PriMera Scientific Engineering (ISSN: 2834-2550)

The X model hence has four major modelling consideration as 1: Sensors/Inputs shown on Upper-Left of X, 2: Processors/Quantum A.I shown on Upper-Right of X, 3: Controllers shown on Lower-Right of X and finally 4: Outputs/Actuators/Executors shown on Lower-Left of X and the fusion gives complete engineering to Phantom Robots designing. Here in each section of X of Phantom Robots three essential designing aspects are “Analysis to use different colors lights wavelengths/Quanta’s to use as Sensors/Inputs , Processors/Quantum ALUs, Light Controllers and Outputs/Actuators/Executors” using modulation, modes, means, transport, hoping, tunneling, Spins , Amplification, Stimulated Emission, multiplications, rotation, reflection, transformation, splits, beam narrowing, beam expansion, diffractions, reflection, refraction of lights/Wavelengths/Quanta’s/Photons respectively. These are further as indicated on X model of Phantom Robotics Engineering as 1: Sensors/Inputs with modelling layers 1-L1, 1-L2 and 1-L3, 2: Processors/Quantum A.I with modelling layers 2-L1, 2L2 and 2-L3, 3: Controllers with modelling layers 3-L1, 3-L2 and 3-L3 and finally: Outputs/Actuators/Executors with modelling layers 4-L1, 4-L2 and 4-L3. The Fig2 is the compact representation of idea in the form of small cube called “X Phantom Robot Optical Illusion Box is a in hand small unit after operate project Phantom/Aura Robot at designed remote distance/direction/place using complicate different wavelengths/Quantum with mix colors light network. The two major benefits if mankind able to get to design such a robotics (Phantom/Aura) form which look like projected multicolor light spot but in actual Intelligence Robots and work similar like A.I based. The major benefit of this QAI based Phantom Robots several times faster, smarter and can travel in space to explore Universe, Stars, Planets, Exo-Planets, Black Holes, Worm Holes, New Life forms, intelligence life forms and Intelligence Alien lives. Equally possibility in several locations of the Universe already such a kind of Intelligence Light forms already exist and act as Intelligence Alien Civilization hence in such possibility Phantom Robots provide great compatibility and close understanding with them with reaching “Speed of Light”. Second thing as Earth-like planet “Proxima Centauri-B” which is discovered nearest exoplanet orbiting to habitable zone of the red dwarf and has signs and possibility of Exoworld with intelligence life forms. Proxima Centauri-B is on 4.24 (ly) Light Years distance from Earth and need to 63000 Years to be reach there and to communicate with intelligence human-like life-forms exist there, which is further NASA trying to reduce in 200 Years with proposed “Tree of Life” Project and “Cubesat”, but still time-travel can reduce if we able to engineer and transmits “Phantom/Aura Space Robots” to Proxima Centauri-B which travel with or more than “Speed of Light” I used the word more than to design such light which directly disappear and appear in deep space from one to another planets with synching such speed of light with the “Speed of Thoughts”. As our thought in a second reach and touch to Proxima Centauri-B in brain imagination. Such speed converts and implement in “Phantom/Aura Robots” with using Ultra Quantum Artificial Intelligence Engineering. In conclusion want to state that after successful implementation of Phantom Robots for space applications we can sync them with incharge human thoughts to achieve speed of thoughts more than of speed of light.

The concept of “Phantom/Aura Robots” I brought with further enhancement in my last research communication on “Quantum Artificial Intelligence (QAI)” published last year and discussed how we can engineer, group and gather various lights as single “Intelligence Light” or can say Optical Artificial Intelligence in other words for clear understandings using various LEDs and LASERS Diodes and another light emitting sources. Hence in present research communication I want to boost it on next level with the origin of idea “Phantom/Aura Robotics” and how one can think about it, how can stat its analysis, engineering and modeling for its implementation, therefore I have developed “X of Phantom Robotics Engineering” model as depicted in fig1. The “X” of phantom engineering stated with same as universal intelligence model aspects “Input, Process, Control and Output” but here in virtual light form so strong analysis required to work out on this fresh idea.

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In the present article, an ideal equivalent three Degrees of Freedom (DoF) system of a onebay-bridge (that is supported on elastometallic bearings) that has distributed stiffness and mass along its length is given. From the naturally point of view, the bridge has infinity number of degrees of freedoms, but based on the free vibration study, using partial differential equation, a mathematical ideal Three-Degrees of Freedoms system is obtained, where its ideal mass matrix is analytically written at specific mass locations on the bridge. Thus, using this abovementioned three Degree of Freedom system, the first three fundamental mode-shapes of the real bridge are identifying. Moreover, we consider the 3x3 mass matrix, we can attempt an estimation of the future feasible damages on the bridge, if a known technique for the identification of dynamic characteristics applied. Furthermore, the way of installation of a local network of three uniaxial accelerometers must be compatible with the abovementioned Three-Degrees of Freedom. It is noting worthy, this technique can be applied on bridges, where the sense of concentrated mass is fully absent.

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This paper presents a mini-review on our previous work presented in ref. [1-6] in which the neural networks (NNs) were used for estimating and then detecting the robot’s collisions with the human operator during the cooperation task. This review investigates and compares the designed NN architectures, their application, the resulted mean squared error (MSE) from training, and their effectiveness (%) in detecting the robot’s collisions. This review reveals that the NN is an effective method in estimating and detecting the human-robot collisions.

Keywords; Electrical resistivity; thermal aging; scattering centers; conduction electrons; precipitation process

This work aims to study a new lead-free solder alloy as a substitute for lead-tin alloys in order to solve the problem of protecting human health. The effect of thermal aging on the electrical resistivity of a eutectic Sn-3.5wt%Ag lead-free solder alloy has been investigated. The samples have been aged at the temperature range of 100°C - 160°C. During aging, the precipitation behavior has been followed by the electrical resistivity measurements. The resistivity change of Sn-3.5wt%Ag eutectic alloy rises to a maximum value and then decreases to a constant value at each aging temperature. The maximum value increases as aging temperature increased. The little reduction in resistivity after the maximum value is due to the coalescence and growth of precipitates and then decreasing in the scattering effect to the electrons. Microstructural characterizations using scanning electron microscope (SEM) were conducted to follow in detail the precipitation behavior. The SEM results cleared that the precipitation process increases with increasing thermal aging time and the coarsening of fine precipitates occurs in the samples aged at a long time.

Keywords and phrases: Star Laplace Transform s-step; Laplace Transform; Star Coefficient; Star-System; Equations Matrix

The Laplace transform has many applications in science and engineering because it is a tool for solving differential equations; in this paper we propose a Star-Laplace transform s-step. We give the definition of this Star-Laplace transform s-step of function f(t), t ∈ [0,+∞], some examples and basic properties. We also give the form of its inverse by using the theory of the Laplace Transform.

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With the mounting demand for oil and gas worldwide, the maintenance of repaired pipelines has posed great importance in recent decades, in which composite materials have eased the way to rehabilitate corroded specimens. This study aims to investigate the failure probability of pipes suffering from internal corrosion repaired by fiber-reinforced composite materials which undergo internal pressure as well as temperature gradient, employing the Monte Carlo method, along with the Spearman rank correlation coefficient signifying the role of input parameters in the level of failure probability. 

Keywords: Regioanalism; Modern Regionalism; Modern Hospital; Culture; Tradition, Society

The present article examines the link between modern regionalism and modern hospitals, and states that addressing community culture in architecture is increasingly debated. The contemporary period has brought about extensive changes in most fields, including architectural theories, has put forward diverse and numerous points of view, and in some cases has led to the emergence of new approaches regarding ancient and rooted thoughts. Hence, it is suggested that a relationship is established between the building and human beings, which makes the environment and space better understood by human beings, and a kind of emotional connection is established between the two. Therefore, attention to the new regionalism that is the subject of this article is examined and according to the progress of science and technology, its relationship with medical centers, including hospitals its relationship with medical centers, including hospitals, is examined.

Keywords: gyroscope theory; spinning disc property; inertial torque

The main property of gyroscopic devices is maintaining the axis of a spinning rotor in space. All gyroscopes manifest the action of the inertial torques and motions which mathematical models based on the principle of the mechanical energy conservation. New studies show the external force to the spinning rotor generates the net of the interrelated inertial torques acting around axes of its rotation. The four centrifugal, two Coriolis torques and two torques of the change in the angular momentum are looped around two axes and express the resistance and precession torques of the spinning rotor. Blocking of the gyroscope precessed motion leads to the vanishing of inertial torques around two axes except the precessed torque-generated by the center mass of the spinning rotor. The vanish of inertial torques of the running gyroscope is not well described in known publications that needs clarification. This paper represents the detailed explanation of the physics of the vanish of inertial torques for the case of the blocking of gyroscopic precessed motion.

Keywords: Preventive Medicine; Milk protein; Nutrition proteins; Health informatics; Medication management; HPLC

The nutrition value of milks is commonly evaluated by determining the total amount of nitrogen, amino acids, and proteins. Because of all these methods only base on the determination of a single parameter, lacking high selectivity, a lot of problems have been met. The presentation reports a highly selective method for the quality control of bovine milk by determining the relative amount of five proteins in milks by reversed-phase liquid chromatography. The fingerprint of main nutrition proteins comprising of five regions of bovine milk is firstly established and then it is characterized by a so-called as “characterization curve of nutrition protein, CCNP” which is drawn basing on the relative amount of the peak area of the five group proteins. Any significant changes in the profile of the CCNP qualitatively indicates the existence of some additives in milk, while the magnitude of the deviation from the standard CCNP is the measurement of the changes in the content of each nutrition proteins in milks. With separately adding eve’s milk, soybean milk, and water into a bovine milk, these additives could be quantitatively determined with the RSD below 5% by the presented method.

What is the solution?
1.    If the driver's seat is on the left side, if you look in the mirror on the immediate left side, you can see people getting on, off, and falling, as well as oncoming vehicles and pedestrians at the same time. If any passenger falls from the vehicle, the vehicle can be stopped immediately.
2.    There is no need to turn the head to the left side so that time can be obtained. The danger due to it can also be avoided.
3.    When passing another vehicle on the right side, the driver's seat is on the left side so that the sides of the vehicle on the left side can be clearly seen.
4.    When a driver driving a vehicle sitting on the left side on the roads allowing travel in both directions tries to overtake another vehicle on the right side, when a vehicle suddenly comes from the opposite direction, the driver's action is according to the reflex action, so the vehicle on the left side is likely to be in danger, so the vehicle is kept to the left side only with great care.
When overtaking another vehicle on the right side, if the driver's seat is on the left side, the sides of the vehicle on the immediate left can be clearly seen.
5.    The life of the driver sitting on the left side of the vehicle is not safe if the vehicle hits the vehicle in front from the rear or side while overtaking another vehicle on the right side of the one way roads. Be very careful and force the other vehicle to pass as your life may be in danger.
6.    A driver driving a vehicle sitting on the left side will have to maintain a greater distance from the vehicle in front to see the position of the vehicle coming from the opposite side to overtake the vehicle in front. This reduces the risk of hitting the rear of the vehicle in front.
A driver sitting on the left side of the vehicle is more likely to have an accident while the vehicle moves to the left side to avoid an accident. The driver will realize that his life is in danger if he hits the one immediately to his left. This will prompt the driver to drive cautiously.
The accident which caused the death of 9 people in Vadakancheri took place on a one-way road. It can be seen that the vehicle that caused the accident hit from the rear and right side of the vehicle in front. . Had the driver's seat been on the left side, such an accident would never have happened. The driver himself tried to save the life of the driver. The driver was not seriously injured.
7.    If the driver gets out of the left side driver's seat by opening the door, such accidents will be avoided as the two-wheeler coming behind will not be affected.
8.    Driver's position in vehicles should be on the left side of the vehicle if pedestrians are also walking on the right side.
In right-hand driving, the advantage is that the driver sits on the right side
1.    Pedestrians can be made to walk on the left side as pedestrians have a natural tendency to walk on the left side in right-hand driving. It is also possible to back off to the left side as per the reflex action when any vehicle is approaching.
It is said that pedestrians should walk on the right side of the road in order to see the vehicles coming from the opposite side.
2.    If you want to overtake the vehicle while driving on the right side, you should keep more distance from the vehicle in front.
As the driver has an innate tendency to veer to the left side while driving on the right side
On one-way roads, when the driver tries to pass the vehicle in front of him on the left side, the driver's action is based on the reflex action, so the risk of accident is high for the driver.
This defect cannot be avoided as passengers should be prioritized over the driver. A driver who has experienced the risk of an accident many times will be more cautious.
3.    If the driver gets down from the right side driver's seat by opening the door, such accidents will be avoided as it will not affect the two-wheeler coming behind.
4.    When stopping a two-wheeler while driving on the right side, the vehicle does not lean towards the footpath as the left foot sticks. This will make it more convenient for pedestrians to walk.
If the driver's seat is on the right side of the vehicle then it should be right side driving and the door of the vehicle should be on the right side.
Why walk on the left side?
Indians generally have a tendency to walk on the left side. A soldier starts his march on the left foot. When one stands to attack another, stand with the left leg crossed. The left side is always defensive and retreating.
While walking along the footpath, you can see that most of the people walk on the left side to pass the person coming from the opposite side.
A person or a group of people walking in the middle of the road on a road with few vehicles has been observed to move to the left side of the road when they hear the bell of a bicycle behind them.
Those standing beyond the halfway mark have also been seen shifting to the left side of the road.
A cyclist should hit the ground first with the left foot.
It is wrong to suggest walking on the right side when there is a natural tendency to walk on the left side. Therefore, it is essential that pedestrians walk on the left side, vehicles drive on the right side, and the driver's seat is on the right side of the vehicle.
The need for uniform traffic rules all over the world
Today, many countries have different traffic laws and practices. In some countries you drive on the right side while in some countries you drive on the left side. Similarly there are vehicles with driver's seat on left side and right side.
Because of this, many people do not like to drive for fear of getting into trouble when going to countries that are against the ways they are used to. Many people are not used to driving against a traffic pattern in another country.
Many people travel and work in different countries with different traffic laws on the same day. Different road traffic rules cause various doubts in the mind.
Many existing traffic laws are against the reflex action that comes from our unconscious mind.
Most drivers cannot concentrate on driving full time. Many people think about many other things while driving. Most of the time driving is by reflex action.
When danger occurs, the mind often simultaneously reacts in favor of reflex action but against traffic law. This causes various types of accidents.
If the traffic rules are the same around the world based on reflex action, accidents will be reduced for those who cannot concentrate on driving full time.
It is very important to enforce the same traffic rules all over the world.
Keywords: pedestrians; right hand driving; safety; drivers seat; accident

An anomaly of the current right-hand driver's position
1.    It is difficult for the driver sitting on the right side of the vehicle to accurately know the position of the vehicle passing on his left side and the pedestrian passenger.
It is common for pedestrians to be struck by vehicles because the left front of the vehicle, including the driver's seat, cannot be seen clearly enough.
2.    Presently the driver's seat is on the right side and the bus door is on the left side. The driver sitting on the right side has to turn his head to the left side to look at the mirror sitting on the left side to see the people getting on and off on the left side.
When the head is turned to the left, it is impossible to see the vehicles coming from the right behind and the opposite direction in front. Similarly, when the head is turned from the left side to the right side, it is not possible to see what is happening on the left side of the vehicle.
Buses do not have doors in most places in India. Do not close the doors. Due to this, a large number of passengers are seriously injured and die every year due to falling from the vehicle, people getting out of the vehicle after the vehicle starts moving, and getting into the vehicle.
3.    Take time to turn the head to the left and back to the right to look at the mirror on the left.
This makes it impossible to look in the left side mirror and pay attention to the oncoming vehicle in front at the same time. This causes danger.
4.    When overtaking another vehicle on the right side, the overtaking driver's seat is on the right side and cannot see the sides of the vehicle on the left.
5.    When a driver driving a vehicle sitting on the right side on the roads that allows travel in both directions tries to pass another vehicle on the right side, when a vehicle suddenly comes from the opposite direction, the driver's action is based on reflex action, without the ability to pay attention to the vehicle on the left side, the driver keeps the vehicle to the left side to save his life
6.    When overtaking another vehicle on the right side of one-way roads, even if the vehicle hits the vehicle in front from the rear or side, the life of the driver sitting on the right side is safe. This makes many drivers arrogant.
This is what happened in the accident that resulted in the immediate death of 9 people in Vadakancherry.
The accident took place in Vadakancherry on one way. It can be seen that the vehicle that caused the accident hit from the rear and right side of the vehicle in front.
7.    It is comparatively difficult to get out from the right side driver's seat to the left side when the vehicle is stopped, so the driver opens the right side door and gets out to the right side. When the door is opened in this way, there are many incidents of death due to being hit by the following two-wheeler.
The main reason for the sudden death of 9 people in Vadakancherry. Driving on the left side is the driver's seat on the right side.
8.    Pedestrians should be given more priority on the road.
In India, vehicles stop and park on the left. Two-wheeler drivers put their left foot on the ground and stop the vehicle by tilting it to the left side. This also causes more inconvenience and danger to the pedestrians walking on the right side.
If the two-wheelers passing by on the left side lean to the right side to avoid causing inconvenience to the pedestrians, the danger to the two-wheeler will increase. If one tilts to the right side and stops the vehicle, the balance will go. The other will go through the leg of the person driving the two-wheeler stopped in front and the wheel of the vehicle of the motorist coming from behind will go up.

Keywords: Photonic Crystal fiber (PCF); Macro-bending Loss; Effective index method (EIM)

This paper highlights the losses arising from bending that plays a vital role in long-term communication purposes. To get better transmission it is very essential to control the losses. Losses arising from bending of photonic crystal fiber (PCF) can also be controlled by altering the structural parameter (d/? and ?) that regulates the photonic crystal fiber structure. Different materials (viz. crown and phosphate) based PCF with lower bending losses has been reported by exploring the whole communication band (0.2-2µm) with variable structural parameters and bending radii. During bending of a fiber, evanescent field energy drives light towards cladding and a subsequent attenuation in the guidance of light energy occurs, which generates losses and can be controlled by altering the structural parameters in the PCF. Therefore, failure in total internal reflection is the major contribution towards these losses. In this study, a very low bending loss ~ 0.11 dB/km and ~0.08 dB/km have been observed for crown and phosphate based PCF. Also, an interesting phenomenon has been observed during the study that with increase in ? for constant d/?, losses are found to increase up to the λ ~1.2µm and then started decreasing with increase in ? which can be useful for the experimentalist during fabrication. Although this work can provide a valuable approach to the engineers to develop a newer kind of PCF model that can lower losses with respective change in material domain.

Keywords: open channel hydraulics; side weir; discharge coefficient; rectangular side weir

More than eighty years ago, De-Marchi indicated an assumption for hydraulics of flow-through side weir. Many researchers who study side weir depended on this assumption in their studies. Many studies for side weir were carried out in different channels, and numerical studies supported the results. This study deal with a comprehensive review of discharge coefficient for side weir.

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Wireless communication evolution from 1G to 6G, evolution of technology based on demand of the customer requirement. The continuous requirement increases the demand on technology. 6 G is the successor of 5 G technology. Sixth-generation (6G) operates the frequency of THz. Terahertz waves (THz), which are sub millimeter waves sitting between microwave and infrared light on the electromagnetic spectrum, have been used to achieve data rates greater than 100 Gbps It incorporate varies technologies machine-to-machine (M2M) communication. 6G communication support, 3 D media technology optical wireless communication (OWC), 3D networking, unmanned aerial vehicles (UAV), and wireless power transfer and also support Internet of Nano Things (IoNT). 6G will support technology like automated cars and smart-home networks, helping create seamless connectivity between the internet and everyday life. In development for 2030, 6G will support advancements in technology, such as virtual reality (VR), augmented reality (AR), metaverse, and artificial intelligence (AI). China successfully launched the world's first 6G satellite. The satellite uses Terahertz waves that could send data at speeds several times faster than 5G. 12 other Earth-observing satellites were aboard the rocket. The new era start with reality connecting with surface rather than devices. 6 G will become first network which are connected all the three system (space, earth and aerial) in a line.

Keywords: Rehabilitation; COVID-19; SpO₂; Monitoring; Oximetry

Implementation of a pulse oximeter with measurement of altitude, heart rate (HR) and oxygen saturation (SpO₂) with bluetooth communication, in the need of real-time as well as remote monitoring by the therapist with the purpose of avoiding the spread of the SARS-CoV-2 virus. With the use of bluetooth technology, the data is transmitted in real-time to a mobile phone through an application in the Android operating system, a medical history record is generated using the MIT App Inventor platform, likewise an audible alarm is activated in case the saturation level is out of range, taking into consideration the variation in oxygen saturation depending on the altitude where measurement is taken.

Keywords: Analog Signal; Arduino UNO; Digital Signal; ESP8266; Internet of Things (IoT); Infrared Sensor (IR); Light Emitting Diode (LED); NodeMCU; Object Detection; Signal; Ultrasonic Sensors

Sensors which are connected to IoT board send data to either a local network or the Internet. The Internet of Things is the accretion of connected sensors. It is assumed that in the year 2025 approximately 75.44 billion devices are connected to the Internet of Things. In IoT greatest number of devices will be small sensors that send information about what they are sensing. Here we are going to design how an infrared sensor works with ESP8266 NodeMCU board and also implement these sensors to select object and measure the distance of the object by using ultrasonic sensor. These sensors provides both digital and analog output. Here we are performing only digital output which will be directly connected to a NodeMCU with Arduino platform to read the sensor output. Infrared sensor is used for object detection and Ultrasonic sensor is used for distance measurement.

Keywords: Natural Resources; Revenue sharing; Livelihoods; Sustainable Development; Investment

Most of communities depend on natural resources such as forests, land, and water for their livelihoods. Nature is the source of human health due to daily needs from ecosystem goods and services. From the relationship existing in ecological and economic systems, the aspects like improved living standards of people, livelihoods, biodiversity conservation, and human wellbeing require a multi-disciplinary collaboration between communities and stakeholders to find a good approach that integrates biodiversity conservation and human wellbeing. We are interested to know the status of community based conservation projects in the tropical region and bring the scientific contribution referring to the findings. We reviewed existing documentation on community conservation and compiled the similarities of conservation practices that involve local communities. We did a comparative study in some countries that are located in tropical region. The data show that in all mentioned countries, there is a will to integrate biodiversity conservation and community development but there is a need to improve the policies and regulations and increase the investment in community development projects. We also detected the issue of lack of conservation professionals in the decision making and this causes the reluctance in implementing community conservation projects in some countries in the tropical region. The assessment of the contribution of community conservation projects on improved livelihoods, and sustainable biodiversity conservation in and around the protected areas will help to improve community conservation. There is a need to assess the perceptions of local community towards co-management in biodiversity conservation, and ecosystem services in and around the protected areas in tropical regions.

Keywords: Metamaterial; absorber; stealth; aircrafts; surveillance

This paper suggests a Ku band metamaterial absorber simulation using CST Microwave Studio Software at a frequency ranging from 12-18 GHz [2]. The work proposes a design which has a square based ground Metamaterial Absorber (MA). The overall measurement of the unit cell assembly is 5* 5 mm^2. This assembly covers the Ku band which gives a wideband of absorption [1]. The optimization of structure is achieved at 13.3 GHz and 14.9 GHz respectively. The designed metamaterial absorber serves in stealth technologies. The major applications also include airborne surveillance and military protection.

Keywords: Computational Path Delay; Latency; Vedic Multiplier; Vivado; Speed

We live in a technologically advanced society. The use of diverse electronic gadgets is interwoven with even the most fundamental aspects of our daily lives. They increase and smoothen the pace of our life. The multiplier component controls the speed of most electronic systems with high-speed applications that employ the IEEE 754-2008 standard for single-precision FPUs. Several existing methods have been included to enhance the multiplier's speed of operation. They have, however, not demonstrated a substantial difference in speed, raising it by a maximum of 1.182 times.

As a result, we presented "Vedic Design," a novel algorithm with a distinctive architecture. When this was simulated in Vivado, it improved the multiplier's speed by 3.4478 times, resulting in a multiplier that is nearly 3.5 times more efficient. The gadget is better equipped to function as a result of the reduced computational path latency.

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     Confined Quantum Field Theory is the extension of the moral of the special and general relativity into the quantum domain. Thereby each quantum object is represented by a bounded and connected manifold with a metric as the function of its energy and topology representing type of particle. This gives any quantum object well-defined size and position. This simple and basic statement bring us beyond uncertainty and paradox in the quantum theory and make it much stronger instrument to solve many fundamental problem in many domain in physics. Here I just give some example to demonstrate how this theory works. Take for example emission of photon by accelerating electron. Since both electron and photon is bounded connected manifold and are not probable points and photon is a sub-manifold of electron in the beginning of the emission both manifolds have overlaps and this take a short time before the separation. Therefore energy conservation is valid all the time and here we have no time-energy uncertainty. The cases that show superiority of this theory are numerous. I recommend the book “Confined Quantum Field Theory” second edition.

Keywords: Design Research; Philosophy; Prosperity; Well-being; Life

Design by research is a design methodology within the context of subject(s) for a defined group. Descriptive research performs in the context of qualitative research. Philosophy, behavioural sciences, sociology, economics, and other scientific fields contribute to the design domain. The design ability can potentially change our behaviour and initiations in the social and economic context. Design activities must be forward-looking so that design energy can harness humanity's necessary progress. In the context of the elderly, the vital energy of the elderly will stimulate their valuable knowledge, skills and experience for social and economic development. This positive practice brings satisfaction and happiness to the elderly. It benefits society that expects the elderly to enjoy life by spending their money, but often without acquiring knowledge. According to the philosopher Peter Sloterdijk, changes are necessary for life. A life with initiative leads to the collective growth of the personality. This needed energy comes to us from the universe, which opens up our 'Self' and leads to changes in design thinking in the design domain. Discover perspective in design by looking at the design methods that form an umbrella for the design domain. A holistic approach to design will stick to the context of the design, and the need will change through human thinking and action. All design entities will consider the need for humanity and present it during the design process. Philosophy and research into the phenomenon of design can lead to new insights into how life changes through design. The design has intrinsic energy to transform ideas into material and spiritual prosperity and well-being. Design Research is concerned with design by research and how it may apply by designers from two different cultures of the world but with a convergence.

After identifying the central idea of Nano [1], as "the power of mind over matter", was to characterize with these elements of Lagrangian and Hamiltonian dynamics concepts such as conscience of a particle, due action to a force of the field, the field itself, seen as an intention [2, 3] and to unite the synergistic action of many particles acting together to create an organized transformation on matter.F.1

     These are the first elements considered to create a mathematical theory of nanotechnology [1] at the advanced level of field theory, which could be achieved in the coming years. Although the latter is mentioned in a first tribute to me [4], this theory is now reality (figures 1).

     While we carry out some interesting research in nanomedicine [5], and nanomaterials [6, 7], being part of the technological developments realized around of the mathematical theory of nanotechnology that are being and have been realized and published in many references.

     This study consider the causal structure of the scattering phenomena through past and future light cones, create the possibility of energy for one thousand years, perpetual motion machines and star-gates (worm holes in the space) with advanced analogues as the synchrotronic propulsion (advanced spaceships) and disintegrative mass weapons, using the same principles in all case.

     During many years developing many ideas around the electromagnetic propulsion as a source of take-off and movement, including landing, of advanced magnetic levitation vehicles. In the year 2010, [8] starts a research sub-program of his research program, dedicated to the development of advanced vehicles by electromagnetic propulsion.

     Several papers are published in more than 20 different journals and book chapters on quantum mechanics and superconductivity [9, 10] for the purpose of proving a consistent theory for the creation of these advanced vehicles and reinforces in that sense, taking advantage of his position as editor-in-chief of the journal on photonics and spintronics, with works by other researchers related to the study of supercinductivity, Majorana fermions, and other related topics (figure 2 and figure 3). F.2

     Just as electromagnetic fields are caused by a charge and gravitational fields are caused by weight (mass and force), any rotating objects create torsion fields.

     Torsion fields can interact with laser beams (change frequency); creating effects of diverse nature, for example, in the biological processes, where torsion affect directly the ADN. Also can melting or solidifying some materials, affect quartz crystals increasing their properties as resonators. Also affect some electronic components creating radiation coverings. The torsion can favourably change some beverages, and have been noted to affects gravity. F.3

     According to this theory, every substance has its own "chronal charge" defined by the quantity of "chronal" particles which were named "chronons". In [13] was supposed that while the object is spinning, "chronons" are interacting with other "chronons" that surround this object and therefore the weight of the object changes. According to A.I. Veinik's theory, "chronons" generate the so called "chronal" field. A.I. Veinik found experimentally that strong "chronal" fields can be generated by spinning masses. In [14] explained through schemes of coverings and measured some properties of "chronal" fields and found that two types of "chronons" exist ("plus" and "minus" chronons). It is important to emphasize that can be concluded that the sign of the "chronon" depended on orientation of their spin. Some of these facts could explain the hyperbolicity of the space and their law of the minimal action and geometrical trajectories “braquistrocrona curve” satisfying this law of minimal action, and consigned in the inertial law inside the Einstein equations with expansion reflected into the Christoffel symbols considered in the gravitation equations.

     The spinning space can be consigned in a smooth space (as the apparent uniformity of the space-time in the ground) when the energy fluxes of the spins derive in neutrinos and these full all space of energy. The problem with the manage of the chronogeometry with this particles is the definition of the synchronicity that is required to some process to quantum level to create some process and their integration inside synergic action or “organized transformations” to obtain the reality of the space-time [2].

     Actually, and with research groups that I direct, we are considering the torsion [15, 16] as principal effect to generate propulsion in an advanced model of ship, considering the way through electromagnetic plasma [17] (figure 4 A).

     Likewise through of a caption and detection camera, is measured the electromagnetic properties of the ionic flow of the space _~II_e (ρ, u), derived from the electromagnetic plasma _~II_H, and is proposed an ionic propeller considering the pressure gradients due to electrons and ions concentrated in a little region of the shock waves produced with an electric field. Also is used mean curvature energy [18, 19] to measure and control the ionic flow. Then researches are in this step (figure 4 B). F.4

     We must to create or invent new mathematics to describe a complete quantum mechanics based in the synchronicity; a synchronic quantum mechanics, which is there in the causal structure of the Universe but in a more deep level. All these are theories developed around field theory to define the intentionality of a field and apply it in nanotechnology, on matter.

     Studies in condensed matter and MHD, also have been incorporated. The only one energy to all ship (even to nanomedicine process) must come from the reactor of electromagnetic plasma.

References

1. F Bulnes. Mathematical Nanotechnology, Generis Publishing, Slovenia (2021).
2. Bulnes F. “Mathematical Nanotechnology: Quantum Field Intentionality”. Journal of Applied Mathematics and Physics 1 (2013): 25-44.
3. Francisco Bulnes. “Quantum Intentionality and Determination of Realities in the Space-Time Through Path Integrals and Their Integral Transforms”. Advances in Quantum Mechanics, Prof. Paul Bracken (Ed.), InTech (2013).
4. Helen Thrift, Yuri Stropovsvky and Francisco Bulnes (Honored). “A Mathematician’s Search for Technologies of Understanding the Universe”. Cambridge Scholars Publishing, New Castle, United Kingdom (2020).
5. F. Bulnes., et al. “Quantum Developments in Nanomedicine: Nanocurative Actions by Soft Photons Sources and their Path Integrals”. Open Central Press, United Kingdom (2015).
6. F Bulnes, Y Stropovsvky and V Yermishkin. "Quasi-Relaxation Transforms, Meromorphic Curves and Hereditary Integrals of the Stress-Deformation Tensor to Metallic Specimens". Modern Mechanical Engineering 2.3 (2012): 92-105.
7. F Bulnes, V Yermishkin and E Toledano. “Constitutive Equations of the Stress-Strain Tensor for a Metal Speci- men Rehearsal in Quasi-Relaxation Regime and Their Generalized Functional of Energy”. Proceedings of the 2nd CIMM, Vol. III, Department of Mechanical Engineering, Universidad Nacional de Colombia, Bogota (2009).
8. F Bulnes, E Hernández and J Maya. Volume 7: Fluid Flow, Heat Transfer and Thermal Systems, Parts A and B, ASME 2010 International Mechanical Engineering Congress and Exposition, Vancouver, British Columbia, Canada (2010).
9. F Bulnes, J Maya and I Martínez. "Design and Development of Impeller Synergic Systems of Electromagnetic Type to Levitation/Suspension Flight of Symmetrical Bodies". Journal of Electromagnetic Analysis and Applications 4.1 (2012): 42-52.
10. F Bulnes and A Álvarez. "Homological Electromagnetism and Electromagnetic Demonstrations on the Existence of Superconducting Effects Necessaries to Magnetic Levitation/Suspension". Journal of Electromagnetic Analysis and Applications 5.6 (2013): 255-263.
11. Francisco Bulnes. “A Lie-QED-Algebra and their Fermionic Fock Space in the Superconducting Phenomena, Selected Topics in Applications of Quantum Mechanics”. Prof. Mohammad Reza Pahlavani (Ed.), InTech (2015).
12. F Bulnes. An electromagnetic ship designed by Lie-QED and their fermionic fock space principles in superconducting to the future flights, 4th International Conference and Exhibition on Mechanical & Aerospace Engineering, Orlando, USA (2016).
13. Veinik AI. Thermodynamics of Real Process, Minsk (1991) (In Russian).
14. Ramirez M., et al. “Field Ramifications: The Energy-Vacuum Interaction that Produces Movement”. Journal on Photonics and Spintronics 2.4 (2013): 4-11.
15. Francisco Bulnes., et al. “Detector of Torsion as Field Observable and Applications”. American Journal of Electrical and Electronic Engineering 8.4 (2020): 108-115.
16. Francisco Bulnes. “Deep Study of the Universe through Torsion, Cambridge Scholars Press”. United Kingdom (2022).
17. Francisco Bulnes., et al. “Electromagnetic plasma reactor: Implicit application of field torsion I”. Int J Electron Microcircuits 1.1 (2021): 30-35.
18. F Bulnes, I Martínez and O Zamudio. “Fine Curvature Measurements through Curvature Energy and their Gauging and Sensoring in the Space”. Book of Sensors and Applications in Measuring and Automation Control Systems (Advances in Sensors: Reviews, (Ed.) Sergey Y. Yurish, IFSA Publishimg, Barcelona, Spain 4.20 (2017).
19. F Bulnes., et al. “Electronic Sensor Prototype to Detect and Measure Curvature Through Their Curvature Energy”. Science Journal of Circuits, Systems and Signal Processing 4.5 (2015): 41-54.
20. Francisco Bulnes., et al. “Electromagnetic plasma reactor: Implicit application of field torsion I”. Int J Electron Microcircuits Accepted (2022).

The central idea of Lagrangian dynamics is to study the movement of particles, even in continuous media, and the causes that originate this movement, which brings an intrinsic transformation of space due to energy. Not so the physical entities, which must be invariant under the choice of transformations of coordinate systems.

Keywords: Face Recognition; Face Detection; Security; Authentication

ML has grown in popularity over the previous decade as a result of strong computers that can process large amounts of data in a reasonable length of time. A well-known problem in Machine Learning is the Dog breed classifier. The issue is determining a dog’s breed. The input can be a dog or a human image, and the proposed algorithm should be able to forecast the dog’s breed or, if the human is a dog, which breeds it belongs to. For human face detection, OpenCV is utilised, while for dog face detection, the VGG16 model is employed. With the ResNet101 architecture, a convolutional neural network is utilized for classification. On test data, the final model had an accuracy of 81 percent.

Keywords: advanced optimization techniques; algorithm-specific parameters; Jaya algorithm; Rao algorithm; teaching-learning-based optimization algorithm

The performance, utilization, reliability, and cost of the system are all improved when optimization techniques are used to solve engineering problems. Researchers have used a number of traditional optimization techniques like geometric programming, nonlinear programming, sequential programming, dynamic programming, etc to solve these problems. Traditional optimization techniques have been effective in many real-world problems, but they have some drawbacks that are primarily caused by the search algorithms they have built into them. The researchers have developed a number of advanced optimization algorithms commonly referred to as metaheuristics, to overcome the limitations of traditional optimization techniques. All of the probabilistic evolutionary and swarm intelligence-based algorithms used to solve optimization problems require common control parameters like population size, generational number, elite size, etc. along with these need their own algorithm-specific control parameters. The effectiveness of these algorithms is significantly influenced by the proper tuning of the algorithm-specific parameters. When tuning algorithm-specific parameters incorrectly, the result is either an increase in computational effort or the local optimal solution. This article presents a review of the application of algorithm-specific parameterless algorithms in electrical engineering applications. This article is expected to play a major role in guiding research scholars in the application of advanced intelligent optimization techniques.

Keywords: Digital model; teaching identity; professionalism; transforming school

To transform education, it is necessary to innovate in knowledge, identity, and teaching practice; that is why the teacher must conceive his pedagogical practice as a way to strengthen his professional identity and exercise his teaching work following trends and challenges of education, showing good preparation, developing research, using technology and reflecting on their teaching practice. In order to improve the development of teacher professionalism and identity (DPID), the sustainable digital model of a transforming school was applied, the design was pre-experimental of an explanatory type, a questionnaire was applied to 31 teachers from a school in Peru, taking into account the dimensions of teacher performance established in the Good Teacher Performance Framework (MBDD), the dimension where there was the greatest problem was chosen. By applying the model, it was possible to reverse the identified problem, increasing the high level to 67.74%.

Keywords: Analytical Hierarchy Process; Profile Matching; Linear Interpolation; Dean’s List

Determining the best dean's list is one of the steps to motivate students to complete their studies at a tertiary institution. However, the process of determining the best candidate dean's list is not an easy thing to make decisions consistently and transparently. So, in this study we combined the AHP and Profile Matching Method, as well as a linear interpolation model with criteria grade point averages (GPA) obtained, subjects taken and criteria repeated subjects. This study aims to provide specific knowledge about how to combination of the AHP-Profile Matching method and the linear interpolation model in building the best Dean's List decision support system. Where the two methods work together to determine the best dean's list according to their respective rules, namely the AHP method to calculate priority levels and criteria consistency values, while the Profile Matching method, to match data values with target data, determine the weight of competency GAP values, calculate the ranking value of each candidate with a priority level value obtained from the results of the calculation by AHP method, and calculating the mapping weight value in decimal form can use the linear interpolation model which will be used as the weight of the competency GAP value. The results showed that the two methods were successfully combined and were able to determine the consistency ratio for each criterion rating scale of 0,030, and were able to determine the best dean's list of 52 candidates, with the highest ranking value of 5,388.

After the Sumerians, people learned to write on clay board. This when people came with the idea of abacus using the wood and clay board. This board is divided into columns with order of base 60 number system. They used different shaped and sized objects in those columns for calculations. The order use to be 1’s, 10’s, 60’s, 600’s, and 3600’s and placing tokens and removing does the addition and subtraction.

After this came modern abacus, as people are used to count with fingers, and we can do this to count till 10. So, to count more than 10 we need more fingers or any object. This made to implement the abacus with base ten i.e., 1’s, 10’s, 100’s, 1000’s…… etc.

Now, the era of computers came and all we have is just electricity to communicate with these electronic devices. So, like morse code dots and dashes to decode alphabets for communicating with people. We have on and off switch to communicate with computer. As calculations are an integral part of computing which is a core functionality of computers, we need to build everything around this as part of communication. This made us to choose the reliable calculation option of 10 finger option of base 10 and reducing it to base 2 to accommodate the 2 level on and off switch functionality. The base 2 binary code has 0’s and 1’s filling the 2⁰, 2¹, 2², …. etc.

Computers

A computer can store and process data. Most computers use binary code, which uses two variables, 0 and 1, to complete storing data and calculations. Throughout history many prototypes have been developed leading to the modern-day computer. During World War II, physicist John Mauchly, engineer J. Presper Eckert, Jr., and their colleagues at the University of Pennsylvania designed the first programmable general-purpose electronic digital computer, the Electronic Numerical Integrator and Computer (ENIAC). Programming languages, such as C, C++, JavaScript and Python, work using many forms of programming patterns. Programming, which uses mathematical functions to give outputs based on data input, is one of the regular ways to provide instructions for a computer.

Binary Code and Transistors

Computers are made using transistors and they operate based on electricity flow. The binary code is just representation of whether transistor is conducting or not. A simple addition operation using transistors:

Let’s see we want to do 1+1=2, take two transistors allow voltage to flow(transfer) from we get the 2 times the voltage.

if we want 0+1=1, same two transistors but make one resist the voltage allowing only voltage we get just 1 time the voltage.

The bigger the number and operation, the more transistors required in computers for computing.

The modern-day computers make computing so easy. But the computing existed for about 4000 years. It was during the bronze age when Sumer developed and rose to prominence being first urban civilization.

 Their Harvests and flocks were not large enough, so to keep track of their livestock and crops using a notch on tally sticks. They used one bigger notch representing ‘10’ and one smaller notch representing ‘1’. They performed calculations by stacking those notches together.

References

1. https://www.circuitstoday.com/optoelectronic-devices
2. Amin S, Elnaz I and Mohammad M. “Efficient attosecond pulse generation from WS2 semiconductor by tailoring the driving laser pulse”. Optics Communications 516 (2022): 128226.
3. Kumbhakar P, Gowda CC and Tiwary CS. “Advance Optical Properties and Emerging Applications of 2D Materials”. Front. Mater (2021).
4. Chen, X., et al. “Multifunctional optoelectronic device based on graphene-coupled silicon photonic crystal cavities”. Optics Express (2021).

Optoelectronics devices are components based on the interaction of light-within different spectral region- with electronics devise. Laser diodes, light emitting diodes LEDs, photodetectors, image sensor, electro optic modulators, opto isolators, phototubes, image intensifiers and photonics integrated circuits are examples of optoelectronics devices. However, these devices are widely used in different applications such as laser technology, optical fiber communications and optical metrology; and the rapid developments in these applications has promoted a demand for high speed or fast response components. For some, the fast response means fast response in communication networks. For others, it refers to high speed imaging which leads the scientists to develop high speed camera with low light and ultra-fast capture without blur. Nevertheless, fast response for many researchers means generating laser light in the attosecond regime, which was easily achieved by [2] and the best is yet to come. Most common way to improve the performance of optoelectronics devices is by using suitable and efficient materials for fabricating these devices. The optoelectronics devices are mainly based on semiconductors materials. However, 2D materials showed an outstanding optical characteristic and performance for fabricating photovoltaic cells, optical fibers, quantum computing, sensing and security [3]. Besides, the 2D materials addressed some challenges for example obtaining high efficiency and speed, lower power consumption and carbon foot print. Recent developments in 2D materials in combining these materials with other structures, yield new tunable ban structure and ultra-high nonlinear coefficiency, ultra-fast carrier mobility. As a consequence, these 2D materials such as black phosphorous BP, graphene and Transition Metal Dihalcogenides TMDs were efficiently utilized in bio-sensing, laser sources, optical communication and photodetector applications [4]. As results, the 2D materials are considered a promising candidates for fabricating an ultra-fast optoelectronics component.

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Wireless sensor networks (WSNs) are paying attention not only in the industry but also in the sector like academia because of their enormous application potential and exclusive safety challenges. Wireless sensor networks have been used for many applications, from ecological monitoring to logistic, tracking etc. In addition, wireless sensor networks can be used in appliances such as wellbeing monitoring and control, environment and terrestrial monitoring, biomedical health monitoring, home automation, travel control, natural disaster relief and seismic sensing etc. WSN technology made a huge and rapid progress in the early 1990s. The latest stage of WSN development lasts 15% till present. With the rapid development of computing, micro-elecrtro mechanical system (MEMS) and other technologies, the sensors are becoming smaller in size and cheaper in price. These advancements provided WSN the opportunities for commercial use in many areas. Companies like Memsic and Crossbow Technology begin to produce wireless motes, sensors and software support. The standardization of protocols also becomes more and more matured. The standards like ZigBee (802.15.4) and 6LoWPAN are built and commonly used in WSN communications. An integration of WSN technology with MEMS makes the motes with enormously stumpy cost, miniatured size and least power. MEMS are the inertial sensors, pressure sensors, temperature sensors, humidity sensors, strain-gage sensors and various piezo and capacitive sensors for proximity. Over the last decade, the technology of Wireless Sensor Network (WSN) has been widely used in many real time applications and these miniaturized sensors can sense, process and communicate. Most wireless sensor nodes are capable of measuring temperature, acceleration, light, illumination, humidity; level of gases and chemical materials in the surrounding environment. Wireless sensor network aims to give co-ordination among the physical conditions and the internet globe.

Keywords: holy Quran text Analysis; Text Mining; Arabic Text Mining; 1N Form; 2Nform; Data Modeling; holy Quran Tafseer text

There is a huge shortage of scientific research in the Arabic language, especially in natural language processing and relationships between Arabic documents and other specific documents. This shortage is also reflected in Arabic Books' introduction, Topics abstraction, and content summary engines. Furthermore, there are some good samples of Arabic words inside the Quran. The Quran is the holy book of Islam, that is divided into chapters (surah) and verses (ayat) of differing lengths and topics. This paper introduces a framework for both specialized researchers in Islamic studies as well as non-specialized researchers to find hidden relationships between one of the most important chapters of the Holy Quran which is Al Fatiha surah and the remaining chapters of the Holy Quran using Hierarchical Technique data modeling as an unsupervised learning technique. a new framework that can access tokens of the Holy Quran in different granule parts such as chapter (sura) part of the chapter (Aya) of the Holy Quran Sura, words, word roots, Aya roots, and Aya meaning in the Arabic language, Moreover, We had developed a lot of statistics related to Fatiha Sura and the holy Quran like (roots distinct for every Sura, Words Redundancy, Roots Redundancy, Matrix report by roots and every sura, Matrix report shows Percentage of roots similarity by every sura and whole Quran distinct roots, etc.). Furthermore, we enhance the search engine results by adding search by roots and Aya meaning for every Sura. And the results for sample queries show accuracy with more than 3% using meaning and roots compared to the text of the Holy Quran only.

Keywords: GeoGebra; Teaching Mathematics; Learning Mathematics; Teachers’ training

The objective of the study is to identify the challenges and difficulties faced by Ecuadorian Teachers in the implementation of GeoGebra as a didactic resource for the teaching and learning of mathematics. The research is framed in a mixed exploratory sequence approach with a population of 832 teachers who were trained by the Ecuadorian Institute of GeoGebra (IEG) at the National University of Education in the period 2017-2020. The sampling was non-random conformed by N=144 teachers, who answered the online questionnaire. The instruments were the 32-item questionnaire grouped into three parts: (1) sociodemographic aspect, (2) difficulties in implementing GeoGebra in the classroom, (3) challenges of implementing GeoGebra rated on a Likert scale, and (4) open-ended questions about the advantages and disadvantages of using GeoGebra in the teaching and learning of mathematics. Quantitative and qualitative data wereanalyzed with SPSS and Atlas.ti. The results show that the difficulties that teachers have faced in the implementation of GeoGebra in the classroom is the digital gap existing in the 21st century through lack of access to technological equipment (87.5%), followed by the lack of training in the use of GeoGebra (79.1%). The advantages of using GeoGebra respond to the potential of using GeoGebra in the teaching and learning of mathematics, which are linked to the dynamic, innovative, interactive and user-friendly tool. The objective of the study is to identify the challenges and difficulties faced by Ecuadorian.

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In essence, this article introduces a digital platform (see Figure 1) that can be located on the Earth’s surface, or anywhere above or below it. It provides a local project “workspace” that can be tied precisely to the Earth-Centered Earth-Fixed (ECEF) coordinate system of an ellipsoid. It is, in nature, like the old Polar Projection, with three differences:

1. It works anywhere on Earth.
2. It works in space (above or below ground), or other celestial bodies.
3. It can hold three-dimensional projects.

References

1. Kruger Hennie, Lynette Drevin and Tjaart Steyn. "A vocabulary test to assess information security awareness". Information Management & Computer Security (2010).
2. Solon Olivia. "Facial recognition database used by FBI is out of control, House committee hears". The Guardian (2017).
3. Silk Robert. "Biometrics: Facial recognition tech coming to an airport near you: Travel Weekly”. Robert Silk (2017).
4. Mahesh PC Senthil, K Sasikala and M Rudra Kumar. "Face Recognition Based Automated Student Attendance System using Deep Learning". International Journal 9.3 (2020).
5. Deagonia M. "Apple’s Face ID[The iPhone X’s facial recognition tech] explained". Computer world 11 (2017).
6. Mohanty Vikram., et al. "Photo sleuth: Combining human expertise and face recognition to identify historical portraits". In Proceedings of the 24th International Conference on Intelligent User Interfaces (2019): 547-557.
7. Reichert C. "Intel demos 5G facial- recognition payment technology| ZDNet”. (2017).
8. Pandya Jigar M, Devang Rathod and Jigna J Jadav. "A survey of face recognition approach". International Journal of Engineering Research and Applications (IJERA) 3.1 (2013): 632-635.
9. Gnanambal, S., et al. "Classification algorithms with attribute selection: an evaluation study using WEKA". International Journal of Advanced Networking and Applications 9.6 (2018): 3640-3644.

We are living in an automated world where technical advancements are taking place and devices are connected to cyber. There has been a technical evolution in internet of things, image processing, and machine learning. Drastic change in the systems to achieve the accurate results is the trend. Evolution of Technical advancements has led to changes in education system. Considerable advancements have to be done on the Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Proxy attendance(s) has been increased because of increase in the number of students. The traditional methods aren’t efficient way of marking the accurate attendance results, hence an advanced face recognition using the Artificial Intelligence is introduced in this research work. In recent years, the problem of automatic attendance marking has been widely addressed using standard biometrics like fingerprint and Radio frequency Identification tags etc., However, these techniques lack the element of reliability. The attendance system is a typical example of this transition, starting from the traditional signature on a paper sheet to face recognition. This paper proposes a method of developing a comprehensive embedded class attendance system using facial recognition with controlling the door access. In this proposed work an automated attendance marking, and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology.

Keywords: Advancements; Artificial Intelligence; Biometrics; Convolution Methods; Cyber; Face recognition technology; Machine learning

References

1. Ahaneku IE and Animashaun IM. “Determination of water quality index of river Asa, Ilorin, Nigeria”. Advances in Applied Science Research 4.6 (2013): 277-284.
2. Akhtar N., et al. “Modification of the Water Quality Index (WQI) Process for Simple Calculation Using the Multi-Criteria Decision-Making (MCDM) Method: A Review”. Water (2021).
3. Animashaun IM., et al. “Receptor Modeling Application on Surface Water Quality and Source Apportionment”. International Journal of Scientific Research in Agricultural Sciences 3.1 (2016): 1-10.
4. Animashaun IM., et al. “Assessment of Water Quality of Bosso Water-Board Using Weighted Arithmetic Index Method”. Proceedings of the Annual Conference (2015).
5. Banda TD and Kumarasamy MA. “Review of the Existing Water Quality Indices (WQIs)”. Journal of Phys. Opt 2 (2020): 1-19.
6. Chauhan A and Singh S. “Evaluation of Ganga water for drinking purpose by water quality index at Rishikesh, Uttarakhand, India”. Rep. Opin 2.9 (2010): 53-61.
7. Ejaro SP and Abdullahi U. “Spatiotemporal Analyses of Land Use and Land Cover Changes in Suleja Local Government Area, Niger State, Nigeria”. Journal of Environment and Earth Science 3.9 (2013): 72-83.
8. Ewaid SH and Abed SA. “Water Quality Index for Al-Gharraf River, Southern Iraq”. Egyptian Journal of Aquatic Research 43 (2017): 117-122.
9. Hasan MM., et al. “Water quality indices to assess the spatiotemporal variations of Dhaleshwari river in central Bangladesh”. Environmental and Sustainability Indicators 8 (2020): 1-13.
10. Lumb A, Sharma TC and Bibeault JF. "A Review of Genesis and Evolution of Water Quality Index (WQI) and Some Future Directions”. Water Qual. Expo. Health 3 (2011): 11-24.
11. Mahi SA and Isah S. “Water Quality Assessment of Gurara Water Transfer Projec and Lower Usuma Dam, Abuja”. Nigeria International Journal of Scientific and Research Publications 6.1 (2016): 125-139.
12. Majeed SA. “Comparison of Water Quality Indices: Bani-Hassan River as a Case Study”. Journal University of Kerbala 16.1 (2018): 53-65.
13. Okunlola IA., et al. “Assessment of Water Quality of Gurara Water Transfer from Gurara Dam to Lower Usuma Dam for Abuja Water Supply, FCT, Nigeria”. American Journal of Water Resources 2.4 (2014): 74-80.
14. Sarkar K and Majumder M. “Application of AHP-Based Water Quality Index for Quality Monitoring of Peri-Urban Watershed”. Environ. Dev. Sustain 23 (2021): 1780-1798.
15. State Bureau of Statistics Facts and Figures on Niger State by Niger State Planning Commission 2011 Edition (2011).
16. Tatawat RK and Singh CP. “A hydrochemical profile for assessing the groundwater quality of Jaipur city”. Environ. Monit. Assess 143.1-3 (2008): 337-343.
17. Todd DK. “Groundwater Hydrology”. second ed. John Wiley and Sons, New York (1980): 535.
18. WHO. Guidelines for drinking-water quality: fourth edition incorporating the first addendum (2017).

Water quality can be affected by either natural or anthropogenic factors. In this study, Water quality indices of Kofa Dam were determined for drinking and irrigation purposes Water samples were analyzed for the selected physicochemical parameter (pH, Electrical conductivity, Chloride, Total dissolve solids, Salinity, Sulphate, Sodium, Nitrate, Calcium, Temperature, Turbidity and Bicarbonate). The results from the analysis were compared with the drinking and irrigation standards of NSDWQ, FAO and WHO. Also, the results in relation to the standards were used to compute indices for drinking and irrigation purposes using the weighted arithmetic index. The results of the physicochemical parameter from each of the sampling locations of Kofa Dam and the overall score show that the Dam failed the index. Therefore, the water is not suitable for drinking purpose. However, for irrigation purposes, the results of the indices show a need for caution in the usage of water. This suggests that anthropogenic activities such as farming around the Dam and the presence of residential houses which discharge their effluent into the Dam are already becoming a source of threat to the reservoir. Hence, there is a need for regulation of activities around the dam to prevent further deterioration of water.

Keyword: Drinking water; Indices; Irrigation; Kofa Dam; Suleja; Water quality; Nigeria

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Pattern recognition is the categorization and classification of specific patterns based on predefined characteristics from sets of available data. Implementation of many human skills such as face recognition, speech recognition, reading handwritten letters with very high stability to noise and different environmental conditions (like what exists in humans) by machines is one of the problems and issues that have been the focus of researchers in various engineering fields such as artificial intelligence and machine vision in the last few decades. Pattern recognition has many applications in various fields of science, including electrical engineering (medicine, computer and telecommunications), biology, machine vision, economics and psychology. Among the applications, we can mention things such as: recognition of voice, face, handwriting, fingerprint and signature, automatic disease detection from medical data (signal or image), detection of DNA strands, industrial automation and remote sensing. Pattern recognition, in short, deals with the problem of clustering and classification supervised and unsupervised and includes a wide range of statistical classical methods, intelligent algorithms, neural networks and fuzzy logic. In this regard, I recommend the book “Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, Second Edition.

References

1. Ayo SA. “Development and Characterization of epoxy/Borassus Palm Leaf Stalk Fiber Reinforced Composite”. Fibers and Polymer 19.2 (2018): 16-122.
2. ASTM D570-98. “Standard Test Method for Water Absorption of Plastics”. ASTM International, West Conshohocken (2018).
3. 3822-07. “Standard Test Method for Tensile Properties of Single Textile, ASTM International West Conshoshocken”. Philadelphia USA 8.1 (2007).
4. Azrin (2011).
5. Bhagat VK, Biswas S and Dehury J. “Physical, Mechanical and Water Absorption Behavior of Coir/Glass Fiber Reinforced Epoxy Based Hybrid Composite”. Polymer Composites (2014): 925-930.
6. Gautam G, Norkey G and Pandey AK. “Mechanical Characterization of Kevlar-29 Fiber Reinforced Polymer Composite”. ELK Asia Pacific Journals 6.6 (2017): 978-993.
7. Kumar R. “Effect of Fiber Type, Weight Percentage Loading, and Fiber Size on Impact Strength and Hardness of Wood and Rice Husk Hybrid Composites”. International journal of material science 12.3 (2017): 443-460.
8. Marian K. “Archimedes’ Principle in action”. Physics Education 42.5 (2007): 484-487.
9. Nekka S, Chebira F and Haddaoui N. “Effect of Fiber Treatment on the Mechanical and Rheological Properties of Polypropylene/Broom Fiber Spartium Junceum Composites”. JEAS 1.3 (2006): 278-283.
10. Nunna S., et al. “A Review on Mechanical Bahavior of Natural Fiber based Epoxy Composite”. JRP 31 (2012): 759-769.
11. Pan Y and Zhong Z. “The Effect of Hybridization on Moisture Absorption and Mechanical Degradation of Natural Fiber Composites: An Analytical Approach”. Elsevier-Composite Sci. and Tech 110 (2015): 132-137.
12. Patel N., et al. “Investigations on Mechanical Strength of Hybrid Basalt/Glass Polyester Composites”. International Journal of Applied Engineering Research 13.6 (2018): 4038-4088.
13. Paul A, Joseph K and Thorma S. “Effect of Surface Treatments on the Electrical Properties of Low Density Polethylene Composites Reinforced with Short Sisal Fibers”. Composite Sci. & Tech 51 (2007): 67-79.
14. Reddy KO., et al. “Effect of Chemical Treatment and Fiber Loading on Mechanical Properties of Borassus (Toddy Palm) Fiber/Epoxy Composites”. International Journal of Polymer Analysis and Characterization 20.7 (2015): 612-626.
15. Reddy KO., et al. “Thermal Degradation parameters and tensile properties of borassus flabellifer fruit fiber reinforcement”. Journal of reinforced plastics and composites 28.18 (2009): 2297-2301.
16. Saba N, Tahirand Jawaid M. “A Review on Potentiality of Nano-Filler/Natural Fiber Filled Polymer Hybrid Composite”. Polymers 2 (2014): 2247-2273.
17. Samal SK, Mohanty S and Nayak SK. “Polypropylene-Bamboo/Glass Fiber Hybrid Composites: Fabrication and Analysis of Mechanical, Morphological, Thermal, and Dynamic Mechanical Behavior”. Journal of reinforced plastics and composites 28.22 (2009): 2729-2747.
18. Singh JK, Rou AK and Kumari K. “A review on Borassus flabellifer lignocellulose fiber reinforced polymer composites”. Carbohydrate Polymers 262 (2021): 117929.
19. Sukanya P., et al. “An insight on the chemistry of epoxy and its curing for coating applications: A detailed investigation and future perspectives”. Polymer-Plastic Technology and Engineering 55.8 (2015): 35-54.
20. Thwea MM and Liao K. “Effects of Environmental aging on the Mechanical Properties of Bamboo-Glass Fiber Reinforced Polymer Matrix Hybrid Composites”. Compos Part A 33 (2002): 43-52.
21. Warbhe NO, Shrivastava R and Adwani PS. “Mechanical Properties of Kevlar/Jute reinforced Epoxy Composite”. International Journal of Innovative Research in Science, Engineering and Technology - IJIRSET 5.9 (2016): 16407-16418.
22. Vineet KB, Biswas S and Dehury J. “Physical, Mechanical and Water Absorption Behavior of Coir/Glass Fiber Reinforced Epoxy Based Hybrid Composites”. Polymer Composites 35.5 (2014): 925-930.
23. Yahaya R., et al. “Investigating Ballistic Impact Properties of Woven Kenaf-Aramid Hybrid Composites”. Fibers and Polymers 17 (2016): 275-281.

In this work, hybrid composites were produced from Borassus flabellifer leaf (BFLF) and Kevlar fibers in Epoxy matrix to establish their effect on physical and mechanical properties of the composites. Hand lay-up technique followed by compression was used to produce hybrid composites samples of different combinations of BLFL and Kevlar fiber. The mechanical and physical analyses were done using associated ASTM standards. The Results revealed that 2K10B had the lowest density of 1.023g/cm³, about 22.91% lower compared to neat sample. The 6K6B sample had the preeminent properties; 53.14, 50.5, 14.8 and 48.54% water absorption, TS, flexural and impact strength respectively higher as compared with samples of 100% K while 11.1% and 44.27% less for elongation and elastic modulus respectively.

Keywords: BorassusFlabellifer Leaf Fiber (BFLF); Tensile Strength (TS); Water Absorption; Impact Strength; Flexural; Hybridization

References

1. Abouelmehdi K, Beni-Hessane A and Khaloufi H. “Big healthcare data: Preserving security and privacy”. Journal of Big Data 5.1 (2018): 1-18.
2. Alexopoulos C., et al. “How machine learning is changing e-government”. In Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance—ICEGOV2019, Part F1481 (2019): 354-363.
3. Bonczek RH, Holsapple CW and Whinston AB. “Foundations of Decision Support Systems”. Academic Press, New York (2014).
4. Broring A., et al. “Enabling IoT ecosystems through platform interoperability”. IEEE Software 1.34 (2017): 54-61.
5. Cresswell AM and Sayogo DS. Developing Public Value Metrics for Returns to Government ICT Investments (2012).
6. Davenport T., et al. “How artificial intelligence will change the future of marketing”. Journal of the Academy of Marketing Science 48.1 (2020): 24-42.
7. De Vries H, Bekkers V and Tummers L. “Innovation in the public sector: A systematic review and future research agenda”. Public Administration 94.1 (2016): 146-166.
8. Edler J., et al. Innovation and Public Procurement. Review of Issues at Stake. In Study for the European Commission (No ENTR/03/24); (2006). Fraunhofer Institute for Systems and Innovation Research (2006).
9. Edquist C, Hommen L and Tsipouri L. Public Technology Procurement and Innovation:16. Springer US (2000).
10. Fu JR, Farn CK and Chao WP. “Acceptance of Electronic Tax Filing: A Study of Taxpayer Intentions”. Information &Management 43 (2006): 109-126.
11. Holzinger A, Tjoa AM and Kieseberg P. “Digital transformation for sustainable development goals (SDGs) - a security, safety and privacy perspective on AI”. in: International Federation for Information Processing (2021): 1-20.
12. Karunasena K and Deng H. “Critical factors for evaluating the public value of e-government in Sri Lanka”. Government Inf. Quart 1.29 (2012): 76-84.
13. Lauterbach A. Artificial Intelligence and policy: QuoVadis? Digital Policy, Regulation and Governance 21.3 (2019): 238-263
14. Lee YJ and Park JY. “Identification of future signal based on the quantitative and qualitative text mining: A case study on ethical issues in Artificial Intelligence”. Quality and Quantity 52.2 (2018): 653-667.
15. Medhora PR. “Data Governance in the Digital Age”. Centre for International Governance Innovation Special Report (2018).
16. Morse RS. “Integrative public leadership: Catalysing collaboration to create public value”. Leadership Quart 2.21 (2010): 231-245.
17. Nilashi M., et al. “Determining the Importance of Hospital Information System Adoption Factors Using Fuzzy Analytic Network Process (ANP).” Technological Forecasting and Social Change 111 (2016): 244-264.
18. Orji IJ and Wei S. “A Detailed Calculation Model for Costing of Green Manufacturing”. Industrial Management & Data Systems 116.1 (2016): 65-86.
19. Pandey SK., et al. “Transformational leadership and the use of normative public values: Can employee be inspired to serve larger public purpose?”. Public Admin 1.94 (2016): 204-222.
20. Rezaei J. “Best-worst multi-criteria decision-making method”. Omega 53 (2015): 49-57.
21. Rezaei J. “Best- Worst Multi- Criteria Decision Making Method: Some Properties and a Linear Model”. Omega 64 (2016): 126-130.
22. Rogers EM. Diffusion of Innovations, Fourth edition, New York: Free Press (1995).
23. Rogers EM. Diffusion of Innovations, Fifth edition, New York: Free Press (2003).
24. Schedler K, Guenduez AA and Frischknecht R. “How smart can government be? Exploring barriers to the adoption of smart government”. Information Polity 24.1 (2019): 3-20.
25. Schrader DE and Ghosh D. “Proactively protecting against the singularity: Ethical decision making in AI”. IEEE Security and Privacy 16.3 (2018): 56-63.
26. Stock T and Seliger G. “Opportunities of sustainable manufacturing in industry 4.0”. Procedia CIRP 40 (2016): 536-541.
27. Sun TQ and Medaglia R. “Mapping the challenges of Artificial Intelligence in the public sector: evidence from public healthcare”. Govern. Inf. Q 36.2 (2019): 368-383.
28. Thesmar D., et al. “Combining the power of Artificial Intelligence with the richness of healthcare claims data: Opportunities and challenges”. Rmaco Economics 37.6 (2019): 745-752.
29. Tornatzky L and Fleischer M. “The process of technology innovation”. Lexington, MA, Lexington Books (1990).
30. van Noordt C and Misuraca G. “Exploratory Insights on Artificial Intelligence for Government in Europe”. Social Science Computer Review (2020).
31. van Noordt C and Misuraca G. Towards a Systematic Understanding on the Challenges of Procuring Artificial Intelligence in the Public Sector (2021).
32. Vellido A. Societal issues concerning the application of Artificial Intelligence in medicine. Kidney Diseases 5.1 (2019): 11-17.
33. Wang ZG., et al. “Energy Performance Contracting, Risk Factors, and Policy Implications: Identification and Analysis of Risks Based on the Best- Worst Network Method”. Energy 170 (2019): 1-13.
34. Yang Y., et al. “Efficacy of China’s Strategic Environmental Management in its Institutional Environment”. International Journal of Operations & Production Management 39.1 (2018): 138-163.
35. Yusof NM, Paul RJ and Stergioulas LK. "Towards a Framework for Health Information Systems Evaluation”. The 39th Hawaii International Conference on System Science (2006).

The study constructed an estimation of the significance of driving factors that influence artificial intelligence (AI) adoption and implementation in the public sector, and accentuated a critical research area that is currently understudied. A theoretical framework, underpinned by the diffusion of innovation (DOI) theory, was developed from a mingle of the technology, organization, and environment (TOE) framework and the human, organisation, and technology (HOT) fit model. The best-worst method was used to scrutinize and rank the identified driving factors according to their weighted averages. The findings of the study pointed to privacy and security; reliability, serviceability and functionality; regulation; interpretability and ease of use; IT infrastructure and data; and ethical issues as the highest ranked driving factors for AI adoption and implementation in government institutions. The study has significant implications for policy makers and practitioners, as it would augment their perspectives on how to adopt and implement AI innovations.

Keywords: privacy and security; innovation; artificial intelligence; government; technology; best-worst method

References

1. Jagdeesh Patil S. ‘BTEX: A Fault Diagnosis Expert System for Broadcast Transmitter’ at Indian Institute of Science Bangalore.
2. Robert A Edmunds. ‘Guide to Expert Systems’, Prentice Hall (1998).
3. MW – (500KW x 6] Brown Boveri Transmitter Manuals, vols 1 to 5 - Super Power Transmitting Station, Bangalore.

With the increasing size and complexity of high-power digital transmitters in the broadcasting network, their fault diagnosis has become cumbersome and time-consuming process. Meanwhile the breakdown transmission is not tolerated. These transmitters located in different parts of the country, are required to provide uninterrupted service round the clock. Sometime they develop faults which are complex in nature and call for services of the transmitter expert. However, the experts may not available all the time, and the fault diagnosis takes long time even for experts using manual techniques, it is highly desirable to provide a computer-based Fault Diagnosis Expert System.

References

1. Zhang Y Harman, M Finkelstein A and Mansouri A. “Comparing the performance of metaheuristics for the analysis of multi-stakeholder tradeoffs”. Requirements Optimization (2011): 761-773.
2. Atladottir G Thora HE and Gunnarsdottir. “Comparing task practicing and prototype fidelities when applying scenario acting to elicit requirements”. Requirements Engineering (2012): 157-170.
3. Meth H, Brhel M and Maedche A. “The state of the art in automated requirements elicitation information and Software Technology”. (2013): 1695-1709.
4. Pacheco C and Garcia. “A systematic literature review of stakeholder identification methods in requirements elicitation”. Journal of Systems and Software (2012): 2171-2181.
5. Sharmila P and R Umarani. “A walkthrough of Requirement Elicitation Techniques”. International Journal of Engineering Research and Applications (2011): 1583-1586.
6. Razali R and F Anwar. “Selecting the Right Stakeholders for Requirements Elicitation: A Systematic Approach”. Journal of Theoretical and Applied Information Technology (2011).
7. Vlas R and Robinson W. “A rule-based natural language technique for requirements discovery and classification in open-source software development projects”. Proceedings of the 44th Hawaii International Conference on System Sciences. Manoa, Hawaii (2011): 110.
8. Aranda G Vizca?no. “A framework to improve communication during the requirements elicitation process in GSD projects”. Requirements Engineering (2010): 397-417.
9. Sabahat N., et al. “An iterative approach for global requirements elicitation: a case study analysis”. Proceedings of the IEEE International Conference on Electronics and Information Engineering (ICEIE). Kyoto, Japan (2010): 361-366.
10. Sutcliffe A and Maiden N. “The domain theory for requirements engineering”. IEEE Transactions on Software Engineering, IEEE (1998): 174-196.
11. Breitman KK, Leite JCSP and Berry DM. “Supporting scenario evolution”. Requirements Engineering (2005): 112131.
12. Soltanian A, Binti R and Soltanian H. “WEBSTUIRE: web-based support tool for user interface requirements elicitation”. Proceeding of the International Conference on Computer and Knowledge Engineering (ICCKE). Ferdowsi, Mashhad (2013): 17.
13. Fernandes J., et al. “I Think: a game based approach towards improving collaboration and participation in requirement elicitation”. Procedia Computer Science (2013): 66-77.
14. Carrizo D, Dieste O and Juristo N. “Systematizing requirements elicitation technique selection”. Journal: Information and Software Technology (2014): 644-669.
15. Burnay C and Faulkner S. “What stakeholders will or will not say: A theoretical and empirical study of topic importance in requirements engineering elicitation interviews”. Information Systems (2014): 61-81.
16. Castro C and Cleland J. “Utilizing recommender systems to support software requirements elicitation”. Second International Workshop on Recommendation Systems for Software Engineering (RSSE’10). South Africa (2010): 610.
17. Wnuk K, Gorschek T and Zahda S. “Obsolete software requirements. Information and Software Technology”. (2013): 921-940.
18. Sakhnini V, Mich L and Berry D. “The effectiveness of an optimized EPM create as a creativity enhancement technique for web site requirements elicitation”. Requirements Engineering (2012): 171-186.
19. Moros B., et al. “Transforming and tracing reused requirements models to home automation model”. Information and Software Technology (2013): 941-965.
20. Lenis R Wong, David S Mauricio and Glen D Rodriguez. “A Systematic Literature Review About: Software Requirements Elicitation”. Journal of Engineering Science and Technology 12 (2017): 296-317.
21. Fares Anwar and Rozilawati Razali. “A Practical Guide to Requirements Elicitation Techniques Selection: An Empirical Study”. Middle-East Journal of Scientific Research 11 (2012): 1059-1067.
22. Didar Zowghi and N Nurmuliani. “A Study of the Impact of Requirements Volatility on Software Project Performance”. University of Technology, Sydney (2007).
23. Jane Coughlan, Mark Lycett and Robert D Macredie. “Communication issues in requirements elicitation: A content analysis of stakeholder experiences”. Information and Software Technology 45 (2003): 525-537.

Requirement engineering has taken the attention in both academic and industries, as today’s software’s expected to fulfil and provide highly customers’ centralize functionality and qualities. Requirement elicitation is the main and major step of any software project development life. It has direct impact on development lifecycle of any software. The incomplete or ambiguous requirement create confusion for the stakeholders. This is the step which leads the project to the success or fulfills the desire of user or it is the step or reason which may leads the project to the downfall. In requirement elicitation process there are many factors which effects on requirement elicitation. In this paper we are proposing the impact of Organizational factors on requirement engineering. Stakeholder needs include an initial, but continuous and critical phase in program development. This section of development is characterized by a high degree of error, influencing important factors based on communication problems. Software development is considered as a powerful process in which the needs of change seem to exist inevitable. Software updates are encouraged by all types of changes including changes in requirements. These types of changes cause internal flexibility, which has several implications for software development life cycle. In particular, the findings reveal that the instability of services is at its highest significant impact on time and cost skip to software projects. Our investigation too tested features that contribute to the level of demand flexibility and I found that flexible as common communication between users and developers and the application of a clear approach to needs analysis and modeling contribute to the stability of requirements. The main purpose of this survey is to look for the requirement elicitation process from the intra and extra factors perspective. The quality of services is critical to the success of the project. Negotiation needs, however these are not an easy task. Vision, psychological model and differences in expectations between users and analysts do this work is difficult and controversial. In most cases, clients are completely convinced of their real needs. In others, the current operating procedure does not meet the expectations of management. In this paper we have search the problems, factors of elicitation process.

Keywords: Impact of organizational factors RE; Requirement elicitation; intra organization

References

1. Rao SR Ramachandra. “Resource recovery and recycling from metallurgical wastes”. Elsevier (2011).
2. Yang Tianzu., et al. "Hydrometallurgical treatment of copper smelting dust by oxidation leaching and fractional precipitation technology”. JoM 69.10 (2017): 1982-1986.
3. Xue J., et al. “Comprehensive recovery of arsenic and antimony from arsenic-rich copper smelter dust”. Journal of Hazardous Materials 413 (2021): 125365.
4. Gao Jintao., et al. "Recovery of crown zinc and metallic copper from copper smelter dust by evaporation, condensation and super-gravity separation”. Separation and Purification Technology 231 (2020): 115925.
5. Okanigbe Daniel Ogochukwu, API Popoola and Abraham A Adeleke. "Characterization of copper smelter dust for copper recovery”. Procedia Manufacturing 7 (2017): 121-126.
6. Okanigbe DO, API Popoola and AA Adeleke. "Hydrometallurgical processing of copper smelter dust for copper recovery as nano-particles: A review”. Energy Technology (2017): 205-226.
7. Okanigbe Daniel., et al. "Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust”. Cogent Engineering 5.1 (2018): 1551175.
8. Flores Gerardo Alvear, Carlos Risopatron and Joe Pease. "Processing of complex materials in the copper industry: challenges and opportunities ahead”. Jom 72.10 (2020): 3447-3461.
9. Okanigbe DO, API Popoola and AA Adeleke. "Thermal analysis and kinetics of the oxidative roasting process of a copper smelter dust”. The International Journal of Advanced Manufacturing Technology 94.5 (2018): 2393-2400.
10. Moldabayeva GZ., et al. "Electrosmelting of lead-containing dusts from copper smelters”. Journal of Sustainable Metallurgy 1.4 (2015): 286-296.
11. Priya Jayati., et al. "High-purity copper recycled from smelter dust by sulfation roasting, water leaching and electrorefining”. Environmental Chemistry Letters 18.6 (2020): 2133-2139.
12. Gorai Bipra, Ranajit Kumar Jana and Zahid Husain Khan. "Electrorefining electrolyte from copper plant dust”. Materials Transactions 43.3 (2002): 532-536.
13. Ha Tae Kyung., et al. "Selective leaching and recovery of bismuth as Bi2O3 from copper smelter converter dust”. Separation and Purification Technology 142 (2015): 116-122.
14. Qiang Li, Isabel SS Pinto and Zhao Youcai. "Sequential stepwise recovery of selected metals from flue dusts of secondary copper smelting”. Journal of cleaner production 84 (2014): 663-670.
15. Vítková Martina., et al. "Leaching of metals from copper smelter flue dust (Mufulira, Zambian Copperbelt)”. Applied Geochemistry 26 (2011): S263-S266.
16. Bakhtiari F., et al. "Bioleaching of copper from smelter dust in a series of airlift bioreactors”. Hydrometallurgy 90.1 (2008): 40-45.
17. Vakylabad, Ali Behrad. "A comparison of bioleaching ability of mesophilic and moderately thermophilic culture on copper bioleaching from flotation concentrate and smelter dust”. International journal of mineral processing 101.1-4 (2011): 94-99.
18. Vakylabad Ali Behrad., et al. "Bio-processing of copper from combined smelter dust and flotation concentrate: A comparative study on the stirred tank and airlift reactors”. Journal of hazardous materials 241 (2012): 197-206.
19. Morales A., et al. “Treatment of copper flash smelter flue dusts for copper and zinc extraction and arsenic stabilization”. Hydrometallurgy, 105(1-2) (2010): 148-154.
20. Montenegro Victor, Hiroyuki Sano, and Toshiharu Fujisawa. "Recirculation of Chilean copper smelting dust with high arsenic content to the smelting process”. Materials transactions 49.9 (2008): 2112-2118.
21. Alguacil Francisco Jose., et al. "Recycling of copper flue dust via leaching-solvent extraction processing”. Desalination and Water Treatment 56.5 (2015): 1202-1207.
22. Font Oriol., et al. "Copper flash smelting flue dust as a source of germanium”. Waste and biomass valorization (2017).
23. Okanigbe Daniel O, Abimbola PI Popoola and Abraham A Adeleke. "Enhanced Recycling of Waste Copper Smelter Dust from Existing and Proposed Smelter Plant at the Palabora Copper (pty) ltd, impopo, South Africa”. IOP Conference Series: Materials Science and Engineering. IOP Publishing 391 (2018): 1.
24. Adeleke AA., et al. "Experimentation, Modeling, and Optimum Conditions of Pyro-Hydrometallurgical-Precipitation Reaction Technology for Recovery of Copper as Oxide of Nanoparticles from a Copper Dust”. Energy Technology 2020: Recycling, Carbon Dioxide Management, and Other Technologies. Springer, Cham (2020): 189-201.
25. Chu Yingying., et al. "Micro-copper powders recovered from waste printed circuit boards by electrolysis”. Hydrometallurgy 156 (2015): 152-157.
26. Cook Benjamin S., et al. "Inkjet catalyst printing and electroless copper deposition for low-cost patterned microwave passive devices on paper”. Electronic Materials Letters 9.5 (2013): 669-676.
27. Bakhtiari Fereshteh. "Synthesis and characterization of tenorite (CuO) nanoparticles from smelting furnace dust (SFD)”. Journal of mining and metallurgy, section B: Metallurgy 49.1 (2013): 21-21.
28. Gawande Manoj B., et al. "Cu and Cu-based nanoparticles: synthesis and applications in catalysis”. Chemical reviews 116.6 (2016): 3722-3811.
29. Jones John?Paul, GK Surya Prakash and George A Olah. "Electrochemical CO2 reduction: recent advances and current trends”. Israel Journal of Chemistry 54.10 (2014): 1451-1466.
30. Xie Huan., et al. "Cu-based nanocatalysts for electrochemical reduction of CO2”. Nano Today 21 (2018): 41-54.
31. Ingle Avinash P, Nelson Duran and Mahendra Rai. "Bioactivity, mechanism of action, and cytotoxicity of copper-based nanoparticles: a review”. Applied microbiology and biotechnology 98.3 (2014): 1001-1009.
32. Ameh Thelma and Christie M Sayes. "The potential exposure and hazards of copper nanoparticles: A review”. Environmental Toxicology and Pharmacology 71 (2019): 103220.
33. Ogunsona Emmanuel O., et al. "Engineered nanomaterials for antimicrobial applications: A review”. Applied Materials Today 18 (2020): 100473.
34. Foley Jonathan A., et al. "Solutions for a cultivated planet”. Nature 478.7369 (2011): 337-342.
35. Mueller Nathaniel D., et al. "Closing yield gaps through nutrient and water management”. Nature 490.7419 (2012): 254-257.
36. Li Peng, et al. "Eco?friendly biomolecule?nanomaterial hybrids as next?generation agrochemicals for topical delivery”. EcoMat 3.5 (2021): e12132.
37. Guan Xiangyu., et al. "CuO nanoparticles alter the rhizospheric bacterial community and local nitrogen cycling for wheat grown in a calcareous soil”. Environmental science & technology 54.14 (2020): 8699-8709.
38. Camara Marcela Candido., et al. "Development of stimuli-responsive nano-based pesticides: emerging opportunities for agriculture”. Journal of nanobiotechnology 17.1 (2019): 1-19.
39. Ngo Quoc Buu., et al. "Effects of nanocrystalline powders (Fe, Co and Cu) on the germination, growth, crop yield and product quality of soybean (Vietnamese species DT-51)”. Advances in Natural Sciences: Nanoscience and Nanotechnology 5.1 (2014): 015016.
40. CO Dimpka., et al. “Bioactivity and biomodification of Ag, ZnO, CuO nanoparticles with relevance to plant performance in agriculture”. IndBiotechnol 8 (2012): 344-357.
41. Tamilvanan A, K Balamurugan and M Vijayakumar. "Effects of nano-copper additive on performance, combustion and emission characteristics of Calophylluminophyllum biodiesel in CI engine”. Journal of Thermal Analysis and Calorimetry 136.1 (2019): 317-330.
42. Lee Youngil., et al. "Large-scale synthesis of copper nanoparticles by chemically controlled reduction for applications of inkjet-printed electronics”. Nanotechnology 19.41 (2008): 415604.
43. Conway Jon R., et al. "Aggregation, dissolution, and transformation of copper nanoparticles in natural waters”. Environmental science & technology 49.5 (2015): 2749-2756.
44. Adeleye Adeyemi S., et al. "Influence of extracellular polymeric substances on the long-term fate, dissolution, and speciation of copper-based nanoparticles”. Environmental science & technology 48.21 (2014): 12561-12568.
45. Slotte Martin and Ron Zevenhoven. "Energy requirements and life cycle assessment of production and product integration of silver, copper and zinc nanoparticles”. Journal of Cleaner Production 148 (2017): 948-957.
46. Li T., et al. "Concentrations and solubility of trace elements in fine particles at a mountain site, southern China: regional sources and cloud processing”. Atmospheric Chemistry and Physics 15.15 (2015): 8987-9002.
47. Xu Bin., et al. "A review of the comprehensive recovery of valuable elements from copper smelting open-circuit dust and arsenic treatment”. Jom 72.11 (2020): 3860-3875.
48. Bakhtiari F., et al. "Continuous copper recovery from a smelter's dust in stirred tank reactors”. International journal of mineral processing 86.1-4 (2008): 50-57.
49. Chen Ya., et al. "Recovery of bismuth and arsenic from copper smelter flue dusts after copper and zinc extraction”. Minerals Engineering 39 (2012): 23-28.
50. Guo Xueyi., et al. "Separation and recovery of arsenic from arsenic-bearing dust”. Journal of environmental chemical engineering 3.3 (2015): 2236-2242.
51. Zhang Rong-Liang., et al. "Treatment process of dust from flash smelting furnace at copper smelter by oxidative leaching and dearsenifying process from leaching solution”. Zhongnan DaxueXuebao (ZiranKexue Ban)/Journal of Central South University(Science and Technology) 37.1 (2006): 73-78.
52. Jia YF., et al. "Preparation, Characterization and Solubilities of Adsorbed and Co?Precipitated Iron (III)?Arsenate Solids”. Electrometallurgy and Environmental Hydrometallurgy 2 (2003): 1923-1935.
53. Shibayama Atsushi., et al. "Treatment of smelting residue for arsenic removal and recovery of copper using pyro–hydrometallurgical process”. Journal of hazardous materials 181.1-3 (2010): 1016-1023.
54. Pérez-Moreno SM., et al. "Diagnose for valorisation of reprocessed slag cleaning furnace flue dust from copper smelting”. Journal of Cleaner Production 194 (2018): 383-395.
55. Yuan Gao-Qing., et al. "Shape-and size-controlled electrochemical synthesis of cupric oxide nanocrystals”. Journal of Crystal Growth 303.2 (2007): 400-406.
56. Neupane Madhav Prasad., et al. "Temperature driven morphological changes of hydrothermally prepared copper oxide nanoparticles”. Surface and Interface Analysis: An International Journal devoted to the development and application of techniques for the analysis of surfaces, interfaces and thin films 41.3 (2009): 259-263.
57. Outokesh M., et al. 2011. “Hydrothermal synthesis of CuO nanoparticles: study on effects of operational conditions on yield, purity, and size of the nanoparticles”. Industrial & engineering chemistry research, 50(6), pp.3540-3554.
58. Zhu Junwu., et al. "Highly dispersed CuO nanoparticles prepared by a novel quick-precipitation method”. Materials Letters 58.26 (2004): 3324-3327.
59. Nikam Arun V., et al. "pH-dependent single-step rapid synthesis of CuO and Cu2O nanoparticles from the same precursor”. Crystal Growth & Design 14.9 (2014): 4329-4334.
60. Vidyasagar CC., et al. "Solid-state synthesis and effect of temperature on optical properties of Cu–ZnO, Cu–CdO and CuO nanoparticles”. Powder technology 214.3 (2011): 337-343.
61. Balá? Matej., et al. "Synthesis of copper nanoparticles from refractory sulfides using a semi-industrial mechanochemical approach”. Advanced Powder Technology 31.2 (2020): 782-791.
62. Gupta Kalyani, Marco Bersani and Jawwad A Darr. "Highly efficient electro-reduction of CO2 to formic acid by nano-copper”. Journal of Materials Chemistry 4.36 (2016): 13786-13794.
63. Elouali Sofia., et al. "Gas sensing with nano-indium oxides (In2O3) prepared via continuous hydrothermal flow synthesis”. Langmuir 28.3 (2012): 1879-1885.
64. Seredkin Maxim, Alexander Zabolotsky and Graham Jeffress. "In situ recovery, an alternative to conventional methods of mining: Exploration, resource estimation, environmental issues, project evaluation and economics”. Ore Geology Reviews 79 (2016): 500-514.
65. Pappu Asokan, Mohini Saxena and Shyam R Asolekar. "Solid wastes generation in India and their recycling potential in building materials”. Building and environment 42.6 (2007): 2311-2320.
66. Hazra Tumpa and Sudha Goel. "Solid waste management in Kolkata, India: Practices and challenges”. Waste management 29.1 (2009): 470-478.
67. Hudson-Edwards., et al. "Mine wastes: past, present, future”. Elements 7.6 (2011): 375-380.
68. Martens Evelien., et al. "Toward a more sustainable mining future with electrokinetic in situ leaching”. Science Advances 7.18 (2021): eabf9971.
69. Okanigbe Daniel Ogochukwu. "Production of copper and copper oxide nano-particles from leach solution of low grade copper smelter dust”. (2019).
70. Crane RA and DJ Sapsford. "Selective formation of copper nanoparticles from acid mine drainage using nanoscale zerovalent iron particles”. Journal of hazardous materials 347 (2018): 252-265.
71. Stefanowicz T, Osi?ska M and Napieralska-Zagozda S. “Copper recovery by the cementation method”. Hydrometallurgy 47.1 (1997): 69-90.
72. Gauteng Scrap Metal Prices - South Africa.              
73. Alibaba.com.
74. Crane RA and DJ Sapsford. "Towards “Precision Mining” of wastewater: Selective recovery of Cu from acid mine drainage onto diatomite supported nanoscale zerovalent iron particles”. Chemosphere 202 (2018): 339-348.
75. Borglin Sharon E, George J Moridis and Curtis M Oldenburg. "Experimental studies of the flow of ferrofluid in porous media”. Transport in porous media 41.1 (2000): 61-80.
76. Nicol Michael J and Zaimawati Zainol. "The development of a resin-in-pulp process for the recovery of nickel and cobalt from laterite leach slurries”. International Journal of Mineral Processing 72.1-4 (2003): 407-415.
77. Udayar T, MH Kotze and V Yahorava. "Recovery of uranium from dense slurries via resin-in-pulp”. Proceedings of the 6th Southern African Base Metals Conference (2011).

This Century will see an urgent up-scaling in the global production of copper nanoparticles due to the continuous industrial development and mounting need to address pressing global issues including non-pharmaceutical disease management and climate change. Mining, benefaction, refining, reagent synthesis, and finally nanoproduct synthesis are the typical linear multistage steps in the traditional copper nanoparticle synthesis process, which is energy and resource intensive. The utilization of nanometer-scale zerovalent iron particles as a reducing agent for environmentally friendly copper nanoparticle manufacturing from waste copper dust is discussed in this paper. Based on the revision, it is clear that this method has significant potential and could represent a completely new paradigm for the conversion of low-grade Cu bearing waste (such as waste copper dust) into useful nanoparticulate Cu compounds for a variety of industrial applications.

Keywords: waste copper dust; cementation; chemical reduction; hydrometallurgy; valorization

     In vapour compression refrigeration system, refrigeration is obtained by giving electric power to compressor and mainly liquid type of refrigerant is used. In vapour absorption refrigeration system, refrigeration is obtained by supplying heat energy to generator (heat receiving device from external source and does the work in place of compressor in compression refrigeration system).It has both refrigerant(liquid type) and absorbent(solid type). In gas cycle refrigeration system, gas type refrigerant is used. Ejector type refrigeration system is a modified form of vapour compression refrigeration system where ejector is used in place of compressor. Here mainly water as refrigerant is used. Lastly thermoelectric refrigeration system is there which works on thermoelectric effect (evolution of heat at one junction and absorption of heat at other junction after passing electric current through a circuit of a thermocouple). Thermocouple is an electrical device which consists of two dissimilar electrical conductors thus forming an electrical junction.

     The COP (coefficient of performance) i.e. performance index of refrigeration are different for different refrigeration types. COP of compression refrigeration system is always more than 1 and higher than absorption system. COP of gas cycle is higher than compression refrigeration system. COP of ejector type is upto 0.3 and for thermoelectric refrigeration system typically between 0.3 to 0.7.

Keywords: Absorption; Compression; Ejector; Gas; Refrigeration; Temperature; Thermoelectric

     Refrigeration is an art of maintaining a system/object at a temperature lower than ambient temperature or surroundings. Refrigeration is used in every day to day life. Foodstuffs are stored at congenial temperature for preventing them from spoilage. The spoilage is reduced as micro-organisms are inactive at lower temperature and thus spoilage is reduced. In medicinal field, different medicines are stored at refrigerated temperature preventing them from spoilage. Also refrigeration is utilized to maintain a given volume of space at comfortable temperature. During summer the required space is maintained at lower temperature than ambient temperature for comfort. While during winter the required space is maintained at higher temperature than ambient temperature for comfort. For achieving refrigeration different types of refrigeration are available.

References

1. Ajzen Icek. “The Theory of Planned Behavior: Frequently Asked Questions”. Human Behavior and Emerging Technologies 2.4 (2020): 314-24.
2. Circella Giovanni., et al. “What Affects Millenials’ Mobility? Part 1: Investigating the Environmental Concerns, Lifestyles, Mobility-Related Attitudes and Adoption of Technology of Young Adults in California”. UC Davis: National Center for Sustainable Transportation (2016).
3. Claflin Anne and Fawkes Steinwand. “Greenhouse Gas Emissions in Minnesota: 1990-2016”. Minnesota Pollution Control Agency (2019).
4. World Commission on Environment and Development, ed. Our Common Future. Oxford New York: Oxford university press (1991).
5. Connectics Transportation Group. “Better Bus Blueprint”. Technical Memorandum (2021).
6. Deci Edward L and Richard M Ryan. “Intrinsic Motivation and Self-Determination in Human Behavior”. New York: Plenum (1985).
7. Deci Edward and Richard Ryan. “The ‘What’ and ‘Why’ of Goal Pursuits: Human Needs and the Self-Determination of Behavior”. Psychological Inquiry 11.4 (2000): 227-68.
8. Deloitte Datawheel and Cesar Hidalgo. “DATA USA: Duluth, MN”. (2020).
9. Eboli Laura and Gabriella Mazzulla. “A Stated Preference Experiment for Measuring Service Quality in Public Transport”. Transportation Planning and Technology 31.5 (2008): 509-23.
10. Elswick Frank. “How Much Does It Cost to Build a Mile of Road?” MIDWEST (blog) (2016).
11. Henao Alejandro., et al. “Sustainable Transportation Infrastructure Investments and Mode Share Changes: A 20-Year Background of Boulder, Colorado”. Transport Policy 37 (2015): 64-71.
12. Large Lakes Observatory. “About Large Lakes Observatory”. Large Lakes Observatory (2023).
13. NASA’s Jet Propulsion Laboratory. “Global Climate Change: Vital Signs of the Planet”. (2023).
14. Natural Resources Research Institute. “About NRRI”. (2023).
15. Ohio University. “Millenials, Cars and the Future of Transportation”. (2021).
16. Ryan Richard M and Edward L Deci. “Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being”. American Psychologist 55.1 (2000): 68-78.
17. Sansom Rebecca. “Theory of Planned Behavior”. Accelerating Systemic Change in STEM Higher Education (2022).
18. Schneider Robert and Lingqian Hu. “Improving University Transportation Sustainability: Reducing Barriers to Campus Bus and Bicycle Commuting”. The International Journal of Sustainability Policy and Practice 11.1 (2015): 17-33.
19. Shaaban Khaled and Inhi Kim. “The Influence of Bus Service Satisfaction on University Students’ Mode Choice: Bus Service Satisfaction and Mode Choice”. Journal of Advanced Transportation 50.6 (2016): 935-48.
20. Steg Linda and Robert Gifford. “Sustainable Transportation and Quality of Life”. Journal of Transport Geography 13.1 (2005): 59-69.
21. United States Environmental Protection Agency. “Inventory of U.S. Greenhouse Gas Emissions and Sinks”. (2022).

In Duluth, the largest demographic living in poverty is 18-24-year-olds. Drivers within this age range are also over-represented in crash statistics in the state of Minnesota. Further, owning and operating a personal vehicle can be costly, especially for young drivers with no stable or high income. Sustainable commute modes include commuting with low impact on the environment, transporting more than one passenger, or replacing fossil fuels with green energy. Behavioral changes are necessary to get the maximum benefits from sustainable commuting such as encouraging the use of alternative modes of transportation like the public transportation system.

     Although the benefits of sustainable commuting include saving money, being eco-friendly, and having a positive social impact on society, a survey of 370 18-24-year-old drivers found that 46% choose their vehicle as their primary commuting option. This research explores the perception of young drivers in Duluth toward the use of public transportation. Based on the factors from the Theory of Planned Behavior (TPB), the study shows that even if their attitude was favorable and there existed a strong social structure, within Duluth, toward using the bus, control factors exist that impede their decision to use the bus. If these factors are not addressed, then ridership will continue to be low.

Keywords: travel behavior; young drivers; sustainable commuting; public transportation

References

1. World Health Organization. Global Status Report on Road Safety 2018: Summary; World Health Organization: Geneva, Switzerland (2018).
2. Timothy M Pickrell, Hongying (Ruby) Li and Shova KC. Traffic Safety Facts (2016).
3. Berri Rafael A., et al. “A pattern recognition system for detecting use of mobile phones while driving”. 2014 International Conference on Computer Vision Theory and Applications (VISAPP). IEEE 2 (2014).
4. Yan C, Coenen F and Zhang B. “Driving posture recognition by joint application of motion history image and pyramid histogram of oriented gradients”. International journal of vehicular technology (2014).
5. Abouelnaga Yehya, Hesham M Eraqi and Mohamed N Moustafa. “Real-time distracted driver posture classification”. arXiv preprint arXiv:1706.09498 (2017)
6. Chollet F. “Xception: Deep Learning with Depthwise Separable Convolutions”. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017).
7. Szegedy C., et al. “Rethinking the Inception Architecture for Computer Vision”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016): 2818-2826.
8. Ou C, Ouali C and Karray F. “Transfer learning based strategy for improving driver distraction recognition”. International Conference Image Analysis and Recognition. Springer, Cham (2018): 443-452.
9. Wang J., et al. “A data augmentation approach to distracted driving detection”. Future internet 13.1 (2021): 1.
10. Saito K., et al. “Adversarial dropout regularization”. arXiv preprint arXiv:1711.01575 (2017).
11. Zhang Z. “Improved adam optimizer for deep neural networks”. 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS). IEEE (2018): 1-2.
12. Wichrowska O., et al. “Learned optimizers that scale and generalize”. International Conference on Machine Learning. PMLR (2017): 3751-3760.

Distracted driving has become a serious tra?ic problem. This study proposes an image process- ing and multi-model fusion scheme to maximize the discrimination accuracy of distracted driving. First, the training dataset and the test dataset are processed to specific specifications by translation and clipping. Second, we set vgg16 as the benchmark model for evaluation, and train ResNet50, InceptionV3 and Xception model input images. Finally, considering that each model has its own advantages, we use frozen part of the network layer to fine-tune the model, remove the weights of each single model Fine-tune from the output before full connection, connect them in series, and then calculate each model weights through neural network training.

References

1. Abdi H. “The interpretation of principal component analysis”. In S. Kotz, N. Balakrishnan, & C. Read (Eds.), Encyclopedia of statistical sciences, Wiley 8 (2007): 1-16.
2. Comrey AL and Lee HB. A first course in factor analysis (2nd ed.). Erlbaum (1992).
3. Gorsuch RL. Factor analysis (2nd ed.). Erlbaum (1983).
4. Hotelling H. “Analysis of a complex of statistical variables into principal components”. Journal of Educational Psychology 24.6 (1933): 417-441.
5. International Organization for Standardization and Commission Internationale de l'Eclairage. (2019). ISO/CIE 11664-1:2019(E) colorimetry -- Part 1: CIE standard colorimetric observers. ISO. Geneva, Switzerland.
6. Jackson JE. “A user's guide to principal components”. Wiley (1991).
7. Jolliffe IT. Principal component analysis (2nd ed.). Springer (2011).
8. Johnson RA and Wichern DW. Applied multivariate statistical analysis (2007).
9. O'Connor BP. “SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test”. Behavior Research Methods, Instruments, & Computers 32.3 (2000): 396-402.
10. Otsu N. “A threshold selection method from gray-level histograms”. IEEE Transactions on Systems, Man, and Cybernetics 9.1 (1979): 62-66.
11. Pearson K. “On lines and planes of closest fit to systems of points in space”. Philosophical Magazine 2.11 (1901): 559-572.
12. Rencher AC. Methods of multivariate analysis (2nd ed.). Wiley (2002).
13. Shlens J. A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100 (2009).
14. Tabachnick BG and Fidell LS. Using multivariate statistics (6th ed.). Pearson (2013).
15. Mardia KV, Kent JT and Bibby JM. “Multivariate analysis”. Academic Press (1979).
16. Velicer WF. “Determining the number of components from the matrix of partial correlations”. Psychometrika 41.3 (1976): 321-327.

Face recognition is an important application in computer vision and biometrics. In this paper, we propose a novel approach to face recognition based on modular PCA (Principal Component Analysis). The proposed method improves the accuracy and efficiency of face recognition by dividing the face image into multiple overlapping sub-blocks, and then applying PCA to each sub-block independently. The resulting sub-block eigenfaces are then combined to form a composite face feature vector, which is used for face identification. Experimental results on several standard face recognition datasets demonstrate that our approach outperforms other state-of-the-art methods in terms of recognition accuracy and computational efficiency. The proposed method is also shown to be robust to variations in lighting, facial expressions, and occlusion.

Keywords: modular Analysis; principal component analysis; face recognition; modular PCA; pattern recognition

References

1. Wang J, Yu C and Li W. “Peer victimization and online game disorder in adolescents”. J. Central China Norm. Univ. (Hum. Soc. Sci.) 59.04 (2020): 184-192.
2. Zhou F., et al. “The influence of teenagers’ negative emotion on internet addiction: the moderating effect of happiness tendency”. Chin. J. Clin. Psychol 25.02 (2017): 208-212.
3. Gao T. “A Study on the Development of Internet Addiction and its Influencing Factors in Senior High School Students”. Jilin University (2020).
4. Zhang Y., et al. “Psychosocial factor influencing online game disorder in university students”. J. South China Norm. Univ. (Soc. Sci. Ed.) 04 (2017): 86-91+191.
5. Liu P. “Study of Stability on Three Classes of Nonlinear Dynamical Systems”. Northwest A&F University (2018).
6. Ma Z and Chou Y. “Mathematical Modeling and Research on Dynamics of Infectious Diseases”. Science Press, Beijing (2004): 34-38.
7. Huang S., et al. “The relationship between boredom proneness and online game addiction of freshmen: the moderating effect of sense of meaning of life”. China J. Health Psychol 28.04 (2020): 580-585.
8. Wei, H., Zhou, Z., Tion, Y., Bao, N.: Online game addiction: effects and mechanisms of flow experience. Psychol. Dev. Educ. 28(06), 651-657 (2012)
9. Li, X., Huang, R., Li, H.: Dynamic analysis of epidemic model with vertical transmission. J. Jilin Univ. (Sci. Ed.) 58(03), 569-574 (2020).

This paper constructs a kind of communication model with forced treatment of online game addicts. First, the regeneration matrix method is used to determine the basic regeneration number of the model, and then the local stability of online game model and online game addiction transmission conditions is judged according to the basic regeneration number. Further, the effect of treatment delay on online game addicts is analyzed. And last, with the help of numerical simulation to verify the stability of the equilibrium point, the author of this paper puts forward effective treatment cycle for online game addicts.

Keywords: online game addictive equilibrium; online game non-addictive equilibrium; time delay; stability

Reference

1. Aakash Bansal and Richa Gupta. “A review on microstrip patch antenna and feeding techniques”. International Journal of Information Technology 12 (2020): 149-154.
2. S Palanivel Rajan and C Vivek. “Analysis and Design of Microstrip Patch Antenna for Radar Communication”. Journal of Electrical Engineering & Technology 14 (2019): 923-929.
3. S Shrivastava and A Bhargava. “A Comparative Study of Different Shaped Patch Antennas with and Without Slots”. 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE Xplore (2019).
4. Singh I and Tripathi VS. “Microstrip Patch Antenna and its Applications: A survey”. International Journal of Computer Applications in Technology 2.5 (2011): 1595-1599.
5. Kusiek A and Lech R. “Resonance frequency calculation of a multilayer and multipatch spherical microstrip structure using a hybrid technique”. IEEE Transactions on Antennas and Propagation 64.11 (2016): 4948-4953.
6. Zhang JD., et al. “A Compact Microstrip-fed patch antenna with Enhanced Bandwidth and Harmonic Suppression”. IEEE Transactions on Antennas and Propagation 64.12 (2016): 5030-5037.
7. Kusiek A and Lech R. “Resonance Frequency Calculation of a Multilayer and Multipatch Spherical Microstrip Structure using a Hybrid Technique”. IEEE Transactions on Antennas and Propagation 64.11 (2016): 4948-4953.
8. Kangan Saxena. “Comparison of Rectangular, Circular and Triangular Patch Antenna with CPW Fed and DGS”. International Journal of Electronics & Communication Technology 7 (2016): 2230-9543.
9. Chandraveer Singh and Gaurav Kumawat. “A Compact Rectangular Ultra-Wideband Microstrip Patch Antenna with Double Band Notch Feature at Wi-Max and WLAN”. Wireless Personal Communications 114 (2020): 2063-2077.
10. Dattatreya Gopi., et al. “DGS based monopole circular?shaped patch antenna for UWB applications”. SN Applied Sciences, A Springer Nature (2021).
11. Mishra RG., et al. “Analysis of the Relationship Between Substrate Properties and Patch Dimensions in Rectangular-Shaped Microstrip Antennas”. In Intelligent communication, control and devices, Singapore: Springer (2018): 65-72.
12. Sathishkumar N., et al. “Design and Study of Rectangular Micro strip Patch Antenna for WLAN Applications”. International Journal of Advanced Science and Technology 29 (2020): 3554- 3558.
13. Simranjit Kaur Josan and JS Sohal. “Design of Elliptical Microstrip Patch Antenna using Genetic Algorithms”. IEEE International Conference on Communication Systems (ICCS), IEEE (2012).
14. A. Daliri., et al. “Strain Measurement in Composite Materials using Microstrip Patch Antennas”. Proceedings of the ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS (2010): 591-598.
15. Bondili Kohitha Bai., et al. “Design and simulation of circularly polarized pentagonal-shaped microstrip patch antenna at RFID frequency 2.4 GHz”. Computer Science & Information Technology (CS & IT), (2010): 01-10.
16. Mary Swarna Latha Gade., et al. “Design of Circular Microstrip Patch Antenna and Its Simulation Results”. Journal of Advanced Research in Dynamical and Control Systems 9 (2017): 230-240.
17. MM Abd-Elrazzak and IS Al-Nomay. “A design of a circular microstrip patch antenna for Bluetooth and HIPERLAN applications”. 9th Asia-Pacific Conference on Communications, IEEE (2004).

The shape of the radiating patch exerts influence greatly on the performance of the microstrip patch. The vital target of this research article is to constitute the microstrip antennas of Circular and Pentagonal patches on FR4 substrate employing partial ground that notably lower the shape complication as a consequence of the antenna size reduction combined with a profitable radiation efficiency on the basis of the center frequency of 8.5 GHz. The specification of return loss, VSWR, gain, directivity, radiation pattern, and the radiation efficiency of the antenna are investigated using ANSYS HFSS 13.0 software which reveals beneficial outcomes of dual - band over the entire band frequency range of 5-15 GHz relating with the earlier analysis. The triangular patch antenna is detected far well compared to the alternatives which is achieved the best gain of 2.2 dB, 6.8 dB, minimum return loss of 19.0144 dB, 34.5612 dB, enhanced bandwidth of 0.50 GHz, 1.15 GHz, and the higher radiation efficiency of 81%, 94% at 9 GHz, 12.34 GHz respectively. This is applicable for Marine Radar communication (SART), productive Satellite Communications, and also it is the best candidate for weather monitoring, defense and military purposes in Wireless Communication applications.

Keywords: Microstrip patch antenna; Circular & Pentagonal shape; FR4 Epoxy; S11 parameter VSWR; bandwidth; gain; directivity; radiation pattern and radiation efficiency

References

1. Li B, B Wu and C-H Liang. “Study on high gain circular waveguide array antenna with metamaterial structure”. Progress in Electromagnetics Research 60 (2006): 207-219.
2. L Pazin, N Telzhengsky and Y Leviatan. “Multiband flat-plate inverted-F antenna for Wi-Fi/WiMax operation”. IEEE Antennas and Propag 7 (2008): 197-200.
3. W-C Liu, C-M Wu and N-C ChuA. “Compact low-profile dual-band antenna for WLAN and WAVE applications”. International Journal of Electronics and Communications 6 (2012): 467-471.
4. Ajay Dadhich., et al. “Design and Investigations of Multiband Microstrip Patch Antenna for Wireless Applications”. Ambient Communications and Computer Systems (2019): 37-45.
5. CM Su., et al. “Dual-band slot antenna for 2.4/5.2 GHz WLAN operation”. Microwave and Optical Technology Letters 35 (2002): 306-308.
6. M Salehi and A Tavakoli. “A novel low mutual coupling microstrip antenna array design using defected ground structure”. Int. J. Electron. Comm 60 (2006):718-723.
7. AHW Yang., et al. “A novel DGS microstrip antenna simulated by FDTD”. Optik 124 (2013): 2277-2280.
8. J-Y Jan and L-C Tseng. “Small planar monopole antenna with a shorted parasitic inverted-L wire for wireless communications in the 2.4-5.2-, and 5.8-GHz bands”. IEEE Trans. Antennas Propag 52 (2004): 1903-1905.
9. Li Li., et al. “A compact triple-band printed monopole antenna for WLAN/WiMAX applications”. IEEE Antennas Wirel Propag Lett 15 (2014): 1853-1855.
10. Sun J-Shiun., et al. “Triple-band MIMO antenna for mobile wireless applications”. IEEE Antennas Wirel Propag Lett 15 (2015): 500 - 503.
11. S Shen and L Gong. “Investigation of gain effect of multi-band patch antenna based on composite rectangular SRRs”. Optik 125 (2014): 930-933.
12. Zhang Rui, Kim Hyung-Hoon and Kim Hyeondong. “Triple-band ground radiation an-tenna for GPS, WiFi 2.4 and 5 GHz band application”. Electron Lett 51 (2015) 2082-4.
13. Dang Lin., et al. “A compact microstrip slot triple-band antenna for wlan/wimax applications”. IEEE Antennas Wirel Propag Lett 9 (2010): 1178-1181.
14. Ali T and Biradar RC. “A compact multiband antenna usingλ/4rectangular stub loaded with metamaterial for IEEE 802.11N and IEEE 802.16E”. Microw Opt Technol Lett 59 (2017): 1000-1006.
15. YF Cao, SW Cheung and TI Yuk. “A multiband slot antenna for GPS/WiMAX/WLAN systems”. IEEE Trans. Antennas and Propag 63 (2015): 952-958.
16. Kaur Amanpreet, Gurmohan Singh and Manjit Kaur. "Miniaturized Multiband Slotted Microstrip Antenna for Wireless Applications". Wireless Personal Communications 96 (2017): 441-453.
17. Ali Tanweer, Nikhat Fatima and Rajashekhar C Biradar. "A miniaturized multiband reconfigurable fractal slot antenna for GPS/GNSS/Bluetooth/WiMAX/X-band applications". AEU-International Journal of Electronics and Communications 94 (2018): 234-243.
18. M Bod and HR Hassani. “Compact UWB Printed Slot Antenna with Extra Bluetooth, GSM, and GPS Bands”. IEEE Antennas and Wireless Propagation 11 (2012): 531-534.
19. Ali Tanweer., et al. "A miniaturized slotted multiband antenna for wireless applications". Journal of Computational Electronics 17 (2018): 1056-1070.
20. N Prema and Anil kumar. “Design of multiband microstrip patch antenna for C and X band”. Optik (2016): 8812-8818.
21. Mukesh Kumar., et al. “Analysis and design of dual band compact stacked Microstrip patch antenna with defected ground structure for WLAN/WiMax applications”. International Journal of Electronics and Communications 69 (2015): 39-47.

This research paper bestows a rectangular slot-loaded microstrip antenna in possession of DGS, which may give multi-band and the highest gain for C-band, X-band, and Ku-band applications. The distinct antenna structure is simulated through HFSS 13.0 simulation software for the illustrative investigation of enhanced gain. The newborn evolution of the advancement of wireless internet means of approach is concerned with the insistence on multi-band antennas. The investigation of nine-band frequency antennas is an achiever of 6.0350 GHz (Global Navigation Satellite System (GNSS)), 6.7950 GHz (Satellite TV), 7.46 GHz (Long-distance Communication), 8.5050 GHz (Terrestrial Broadcast Radar), 11.45 GHz (Space Communication), 13.35 GHz (detector), 15.06 GHz (Military), 17.24 GHz (Aerospace), and 19.05 GHz (Astronomical observation). The multi- band aspect is accomplished by novel U-slot cuttings and rectangular slots in the antenna. It resonates at quad- band without any patch or ground modification; when a U- slot cutting is made at the left and top of a patch, it resonates at six bands; and when rectangular slot cuttings are united at the ground plane, it resonates at nine bands. The slot length, width, and position are optimized to attain the highest gain. The achieved gains of the proposed antenna are 1.5865 dB, 1.1344 dB, 1.0416 dB, 0.92179 dB, 3.7586 dB, 6.2776 dB, 5.1998 dB, 14.679 dB, 5.4279 dB, 6.0350 GHz, 6.7950 GHz, 7.4600 GHz, 8.5050 GHz, 11.45 GHz, 13.3500 GHz, 15.0600 GHz, 17.2450 GHz, and 19.0500 GHz, In wireless communication, a successful multi-band antenna is advantageous.

Keywords: Multiband Microstrip antenna; Slot cuttings techniques; DGS; C; X and Ku band

.

The I.S. of units implies a damaging separation between engineers and theoreticians. In particular, the electrical units are useless. Thus, in cosmology, we must first calculate what distance corresponds to three universal constants excluding the speed of light c, too slow to ensure cosmic coherence. In the standard triplet G, c, ħ, which defines mass, length and Planck time (the last two constituting the "Planck wall"), the first choice is to replace c with the average mass of the 3 main particles of physics atomic (electron, proton, neutron), and calculate a length, since it is what is really measured in the Hubble-Lemaître law. This gives the half of 13.8 billion light-years (see French wikipedia, “analyse dimensionnelle”). This invariable length, calculated in the first three minutes of the sabbatical year 1997 (Univ. Paris 11), is now identified (2023) not only with the Hubble radius, directly measured, but also with the product by c of the time characteristic of standard cosmology, which cannot therefore be considered no more as the "age" of the Universe, but as the time constant of exponential recession, the unique parameter of steady-state cosmology, so which has also predicted the recession acceleration. This "3-minutes formula" implies that the critical cosmic condition, observed with surprise around 2000, does not need any inflation, it corresponds to a very simple relationship. It is the equalization of the diametral area of the visible Universe compared to the Planck area (the entropy of Bekenstein-Hawking of the visible Universe considered as a black-hole) with the perimeter of the Universe, relative to its wavelength, which pushes back the "wall of Planck" by a factor of 10⁶¹, enlighting the vacuum energy enigma. By separating the electron from the proton-neutron couple, another relation of the same "holographic" type extends to the wavelength of the thermal background, with a precision which excludes any hazardous numerology. The great quanto-gravitational unification, vainly sought for a century, is therefore within everyone's reach, and is deposited in a sealed envelope at the Paris Academy of Sciences (March 1998). We have thus predicted that the far field Universe must be identical to the near domain, which is indeed indicated by the first observations of the JWST. This marvel of technology thus consecrates the triumph of physical common sense and the talent of engineers over the theoreticians drowned in their formalism who imposed this illusion of the Expansion of the Universe and the Initial Big Bang. The JWST must know observe the Universe isothermy (http://holophysique.free.fr).

References

1. Srinivas K and Saroja L. “Comparative financial performance of HDFC Bank and ICICI Bank”. International Refereed Multidisciplinary Journal of Contemporary Research 1.2 (2013): 108-126.
2. Koley J. “Analysis of financial position and performance of public and private sector banks in India: A comparative study on SBI and HDFC Bank”. A Multidisciplinary Online Journal of Netaji Subhas Open University, India 2.1 (2019): 1-14.
3. Sai VRN and Sultan DST. “Financial performance analysis in Banking sector-A Pre & Post Merger perspective”. International Monthly Refereed Journal of Research in Management & Technology 2 (2013): 56-66.
4. Bag S and Omrane A. “Corporate social responsibility and its overall effects on financial performance: Empirical evidence from Indian companies”. Journal of African Business 23.1 (2022): 264-280.
5. www.moneycontrol.com
6. www.investopedia.com
7. www.southindianbank.com

Every country's financial system relies on the banking sector. It has an impact on the economy of the country by providing loans, infrastructure, and investment. The banking sector is critical to any country's growth and expansion. Hence this study focuses on the performance assessment of the new-generation South Indian Bank. The financial performance analysis of South Indian Bank involves an evaluation of the Bank's financial health, profitability, and efficiency. This analysis provides valuable insights into the Bank's overall performance and ability to generate sustainable returns for its stakeholders. The Bank's financial performance is evaluated using various financial ratios and indicators, such as Credit- deposit ratio, Investment-deposit Ratio, Cash- deposit ratio, Cost- income ratio, Deposit- cost ratio, Yield on advance ratio, Yield on investments ratio, fixed assets to net-worth Ratio, and other ratios. These ratios are calculated by analyzing the Bank's income statement and balance sheet. The analysis reveals that South Indian Bank has maintained a stable financial position recently, with steady net interest income and profitability growth. The Bank's asset quality has also improved over time, with a reduction in non-performing assets and an increase in the provision coverage ratio. The financial performance analysis of South Indian Bank suggests that the Bank has a stable financial position. Still, it needs to address specific areas of concern to sustain its growth and profitability in the future.

Keywords: Financial performance analysis; Non-Performing Asset; Ratios; Private Sector Bank

References

1. M Dholu and KA Ghodinde. “Internet of Things (IoT) for Precision Agriculture Application”. Proc. 2nd Int. Conf. Trends Electron. Informatics, ICOEI (2018): 339-342.
2. AS Selvaraj and S Anusha. “RFID Enabled Smart Data Analysis in a Smart Warehouse Monitoring System using IoT”. J. Phys. Conf. Ser 1717.1 (2021): 012022.
3. H Fan. “Theoretical basis and system establishment of China food safety intelligent supervision in the perspective of internet of things”. IEEE Access 7 (2019): 71686-71695.
4. RK Kodali, J John and L Boppana. “IoT Monitoring System for Grain Storage”. Proc. CONECCT 2020 - 6th IEEE Int. Conf. Electron. Comput. Commun. Technol (2020).
5. BJ Olorunfemi and SE Kayode. “Post-Harvest Loss and Grain Storage Technology-A Review”. (2021).
6. S Muthukumar., et al. “Sensor based warehouse monitoring and control”. Proc. 2nd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA (2018): 155-157.
7. RN Dean., et al. “A PCB sensor for status monitoring of stored food stocks”. IEEE Sensors Lett 3.4 (2019).
8. M Haji., et al. “Roles of Technology in Improving Perishable Food Supply Chains”. Logist 4.4 (2020): 33.
9. G Elmasry., et al. “Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring-An Overview”. Sensors 19.5 (2019): 1090.
10. A Akila and P Shalini. “Food grain storage management system”. Int. J. Eng. Technol 7.2 (2018): 170-173.
11. L J., et al. “Automated food grain monitoring system for warehouse using IOT”. Meas. Sensors 24 (2022): 100472.
12. É Lutz and PC Coradi. “Applications of new technologies for monitoring and predicting grains quality stored: Sensors, Internet of Things, and Artificial Intelligence”. Measurement 188 (2022): 110609.
13. O Elijah., et al. “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges”. IEEE Internet Things J 5.5 (2018): 3758-3773.
14. M Dhanaraju., et al. “Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture”. Agric 12.10 (2022): 1745.
15. J Doshi, T Patel and SK Bharti. “Smart Farming using IoT, a solution for optimally monitoring farming conditions”. Procedia Comput. Sci 160 (2019): 746-751.
16. J Xu, B Gu and G Tian. “Review of agricultural IoT technology”. Artif. Intell. Agric 6 (2022): 10-22.
17. M Ayaz., et al. “Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk”. IEEE Access 7 (2019): 129551-129583.

This work is carried out to develop a system to maintain silos in a safe and hygienic environment to store food grains by monitoring them at regular intervals. We keep track of the parameters in the silo such as temperature, humidity, and carbon-di-oxide level concentrations. Values recorded at regular intervals of time help us analyze and visualize the data and observe the effects of these parameters on the food grains stored, while providing protection against pests, rodents and other organisms that can affect the yield. It has also been observed that while the grains are stored in such silos, due to the natural processes we have a lot of carbon-di-oxide concentration in the silos which has caused many fatal deaths of farmers. This issue has been mitigated in this work by implementing an emergency procedure which will be activated to open the grain bin and release the toxic gases from the silo. Through this work, we believe we can innovate in the grain storage space and solve the problems faced by the farmers.

Keywords: IOT; Food; Grains; ESP32; Google Apps Script; Cloud

References

1. Akyildiz Ian F., et al. "A survey on sensor networks”. IEEE Communications magazine 40.8 (2002): 102-114.
2. Yoneki Eiko and Jean Bacon. "A survey of Wireless Sensor Network technologies”. UCAM-CL-TR-646 (2005).
3. Karl Holger and Andreas Willig. Protocols and architectures for wireless sensor networks. John Wiley & Sons (2007).
4. Da Xu Li, Wu He and Shancang Li. "Internet of things in industries: A survey”. IEEE Transactions on industrial informatics 10.4 (2014): 2233-2243.
5. Palattella Maria Rita., et al. "Internet of things in the 5G era: Enablers, architecture, and business models”. IEEE Journal on Selected Areas in Communications 34.3  (2016): 510-527.
6. Al-Fuqaha Ala., et al. "Internet of things: A survey on enabling technologies, protocols, and applications”. IEEE Communications Surveys & Tutorials 17.4 (2015): 2347-2376.
7. Rawat Priyanka., et al. "Wireless sensor networks: a survey on recent developments and potential synergies”. The Journal of supercomputing 68.1  (2014): 1-48.
8. Li Shancang, Li Da Xu and Shanshan Zhao. "The internet of things: a survey”. Information Systems Frontiers 17.2 (2015): 243-259.
9. Evans Dave. "The internet of things: How the next evolution of the internet is changing everything. 2011”. (ä??? ?????????: 5.02. 2016) (2015).
10. Seah Winston KG, Zhi Ang Eu and Hwee-Pink Tan. "Wireless sensor networks powered by ambient energy harvesting (WSNHEAP)-Survey and challenges”. Wireless Communication, Vehicular Technology, Information Theory and Aerospace                & Electronic Systems Technology, 2009. Wireless VITAE 2009. 1st International Conference on. IEEE (2009).
11. Akpakwu Godfrey Anuga., et al. "A survey on 5G networks for the internet of things: communication technologies and challenges”. IEEE 6 (2018): 3619-3647.
12. Michalski Ryszard S., et al. “Machine learning: An artificial intelligence approach”. Springer Science & Business Media (2013).
13. Ayodele Taiwo Oladipupo. "Machine learning overview”. New Advances in Machine Learning. InTech (2010).
14. Thrun Sebastian and Lorien Pratt eds. Learning to learn. Springer Science & Business Media (2012).
15. Langley Pat and Herbert A Simon. "Applications of machine learning and rule induction”. Communications of the ACM 38.11 (1995): 54-64.
16. Benhaddou Driss and Ala Al-Fuqaha eds. “Wireless sensor and mobile ad-hoc networks: vehicular and space applications”. Springer (2015).
17. Madhu Arun and A Sreekumar. "Wireless sensor network security in military application using unmanned vehicle”. International Journal of Electronics and Communication Engineering (2014): 51-8.
18. Ohbayashi Riki., et al. "Monitoring system for landslide disaster by wireless sensing node network”. SICE Annual Conference, 2008. IEEE (2008).
19. Suganya E and S Vijayashaarathi. "Smart vehicle monitoring system for air pollution detection using Wsn”. Communication and Signal Processing (ICCSP), 2016 International Conference on. IEEE (2016).
20. Hilmani Adil, Abderrahim Maizate and Larbi Hassouni. "Designing and Managing a Smart Parking System Using Wireless Sensor Networks”. Journal of Sensor and Actuator Networks 7.2 (2018): 24.
21. A Lavric, V Popa and S Sfichi. “Street lighting control system based on large-scale WSN: A step towards a smart city”. in Proc. Int. Conf. Expo. Electr. Power Eng. (EPE) (2014): 673-676.
22. Akyildiz Ian F., et al. "Wireless sensor networks: a survey”. Computer networks  38.4 (2002): 393-422.
23. Lee In and Kyoochun Lee. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises”. Business Horizons 58.4 (2015): 431-440.
24. Akpakwu Godfrey Anuga., et al. "A survey on 5G networks for the internet of things: communication technologies and challenges”. IEEE       Access 6 (2018): 3619-3647.
25. Rani Shalli, Syed Hassan Ahmed and Sayed Chhattan Shah. "Smart Health: A Novel Paradigm to Control the Chickungunya Virus”. IEEE Internet of Things Journal (2018).
26. Gupta Punit and Jasmeet Chhabra. "IoT based smart home design using power and security management”. Innovation and Challenges in Cyber Security (ICICCS-INBUSH), 2016 International Conference on. IEEE (2016).
27. Chang Sei and Kisik Jeong. "A Mobile Application for Fine Dust Monitoring System”. Mobile Data Management (MDM), 2017 18th IEEE International Conference on. IEEE (2017).
28. Chen Shanzhi., et al. "A vision of IoT: Applications, challenges, and opportunities with china perspective”. IEEE Internet of Things journal 1.4 (2014): 349-359.
29. Kulkarni Sanjeev R, Gábor Lugosi and Santosh S Venkatesh. "Learning pattern classification-a survey”. IEEE Transactions on Information Theory 44.6 (1998): 2178-2206.
30. Morelande Mark R, Bill Moran and Marcus Brazil. "Bayesian node localisation in wireless sensor networks”. Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on. IEEE (2008).
31. Lu Ching-Hu and Li-Chen Fu. "Robust location-aware activity. recognition using wireless sensor network in an attentive home". IEEE Transactions on Automation Science and Engineering 6.4 (2009): 598-609.

In the IoT and WSN period, large number of connected objects and seeing bias are devoted to collect, transfer, and induce a huge quantum of data for a wide variety of fields and operations. To effectively run these complex networks of connected objects, there are several challenges like topology changes, link failures, memory constraints, interoperability, network traffic, content, scalability, network operation, security, and sequestration to name a many. therefore, to overcome these challenges and exploiting them to support this technological outbreak would be one of the most pivotal tasks of ultramodern world. In the recent times, the development of Artificial Intelligence (AI) led to the emergence of Machine Learning (ML) which has come the crucial enabler to figure out results and literacy models in an attempt to enhance the QoS parameters of IoT and WSNs. By learning from once gests, ML ways aim to resolve issues in the WSN and IoT’s fields by erecting algorithmic models. In this paper, we're going to punctuate the most abecedarian generalities of ML orders and Algorithms. We start by furnishing a thorough overview of the WSN and IoT’s technologies. We also bandy the vital part of ML ways in driving up the elaboration of these technologies. also, as the crucial donation of this paper, a new taxonomy of ML algorithms is handed. We also epitomize the major operations and exploration challenges that abused ML ways in the WSN and IoT. ultimately, we dissect the critical issues and list some unborn exploration directions. 

Keywords: Wireless Sensor Network; Internet of Things; Machine learning categories; Machine Learning Algorithms

References

1. Mudit Gupta, Pramod Kumar Morya and Satyajit Das. “Design & Fabrication of Rectangular Microstrip Patch Antenna for WLAN using Symmetrical slots”. International Journal on Emerging Technologies 8 (2017): 11-15.
2. Dheeraj Bhardwaj., et al. “Gain and Bandwidth Enhancement of Rectangular Patch Antenna with Trapezoidal Slot for Wi-Max Applications”. International Journal of Engineering Research & Technology (IJERT) 3 (2014).
3. Harshit Srivastava., et al. “Bandwidth and Gain Enhancement of Rectangular Microstrip Patch Antenna (RMPA) Using Slotted Array Technique”. Wireless Personal Communications (2020).
4. Vivek Hanumante., et al. “Performance Analysis of Rectangular Patch Antenna for Different Substrate Heights”. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 2 (2014).
5. Lahcen Aguni., et al. “Comparision of the Resonant Frequency Determinattion of a Microstrip Patch Antenna using ANN and Analytical Methods”. International Journal of Informatics and Communication Technology (IJ-ICT) 6 (2017): 1-9.
6. Ram Krishan and Vijay Laxmi. “Design of Microstrip Antenna for Wireless Local Area Network”. International Journal of Computer Science and Mobile Computing 4 (2015): 361-365.
7. Gnanamurugan S., et al. “On the Performance of Rectangular Microstrip Patch Antenna using Rogers RT/Duriod 5880”. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 8 (2019).
8. P Akila., et al. “Design and Analysis of Microstrip Patch Antenna Using Alumina and Paper Substrate for Wifi Application”. International Research Journal of Engineering and Technology (IRJET) 5 (2018).
9. Hind S Hussain and Al-Nahrain. “Design and Analysis of Rectangular Microstrip Patch Antenna Operating at TM 03 mode with Single and Stacked Structure for Bandwidth Enhancement”. Al-Nahrain University, College of Engineering Journal (NUCEJ) 19 (2016): 385-394.
10. Ashish Kumar and Nikhil Kumar Nitesh. “Design of Microstrip Patch Antenna for High Gain & Directivity at 3.5 GHz by Simulation studies using ADS”. International Journal of Engineering Trends and Technology (IJETT) 55 (2018).
11. M Karthigai Pandian and T Chinnadurai. “Design and Optimization of Rectangular Patch antenna based on FR4, Teflon and Ceramic Substrates”. Recent Advances in Electrical & Electronic Engineering 12 (2019).
12. Karim A Hamad. “Design and Enhancement Bandwidth Rectangular Patch Antenna using single Trapezoidal Slot Technique”. ARPN Journal of Engineering and Applied Sciences 7 (2012).
13. Vivek Hanumante and Sahadev Roy. “Comparative Study of Microstrip Patch Antenna using different Dielectric Materials”. 9th International Conference on Microwaves, Antenna Propagation and Remote Sensing ICMARS, Jodhpur, India (2013).
14. KV Rop and DBO Konditi. “Performance analysis of a Rectangular Microstrip patch Antenna on different Dielectric Substrates”. Innovative System design and Engineering 3 (2012).
15. Omar Darboe., et al. “A 28 GHz Rectangular Microstrip Patch Antenna for 5G Applications”. International Journal of Engineering Research and Technology 12 (2019): 854-857.
16. Juin Acharjee., et al. “Defected Ground Structure toward Cross Polarization Reduction of Microstrip Patch Antenna with Improved Impedance Matching”. Radioengineering 28.1 (2019).
17. Md. Abubakar Siddik., et al. “Design and Radiation Characterization of Rectangular Microstrip Patch Antenna for Millimeter-wave Communication”. American Journal of Engineering Research (AJER) 8 (2019): 273-281.
18. Amba Srivastava and Nandita Pradhan. “Analysis of Different Substrate Material and Frequency on Microstrip Patch Antenna”. International Journal of Electronics, Electrical and Computational System 6 (2017).
19. Subhadra Panda., et al. “An Extensive Judgment of Rectangular Microstrip Patch Antenna with Flexible Substrates”. International Journal of Recent Technology and Engineering (IJRTE) 8 (2019).
20. Ahmad H Abdelgwad. “Microstrip Patch Antenna Enhancement Techniques”. International Journal of Electronics and Communication Engineering 12.10 (2018).
21. Barun Mazumdara., et al. “Design of Compact Printed Antenna for WiMAX & WLAN Applications”. Procedia Technology 4 (2012): 87-91.
22. SE Jasim., et al. “Finding the Best Feeding Point Location of Patch Antenna using HFSS”. Journal of Engineering and Applied Sciences 10.23 (2015).
23. Sameh Khmailia., et al. “Impedance Matching of a Microstrip Antenna”. (IJACSA) International Journal of Advanced Computer Science and Applications 8.7 (2017).

The goal of this research is to enhance the bandwidth of a rectangular patch microstrip antenna. The bandwidth of the antenna is tuned by analyzing the various dielectric substrate mat erials, height & width, coaxial feed line, input and output impedances with effective dielectric constant s, and effective length. The suggested rectangular microstrip patch antenna operates at a frequency of 4.4 GHz. As a function of various frequencies, the VSWR, S11 and efficiency are analyzed. This analysis emphasizes that a low dielectric constant with an appropriate height of the dielectric substrate material for a microstrip patch antenna is utterly important in terms of enhancing bandwidth as well as a surface wave. This analysis gives better results for the RT-Turoid and attains a bandwidth of 56.65% at 4.4 GHz of resonance frequency. This high bandwidth makes it useful in many wideband applications.

Keywords: Dielectric substrates; Rectangular patch; Bandwidth; Coaxial Feedline; S11 parameter; VSWR and Efficiency

     On the other side, when sensitive information is stored on a cloud server, which is not in a direct control of end user, then the risk for the information is increased dramatically. Many unauthorised users may try to intercept secure data to compromise data centre server. Therefore in cloud communication, cloud provider will help to provide complete security measures for user to user communication but at the end cloud protection is not a network protection. Network level threads can compromise the security of information, so to provide the protection of information from intruders, security shield at the different level of cloud network is required.

We need three layers of security shield in cloud based network (1) Connectivity Level (2) Storage Level (3) Application Level. Cloud based protocol provide a broad set of policies and technologies to control these attack.

1. In connectivity level, when the real world communicate with multiple data centres and multiple end users in a cloud network, we have to consider all aspects of infrastructure including computer centre, data centre and cloud network to overcome the challenges related to architecture, performance , reliability, security, maintainability and virtualization as well.

As the information move between the communication channels, end to end encryption and authentication with no data leakage is mostly needed. During this transmission to protect our cloud network, Host Identity Protocol is used for authenticate IPv4, IPv6 client server cloud network from the intruders. MQTT and HTTP Protocol provide a core support for device connection and communication. In general MQTT is supported by embedded devices and for machine to machine interaction. In HTTP protocol based devices do not maintain a connection to IoT cloud, however it maintain a half duplex TCP connection where transmission is connectionless.

At this level some standardized protocol like connectionless network protocol, Secure Shell protocol, Spanning Tree Protocol, Equal Cost Multi-Pathing Protocol and many more are used. In these protocols some are communication protocols which are used for message failure detection, message monitoring and data unit identification.

2. In storage level, privacy plays an important role for the cloud service customer. Cloud service provider must ensure that the information and identity of client service customer will not disclose in any case. On the other hand, most pressing issue is that the data must be encrypted so no one, even administrator can’t see the information without permission of customer. It is a duty of customer service provider to make sure the customer that no duplicate copy of keys (Private /Public) is generated and data form stored in is in encrypted multiple safe locations.

Since the cloud is a very vast storage and it deals with a large amount of data information, the service provider should provide a separate address space to each customer with their individual memory space. This virtual isolation is provided with the help of dedicated virtual machines. Some time cloud deploys firewalls to protect the data and to overcome it Session Initiation Protocol (SIP) is used on VoIP based communication. This protocol helps the cloud for protecting their network by attacks like Denial of service, IP traffic management, Toll fraud protection and encryption of data.

3. In application level, cloud provides a facility to the end user to design their own application as per their requirement and the platform provided by service provider. Each cloud has its own different platform to execute user idea. The application must be tested and verified by the cloud service provider. The complete testing and module checking are done at the cloud service provider level before application being made available for the end user. There are some chances that attacker can also create a malicious application on cloud and launch it with attractive features to attract new user in a network. Application security protocol can handle all these malicious application service provider and user also. In addition, Cloud service providers have their own application firewall for monitoring incoming and outgoing traffic.

     In these days, Equal Cost Multi-Pathing protocol is widely adopted by the cloud computing because it has the ability to create multiple load balanced paths which play a very important role for providing variable bandwidths depending upon the requirement of the application. Moreover, Extensible messaging and presence protocol (XMPP) can be used for public subscribe system and file transfer.

     To conclude, there is no doubt that Cloud computing is the latest field in communication and for technology friendly users which promises immense benefits. Most of the Information technology giants like IBM, Cisco, Google and Microsoft have adopted it and continuously working in this area to handle security and privacy issues. It is expected that the use of cloud computing would exponentially increases in the upcoming days and simultaneously we all will face new challenges in cloud security. Hacking and various attacks to cloud infrastructure and cloud network would affect multiple clients even if only one site or one machine is attacked. These risks can be mitigated by using most secure protocols, security applications, most dynamic encrypted file systems, minimum data leakage and recovery software, and buying security hardware to track unusual behaviour across servers. However, there a lot of research work by the experts is still required in cloud area because many of the concerns related to security and privacy issues are not been answered yet.

     Dr. Varun Prakash Saxena (BE, ME, PhD) is presently Assistant Professor, Department of Computer Engineering, Government Women Engg. College Ajmer since 2012. He has 10 year of teaching and research experience. He has published more than 18 research paper and 5 research article in national/International journal of repute. He is a member of 5 national/ international professional societies. His interest includes cryptography, Networks and programming. He is also guiding more than 10 PG students.

     Over the past decade, cloud computing is the most emerged fast growing and widely accepted concept for information exchange. This is smartly designed for information exchange paradigm which builds around the core concepts of data encryption, data transmission, media transmission and communication with different remote login.

     In highly programmable and high performance cloud based network, the central remote server or data centres are used for the storage of information in secure manner. They monitor everything and help the end user to retrieve the information without breaking its integrity, confidentiality and access controllability.

     This task to manage and maintain the security at highest level in cloud is, inherently challenging. These cloud network are operated by many security level protocols to provide the efficient and secure service to end user who often lack of technical knowledge about the security mechanism and threats in a network.

References

1. S Glass, I Mahgoub and M Rathod. “Leveraging MANET Based Cooperative Cache Discovery Techniques in VANETs: A Survey and Analysis”. IEEE Communications Surveys Tutorials 19 (2017): 2640-2661.
2. Q Ye., et al. “Traffic-load-adaptive medium access control for fully connected mobile ad hoc networks.” IEEE Transactions on Vehicular Technology 65 (2016): 9358-9371.
3. P Barik., et al. “A Resource Sharing Scheme for Intercell D2D Communication in Cellular Networks: A Repeated Game Theoretic Approach”. IEEE Transactions on Vehicular Technology 69 (2020): 7806-7820.
4. TA Assegie and HD Bizuneh. “Improving network performance with an integrated priority queue and weighted fair queue scheduling”. Indonesian Journal of Electrical Engineering and Computer Science 19.1 (2020): 241-247.
5. S Patel and H Pathak. “A mathematical framework for link failure time estimation in MANETs”. Engineering Science and Technology, an International Journal (2022).
6. S Kakuba, K Kawaase and M Okopa. “Modeling Improved Low Latency Queuing Scheduling Scheme for Mobile AdHoc Networks”. in International Journal of Digital Information and Wireless Communication (2017).
7. MA Muwumba, OS Eyobu and J Ngubiri. “An Improved Low Latency Queueing Scheduling Algorithm for MANETs”. In K. Arai (Eds.) Advances in Information and Communication FICC 2023, Lecture Notes in Networks and Systems 651 (2023).
8. A Farzad, K Sahar and S Bahram. “A New Scheduling Algorithm Based on Traffic Classification using Imprecise Computation”. International Journal of Computer, Control, Quantum and Information Engineering 2.9 (2008): 78-82.
9. J Hyunchul, AK Jin and S Hwangjun. “Urgency-based Packet Scheduling and Routing Algorithms for Video Transmission over MANETS.” IET International Communication Conference on Wireless Mobile and Computing (2011): 78-82.
10. A Jesus, V Perez and C Christian. “A Network and Data Link Layer QoS Model to Improve Traffic Performance”. Emerging Directions in Embedded and Ubiquitous Computing. Lecture Notes in Computer Science, Springer Berlin (2006).
11. D Brunonas, A Tomas and B Aurelijus. “Analysis of QoS Assurance using Weighted Fair Queuing (WFQ) Scheduling Discipline with Low Latency Queue (LLQ)”. in Proc. In: 28th International Conference Information Technology Interfaces June 19-22, IEEE, Croatia (2006).
12. H Eric, IH Ming and L Hsu-Te. “Low Latency and Efficient Packet Scheduling for Streaming Applications.” Journal of Computer Communications 29.9 (2006): 1413-1421.
13. B Shaimaa, B Fatma and D Gamal. “QoS Adaptation in Real Time Systems based on CBWFQ”. in Proc. 28th National Radio Science Conference (NRSC), Cairo (2011).
14. P Rukmani and R Ganesen. “Scheduling Algorithm for Real Time Applications in Mobile Ad-Hoc Network with OPNET Modeler”. Procedia Engineering Journal 64 (2013): 94-103.
15. A Sohail., et al. “Implementation of class-based low latency fair queueing (cbllfq) packet scheduling algorithm for hsdpa core network”. KSII Transactions on Internet and Information Systems 14.1 (2020): 473-494.
16. P Rukmani and RG. “Enhanced Low Latency Queuing Algorithm for Real Time Applications in Wireless Networks”. International Journal of Technology (2016): 663-672.
17. IA Rai and M Okopa. “Modeling and Evaluation of SWAP Scheduling Policy Under Varying Job Size Distributions”. in Proc. The Tenth International Conference on Networks (2011).
18. IA Rai. “QoS Support in Edge Routers”. Ph.D. dissertation, Paris Telcom, France (2004).
19. A Wierman. “Scheduling for Today’s Computer Systems: Bridging Theory and Practice”. Ph.D. dissertation, Carnegie Mellon University, Pittsburgh, PA 15213 (2007).
20. L Byeongchan. “Asymptotic tail distribution analysis of queueing systems with heavy-tailed input traffic”. Ph.D. dissertation, Korea Advanced Institute of Science and Technology, College of Natural Sciences (2018).
21. L Clavier., et al. “Experimental Evidence for Heavy Tailed Interference in the IoT”. IEEE Communications Letters (2021): 692-695.

     Delay is a major Quality of Service (QoS) metric in Mission Critical Applications and some of these include health, vehicle and inspection safety applications. Some of such applications run on Mobile Ad Hoc Network (MANET) set ups which comes with transmission challenges arising from the size of traffic packets, environmental conditions and others. These challenges cause transmission delays, packet loss and hence a degraded network performance. In this article we study a Low Latency Queueing (LLQ) Scheduling Algorithm that makes use of three priority queues each transmitting voice, video and text packets. For the purposes of improving delay performance piggy backing off video packets on voice transmission is used. The LLQ model is developed under two scenarios as follows: (I) when voice packet is delayed once and piggybacked with video on transmission. (II) when voice packet is delayed only if there is a partial video packet being transmitted. During scheduling of traffic voice packets are combined with the partial video packet. We investigate the performance variation of the LLQ in an M/G/1 queue under different scenarios and under two service distributions namely: Exponential and Bounded Pareto (BP). The numerical results for the first scenario revealed that the video packets experienced the least conditional mean response time/conditional mean slowdown, followed by voice and least were text packets under LLQ Algorithm. While for second scenario, it was observed that voice packets experienced the least conditional mean response time/conditional mean slowdown, followed by video packets and then text packets in that order under LLQ Algorithm.

Keywords: delay; video; voice; text

References

1. Kaur Harsh Vineet. "Analysis of banks in India-A CAMEL approach". Global Business Review 11.2 (2010): 257-280.
2. Gupta Sumeet and Renu Verma. "Comparative Analysis of Financial Performance of Private Sector Banks in India: Application of CAMEL Model". Journal of Global Economy 4.2 (2008): 160-180.
3. Varghese Titto and Sneju Sajan. "A Comparative Analysis of South Indian Bank's Performance Post Implementation of Information Technology: An Empirical Analysis". IITM Journal of Management and I.T 8.2 (2017): 56-69.
4. Meena GL. "Financial analysis of select banks using CAMEL approach a study concerning Indian banking industry". International Journal of Research and Scientific Innovation (IJRSI) 3.10 (2016): 30-35.
5. Annual Report of South Indian Bank.
6. Annual Report of HDFC Bank.
7. https://www.moneycontrol.com
8. http://www.investopedia.com

     An efficient, functional banking system may catalyze rapid growth in numerous areas of the economy and is a critical prerequisite for a country's progress, and India is no exception. This research study will investigate and compare the financial performance of South Indian Bank and HDFC Bank using the CAMEL Model. The purpose of this article is twofold: first, to examine South Indian Bank's financial performance, and second, to compare South Indian Bank's performance to that of HDFC Bank. The study utilizes secondary data from the banks' annual reports over five years, from 2017-18 to 2021-22. The CAMEL Model is used as a framework for the analysis, comprising five dimensions - capital adequacy, asset quality, management quality, earnings, and liquidity. According to the study's findings, both banks performed satisfactorily regarding capital sufficiency, managerial quality, and liquidity. However, regarding asset quality and earnings, HDFC Bank outperforms South Indian Bank. This research will help investors, stakeholders, and policymakers make informed judgments concerning these banks.

Keywords: Capital Adequacy; Asset Quality; Management Efficiency; Earning Capacity; Liquidity; Non-Performing Asset

References

1. Atkinson AA., et al. Management Accounting: Information for Decision-Making and Strategy Execution (7th ed.). Pearson (2018).
2. Bansal R. “A Comparative Analysis of the Financial Performances of Selected Indian IT Companies During 2010-2014”. IUP Journal of Accounting Research & Audit Practices 14.4 (2015).
3. Brigham EF and Houston JF. Fundamentals of Financial Management (15th ed.). Cengage Learning (2017).
4. Ehiedu VC. “The impact of liquidity on profitability of some selected companies: The financial statement analysis (FSA) approach”. Research Journal of Finance and Accounting 5.5 (2014): 81-90.
5. Gitman LJ and Zutter CJ. Principles of Managerial Finance (15th ed.). Pearson (2018).
6. Hansen DR and Mowen MM. Cost Management: Accounting and Control (6th ed.). Cengage Learning (2018).
7. Horngren CT., et al. “Introduction to Management Accounting (16th ed.). Pearson (2018).
8. Mohanty S. “A comparative financial performance of selected public and private sector banks in India”. Parikalpana KIIT Journal of Management 17.2 (2021): 155-174.
9. Philip B and M Prasad. “A Study on The Financial Performance of The South Indian Bank”. PriMera Scientific Engineering 2.5 (2023): 2-13.
10. Ross SA., et al. Essentials of Corporate Finance (10th ed.). McGraw-Hill Education (2019).
11. Srinivas K and Saroja L. “Comparative financial performance of HDFC Bank and ICICI Bank”. International Refereed Multidisciplinary Journal of Contemporary Research 1.2 (2013): 108-126.
12. Van Horne JC, Wachowicz JM and Bhaduri SN. Fundamentals of Financial Management (16th ed.). Pearson (2019).

     Pestology Combines is a manufacturing company that specializes in producing hygiene products and partners with renowned brands. The company emphasizes integrity, innovation, and customer satisfaction while promoting eco-friendly pest elimination solutions. With a state-of-the-art manufacturing unit, Pestology Combines ensures the production of high-quality defect-free products by utilizing top-notch raw materials and professional management. Additionally, the company exports its products to several countries and serves as an OEM supplier, offering customized B2B solutions. This study evaluates Pestology Combines' financial performance using four years of balance sheet and income statement data. By analyzing the changes in assets and liabilities over time, the research provides management strategies and problem-solving recommendations. Furthermore, the study explores the manufacturing sector's significance, technological advancements, and its contribution to economic growth and innovation. The findings of this research contribute to a better understanding of financial statement analysis within specific industry contexts, highlighting the importance of managing assets and liabilities effectively for sustained financial health.

Keyword: Financial performance analysis; Financial performance analysis; Balance sheet and income statement analysis; Manufacturing company; Hygiene products

References

1. Bansal R. “A comparative financial study: Evidence from selected indian retail companies”. Journal of finance and investment Analysis 3.3 (2014): 13-35.
2. Ehiedu VC. “The impact of liquidity on profitability of some selected companies: The financial statement analysis (FSA) approach”. Research Journal of Finance and Accounting 5.5 (2014): 81-90.
3. Foster G. Financial Statement Analysis, 2/e. Pearson Education India (1986).
4. Haralayya B. “Financial Statement Analysis Using Common Size on Mahindra Sindol Motors”. Iconic Research and Engineering Journals 5.1 (2021): 150-159.
5. Hofmann E and Lampe K. “Financial statement analysis of logistics service providers: ways of enhancing performance”. International Journal of Physical Distribution & Logistics Management (2013).
6. Philip B and M Prasad. “A Study on The Financial Performance of The South Indian Bank”. PriMera Scientific Engineering 2.5 (2023): 2-13.

     The research paper focuses on the increasing demand for bakery products and the growth of the bakery industry, with a particular emphasis on Sheen Confectionery in India. The study utilizes comparative statements to analyze the financial position and income position of Sheen Confectionery. The objectives of the study are to assess the company's assets and liabilities, provide recommendations for improvement, and understand the changes in the company's financial performance over time. The paper acknowledges limitations in terms of the time frame and the use of a single analytical tool. The findings highlight trends and fluctuations in various financial aspects of Sheen Confectionery, and based on the analysis, recommendations are provided to enhance the company's financial position and profitability.

Keywords: Bakery business, Sheen Confectionery, Financial statement, Comparative analysis, Assets and liabilities, Financial performance

References

1. Gao SL. “Nanostructured coatings of glass fibers: Improvement of alkali resistance and mechanical properties”. Acta Materialia 55.3 (2007): 1043-1052.
2. Kevin D. “Validation of Asphalt binder and mixture tests that measure rutting susceptibility”. FHWARD-99-204 (2000): 17-19.
3. Akulich AV. Structure and properties of dispersed reinforced asphalt concrete: Ph.D. dis. ... cand. tech. Sciences: 05.23.05 (1987): 17.
4. Grigoriev DA. “The use of fibroasphalt concrete for the upper layers of the pavement of the carriageway of roads”. Prospects for the development of science in the modern world: a collection of articles based on the materials of the XVI International Scientific and Practical Conference (April 5, 2019., Ufa). - Ufa: Publisher: Dendra (2019): 40-44.
5. Sayyed MA, Sheikhzadeh M and Hejazi SM. “Fiber-reinforced asphalt-concrete”. Construction and Building Materials 24.6 (2010): 871-877.
6. Efanov NE. “Influence of the technology of dispersed reinforcement of asphalt concrete mixtures on the processes of their structure formation”. Vestnik TGASU 1 (2007): 204-209.
7. Dean A Maurer and Gerald J Malasheskie. Field performance of fabrics and fibers to retard reflective cracking. Geotextiles and Geomembranes 8.3 (1989): 239-267.
8. MA Dalhat., et al. “Chicken Feather fiber modified hot mix asphalt concrete: Rutting performance, durability, mechanical and volumetric properties”. Construction and Building Materials 239.10 (2020): 117849.
9. Iliopolov SK., et al. “Modified crushed-stone-mastic asphalt-concrete mixture with a dispersed-reinforcing additive”. Internet journal Nauko Vedenie 3.12 (2012): 55-64.
10. EA Oluwasola, MR Hainin and MMA Aziz. “Evaluation of rutting potential and skid resistance of hot mix asphalt incorporating electric arc furnace steel slag and copper mine tailing”. Indian Journal of Engineering and Materials Sciences 22.5 (2015): 550-558.
11. M Shukla, D Tiwari and K Sitaramanjaneyulu. “Performance characteristics of fiber modified asphalt concrete mixes”. International Journal on Pavement Engineering & Asphalt Technology 15.1 (2014): 38-50.
12. Z Hong and L Liang. “Research on the performance of asphalt mixtures of polymer fibers”. Applied Mechanics and Materials 505-506 (2014): 228-233.

     A wide variety of destructions and deformations of the pavement, such as rutting, waves, fatigue cracks, is shown by the experience of operating roads with asphalt concrete pavements. The actual service life of road structures is often much lower than the standard. The service life of asphalt concrete pavements of roads in Ukraine is 5-7 years instead of 12 before overhaul.

     Thus, a decrease in the service life generally leads to a deterioration in the transport and operational condition of the road network.

     Bitumen is one of the materials most susceptible to changes in asphalt mixes.

     In the world practice of road construction, the opinion is firmly established that a radical way to improve the quality of bitumen is their modification with polymers. However, the introduction of polymer additives into petroleum bitumen is not always able to provide the required performance of asphalt concrete in terms of shear resistance at high positive temperatures, crack resistance at low temperatures, and fatigue life when exposed to long-term dynamic loads.

     The compositions and technologies for the production of technological complex-modified road asphalt mixtures for the construction of non-rigid pavements of increased durability based on basalt fiber have been developed at the National Aviation University.

Keywords: asphalt concrete; dispersed reinforcement; basalt fiber; crack resistance; shear resistance

     It is known that phases are formed in the Fe-N system: α-phases (nitrogenous austenite with a face-centered cubic lattice) [1, 2]. At a temperature of 590°C, the α-phase represents the eutectoid decomposition γ→α+ γ'. When supercooling the γ-phase by the shear mechanism, it represents a martensitic transformation. Nitrogenous martensite (α ' - phase) has a tetragonal body-centered lattice [2, 3]. A separate γ'-phase is a solid solution of Fe and N nitride, the ε-phase is also formed during nitriding and is a solid solution of Fe3N. In the Fe-C-N system in steel, the main strengthening phase is ε-carbonitride Fe2-3(N-C) formed during nitriding, the carbonitride phase obtained by simultaneous diffusion of carbon and nitrogen into steel has high hardness and high wear resistance. In the γ-phase, carbon practically does not dissolve, and the γ-phase is a solution of nitrogen and carbon intercalation. According to [4]. The introduction of nitrogen into the cementite lattice facilitates the formation of the carbonitride phase. Carbonitride with a cementite lattice is formed in the process of nitrocarburizing at a temperature of 680°C. When steel is nitrided, cementite, after saturation with nitrogen atoms, turns into ε-carbonitride. It has been established in the studies that during the nitriding of alloyed steels, phase points are formed, which are also formed during the nitriding of iron, only during alloying does the composition of the phases and the temperature intervals of their formation change. Studies have shown that in alloyed steel, due to the nitrogen content in the ε-phase, the hardness is increased to HRC 63.

     Following the nitride zone during nitriding in steels, there is a layer of the α-phase, which is the main part of the diffusion layer. Refractory alloying elements increase the solubility of nitrogen in the α-phase. During nitriding, the mosaic blocks are also refined and the α-phase lattice is distorted. The overall change in the defectiveness of the crystal structure of the α-phase depends on the nitriding temperature. When nitriding in the region of 500-600°C, the α-phase zone provides ferrite grains, and at a higher temperature of more than 600°C, a darkly etched zone is formed, also consisting of ferrite grains. Moreover, the darkening of ferrite grains increases with an increase in the content of alloying elements. During slow cooling after nitriding in steel, a γ'-phase of an acicular character is released from the α-phase. All alloying elements to some extent reduce the diffusion coefficient of nitrogen in the α-phase and, accordingly, reduce its depth.

     The structure of the nitrided layer is formed not only at saturation temperature but also during subsequent cooling. During cooling of the nitrided layer, the α-solid solution decomposes. This decay is strongly influenced by the rate of cooling. If there is a slow cooling, then simultaneously granular needle-like nitrides are formed that are released from the α-phase. The properties of the nitrided layer are determined by the structure that was formed during the saturation of the steel with nitrogen and subsequent transformations occurring during cooling. Two phases have high hardness: γ'-phase and nitrogenous martensite α'-phase. All alloying elements reduce the thickness of the nitrided layer but significantly increase the hardness of the steel surface. It was found that the high hardness of the nitrided layer is obtained by separating dispersed nitride alloying elements from solid solutions, which distort the α-phase lattice and serve as an obstacle to the movement of dislocations.

     Moreover, the greatest increase in hardness corresponds to the nitriding temperatures at which nitrides are actively formed. The nitride layer forms strong elastic distortions of the crystal lattice of the α-phase. These distortions prevent the movement of dislocations and contribute to the hardening mechanism of the steel. By changing the temperature and time of nitriding, it is possible to fix various stages of nitride precipitation in the diffusion zone and thus control the degree of steel hardening. When alloying steel with several elements, the degree of distortion of the diffusion layer is much higher than that of steel alloyed with one element. Therefore, complex alloy steels tend to have higher hardness than low alloy steels.

References

1. Lakhtin YuM and Kozlovsky IS. “Fundamentals of chemical heat treatment technology”. M. Mashinostroeniya (1985): 256.
2. Guriev AM., et al. “Improving the technology of chemical-thermal processing of tool steels and metal processing”. 6.1 (2009): 19-21.
3. Dolzhenkov VN. Cyanidation of improved steels in pastes: Cand. tech. Sciences. - Kursk (2001): 127.
4. Lakhtin YuM and Kogan YaD. “Nitriding of steel”. M.: Engineering (2005): 254.
5. Norkhudjayev FR., et al. “Influence of nitrocementation modes on the change in the hardness of the surface layer of structural steels”. Journal NX- A Multidisciplinary Peer Reviewed Journal 7.11 (2021): 75-77.
6. Alimbabaeva ZL and Bektemirov BSh. “Composite materials production technology for machining materials”. Lityo i metallurgiya 2020: sbornik nauchnih rabot III Mejdunarodnoy nauchno-prakticheskoy internet konferenciy studentsov i magistrantov, 18-19 noyabrya 2020 g./sost. AP Bejok. Minsk: BNTU (2021): 92-93.
7. Tilavov Yunus Suvonovich., et al. “Research of Technological Modes of Production of Small Diameter Rods from Niobium”. In: Cioboată, D.D. (eds) International Conference on Reliable Systems Engineering (ICoRSE) - 2022. ICoRSE 2022. Lecture Notes in Networks and Systems, Springer 534 (2022).
8. Alimbabaeva ZL., et al. “Physical and technological basis for formation of coatings by electric contact sintering”. Casting and metallurgy 2022: collection of scientific papers of the International Scientific and Practical Internet Conference of Students and Undergraduates. Minsk: BNTU (2022): 175-176.

     This article presents the results of a study of the processes of nitriding of alloyed steels. It is shown that to obtain a high hardness of the surface layer of steel, it is necessary to obtain dispersed nitrides of alloying elements in the surface layer of steel. The resulting nitrides have high hardness and are an obstacle to the movement of dislocations in the steel, thereby strengthening the surface layer of the steel.

Keywords: nitriding; nitrides; hardness; temperature; alloyed steels

References

1. Shubham Sharma., et al. "Detecting Insider Attacks on Databases using Blockchains". In proceedings of Workshop on Blockchain Technologies and its Applications, ISRDC, IIT Bombay (2017).
2. Weizhi Meng., et al. "When Intrusion Detection Meets Blockchain Technology: A Review". IEEE Access 6 (2018): 10179-10188.
3. Deepak K Tosh., et al. "Security Implications of Blockchain Cloud with Analysis of Block Withholding Attack". 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (2017).
4. Tzvi Chumash and Danfeng Yao. "Detection and prevention of insider threats in database driven web services". IFIPTM 2009: Trust Management III (2009): 117-132.

Many web applications that rely on centralized databases face vulnerabilities to insider attacks. While these systems implement multiple layers of security measures against external hackers, they may overlook the threat posed by employees who are already within these security layers and have access to privileged information. Users with administrative privileges in the database system can potentially access, modify, or delete data, while also manipulating corresponding log entries to erase any evidence of tampering, making detection nearly impossible. While one approach could involve developing methods to detect and trace such attacks, along with recovering the original data, this report takes a different perspective. Instead of focusing on detection and recovery, we explore a new direction: ensuring that attacks do not occur in the first place. By establishing a system that comprehensively safeguards data integrity, the need for detection, tracing, and recovery can be minimized or eliminated. This report investigates the prevention of insider attacks on databases by utilizing Bluzelle, a NoSQL database that offers decentralized database solutions for decentralized applications.

Keywords: Tampering; Centralized Database Systems; Insider Attack; Detection; Recovery; Integrity; Bluzelle

References

1. Sun X. “Effect of Projection Height on Projection Collapse and Nugget Formation”. A Finite Element Study, Welding Research Supplement (2001): 211-s to 216-s.
2. Gould JE. “Challenges and Advances in Resistance Spot Welding Aluminium Sheet”. Resistance and Solid-State Welding, Edison Welding Institute, EWI (2014).
3. Wang X and Zhang Y. “Effects of Welding Procedures on Resistance Projection Welding of Nuts to Sheets”. ISIJ International 57.12 (2017): 2194-2200.
4. Huang H-Y and Tseng K-H. “Process Parameters in Resistance Projection Welding for Optical Transmission Device Package”. Journal of Materials Engineering and Performance 20.2 (2011): 244-249.
5. Han G., et al. “Shape optimization of square weld nut in projection welding”. The International Journal of Advanced Manufacturing Technology 7-8 (2021).
6. Voiculescu I, Oprea VA and Vasile IM. “Case Study About Resistance Projection Welding of Aluminized Steel Parts”. Annals of “Dunarea de Jos” University of Galati, Fascicle XII, Welding Equipment and Technology 32 (2021): 23-29.
7. Song Q. “Testing and Modeling of Contact Problems in Resistance Welding”. Ph.D Thesis, Technical University of Denmark, Kongens Lyngby, Denmark (2003).
8. Pakala H. “An investigation into the processes of projection welding of galvanized sheet”. Welding International 10.1 (2009): 5-13.
9. Vedani M. “Microstructural and mechanical properties of stainless steel electrical resistance projection welds”. Welding International 16.9 (2002): 696-703.
10. Saha DC and Park Y-D. “A Review on Al-Al/Al-Steel Resistance Spot Welding Technologies for Light Weight Vehicles”. Journal of KWJS 29.4 (2011): 397-40.
11. Gullino A, Matteis P and D’Aiuto F. “Review of Aluminum-To-Steel Welding Technologies for Car-Body Applications”. Metals 9.315 (2019).

Projection welding is a joining process based on the electrical resistance of metal components that have small protrusions on the areas where assembly is desired. In this case, the welding electrodes that ensure the transfer of the electric current have a flat geometry, which allows uniform pressing of the areas with protrusions, which have the role of ensuring the concentration of the current lines for the formation of welding points. Excessive deformation can cause the protrusions to collapse rapidly, causing greater dispersion of the current and limiting the effect of localized heating and melting, and welding no longer occurs. The process is widely used in the electrical, electronic, automotive, food and construction industries, etc. The paper analyzes the effect of regime parameter values during the resistance welding process on the joints made between 2 steel components with surfaces protected by aluminization. The surface of one of the components was imprinted with 5 equidistant protrusions created by plastic deformation to concentrate the electric current lines and achieve melting points with a limited surface area. The thin layer of aluminum deposited on the surfaces of the parts has the role of protection against corrosion, but it produces hard compounds located on the welding interface. The chemical micro-composition analyzes performed with the EDAX method highlighted the diffusion effects of chemical elements in the welding area and the formation of hard compounds Al-rich loccated on the fusion line of the welded points. The microstructure of the welded areas was analyzed and the fracture strength tests of some welded samples were performed to establish the most suitable values of the welding process for this application.

Keywords: projection welding; current; microstructure; tensile test; microhardness

References

1. Cabrero Julio and Crying Carmen. “The application of expert judgment as a technique for evaluating information and communication technologies (ICT). Eduweb”. Journal of Information and Communication Technology in Education 7 (2013): 11-22.
2. Escobar-Pérez J and Cuervo-Martínez A. “Content validity and expert judgment: an approach to its use”. In Advances in Measurement 6 (2008): 27-36.
3. González Alonso J and Pazmiño Santacruz M. “Calculation and interpretation of Cronbach's Alpha for the case of validation of the internal consistency of a questionnaire, with two possible Likert-type scales”. Publishing Magazine 2.1 (2015): 62-67.
4. Jornet Meliá., et al. The validity in the evaluation of programs (2000).
5. Martin Cuadrado AM. (s.f.). Impact of internal training on productivity and standardization of production processes: A case study.
6. Marszowski Ryszard., et al. “Education in the Times of Demographic Change and Globalization. Case Study on the Example of the Silesian Voivodeship”. Sustainability 12.14 (2020): 5688.
7. Mingorance-Estrada AC., et al. “Validation of a questionnaire on the use of Interactive Response System in Higher Education”. Rev. Latino-Am. Sick 29 (2021): e3418.
8. Padilla R and Juárez M. “Effects of training on the competitiveness of the manufacturing industry. ECLAC, ECLAC Subregional Headquarters in Mexico”. Mexico D.F.: United Nations (2006).
9. Pedrosa Ignacio, Javier Suárez-Álvarez and Eduardo García-Cueto. “Content Validity Evidences: Theoretical Advances and Estimation Methods”. Psychological Action 10.2 (2014): 3.
10. Quero Virla Milton. “Reliability and Cronbach's Alpha coefficient”. Telos 12.2 (2010): 248-252.
11. Rodríguez-Rodríguez J and Reguant-Álvarez M. “Calculate the reliability of a questionnaire or scale using SPSS: Cronbach's alpha coefficient”. REIRE Magazine of Innovation and Research in Education 13.2 (2020): 1-13.
12. Suárez Yerikson. Impact of Virtual Education and ICT on Andragogy (Educational Sciences Magazine, University of Carabobo) 28 (2018): 326-347.
13. Valencia Rodríguez WA and Vallejo Cardona JD. “Educational evaluation: more than an action, an ethical question”. Virtual Magazine Catholic University of the North 45 (2015): 210-234.
14. Zineldin M, Akdag HC and Vasicheva V. “Assessing quality in higher education: new criteria for evaluating students' satisfaction”. Quality in Higher Education 17.2 (2011): 231-243.

     This article focuses on the importance of expert validation in the construction and evaluation of questionnaires, particularly in the context of satisfaction surveys. The article highlights that the involvement of experts provides an overview and helps understand the limitations of the coefficients used, such as Aiken's V and Cronbach's Alpha, as well as their proper interpretation.

     The text mentions that expert validation can also provide additional information about the interpretation of results and key concepts in the evaluation of measurement instruments. Furthermore, it points out that expert validation can be useful in assessing the agreement among experts' ratings in an impact study.

     Next, the importance of Aiken's V and Cronbach's Alpha in evaluating the validity of data obtained through questionnaires is emphasized. Aiken's V assesses the convergent and discriminant validity of a questionnaire, while Cronbach's Alpha evaluates the internal consistency of scale items.

     It is highlighted that obtaining positive results in both coefficients enhances the validity of data by demonstrating good convergence, discrimination, and consistency in measuring the target construct.

In conclusion, the article emphasizes that expert validation and the use of coefficients such as Aiken's V and Cronbach's Alpha are fundamental in improving the validity of data in satisfaction surveys and other measurement instruments.     These tools provide valuable information to ensure the accuracy and reliability of the results.

Keywords: Continuous education; Professional development; Satisfaction survey; Content validity; Reliability

References

1. Schmidt E, Schurig W and Sellschopp W. “Experiments on the condensation of water vapor in the form of films and drops”. tech Mech. Thermodyne 1 (1930): 53-63.
2. EJ LeFevre and JW Rose. “A theory of heat transfer by dropwise condensation”. in: Chem. Eng. Prog., Amer Inst Chemical Engineers 345 E 47th St, New York, NY 10017 (1966): 86.
3. DF Othmer. “The condensation of steam”. Ind. Eng. Chem 21 (1929): 576-583.
4. Davies G, Mojtehedi W and Ponter A. “Measurement of contact angles under condensation conditions. The prediction of dropwise film wise transition”. Int. J. Heat Mass Transf 14 (1971): 709-713.
5. S Kim and KJ Kim. “Dropwise condensation modelling suitable for superhydrophobic surfaces”. J. Heat Transf 133 (2011): 081502.
6. N Fatica and DLV Katz. “Dropwise condensation”. University of Michigan (1949).
7. JW Rose. “Dropwise condensation theory and experiment: a review”. Proc. Inst. Mech. Eng. Part J. Power Energy 216 (2002): 115-128.
8. JT Han, CX Lin and MA Ebadian. "Condensation heat transfer and pressure drop characteristics of R-134a in an annular helical pipe". International Communications in Heat and Mass Transfer 32 (2005): 1307-1316.

To assess the distribution of droplet sizes on a flat plate during dropwise condensation, a droplet detection technique has been devised. By integrating an equation for the single droplet heat transfer rate with the droplet size distribution, dropwise condensation heat transfer can be modelled. This chapter provides a thorough introduction to the condensation process. Both boiling and condensation take place throughout the thermodynamic heat transfer process. This article contains information about homogeneous and heterogeneous drop-wise condensation. The drop-wise condensation mechanism has been described with a diagram. The figure shows several contact angles and the resistance provided in dropwise condensation are recognized. The relevance of the condensation process and certain fundamental applications have been discussed recently.

References

1. AM Obeso., et al. “Forward- backward visual saliency propagation in Deep NNs vs internal attentional mechanisms”. 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA (2019).
2. V Mnih., et al. “Recurrent models of visual attention”. Advances in Neural Information Processing Systems 3 (2014): 2204-2212.
3. X He and Y Peng. “Fine-grained image classification via combining vision and language”. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2017): 7332-7340.
4. X Bai., et al. “Integrating scene text and visual appearance for fine-grained image classification”. IEEE Access 6 (2018): 66322-66335.
5. Z Yu., et al. “Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering”. IEEE Transactions on Neural Networks and Learning Systems 29.12 (2018): 5947-5959.
6. Y Zhou., et al. “Joint image and text representation for aesthetics analysis”. MM 2016 - Proceedings of the 2016 ACM Multimedia Conference (2016): 262-266.
7. X Tang, W Luo and X Wang. “Content-based photo quality assessment”. IEEE Transactions on Multimedia 15.8 (2013): 1930-1943.
8. R Datta., et al. “Studying aesthetics in photographic images using a computational approach”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3953 (2006): 288-301.
9. L Guo., et al. “Image esthetic assessment using both hand-crafting and semantic features”. Neurocomputing 143 (2014): 14-26.
10. M Nishiyama., et al. “Aesthetic quality classification of photographs based on color harmony”. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2011): 33-40.
11. DY Kao, R He and K Huang. “Deep Aesthetic Quality Assessment with Semantic Information”. IEEE Transactions on Image Processing 26.3 (2017): 1482-1495.
12. M Kucer., et al. “Predicting Image Aesthetics”. 27.10 (2018): 5100-5112.
13. Y Chen., et al. “Engineering deep representations for modeling aesthetic perception”. IEEE Transactions on Cybernetics 48.11 (2018): 3092-3104.
14. Y Luo and X Tang. “Photo and Video Quality Evaluation”. Quality 8.08 (2008): 386-399.
15. Y Ke, X Tang and F Jing. “The design of high-level features for photo quality assessment”. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 (2006): 419-426.
16. K Xu., et al. “Show, attend and tell: Neural image caption generation with visual attention”. 32nd International Conference on Machine Learning, ICML 3 (2015): 2048-2057.
17. S Kong., et al. “Photo aesthetics ranking network with attributes and content adaptation”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9905 (2016): 662-679.
18. TY Lin., et al. “Microsoft COCO: Common objects in context”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5 (2014): 740-755.

Different kinds of images induce different kinds of stimuli in humans. Certain types of images tend to activate specific parts of our brain. Professional photographers use methods and techniques like rule of thirds, exposure, etc, to click an appealing photograph. Image aesthetic is a partially subjective topic as there are some aspects of the image that are more appealing to the person’s eye than the others, and the paper presents a novel technique to generate a typical score of the quality of an image by using the image captioning technique. The model for Image Captioning model has been trained using Convolutional Neural Network, Long Short Term Memory, Recurrent Neural Networks and Attention Layer. After the Image caption generation we made, a textual analysis is done using RNN- LSTM, embedding layer, LSTM layer, and sigmoid function and then the score of the image is predicted for its aesthetic quality.

Keywords: Image Aesthetic; Convolutional Neural Network; Long Short Term Memory; Recurrent Neural Networks; Attention Layer; Embedding Layer; Image Captioning

Dynamic of the electron

Let’s briefly describe the dynamic of an electron in the quantum computer. An electron can be in a three states in the quantum computer. It can be in a bounded state in an atom or molecule, it can be in a transitory state and it can move in a stabile state or pre-superconducting state. Concerning bounded state there is not much difference in the Confined Quantum Field Theory compared with the standard quantum. The only difference is that state functions have a cut-off and do not go to infinity. One can accept solution to the Schrödinger but on a bounded domain. The difference is mainly on the transitory and stable or pre-superconducting state as we call it in the Confined Quantum Field Theory. Transitory state is a state that the electron moves around and has a size or energy that do not match with the potentials around to be captured. In this state the electron frequently exchanges energy with the bulk until it finds energy or size and position to be in a bounded state or pre-superconducting state. Pre-superconducting state is a state which specifically recognized by Confined Quantum Field Theory in that an electron moves in a periodic potential and the size of the electron is a multiple of the periodicity. In hardware engineering avoiding impurity and defect is especially important just because they destroy the periodicity. We all are aware that computers works always better in a lower temperature. It is just because the temperature disturbs the potential periodicity. Since in the quantum computing we deal with single atom or molecule, if this atom or molecule is disturbs by high energy radiation can be a problem for the computing. Disturbance due to the high energy radiation like cosmic radiation is more difficult to prevent. For this one need a thick jacket of lead which is not practical for small mobile computer. One possible solution is to construct several parallel computing lines. The probability that two lines disturbs in a same way by a high energy radiation at the same time is almost zero.

Today Quantum Computing is central in the advance engineering. This is because there is more demand for faster and more effective computer to treat huge amount of information in a very short amount of time. From engineering point of view this means that we must go in to the atomic level in the constructing computer. This is in fact what Quantum Computing is about. For this one should have good understanding about electrons dynamic behavior. This in turn demands a good understanding of the quantum. Quantum that we teach student now is limited by uncertainty and paradox. First it is not transparent. One may not be interested in transparency and only be interested in the result of the calculation. Even if one is directed to the result, we should say that the calculation is often complicated and confronted with ambiguity. We must say that we can never separate transparency from the other aspects of the calculation. It is through the transparency that we can find the best way of the calculation. Here we come to the Confined Quantum Field Theory. In confined Quantum Field Theory we have no uncertainty and paradox. Each quantum object like electrons are represented by a well defined bounded and connected manifold with well defined size. This size is a function of the quantum objects energy. Therefore we often refer to the energy as the size. Quantum objects with higher energy have smaller size. More in the confined quantum field theory quantum objects have well defined position. Well defined size and position is the transparency we receive from the Confined Quantum Field Theory.

References

1. Adebabay A. “Promoting and strengthening professionalism in the civil service”. The Ethiopian case, Addis Ababa (2011).
2. Akintola A, Root D and Venkatachalam S. “Key Constraints to Optimal and Widespread, Implementation of BIM in the South African Construction Industry”. Association of Researchers in Construction Management 33 (2017): 25-34.
3. Amante G. “Performance Evaluation of Governance and Political System of Ethiopia: Post 1991”. In B. W. Debela.B (Ed.), Public Administration in Ethiopia: Case Studies and Lessons for Sustainable Development. Belgium: Leuven University Press (2020): 444-472.
4. Awuzie B and Monyane G. “Conceptualizing Sustainability Governance Implementation for Infrastructure Delivery Systems in Developing Countries: Success Factors”. Sustainability, 12(3), (2020): 961.
5. Awuzie BO and McDermott P. “Understanding complexity within energy infrastructure delivery systems in developing countries: Adopting a Viable Systems Approach”. Journal of Construction Project Management and Innovation 3.1 (2013): 554-559.
6. Awuzie BO and McDermott P. “Evaluating the Impact of National Culture on Viability with Infrastructure Delivery Systems”. UK: University of Salford (2014).
7. Ayee JR. Public sector Management in Africa, Legon, Africa Development Bank (2005).
8. Bolton R and Foxon TJ. “Infrastructure transformation as a socio-technical process”. Implications for the governance of energy distribution networks in the UK. Technological Forecasting & Social Change 90.1 (2014): 538-5550.
9. Burches E and Burches M. “Efficacy, Effectiveness and Efficiency in the Health Care: The Need for an Agreement to Clarify its Meaning”. International Archives of Public Health and Community Medicine 4.1 (2020): 2643-4512.
10. Cahen M. “Effective Delivery of Infrastructure”. OECD better policies for better lives (2016).
11. Chekole TGN. “Key Factors Contributing to Time and Cost Overrun in Mega Sugar Construction Projects in Ethiopia”. In B. W. Debela.B (Ed.), Public Administration in Ethiopia: Case Studies and Lessons for Sustainable Development. Belgium: Leuven University Press (2020): 474-489.
12. Chemouni B. “The politics of core public sector reform in Rwanda”. Manchester: ESID Working (2017).
13. CIDB.: The standard for the implementation of the IDMS through the Infrastructure Gateway System.
14. Crudgington A. “A Systems Approach to Infrastructure Delivery, A review of how systems thinking can be used to improve the delivery of complex infrastructure projects”. UK: ICE (2020).
15. Daniel EI. “Exploratory Study into The Use of Last Planner® System and Collaborative Planning for Construction Process Improvement”. Nottingham: Nottingham Trent University (2017).
16. Desta S. “The Management of the Construction Processes in Developing Countries: A Case Study of the Ethiopian Roads Authority”. University of Cape town (2015).
17. Development Bank of South Africa.: The State of South Africa Socio- Economic Infrastructure: Opportunities and Challenges. Johannesburg: DBSA (2012).
18. Egan S. “Rethinking Construction”. UK: Department of Trade and Industry (1998).
19. Ejigu A. “A Systems Approach to Infrastructure Planning: Understanding Complexity and Uncertainty”. Stockholm: Department of Urban Planning and Environment (2007).
20. Fawcett and Marsh D. “Policy Transfer and Policy Success: The case of Gateway Review Pro-cess 2001-10”. Journal of Comparative Politics 47.2 (2012): 162-185.
21. Foster V and Morella E. “Ethiopia's Infrastructure: A Continental Perspective”. Ethiopia: World bank (2011).
22. Ghrmay TM. “New Public Management and Path Dependence in Public Organizations in Ethiopia: A Multiple Case Study”. In B. W. Debela.B (Ed.), Public Administration in Ethiopia: Case Studies and Lessons for Sustainable Development. Belgium: Leuven University Press (2020): 251-275.
23. Hudson BH. Policy failure and the policy implementation gap: Policy Design and Practise 4.1 (2019): 1-14.
24. Khatleli N. Utilizing Principal Agent and Principal Steward Theories to Assess the Efficacy of Public Private Partneships in Delivering Black Economic Empowerment, Cape Town: University of Cape Town (2009).
25. Kibuuka P and Fourie D. “Effective regional infrastructure delivery in Africa: an analysis of the financial skills of the technical staff”. Public and Municipal Finance 5.1 (2016): 18-28.
26. Koskela L. Is structural change the primary solution to the problems of construction? 31.2 (2003): 85-96.
27. Kwofie TE, Aigbavboa CO and Matsane Z. “Collaborative practices in South African construction project delivery”. International Journal of Construction Supply Chain Management 7.2 (2014): 39-55.
28. Latham M. “Constructing the Team, Joint Review of Procurement and Contractual Arrangements in the United Kingdom Construction Industry”. London: HMSO (1994).
29. Liu Y. “From Best Practices to Next Practices: Project-based learning in the development of large infrastructure”.
30. Mabugu R. “Chapter 1 Responding to South Africa’s Infrastructure Challenge”. In Submission for the 2016/17 Division of Revenue. Pretoria: Financial and Fiscal Commission (2016): 16-36.
31. Malete R and Khatleli N. Efficacy in infrastructure delivery post-apartheid: An Analysis of infrastructure delivery management system in the public sector in South Africa: South African Council of Quantity Surveyors Conference, (2019).
32. Master DM. “Policy-Oriented Learning and Policy Change in South Africa Between 1994 - 2006: An Assessment of South Africa’s Economic Policies (Rdp, Gear and Asgisa)”. JHB: University of Witwatersrand (2019).
33. Matji MP and Ruiters C. “Funding and Financing Mechanisms for Infrastructure Delivery: Multi-Sector Analysis of Benchmarking of South Africa Against Developed Countries”. Pretoria: Asset Management Conference (2015).
34. Mihaiu D and Opreana A Christescu. “Efficiency, Effectiveness and Performance of Public Sector”. Romanian Journal of Economic Forecasting 4.1 (2010): 132-147.
35. Miller JB., et al. “Toward a New Paradigm: Simultaneous Use of Multiple Project Delivery Methods”. Journal of Management in Engineering (2002): 58-67.
36. National Treasury.: Standard for Infrastructure Delivery Management System. Pretoria: National Treasury Republic of South Africa (2016).
37. Ntliziywana P. “Local Government Service Delivery in South Africa: The Failures and Limits of Legislating New Public Management”. Cape Town: University of The Western Cape (2017).
38. Nuramo DA and Haupt T. “BIM for Infrastructure Sustainability in Developing Countries: the case of Ethiopia”. KZN: University of KwaZulu-Natal (2016).
39. Pisu M, Pels B and Bottini M. “Improving infrastructure in the United Kingdom, OECD”. Paris: OECD Publishing (2015).
40. Practise Note 22a.: Infrastructure Gateway. CIDB IDMS Toolkit 2010: Delivery Processes. IDM Toolkit management companion (2010).
41. Regar M, Smith J and Love P. “Government institutions and infrastructure skills development”. UK: Association of Researchers in Construction Management (2014).
42. Saunders M, Lewis P and Thornhill A. Research Methods for Business Students (7th ed.). England: Pearson (2016).
43. Sharpe W. “The Gateway Review Process in Victoria. In Wanna.J (Ed.), Improving Implementation: Organisational Change and Project Management”. New Zealand: Anu Press (2007): 199-217.
44. SMEC: Domestic Construction Industry Study. Snowy Mountain Engineering Company (1999).
45. Tadesse W. “Public service reform in Ethiopia: Challenges and gaps”. Africa's Publis Service Delivery and Perfomance Review 7.1 (2018): 5.
46. Takeuchi S. “Development and Developmentalism in Post-genocide Rwanda”. Developmental State Building, Emerging-Economy State and International Policy Studies (2019): 121-134.
47. Watermeyer R. “A systems approach to the effective delivery of infrastructure”. JHB: in proceedings (2012).
48. Watermeyer R and Phillips S. “Public infrastructure delivery and construction sector dynamism in the South African economy”. Pretoria: National Planning Commission (2020).
49. Winter.: Evidence for the value of a systems approach to infrastructure planning, delivery and operation. UK: UCL Press (2013).
50. World Economic Forum.: Strategic Infrastructure Steps to Prepare and Accelerate Public-Private Partnerships (2013).
51. WorldBank T. “Private solutions for infrastructure in Rwanda”. United States of America: The International Bank for Reconstruction and Development (2005).
52. Yin R. Case Study Research Design and Methods (5th ed.). (5th ed.). CA: Sage (2014).
53. DTN Huy. “Estimating Beta of Viet Nam listed construction companies groups during the crisis”. Journal of Integration and Development 15.1 (2012): 57-71.
54. ND Trung, DTN Huy and T Van Thanh. “Practical Analysis of Building Industrial Clusters Infrastructure in Hanoi Vietnam”. International Journal of Ecosystems and Ecology Science 12.3 (2022).

South Africa, like many other developing nations, faces significant challenges in delivering effective and fair public services. Africa in particular suffers from a catastrophic shortage of public infrastructure, and a variety of factors contribute to the infrastructure deficit. Public entities around the world are battling with effective service delivery and have adopted different models to enhance and improve infrastructure delivery. However, the models currently deployed have shortcomings, thus frustrating the efforts to deliver infrastructure effectively to the general populace. South Africa has similarly had its fair share of false starts. The 2010 introduction of Infrastructure Delivery Management System (IDMS) was specifically to facilitate effective, timely and sustained infrastructure development, and tackle the challenges in public sector infrastructure delivery. The study employs a multi-case study, qualitative approach through content analysed data to look at four nations that implements infrastructure projects in Europe and Sub-Saharan Africa and analyze the advancement of infrastructure delivery. A systematic review of infrastructure delivery models/reforms in the context of public sector was carried out through literature and descriptive analysis was applied. The findings reveal a knowledge vacuum about the diverse techniques taken by various countries in the execution of public sector infrastructure projects, and provide little precise evidence on the performance of delivery systems and lessons learned. It is here recommended that interventions such as IDMS should be contextualized cognizant of the country’s developmental imperatives.

Keywords: reforms; infrastructure; delivery; construction industry; public sector

References

1. Gambi., et al. “A home automation architecture based on LoRa technology and message queue telemetry transfer protocol”. Int. J. Distrib. Sens. Netw 14.10 (2018): 1550147718806837.
2. R Islam., et al. LoRa and server-based home automation using the internet of things (IoT)". Journal of King Saud University - Computer and Information Sciences 34.6 (2022): 3703-3712
3. ElShafee et al. Proposed Wi-Fi-based home automation and security system (2012).
4. Risteska Stojkoska., et al. “A review of Internet of Things for smart home: challenges and solutions”. Cleaner Prod., 140 (2017): 1454-1464.
5. Bababe., et al. “Kumar Lora based intelligent home automation system”. Int. J. Eng. Adv Technol. (IJEAT) 6 (2017): 88-92
6. MR Bashir and AQ Gill. "IoT enabled smart buildings: A systematic review". 2017 Intelligent Systems Conference (2017): 151-159.
7. Pavithra and Balakrishnan. wi-fi-based home automation systems (2015).

Smart building management systems (SBMS) are becoming increasingly popular due to their ability to improve the energy efficiency and sustainability of buildings. This research uses Lora technology- the latest trend to deliver a secure wireless SBMS using very little power. LoRa devices involve incorporating wireless communication technology into building management systems to increase power efficiency, ensure safety by earlier earthquake and fire detection, providing safety instructions, escape routes to the occupants and alert to the local emergency response team, security to the end user, automation and monitoring capabilities. SBMS also provides advanced automation capabilities, such as automated lighting, heating, and cooling systems. These systems adjust in real-time based on the occupancy levels and other building parameters, providing optimal comfort to occupants while minimizing energy usage. The goal is to enhance comfort, safety, security and energy efficiency for the occupants.

LoRa offers long-range coverage and low power consumption, making it ideal for use in smart buildings.

Keywords: Internet of things (IOT); Smart buildings; Lora; Cloud computing

References

1. Akhmedov Yu and Asadov ShK. Contruction of the shadow of polyhedral. International journal of progressive scinces and technologies (IJPSAT) 24.2 (2021): 370-374.
2. Yunus Akhmedov, Shuhrat Asadov and Bobir Azimov. Two-sided estimation of linear approximation error second-order hypersurfaces. Apitech-iv-2022 Journal of Physics: Conference Series 2388 (2022): 012124.
3. Akhmedov Yu and Sharipov K. Geometric modeling of drilling bit rolling cutters by generalized Hermitian splines. Apitech-iv-2022 Journal of Physics: Conference Series 2388 (2022): 012164.
4. Asadov ShK and Akhmedov YuKh. Approximation of hypersurfaces by polyhedra in relation to calculations of the bearing capacity of coating shells in En space. Universum: technical sciences: electronic scientific journal 4.97 (2022).
5. 5. Asadov Sh K. Mathematical modeling under plasticity conditions for problems of zero Gaussian curvature of the stress state of plates under variable loads. Universum: Engineering sciences 5.86 (2021): 35-38.
6. Ahmedov Yu and Shuhrat Asadov. Construction of The Shadows of Polyhedra. International Journal of Progressive Scinces and Technologies (IJPSAT) 24.2 (2021): 370-374.
7. Shukhrat Muradov. Research of Working Bodies for Uncomplacing the Underground Layer of Soil. International Scientific Conference on Agricultural Machinery Industry “Interagromash”, XV International Scientific Conference “Interagromash 2022”, Lecture Notes in Networks and Systems book series 574.05 (2023): 1099-1108.

Based on the generalization of the theory of generalization of polyhedra, a method for linearizing the yield conditions of an arbitrary form, for plastic shells by inscribed and circumscribed hyperpolyhedra, has been developed. The technique allows one to obtain hyperpolyhedra with an arbitrary number of faces. The linearization technique is used to construct an algorithm and a program for automatic approximation of convex hypersurfaces by inscribed and circumscribed hyperpolyhedra with any number of faces. Automatic linearization of plasticity conditions for rigid-plastic shells made it possible to construct an effective method and PPP for calculating lower estimates for the bearing capacity of shallow shells with rectangular plan. The possibility of increasing the efficiency of programs due to the optimal choice of the number of faces of hyperpolytopes is obtained - polyhedra with a minimum number of faces are found, leading to estimates of the bearing capacity with a given accuracy. On the basis of the kinematic method of the theory of limit equilibrium and the use of the generalized Ioganson's yield condition, a method has been developed for calculating the bearing capacity of plates of a complex shape.

Keywords: Geometric modelling; bearing capacity; yield conditions; approximation; hypersurface; polyhedron; equilibrium; hyperpolyhedron; seismic resistance; design; plasticity theory; design; geometric modelling; rigid plasticity; six-dimensional space; surface; linearization; ultimate load; deformation; optimization; coefficient

References

1. Dewi Suriyani Che Halin. Metal Processing. Presentation. (13) (PDF) Lecture 2- Metal Processing (2018).
2. Guides to Industrial Art. Metal Casting 101: Learn to Cast Metal [Types & Processes].
3. Manufacturing Technology-1 Note by R. Ganesh Narayanan. Metal casting processes.
4. E Abbilash, B Ravi and SS joshi. “Opportunities in Aerospace Casting Manufacte”. 26th Indian Engineering Congress (2011).
5. Sayed M Amer. Aluminum and Its Alloy (2022).
6. M Thiugnanam. “Modern High Pressure Die-Casting Processes for Aluminum Casting”. 61st Indian Foundry Congress (2013).
7. Aluminium - Wikipedia.
8. Frank M White. Viscous Fluid Flow. Mc Graw Hill Companies. 3rd ed (2006).
9. MJ Assael., et al. J. Phys. Chem. Ref. Data 35.1 (2006): 285-300.
10. Matthias Leitner., et al. “Thermophysical Properties of Liquid Aluminum”. Metallurgical and Materials Transactions. Cross Mark 48A (2017): 3036-3045.
11. Ron Cobden Alcan and Banbury. Aluminum: Physical Properties, Characteristics and Alloys. TALAT Lecture 1501. EAA (1994).

Aluminum casting is a manufacturing process that has been around for some time. This method has been used in making many aluminum products used as parts of aircraft, automobiles, turbines, and structures like bridges. Every cast product is expected to be of the desired and required strength so it will not fail on application. It is good to predetermine the strength of aluminum cast accurately at the design stage before casting. Therefore, we developed a model that can predict the aluminum cast's strength and other mechanical properties by studying the flow profile of liquid aluminum flowing through the mold.

Keywords: Strength of cast; Aluminum casting; Fluid mechanics; Flow properties

Applications in Various Industries

     The versatility of Functionally Graded Materials has sparked interest and found applications in a wide range of industries. In aerospace, FGMs are used in turbine blades, where the gradual variation in material properties helps withstand high temperatures and reduce thermal stresses. In automotive applications, FGMs contribute to lightweight designs while improving structural integrity. The energy sector benefits from FGMs in heat exchangers, which efficiently manage thermal gradients. Biomedical implants take advantage of FGMs' ability to mimic the natural transition between bone and implant materials, promoting better integration and reducing complications. Moreover, FGMs find applications in electronics, where they enable precise control of electrical conductivity and thermal management. Some key application areas where FGMs are used.

Thermal Management Systems

     FGMs find use in thermal management systems, such as heat sinks and cooling devices. By tailoring the thermal conductivity gradient within the material, FGMs can efficiently dissipate heat, ensuring optimal thermal performance and minimizing temperature gradients.

Wear-Resistant Coatings

     FGMs can be employed as wear-resistant coatings in various industries. By gradually transitioning from a hard and wear-resistant material to a tough and ductile material, FGM coatings can provide superior resistance to wear, abrasion, and erosion.

Optics and Photonics

     FGMs play a vital role in optical and photonics applications. They can be designed to possess varying refractive indices, allowing for the controlled manipulation of light and the creation of gradient-index lenses, waveguides, and optical filters.

Energy Storage and Conversion

     FGMs have the potential to enhance energy storage and conversion devices. For example, in lithium-ion batteries, FGM electrodes can optimize the transport of ions and electrons, improving the battery's performance and durability.

Structural Components

     FGMs offer advantages in structural applications where there are significant thermal or mechanical loads. By gradually adjusting the material properties, FGMs can minimize stress concentrations, reduce thermal expansion mismatches, and enhance the overall structural integrity of components.

Aerospace Propulsion Systems

     FGMs have garnered attention in aerospace propulsion systems, particularly in combustion chambers and turbine components. The tailored composition and property gradients can withstand extreme temperatures, resist thermal fatigue, and enhance the efficiency and durability of propulsion systems.

Acoustic Applications

     FGMs can be utilized in acoustic devices and systems. By controlling the density and stiffness gradients, FGMs can effectively manipulate sound waves, enabling the design of improved acoustic lenses, sound barriers, and noise-reduction materials.

Microelectronics and MEMS

     FGMs find applications in microelectronics and microelectromechanical systems (MEMS). By tailoring the electrical conductivity and thermal properties, FGMs can facilitate better heat dissipation, improved electrical interconnects, and enhanced performance of microdevices.

Corrosion Protection

     FGMs can serve as protective coatings in corrosive environments. By gradually transitioning from a corrosion-resistant material to a sacrificial layer, FGM coatings can effectively inhibit corrosion and extend the lifespan of structures and equipment.

     These applications highlight the diverse range of industries that benefit from the unique properties and capabilities of Functionally Graded Materials. By tailoring material compositions and properties, FGMs offer opportunities for innovation and optimization, leading to improved performance, efficiency, and reliability in various technological domains.

Design Challenges and Fabrication Techniques

     The design and fabrication of Functionally Graded Materials present unique challenges. Achieving the desired composition gradients requires careful consideration of material selection, processing techniques, and modeling approaches. Techniques such as powder metallurgy, thermal spray, sol-gel processes, and additive manufacturing methods like 3D printing offer the means to fabricate FGMs with controlled composition variations. Additive manufacturing, in particular, allows for the creation of complex geometries and precise property gradients, opening up new possibilities for material design.

Future Prospects

     Functionally Graded Materials continue to captivate researchers and industry professionals, with ongoing efforts to refine fabrication techniques, characterize properties, and optimize designs. Advances in computational modeling and simulation techniques aid in predicting material behavior and guiding the development of novel FGMs. As additive manufacturing technologies advance, FGMs are poised to revolutionize material design, offering unprecedented opportunities for customization and performance optimization.

Conclusion

     Functionally Graded Materials represent a remarkable advancement in material science and engineering. By carefully tailoring composition gradients, FGMs offer unique combinations of properties that can overcome the limitations of homogeneous materials. From aerospace and automotive applications to biomedical implants and electronics, FGMs unlock new possibilities for innovation and performance optimization. As research and development in this field continue to progress, we can anticipate exciting breakthroughs that will shape the future of advanced materials and their applications in various industries.

     In the quest for materials with enhanced performance and tailored properties, scientists and engineers have turned to Functionally Graded Materials (FGMs). These engineered materials exhibit a gradual transition in composition, structure, and properties, offering unique opportunities for innovation across numerous industries. By designing materials with specific property gradients, FGMs enable the development of advanced technologies that push the boundaries of traditional homogeneous materials.

Composition and Property Variations

     Functionally Graded Materials are characterized by a controlled variation in composition from one end to the other. This composition gradient can include different materials or phases, such as metals, ceramics, polymers, or composites. The gradual change in composition leads to corresponding variations in material properties, including mechanical strength, thermal conductivity, electrical conductivity, magnetic and optical properties. This property tailoring allows FGMs to excel in applications that demand specific material behavior.

     This eons-old parallel development could be accepted as casual and based on weak statistics, but any further analysis pushes us to consider a strong participation of creation, and entangled development within that.

     Similar thinking can be used to look at fleas and humans, snakes and insects, whales and sardines, monkeys and bananas, etc.

     If one brings this into the classroom, it may be that this would be a good introduction to quantum entanglement. The above entanglement would not exist if physics had no part in it.

     After all, it is physics that teaches us about energy. But it does not teach us about the energy that exists, for example, when a dog nurses a newborn cat.

     In the above examples, it is clear that creation demands a certain proximity to be relevant, like apple trees and humans over eons, but this is something that quantum entanglement has proven to be unnecessary. This suggests that nature has a way to act along certain rules of creation. Can one then expect that there are humans elsewhere in this universe, as long as the conditions of creation are similar? Is love a universal energy we can count on?

     This means that we are not giving creation the proper amount of mention and respect. Science and engineering should be seen as extensions of creation.

     The easiest example of that entanglement is the emergence of nature, and humans within it. If one observes the most important natural food for humans, fruit was clearly shaped to work with humans. For example, our hands are not optimized for ripping oxen apart but shaped ideally for picking apples from a tree.

     If we follow that thinking, we reap major benefits (energy, vitamins, fiber, clean water, etc.). The apple tree, since we are eating its gonads, benefits from our digestive system simply passing its undigested seeds along to Earth, surrounded by nutritious cover.

References

1. S Stewart., et al. “Population prevalence, incidence, and predictors of atrial fibrillation in the Renfrew/Paisley study”. Heart 8 (2001): 516-521.
2. Blum S., et al. “Incidence and Predictors of Atrial Fibrillation Progression”. Journal of the American Heart Association 8 (2019): e012554.
3. William M., et al. “Prevalence, Age Distribution, and Gender of Patients with Atrial FibrillationAnalysis and Implications”. Arch Intern Med 155.5 (1995): 469-473.
4. Vermond RA., et al. “Incidence of Atrial Fibrillation and Relationship with Cardiovascular Events, Heart Failure, and Mortality: A Community-Based Study from the Netherlands”. J Am Coll Cardiol 66.9 (2015): 1000-7.
5. Benjamin Emelia J., et al. “Impact of Atrial Fibrillation on the Risk of Death”. Circulation 98 (1998): 946-952.
6. Schläpfer J and Wellens HJ. “Computer-interpreted electrocardiograms: benefits and limitations”. J. Am. Coll. Cardiol 70 (2017): 1183-1192.
7. Shah AP and Rubin SA. “Errors in the computerized electrocardiogram interpretation of cardiac rhythm”. J. Electrocardiol 40 (2007): 385-390.
8. Guglin ME and Thatai D. “Common errors in computer electrocardiogram interpretation”. Int. J. Cardiol 106 (2006): 232-237.
9. Poon K, Okin PM and Kligfield P. “Diagnostic performance of a computer-based ECG rhythm algorithm”. J. Electrocardiol 38 (2005): 235-238.
10. Gulshan V., et al. “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”. JAMA 316 (2016): 2402-2410.
11. Esteva A. “Dermatologist-level classification of skin cancer with deep neural networks”. Nature 542 (2017): 115-118.
12. Poungponsri S and Yu X. “An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks”. Neurocomputing 117 (2013): 206-213.
13. Ochoa A, Mena LJ and Felix VG. “Noise-tolerant neural network approach for electrocardiogram signal classification”. In Proc. 3rd International Conference on Compute and Data Analysis (2017): 277-282.
14. Javadi M., et al. “Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning”. Biomed. Signal Process. Control 8 (2013): 289-296.
15. Acharya UR., et al. “A deep convolutional neural network model to classify heartbeats”. Comput. Biol. Med 89 (2017): 389-396.
16. Banupriya CV and Karpagavalli S. “Electrocardiogram beat classification using probabilistic neural network”. In Proc. fea Learning: Challenges and Opportunities Ahead (2014): 31-37.
17. Al Rahhal MM., et al. “Deep learning approach for active classification of electrocardiogram signals”. Inf. Sci. (NY) 345 (2016): 340-354.
18. Acharya UR., et al. “Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network”. Inf. Sci. (NY) 405 (2017): 81-90.
19. Zihlmann M, Perekrestenko D and Tschannen M. “Convolutional recurrent neural networks for electrocardiogram classification”. Comput. Cardiol (2017).
20. Xiong Z, Zhao J and Stiles MK. “Robust ECG signal classification for detection of atrial fibrillation using a novel neural network”. Comput. Cardiol (2017).
21. Clifford G., et al. “AF classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017”. Comput. Cardiol (2017).
22. Teijeiro T., et al. “Arrhythmia classification from the abductive interpretation of short single-lead ECG records”. Comput. Cardiol (2017).
23. Tison GH., et al. “Passive detection of atrial fibrillation using commercially available smatwatch”. JAMA Cardiology 3 (2018): 409-416.
24. Moody GB and Mark RG. “A new method for detecting atrial fibrillation using R-R intervals”. Computers in Cardiology 10 (1983): 227-230.
25. Mark RG., et al. “An annotated ECG database for evaluating arrhythmia detectors”. IEEE Transactions on Biomedical Engineering 29.8 (1982): 600.
26. Moody GB and Mark RG. “The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it”. Computers in Cardiology 17 (1990): 185-188.
27. Goldberger AL., et al. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”. Circulation 101.23 (2000): e215-e220.
28. Hand DJ and Till RJ. “A simple generalisation of the area under the ROC curve for multiple class classification problems”. Mach. Learn 45 (2001): 171-186.
29. Fawcett T. “An introduction to ROC analysis”. Pattern Recognit. Lett 27 (2006): 861-874.
30. Sanders P., et al. “Reveal LINQ Usability Investigators. Performance of a new atrial fibrillation detection algorithm in a miniaturized insertable cardiac monitor: Results from the Reveal LINQ Usability Study”. Heart rhythm 13 (2016): 1425-1430.

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia but is currently under-diagnosed since it can be asymptomatic. Early detection of AF could be highly beneficial for the prevention of stroke, which is a major risk associated with AF, with a fivefold increase. The advent of portable monitoring devices can help uncover the underlying dynamics of human health in a way that has not been possible before.

Method: The purpose of this study was to validate the automated analysis of AF by the SmartCardia’s proprietary health monitoring device (ScaAI patch, SmartCardia S.A., Lausanne, Switzerland). To this end, a model was created and tested on three publicly available databases comprised of 243,960 ECG segments of 30-seconds. The model was further tested against a set of 500 ECG streams of 30-seconds (recorded by ScaAI patch - across different clinical trials; annotated by 3 different cardiologists), especially representing problematic conditions, when determination the underlying rhythm was challenging.

Results: The created model obtained F1-scores of 94.42 against a test set from two published available databases, and an F1-score of 92.61 (average F1-scores w.r.t each cardiologist) on the SmartCardia assembled database.

Conclusion: We demonstrated that the new wireless ScalAl patch had a high capacity to automatically detect AF when compared with public database. Further studies will help identify the optimal role of the the ScaAl patch in the management of cardiac arrhythmias.

Keywords: Atrial fibrillation; arrhythmias; automatic arrhythmia detection; wireless system

References

1. G Divyashree., et al. “Intrusion detection system in wireless sensor network”. Int. J.Recent Technol. Eng 8.1 (2019): 2047-2051.
2. PR Chandre, PN Mahalle and GR Shinde. “Machine Learning Based Novel Approach for Intrusion Detection and Prevention System: A Tool Based Verification”. Proc. - 2018 IEEE Glob. Conf. Wirel. Comput. Networking, GCWCN 2018 (2019): 135-140.
3. L Sheeba and V Meenakshi. “A Brief survey on Intrusion Detection System for WSN”. Int. J. Comput. Trends Technol 40.3 (2016): 109-113.
4. AH Farooqi and FA Khan. “A survey of intrusion detection systems for wireless sensor networks”. Int. J. Ad Hoc Ubiquitous Comput 9.2 (2012): 69-83.
5. SHAH Baddar, A Merlo and M Migliardi. “Anomaly detection in computer networks: A state-of-the-art review”. J. Wirel. Mob. Networks, Ubiquitous Comput. Dependable Appl 5.4 (2014): 29-64.
6. I Butun, SD Morgera and R Sankar. “A survey of intrusion detection systems in wireless sensor networks”. IEEE Commun. Surv. Tutorials 16.1 (2014): 266-282.
7. S Ifzarne., et al. “Anomaly Detection using Machine Learning Techniques in Wireless Sensor Networks”. J. Phys. Conf. Ser 1743.1 (2021).
8. F Cauteruccio., et al. “Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance”. Inf. Fusion 52 (2019): 13-30.
9. Yihao Zang, Xianhao Shen and Shaohua Niu. “A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network”. Electrical Engineering and Systems Science (2021): 1-12.

Cloud computing has transformed the technological landscape by providing businesses with scalable virtual resources and by transforming the e-learning industry. With this transformation, however, comes a paramount concern for security. The improvement of e-learning systems requires substantial investments in hardware and software. Cloud computing provides a cost-effective solution for institutions with limited resources. A comprehensive security framework adapted to the specific requirements of the e-learning platform is crucial for maximising the utility of common applications. Skilled security experts and software architects are essential to the design and implementation of such solutions. Authentication, encryption, and access controls serve as the security arsenal's armour and weaponry. The ongoing pursuit of a secure educational environment necessitates a dedicated team that stays current on the most recent threats and countermeasures. Combining cloud computing and e-learning offers numerous opportunities, but security must remain a top priority. This report examines the principles of neural networks and high-performance computing for fortifying cloud-based e-learning platforms, resulting in a tapestry of safeguards that protect the treasures of online education.

Keywords: Cloud computing; Scalable; Authentication; Encryption; Neural Networks

References

1. S Kumari and P Muthulakshmi. “A Wide Scale Survey on Weather Prediction Using Machine Learning Techniques”. J. Inf. Knowl. Manag (2022): 2250093.
2. F Mehrpour. “Prediction of Bridge Fires Characteristics Using Machine Learning”. Text, Carleton University (2022).
3. X Ren., et al. “Deep Learning-Based Weather Prediction: A Survey”. Big Data Res 23 (2021): 100178.
4. K Maaloul and L Brahim. “Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities”. WSEAS Trans. Comput 21 (2022): 248‑256.
5. KY Ngiam and IW Khor. “Big data and machine learning algorithms for health-care delivery”. Lancet Oncol 20.5 (2019): e262‑e273.
6. I Munoko, HL Brown-Liburd and M Vasarhelyi. “The Ethical Implications of Using Artificial Intelligence in Auditing”. J. Bus. Ethics 167.2 (2020): 209‑234.
7. TK Shivaprasad and J Shetty. “Sentiment analysis of product reviews: A review”. 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT) (2017): 298‑301.
8. DV Sahasrabuddhe and P Jamsandekar. “Data Structure for Representation of Big Data of Weather Forecasting: A Review”. International Journal of Computer Science Trends and Technology 3.6 (2015): 10.
9. H Jain and R Jain. “Big data in weather forecasting: Applications and challenges”. 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017): 138‑142.
10. NH Rao. “Big Data and Climate Smart Agriculture - Review of Current Status and Implications for Agricultural Research and Innovation in India”. Rochester, NY 25 (2017).
11. S Murugan Bhagavathi., et al. “Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm”. Int. J. Commun. Syst 34.10 (2021): e4805.
12. S Mittal and OP Sangwan. “Big Data Analytics Using Data Mining Techniques: A Survey”. in Advanced Informatics for Computing Research, Singapore (2019): 264‑273.
13. A Moosavi, V Rao and A Sandu. “Machine learning based algorithms for uncertainty quantification in numerical weather prediction models”. J. Comput. Sci 50 (2021): 101295.
14. M Alam and M Amjad. “Weather forecasting using parallel and distributed analytics approaches on big data clouds”. J. Stat. Manag. Syst 22.4 (2019): 791‑799.
15. A Shehadeh., et al. “Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression”. Autom. Constr 129 (2021): 103827.
16. D Mukhin., et al. “Revealing recurrent regimes of mid-latitude atmospheric variability using novel machine learning method”. Chaos Interdiscip. J. Nonlinear Sci 32.11 (2022): 113105.
17. IH Sarker. “Machine Learning: Algorithms, Real-World Applications and Research Directions”. SN Comput. Sci 2.3 (2021): 160.
18. F Kamalov, S Moussa and J Avante Reyes. “KDE-Based Ensemble Learning for Imbalanced Data”. Electronics 11.17 (2022).
19. N Boonnam., et al. “Coral Reef Bleaching under Climate Change: Prediction Modeling and Machine Learning”. Sustainability 14.10 (2022).
20. EK Ampomah., et al. “Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm”. Informatica 45.2 (2021).
21. Z Qinghe., et al. “Optimised extreme gradient boosting model for short term electric load demand forecasting of regional grid system”. Sci. Rep 12.1 (2022): 19282.
22. R Sibindi, RW Mwangi and AG Waititu. “A boosting ensemble learning based hybrid light gradient boosting machine and extreme gradient boosting model for predicting house prices”. Eng. Rep (2022).
23. C Jayakumar., et al. “Using the Artificial Neural Networks to Predict the Solubility Effects of Theophylline Drug in Hydrotropic Solutions”. International Journal of Pharmaceutical Research (2021).
24. F Wang., et al. “Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method”. Appl. Energy 323 (2022): 119579.
25. K Maaloul, NM Abdelhamid and B Lejdel. “Machine Learning Based Indoor Localization Using Wi-Fi and Smartphone in a Shopping Malls”. Artificial Intelligence and Its Applications, Cham (2022): 1‑10.
26. M Madhavi and D Nethravathi. “Gradient Boosted Decision Tree (Gbdt) And Grey Wolf Optimization (Gwo)Based Intrusion Detection Model”. 16 (2022): 15.
27. G Sambasivam and GD Opiyo. “A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks”. Egypt. Inform. J 22.1 (2021): 27‑34.
28. P Lipinski, E Brzychczy and R Zimroz. “Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space”. Sensors 20.21 (2020).
29. A Kanervo. “Random Forests an Application to Tumour Classification”.
30. Maswadi., et al. “Human activity classification using Decision Tree and Naïve Bayes classifiers”. Multimed. Tools Appl 80.14 (2021): 21709‑21726.

Weather forecasting, a crucial and vital process in people's everyday lives, assesses the change taking place in the atmosphere's current state. Big data analytics is the practice of studying big data to uncover hidden patterns and useful information that might produce more beneficial outcomes. Big data is currently a topic of fascination for many facets of society, and the meteorological institute is no exception. Big data analytics will therefore produce better results for weather forecasting and assist forecasters in providing more accurate weather predictions. Several big data techniques and technologies have been proposed to manage and evaluate the enormous volume of weather data from various resources in order to accomplish this goal and to identify beneficial solutions. A smart city is a project that uses computers to process vast amounts of data gathered from sensors, cameras, and other devices in order to manage resources, provide services, and address problems that arise in daily life, such as the weather. Forecasting the weather is a crucial process in daily life because it assesses changes in the atmosphere's current state. A machine learning-based weather forecasting model was proposed in this paper, and it was implemented using 5 classifier algorithms, including the Random Forest classifier, the Decision Tree Algorithm, the Gaussian Naive Bayes model, the Gradient Boosting Classifier, and Artificial Neural Networks. These classifier algorithms were trained using a publicly available dataset. When the model's performance was assessed, the Gradient Boosting Classifier algorithm, which had a plus 98% predicted accuracy, came out on top.

Keywords: Weather forecasting; Big data; Machine Learning; smart city; Gradient Boosting Classifier

References

1. The Treatise on Light Edited by Stephen Gaukroger, University of Sydney Publisher: Cambridge University Press.
2. Pierre Gassendi. Fisher Saul. Stanford Encyclopedia of Philosophy (2009).
3. Isaac Newton. Smith, George. Stanford Encyclopedia of Philosophy (2007).
4. Robert Hooke Source: Thomas Birch, The History of the Royal Society 3 (1757): 10-15.
5. C. Huygens (translated by Silvanus P. Thompson), Treatise on Light, London: Macmillan (1912).
6. "Young, Thomas (YN797T)". A Cambridge Alumni Database. University of Cambridge.
7. A. Fresnel, 1818, "Mémoire sur la diffraction de la lumière" (deposited 29 July 1818, crowned" 1819), in Fresnel, 1866-70, vol. 1, pp. 247-364; partly translated as "Fresnel's prize memoir on the diffraction of light", in Crew, 1900, pp. 81-144. Not  to be confused with the earlier memoir of the same title (Fresnel, 1816).
8. Planck M. “About the law of energy distribution in normal spectrum”. ann. physics (1901): 4.
9. Kahr Bart and Claborn Kacey. "The lives of Malus and his bicentennial law.", Chemphyschem: a European journal of chemical physics and physical chemistry 9.1 (2008): 43-58.
10. L. de Broglie: a) Some important papers: C.R.A.S., 177, p. 517, p. 548, p. 630, 1923; Philosophical Mag., XLVII, p. 446, 1924; J. Phys., série VI, t. VII, n° 11, p. 321, 1926; J. Phys., série VI, t. VIII, n° 5, p. 225, 1927; J. Phys., 20, p. 963, 1959; C.R.A.S., 277, série B, p. 71, 1973; Found. of Phys. (with G. Lochak, J.A. Beswick and J. Vassalo Pereira), 6, p. 3, 1976. b) Books (among 44 titles): — Recherches sur la théorie des quanta (Thesis, 1924): first published in: Annales de Physique, 10e série, t. III, p. 22-128, 1925; german translation: Akademische Verlaggesellschaft, Leipzig, 1927; reprinted by Masson, Paris, 1963. — Selected papers on wave mechanics (with Léon Brillouin), Blackie & Son, Glasgow, 1928. — Une tentative d’interpretation causale et non linéaire de la mécanique ondulatoire: la théorie de la double solution, Gauthier-Villars, Paris, 1956; english translation: Elsevier, Amsterdam, 1960. — La théorie de la mésure en mécanique ondulatoire (interprétation usuelle et interprétation c usale), Gauthier-Villars, Paris, 1960. — Etude critique des bases de l—interprétation actuelle de la mécanique ondulatoire, Gauthier-Villars, Paris, 1963; english translation, Elsevier, Amsterdam, 1965. — La réinterprétation de la mécanique ondulatoire, Gauthier-Villars, Paris, 1971. — Contribution to: Foundations of quantum mechanics, Proc. Int. Schools Enrico Fermi, Varenna, 1971. — Les incertitudes d’Heisenberg et l’interprétation probabiliste de la mécanique ondulatoire, Gauthier-Villars, Paris, 1982 (with a long preface of G. Lochak on The evolution of the ideas of Louis de Broglie on the interpretation of wave-mechanics: translated in English in the Special issue of Found, of Physics devoted to Louis de Broglie, 1982.
11. S Orlov. Foundation of vortex gravitation, cosmology and cosmogony. Global journal of science Frontier.
12. VA Atsurovskiy. General ether-dynamics. Energoatomizdat. Moscow, Russia (1990).
13. S Orlov. "Gravitational Properties of Atom". Journal of Physics & Astronomy (2017).
14. The First Three Minutes: A Modern View of the Origin of the Universe (1977), updated with new afterword in (1993).
15. Walter Hohmann. “The accessibility of the celestial bodies”. Oldenbourg publishing house in Munich (1925).
16. Castka Joseph F., et al. “Modern Chemistry”. Holt, Rinehart and Winston (2002).
17. Dehmelt Hans. "A Single Atomic Particle Forever Floating at Rest in Free Space: New Value for Electron Radius". Physica Scripta T22 (1988): 102-110.

The atomic structure is presented on the basis of the theory of vortex gravitation. The feasibility and calculation of the values of the density and mass of electromagnetic particles are proposed. A calculation is made, which proves that the photon must have mass. In the calculations, some physical characteristics of electromagnetic particles that are accepted by modern physics are refuted.

Keywords: theory of vortex gravity; cosmology and cosmogony; Celestial mechanics

References

1. Hong J and Curran NM. “Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence”. ACM Trans. Multimed. Comput. Commun. Appl 15 (2019): 58.
2. McCarthy J. “What Is Artificial Intelligence?”. How Does AI Work? (2020).
3. Ali M. “The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning”. Int. J. Engl. Linguist 8 (2020): 259.
4. Goodfellow I, Bengio Y and Courville A. “Deep Learning”. MIT Press: Cambridge, UK (2016).
5. Elgammal A., et al. “ CAN: Creative Adversarial Networks, Generating “Art” by Learning about Styles and Deviating from Style Norms”. Cornell University: Ithaca, NY, USA (2017).
6. Elgammal A. What the Art World Is Failing to Grasp about Christie’s AI Portrait Coup (2018).
7. Sawyer RK. Explaining Creativity: The Science of Human Innovation; Oxford University Press: New York, NY, USA (2014).
8. Boden MA. “The Creative Mind: Myths and Mechanisms”. Psychology Press: London, UK (2004).
9. Regan M. “Generative Adversarial Networks & The Art Market”. Ph.D. Thesis, State University of New York, New York, NY, USA (2018).
10. Kurt DE. “Artistic Creativity in Artificial Intelligence”. Ph.D. Thesis, Radboud University, Nijmegen, The Netherlands (2018).
11. Cetinic E and She J. “Understanding and Creating Art with AI: Review and Outlook”. (2021): arXiv:2102.09109.

Especially new tendencies in the area of synthetic focus are dynamic in that they exchange the house of expression. Programs based totally on simulated brain are hastily turning into famous in areas such as technology, music, human expression, and science. A new close-up of Christie's Edmund portrait adjustments our modern-day appreciation of the science of synthetic talent and raises questions about the creativeness related with technology. Can craft be seen as resourceful?” Against this background, this lookup makes use of a range of AI functions and modifications the point of view on AI craft and the manufacturing cycle of AI craft to create synthetic intelligence. Show that it is achievable Innovative imagination.

Keywords: Artificial intelligence-generated art; computational creativity; machine learning

.

Since Confined Quantum Field Theory is the extension of the special and general relativity, people may think this is a concern for the people working on astrophysics black hole and so on. But on the contrary its simplicity makes it useable on the everyday modern engineering. All from electrical resistivity to the super conductivity and thermoelectric effect to the crystal growth. In thermoelectric effect we show why two metals must join together and not smelt together to preserve the crystal structure in the both side of the junction. This theory shows how impurities and defect affects performance of the electronic devices in different temperature. Recently in the Crystal Growth and Reproductive Entities article we showed how a bigger crystal eats up a smaller one and the theory was approved by recent experiment.

References

1. Weakness and Problem in Quantum Field Theory, Which Leads to the Development of the Confined Quantum Field Theory.
2. Confined Quantum Field Theory.
3. Do we teach our students the right physics?.
4. Symmetry aspect of Confined Quantum Field Theory.
5. Why the light changes direction in entering the water.
6. Universalities in the Complex Science.
7. Credit Q Zheng., et al. /Nat.Communications https://lnkd.in/dEMKfuss.
8. # #chemistry #university #video #TEM #nanoparticle

Crystal Growth and Reproductive Entities play a more important role in modern technology and in medical physics.

We will use Confined Quantum Field Theory in Crystal Growth and Reproductive Entities which will give us a more fundamental perspective.

References

1. Healthcare-Barometer 2023, PwC Germany (2023).
2. Ries E. “The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses”. Vereinigtes Königreich: Crown (2011).
3. Zierock B. Chaotic Customer Centricity, HCI International 2023 Posters, Springer Nature Switzerland (2023).
4. Spitta I and Spitta T. Software Engineering und Prototyping: Eine Konstruktionslehre für administrative Softwaresysteme. Deutschland: Springer Berlin Heidelberg (2013).
5. Ethical Implications of Reshaping Healthcare with Emerging Technologies. (2021). USA: IGI Global.
6. Internet of Things for Human-Centered Design: Application to Elderly Healthcare. Singapur: Springer Nature Singapore (2022).

In an era marked by rapid transformation and shifting priorities, the need for strategic adaptation has never been more critical. Recent findings emphasize the challenges faced by the German healthcare sector in maintaining public approval. With the healthcare landscape evolving, the industry is increasingly recognizing the importance of user-centered design. This article delves into the pivotal role that prototyping and testing, as integral components of user-centered design, can play in revolutionizing the healthcare domain. It explores how these methods can address the unique considerations of healthcare and highlights their potential benefits and challenges.

Moreover, as digital services reshape the German healthcare industry daily, the landscape is characterized by shorter planning cycles, global competition, and stricter regulatory requirements. In this dynamic environment, traditional project management approaches often struggle to keep pace. This article underscores the transformative power of Agile Project Management, a methodology that has redefined how projects and companies operate. Agile principles, known for their adaptability and value-driven focus, are examined in the context of 21st-century demands, shedding light on how they enhance project outcomes and drive organizational success.

Additionally, the article explores the Agile mindset and its alignment with methodologies like Design Thinking, Scrum, and Objectives and Key Results (OKR). The Agile mindset champions customer collaboration, welcomes change as an opportunity, and emphasizes delivering value promptly. It operates on principles such as customer-centricity, iterative development, continuous feedback, flexibility, adaptability, transparency, and collaboration.

In conclusion, prototyping emerges as a central theme, described as the art of transforming ideas into testable realities. It's defined as the process of creating preliminary versions of products or systems to visualize, evaluate, and refine their design and functionality. The article emphasizes the significance of choosing the right fidelity level for prototypes based on project goals and stages, promoting early exploration and iterative refinement throughout the design process.

Keywords: agile; prototyping; healthcare

References

1. Sanjeev Chauhan and RM Belokar. “Conversion of intermittent water supply to continuous water supply of Chandigarh: A case study”. Proceedings of Third International Conference on Computing, Communications, and Cyber-Security (2022): 21-36.
2. Anadi Gayen. Aquifer Characteristics, Long Term Groundwater Level Trend and Hydrochemistry in the Central Indian Groundwater Province: An Overview of the Chhattisgarh State (2021).
3. Alegre., et al. Performance Indicators for water supply services, 2nd Ed, IWA Publications (2006).
4. Kojima., et al. Basic design study report on the project for improvement of water supply system (2005).
5. Dahasahasra DS and Dahasahasra DV. A Model Transforming an Intermittent into a 24x7 water supply system (2010).
6. Basic design study report on the project for improvement of water supply system, Seiji Kojima, Toshifumi Okaga (2005).
7. Neeraj Sharma, Kafeel Ahmad and Syed Khursheed Ahmad. “Analysis of Water Distribution Network by Forming District Metering Areas to achieve Continuous Water Supply with Minimizing Non-Revenue Water, A Case Study”. International Journal of Scientific & Engineering Research 9.9 (2018).
8. Baietti A, Kingdom W and Ginneken M. “Characteristics of Well-Performing Public Water Utilities”, Water Supply and Sanitation Working Notes 9, February 2006, World Bank, Washington DC (2006).
9. Farley M and Trow S. “Losses in Water Distribution Networks: A practionner's Guide to Assessment, Monitoring and Control”. IWA Publishing, London (2003).
10. Charalambous B. Use of district metered areas coupled with pressure optimization to reduce leakage, Water Science and Technology: Water Supply 8.1 (2008): 57-62.
11. Fanner P., et al. Leakage Management Technologies, Awwa Research Foundation, AWWA, Denver, USA; IWA, London (2007).
12. Chandi Rajak., et al. Modelling and Mapping of Groundwater Potential Zones using AHP and GIS Technique: A case study of Jhalda-I Block, Puruliya, West Bengal (2021).
13. Vasant Wagh., et al. “Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques”. Journal of Water Supply: Research and Technology – AQUA (2020).
14. Allen DJ., et al. “Interaction between groundwater, the hyporheic zone and a Chalk stream: a case study from the River Lambourn, UK”. Hydrogeol J 18 (2010): 1125-1141.

Chandigarh is a well planned city and one of the modern cities of India. The city is having spacious roads, lush green gardens, good network of Sewerage and Storm water system. The city also owns a well designed Water supply system. Due to the importance of the city, people all across the country has been settled and enjoy facilities of International level. Chandigarh is the only city which earns its 70% of the revenue helping the govt. to plan its funds in darker zones. This level of service has been achieved by the Municipal Corporation Chandigarh by increasing the reliability of the mechanism involved in water supply from the source to disposal of the water. The optimization of the system also has features like 24 Hrs. Water supply in some sectors, 3 times regular supply in routine to all the sectors, villages and slum colonies. The system include pumping the water from Kajauli Water Works (On Bhakhra Canal), Distt. Ropar, Punjab to Sector 39 Chandigarh, Treatment of water, distribution, metering, disposal and further treating the sewage for recycling purpose. This paper reflects the application of “DFR” in Water Supply System of Chandigarh.

Notes

1. “The obligation is the patrimonial content of the performance owed by the debtor to the creditor. The obligation is not primarily a legal relationship, as are the contract or the legal bond that binds the parties; it is a relationship between two things, between the fact of enrichment and the fact of impoverishment that the law places face to face” BETTI, E. (1955). Teoría General del Negocio Jurídico. Buenos Aires: Editorial Universitaria de Buenos Aires.
2. “Smart contracts are mainly based on Blockchain technology. Proof of this is that they share many characteristics with it, such as unchangeability, transparency, security or pseudonymity. For this reason only the specific characteristics of smart contracts will be analyzed now, in order to avoid superfluous reiterations. We can mainly point out that smart contracts are autonomous, which means that they do not require the intervention of the parties for the services that comprise them to be executed. They use a computer language that must be translated into the common language, so that any average citizen can understand its content. Otherwise, the contract could be annulled, as there is a vitiated consent” SÁNCHEZ ÁLVAREZ, E. and GARCÍA PACIOS, A. (2021). Blockchain technology and electronic contracting: critical points of integration of the so-called smart contracts in our contract law system. In Revista CEFLegal, 246, 71–98.
3. “Contractware is the translation of legal (contractual) prose into computer code. The coding incorporates not only the agreements reached but also the consequences that may arise from their fulfillment or non-fulfillment. If, for example, the transfer of a token is formalized, the smart contract is not limited to stating the transfer agreements. It also verifies the data and executes the consequences agreed upon by the signatories. Thus, for example, the code receives the parties’ performances (the payment in cryptocurrencies and the token transfer mandate) and forwards to each party the other party’s performance. Many authors reject that these sequences of code can be considered authentic contracts, since while simple contracts merely incorporate promises of future performance, intelligent ones execute them”.
4. “The United Nations Commission on International Trade Law (UNCITRAL) has developed a set of legislative texts to enable and facilitate the use of electronic means in commercial activities, which have been adopted by more than 100 States. The most widely adopted text is the UNCITRAL Model Law on Electronic Commerce (1996), which sets out the rules for equal treatment of electronic and paper-based information and legal recognition of electronic transactions and processes, based on the fundamental principles of non-discrimination in the use of electronic means, functional equivalence and technological neutrality. The UNCITRAL Model Law on Electronic Signatures (2001) provides further rules on the use of electronic signatures.

The United Nations Convention on the Use of Electronic Communications in International Contracts (New York, 2005) takes as its point of departure the earlier texts of UNCITRAL to become the first treaty to give legal certainty to electronic contracting in international trade.

More recently, the UNCITRAL Model Law on Electronic Transferable Documents (2017) applies the same principles to enable and facilitate the use of transferable documents and securities in electronic form, such as bills of lading, bills of exchange, checks, promissory notes and warehouse receipts.

In 2019, UNCITRAL approved the publication of the Notes on key issues related to cloud computing contracts, while continuing to work on the development of a new instrument on the use and cross-border recognition of electronic identity management services and authentication services (trust services)”.

5. “P2P technology can be defined as a network in the form of a backbone, composed of nodes that act as clients and servers of other nodes. When a client enters this system”, it makes a direct connection to one of the latter, where it collects and stores all the information and content available for sharing. It is then a program whose function is to connect users through a serverless network that facilitates the download53 of music, movies, books, photos and software among all other users, free of charge. These files are shared “from computer to computer” by the mere fact of having access to the system”. VARELA PEZZANO, Eduardo Secondo.

Peer-to-peer technologies, author’s rights and copyright. Bogotá: Editorial Universidad del Rosario, 2009.

References

1. Alfonso Sánchez Rosalía. “Collaborative economy: a new market for the social economy. CIRIEC-Spain”. Journal of Public, Social and Cooperative Economy [online] 88 (2016): 230-258.
2. Anguiano JM. "'Smart Contracts'. Introduction to 'contractware'", Garrigues opina (2018).
3. Betti E. Teoría General del Negocio Jurídico. Buenos Aires: Editorial Universitaria de Buenos Aires (1955).
4. Uncitral Guide. Basic Facts and Functions of the United Nations Commission on International Trade Law, United Nations (2013).
5. Josserand Louis. Teoría General de las Obligaciones (2nd Edition), Ediciones Jurídica de Santiago (2014).
6. Llambías Jorge J. Tratado de Derecho Civil - Parte General (Volume II). Editorial Perrot (1997).
7. Sánchez Álvarez E and García Pacios A. “Blockchain technology and electronic contracting: critical points of integration of the so-called smart contracts in our contract law system”. In Revista CEFLegal 246 (2021): 71-98.
8. Szabo N. Smart Contracts: Building Blocks for Digital Markets [PDF File] (1996).
9. Varela Pezzano, Eduardo Secondo Peer-to-peer technologies, author's rights and copyright. Bogotá: Editorial Universidad del Rosario (2009).

Regulations

1. UNCITRAL Model Law on Electronic Commerce Guide to Enactment 1996 with new article 5 adopted in 1998.
2. UNCITRAL Model Law on Electronic Signatures with Guide to Enactment 2001.
3. UNCITRAL Model Law on Electronic Transmittable Documents 2017.

The present work, explores from the nature of Civil Law and the source theory of legal obligation, towards the evolution of development of Smarts Contracts and Blockchain Technology in the implementation of a system of RC20 and RC21 token issuing machine, with fiduciary support as a mechanism of legal security in order to propose a model of potentialization of destinations with high tourism demand in developing countries, within the framework of design, implementation and commissioning of the destination Hotel Aiden by Best Western Quito DM, Republic of Ecuador City and Beach.

Keyworks: Smart Constracts; Smart Tourism; Tokenization; Blockchain; contractware

References

1. Alfarizi M and Sari RK. “Analysis of Factors Affecting Customer Behavior of Marketplace Applications: A Case Study of Cash on Delivery (COD) Payment Systems”. 13th International Conference on Advanced Computer Science and Information Systems, ICACSIS (2021).
2. Alkis A and Kose T. “Privacy concerns in consumer E-commerce activities and response to social media advertising: Empirical evidence from Europe”. Computers in Human Behavior 137 (2022).
3. Almtiri Z, Miah SJ and Noman N. “Application of E-commerce Technologies in Accelerating the Success of SME Operation”. In Y. X.-S., S. S., D. N., & J. A. (Eds.), 7th International Congress on Information and Communication Technology, ICICT 2022 (2023): 463-470.
4. Anjum S and Chai J. “Drivers of Cash-on-Delivery Method of Payment in E-Commerce Shopping: Evidence from Pakistan”. SAGE Open 10.3 (2020).
5. Armstrong CEJ., et al. “Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra”. Food Chemistry 403 (2023).
6. Ashrafpour N., et al. “The prerequisites and consequences of customers’ online experience regarding the moderating role of brand congruity: evidence from an Iranian bank”. Journal of Islamic Marketing 13.10 (2022) 2144-2172.
7. Bighrissen B. “A Study of Barriers to E-Commerce Adoption Among Cooperatives in Morocco”. In A. B. & H. A. (Eds.), International Conference on Business and Technology, ICBT 2021 485 (2023): 557-570.
8. Bopage G, Nanayakkara J and Vidanagamachchi K. “A strategic model to improve the last mile delivery performance in ecommerce parcel delivery”. 9th International Conference on Industrial Engineering and Operations Management, IEOM 2019, 2019(MAR), 2018-2019 (2019).
9. Chang S., et al. “Joint optimization of e-commerce supply chain financing strategy and channel contract”. European Journal of Operational Research 303.2 (2022): 908-927.
10. Cosmi M, Nicosia G and Pacifici A. “Lower bounds for a meal pickup-and-delivery scheduling problem”. In H. J., K. S., M. B.R., U. V., & U. M.S. (Eds.), 17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW, University of Twente (2019): 33-36.
11. Das D, Kumar R and Rajak MK. “Designing a reverse logistics network for an e-commerce firm: A case study”. Operations and Supply Chain Management 13.1 (2020): 48-63.
12. Dasgupta S, Kanchan S and Kundu T. “Creating a KPI tree for monitoring and controlling key business objectives of first mile logistics services”. 9th International Conference on Industrial Engineering and Operations Management, IEOM 2019 (2019): 716-727.
13. Datta S., et al. “Sanskriti—A Distributed E-Commerce Site Implementation Using BlockChain”. In Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH 139 (2023): 329-346.
14. Etumnu CE. “A competitive marketplace or an unfair competitor? An analysis of Amazon and its best sellers ranks”. Journal of Agricultural Economics 73.3 (2022): 924-937.
15. González-Mora C., et al. “Improving open data web API documentation through interactivity and natural language generation”. Computer Standards and Interfaces 83 (2023).
16. Ha XS., et al. “Scrutinizing Trust and Transparency in Cash on Delivery Systems”. In W. G., C. B., L. W., D. Pietro R., Y. X., & H. H. (Eds.), 13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020: Vol. 12382 LNCS (2021): 214-227.
17. Handhayani T and Hendryli J. “Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite”. In A. K. (Ed.), Intelligent Systems Conference, IntelliSys 2022: Vol. 544 LNNS (2023): 53-67.
18. Hasan F., et al. “E-commerce Merchant Fraud Detection using Machine Learning Approach”. 7th International Conference on Communication and Electronics Systems, ICCES (2022): 1123-1127.
19. Jaller M and Dennis S. “E-Commerce and Mobility Trends During COVID-19”. In Springer Tracts on Transportation and Traffic 20 (2023): 79-93.
20. Kahr M. “Determining locations and layouts for parcel lockers to support supply chain viability at the last mile”. Omega (United Kingdom) 113 (2022).
21. Law C-Y., et al. “Mobile Application for After School Pickup Solution: Malaysia Perspectives”. In A. K. (Ed.), Intelligent Systems Conference, IntelliSys 2022: Vol. 544 LNNS (2023): 68-78.
22. Li Y., et al. “Product Reviews Analysis of E-commerce Platform Based on Logistic-ARMA Model”. 2021 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS (2021): 714-717.
23. Lu S-H., et al. “Improving the efficiency of last-mile delivery with the flexible drones traveling salesman problem”. Expert Systems with Applications 209 (2022).
24. Luo X., et al. “Nondestructive determination of common indicators of beef for freshness assessment using airflow-three-dimensional (3D) machine vision technique and machine learning”. Journal of Food Engineering 340 (2023).
25. Luo Z., et al. “A last-mile drone-assisted one-to-one pickup and delivery problem with multi-visit drone trips”. Computers and Operations Research 148 (2022).
26. McKinlay A, Mitchell G and Bertenshaw C. “Review article: DINED (Delivery-related INjuries in the Emergency Department) part 1: A scoping review of risk factors and injuries affecting food delivery riders”. EMA - Emergency Medicine Australasia 34.2 (2022): 150-156.
27. McKinlay A, Mitchell G and Bertenshaw C. “Review article: DINED (Delivery-related INjuries in the Emergency Department) part 1: A scoping review of risk factors and injuries affecting food delivery riders”. EMA - Emergency Medicine Australasia 34.2 (2022): 150-156.
28. Muniasamy A and Bhatnagar R. “Analyzing Online Reviews of Customers Using Machine Learning Techniques”. In R. V.S., S. S.C., T. J.M.R.S., M. C., & S. B. (Eds.), 2nd FICR International Conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 434 (2022): 485-493.
29. Muñoz-Villamizar A., et al. “The environmental impact of fast shipping ecommerce in inbound logistics operations: A case study in Mexico”. Journal of Cleaner Production 283 (2021).
30. Nagpal G., et al. “Use of data analytics to increase the efficiency of last mile logistics for ecommerce deliveries”. In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics IGI Global (2020): 167-180.
31. Oviedo-Trespalacios O, Rubie E and Haworth N. “Risky business: Comparing the riding behaviours of food delivery and private bicycle riders”. Accident Analysis and Prevention 177 (2022).
32. Peng Y and Yi J. “Research on the Application of Big Data Technology in the Process of Cross-Border E-Commerce Product Selection”. In W. T., P. S., H. J. W.C., & R. V. M.L. (Eds.), 4th International Conference on Decision Science and Management, ICDSM 260 (2023): 29-37.
33. Pereira Marcilio Nogueira G., et al. “The environmental impact of fast delivery B2C e-commerce in outbound logistics operations: A simulation approach”. Cleaner Logistics and Supply Chain 5 (2022).
34. Pérez-Morón JM. “E-commerce in China and Latin America. A Review and Future Research Agenda”. In A. B. & H. A. (Eds.), International Conference on Business and Technology, ICBT 2021 485 (2023): 571-587.
35. Purwandari B., et al. “Factors Affecting Switching Intention from Cash on Delivery to E-Payment Services in C2C E-Commerce Transactions: COVID-19, Transaction, and Technology Perspectives”. Emerging Science Journal 6 (2022): 136-150. 
36. Raj NV and Saini JR. “Loyalty Score Generation for Customers Using Sentimental Analysis of Reviews in e-commerce”. In D. P., C. S., B. A., D. S., & S. C. (Eds.), 3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022 490 (2023): 461-473.
37. Sandoval MG., et al. “A novel districting design approach for on-time last-mile delivery: An application on an express postal company”. Omega (United Kingdom) 113 (2022).
38. Stefko R., et al. “Gender-generation characteristic in relation to the customer behavior and purchasing process in terms of mobile marketing”. Oeconomia Copernicana 13.1 (2022) 181-223.
39. Thematic analysis. In Wikipedia (2022).
40. Tran NAT., et al. “Health and safety risks faced by delivery riders during the Covid-19 pandemic”. Journal of Transport and Health 25 (2022).
41. Verma R, Dhanda N and Nagar V. “Towards a Secured IoT Communication: A Blockchain Implementation Through APIs”. In S. P.K., W. S.T., T. S., R. J.J.P.C., R. J.J.P.C., & G. M. (Eds.), 3rd International Conference on Computing, Communications, and Cyber-Security, IC4S 2021 421 (2023): 681-692.
42. Wang M., et al. “A branch-and-price algorithm for location-routing problems with pick-up stations in the last-mile distribution system”. European Journal of Operational Research 303.3 (2022): 1258-1276.
43. Wu G. “Research on the development path of logistics management innovation in e-commerce environment”. 2020 6th International Conference on Environmental Science and Material Application, ESMA 2020 714.4 (2021).
44. Xu J., et al. “Flexible sensing enabled packaging performance optimization system (FS-PPOS) for lamb loss reduction control in E-commerce supply chain”. Food Control 145 (2023).
45. Yin L, Zhong RR and Wang J. “Ontology based package design in fresh E-Commerce logistics”. Expert Systems with Applications 212 (2023).
46. Yun KK, Yoon SW and Won D. “Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection”. Expert Systems with Applications 213 (2023).

The objective of this paper is to share the details related to the standard Delivery Attempt Verification Techniques for accurately determining if the Delivery Attempt made by the Delivery Rider is valid or not against the Order or Parcel ordered Online by the Customer through Shipper E-commerce Site. The Researchers who are working on this research area are using ‘Qualitative Research.’ The Data for the research was taken from the Delivery Mobile Applications, Blogs, social media, and E-commerce Sites, and then it was presented descriptively. The study in this paper tells us that the formation of the Standard Regulation will improve the visibility of Delivery Attempts made on the logistics operations. In Order to achieve it, several steps need to be taken for the successful implementation of the Regulation. This paper will only be limited to the extent to which the challenge currently facing is clearly described, then suggest possible solutions like Live Tracking, Lat-long Capture, Delivery Code, Verification Calls or SMS, etc., for resolving it and how it will work, and finally, what positive impact the solution will have on the Customer and the logistics sector.

Keywords: First Mile; Mid Mile; Last Mile; Logistics; E-Commerce; COD (Cash on Delivery)

     At the forefront of biomedical engineering are innovative medical devices, each meticulously crafted with the intention of transforming patient care, alleviating pain and suffering, and extending human lifespans. From magnetic resonance imaging (MRI) machines to pacemakers, artificial organs to state-of-the-art prosthetic limbs, these life-saving devices are a testament to the profound impact of biomedical engineering on healthcare.

     Beyond these visible marvels, biomedical engineers delve into the intricate realm of biomechanics and rehabilitation. They design orthopedic implants, rehabilitation equipment, and assistive devices that empower individuals to regain mobility and independence after injuries or surgeries. This field represents not just physical healing but also the restoration of hope and human dignity.

     Imagine a world where damaged organs can be repaired or replaced without the need for organ donors. Biomedical engineers are propelling us toward this extraordinary future through groundbreaking work in tissue engineering and regenerative medicine. Their efforts hold the promise of growing human organs in the laboratory, offering hope to countless patients awaiting life-saving transplants.

     Effective drug delivery is another vital aspect of biomedical engineering. It can mean the difference between a successful treatment and an ineffective one. Biomedical engineers engineer drug delivery systems that ensure medications reach their intended targets with precision, minimizing side effects and maximizing therapeutic benefits.

     The field also plays a pivotal role in the development of diagnostic tools and medical imaging technologies that have revolutionized healthcare. Computed tomography (CT) scans, positron emission tomography (PET) scans, and genetic testing have empowered physicians to detect diseases at earlier stages, resulting in more effective treatments and improved patient outcomes.

      In the age of artificial intelligence, biomedical engineers are harnessing the power of machine learning and data analytics. They create tools capable of analyzing vast amounts of medical data, assisting with diagnostics, and even predicting disease outbreaks. These innovations have the potential to revolutionize medical decision-making and improve patient care on an unprecedented scale.

     Moreover, biomedical engineers are making a global impact by crafting low-cost, easily maintainable medical devices for resource-constrained regions. These innovations extend modern healthcare to underserved communities worldwide, bridging the gap between medical technology and accessible healthcare.

     Biomedical engineering is not merely about machines and gadgets; it's about elevating the human condition. As technology continues to advance, the horizons of innovation within this field remain boundless. Biomedical engineers tirelessly address complex health challenges, propelling us toward a future where healthcare is more effective, accessible, and patient-centric than ever before. Their work is nothing short of remarkable, and its potential to positively impact our lives is immeasurable.

     As we navigate the challenges of the 21st century, engineers stand at the forefront of innovation and progress. Their work not only shapes our daily lives but also holds the key to a sustainable and prosperous future. One such field of engineering is Biomedical engineering. It is a dynamic and rapidly advancing field, sitting at the captivating crossroads of medicine, biology, and engineering. These dedicated professionals serve as the vanguards of healthcare innovation, leveraging their deep knowledge of engineering, biology, and medicine to bring about groundbreaking advancements that not only enhance patient outcomes but also revolutionize our entire approach to healthcare challenges. Its primary mission is to elevate healthcare standards, enhance patients' quality of life, and, most importantly, save lives. This remarkable discipline achieves these goals through the ingenious application of cutting-edge technology and profound scientific insights.

References

1. Schwab K. “The Fourth Industrial Revolution: what it means and how to respond”. World Economic Forum (2016).
2. AI in the UK: ready, willing and able? United Kingdom: authority of the house of lords (2018). (Intelligence SCoA, editor).
3. Ravi D., et al. “Deep learning for health informatics”. IEEE J Biomed Health Inform 21.1 (2017): 4-21.
4. LeCun Y, Bengio Y and Hinton G. “Deep learning”. Nature 521.7553 (2015): 436-44.
5. Price Waterhouse Cooper. Sizing the prize: What’s the real value of AI for your business and how can you capitalize? (2017).
6. Murphy K., et al. “Artificial intelligence for good health: a scoping review of the ethics literature”. BMC medical ethics 22.1 (2021): 14.
7. Brady A P and Neri E. “Artificial intelligence in radiology—ethical considerations”. Diagnostics 10.4 (2020): 231.
8. WHO Consultation, Development of guidance on ethics and governance of artificial intelligence for health, Geneva, Switzerland (2019).
9. Gibney E. “The battle for ethical AI at the world’s biggest machine-learning conference”. Nature 577.7792 (2020): 609-609.
10. Jackson BR., et al. “The ethics of artificial intelligence in pathology and laboratory medicine: principles and practice”. Academic Pathology 8 (2021): 2374289521990784.
11. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; Licence: CC BY-NC-SA 3.0 IGO (2021).
12. Kooli C and Al Muftah H. “Artificial intelligence in healthcare: a comprehensive review of its ethical concerns”. Technological Sustainability (2022).
13. DeCamp M and Lindvall C. “Latent bias and the implementation of artificial intelligence in medicine”. J. Am. Med. Informatics Assoc 27 (2020): 2020-2023.
14. Salim M., et al. “External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms”. JAMA Oncol 6 (2020): 1581-1588.
15. Pianykh OS., et al. “Continuous learning AI in radiology: implementation principles and early applications”. Radiology 297 (2020): 6-14.
16. ChenIY., et al. “Ethical machine learning in health. ligence in medicine”. J. Am. Med. Informatics Assoc (2020).
17. NHSX. “A buyer’s guide to AI in health and care”. Radiology 286 (2018): 800-809.
18. Hosny A., et al. “Artificial intelligence in radiology”. Nat. Rev. Cancer 18 (2018): 500-510.
19. Hickman SE, Baxter GC and Gilbert FJ. “Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations”. British journal of cancer 125.1 (2021): 15-22.
20. Carter SM. “Valuing healthcare improvement: implicit norms, explicit normativity, and human agency”. Health Care Anal 26.2 (2018): 189-205.
21. Cabitza F, Rasonini R and Gensini GF. “Unintended consequences of machine learning in medicine?”. J Am Med Assoc 318.6 (2017): 517-518.
22. Holzinger A., et al. What do we need to build explainable AI systems for the medical domain?. arXiv:1712.09923 (2017).
23. Cowls J and Floridi L. “Prolegomena to a white paper on an ethical framework for a good AI society”. SSRN (2018).
24. O’Neil C. Weapons of math destruction. London: Penguin Random House (2016).
25. Rogers WA. “Evidence based medicine and justice: a framework for looking at the impact of EBM upon vulnerable or disadvantaged groups”. J Med Ethics 30.2 (2004): 141-5.
26. Carter SM., et al. “The ethical, legal and social implications of using artificial intelligence systems in breast cancer care”. The Breast 49 (2020): 25-32.
27. Latrice G Landry., et al. “Lack of diversity in genomic databases is a barrier to translating precision medicine research into practice”. Health Affairs 37.5 (2018): 780-785.
28. Adewole S Adamson and Avery Smith. “Machine Learning and Health Care Disparities in Dermatology”. JAMA Dermatology 154.11 (2018): 1247-1248.
29. Stuart Geman, Elie Bienenstock and René Doursat. Neural networks and the bias/variance dilemma. Neural computation 4.1 (1992): 1-58.
30. David Danks and Alex John London. “Algorithmic bias in autonomous systems”. In IJCAI (2017): 4691-4697.
31. Rudzicz F and Saqur R. Ethics of Artificial Intelligence in Surgery (2020).
32. Rice University David M. Lane. “Chapter 6 research design - sampling bias”. in Online Statistics Education: A Multimedia Course of Study, Rice University.
33. Alexandra Olteanu., et al. “Social data: Biases, methodological pitfalls, and ethical boundaries”. Frontiers in Big Data (2019).
34. CNN World (2016).
35. Anupam Datta., et al. “Proxy non- discrimination in data-driven systems”. CoRR, abs/1707.08120 (2017).
36. Burton G Malkiel. “Returns from investing in equity mutual funds 1971 to 1991”. The Journal of Finance 50.2 (1995): 549-572.
37. Hellström T, Dignum V and Bensch S. “Bias in Machine Learning--What is it Good for?”. arXiv preprint arXiv:2004.00686 (2020).
38. Ouyang L., et al. “Training language models to follow instructions with human feedback”. arXiv preprint arXiv:2203.02155 (2022).
39. Gerke S, Minssen T and Cohen G. “Chapter 12—Ethical and Legal Challenges of Artificial Intelligence-Driven Healthcare”. Artificial Intelligence in Healthcare; Bohr, A., Memarzadeh, K., Eds.
40. Currie G, Hawk KE and Rohren EM. “Ethical principles for the application of artificial intelligence (AI) in nuclear medicine”. European Journal of Nuclear Medicine and Molecular Imaging 47 (2020): 748-752.
41. Fletcher RR, Nakeshimana A and Olubeko O. “Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health”. Frontiers in Artificial Intelligence 3 (2021): 561802.
42. High-Level Expert Group on Artificial Intelligence. Ethics Guidelines for Trustworthy AI (2019).
43. Floridi L., et al. “AI4People - An ethical framework for a good AI society: opportunities, risks, principles, and recommendations”. Minds Mach 28.4 (2018): 689-707.
44. D’Antonoli TA. “Ethical considerations for artificial intelligence: An overview of the current radiology landscape”. Diagnostic and Interventional Radiology 26.5 (2020): 504-511.

The field of artificial intelligence (AI) in healthcare is rapidly expanding worldwide, with successful clinical applications in orthopedic disease analysis and multidisciplinary practice. Computer vision-assisted image analysis has several U.S. Food and Drug Administration-approved uses. Recent techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, and natural language processing to infer health trajectories from medical notes. Advanced clinical decision support systems that combine genomics and clinomics are also gaining popularity. Machine/deep learning devices have proliferated, especially for data mining and image analysis, but pose significant challenges to the utility of AI in clinical applications. Legal and ethical questions inevitably arise. This paper proposes a training bias model and training principles to address potential harm to patients and adverse effects on society caused by AI.

References

1. Allport GW. “The historical background of social psychology”. The handbook of social psychology, Reading, MA: Addison Wesley 1 (1985): 1-46.
2. Asch SE. “Opinions and social pressure”. Scientific American 193 (1955): 31-35.
3. Bond FC. “Social facilitation: a self-presentational view”. Journal of Personality and Social Psychology 42 (1982): 1042-1050.
4. Bond FC and Titus LJ. “Social facilitation: a meta-analysis of 241 studies”. Psychological Bulletin 94.2 (1983): 265-292.
5. Baron RS. “Distraction-conflict theory: Progress and problems”. In L. Berkowitz (Ed.), Advances in experimental social psychology 19 (1986): 1-40.
6. Eysenck HJ. “Personality and experimental psychology: the unification of psychology and the possibility of a paradigm”. Journal of Personality and Social Psychology 73.6 (1997): 1224-1237.
7. Heingartner A and Herman EM. “Category accessibility and social perception: Some implications for the study of person memory and interpersonal judgments”. Journal of Personality and Social Psychology 38 (1980): 841-856.
8. Leary MR and Baumeister RF. “The nature and function of self-esteem: sociometer theory”. In M. P. Zanna (Ed.). Advances in experimental social psychology, San Diego: Academic Press 32 (2000): 1-62.
9. Neiss R. “Reconceptualizing arousal: psychobiological states in motor performance”. Psychological Bulletin 103.3 (1988): 345-366.
10. Sanders GS. “The motivating effects of distraction on task performance”. Journal of Personality and Social Psychology 32 (1975): 956-963.
11. Schmitt. “Mere presence and social facilitation: One more time”. Journal of Experimental Social Psychology 22.3 (1986): 242-248.
12. Zajonc RB, Heingartner A and Herman EM. “Social enhancement and impairment of performance in the cockroach”. Journal of Personality and Social Psychology 13 (1969): 83-92.

This study aimed at investigating the role of social facilitation on performance. This study was done on 28 university students at Psychology department Karachi University. A test was conducted through Speed and Accuracy Cancellation Sheet individually and within a group. The result for this study was a mean average of 50.036 when working alone whereas 46.714 when working in group. It was found that the effect of social facilitation on performances was false. There can be different reasons/factors that can affect social facilitation, which can be fear of audience evaluation, opposite gender audience, bad mood, an inner drive for performance or distraction/ conflict within oneself. Even when performing a seemingly simple task, fear of public shame can be a reason for harmful social facilitation of performance. A drop in performance is just one of the potential adverse outcomes of extreme fear.

Keywords: Social Facilitation; Speed and Accuracy; Performance

References

1. Honeypots: A Guide to Increasing Security (2021).
2. Wafi H., et al. “Implementation of a modern security systems honeypot Honey Network on wireless networks”. 2017 International Young Engineers Forum (YEF-ECE), (2017): 91-96.
3. Easier Honeypot Deployment and Management with Modern Honey Network.
4. Ci-Bin Jiang., et al. “Novel intrusion prediction mechanism based on honeypot log similarity”. International Journal of Network Management 26.3 (2016):
5. Ioannis Koniaris., et al. “Honeypots deployment for the analysis and visualization of malware activity and malicious connections”. IEEE (2014).
6. Emmanouil Vasilomanolakis., et al. “A honeypot-driven cyber incident monitor: lessons learned and steps ahead”. Proceedings of the 8th International Conference on Security of Information and Networks (2015): 158-164.
7. Amit D. Lakhani and Kenneth G Paterson. “Deception Techniques Using Honeypots”.
8. SM Jigneshkumar. “Modern Honey Network”. Int. J. Res. Advent Technol (2016).
9. J Gondohanindijo. “IPS (Intrusion Prevention System) Untuk Mencegah Tindak Penyusupan/Intrusi”. Maj. Ilm. Inform (2012).
10. A Muhammad. “Implementasi Honeypot Dengan Menggunakan Dionaea Di Jaringan Hotspot Fizz”. Politek. Telkom (2011).

It would be an understatement to say that the internet is hazardous in this age of constantly evolving attack mechanisms and pervasive data thefts. For security specialists, it is akin to engaging in an endless game of cat-and-mouse as they traverse an ever-changing landscape. Using only firewalls and antivirus software against a modern, well-equipped army is equivalent to using spears and stones. Social engineering or malware employing packing or encoding techniques that evade our detection tools are all that an adversary needs to compromise our system. Therefore, it is imperative to transcend the limitations of edge defence, which primarily focuses on tool validation, and adopt a proactive strategy that emphasises intrusion identification and prompt response. This can be accomplished through the implementation of an ethereal network, a comprehensive end-to-end host and network approach that not only scales effectively but also ensures accurate intrusion detection. Our objective is not limited to mere obstruction; it also includes a substantial reduction in time. When conventional security measures, such as firewalls and antivirus software, fail, we must swiftly ascertain the nature of the incident and respond accordingly. In industry reports, response times are frequently measured in weeks, months, or even years, which is untenable. Our objective is to reduce this timeframe to hours, a significant reduction that will improve our response capabilities. Therefore, an effective approach to breach detection becomes essential. Together with a robust honeypot system, we employ a Modern Honey Network (MHN) to facilitate honeypot management and deployment while ensuring their security. This fusion includes honeypots such as Glastopf, Dionaea, and Kippo, which document suspicious activities and capture crucial details of the attacks on the MHN server. In addition, reconnaissance is essential to our research. Recognising the complexities of reconnaissance, we make it the focal point of our efforts. When malware or insider threats penetrate our network, they frequently conduct reconnaissance to determine the extent of their access. By closely observing this type of activity, we can readily identify any suspicious network intrusions or compromised Internet of Things devices. Our deployment strategy concludes with the installation of MHN, the deployment of Dionaea, Kippo, and Snort honeypots, and their integration with Splunk for effective analysis of captured attacks. This integration enables us to identify the specific service ports under attack and trace the assailants’ source IP addresses, providing invaluable information for further investigation and mitigation.

Keywords: Breach; Ethereal; Intrusion Detection Systems; Honeypot; Reconnaissance

References

1. Zhao Tao, Zhang Zhi and Liang Shangkun. “Digital economy, entrepreneurship activity, and high-quality development: empirical evidence from Chinese cities”. Management World 36.10 (2020): 65-76 (in Chinese).
2. Zhang Xun., et al. “Digital economy, inclusive finance, and inclusive growth”. Economic Research 54.08 (2019): 71-86 (in Chinese).
3. Бабкин АВ and Алексеева НС. “A Methodology for assessing the intellectual capital of an innovative-active industrial cluster in the context of the digital economy”. Ekonomika i Upravlenie / Economics & Management 26.7 (2020): 739-749.
4. Zhang Yuzhe. “Development ideas and main tasks for the digital economy driving industrial structure towards the mid to high end”. Economic Review 394.09 (2018): 85-91 (in Chinese).
5. Wei Qian, Huan Liu and Fanghui Pan. “Digital economy, industry heterogeneity, and service industry resource allocation”. Sustainability 14.13 (2022): 8020.
6. Zhang Hui and Shi Lin. “Digital economy: new power in the new era”. Journal of Beijing Jiaotong University (Social Science Edition) 18.02 (2019): 10-22 (in Chinese).
7. Lifang Chen., et al. “Research on the teaching system of creative and entrepreneurial software engineering(digital media technology) talents training”. Computer Education 12 (2018): 75-81.
8. Prashant M. Dolia. “Evaluation: The new philosophical roles & psychological means”. IJMECS 5.7 (2013): 34-40.
9. Yong Gu, Di Liu and Zhiping Liu. “Training and evaluation system of intelligent supply chain talent based on digital teaching and collaborative innovation”. Advances in Computer Science for Engineering and Education (2022): 134.
10. Dong Wei, Ling Guo and WenRan Dong. “Analysis on the classification and evaluation system of talents in colleges and universities from the perspective of AHP”. Mobile Information Systems (2022).
11. Yue Shujing and Zhang Xinhe. “Research on the impact of digital economy on energy intensity”. Journal of Nanchang University (Humanities and Social Sciences Edition) 54.01 (2023): 77-90 (in Chinese).
12. Rikshat Verma and Patel Parth. “Digital talent management: Insights from the information technology and communication industry”. Thunderbird International Business Review 65.2 (2023): 253-254.
13. Umasankar Murugesan., et al. “A study of artificial intelligence impacts on human resource digitalization in industry 4.0”. Decision Analytics Journal 7 (2023): 100249.
14. Xiong Ziyue, Zhang Yi and Wang Yanan. “A study on the coupling and coordination of scientific research investment and achievement transformation in universities under the background of industry education integration”. Jiang Ke Academic Research 17.4 (2022): 1-8 (in Chinese).
15. Sun Jinghao and He Haiyan. “Research on the influencing factors of scientific and technological innovation performance of State Key Laboratory in universities based on BP neural network”. Scientific Management Research 40.04 (2022): 48-54 (in Chinese).
16. Palab Naidu and Lai Meiyun. “SPOT team guided technology - ignite the wisdom of group management[M]”. Nanjing: Jiangsu People's Publishing House (2014) (in Chinese).
17. Li Yang and Pan Haisheng. “The action logic and reform direction of integrating digital abilities into vocational education in the European Union”. Comparative Education Research 44.10 (2022): 76-85 (in Chinese).
18. Lan Guoshuai., et al. “The digital literacy framework for eu educators: key interpretation and enlightenment”. Modern Distance Education Research 32.06 (2020): 23-32 (in Chinese).
19. Liu Ping, Chen Weizheng. “Analysis of the value and pricing of human capital”. Financial Science 230.05 (2007): 81-88 (in Chinese).
20. Ge Minglei., et al. “Contradictions and management of multiple institutional logic in human resources consulting projects”. Management Review 34.09 (2022): 254-270 (in Chinese).
21. Abdul Rahman Abuduaini. “Evaluation of cross-border e-commerce logistics business based on game theory combination weighting model”. Statistics and Decision 38.19 (2022): 184-188 (in Chinese).

Digital transformation and upgrading pose new requirements for talent quality. Taking human resources positions as an example, this article constructs a "3+X" talent quality evaluation index system that includes knowledge, ability, attitude, and professional skills. Based on the data characteristics of 55 employees in human resources positions, a combination weighting model, entropy method, and BP neural network model are used, and an evaluation index system and weight evaluation index system suitable for the quality characteristics of talents in China's hydropower industry have been constructed. The research results show that employee competence is an important indicator for driving digital transformation, with knowledge and professional skills playing a relatively central role, and attitudes being less differentiated. Finally, the research significance and shortcomings of the study are summarized, and the prospects for future development are provided.

Keywords: Digital transformation; Talent quality characteristics; Evaluation index; BP neural network; Entropy method

References

1. Grieves M. “Digital twin: manufacturing excellence through virtual factory replication”. White paper 1 (2014): 1-7.
2. Boschert S and Rosen R. “Digital Twin-The Simulation Aspect “in Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and their Designers”. P. Hehenberger und D. Bradly, Hg (2016).
3. Kit KT. “Paradigm of Digital Twin Application in Project Management in Architecture, Engineering and Construction”. International Journal of Civil and Architectural Engineering 16.4 (2022): 122-127.
4. Li CZ., et al. “The application of BIM in the AECO industry”. Journal of Civil Engineering and Management 29.3 (2023): 202-222.
5. Kazado D, Kavgic M and Eskicioglu R. “Integrating building information modeling (BIM) and sensor technology for facility management”. Journal of Information Technology in Construction (ITcon) 24.23 (2019): 440-458.
6. Scherer RJ and Schapke SE. “A distributed multi-model-based management information system for simulation and decision-making on construction projects”. Advanced Engineering Informatics 25.4 (2011): 582-599.
7. Pan Y and Zhang L. “A BIM-data mining integrated digital twin framework for advanced project management”. Automation in Construction 124 (2021): 103564.
8. Howell S and Rezgui Y. “Beyond BIM: Knowledge Management for a Smarter Future”. IHS Markit (2018).
9. Beetz J, van Leeuwen JP and de Vries B. An ontology web language notation of the industry foundation classes. In Proceedings of the 22nd CIB W78 Conference on Information Technology in Construction, Technische Universität Dresden (2006): 670.
10. Pauwels P., et al. “Enhancing the ifcOWL ontology with an alternative representation for geometric data”. Automation in Construction 80 (2017): 77-94.
11. FH Abanda, JHM Tah and R Keivani. “Trends in built environment Semantic Web applications: where are we today?”Expert Syst. Appl 40 (2013): 5563-5577.
12. Falquet G, Metral C and Cutting-Decelle AF. “Towards semantically enriched 3D city models: an ontology-based approach”. Proceeding GeoWeb 2009 Academic Track-Cityscapes-International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (2009): 40-45.
13. Pauwels P, Zhang S and Lee YC. “Semantic web technologies in AEC industry: A literature overview”. Automation in construction 73 (2017): 145-165.
14. Fuchs S and Scherer RJ. “Multimodels—Instant nD-modeling using original data”. Automation in Construction 75 (2017): 22-32.
15. International Standardization Organization. Information Containers for linked document delivery (2020).
16. Scherer RJ and Katranuschkov P. “Context capturing of multi-information resources for the data exchange in collaborative project environments”. InProceedings of the European Conference on Computing in Construction (EC3 2019) (2019).
17. Al-Sadoon N and Scherer RJ. “IFC Semantic Extension for Dynamic Fire Safety Evacuation Simulation”. InProc. of the Conference CIB W78 2021 (2021): 11-15.
18. Al-Sadoon N, Katranuschkov P and Scherer RJ. “Extending ICDD Implementation to a Dynamic Multimodel Framework”. InProc. of the Conference ECPPM (2022).
19. Al-Sadoon N and Scherer RJ. “From Static to Dynamic Information Containers _ Dynamic ICDD”. In Proc. of the Conference EC3 (2023).
20. IECO Consortium (2022).

In recent years, digital twins have become a more significant strategic trend in the construction industry. Stakeholders in the industry view it as a technology-driven innovation that has the potential to support the design, building, and operation of constructed assets, alongside advancements in other new-generation information technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and edge computing. However, the construction project context generates various organizational and functional information through model-based domain-specific information models that require integration and analysis. Furthermore, commercial technologies enable the integration of real-time data sources with building information models (BIM), but these tools are often proprietary and incompatible with other applications. This lack of interoperability among heterogeneous data formats is a major obstacle to the reliable application of digital twins in the construction industry. To address this challenge, this study presents a multimodel framework developed using Information Container for Linked Document Delivery (ICDD) that can integrate multiple data models from autonomous and heterogeneous sources, including real-time data sources, in their original format at the system level. This framework enables stakeholders to analyze, exchange, and share linked information among the built asset stakeholders, relying on linked data and Semantic Web technologies.

Keywords: BIM; Multimodel; Digital Twin; Linked data; Information Containers

References

1. Wang Chen and Wang Yuan. Introduction to Cultural Heritage, Beijing, Tsinghua University Press (2016).
2. Ye Han. The Apocalypse of World Heritage Conservation, Beijing: Zhejiang Gongshang University Press (2013).
3. Zhang Xin. The Value and Significance of the Principle of "Authenticity" in the Protection of Cultural Heritage, Hubei Academy of Fine Arts Newspaper 4 (2008).
4. Paolo Ceccarelli and Wu Qingzhou. Yesterday Tomorrow: 50 Years of Italian Urban Conservation and Development, Beijing: China Architecture and Construction Press (2016).
5. Lin Yuan. Basic Theory of Architectural Heritage Protection in China, Beijing: China Architecture and Architecture Press (2012).
6. State Administration of Cultural Heritage: Italian Code of Cultural Landscape Heritage (Jing), Beijing: Cultural Relics Publishing House (2009).
7. Lv Zhou. "The Spirit of Authenticity of the Venice Charter". column commemorating the 50th anniversary of the publication of the Venice Charter.
8. Zhang Song. "Exploration of Several Issues in the Protection of Architectural Heritage--Enlightenment of the International Charter for the Protection of Cultural Heritage". Urban Architecture Thematic Column.
9. He Yujie, Chang Chunyan and Tang Xiaotao. “Overview of the Protection of Italian Cultural Heritage”. Journal of Central South University of Forestry and Technology (Social Science Edition) 5.5 (2011): 150-152.
10. Monroe Beardsley. History of Aesthetics: From Ancient Greece to Contemporary China, translated by Gao Jianping, Beijing: Higher Education Press (2018).
11. Gao Jianping. "The Modern Course of Western Aesthetics". Anhui: Anhui Education Press (2014).V

Italy has a rich world cultural heritage, and its protection strategy has also been developed. It has experienced value changes that emphasize "restore the original shape of the building", "historical authenticity", and "integrity protection". The aesthetics presented here are also Changes have taken place, highlighting the "beauty of historical truth and the beauty of differences of contemporary imprints."

Keywords: architectural cultural heritage; protection strategy; aesthetic development; gestalt and authenticity

     A well-known classification algorithm we often hear about and use in solving several problems is the k-nearest Neighbor (KNN) Classifier algorithm, which can also be used in regression processes such as the KNN regression algorithm. Also, the Naïve Bayes Classifier algorithm, including the Artificial Neural Network (ANN) algorithm, where the Neural Network (NN) algorithm can also carry out the regression process. Furthermore, the Support Vector Machine (SVM) algorithm can also conduct the regression process with the SVM regression algorithm and the Decision Tree Classifier (DT) algorithm, including the regression process with the DT regression algorithm. Apart from that, the random forest classifier algorithm can also be used for classification, and the regression process is carried out with the RF regression algorithm. Furthermore, the classification process can also be carried out using the Generalized Regression NN (GRNN) algorithm as a variation of the Radial Basis NN (RBNN) algorithm, where GRNN can be used for classification or regression. Finally, the classification process can also use the algorithm. The gradient-boosted tree classifier can also be chosen to carry out the classification. The regression process can also be done using the Gradient gradient-boosting machine-regression algorithm. Furthermore, the classification process can be carried out with multilayer perceptron Classifier algorithms, One-vs-Rest (aka One-vs-All) Classifier algorithms, and factorisation machine Classifier algorithms.

     In addition, the classification process can be used to classify with two output results, namely the binary classification algorithm. The following classification algorithms can be used for binary classification and regression processes: the Logistic Regression algorithm, LightGBM algorithm, XGBoost algorithm and Neural Network (NN) algorithm (Deep Learning). Apart from that, the following binary classification algorithms are only specific for classifying with two output results. They are the Naive Bayes (Gaussian) algorithm, the Naive Bayes (Bernoulli) algorithm, the K Nearest Neighbors (KNN) algorithm, the Support Vector Machine (SVM) algorithm, the Decision Tree (DT) algorithm, Random Forest (RF) algorithm and Gradient Boosting Machine algorithm.

     A classification model can be measured using a confusion matrix, a Matrix for Summarizing the performance of the Classification algorithm and is known as an error matrix. A confusion matrix is a matrix between the predicted and actual conditions in the population. The predicted condition has PP as the number of positively predicted cases in the population, whilst PN is the number of negatively predicted cases in the population. Meanwhile, the actual condition has P as the number of positive actual cases in the population and N as the number of negative actual cases. The confusion matrix has 4 scores: TP as True Positive, TN as True Negative, FN as False Negative and FP as False Positive. TP is a test result that correctly indicates the presence of a condition or characteristic, whilst TN is a test result that accurately displays the absence of a condition or characteristic. Meanwhile, FN is a test result that wrongly indicates that a particular condition or attribute is absent, and FP is a result that wrongly suggests that a specific condition or attribute is present.

The following are metrics that can be used in measuring classification models, and they are:

1. Accuracy.
2. Classification Error Rate.
3. True Positive Rate (TPR).
4. False Positive Rate (FPR).
5. False Negative Rate (FNR).
6. True Negative Rate (TNR).
7. Positive Predictive Value (PPV).
8. Negative Predictive Value (NPV).
9. False Discovery Rate (FDR).
10. False Omission Rate (FOR).
11. Misclassification Rate.
12. F1 Score.
13. Kappa Cohen.
14. Matthew Correlation Coefficient (MCC).
15. +LR (Positive Likelihood Ratio).
16. -LR (Negative Likelihood Ratio).
17. J Youden.
18. G Measure.
19. D2H (Distance to Heaven).
20. AUC=Area under the Receiver Operating Characteristic (ROC) curve.

     This metric can only be applied to the classification process. It cannot possibly be used for other supervised methods, such as regression, and is even less applied to clustering processes as unsupervised models.

     When dealing with supervised models containing classification and regression, we need special matrices, such as confusion matrices. In software engineering, the final step, such as testing, is the most crucial stage in software creation, and that is the confusion matrix. We must have software that has been tested, and in this case, it is called white box testing, which is testing carried out from the internal side of the software. It is essential that software is not only created but can also be tested internally and even better if it is also tested externally, which is also often referred to as black box testing. Black box testing is a software testing process carried out by distributing questionnaires to software users as a user acceptance test.

References

1. Carlo Brunetta., et al. “Non-interactive, secure verifiable aggregation for decentralized, privacy preserving learning”. In Australasian Conference on Information Security and Privacy, Springer (2021): 510-528.
2. Haokun Fang and Quan Qian. “Privacy preserving machine learning with homomorphic encryption and federated learning”. Future Internet 13.4 (2021): 94.
3. Jiale Guo., et al. “Secure weighted aggregation for federated learning”. arXiv preprint arXiv:2010.08730 (2020).
4. Swanand Kadhe., et al. “Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning”. arXiv preprint arXiv:2009.11248 (2020).
5. Renuga Kanagavelu., et al. “Two-phase multi-party computation enabled privacy-preserving federated learning”. In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE (2020): 410-419.
6. Zaixi Zhang., et al. “Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients”. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022): 2545-2555.

Federated learning (FL) is a distributed machine learning technique that enables remote devices to share their local models without sharing their data. While this system benefits security, it still has many vulnerabilities. In this work, we propose a new aggregation system that mitigates some of these vulnerabilities. Our aggregation framework is based on: Connecting with each client individually, calculating clients’ model changes that will affect the global model, and finally preventing aggregation of any client model until the accepted range of distances with other clients is calculated and the range of this client is within it. This approach aims to mitigate against Causative, Byzantine, and Membership Inference attacks. It has achieved an accuracy of over 90 percent for detecting malicious agents and removing them.

Keywords: Federated Learning; Security; Step - wise Model Aggregation

References

1. Wakil M, Burhan M and Choon K. “Pushing desalination recovery to the maximum limit: Membrane and thermal processes integration”. Desalination 416 (2017): 54-64.
2. International Desalination Association, Desalination by numbers (2015).
3. Prasad TL., et al. “Pathways and Challenges for Efficient Desalination [Internet]”. London: Intech Open (2022).
4. Mehetre ST and Prasad TL. “Microbial desalination of Brine (RO reject effluent) for potential reuse for agriculture applications”. Presented at International conference 74th Indian Chemical Engineering congress CHEMCON-2021, Organised by Indian Institute of Chemical Engineers in association with Bhubhaneshwar regional centre held at IMMT Bhubhaneshwar in hybrid mode (2021).

Seawater desalination is an alternative that can extend water supplies beyond what is available in the hydrological cycle, with a constant and climate-independent supply. Radical transformation in the way we use natural resources is central to meeting the needs of future generations. The growing desalination market across the world has thrown up the challenges of management of Brine and EoL (End-of-Life) membranes. For achieving sustainable desalination strategy, the current economy based on linear model has to be replaced with circular economy model through suitable technologies. Here in our study, we have carried out various process studies for brine management and membrane management for facilitating the value addition to desalination plants and facilitating the reuse and recycle for lower end applications. The process technologies based on these studies are presented in this engineering article. The coupling of recovery of technology trace metals from brine using Radiation Induced Grafted sorbents is highlighted. These process studies and technologies will help to incorporate principles of the circular economy for the sustainable development of desalination.

Keywords: Desalination; Brine management; Membrane management; Circular economy

References

1. Konishchev VN. “Reaction of permafrost to climate warming yuPermafrost response to the climate of Warming”. Cryosphere of the earth - Cryosphere of the earth 15.4 (2011): 15-18.
2. Shur Y and Goering DJ. “Climate Change and Foundations of Buildings in Permafrost Regions”. In: Margesin, R. (eds) Permafrost Soils. Soil Biology, vol 16. Springer, Berlin, Heidelberg (2009): 251-260.
3. Bockheim JG., et al. “Soil organic carbon storage and distribution in arctic tundra, Barrow, Alaska”. Soil Sci. Soc. Am. J 63 (1999): 934-940.
4. Burke EJ, Hartley IP and Jones CD. “Uncertainties in the global temperature change caused by carbon release from permafrost thawing”. The Cryosphere 6 (2012): 1063-1076.
5. Frank-Fahle BA., et al. “Microbial functional potential and community composition in permafrost-affected soils of the NW Canadian Arctic”. PloS ONE 9 (2014): e84761.
6. Sullivan Taylor D., et al. “Influence of permafrost thaw on an extreme geologic methane seep”. Permafrost and Periglacial Processes 32.3 (2021): 484-502.
7. Dutta K., et al. “Potential carbon release from permafrost soils of North-eastern Siberia”. Glob. Change Biol 12 (2006): 2336-2351.
8. Semenov SM., et al. “Methods for Assessing the Impacts of Climate Change on Physical and Biological Systems”. M. Roshydromet (2012): 508.
9. Alexandrov I. “Russia: thawing permafrost is fraught with trillions in losses”. Eastern Europe, Russia www.eurasianet.ru (2021).
10. Harris SA. “Comparison of the climatic and geomorphic methods of predicting permafrost distribution in western Yukon Territory”. In Proceedings of the 4th International Conference on Permafrost, Fairbanks, Alaska (1983): 49-58.
11. Swanson David K. Permafrost thaw-related slope failures in Alaska's Arctic national parks, 1980-2019. Permafrost and Periglacial Processes 32.3 (2021): 392-406.
12. Bonnaventure PP and Lewkowicz AG. “Impacts of mean annual air temperature change on a regional permafrost probability model for the southern Yukon and northern British Columbia, Canada”. The Cryosphere 7 (2013): 935-946.
13. Gagnon Samuel and Allard Michel. “Modeled (1990-2100) variations in active-layer thickness and ice-wedge activity near Salluit, Nunavik (Canada)”. Permafrost and Periglacial Processes 32.3 (2021): 447-467.
14. Popescu Razvan., et al. “New findings regarding the ground air circulation by chimney effect in low-altitude permafrost susceptible porous screes (Detunata Goala, Romanian Carpathians)”. Meeting: European Geosciences Union general assembly (2021).
15. Blucher Johanna, Mayer Pert and Kuraz Michal. “Simple numercal strategies to model freezing in variably-saturated soil with the standard finite element method”. European Geosciences Union general assembly (2021).
16. Yukon Permafrost Network (YPN). Government of Yukon, Whitehorse, YT. Zhao, L., Wu, Q., Marchenko, S.S. and Sharkhuu, N. Thermal state of permafrost and active layer in Central Asia during the international polar year. Permafrost and Periglacial Processes 21 (2010): 198-207.
17. Kong Xiangbing., et al. “Field and numerical studies on the thermal performance of air convection embankments to protect side slopes in permafrost environments”. Cold Regions Science and Technology 189 (2021).
18. Jorgenson MT, Shur YL and Pullman ER. “Abrupt increase in permafrost degradation in Arctic Alaska, Geophys”. Res. Lett 33 (2006): L02503.
19. Bense VF., et al.” Permafrost degradation as a control on hydrogeological regime shifts in a warming climate”. Journal of geophysical research 117 (2012): F03036.
20. Dashjamts D. “Scientific substantiation of construction on structurally unstable soils in Mongolia”. Ulaanbaatar (2013): 504.
21. Pan CG., et al. “Glacier Recession in the Altai Mountains of Mongolia in 1990-2016”. Geografiska Annaler: Ser. A, Phys.Geogr 100.2 (2018): 185-203.
22. Goodrich LE. “The influence of the snow cover on the ground thermal regime”. Revue canadienne de gеotechnique 19.4 (1982): 421-432.
23. Fierz C., et al. “The International Classification for Seasonal Snow on the Ground”. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris (2009).
24. Cornellissen JHC., et al. “Global negative vegetation feedback to climate warming responses of leaf litter decomposition rates in cold biomes”. Ecol. Lett 10 (2007): 619-27.
25. Epstein HE, Myers-Smith I and W alker DA. “Recent dynamics of arctic and sub-arctic vegetation”. Environ. Res. Lett 8.6 (2013): 15040.
26. Igor I Sakharov., et al. “Design and construction of footings of buildings and structures permofrost soil in conjunction with environmental requirements”. International Journal for Computational Civil and Structural Engineering 15.4 (2019): 143-149.
27. Igor Holubec. “Flat Loop Thermosyphon Foundations in Warm Permafrost. Prepared for Government of the NT Asset Management Division Public Works and Services and Climate Change Vulnerability Assessment Canadian Council of Professional Engineers”. Holubec Consulting Inc (2008): 119.
28. Efimov VM, Popenko FE and Rozhin II. “Formation of base soil temperature when using seasonal cooling devices (SDA) in the permafrost zone of Central Yakutia”. Journal: Arktika end Antarktika (2017): C98-105.
29. Mangushev RA, Nyamdorj S and Sakharov II. “Bases and foundations of buildings and structures”. Ulaanbaatar (2023): 427C.
30. Fei Wang., et al. “Applicability analysis of thermosyphon for thermally stabilizing pipeline foundation permafrost and its layout optimization”. Cold Regions Science and Technology. Elsevier 208 (2023).
31. The PLOS ONE staff. “The environmental consequences of climate-driven agricultural frontiers”. PLOS ONE 15.7 (2020): e0236028.
32. Romanovskiy NN. “Osnovy kriogeneza Litosfery [Fundamentals of cryogenesis of the lithosphere)”. Moscow, MGU Publ (1993): 336.
33. Boike J., et al. Baseline characteristics of climate, permafrost and land cover from a new permafrost observatory in the Lena River Delta, Siberia (1998-2011), Biogeosciences 10 (2013): 2105-2128.
34. Shatilov IS, Shibasov GV and Litvinchuk AS. “Ecological features of establishment and operation of road structures in the conditions of permafrost”. European Journal of Natural History 6 (2021): P76-81.
35. Gevorkyan SG. Permafrost in China at the beginning of the 21st century (review). Electronic scientific publication Almanac Space and Time. T-11, UDK 1 (2016).
36. Qiu, Guoqing; Cheng Guodong. “Permafrost in China: Past and Present”. Permafrost and Periglacial Processes 6.1 (1995): 3-14.
37. Ma Wei, Wang Dayan and Wen Zhi. "Progress in Frozen Soil Mechanics Research in China: A Review". Proceedings of the 10th International Conference on Permafrost. Salekhard, Yamal-Nenets Autonomous District, Russia (2012): 249-25
38. Jin HJ, Cheng GD and Zhu YL. “Chinese Geocryology at the Turn of the Twentieth Century”. Permafrost and Periglacial Processes 11.1 (2000): 23.33.
39. Zhou Youwu., et al. "The Map of Geocryological Regionalization and Classification in China". Geocryology in China. Beijing: Science Press (2000): 450. (In Chinese).
40. Zhao Lin, Cheng Guodong and Ding Yongjian. "Studies on Frozen Ground of China". J. Geogr. Sci 14.4 (2004): 411-416.
41. Zhang Guofei., et al. “Qinghai-Tibet Plateau wetting reduces permafrost thermal responses to climate warming”. Earth and Planetary Science Letters (2021).
42. de Bruin Jelte, Bense Victor and Van der Ploeg Martin. Determining permafrost active layer thermal properties of the Qinghai-Tibet Plateau using field observation and numerical modeling. European Geosciences Union general assembly (2021).
43. Immerzeel Walter., et al. “Modeling recent permfrost thaw and associated hudrological changes in an endorheis Tibetan watershed”. European Geosciences Union general assembly (2021).
44. Lu Guowei, Wang Binlin and Guo Dongxon. “The geographic southern boundary of permafrost in the northeast of China”. Proceedings of the Sixth International Conference on permafrost (1993): 1186-1189.
45. Popov AI. Podzemnyy led. Sb. it. (Underground ice. Coll. of articles]. Iss. II. Moscow, MGU Publ (1965): 182.
46. Kaplina TN. “Alasnye komleksy Severnoy Yakutii [Alas complexes of Northern Yakutia]”. Kriosfera zemli -Cryosphere of the earth 13.4 (2009): 3-17.
47. Lonjid N, Tumurbaatar G and Tsagaan TS. “Permafrost of Mongolia”. Ulaanbaatar (1969).
48. Tumurbaatar D. Seasonaly land perennially frozen ground around Ulaanbaatar, Mongolia. "In abstracts of 7-th international Conference on permafrost". Yellowkift, Ganada.
49. Gravis GF., et al. “Geocryological conditions in the People’s Republic of Mongolia”. Nauka Publ. Moscow (1974): 200.
50. Sharkhuu N. “Trends of permafrost development in the Selenge River Basin, Mongolia”. Proceedings of the Seventh International Conference on Permafrost, Yellowknife, Canada (1998): 976-986.
51. Sharkhuu N. “Dynamics of permafrost in Mongolia. Tohoku”. Geophysical Journal, Sendai, Japan 36.2 (2001): 91-100..
52. Jambaljav YA., et al. “The temperature regime in boreholes at Nalaikh and Terelj sites in Mongolia”. Proceedings of the Ninth International Conference on Permafrost. University of Alaska, Fairbanks 1 (2008): 821-825.
53. Saruulzaya Adiya and Enkhbat Erdenebat. Permafrost Distribution in Mongolia Under the Scenarios of RCP2.6 and RCP8.5. Proceedings of the Environmental Science and Technology International Conference (ESTIC 2021). Advances in Engineering Research 206 (2021): 121-125.
54. Qinxue Wang., et al. “Ground warming and permafrost degradation in various terrestrial ecosystems in Northcentral Mongolia”. Permafrost and Periglac Process 33.4 (2022): 406-424.
55. Jukov MF. “Construction features of seasonal deep freezing and permafrost soil of Mongolia”. Industrial Construction 4 (1957): 52-54.
56. Dalain Dashjamts. Geotechnical problems of construction on permafrost in Mongolia International Forum on Strategic Technology (2007).
57. Nyamdorj Setev., et al. “Somе results of studying cracks and cavities formed due to the degradation and disappearance of fermafrost zone of the Nalayh town in Mongolia”. Bulletin of Civil Engineers. St. Petersburg GASU. Russia 3.74 (2019): 52-62.

Permafrost is the subject of global research, it occupies about 25% of the entire land area of the globe, and also its degradation. Many works of international researchers reflect the results of work on permafrost soils common in North America, Canada, Europe, Asia, and naturally in the Arctic and Antarctica. As a result of the degradation of permafrost all over the world, including in Mongolia, permafrost of mainly discontinuous and island types is thawing, since here the thickness of frozen soils is from 2.0 ... 4.0 meters to several tens of meters, as a result of the last few decades, complete thawing is possible most island permafrost. From the point of view of permafrost engineering, thawing and degradation of permafrost leads to a decrease in the bearing capacity of the base soil, the latter leading to a loss of stability of buildings and engineering structures with possible catastrophic consequences.

Keywords: geocryology; thawing; methane release; active layer; mechanical properties of frozen soils

.

To attract every farmer to do agriculture business digitally and smart agriculture to get more productivity without soil or land. There is dire need of Internet of Things (IoT) devices in Hydro- culture Farming sector in India., where as agriculture is backbone of the country. Indian Agriculture is facing several problems such as small and fragmented land holdings, fertilizers, pesticides, chemicals used for agriculture. The solution is hydroponic system. Hydroponics is a method in which plant grow without using soil and gives you more production than soil farming within less time. However, purity of water control by this system which can result to growing of plant. Microcontroller automatically maintains purity level in water solution using turbidity sensor. We have to develop and implement Internet of Things (IoT) devices to solve above difficulties. Hydroponic system does not required land so its land free farming, Human being demanding quality food which is free from chemicals and pesticides Here we can go organic ,it should done in controlling environment .you also can do it at garden, balcony or in some controlling area. Such research paper is basic study of Internet of Things (IoT) devices used in Hydroponic System and its Impact on Productivity of Agricultural Sector upcoming as a Hydro culture Farming.

References

1. Dargan S., et al. “Augmented Reality: A Comprehensive Review”. Arch Computat Methods Eng 30 (2023): 1057-1080.
2. Abdullah M Al-Ansi., et al. “Analyzing augmented reality (AR) and virtual reality (VR) recent development in education”. Social Sciences & Humanities Open 8.1 (2023): 100532.
3. Taras Terletskyi., et al. “Determining the Feasibility of Applying Existing Criteria, for Solving Operational Problems in the Design of CCTV”. Information Systems Security of Info communication Systems and Internet of Things 1.1 (2023): 1-6.
4. Shervin Minaee, Xiaodan Liang and Shuicheng Yan. “Modern Augmented Reality: Applications, Trends, and Future Directions”. Computer Science (2022).
5. Fahmi Bellalouna. “The Augmented Reality Technology as Enabler for the Digitization of Industrial Business Processes: Case Studies”. Procedia CIRP 98 (2021): 400-405.
6. Yeung AWK., et al. “Virtual and Augmented Reality Applications in Medicine: Analysis of the Scientific Literature”. J Med Internet Res 23.2 (2021): e25499.
7. Marougkas A., et al. “Virtual Reality in Education: A Review of Learning Theories, Approaches and Methodologies for the Last Decade”. Electronics 12 (2023): 2832.
8. Ryan Lege and Euan Bonner. “Virtual reality in education: The promise, progress, and challenge”. Jaltcall journal 16.3 (2022): 167-180.

AR applications have become an integral part of our daily life as a form of communication, for entertainment, shopping, travel, education, medicine, robotics, manufacturing, etc. [1, 2]. The development of simulation applications has recently become quite common. The development and management of applications with an interactive environment and the interaction of better functions, updates such as the use of augmented reality technologies such as AR and VR [1], recognition methods [3, 4] to improve user interaction play an important role in the current development trends.

The introduction of AR and VR in educational institutions and digital transformation in industrial and non-industrial areas has grown exponentially in recent years [5]. The article [5] presents the application of augmented reality technology in the field of engineering.

For example, in medicine, VR is used as a field for training future doctors, especially surgeons, which allows you to get not only theoretical knowledge, but also real practical experience from training, while eliminating the risk of harming a real patient [6]. In addition, it provides an opportunity to analyze rare diseases and acquire skills to deal with them in emergency situations. In addition, R can help in the rehabilitation of patients after severe injuries. In science, virtual reality technology can be used to simulate complex systems and processes, allowing researchers to study them in detail and make new discoveries.

In education, VR has long been used to conduct interactive lessons and create unique simulators that help accelerate the progress of learning the material and contribute to the acquisition of practical skills [7, 8].

This paper provides experiences and suggestions for the practice-oriented development and deployment of augmented reality technology in various fields that can be used for future augmented reality research.

Keywords: Augmented reality (AR); Virtual reality (VR); Web AR; image; configuration

     Recent developments in composite material research focus on improving performance, cost-effectiveness, and sustainability. Innovations in fiber technology, matrix materials, and manufacturing processes enable the deployment of composites with enhanced properties. The integration of nanomaterials and advanced manufacturing techniques, such as automated fiber placement and 3D printing, has further expanded the design possibilities and applications of composite materials [2].

     In static testing, advancements have been done in different fields [3]. Digital image correlation (DIC) has been used to capture deformations and crack growth. Integration of acoustic emission testing with machine learning algorithms has improved the identification of specific failure modes. Advanced thermographic techniques, such as lock-in thermography and pulsed thermography, were tailored to offer improved sensitivity and resolution to detect subtle defects. In dynamic testing, the incorporation of servo-hydraulic testing systems with high-speed cameras was developed to facilitate a more accurate representation of real-world loading conditions. The integration of instrumented impactors with advanced sensors in drop weight impact testing was set to provide detailed data on impact energy, force, and deformation. Implementation of shearography in dynamic testing made possible the real-time monitoring of damage evolution during impact. Other developments include the use of DMA in conjunction with other testing methods, such as rheometry and spectroscopy, to obtain a more comprehensive understanding of dynamic behavior.

     Numerical modeling plays a fundamental role in understanding and predicting the behavior of composite materials, complementing experimental testing. Finite Element Analysis (FEA) is a widely used numerical technique that simulates the response of composite structures under various loading conditions. FEA allows researchers and engineers to assess stress distribution, deformation, and failure modes, providing deep understanding into the material's performance. Recent advancements in numerical modeling include the incorporation of multiscale modeling, allowing for a more accurate representation of the complex interactions between fibers and the matrix at different length scales. Computational tools based on machine learning algorithms are also emerging as powerful tools to predict the material properties, optimize designs, and reduce the time and cost associated with traditional trial-and-error approaches [4].

References

1. Irving PE and C Soutis. “Polymer composites in the aerospace industry”. Woodhead Publishing (2019).
2. Dikshit V., et al. “Recent progress in 3D printing of fiber-reinforced composite and nanocomposites”. Fiber-Reinforced Nanocomposites: Fundamentals and Applications (2020): 371-394.
3. Wang B., et al. “Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review”. Advances in mechanical engineering 12.4 (2020):  1687814020913761.
4. Chen C-T and GX Gu. “Machine learning for composite materials”. MRs Communications 9.2 (2019): 556-566.

Fiber-reinforced composites are engineered materials that derive their enhanced properties from the combination of the fibers and matrix. The integration of carbon, glass, or aramid fibers with a matrix material forms a composite that exhibits superior mechanical properties compared to traditional materials. Actually, the fibers provide the composite with high strength and stiffness, while the matrix material, often of polymeric, metallic, or ceramic nature, binds the fibers together and ensure load transfer. This fiber/matrix synergistic combination results in a material with a superior strength- and stiffness-to-weight ratios and improved mechanical performance. The extensive utilization of these materials in different industries justifies the need for continuous advancements in this topic to reduce costs and assure design confidence, aiming to make these materials applicable in a larger number of applications [1]. In the aerospace industry, composite materials are extensively used in aircraft structures, reducing weight and fuel consumption while maintaining structural integrity. The automotive sector benefits from composites in the form of lightweight components, enhancing fuel efficiency and overall performance. In the construction industry, composite materials contribute to the development of durable and corrosion-resistant structures, as primary structural elements or for reinforcement of existing structures. Sports equipment, such as tennis rackets and golf clubs, capitalizes on the exceptional strength and design flexibility offered by composite materials.

References

1. B Alexey, C-Y Wang and H-YM Liao. “YOLOV4: Optimal Speed and Accuracy of Object Detection”. In Arxiv, Arxiv:2004.10934 (2020).
2. I-H Yeh., et al. “CSPNET: A New Backbone That Can Enhance Learning Capability of CNN”. In Arxiv, Arxiv:1911.11929 (2019).
3. IK He., et al. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition”. In Arxiv, Arxiv:1406.4729 (2015).
4. Yang Boyu. “Design And Implementation of Hospital Fire Escape Guidance System Based on Image Recognition Indoor Positioning Technology: A Case Study of Oriental Memorial Hospital”. Master Of Information and Communication Engineering, Department of Communication Engineering, Oriental Institute of Technology (2021).
5. Krishna Sajja Tulasi and Hemantha Kumar Kalluri. “Deep Learning and Transfer Learning Approaches for Image Classification”. International Journal of Recent Technology and Engineering (IJRTE) 7.5S4 (2019): 427-432.
6. Huang L, Y Zhou and L Zhang. “Automated Defect Detection and Classification on Wafer Fabrication Using Machine Learning". Journal of Sensors (2021).
7. Kim H., et al. “Real-Time Defect Detection on Semiconductor Wafer Using Machine Learning-Based Approaches". IEEE Transactions on Industrial Informatics (2018).
8. Ng KC., et al. "Defect Detection on Semiconductor Wafers Using Convolutional Neural Networks". IEEE Transactions on Semiconductor Manufacturing (2017).
9. Zhang T., et al. "Deep Learning-Based Defect Detection for Semiconductor Wafer Inspection". IEEE Transactions on Industrial Electronics (2019).
10. Wang W., et al. "Automatic Defect Detection in Semiconductor Wafer Images Using Deep Convolutional Neural Networks". Journal of Electronic Testing (2020).

Accurately detecting and classifying defects in wafers is a crucial aspect of semiconductor manufacturing. This process provides useful insights for identifying the root causes of defects and implementing quality management and yield improvement strategies. The traditional approach to classifying wafer defects involves manual inspection by experienced engineers using computer-aided tools. However, this process can be time-consuming and less accurate. As a result, there has been increasing interest in using deep learning approaches to automate the detection of wafer defects, which can improve the accuracy of the detection process.

Keywords: Wafer detection; Deep learning; Object detection; Classification

References

1. Hui Lin., et al. “Virtual Geographic Environments (VGEs): A New Generation of Geographic Analysis Tool”. Earth-Science Reviews 126 (2013): 74-84.
2. Benenson I and Torrens P. “Geosimulation: Automata-based modelling of urban phenomena”. Wiley: Chichester (2002).
3. Agbossou I. “Urban Augmented Reality for 3D Geosimulation and Prospective Analysis”. In: Pierre Boulanger. Applications of Augmented Reality - Current State of the Art. [Working Title] IntechOpen (2023).
4. Bazzanella Liliana., et al. Future Cities and Regions. “Simulation, Scenario and Visioning, Governance and Scales”. New York, Heidelberg: Springer (2011).
5. Batty M. “Cities as Complex Systems: Scaling, Interaction, Networks, Dynamics and Urban Morphologies”. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY (2009).
6. Portugali J. (Eds.). Handbook on Cities and Complexity. Cheltenham, UK: Edward Elgar Publishing (2021).
7. Giuliani G., et al. “Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation”. International Journal of Applied Earth Observation and Geoinformation 88 (2020): 102068.
8. Yigitcanlar T, Agdas D and Degirmenci K. “Artificial intelligence in local governments: Perceptions of city managers on prospects, constraints and choices”. AI & Society (2022a).
9. Zhou M., et al. “SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion”. Environmental Science and Pollution Research 29.48 (2022): 72797-72818.
10. Hajjaji Y., et al. “Big data and IoT-based applications in smart environments: A systematic review”. Computer Science Review 39 (2021): 100318.
11. Herath H and Mittal M. “Adoption of artificial intelligence in smart cities: A comprehensive review”. International Journal of Information Management Data Insights 2.1 (2022): 100076.
12. Sanchez T., et al. “The prospects of artificial intelligence in urban planning”. International Journal of Urban Sciences (2022).
13. Alix-Garcia J., et al. “Drivers of forest cover change in Eastern Europe and European Russia, 1985-2012”. Land Use Policy 59 (2016): 284-297.
14. Manogaran G and Lopez D. “A survey of big data architectures and machine learning algorithms in healthcare”. International Journal of Biomedical Engineering and Technology 25 (2017): 182.
15. Berawi M. “The role of industry 4.0 in achieving sustainable development goals”. International Journal of Technology 10.4 (2019): 644-647.
16. Imdat A, Prithwish B and Pratap T. “Artificial Intelligence in Urban Planning and Design”. Technologies, Implementation, and Impacts. Elsevier (2022).
17. Grimaldi D and Carrasco-Farré C. “Implementing Data-Driven Strategies in Smart Cities A Roadmap for Urban Transformation”. Elsevier Science (2021).
18. Popelka S, Narvaez Zertuche L and Beroche H. “Urban AI Guide”. Urban AI (2023).
19. Lăzăroiu G and Harrison A. “Internet of Things Sensing Infrastructures and Data-driven Planning Technologies in Smart Sustainable City Governance and Management”. Geopolitics, History, and International Relations 13.2 (2021): 23-36.
20. Hai Lan, Puyang Zheng and Zheng Li. “Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities”. Technological Forecasting and Social Change 165 (2021): 120569.
21. Yigitcanlar T., et al. “Artificial intelligence technologies and related urban planning and development concepts: How are they perceived and utilized in Australia?”. Journal of Open Innovation: Technology, Market, and Complexity 6.4 (2020): 187.
22. Tekouabou S., et al. “Artificial intelligence based methods for smart and sustainable urban planning: A systematic survey”. Archives of Computational Methods in Engineering (2022).
23. Mikulsen M and Diduck A. “Towards an integrated approach to disaster management and food safety governance”. International Journal of Disaster Risk Reduction 15 (2016): 116-124.
24. Jha AK., et al. “A review of AI for urban planning: Towards building sustainable smart cities”. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE (2021): 937-944.
25. Li B., et al. “POI detection of high-rise buildings using remote sensing images: A semantic segmentation method based on multitask attention Res-U-Net”. IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-16.
26. King BA, Hammond T and Harrington J. “Disruptive technology: Economic consequences of artificial intelligence and the robotics revolution”. Journal of Strategic Innovation and Sustainability 12.2 (2017): 53-67.
27. Tomitsch M and Haeusler MH. “Infostructures: Towards a Complementary Approach for Solving Urban Challenges through Digital Technologies”. Journal of Urban Technology 22.3 (2015).
28. City of Los Angeles SmartLA 2028. Technology for a better Los Angeles (2020).
29. Breeze Technologies.
30. CurbIQ platform.

Urban Computing (UC) stands as an interdisciplinary field where urban challenges are examined and, where applicable, addressed through cutting-edge computing technologies. The swift pace of urbanization has brought about significant improvements in many aspects of people's lives, but it has also given rise to substantial challenges like traffic congestion, energy consumption, pollution, soil artificialization, and heat islands. In response, Urban Computing seeks to confront these issues by leveraging the data generated in cities, facilitated by urban sensing, data management, data analytics, and service provision. This iterative process aims for unobtrusive and continuous enhancements in the quality of life, city operations, and environmental conditions. This paper introduces a comprehensive framework tailored for Urban Computing, specifically attuned to the requirements of 3D geosimulation and informed prospective analysis. Given the dynamic evolution of urban environments, the demand for sophisticated computational tools has become increasingly imperative. The proposed framework integrates cutting-edge technologies to address the intricacies associated with urban dynamics, providing a foundational basis for well-informed decision-making. Encompassing components for data acquisition, processing, modeling, simulation, and analysis, the framework underscores the synergy among these elements, promoting a holistic understanding of urban phenomena.

Keywords: Urban Sensing; Urban Data Analytics; Explainability, Smart Cities; Sustainable Development Goals; Machine Learning

References

1. Lamb W. “Pore pressure in a foundation clay”. J.soil of mech. ASCE 88.2 (1962).
2. Papaleksi ND. Physics course 1.2 (1948).
3. Ter - Martirosyan ZG. “Forecast of subsidence of the earth's surface during pumping of groundwater”. Problems of geomechanics 6 (1973).
4. Leibenzon LS. “Movement of natural liquids and gases in porous media”. Gostekhizdat (1948).
5. Togtokh N. “Study of pore pressure in multiphase clayey soil under complex stress state taking into account the time factor”. Abstract. diss... Ph.D (1975): 26C.
6. Hamilton LW. “The effect of internal hydrostatic pressure on shearing strengths of soils”. Proc. ASTM vol.39.1939.
7. Togtokh N. “On the theoretical problems of compaction of clayey soils”. MSU. Collection of scientific papers 47 (1974).
8. Tsytovich NA. Soil mechanics (1963).
9. Pokrovsky GI. “On the physical principles of calculating soil deformations”. Mat. All-Union conference on foundations and foundations. Gosstroyzdat (1939).
10. Dalmatov BI and Ngun Van Quang. Study of the deformability of gas-containing clay soils (1972).
11. Togtokh N. “Equation of state of compressible fluids in multiphase physical soils”. MSU. Collection of scientific papers 4.54 (1975).

The newly discovered general law on the compressibility of gas-containing fluid is used to calculate the density of multiphase clayey soil in a closed system under high pressure in the field of construction, taking into account the parameters of water saturation and dissolution of gases containing in thick layers of clayey soils of the foundations of high-rise buildings, the clay core of hydraulic dams and the environment of underground structures will allow you to perform engineering calculations of the foundations of buildings and structures with high accuracy.

Keywords: foundation soil; pore pressure; compressibility coefficient; Henry's law; Pauson's law

References

1. Grieves M., et al. “Virtually Intelligent Product Systems: Digital and Physical Twins”. In Complex Systems Engineering: Theory and Practice, edited by S. Flumerfelt, et al., 175-200. American Institute of Aeronautics and Astronautics (2015).
2. Grieves M and Vickers J. “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems”. In: Kahlen, J., Flumerfelt, S., Alves, A. (eds) Transdisciplinary Perspectives on Complex Systems. Springer, Cham (2017).
3. USDZ: 3D interoperability around the Augmented Reality format (2023).
4. OGC CityGML 3.0 Conceptual Model (2022).
5. Kutzner T, Chaturvedi K and Kolbe TH. “CityGML 3.0: New Functions Open Up New Applications”. PFG J. Photogramm. Remote Sens. Geoinf. Sci 88 (2020): 43-61.
6. Pixar Animation Studios (2020).
7. Ledoux H. “val3dity: validation of 3D GIS primitives according to the international standards”. Open geospatial data, softw. stand (2018).
8. Ledoux H., et al. “CityJSON: a compact and easy-to-use encoding of the CityGML data model”. Open geospatial data, softw. stand (2019).
9. Liao T. “Standards and Their (Recurring) Stories: How Augmented Reality Markup Language Was Built on Stories of Past Standards”. Science, Technology, & Human Values 45.4 (2020): 712-737.
10. Chicago area transportation study. Final report 3 (2020).
11. Boyce DE and Williams HCWL. “Forecasting urban travel: Past, present and future”. Edward Elgar Publishing.
12. Batty M. “Fifty years of urban modeling: Macro-statics to micro-dynamics”. In The dynamics of complex urban systems. Physica-Verlag HD (2008): 1-20.
13. Boyce DE. Urban transportation network-equilibrium and design models: Recent achievements and future prospects: Environment and planning A: Economyand space 16.11 (1984): 1445-1474.
14. Echenique M. “The use of integrated land use and transport models: The cases of Sao Paulo, Brazil and Bilbao”. In The practice of transportation planning. Amsterdam: Elsevier (1985).
15. Waddell P. “UrbanSim: Modeling urban development for land use, transportation, and environmental planning”. Journal of the American Planning Association 68.3 (2002): 97-314.
16. Allen PM. “Cities and regions as self-organizing systems: Models of complexity”. Amsterdam: Gordon and Breach Science (1997).
17. Couclelis H. “Cellular worlds: A framework for modeling Micro—Macro dynamics”. Environment and Planning 17.5 (1985): 585-596.
18. Tobler WR. “A computer movie simulating urban growth in the detroit region”. Economic Geography 46 (1970): 234.
19. White RW and Engelen G. “Cellular automata as the basis of integrated dynamic regional modelling”. Environment and Planning 24.2 (1997): 235-246.
20. White G., et al. “A digital twin smart city for citizen feedback”. Cities 110 (2021).
21. Alonso W. Location and land use: Toward a general theory of land rent (1964).
22. Anas A. “A dynamic disequilibrium model of residential location”. Environment and Planning 5.5 (1973): 633-647.
23. Whyte J., et al. Analysing systems interdependencies using a digital twin (2019).
24. Ding K., et al. “Smart steel bridge construction enabled by BIM and Internet of Things in industry 4.0: A framework”. In 2018 IEEE 15th international conference on networking, sensing and control (ICNSC) (2018): 1-5.
25. Nebiker S., et al. “Fusion of airborne and terrestrial image-based 3d modelling for road infrastructure management - vision and first experiments”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B4 (2012): 79-84.
26. Melo HC., et al. “City information modeling (CIM) concepts applied to the management of the sewage network”. IOP Conference Series: Earth and Environmental Science 588.4 (2020).
27. Farruggio D and Glattfelder AH. “Modeling and control of electric power trans-mission lines”. In 2001 European control conference (ECC 2001) (2001): 2322-2327.
28. Biljecki F., et al. “The variants of an LOD of a 3D building model and their influence on spatial analyses”. ISPRS Journal of Photogrammetry and Remote Sensing 116 (2016): 42-54,
29. Sinyabe E., et al. “Shapefile-based multiagent geosimulation and visualization of building evacuation scenario”. Procedia Computer Science 220 (2023): 519-526.
30. Kalogianni E., et al. “3D land administration: A re-view and a future vision in the context of the spatial development lifecycle”. ISPRS Int. J. Geo-Inf 9.2 (2020): 107.
31. Biljecki F., et al. “Applications of 3D City Models: State of the art review”. ISPRS Int. J. Geo-Inf 4.4 (2015): 2842-2889.
32. Li L., et al. “Semantic 3D modeling based on CityGML for ancient Chinese-style architectural roofs of digital heritage”. ISPRS Int. J. Geo-Inf 6.5 (2017): 132.
33. Nys G-A, Poux F and Billen R. “CityJSON Building Generation from Airborne LiDAR 3D Point Clouds”. ISPRS International Journal of Geo-Information 9.9 (2020): 521.
34. Huang MQ, Ninić J and Zhang QB. “BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives”. Tunnelling and Under-ground Space Technology 108 (2021).
35. Zheng Y., et al. “Urban computing: Concepts, methodologies, and applications”. ACM Trans. Intell. Syst. Technol (2014).
36. Agbossou I. “Urban Resilience Key Metrics Thinking and Computing Using 3D Spatio-Temporal Forecasting Algorithms”. In: Gervasi, O., et al. Computational Science and Its Applications - ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, Springer, Cham 13957 (2023): 332-350.
37. Agbossou I. “Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model”. Artificial Intelligence. IntechOpen (2023).
38. Cherdo L. The 8 Best 3D Scanning Apps for Smartphones and IPads in (2019).
39. Ming H., et al. “A topological enabled three-dimensional model based on constructive solid geometry and boundary representation”. Cluster Comput 19 (2016): 2027-2037.
40. Kang TW and Hong CH. “IFC-CityGML LOD mapping automation using multiprocessing-based screen-buffer scanning including mapping rule”. KSCE J Civ Eng 22 (2018): 373-383.
41. Stoter JE., et al. “State of the Art in 3D City Modelling: Six Challenges Facing 3D Data as a Platform”. GIM Interna-tional: the worldwide magazine for geomatics 34 (2020).
42. Vázquez-Canteli JR., et al. “Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities”. Sustainable Cities and Society 45 (2019): 243-257.
43. Yigitcanlar T., et al. “Can cities become smart without being sustainable?”. A systematic review of the literature Sustain Cities Soc 45 (2019).
44. Agbossou I. “Urban Augmented Reality for 3D Geosimulation and Prospective Analysis”. In: Pierre Boulanger. Applications of Augmented Reality - Current State of the Art. [Working Title] IntechOpen (2023).
45. Zheng Y., et al. “Visual Analytics in Urban Computing: An Over-view”. IEEE Transactions on Big Data 2.03 (2016): 276-296.
46. Gautier J, Brédif M and Christophe S. “Co-Visualization of Air Temperature and Urban Data for Visual Exploration”. in 2020 IEEE Visualization Conference (VIS), Salt Lake City, UT, USA (2020): 71-75.
47. Li C., et al. “DDLVis: Real-time Visual Query of Spatiotemporal Data Distribution via Density Dictionary Learning". IEEE Transactions on Visualization & Computer Graphics 28.01 (2022): 1062-1072.
48. Rosen R., et al. “About the Importance of Autonomy and Digital Twins for the Future of Manufacturing”. IFAC-PapersOnLine 48.3 (2015): 567-572.
49. Wang S., et al. “Fog manufacturing: New paradigm of industrial internet manufacturing based on hierarchical digital twin”. Computer Intergrated Manufacturing Systems 25 (2019): 3070-3080.
50. Pan YH., et al. “Digital Twin Based Real-time Production Logistics Synchronization System in a Multi-level Computing Architecture”. Journal of Manufacturing Systems 58.B (2021): 246-260.
51. USDZ: 3D interoperability around the Augmented Reality format (2023).
52. OGC CityGML 3.0 Conceptual Model (2022).
53. Semeraro C., et al. “Digital twin paradigm: A systematic literature review”. Computers in Industry 130 (2021): 103469.

Digital twins have become indispensable tools in urban planning, providing dynamic and interactive portrayals of urban landscapes. This paper presents an innovative perspective on urban digital twin design, placing a pronounced emphasis on a multi-scalar framework that captures the intricate dynamics of urban systems across macro, meso, and micro scales, enriched by the concept of varying levels of detail. The proposed architectural model is rooted in OpenUSD standards, harnessing the Universal Scene Description format to amplify interoperability, facilitate seamless data exchange, and enable nuanced capture of urban visual features. Our exhaustive methodology addresses the constraints observed in existing urban digital twin frameworks and showcases the effectiveness of our approach through practical implementation in real-world urban settings. The outcomes underscore the critical role of multi-scalar representation and the integration of OpenUSD standards in propelling the capabilities of urban digital twins, thereby fostering more enlightened and responsive urban planning.

Keywords: Urban digital twin; Multi-Scale representation; OpenUSD standards; Urban planning; Level of detail; Urban visual features

References

1. Jalloh A, MD Faye and GC nelson. West African Agriculture and Climate Change 14 (2013): 384-391.
2. Magatte S., et al. “Uncertainties in the Annual Cycle of Rainfall Characteristics over West Africa in CMIP5 Models”. J. CLIM 11 (2020): 216.
3. Sylla MB., et al. “Projected Changes in the Annual Cycle of High-Intensity Precipitation Events over West Africa for the Late Twenty-First Century”. J. Clim 28 (2015): 6475-6488.
4. Sarr AB. Moctar Camara. Analysis of the extremes precipitation indices evolution with the Regionals Climatics Models 13 (2017): 206-219.
5. Faye A and Akinsanola AA. Evaluation of extremes precipitation indices over West Africa in CMIP6 models (2021): 1-13.
6. Sylla MB., et al. Climate Change over West Africa: Recent Trends and Future Projections (2016): 7-9.
7. Todzo S and Bichet A. Diedhiou. A Intensity of the hydrological cycle expected in West Africa over the 21st century 11.1 (2020): 319-328.
8. Barry AA and Caesar J. West Africa climate extremes and climate change indices 38 (2018): 921-938.
9. Quenum GMLD., et al. Spatiotemporal change in temperature and precipitation in West Africa 13 (2021): 1-7.
10. Kumi N and Abiodun BJ. Potential impact of 1.5oc and 2oc global warming on rainfall onset, cessation and length in West Africa 13.5 (2018): 2-11.
11. Felix OA., et al. “Evaluation of the Performance of CMIP6 HighResMIP on West African Precipitation”. J. Clim 11.10 (2020): 1053.
12. Kouassi A and Assamoi P. Study of the African Western climate with regional atmospheric model M.A.R 7 (2015): 39-55.
13. Nka BN and Oudin L. Trends in flood in West Africa 19 (2015): 4707-4717.
14. Wagner S., et al. “When does risk become residual? A systematic review of research on flood risk management in West Africa”. Reg Environ Change 21.3 (2021): 84.
15. Data source: https://esgf-node.llnl.gov/search/cmip/

An analysis of spatial distributions of West African rainfall during monsoon period (JJAS) of six ensemble members from Coupled Intercomparison Model Project Phase 5 and 6 (CMIP5 and CMIP6) and compared to two observational datasets of Global Precipitation Climatologic Center and Climate Hazard Group InfraRed Precipitation with Station (GPCC and CHIRPS) using six extremes precipitation indices from Expert Team on Climate Change Detection and Indices (ETCCDI). The annual cycle of indices based on daily rainfall such as consecutive dry (CDD) and wet (CWD) days, was used over the Sahelian, Savannah and Guinean regions with satellite daily precipitation estimates. The root mean square error (RSME) and standard deviation were compared using a Taylor diagram for each subregion over West Africa. A higher positive correlation is found between CMIP6 and the reference dataset. Despite the high uncertainties, a strong correlation was found over the Savannah region between the GPCC and model simulations with extreme precipitation events (EPEs). This indicates that CMIP6 reproduces the rainfall pattern over the areas better than its CMIP5 counterpart.

Keywords: West Africa; rainfall; CMIP5 and CMIP6 models; climate change

.

The automotive industry faces many challenges caused by AI and is pursuing more ecological solutions. We can see a significant increase in articles announcing the implementation of the latest trends in the automotive industry. Buzzwords, including Artificial Intelligence, Machine Learning, Big Data, and blockchain, stand out among others. Although very promising, these technologies only sometimes match the needs of the automotive market, although they may have (narrow) applications. Quantum computing represents a revolution to transform the IT industry and extend to the automotive sector, posing a significant threat to automotive security in the coming decades.

On the other hand different renewable/non conventional sources are solar energy(energy derived from sun’s radiation falling on earth’s surface), wind power(energy generated using air’s velocity), tidal energy(energy generated by using different level of water during high and low tides), hydropower(energy generated by movement of water from higher to lower heights), ocean energy(energy generated by using waves and currents of sea water), bioenergy (energy derived from decomposition of organic materials, called biomass), geothermal energy(energy derived from below of earth’s surface which got trapped during formation of earth and with radioactive decay) and lastly by OTEC(ocean thermal energy conversion)(energy derived by using temperature gradient between surface and deep ocean waters).

The advantages of renewable sources are that they can be renewed in nature in short time with unlimited supply; less polluting to environment due to less carbon emissions; and low cost.

At present share of renewable energy in India is 42.5% (as on February 2023) [1]. In the world’s perspective it is 29% of electricity generation from renewable (as on 2020) [2]. However it will need lot of good innovations for utilizing renewable sources upto maximum limits.

In today’s world energy consumption is increasing at an alarming rate with rapid developments in any society or places. From the industrial revolution energy production has made rapid developments by utilizing conventional sources like coal, petroleum, natural gas, nuclear energy etc in different equipments/power plants. However these conventional sources have drawbacks like not being able to replace after consumption with having limited stocks and millions of years to generate it. They emit high carbonaceous element after use and as a result not environment friendly. Also costs of conventional resources are high. Only benefits of using conventional sources are that they need less land requirements and maintenance costs compared to renewable/non conventional sources.

References

1. Benjamin LR., et al. “Using stochastic dynamic programming to support weed management decisions over a rotation”. Weed Res 49 (2009): 207-216.
2. Bessette D., et al. “An online decision support tool to evaluate ecological weed management strategies”. Weed Sci 67 (2019): 463-473.
3. Bohanec M., et al. “A qualitative multi-attribute model for assessing the impact of cropping systems on soil quality”. Pedobiologia 51.3 (2007): 239-250.
4. Bennett AC., et al. “Decision aids for field crops”. Weed Technol 17 (2003): 412-420.
5. Derby NE. “Comparison of nitrogen management zone delineation methods for corn grain yield”. Agronomy Journal 99 (2007): 405-414.
6. Emelyanov YuYa, Kopylov EV and Kirillova EV. “The effectiveness of herbicides in combination with fertilizers on spring wheat”. Niva Zauralya 6.106 (2014): 18-23.
7. Gonzalez-Andujar JL., et al. “Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals”. Weed Res 51 (2011): 304-309.
8. Heatherly LG and Elmore TW. “Managing inputs for peak production”. In J. E. Specht & H. R. Boerma (Eds.). Soybeans: Improvement, production and uses, Madison: ASA-CSSA-SSSA (2004): 451-536.
9. Jørgensen LN., et al. “Decision support systems: Barriers and farmers’ need for support”. Bull. OEPP 37 (2007): 374-377.
10. Jouven M, Carrère P and Baumont R. “Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures”. 1. Model description. Grass & Forage Science 61.2 (2006): 112-124.
11. Korsakov KV, Strizhkov NI and Pronko VV. “Combined use of fertilizers, herbicides and growth regulators in the cultivation of oats and millet in the Volga region”. Bulletin of Altai State Agrarian University 4.120 (2013): 24-32.7.
12. Kim K and Chavas JP. “Technological change and risk management: An application to the economics of corn production”. Agricultural Economics 29 (2003): 125-142.
13. Kazakov IE. “Methods of optimization of stochastic systems”. Moscow: Nauka (1987): 349.
14. Mikhailenko IM and Timoshin VN. “The program level of the general management of agrocenoses, taking into account the influence of weeds on the state of crop sowing”. Agricultural biology 57.3 (2022): 500-517.
15. Mikhailenko IM. “Theoretical foundations and technical implementation of agricultural technology management”. Ed. SPbSTU (2017): 250.
16. Mikhailenko IM. “Intellectualization of management of agricultural technologies”. Bulletin of Russian agricultural science 2 (2019): 24-28.
17. Mikhailenko IM and Timoshin VN. “Software control of soil condition parameters under spring wheat crops”. Agrochemistry 8 (2020): 86-93.
18. Nemchenko VV., et al. “Modern plant protection products and technologies for their use”. Kurtamysh (2006).
19. Oleson J., et al. “Estimating soil properties in heterogeneous land-use patches: A Bayesian approach”. Environmetrics 17 (2006): 517-525.
20. Oliver M, Bishop T and Marchant B. “An overview of precision agriculture”. In Precision Agriculture for Sustainability and Environmental Protection. Eds. Rout. London (2013).
21. Paoli J., et al. “A technical opportunity index based on the fuzzy footprint of a machine for site-specific management: an application to viticulture”. Precision Agriculture 11 (2010): 379-396.
22. Roudier PB, Tisseyre H and Poilve J-M. “Roger A technical opportunity index adapted to zone-specific management”. Precision Agriculture 12 (2011): 130-145.
23. Roudier P., et al. “A technical opportunity index adapted to zone-specific management”. Precision Agriculture 12 (2011): 130-145
24. Sudduth K., et al. “Analysis of spatial factors influencing crop yield”. Proceedings of the 3rd International Conference on Precision Agriculture, ASA, CSA, and SSSA Madison, WI, USA (1996): 129-140.
25. Sun W., et al. “An integrated framework for software to provide yield data cleaning and estimation of an opportunity index for site-specific crop management”. Precision Agriculture 14 (2013): 376-391.
26. Tisseyre B and McBratney A. “A technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture”. Precision Agriculture 9 (2008): 101-113.

Modernization of the agricultural sector is based on the transition to “smart agriculture”. The intellectualization of agricultural technology management is of greatest interest to science and practice. At the same time, expert systems in which control decisions are made through knowledge bases (KB) are most effective. In this work, knowledge bases are formed using analytical control systems located in data processing centers. Such knowledge bases are transferred to local consumers, who make local control decisions based on them. The purpose of this work is to develop a theoretical basis for solving the problem of intelligent management of the state of agrocenoses containing crops of main crops and weeds. Solving this problem aims to address the limitations of the current paradigm of separate crop and weed management. The application of mineral fertilizers simultaneously stimulates the growth and development of agricultural plants and weeds, and treatment with herbicides simultaneously suppresses the growth of both agricultural plants and weeds. As a result, this leads to significant crop losses and excessive consumption of fertilizers and herbicides. In the presented work, for the first time, the problem of managing agrocenoses is raised and solved at the program level, implemented during one growing season. At this level of management, programs are formed that represent a sequence of technological operations for the application of mineral fertilizers, irrigation and herbicide treatments, ensuring the achievement of a given crop yield. To solve this problem, the previously developed theory modified mathematical models of the state of cultivated crops, reflecting the influence of herbicides. In addition, a model of the state parameters of the dominant weed species was introduced into the control problem, which, in addition to the doses of herbicide treatments, also reflects the influence of mineral fertilizers. The problem is solved using the example of sowing spring wheat as part of agrocenoses.

Keywords: agrocenoses; program control; intelligent expert systems; mathematical models; algorithms

References

1. Templeton Allan, Joan K Morris and William Parslow. "Factors that affect outcome of in-vitro fertilisation treatment". The Lancet 348.9039 (1996): 1402-1406.
2. Minaretzis Demetrios., et al. "Multivariate analysis of factors predictive of successful live births in in vitro fertilization (IVF) suggests strategies to improve IVF outcome". Journal of assisted reproduction and genetics 15 (1998): 365-371.
3. Hunault Claudine C., et al. "A prediction model for selecting patients undergoing in vitro fertilization for elective single embryo transfer". Fertility and sterility 77.4 (2002): 725-732.
4. Nelson Scott M and Debbie A Lawlor. "Predicting live birth, preterm delivery, and low birth weight in infants born from in vitro fertilisation: a prospective study of 144,018 treatment cycles". PLoS medicine 8.1 (2011): e1000386.
5. Van Loendersloot LL., et al. "Individualized decision-making in IVF: calculating the chances of pregnancy". Human Reproduction 28.11 (2013): 2972-2980.
6. Qiu Jiahui., et al. "Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method". Journal of translational medicine 17.1 (2019): 317.
7. McLernon David J., et al. "Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113 873 women". BMJ 355 (2016).
8. Meijerink AM., et al. "Prediction model for live birth in ICSI using testicular extracted sperm". Human Reproduction 31.9 (2016): 1942-1951.
9. Metello José Luis, Claudia Tomás and Pedro Ferreira. "Can we predict the IVF/ICSI live birth rate?". JBRA Assisted Reproduction 23.4 (2019): 402-407.
10. Raef Behnaz, Masoud Maleki and Reza Ferdousi. "Computational prediction of implantation outcome after embryo transfer". Health Informatics Journal 26.3 (2020): 1810-1826.
11. Goyal Ashish, Maheshwar Kuchana and Kameswari Prasada Rao Ayyagari. "Machine learning predicts live-birth occurrence before in-vitro fertilization treatment". Scientific reports 10.1 (2020): 20925.
12. Kaptein Maurits and Paul Ketelaar. "Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream". arXiv preprint arXiv:1802.10529 (2018).
13. Li Gen. "Application of finite mixture of logistic regression for heterogeneous merging behavior analysis". Journal of Advanced Transportation (2018): 1-9.

Predicting pregnancy and live births using machine learning in the field of in-vitro fertilization (IVF) has long posed a significant challenge due to the difficulty in achieving consistent performance across various studies. In this paper, we conduct a comprehensive review and analysis of the existing limitations in current research. Additionally, we introduce a standardized machine learning pipeline, which serves as a valuable guide for future researchers. Furthermore, we propose two alternative modeling approaches: phase-by-phase modeling and subgroup FMLR modeling. These two alternatives not only enhance prediction performance but also offer clinically sensible explanations and timely guidance for users. Most notably, they shed light on the complexities of the IVF cycle, highlighting when, who, and where machine learning tasks face their greatest challenges. This insight can inspire future efforts in data collection and patient engagement processes.

Keywords: In-vitro fertilization; Machine Learning; Explainable AI; Medical AI

References

1. Olsson J-O, Tunestål P and Johansson B. “Closed-Loop Control of an HCCI Engine”. SAE Technical Paper Series (2001).
2. Beasley M Cornwell R., et al. “Reducing Diesel Emissions Dispersion by Coordinated Combustion Feedback Control”. SAE Technical Paper (2006).
3. Beaumont A., et al. “Design of a rapid prototyping engine management system for development of combustion feedback control technology”. SAE Technical paper. Detroit, MI, USA: SAE (2006).
4. Hülser H., et al. “EmIQ: Intelligent Combustion and Control for Tier2 Bin5 Diesel Engines”. SAE Technical Paper Series (2006).
5. Kakoee A., et al. “Modeling combustion timing in an RCCI engine by means of a control-oriented model”. Control Engineering Practice 97 (2019): 104321.
6. Kondipati NNT., et al. “Modeling, design and implementation of a closed-loop combustion controller for an RCCI engine”. 2017 American Control Conference (ACC) (2017).
7. Sui W, González JP and Hall CM. “Modeling and control of combustion phasing in dual-fuel compression ignition engines”. Journal of Engineering for Gas Turbines and Power 141.5 (2018).
8. Bahri B, Shahbakhti M and Aziz AA. “Real-time modeling of ringing in HCCI engines using artificial neural networks”. Energy 125 (2017): 509-518.
9. Basina LNA., et al. “Data-driven modeling and predictive control of maximum pressure rise rate in RCCI engines”. 2020 IEEE conference on control technology and applications (CCTA). IEEE (2020): 94-99.
10. Irdmousa BK., et al. “Data-driven modeling and predictive control of combustion phasing for RCCI engines”. 2019 American Control Conference (ACC). IEEE (2019): 1617-1622.
11. Mohammad A, Rezaei R and Hayduk C. “Hybrid physical and machine learning-oriented modeling approach to predict emissions in a diesel compression ignition engine”. SAE Technical Paper (2021).
12. Mishra C abd Subbarao PMV. “A comparative study of physics based grey box and neural network trained black box dynamic models in an rcci engine control parameter prediction”. SAE Technical Paper (2021): 1-17.
13. Mishra C and Subbarao PMV. “Stochastic Cycle to Cycle Prediction in a Reactivity Controlled Compression Ignition Engine Using Double Wiebe Function”. SAE International Journal of Advances and Current Practices in Mobility (2021): 2672-2689.
14. Mishra C and Subbarao PMV. “Design, Development, and Testing of RCCI Engine Hybrid Control Models Using Dual Wiebe Functions and Random Forest Machine Learning”. Control Engineering Practice 113 (2021): 104857.
15. S Loganathan., et al. “Heat release rate and performance simulation of DME fuelled diesel engine using oxygenate correction factor and load correction factor in double Wiebe function”. Energy (2018): 77-91.
16. Payri F., et al. “A complete 0D thermodynamic predictive model for direct injection diesel engines”. Applied Energy 88.12 (2011): 4632-4641.
17. Sawant P, Warstler M and Bari S. “Exhaust Tuning of an Internal Combustion Engine by the Combined Effects of Variable Exhaust Pipe Diameter and an Exhaust Valve Timing System”. Energies 11.6 (2018): 1545.
18. Firoozabadi MD., et al. “Thermodynamic control-oriented modeling of cycle-to-cycle exhaust gas temperature in an HCCI engine”. Applied Energy 110 (2013): 236-243.
19. Jaeheun Kim, Chongsik Bae and Gangchul Kim. “Simulation on the effects of combustion parameters on the piston dynamics and engine performance using the Wiebe function in a free-piston engine”. Applied Energy (2013): 107.
20. Galindo J., et al. “Correlations for Wiebe function parameters for combustion simulation in two-stroke small engines”. Applied thermal engineering 31.6-7 (2011): 1190-1199.
21. Hu S., et al. “Automatic calibration algorithm of 0-D combustion model applied to DICI diesel engine”. Applied Thermal Engineering 130 (2018): 331-342.
22. Jonathan M Borg. “On the application of Wiebe functions to simulate normal and knocking spark-ignition combustion”. International Journal of Vehicle Design 49.1/2/3 (2009): 52-69(18).
23. Zhu Zhenxia., et al. “Zero-dimensional predictive combustion model and modeling method based on neural network”. Journal of Internal Combustion Engines 33.02 (2015): 163-170.
24. Maroteaux F and Saad C. “Diesel engine combustion modeling for hardware in the loop applications: Effects of ignition delay time model”. Energy 57 (2013): 641-652.
25. Yongrui Sun., et al. “Development and Validation of a Marine Sequential Turbocharging Diesel Engine Combustion Model Based on Double Wiebe Function and Partial Least Squares Method”. Energy Conversion and Management (2017): 69.
26. Naber JD and Siebers DL. “Effects of gas density and vaporization on penetration and dispersion of diesel sprays”. SAE transactions (1996): 82-111.
27. Jung D and Assanis DN. “Multi-zone DI diesel spray combustion model for cycle simulation studies of engine performance and emissions”. SAE transactions (2001): 1510-1532.
28. Liu J and Dumitrescu CE. “Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition”. Applied energy 248 (2019): 95-103.

Combustion closed-loop control is an important technology for intelligent energy saving and emission reduction of internal combustion engines. The real-time feedback of combustion indicators plays an important role in the accuracy and rapidity of closed-loop control. However, the calculation of the combustion midpoint based on the complete heat release rate curve often consumes more computing resources. In order to speed up the calculation speed, this paper proposed a method that the Wiebe model combined with neural network prediction combustion metric. Firstly, we match the Wiebe basis function for different working conditions by analyzing the heat release rate curve. then the RLS-DE algorithm is developed to identify the heat release rate curve with high precision, and the BP neural network combined with the Wiebe model parameters is used to calculate CA50. Finally, in the HIL real-time simulation environment, the calculation accuracy and calculation speed of the algorithm are verified. The results show that the use of different Wiebe basis functions combined with the RLS-DE algorithm can fit the heat release rate curves under different working conditions with high precision, and the fitting error is within 5%. The CA50 prediction algorithm based on the parameters of the Wiebe model has a different calculation accuracy under different loads. The algorithm error is 6%-8% at low load, and the error is 2%-4% under high load conditions. It is developed in the cRIO-9047 real-time computing platform. The algorithm time-consuming is 8-12 us, which has high real-time performance and engineering application value.

References

1. J Chaki and M Woźniak. “A deep learning based four-fold approach to classify brain MRI: BTSCNet”. Biomedical Signal Processing and Control 85 (2023): 104902.
2. S Suyanto., et al. “A new nearest neighbor-based framework for diabetes detection”. Expert Systems with Applications 199 (2022): 116857.
3. A Bilal. “Diabetic retinopathy detection and classification using mixed models for a disease grading database”. IEEE Access 9 (2021): 23544-23553.
4. M Woźniak, M Wieczorek and J Siłka. “BiLSTM deep neural network model for imbalanced medical data of IoT systems”. Future Generation Computer Systems 141 (2023): 489-499.
5. T Le., et al. “A novel wrapper–based feature selection for early diabetes prediction enhanced with a metaheuristic”. IEEE Access 9 (2020): 7869-7884.
6. P Chaudhary and P Ram. “Automatic diagnosis of different grades of diabetic retinopathy and diabetic macular edema using 2-D-FBSE-FAWT”. IEEE Transactions on Instrumentation and Measurement 71 (2022): 1-9.
7. J Chaki and M Woźniak. “Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review”. Biomedical Signal Processing and Control 80 (2023): 104223.
8. W Siłka., et al. “Malaria Detection Using Advanced Deep Learning Architecture”. Sensors 23.3 (2023): 1501.
9. F Khademi., et al. “A weighted ensemble classifier based on WOA for classification of diabetes”. Neural Computing and Applications (2022): 1-9.
10. F Haque., et al. “Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait”. Sensors 22.9 (2022): 3507.

Machine learning is widely utilized across various scientific disciplines, with algorithms and data playing critical roles in the learning process. Proper analysis and reduction of data are crucial for achieving accurate results. In this study, our focus was on predicting the correlation between cigarette smoking and the likelihood of diabetes. We employed the Naive Bayes classifier algorithm on the Diabetes prediction dataset and conducted additional experiments using the k-NN classifier. To handle the large dataset, several adjustments were made to ensure smooth learning and satisfactory outcomes. This article presents the stages of data analysis and preparation, the classifier algorithm, and key implementation steps. Emphasis was placed on graph interpretation. The summary includes a comparison of classifiers, along with standard deviation and standard error metrics.

Keywords: Machine Learning; Naive Bayes classifier; k-NN; Diabetes prediction dataset

References

1. Attree EA, Turner MJ and Cowell NA. “Virtual Reality Test Identifies the Visuospatial Strengths of Adolescents with Dyslexia”. Cyberpsychology & Behavior 12.2 (2009) 163-168.
2. Baker RS and Hawn A. “Algorithmic Bias in Education”. International Journal of Artificial Intelligence in Education 32.4 (2021): 1052-1092.
3. Choi M., et al. “Robot-Assisted ADHD screening in diagnostic process”. Journal of Intelligent and Robotic Systems 95.2 (2018): 351-363.
4. Chrysochoou M, Zaghi AE and Syharat CM. “Reframing neurodiversity in engineering education”. Frontiers in Education 7 (2022).
5. Deetjen-Ruiz R., et al. “Applying Ant colony optimisation when choosing an individual learning trajectory”. In Lecture notes in networks and systems (2023): 587-594.
6. Deetjen-Ruiz R., et al. “Using a digital educational environment to monitor student learning results”. In Lecture notes in networks and systems (2023): 363-370.
7. Deho OB., et al. “When the past != the future: Assessing the Impact of Dataset Drift on the Fairness of Learning Analytics Models”. IEEE Transactions on Learning Technologies (2024): 1-14.
8. Dieterle E, Dede C and Walker ME. “The cyclical ethical effects of using artificial intelligence in education”. AI & Society (2022).
9. Gollasch D., et al. “Designing Technology for Neurodivergent Self-determination: Challenges and opportunities”. In Lecture Notes in Computer Science (2023): 621-626.
10. Hosseini M., et al. “Deep Learning for Autism diagnosis and Facial Analysis in children”. Frontiers in Computational Neuroscience 15 (2022).
11. Jesse T. “Creating Neuro-Inclusive learning environments”. In Advances in higher education and professional development book series (2023): 79-99.
12. Lučić A., et al. “Reproducibility as a mechanism for teaching fairness, accountability, confidentiality, and transparency in artificial intelligence”. Proceedings of the . . . AAAI Conference on Artificial Intelligence 36.11 (2022): 12792-12800.
13. Porayska-Pomsta K. “A manifesto for a Pro-Actively Responsible AI in education”. International Journal of Artificial Intelligence in Education (2023).
14. Porayska-Pomsta K and Rajendran G. “Accountability in human and artificial intelligence Decision-Making as the basis for diversity and educational inclusion”. In Perspectives on rethinking and reforming education (2019): 39-59.
15. Reddy P. “Diagnosis of Autism in Children Using Deep Learning Techniques by Analyzing Facial Features”. Eng. Proc 59 (2023): 198.
16. Salas‐Pilco SZ, Xiao K and Hu X. Correction: Salas-Pilco et al. “Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review”. Educ. Sci. 2022, 12, 569. Education Sciences 13.9 (2023): 897.
17. Salas‐Pilco SZ, Xiao K and Oshima J. “Artificial Intelligence and New Technologies in Inclusive Education for Minority Students: A Systematic review”. Sustainability 14.20 (2022): 13572.
18. Sanders CK and Scanlon E. “The digital divide is a human rights issue: Advancing social inclusion through social work advocacy”. Journal of Human Rights and Social Work 6.2 (2021): 130-143.
19. Shahamiri SR and Thabtah F. “Autism AI: a New Autism Screening System Based on Artificial Intelligence”. Cognitive Computation 12.4 (2020): 766-777.

This editorial examines the transformative role of Artificial Intelligence (AI) in enhancing cognitive accessibility for neurodiverse individuals. It explores the evolution from conventional assistive technologies to sophisticated AI-driven solutions, highlighting how these advancements are reshaping inclusivity in education and the workplace. The piece critically analyzes the benefits and challenges of AI in this context, considering ethical implications, user-centered design, and the need for equitable access. It concludes with a call to action for continued innovation and collaboration in developing AI technologies that truly cater to the diverse needs of neurodiverse individuals.

Keywords: Artificial Intelligence in Education; Cognitive Accessibility; Neurodiversity in Learning; AI Ethical Considerations; Inclusive Educational Technology

In recent years, the automotive industry has witnessed major advancements in automation systems, which revolutionized the manufacturing processes and improved vehicle production:

• One significant improvement are collaborative robots, or cobots, which work seamlessly alongside human operators. These robots, equipped with advanced sensors and vision systems, increase the efficiency of tasks such as component assembly and welding [7]. 
• Another major development is the integration of Artificial Intelligence (AI) in automotive production systems. AI algorithms, powered by machine learning, contribute to intelligent decision-making in manufacturing operations. From predictive maintenance to quality control, AI-driven systems optimize production by identifying patterns, predicting failures, and ensuring consistent product quality [8].
• The automotive industry has been implementing the concept of "digital twins”, which consist of virtual replicas of physical manufacturing processes. Digital twins facilitate real-time monitoring and analysis, enabling manufacturers to identify and address potential issues before they impact the production line. This concept increases the production efficiency, and reduces stoppages and maintenance costs [9].
• The adoption of 5G technology has also played a crucial role in advancing automated processes in the automotive sector. Actually, it provides high-speed and low-latency communication, leading to continuous connectivity among robotic systems and allows for real-time data exchange and decision making. As a result, the coordination between various manufacturing systems is significantly improved [10].

References

1. Dzedzickis A., et al. “Advanced applications of industrial robotics: New trends and possibilities”. Appl. Sci 12.1 (2022): 135.
2. Liagkou V., et al. “Challenges and opportunities in industry 4.0 for mechatronics, artificial intelligence and cybernetics”. Electronics 10.16 (2021): 2001.
3. Santos RFL., et al. “The improvement of an APEX machine involved in the tire manufacturing process”. Procedia Manuf 17 (2018): 571-578.
4. Nunes DM, R Campilho and FJG Silva. “Design of a transfer system for the automotive industry”. P. I. Mech. Eng. E-J. Pro 236 (2022): 2044-2055.
5. Culot G., et al. “Behind the definition of Industry 4.0: Analysis and open questions”. Int. J. Prod. Econ 226 (2020): 107617.
6. Osterrieder P, L Budde and T Friedli. “The smart factory as a key construct of industry 4.0: A systematic literature review”. Int. J. Prod. Econ 221 (2020): 107476.
7. Andronas D., et al. “Towards seamless collaboration of humans and high-payload robots: An automotive case study”. Robot. Comput. Integr. Manuf 83 (2023): 102544.
8. Demlehner Q, D Schoemer and S Laumer. “How can artificial intelligence enhance car manufacturing? A Delphi study-based identification and assessment of general use cases”. International Journal of Information Management 58 (2021): 102317.
9. Piromalis D and A Kantaros. “Digital twins in the automotive industry: The road toward physical-digital convergence”. Applied System Innovation 5.4 (2022): 65.
10. Attaran M. “The impact of 5G on the evolution of intelligent automation and industry digitization”. Journal of ambient intelligence and humanized computing 14.5 (2023): 5977-5993.

The evolution of industrial processes through automation has led to increased efficiency, precision, and adaptability. Incorporating automation and robotics into manufacturing and production has been a key driver of progress in various industries [1]. The capacity to enhance quality and repeatability, mitigate human error, and accelerate production rates has rendered these concepts indispensable across diverse sectors. Furthermore, the significance of automation is further emphasized in the era of Industry 4.0, where smart manufacturing and mechatronics play fundamental roles [2]. Automation encompasses the utilization of diverse control and sensor systems alongside actuators to control machinery, thereby diminishing the necessity for human intervention. Automation is widespread across various industries, with the automotive sector emerging as a main driver for the advancement of automation systems, by pursuing high productivity and enhanced product flexibility [3, 4]. A notable advancement in automation is the integration of Industry 4.0 principles, by the combination of digital technologies with industrial processes [5]. As a result, different principles have been adopted, such as Internet of Things (IoT), artificial intelligence (AI), and big data. The communication and decision-making abilities of machines leads to the concept of "smart factory" [6], which excels in production line efficiency, productivity, and fewer stoppages. Using real-time system monitoring and optimization, businesses can reduce material waste and power requirements, while enhancing product quality.

References

1. Kalla D and Smith NB. “Study and Analysis of Chat GPT and its Impact on Different Fields of Study”. ResearchGate (2023).
2. Wu X, Duan R and Ni J. “Unveiling Security, Privacy, and Ethical Concerns of ChatGPT”. ResearchGate (2023).
3. Farhat F, Sohail SS and Madsen DI. “How Trustworthy is ChatGPT?”. The Case of Bibliometric Analyses (2023).
4. Sohail SS., et al. “Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions”. Journal of King Saud University - Computer and Information Sciences; Elsevier BV (2023).
5. Jiang P., et al. “Big data in basic and translational cancer research”. Nature Reviews Cancer; Springer Science+Business Media (2022).
6. Giansanti D. “Artificial Intelligence in Public Health: Current Trends and Future Possibilities”. International Journal of Environmental Research and Public Health; Multidisciplinary Digital Publishing Institute (2022).
7. Jungwirth D and Haluza D. “Artificial Intelligence and Public Health: An Exploratory Study”. International Journal of Environmental Research and Public Health; Multidisciplinary Digital Publishing Institute (2023).
8. Ray PP. “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope”. Internet of Things and Cyber-Physical Systems; Elsevier BV (2023).
9. Haleem A, Javaid M and Singh RP. “An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges”. BenchCouncil Transactions on Benchmarks, Standards and Evaluations; Elsevier BV (2022).
10. Lund B., et al. “ChatGPT and a New Academic Reality: AI-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing”. Social Science Research Network; RELX Group (Netherlands) (2023).
11. Aljanabi M and ChatGPT C. ChatGPT: Future Directions and Open possibilities (2023).
12. Gupta M., et al. “From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy”. IEEE Access; Institute of Electrical and Electronics Engineers (2023).
13. ChatGPT Jailbreak Prompts. Insane - TheInsaneApp.com (2023). Li H. Multi-step Jailbreaking Privacy Attacks on ChatGPT. arXiv.org (2023).
14. Khowaja SA. “ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review”. arXiv.org (2023).
15. A REVIEW OF CHATGPT AI’S BENEFITS AND IMPACT ON E-COMMERCE SECTORS | EPRA International Journal of Research & Development (IJRD). (n.d.).
16. George A, George A and Martin A. A Review of ChatGPT AI’s Impact on Several Business Sectors. Zenodo (CERN European Organization for Nuclear Research); European Organization for Nuclear Research (2023).
17. Coghlan S, Miller T and Paterson JM. Good Proctor or “Big Brother”? Ethics of Online Exam Supervision Technologies. Philosophy & Technology; Springer Nature (Netherlands) (2021).
18. Zhai X. “ChatGPT User Experience: Implications for Education”. Social Science Research Network; RELX Group (Netherlands) (2022).
19. Arman M and Lamiya UR. “Exploring the Implication of ChatGPT AI for Business: Efficiency and Challenges”. Journal of Innovation Information Technology and Application (2023).
20. Li H. “Multi-step Jailbreaking Privacy Attacks on ChatGPT”. arXiv.org (2023).
21. Lund B and Wang T. “Chatting about ChatGPT: how may AI and GPT impact academia and libraries?”. Library Hi Tech News; Emerald Publishing Limited (2023).
22. Biswas S. “Role of Chat GPT in Public Health”. Annals of Biomedical Engineering; Springer Science+Business Media (2023).
23. Sebastian G. Do ChatGPT and Other AI Chatbots Pose a Cybersecurity Risk? - An Exploratory Study. Social Science Research Network; RELX Group (Netherlands) (2023).
24. Biswas S, Dobaria D and Cohen HL. “ChatGPT and the Future of Journal Reviews: A Feasibility Study”. Yale Journal of Biology and Medicine; Yale University Press (2023).
25. Susnjak T. “ChatGPT: The End of Online Exam Integrity?”. arXiv.org (2022).
26. Aggarwal SP. A Review of ChatGPT and its Impact in Different Domains. International Journal of Applied Engineering Research; Research India Publications (2023).
27. Gundu T. “Chatbots: A Framework for Improving Information Security Behaviours using ChatGPT”. IFIP Advances in Information and Communication Technology (2023).
28. Yosifova A. “The Evolution of ChatGPT: History and Future”. 365 Data Science (2023).
29. Hines K. “History of ChatGPT: A Timeline of The Meteoric Rise of Generative AI Chatbots”. Search Engine Journal (2023).
30. Roumeliotis KI and Tselikas ND. “ChatGPT and Open-AI Models: A Preliminary Review”. Future Internet; Multidisciplinary Digital Publishing Institute (2023).
31. Aithal PS and Aithal S. “Application of ChatGPT in Higher Education and Research – A Futuristic Analysis”. International Journal of Applied Engineering and Management Letters (2023).
32. De Silva GHBA, Sandanayake T and Firdhous M. “ChatGPT in Higher Education - A Comprehensive Review of Its Impact on Student Research”. ResearchGate (2023).