The growing adoption of cloud platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), has introduced new complexities in aligning enterprise workloads with federal compliance frameworks. This paper presents a comparative analysis and practical alignment model that integrates these major cloud service providers with the Federal Risk and Authorization Management Program (FedRAMP), Federal Information Processing Standards (FIPS) 199/200, the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF), and the directives established by Executive Order 14110 on the safe, secure, and trustworthy use of Artificial Intelligence. The study develops a multi-dimensional control mapping methodology tailored to ensure technical interoperability, compliance readiness, and risk transparency. Through structured evaluation of service categories, native controls, and governance mechanisms, the paper delivers a unified compliance architecture designed to support secure and auditable cloud deployments in federally regulated environments. Findings contribute actionable insights for IT leaders, cloud architects, and policymakers by offering a scalable and standards-based roadmap for AI-driven cloud governance.
Keywords: cloud compliance; fedramp; fips 199; fips 200; nist ai rmf; executive order 14110; aws; azure; gcp; artificial intelligence governance; cloud security; risk management

The increasing fusion of the global economy with digital infrastructure has elevated cyber threats from isolated technical issues to significant drivers of financial instability. Despite this, a measurement gap persists, as conventional financial risk models are ill-equipped to handle the high-dimensional and non-linear nature of Cyber Threat Intelligence (CTI). This study bridges this gap by developing and validating a predictive framework that translates global CTI into quantitative forecasts of systemic financial risk. Using a comprehensive dataset of over 77,000 daily cyber threat observations across 225 countries from 2015 to 2024, we forecast the U.S. St. Louis Fed Financial Stress Index (STLFSI). We conduct a comparative analysis of advanced deep learning architectures, including a Temporal Fusion Transformer (TFT), against canonical machine learning ensembles. Our results show that a gradient-boosted model (XGBoost) decisively outperforms other models, achieving an R² of 0.9883 and RMSE of 0.0321 on the hold-out test set. Employing Explainable AI (XAI) techniques, we deconstruct the model's predictions and find that its success stems from capturing the complex, non-linear interaction between cyber threat levels and the pre-existing state of financial market stress. This research provides robust empirical evidence of the cyber-financial nexus, offering a novel, data-driven methodology for asset managers, regulators, and security leaders to proactively quantify and manage a critical 21st-century risk.
Keywords: Cyber-Financial Risk; Systemic Risk; Cyber Threat Intelligence (CTI); Predictive Modeling; Machine Learning; Time-Series Forecasting; XGBoost; Explainable AI (XAI)

Accurate forecasting of urban air pollutants is critical for public health management and regulatory compliance. While machine learning models are widely used, their performance is highly dependent on the robustness of the underlying methodological framework. This study proposes and validates a comprehensive, end-to-end framework for forecasting hourly Carbon Monoxide (CO) concentrations using the canonical UCI Air Quality dataset. The methodology integrates context-aware data imputation, an advanced feature engineering pipeline (incorporating temporal, cyclical, autoregressive, and rolling-window features), and a rigorous comparative evaluation of fourteen distinct machine learning models. Crucially, all models are validated using a 5-fold TimeSeriesSplit cross-validation protocol to ensure temporal data integrity and prevent lookahead bias. The results demonstrate the clear superiority of ensemble methods, with an optimized XGBoost model emerging as the top performer, achieving an R-squared score of 0.9216 and a Root Mean Squared Error of 0.3824 mg/m³. Feature importance analysis revealed that the model’s predictions were overwhelmingly driven by non-methanic hydrocarbon (NMHC) sensor readings and their engineered non-linear terms, confirming the model learned scientifically sound relationships. This study validates a synergistic framework where advanced feature engineering paired with powerful ensemble models provides a new benchmark for accuracy and offers a viable template for developing reliable, real-world air quality forecasting systems.
Keywords: Air Quality Forecasting; Time-Series Analysis; Machine Learning; Feature Engineering; Gradient Boosting; Ensemble Methods; Pollutant Prediction

In the world, engineering science and technologies shape our desires and enhance our experiences with pleasure and charm. The materials we use guide and fulfill the needs of society. The potential growth of materials drives technological advancements toward sustainable development on a global scale. The utilization of materials extends from the Earth's surface to outer space.

The importance of efficient digital platforms for monitoring greenhouse gas (GHG) indicators cannot be overstated in contemporary environmental management and sustainability efforts. Such platforms are crucial for accurate, real-time data collection and integration, facilitating regulatory compliance, and enabling strategic decision-making. However, developing a usable digital platform for GHG monitoring is challenging due to the complexity of integrating diverse data sources, ensuring data accuracy and reliability, and providing user-friendly interfaces for varied stakeholders.
Despite the availability of multiple official data sources on GHG emissions, no specific digital platform exists to comprehensively aggregate and manage this data. Existing systems often lack the flexibility to incorporate new indicators and adapt to evolving regulatory requirements. This paper presents a digital platform capable of seamlessly integrating data from various sources and offering robust usability, to enhance the effectiveness of GHG emission monitoring. The platform can be valuable to stakeholders, including regulatory bodies, industry leaders, environmental organizations, and the general public to address the complexities of environmental data and advancing global sustainability efforts.
Keywords: Data Visualization; Greenhouse gas (GHG) emissions; Interactive Dashboard; Usability

Software Engineering is a broad and diverse field that encompasses various aspects of software development, maintenance, and management. Software development with software development lifecycle (SDLC) and software design and architecture. Software project management and software development projects from planning to implementation with data science and machine learning with data processing and analysis and machine learning algorithms to design and develop complex software systems. Software testing and quality assurance. Protecting systems and data from security threats. Data Scientist/Analyst with the task of analyzing data and developing machine learning models. Managing and optimizing the software development and deployment process. Helping to process and analyze data effectively, enabling the development of machine learning and artificial intelligence models. Ensure that software is developed as required, on time, within budget and with high quality. Use technical methods and tools to manage and optimize the software development process. Not too focused on using tools, techniques and technologies to develop and deploy software like the software engineering industry. Not required to always use the latest technologies to develop software efficiently and quickly like software engineering. And the software engineering industry must focus on using databases and database management systems in each future development project. Software engineering focuses on using tools and technologies such as the latest specific databases to develop software efficiently and quickly. And the Software Engineering industry focuses on the technical process and methods (using different techniques, different databases as long as they solve the problem of the project being encountered) to develop software in a systematic and organized manner.
Keywords: software engineering; software development process; data science; machine learning and artificial intelligence models

Cancer, a multifaceted disease with physical, emotional, and psychological burdens, challenges patients well beyond the physiological effects of tumours and treatment side effects. The journey through diagnosis, treatment, and survivorship often entails a complex interplay of distress, anxiety, depression, and cognitive impairments, significantly impacting quality of life. As oncology care advances, it becomes imperative to adopt a holistic approach that addresses not only the physical but also the psychological needs of cancer patients. Integrating psychopharmacology into comprehensive cancer care emerges as a critical, yet often underutilized, strategy to improve overall patient outcomes.

Surgical practice has traditionally emphasized biological and physiological dimensions of health. Yet patients are not only organisms to be repaired; they are social beings whose emotions influence pain perception, treatment adherence, and recovery trajectories. Neuromarketing, a field that investigates how emotions and expectations shape decision-making and bodily experience, offers unique tools to bridge this gap. By acknowledging patients’ emotional responses as integral rather than peripheral, medicine can align more closely with the human condition. This editorial argues that integrating neuromarketing insights into surgical communication and perioperative care highlights the transformative potential of treating patients not only as biological systems but as emotional and social selves.
Keywords: neuromarketing; surgery; emotions; recovery; patient communication; social dimension of health

Back pain is one of the most common medical and social problems worldwide. In 2021 low back pain globally affected to 628.8 million people [8]. Treatment of this condition included exercises, behavioral treatment, physiotherapy, psychological and social support, manual therapy, acupuncture, medications, interventional pain management and surgery. For the treatment of this conditions, initially conservative modalities are using but in cases when they are failing, pain interventions and surgeries are indicated.

History of medicine has it’s roots in ancient times dating before the birth of Christ. Medicine has since then undergone massive evolution, more so in the 20^th and 21^st centuries. Traditional medicine has now mostly been replaced by evidence-based Allopathic medicine. However, the need to align traditional medicine with modern day medicine is being recognized. Countries like Bangladesh, with rich history of traditional systems of medicine, can be in the forefront. For this further work including research, appropriate policy for pharmaceutical industries etc. is now time demanding.
Keywords: Traditional medicine; history; Bangladesh; policy for pharmaceutical industry

Rheumatoid arthritis (RA) is an autoimmune disease with prevalent of clinical expressions on the joints of the body especially in the inflammation of synovial membrane, degeneration of cartilage and bone and multi-organ involvement. The current review is an integrative framework synthesising the view of RA immunopathogenesis, biomarkers development, precision medicine, and novel ways of treating RA. Pathogenesis is the result of an interaction of genetic risk alleles such as HLA-DRB1, environmental exposures, and dysregulated immune pathways which converge around Th1/Th17 cells, B lymphocytes and fibroblast-like synoviocytes. The inflammatory condition in the complex rheumatic disease is a clinico-pathological convergence sampling that is based on epigenomic reprogramming and metabolic adaptations. Recent serological and multi-omics assay developments, anti-citrullinated protein antibodies (ACPAs), 14-3-3eta, microRNAs, and exosomes have made early diagnostic sensitivity, longitudinal pattern of disease, and therapeutic accuracy possible. The emergence of precision medicine as defined by pharmacogenomic testing and synovial testing assists in the support of personalised approaches to treatment. Artificial intelligence is more and more being used in order to normalise heterogeneous data on biomarkers in order to facilitate dynamic risk evaluation. The therapeutic front has advanced as usual beyond traditional disease modifying antirheumatic medicines (DMARDs) to biologics medicines, JAK / BTK blockers, and experimental avenues like tolerazine vaccines, stem cell-based medicine, and nanomedicine. Despite of recent advances in therapeutic practice, patients with malignant neoplasms still pose complications in terms of symptom control, namely pain, fatigue and comorbidities. Further, it has increasingly become more and more common with acquired treatment resistance. Therefore, it is imperative to engage a multidisciplinary patient centred paradigm to facilitate the long term remission and foster long term clinical outcomes.
Keywords: rheumatoid arthritis; pharmacogenomics; biomarkers; immunopathogenesis; regenerative therapies

Romantic relationships are often idealized as the highest form of human intimacy. Yet they are also among the most fragile of human arrangements. Divorce and long-term relationship failure have become common, not because love itself has weakened, but because couples often lack clarity about the kind of relational structure they are building.

Recognizing the environmental impact of aquaculture, a study was conducted to investigate the feasibility of generating aquaculture data using ResourceSat LISS-IV and PAN data in a section of coastal Andhra Pradesh, southern India. The study involved database preparation, including georeferencing and radiometric normalization, on-screen visual interpretation of spaceborne multispectral and multitemporal data, and the creation of area statistics.

The ResourceSat LISS-IV data enabled the identification of areas where aquaculture is practiced, while the LISS-IV and PAN-merged data facilitated the detection of individual aquaculture ponds due to improved spatial resolution. Between 1986 and 2001, the area under aquaculture expanded significantly.

The study aimed to assess the potential of various image fusion techniques, namely:

1. Hue-Intensity-Saturation (HIS),
2. Principal Component Analysis (PCA),
3. Brovey transformation,
4. Cubic spline wavelet transformation,
5. Multiplicative transformation, and
6. High-pass filter (HPF) transformation,

for delineating salt-affected soils in the Kaikalur area of Andhra Pradesh using data from the Indian Remote Sensing Satellite (IRS-ID) Linear Imaging Self-Scanning Sensor (LISS-IV) and Panchromatic sensor data.

The results indicate that, compared to commonly used approaches like IHS transformation, wavelet transformation provides more accurate delineation of salt-affected soils. Furthermore, IHS and HPF transformations have been found to outperform PCA transformation in terms of accuracy and performance.

Keywords: Aquaculture; LISS-IV; PAN-data; Image Fusion

In my journey as a veterinarian and an epidemiologist, I have witnessed firsthand how closely the health of humans, animals, and our shared environment are intertwined. A contaminated water source on a farm, an outbreak of foodborne illness, or the rapid spread of COVID-19 across the globe—these are stark reminders that pathogens do not respect boundaries. This is why the One Health approach is not just a framework but a necessity [1].

It has been known for many years that the last few radiation therapy treatment fractions can increase the tumor cure from a few percent to 80-90 % with classical photon or electron beam treatments [1, 2]. This is indicating that the loss of the last few clonogenic tumor cells are determining the therapeutic outcome and the probability of cure. Interestingly, this also means that both the therapeutic beams and the tumor are Quantized and the treatment outcome is largely dependent on the probability that all tumor clonogens are lethally hit by the particles of the therapeutic beam weather they are primary electrons or secondary electrons in photon and ion beams. In fact, it has recently been demonstrated that the therapeutic entity of most if not all radiation beams is the low energy secondary so called  -electrons or  -rays of energy below ≈1 keV that are capable of delivering a local dose of about 1M Gy to 10 nm size objects. Such critical energy deposition sites are the periphery of the nucleosome and  -rays can induce a very severe Dual Double Strand Break (DDSB) on their DNA as it is wound two turns around the nucleosome [3]. Unfortunately, this kind of damage is very hard to handle by the most common DNA repair system for DSB´s, namely the Non Homologous End Joining process (NHEJ) as the resulting DSB´s are as close as 80 base pairs or 35 nm from each other. The two DNApk molecules needed for repair dose not generally have room enough for each other to work on the DNA [3, 4]. Even if the damage sometimes can be repaired by a switch to the Homologous Recombination DSB repair machinery, this is one of the most severe damage events to nuclear DNA that may often lead to cell death [3, 4]. The interesting fact that the these  -rays are produced by practically all therapeutic beams and it make them the key Effectors of all curative radiation therapy as discussed in further details recently [3-7]. As proven in some of these studies [4, 7] the Relative Biological Efficiency of light ion beams is closely related to the mean  -ray multiplicity along the ion track as they are produced new almost as fast as they are absorbed along the track. The RBE in front of the Bragg peak of Boron, Carbon and Nitrogen ions is about 3, corresponding to ≈3  -rays in average along every section of these high ionization density ion tracks.

Diabetes mellitus represents one of the most formidable global health challenges of the 21st century. The International Diabetes Federation estimates that 589 million adults aged 20-79 years were living with diabetes in 2024, with projections reaching 853 million by 2050 [1].

“Patient-Oriented Evidence that Matters (POEMs) are research studies that focus on outcomes that directly impact patients”.

“From Bench to Bedside: Using POEMs to Guide Clinical Research and Treatment Decisions”.

People regularly share goods, services, or ideas as needed because no one can know or produce everything necessary. How they interact influences their life paths.

Exchanging products can bring happiness or disappointment to players in relationships, depending on the quality of the product, its availability when needed, skilled interaction with producers, and a fair price. Some consumers consider the producer's reputation in both real and online communities when deciding on a possible collaboration, viewing the moral value and expertise of providers as indicators of fostering healthy relationships.

Background: Magnetic field therapies have gained attention for their non-invasive potential to modulate biological processes, yet the underlying molecular mechanisms remain poorly understood, particularly at the proteomic level.

Objective: This pilot study aimed to assess whether short-term exposure to low-frequency magnetic fields affects the proteomic profile of human buccal cells and to evaluate the methodological viability of using buccal cell proteomics as a monitoring tool.

Methods: Nine participants were assigned to either a control group (n=3) or an intervention group (n=6) exposed to the Vitori mat (7.83 Hz Schumann frequency) for five consecutive days. Buccal cell samples were collected pre- and post-intervention, with a follow-up sample collected three days later in the intervention group. Samples were analyzed via LC-MS/MS using a data-independent acquisition (DIA) workflow and processed with DIA-NN [1]. Statistical comparisons employed Wilcoxon rank-sum and Friedman tests.

Results: A total of 108 protein groups differed significantly between groups after exposure (p<0.05), while 67 proteins showed significant temporal changes within the intervention group. Thirteen proteins were identified across both comparisons, implicating biological pathways related to immune regulation, cell proliferation, and stress response. Principal component analysis indicated partial reversibility of proteomic shifts following a short washout phase.

Conclusions: The study confirms both the biological responsiveness of buccal cells to magnetic field exposure and the feasibility of integrating non-invasive sampling with high-resolution proteomic analysis. These findings support future investigations into the molecular mechanisms of magnetic field therapies and the development of minimally invasive monitoring tools.

Keywords: Magnetic field exposure; Proteomics; Electromagnetic fields; Buccal cells; Non-invasive biomarker; Schumann frequency; Immune response

Health care is entering a transformative era. Two rapidly advancing technologies—Quantum Artificial Intelligence (Quantum AI) and Cognitive Analytics—are reshaping the way we discover medicines, predict diseases, and safeguard patient data. These tools are not just about making computers faster; they allow us to tackle medical challenges that traditional computing cannot manage.

Adhesive dentistry has evolved to enhance the longevity and predictability of indirect restorations. One such revolutionary concept is Immediate Dentin Sealing (IDS).

WHO defines adolescent as those people between the age of 10-19 years. Globally, approximately 1 in 7 (14.3%) of the adolescents suffer from mental health issues, yet they constitute the submerged portion of an iceberg with many undiagnosed and untreated [1]. India constitutes the largest adolescent population i.e. 20% of the world’s 1.2 billion adolescents.

The Global burden of disease (2021) estimated that 14% of adolescents in India experienced a mental disorder; 13.4% of them being boys and 13.9% girls [2].

The past thirty years have seen a shift in percutaneous coronary intervention (PCI) from simple balloon angioplasty to modern day practices characterized by drug eluting stents (DES) along with powerful antiplatelet medications and sophisticated intra-vascular imaging. With this progress, complication rates have steadily declined. Yet, the specter of abrupt vessel closure (AVC) continues to haunt the catheterization laboratory. Though the incidence of AVC has fallen from approximately 3% in the balloon angioplasty era to 0.3% in the DES era [1], its occurrence remains a life-threatening challenge.

AVC is defined as the sudden cessation of antegrade coronary blood flow during or immediately after PCI. It is not only catastrophic in its clinical consequences but also a litmus test of the operator’s preparedness, judgment, and technical skill. In this editorial note, we revisit AVC from a contemporary lens, distilling mechanistic insights, risk factors, management strategies, and pearls of wisdom that every interventional cardiologist must carry into the lab.