Hypertension is a chronic heart disease that requires a course of therapeutic drugs; however, traditional oral antihypertensive agents often face constraints related to non optimal adherence to medication, variable pharmacokinetics, and strong first pass hepatic bio-transformation. Transdermal drug delivery systems (TDDS) have become a patient centric option that is able to provide a sustained and controlled delivery of drugs, as well as maintain stable plasma levels, and reduce the number of doses. This review states the latest advances in transdermal patches technology which are specifically designed to control hypertension and it focuses on history of the advancement, design principle of the core, the polymer used, the modes of permeability promotion, and optimization of the formulations. Modern technologies such as microneedle delivery, incorporation of nanocarriers into matrices and stimuli responsive polymers have significantly extended the range of antihypertensive drugs to which transdermal delivery can be applied. In vitro and in vivo techniques of analyzing mechanical integrity, release kinetics, dermal permeation and therapeutic efficacy are discussed. In addition, it discusses some of the new directions like combinatory patches, biosensor based intelligent systems, and AI assisted formulation design, thus showing how TDDS can enable patient specific, effective, and efficient treatment of hypertension.
Keywords: Smart drug delivery systems; controlled drug delivery; hypertension; Transdermal patches; permeation enhancers

Modified release dosage forms are designed in order to control the release of the drug, optimize the pharmacokinetic profiles and the adherence of patients in comparison to conventional immediate release systems. This review defines the historical evolution, categorization and formulation strategies of these technologies ranging from hydrophilic and hydrophobic matrix systems to reservoir based designs, osmotic mechanisms and gastroretentive approaches. It focuses on how the polymer properties, solubility of the drug and the kinetic pathway affect the controlled release of therapeutics. In vitro evaluation methodologies such as dissolution profiling in biorelevant media, mathematical modelling, swelling erosion assessment, morphological characterization and stability evaluation are discussed to demonstrate the use of such methodologies to predict in vivo performance. Pharmaceutical application, throughout chronic disease management and site specific delivery, emphasizes the therapeutic benefit of MR systems in the long lasting plasma concentration, decreasing the number of administrations as well as ensuring safety. Emerging trends such as smart responsive materials, AI assisted formulation design and 3D printing are presented as future directions for personalized modified release dosage therapies.
Keywords: Modified release dosage forms; Matrix technology; Osmotic delivery; Gastroretentive systems; controlled drug release

At its core, engineering remains the application of science in order to solve real-world problems. What has changed is the degree to which information technology (IT) is embedded in engineering work.' It’s no longer just a value-addedit is working its way into the engineer’s toolkit at all stages of design, construction, and operation.

Engineering has always been synonymous with projects. From bridges and power plants to software systems and smart infrastructure, engineering work unfolds through time-bound, goal-oriented undertakings. Yet in recent decades, the intensification of projectification—the increasing reliance on temporary, cross-functional, and performance-driven projects as the primary mode of organizing work—has reshaped not only how engineering is executed, but how engineers experience their profession (Lundin et al., 2015; Hodgson et al., 2019). While this transformation has delivered flexibility, innovation, and responsiveness to technological complexity, it has also altered the foundations of professional identity, agency, and long-term sustainability in engineering careers. Engineering journals have traditionally focused on technical rigor, methodological advancement, and system performance. However, as projectification becomes the dominant organizing logic across private industry, public infrastructure, and digital ecosystems, it is increasingly clear that the sustainability of engineering outcomes depends not only on materials and methods, but on the identity conditions under which engineers work. If engineering is to meet contemporary challenges—climate adaptation, digital transformation, resilient infrastructure—it must confront a parallel question: How does projectification shape the professional identity, well-being, and agency of engineers?

The rapid integration of Large Language Models (LLMs) and autonomous agents into high-stakes decision-making loops has precipitated a critical challenge: the “black box” opacity of AI reasoning fosters user distrust and cognitive misalignment. While previous research, such as the NeuroDigital Adaptive Network (NDAN), established the architectural infrastructure for bidirectional neural data flow, the mechanism by which human physiological states dynamically calibrate trust in real-time remains under-explored. This paper introduces the Neuro-Symbiotic Synchronization (NSS) Protocol, a novel framework that utilizes continuous physiological monitoring (EEG, HRV, eye-tracking) to construct a dynamic “Trust Oscillator”. This closed-loop system adapts the complexity and explanation depth of AI responses based on the user’s inferred cognitive load and trust levels. We present a mathematical model of trust dynamics and validate the architecture through a simulated human-in-the-loop scenario involving crisis management. Results indicate that biologically-informed adaptive interfaces significantly reduce cognitive load and improve decision accuracy compared to static AI interfaces, providing a robust pathway toward trustworthy human-AI symbiosis.
Keywords: Human-AI Collaboration; Trustworthy AI; Human-in-the-loop; Physiological Computing; Adaptive Interfaces; Neuro-Symbiotic Systems

This editorial mainly describes the state of the art of the current version of the public, access-free Google AI Gemini 3, compared to OpenAI ChatGPT-4 and the Turing Co. GPAI, from a Computer Science Professor and Software Engineer perspective. Other 6 AI tools are briefly discussed as well: Grammarly, Sidekicker.ai, JustDone, Turnitin, Moonlight, and Jenni.
Keywords: Gemini 3; ChatGPT-4; GPAI; Grammarly; Sidekicker.ai; JustDone; Turnitin; Moonlight; Jenni.ai

Coconut is an important cash crop in Thailand, especially in the Thap Sakae District, Prachuap Khiri Khan Province. The total annual output exceeds around 600,000 tons for export consumption. Ten years ago, coconut growers suffered a decrease in both quantity and quality because coconut trees were destroyed and eventually died from insect pests, especially coconut beetles and coconut black-headed caterpillars. The obstacle to preventing and eliminating is the nature of tall coconut plants. Wind speed in coastal areas affects labor costs. The quantities of chemicals applied and the risk of chemical hazards that impact health costs. This research is the outcome of an expansion project to control coconut beetles and coconut black-headed caterpillars in Thap Sakae District, Prachuap Khiri Khan Province, in 2021, which involved hand-spraying coconut shoots and expanding the area to 100 Rai. The research teams are focused on developing technology to control the treatment and reach the tops of coconuts, covering as much leaf area as possible. The methods mentioned above aimed to reduce chemical and nutrient waste, and to ensure that conventional manual labor methods could reach a critical part of the coconut shoot. In addition, the research team adjusted the spray nozzle to three levels to maximize pesticide application efficiency. The experiment showed that insecticide could be sprayed at a rate of 4.5-5.2 liters per minute, with a spray radius of 0.5-1.2 meters for continuous spraying and 6.5-8 meters for general spraying. The insect control system was effective continuously. The results indicated that this method could effectively control coconut beetles and coconut black-headed caterpillars in a demonstration plot in Thap Sakae District, Prachuap Khiri Khan Province.
Keywords: Production potential; Coconut milk; Coconut beetle; Black-headed caterpillar

Achieving large-scale content-based publish-subscribe has been an ambitious research agenda that has received tremendous attention over the last two decades by researchers from distributed systems and networking communities. Simulations have been the most common approach for evaluating solutions. However, the research community on this topic neither has shared workload assumptions nor standard workload generation methodologies. As a result, each effort has introduced its own assumptions and ad-hoc workload generation methodologies. Also, comparison to related alternatives has often been neglected. This has made it difficult to understand the performance gains of one contribution over related alternatives. This paper reports an effort to enhance a workload generation tool for content-based publish-subscribe research using Google groups data. It is enhanced with a visual characterization of the generated workload, given a set of parameters. The workload generated can be characterized in terms of popularity and locality. The resulting software contributes to generating well-specified workloads, facilitates experiment reproducibility, and it will also be time-saving in evaluation processes.
Keywords: performance evaluation; distributed systems; workload characterization; workload generation; content-based publish subscribe

Glucagon-like peptide-1 (GLP-1) receptor agonists have been one of the most important advances in pharmacotherapy in the field of metabolic diseases. Originally developed as a glucose-lowering agent in type 2 diabetes mellitus, these drugs have now been shown to have a wide range of pharmacodynamic properties that reach well beyond glucose lowering. From a pharmacological standpoint, GLP-1 analogues are pleiotropic incretin receptor agonists with systemic effects mediated by a widespread distribution of receptors in the pancreatic, cardiovascular, renal, gastrointestinal, and central nervous systems.

Background: Clinical outcomes in wound management are strongly influenced by structured preparation and continuity of care. Implementation observations suggest that approximately 75% of wound-care success depends on adequate preparedness, while sustained healing is highly dependent on continuity across transitions of care. Fragmented workflows, inconsistent materials, and variable debridement techniques remain barriers, particularly in outpatient and resource-limited settings.
Objective: To design and evaluate a standardized, low-resource wound-care eco-system capable of operationalizing ≥75% preparedness domains and mitigating ≥95% of high-priority continuity failure modes.
Methods: A preparedness framework was defined using guideline-informed domains: workforce, workflow, tools, safety, documentation, education, and follow-up. Care-continuity gaps across transitions (home/community ↔ outpatient ↔ referral) were mapped using process mapping and Failure Mode and Effects Analysis (FMEA). Eco-system components—including a standardized kit, competency-based microtraining, decision-support tools, documentation templates, and referral/tele-follow-up bundles—were mapped against preparedness domains and identified failure modes. Coverage metrics were defined as (1) proportion of preparedness domains operationalized with usable tools/processes and (2) proportion of high-priority failure modes mitigated by at least one eco-system component.
Results: The eco-system operationalized 75% of predefined preparedness domains through standardized workflows, minimal equipment sets, and competency-based training. The continuity bundle directly mitigated 95% of high-priority transition failure modes, particularly those related to follow-up scheduling, escalation criteria, shared documentation, and patient/caregiver instructions. Residual gaps were associated primarily with workforce constraints and referral network limitations.
Conclusion: A structured wound-care eco-system can be engineered to achieve measurable preparedness coverage and high continuity mitigation by design. Reliable tools, standardized materials, and clinical autonomy are critical enablers. Prospective studies are warranted to assess clinical outcomes, economic impact, scalability, and sustainability.
Keywords: wound care systems; debridement; continuity of care; preparedness; health systems design; eco-kit; neuropathic wounds

Endodontics has undergone a significant transformation in the past two decades, shifting from predominantly tactile procedures to precision-based, visualization-driven clinical practice. The introduction of the dental operating microscope (DOM) has redefined standards of care, enhancing accuracy in diagnosis, access preparation, and management of complex canal anatomy.

The introduction of Competency-Based Medical Education (CBME) by the National Medical Commission (NMC) represents a paradigm shift in undergraduate medical education in India. Driven by evolving global educational strategies, CBME seeks to produce graduates equipped with essential competencies encompassing knowledge, clinical skills, attitudes, ethics, and professionalism necessary for contemporary medical practice. Unlike the traditional time-based curriculum primarily centred on content delivery and summative examinations, CBME emphasizes defined outcomes, early clinical exposure, integrated learning, and continuous formative assessment. This article examines the impact of CBME on medical students in India, highlighting educational benefits, implementation challenges, effects on learning experiences and well-being, and future directions for curriculum optimization.

Earthworms have gained increasing attention as a source of bioactive compounds with potential applications in modern medicine. Molecules isolated from Lumbricus rubellus, Eisenia fetida, and related species exhibit fibrinolytic, antimicrobial, anti-inflammatory, antioxidant, and regenerative properties. Lumbrokinase, a fibrinolytic enzyme complex, is the most clinically advanced earthworm-derived therapeutic and has been evaluated for ischemic stroke, thrombosis, and microcirculatory disorders. Additional compounds—including antimicrobial peptides and coelomic fluid proteins—demonstrate promising activity in wound healing, infection control, and cancer research. This review summarizes current evidence, highlights translational challenges, and outlines future directions for integrating earthworm-derived biomolecules into biomedical practice.
Keywords: Earthworms; Lumbrokinase; Fibrinolysis; Antimicrobial peptides; Regenerative medicine; Coelomic fluid; Biotherapeutics

Skill laboratories have evolved from being primarily teaching facilities into versatile academic ecosystems that support education, assessment, innovation, and research. While their pedagogical value is well established, their role in fostering research among medical teachers remains underexplored. In institutions with substantial faculty strength, such as medical colleges employing approximately 75 teaching staff, skill labs can serve as catalysts for educational research, simulation-based investigations, procedural innovation, Interprofessional studies, and competency evaluation. This article examines the multidimensional role of skill laboratories in enabling research productivity among medical teachers, highlighting methodological opportunities, infrastructural advantages, ethical considerations, collaborative models, and measurable outcomes. The discussion synthesizes contemporary literature on simulation-based education, translational training environments, and faculty development, offering a practical framework for integrating research into skill lab operations.

The software systems that power today’s enterprises are no longer static applications running in controlled environments. They are dynamic, distributed, and constantly evolving ecosystems spanning cloud infrastructure, microservices architectures, and AI-driven platforms. As complexity grows, so does fragility. Outages, cascading failures, configuration drift, and unforeseen runtime anomalies have become inevitable rather than exceptional. In this context, resilience can no longer rely solely on monitoring dashboards and human intervention. The future demands systems capable of diagnosing and correcting themselves. In their recent work, Mohammad Baqar, Rajat Khanda, and Saba Naqvi articulate a bold and forward-looking vision: software systems that emulate biological healing. In nature, living organisms continuously monitor their internal state. When damage occurs, sensory mechanisms detect the disruption, cognitive processes evaluate its severity, and adaptive repair mechanisms restore equilibrium. This process unfolds autonomously, intelligently, and in real time. The authors argue that modern observability platforms—rich with logs, metrics, traces, and event streams—represent the digital equivalent of biological sensory systems. What has been missing is a true cognitive layer capable of interpreting these signals and orchestrating intelligent recovery. Artificial intelligence now makes such a layer possible. Rather than relying on static rule-based automation, the proposed approach leverages machine learning models that can correlate telemetry signals, infer root causes, and propose corrective actions based on historical patterns. The system does not merely trigger alerts; it reasons. It does not simply restart services; it evaluates context and selects targeted interventions. In enterprise environments where downtime translates directly into financial loss and reputational risk, this shift from reactive automation to adaptive intelligence is profound. From an industry perspective, the implications are transformative. Self-healing capabilities can reduce mean time to recovery, alleviate operational fatigue among engineering teams, and enable organizations to scale infrastructure without proportionally increasing human oversight. As DevOps and SRE practices mature, the next frontier lies in autonomous remediation. Instead of engineers manually investigating incidents at 3 a.m., AI-driven agents could analyze anomalies, apply validated fixes, regenerate tests, adjust configurations, and document their actions—all within seconds. The significance of this paradigm extends beyond operational efficiency. It challenges how we conceptualize software ownership and reliability. Traditionally, resilience has been a property engineered into systems through redundancy and careful design. In the emerging model, resilience becomes an emergent property of continuous learning. Systems accumulate experience, adapt to evolving environments, and refine their corrective strategies over time. This mirrors biological evolution, where organisms grow stronger through exposure to stress. Of course, autonomy must be approached responsibly. Industry adoption will require safeguards, explainability mechanisms, and layered validation to ensure that automated corrections do not introduce new risks. Trust in AI-driven remediation depends on transparency and rigorous testing. Yet the trajectory is clear. As AI models improve in reasoning over code, infrastructure states, and operational telemetry, the boundary between human-led debugging and machine-led recovery will continue to blur. The vision presented by Baqar, Khanda, and Naqvi signals a pivotal moment in software engineering. Observability has given systems the ability to sense. Artificial intelligence now provides the capacity to think. The final step—decisive and controlled action—positions self-healing architectures as the logical evolution of modern digital infrastructure. In an era defined by scale, speed, and complexity, the ability for systems to maintain their own health is not a luxury; it is an operational necessity. The coming decade may well see self-healing mechanisms become a standard layer of cloud-native stacks, much like CI/CD pipelines and container orchestration are today. Organizations that embrace this transition early will likely gain advantages in uptime, operational efficiency, and innovation velocity. More importantly, they will move toward a future where software systems are not merely reactive tools but adaptive partners in enterprise growth.

Artificial Intelligence (AI) is playing a transformative role in renewable energy and thermal engineering by improving efficiency, reliability, and system optimization. One of its most powerful applications is predictive analysis. By analyzing large volumes of weather and operational data, AI helps solar and wind power systems forecast energy generation more accurately. In solar plants, AI can anticipate sunlight intensity and cloud movement, enabling better panel positioning and energy planning. In wind farms, predictive models estimate wind speed and direction, allowing turbines to operate at optimal conditions while reducing wear and downtime.

We are all witnesses to the intensive integration of artificial intelligence (AI) in the field of financial technology. I can say it’s a double-edged sword that offers both promising solutions to problems and potential pitfalls of IT security. This article explores the various impacts of artificial intelligence on security in the financial technology industry and highlights some positive and negative examples of its impact.
Keywords: Fintech; Artificial intelligence; IT security; Financial services

In March 2021, a digital artwork titled Every day: The First 5000 Days sold for $.9 million on Christie's auction site. The question is, what made it special? Spicailly, it was not a physical canvas, but it is a Non-Fungible Token (NFT), a unique digital asset recorded on the blockchain platform. Just 2 years prior, the NFT trade volume was around $ 90 million. In 2022, that number had grown to $17 billion, according to one report by NFT marketplace OpenSea [1].
This meteoric expansion was fuelled by cultural mania, digital scarcity, and speculation. Yet as NFTs amazed the art community and crypto investors, they also caught the interest of another crowd—cybercriminals.

University teaching of computer science, and more specifically programming, faces persistent challenges in terms of deep understanding and abstract reasoning. Intelligent tutors based on large language models (LLMs) offer personalized support but often remain intrusive by proposing final solutions to problems. This article proposes applying the “what-if” concept from UX design—generating hypothetical scenarios to challenge assumptions—in order to encourage proactive exploration and counterfactual reasoning. A conceptual framework is presented, including prompt patterns, interactive interface, and exploration loop. A prospective discussion addresses implementation in a university context: pedagogical integration, ethical and technical challenges, and expected benefits in terms of engagement and comprehension. This work remains a theoretical contribution with practical implications for the evolution of computer science education with AI.
Keywords: AI in education; intelligent tutor; what-if scenarios; UX design; algorithms and data structures; exploratory learning

This research paper mainly describes how to develop MS Blazor safe web applications with Claude AI, version Sonnet 4.5, starting from (Elementary) Mathematical Data Model schemas. In the sequel, it also provides a list of general software engineering best practice rules, as well as issues of the MS Blazor Server platform.
Keywords: (Elementary) Mathematical Data Model; database and software application design; Claude AI; Claude Code; web developing; MS Blazor Server; MatBase; MS Access VBA