Nvpubhouse Library for American Journal of Applied Science and Technology

Defects Arising In The Process Of Sewing Outerwear

2025-11-23T15:38:34+00:00

This article examines defects that arise during the outerwear sewing process. Defects in the sewing seam at garment manufacturing facilities were also examined during the outerwear sewing process. The study identified instances of needle friction and thread burning of clothing materials. The study found that many thread breaks are caused by damage to the warp and weft threads of the clothing material.

Hybrid Streaming Intelligence For Real-Time Transactional Fraud Detection: A Kafka-Centric Machine Learning Framework

2025-12-22T04:51:30+00:00

Financial transaction fraud in contemporary digital ecosystems is a fast-moving adversarial problem that requires detection systems to be not only accurate but immediate, adaptive, and auditable. This article develops a comprehensive theoretical and design-oriented framework for hybrid streaming intelligence that unites stateful event streaming (with Apache Kafka as the canonical substrate), multiple machine learning paradigms (supervised classification, anomaly detection, graph-based link analysis, and ensemble voting), and alarm-verification mechanisms including text analytics for enriched context. Drawing upon prior technical and academic work on streaming architectures, ML workflow automation, hybrid alarm verification, transaction synthesis, and practical implementations of fraud scoring, the framework articulates how Kafka-centric event topologies can host low-latency triage, mid-path contextual scoring, and deferred deep analysis while preserving system stability and supporting continuous model adaptation (Dunning & Friedman, 2016; Crettaz & Dean, 2019; Sima et al., 2018). The article provides a detailed methodological rationale for mapping ML models into stream processors and microservices, explains feature engineering patterns suitable for streaming contexts, addresses class imbalance and concept drift, and presents a layered alarm-verification design that reduces false positives and operational costs (Singh, 2019; Sahai & Gursoy, 2019). It further examines how graph analytics, generative models, and ensemble adaptive online learning contribute to network-level detection and adversarial resilience (Molloy et al., 2016; Goodfellow et al., 2014; Roshan & Zafar, 2024). The discussion evaluates trade-offs in latency, interpretability, and governance, and proposes an empirical research agenda including sandbox pilots, red-team adversarial testing, and standards for forensic logging. The contribution is a publication-ready synthesis intended to guide both researchers and practitioners seeking to deploy robust, production-grade real-time fraud detection in high-throughput FinTech environments.

Innovative Approaches To Assessing Medical Students’ Knowledge And Professional Competence In A Competency-Based Educational Model

2025-12-22T04:51:32+00:00

The article analyzes the results of applying the competency-based approach in the higher education system. The role of biochemistry as a fundamental academic discipline in the formation of basic and professional competencies of future medical workers is considered. Examples are given that the main integral tool for monitoring the results of students' educational and cognitive activities is the rating assessment system.

An Algorithm For Selecting Informative Symbols Based On Determining The Measure Of Information In A Nominally Informed Space

2025-12-22T04:51:54+00:00

The article reveals the issue of reducing the size of the phase of features describing objects, from data mining to brain cancer diseases. Initially, with the support of specialists in the field of medicine, 218 objects of the 4th class were conducted (it is a paid astrocytoma of the right hemisphere of the brain; Adenoma of the cellar region of the brain; Glioblastoma of the right-sided region of the brain; Meningioma of the right frontal region of the brain) and a training sample of 19 characters is formed. In this educational selection, the features characterizing the objects of the class are expressed as a nominal value. For this reason, this article proposes an algorithm for solving the problem of choosing a set of informative symbols based on determining the measurement of information in a nominal data space.

Mplementation Of The Process Of High Temperature Diffusion Treatment

2025-11-23T15:38:32+00:00

The test specimens and finished products were subjected to high-temperature diffusion treatment in low-melting metal melts. The final mechanically processed products, which reached a roughness of Rz40-Ra2.5, were degreased with alcohol and acetone before loading into the furnace. The working ampoules were made of grade 10 carbon steel and were solid metal, with an outer diameter of up to 85 mm, a height of up to 180 mm, and a wall thickness of up to 8 mm, allowing for maximum utilization of the working space of the SShV-1.2.5/25-I1 vacuum furnace. When studying the kinetics of the effect of melt composition on the HTAD process, smaller ampoule specimens with an outer diameter of 20 mm, a height of 35 mm, and a wall thickness of 3 mm were used.

Optimization Of Power Supply Systems For Mobile Network Base Stations

2025-11-23T15:38:35+00:00

The modern era of telecommunications is characterized by an insatiable demand for connectivity, which has created a pressing need to optimize the energy efficiency of base station subsystems. The base station subsystem serves as the backbone of mobile communication networks and is a significant consumer of power. This article explores contemporary trends in the realm of telecommunications infrastructure to promote a more sustainable and eco-friendly future.

Analysis Of The Anticancer Activity Properties Of Octovanadium (Iv) Ion Complex Compounds

2025-12-22T04:51:31+00:00

This article presents a comprehensive analysis of the anticancer biological activity of complexes based on oxovanadium (IV) ions. Vanadium compounds have attracted significant interest in recent decades as potential therapeutic agents with activity against tumor cells. The study examines the cytotoxic activity of oxovanadium (IV) complexes formed with various ligands, their cellular uptake mechanisms, and the relationship between structural features and biological activity. The results indicate that oxovanadium complexes exhibit lower toxicity and higher selectivity compared to conventional platinum-based drugs.

AI-Augmented Devsecops Security: Integrating Neural Vulnerability Detection, Adaptive Learning, And Policy-Driven Automation Across Modern CI/CD Pipelines

2025-12-22T04:51:33+00:00

This research article presents a deeply elaborated and theoretically grounded examination of AI-augmented DevSecOps security, synthesizing findings from neural code-scanning innovations, adaptive learning systems, policy-driven architectures, cloud-native security automation, and AIOps-enabled operational intelligence. Drawing exclusively from the provided references, the study develops an expanded conceptual model illustrating how deep learning, adaptive threat modeling, automated security governance, and continuous vulnerability detection converge to create a mature, self-evolving DevSecOps ecosystem. The article emphasizes the increasing sophistication of neural code-scanning models capable of identifying complex and previously unseen vulnerabilities, along with adaptive learning mechanisms designed to cope with evolving attack surfaces in cloud-native architectures. In addition, policy-driven DevSecOps frameworks are explored as a means of enforcing compliance, providing architectural guardrails, and guaranteeing uniform security enforcement across distributed microservices. Further analysis highlights the relevance of AI safety principles, the operational challenges inherent in large-scale automation, and the implications of continuous security testing for real-world CI/CD environments. This research integrates theoretical reasoning with practical insights arising from case studies on pipeline vulnerabilities, dynamic security testing obstacles, anomaly detection within cloud-native microservice ecosystems, and the emerging importance of AIOps in enabling self-healing pipeline infrastructures. Collectively, the findings offer a detailed conceptual foundation intended to support researchers, security engineers, and DevSecOps practitioners seeking to design robust, AI-enabled security architectures capable of sustained resilience.

The Presence Form Iridium In Waste Gases Of Copper Production

2025-11-23T15:38:33+00:00

The modern development of metallurgy requires comprehensive processing of raw materials with the extraction of valuable components from technogenic waste. Despite the presence of significant quantities of valuable components in technogenic waste, a significant aspect is the form of occurrence and properties of the extracted component, as their physicochemical properties during interaction with process reagents lead to unpredictable interactions. To select an effective technology and assess the quality of the resulting product, it is necessary to investigate and analyze the chemical and mineralogical formations of compounds in the raw materials. Consequently, iridium is present in the exhaust gases of the copper smelting section. Iridium belongs to the rarest and most refractory elements of the platinum group. Despite its extremely low content in nature, it can be present in the waste flows of non-ferrous metallurgy, especially during the processing of copper-nickel and copper-molybdenum sulfide ores containing platinum metal impurities. The capture and extraction of iridium from exhaust gases is becoming both ecologically relevant and resource-saving. This article is dedicated to the analysis of the forms of iridium's presence in gases, the thermodynamic aspects of its behavior in various types of furnaces, and the technical possibilities of its capture and extraction.

Harnessing Generative AI and Intelligent Systems for Enhanced Retail and Industrial Operations: A Comprehensive Analysis

2025-12-22T04:51:30+00:00

The rapid integration of generative artificial intelligence (AI) and advanced computational systems into both retail and industrial sectors has transformed operational frameworks, customer engagement strategies, and data-driven decision-making processes. This study explores the multifaceted applications of generative AI, machine learning (ML), and intelligent automation in optimizing workflows, enhancing consumer experiences, and mitigating algorithmic bias and data privacy concerns. By synthesizing contemporary literature on AI-driven retail environments, automated software pipelines, and privacy-preserving machine learning techniques, the research identifies significant opportunities and challenges associated with AI adoption. The study provides a theoretical elaboration of ambient intelligence in smart retailing, AI-enabled marketing strategies, and the deployment of generative AI in industrial process optimization. Furthermore, critical analysis is conducted on the ethical, methodological, and operational dimensions of AI systems, including bias detection, fairness in algorithmic scoring, and CI/CD pipelines for large language models (LLMs). The findings suggest that while generative AI and intelligent systems offer substantial efficiency gains and innovation potential, they require comprehensive governance frameworks, robust privacy safeguards, and adaptive operational strategies to realize sustainable benefits. The study concludes by proposing a conceptual roadmap for integrating AI-driven solutions across retail and industrial domains while ensuring ethical compliance, operational resilience, and enhanced customer satisfaction.

Operationalizing Trust: A Multi-Layered Framework for Ethical Governance and Explainability in High-Stakes Artificial Intelligence Systems

2025-12-22T04:51:32+00:00

Background: As Artificial Intelligence (AI) systems become integral to high-stakes decision-making in healthcare, finance, and education, the opacity of "black box" models presents significant ethical and legal challenges. While deep learning models offer superior predictive accuracy, their lack of interpretability undermines user trust and complicates compliance with emerging privacy regulations.

Methods: This study employs a comprehensive theoretical analysis, synthesizing literature on Cyber Security, AI Governance, and Explainable AI (XAI). We utilize a systems-design perspective to construct a multi-layered framework that aligns algorithmic transparency with ethical privacy requirements. The research evaluates various XAI approaches—including prototype-based reasoning and counterfactual explanations—against metrics of fairness, privacy preservation, and explanation quality.

Results: Our analysis identifies a critical "Transparency-Privacy Paradox," where granular explanations may inadvertently leak sensitive training data. Furthermore, we find that post-hoc explanation methods often suffer from "deceptive transparency," providing plausible but unfaithful justifications for model behavior. Conversely, interpretable-by-design architectures demonstrate a higher capacity for building sustainable trust without compromising privacy standards.

Conclusion: We conclude that trust in AI cannot be achieved through performance metrics alone. Instead, it requires a holistic governance strategy that integrates "Privacy by Design" with robust XAI mechanisms. We propose a shift from retrospective explanation to prospective, interpretable model architecture as the standard for critical infrastructure.

Scientific And Methodological Foundations For Developing Practical Fire Safety Skills In Students

2025-12-22T04:51:34+00:00

The development of civil society, the introduction of automated systems, the construction of advanced mechanisms and multi-storey buildings and structures, as well as other factors, necessitate aligning the professional and physical preparedness of rescuers with modern requirements.

The Impact Of A Combination Of Healthy Eating And Physical Activity On Student Health

2025-11-23T15:38:35+00:00

This research article explores the significance of healthy nutrition and physical activity in maintaining and improving the health of university students. The study analyzed students’ dietary habits, their participation in sports activities, and the effects on the body’s functional indicators. The findings indicate that the combination of regular physical exercise and balanced nutrition helps regulate metabolic processes, prevent excess weight gain, and improve mental and emotional well-being. Practical recommendations for promoting a healthy lifestyle among students are also provided.

Application Of Innovative Technologies In Basketball Training And Their Didactic Possibilities

2025-12-22T04:51:31+00:00

This article analyzes the theoretical and methodological foundations of the use of innovative technologies in basketball training, their didactic capabilities and effectiveness in the educational process. The study highlights ways to develop students' technical and tactical skills using digital platforms, interactive simulators, video lectures and analytical programs. It also shows the advantages of integrating AR/VR technologies, mobile applications, sensor measurement tools and training programs based on artificial intelligence into the training process. It is scientifically proven that innovative approaches serve to increase student activity, strengthen an individual approach, improve instructional effectiveness and ensure a high level of efficiency of the educational process. At the end of the article, practical recommendations are developed for the introduction of digital technologies in basketball education.

Developing Library Services Through Mobile Applications And Chatbots: A Modern Digital Approach

2025-12-22T04:51:33+00:00

The rapid advancement of mobile technologies and artificial intelligence has introduced new opportunities for transforming library services. This study investigates how mobile applications and chatbots can improve access to library resources, enhance user experience, and optimize internal workflows. Using a mixed-method design combining a literature review, user surveys, and prototype testing, the research explores the practical impact of these technologies in academic libraries. Findings indicate that mobile and chatbot-based services significantly reduce information-search time, increase satisfaction, and decrease the repetitive workload of librarians. The paper concludes with recommendations for implementing smart, user-centered digital systems in higher education libraries.

Urban Planning Foundations For The Design And Organization Of Recreational And Service Areas Along Intercity Highways

2025-11-23T15:38:33+00:00

This article analyzes the types of roadside rest areas along intercity highway routes, their functional structure, and the characteristics of their spatial placement from a scientific and architectural perspective. The increasing traffic flow on major highways, the growing demand for safe travel conditions for drivers and passengers, and the rising need for service facilities highlight the necessity of modern approaches to the design of rest areas. The article presents a classification of rest areas (minimum, basic, and comprehensive service zones), their service categories, sanitary and hygienic requirements, transport-logistical placement criteria, and environmental approaches. In addition, urban planning parameters and international practices are examined to determine optimal spacing, capacity, and functional organization of such zones. The study concludes with practical recommendations aimed at improving highway transport infrastructure, modernizing roadside service networks, and enhancing the efficiency of interregional transport connections.

The Evolution Of Hydraulic Fracturing: A Comprehensive Review Of Techniques, Technological Innovations, And Environmental Consequences

2025-11-23T15:38:36+00:00

Background: Hydraulic fracturing is an indispensable technology for extracting oil and gas from unconventional reservoirs, fundamentally altering the global energy landscape. However, its operational complexity and environmental footprint necessitate continuous innovation. A comprehensive synthesis of traditional techniques, emerging innovations, and associated environmental impacts is required to guide future development.

Objective: This article provides a systematic review of hydraulic fracturing, charting its evolution from foundational methods to cutting-edge technologies. It aims to (1) analyze the principles and applications of various fracturing techniques, (2) highlight key technological innovations in fluids and methods, and (3) critically evaluate the environmental impacts and corresponding mitigation strategies.

Methods: The analysis is based on a structured review of seminal and contemporary literature. The works are categorized to compare proppant and acid fracturing; assess applications in diverse geologies like shale, high-pressure/high-temperature, and mature fields; and examine novel methods, including supercritical CO2, foam-based, and plasma-based fracturing. The review integrates findings from field case studies and advanced simulation models to connect theory with practice.

Results: The review confirms a clear technological progression from water-intensive conventional methods to waterless or reduced-water alternatives designed to enhance efficiency and minimize environmental risks. Innovations like supercritical CO2 and plasma fracturing offer promising solutions for shale reservoirs. Advanced modeling techniques have proven crucial for optimizing fracture network complexity and well performance in challenging environments. While these innovations address key issues like water consumption, significant environmental challenges, particularly concerning water management and induced seismicity, persist.

Conclusion: The future of hydraulic fracturing hinges on developing and deploying technologies that are both economically viable and environmentally sustainable. While significant progress has been made, current predictive models for managing environmental risks remain a key area for improvement. Future research must focus on the long-term scalability of novel techniques and the creation of integrated models that fully account for complex geological and environmental interactions.

Impact Of Salt Washing Rates On The Washing Of Soil Salts

2025-12-22T04:51:32+00:00

This article determines the effectiveness of salt leaching in saline lands based on the soil conditions of the site, the depth of the seepage waters and the amount of salts they contain, the degree of land drainage (type, depth, distance between the ditches, the flow module of the ditch water), the technology of preparing the land for salt leaching (plowing, deep loosening, leveling, the size of the ridges, the relationship of the ridges with the ditches), and the duration of salt leaching.

Evolving Frameworks for Digital Trust: A Multidimensional Analysis of Data Governance in Cloud, IoT, and Multimodal AI Ecosystems

2025-12-22T04:51:34+00:00

Purpose: As organizations transition toward cloud-native architectures and artificial intelligence (AI) integration, traditional data governance (DG) frameworks—originally designing for on-premise, structured data—are proving insufficient. This study aims to analyze the evolving requirements of DG in converged environments, specifically examining the intersection of Cloud Computing, Internet of Things (IoT), and Multimodal AI systems. The research seeks to establish a taxonomy of enabling factors that facilitate secure AI adoption and digital trust.

Methodology: A systematic literature review and qualitative document analysis were conducted on 33 peer-reviewed sources ranging from 2014 to 2025. The analysis utilized a risk-based modeling approach to categorize governance dimensions, contrasting cloud versus non-cloud governance taxonomies and evaluating frameworks for algorithmic auditing and multimodal data fusion.

Findings: The review identifies that successful DG in modern ecosystems requires a shift from static compliance checklists to dynamic, "agile" governance models. Key findings indicate that cloud data governance is distinctively characterized by shared responsibility models that complicate digital forensics and custody. Furthermore, the integration of AI necessitates new governance layers for "grey data" and multimodal inputs, particularly in high-stakes sectors like healthcare and banking. The study confirms that organizational culture and executive sponsorship are as critical as technical controls in enabling secure digital transformation.

Originality/value: This paper proposes a unified conceptual framework that bridges the gap between technical data management and strategic corporate governance. It uniquely addresses the governance of "multimodal" data streams and provides a roadmap for internal auditors to engage with AI systems despite the current lack of standardized guidance.

Decay Of Ni Impurity Accumulations In Si Under The Influence Of Thermal Annealing

2025-11-23T15:38:34+00:00

In this article, the effect of thermal annealing on the electrical conductivity of nickel-doped silicon single crystals is investigated. The results of analyses of morphological parameters of impurity accumulations of nickel under the influence of heat treatment, obtained using the method of electron microscopy, are presented. It is revealed that under the influence of thermal annealing at T=1173 K, impurity accumulations of nickel in silicon decay.

Robust Object Reconstruction From Limited And Noisy Tomographic Data Using The Broken Ray Transform (BRT)

2025-12-22T04:51:31+00:00

Reconstruction of objects from incomplete or corrupted tomographic data remains a fundamental challenge in inverse problems. In particular, the Broken Ray Transform (BRT) leads to highly ill-posed reconstruction tasks when ray paths are limited, noisy, or partially missing due to geometric or physical constraints. This study investigates robust reconstruction methods for BRT under three experimental conditions: (1) noisy full-angle data, (2) limited-angle acquisition, and (3) incomplete boundary measurements with missing ray segments. We analyze the performance of three reconstruction approaches—Filtered Back-Projection (FBP), Tikhonov-regularized minimization, and Total Variation (TV) regularization—across these scenarios. Quantitative evaluations using RMSE, PSNR, and SSIM reveal that regularization-based methods significantly outperform classical FBP under noise and angular limitations. TV produces the highest structural fidelity, especially for sharp-edge phantoms, while Tikhonov demonstrates superior noise suppression. The results indicate that properly tuned variational regularization substantially improves stability and accuracy of BRT reconstruction under practical constraints and limited measurement capabilities.

Organic Fundamentals Of Synthesizing Bioplastics From Plant Oils

2025-12-22T04:51:33+00:00

The transition from petrochemical plastics to bio-based alternatives has accelerated interest in plant-oil-derived polymers. Vegetable oils (castor, soybean, linseed, olive, sunflower, and others) provide a versatile platform of triglyceride molecules that can be chemically transformed into a wide range of polymeric architectures including polyesters, polyurethanes, epoxies, and acrylates. This article reviews the organic-chemistry foundations of converting plant oils into bioplastics: functionalization of triglycerides (epoxidation, transesterification, hydrolysis), generation of reactive monomers (fatty acid-based diacids, polyols, cyclic carbonates), polymerization strategies (step-growth polycondensation, ring-opening polymerization, radical curing, non-isocyanate routes), and structure–property relationships. Attention is given to green chemistry principles — atom economy, benign reagents, catalytic and solvent-free methodologies — and to performance factors (crosslink density, crystallinity, thermal stability) that determine suitability for applications. Analytical and characterization techniques (FTIR, NMR, GPC, DSC, TGA, mechanical testing) commonly used to validate molecular structure and properties are summarized. The review concludes with discussion on biodegradability, life-cycle considerations, industrial challenges and perspectives for future research. Keywords and a curated bibliography are provided to meet Scopus submission expectations.

Apoptosis And The Role Of Caspases In The Regulation Of Cellular Stress

2025-11-23T15:38:32+00:00

Apoptosis is programmed cell death triggered by the destruction of the cytoskeleton by proteases and DNA cleavage by endonucleases. This process occurs through various homeostatic and pathological pathways and plays a vital role in maintaining the body's homeostasis, eliminating cells that pose a threat to its survival.

Caspases play a key role both in triggering apoptosis (programmed cell death) in the event of irreversible damage and in adaptive cellular responses to moderate stress. More precisely, apoptosis is the controlled elimination of cells deemed unnecessary or potentially dangerous to the body.

Cyano-Acylation And Cyano-Esterification Of Unsaturated Hydrocarbons

2025-11-23T15:38:36+00:00

This review aims to summarize the current literature on the direct vicinal cyano-acylation and cyano-esterification of unsaturated hydrocarbons, with a particular focus on the mechanistic features of these reactions. This review is structured into four main sections. The first section covers the cyano-acylation of alkenes, followed by a discussion of cyano-esterification of alkenes in the second section. The third section consists of an overview of cyano-acylation of alkynes, while the final section focuses on the cyano-esterification of alkynes.

From Local Silkworm Seeds Achievement Processed Fine-Fiber Cocoons Processing Of Low-Linear Density Raw Silk Release Technology

2025-12-22T04:51:32+00:00

This article presents the results of laboratory tests conducted at the Uzbekistan Scientific Research Institute of Natural Fibers on creating a technology for producing raw silk with linear densities of 1.56 and 1.89 tex on FY-502 mechanical cocoon reeling machines manufactured in the People's Republic of China and KMS-10 mechanical cocoon reeling machines manufactured in the Republic of Uzbekistan from local mulberry silkworm eggs, and determining the quality indicators of raw silk obtained using this technology.

Orchestrating Elasticity: A Comparative Analysis Of AI-Driven Predictive Scaling Versus Reactive Auto-Scaling In Microservices Architectures

2025-12-22T04:51:34+00:00

As cloud computing paradigms shift towards microservices and containerized architectures, the efficiency of resource allocation remains a critical challenge. Traditional reactive auto-scaling mechanisms, which rely on threshold-based metrics such as CPU and memory utilization, often fail to address sudden workload spikes, leading to service degradation and "cold start" latency. This study presents a comparative analysis between standard reactive scaling, Ansible-based dynamic scaling on Azure PaaS, and a novel AI-driven predictive scaling framework. Drawing on recent developments in Artificial Intelligence and Infrastructure as Code (IaC), we evaluate these approaches using a synthesized workload representative of complex industrial scenarios, such as refinery turnarounds, and high-velocity e-commerce transactions. Our methodology involves the deployment of a Long Short-Term Memory (LSTM) neural network to forecast workload demands 10 minutes in advance, triggering proactive scaling actions. We contrast this with standard Kubernetes Horizontal Pod Autoscaling (HPA) and rule-based Ansible automation. The results demonstrate that the AI-driven predictive model reduces 95th percentile latency by approximately 34% compared to reactive approaches and mitigates cold-start latency by 90%. Furthermore, while the predictive model incurs a marginal computational overhead, it reduces overall cloud expenditure by 18% by minimizing over-provisioning during idle periods. The findings suggest that integrating AI into the orchestration layer is essential for the next generation of cost-efficient, high-performance cloud architectures.

Theoretical Basis Of Cascade Dryer Aerodynamics And Evaluation Of Variable Parameters

2025-11-23T15:38:34+00:00

This article discusses the theoretical foundations of the aerodynamics of a cascade dryer used in the industrial drying of wheat grain and the physical and mathematical models of the heat and mass transfer processes occurring in it. The efficiency of the dryer is evaluated depending on the air flow rate, temperature, partial pressure, and moisture properties of the grain being dried. The study analyzed the effects of parameters such as the drying agent motion mode, resistance coefficients in the grain layer, mass transfer coefficient hm, and evaporation coefficient φs. Also, the relationships between the saturation pressure of water vapor on the grain surface psat(Ts), air humidity Yxavo, and air density ρn.u were developed. The results obtained serve to determine the optimal technological modes that allow for energy-efficient drying of wheat while maintaining quality indicators.

Reimagining Cloud Security and Connectivity: AI-Enabled Automation, Zero-Trust Architectures, and Software-Defined Networks in Multi-Tenant Environments

2025-12-22T04:51:31+00:00

Background: The rapid global adoption of cloud computing has transformed how organisations architect, operate, and secure their information systems. Foundational conceptualisations of cloud computing emphasise on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service (NIST, 2007). However, the concurrent scaling of multi-tenant services, software-defined wide area networks (SD-WANs), and in-network security mechanisms has intensified complexity and introduced novel failure modes and attack surfaces that demand integrated, theory-driven responses (Armbrust, 2010; Buyya et al., 2011; Jain et al., 2013).

Objective: This article constructs a comprehensive, publication-ready theoretical framework that synthesises multi-tenant security, data-driven connectivity, and collaborative in-network security concepts to produce adaptive, resilient cloud infrastructures. The framework is grounded strictly in the supplied literature and explicates mechanisms by which traffic measurement, deep packet inspection, and distributed access control may be combined with data-plane connectivity techniques and SD-WAN practices to reduce risk and maintain service continuity (Ruan et al., 2006; Ni et al., 2007; Chen et al., 2011; Liu et al., 2013).

Methods: We employ a conceptual analytical methodology that integrates prior empirical observations and system descriptions from the reference corpus. We synthesise design patterns, threat models, and operational practices described in the literature into modular components: (1) adaptive tenancy isolation and policy orchestration, (2) connectivity assurance through data-plane mechanisms and SD-WAN routing, (3) cooperative in-network security services, and (4) instrumentation and measurement for feedback control. For each component we present theoretical constructs, presumed interfaces, attack/risk vectors, and mitigation strategies distilled from the references. We further articulate composed operational workflows and failure scenarios and provide prescriptive hardening recommendations.

Results: The integrated framework yields seven principal claims: (1) rigorous, adaptive tenancy control reduces lateral risk in multi-tenant clouds when coupled with distributed access control and role semantics (Brown et al., 2012; Tsai & Shao, 2011; Abdulrahman et al., 2012); (2) data-plane connectivity mechanisms materially improve recovery time and path diversity for tenant traffic in the face of failures (Liu et al., 2011; Liu et al., 2013); (3) SD-WAN patterns support global traffic engineering and hierarchical policy enforcement at scale (Jain et al., 2013); (4) collaborative, in-network security platforms can provide scalable deep traffic analysis and threat coordination when paired with high-speed measurement hardware (Chen et al., 2011; Ruan et al., 2006); (5) multi-stage detection combining URL/behavioural models and signature matching strengthens defence breadth (Sahoo et al., 2017); (6) tenancy and migration policies must be formalised and enforced to avoid data residency and compliance drift (Hay et al., 2012; Wood & Anderson, 2011); and (7) zero-trust principles applied to multi-tenant orchestration achieve superior security posture provided instrumentation and policy automation are mature (Hariharan, 2025).

Conclusions: Integrating tenancy isolation, SD-WAN-informed routing, data-plane connectivity, and collaborative in-network security produces a defensible architecture for modern cloud deployments. The theoretical framework elaborated here offers a precise vocabulary for architects and researchers to evaluate, simulate, and implement adaptive controls. We conclude with a detailed agenda for validating the framework through controlled experimentation and applied measurement, and we identify key limitations and research directions to bridge the gap between conceptual synthesis and empirical deployment.

Methodology For Developing Professional Competence Of Medical Higher Education Students In The Process Of Teaching Biochemistry

2025-12-22T04:51:32+00:00

The article discusses the importance of receiving a quality education from the best universities in the world, not only for our children, but also for adults, and also points out the importance of rankings and education indices for assessing the quality of educational systems in various countries.

The main idea of the article is that in the modern world, with the constant development of science, technology and technology, education should change towards the development of practical skills based on theoretical knowledge. It is important to develop students’ competencies and perform tasks based on the knowledge and skills inherent in a particular profession. It is emphasized that modern education should provide preparation for independent decision-making, changes in life and academic activities, as well as contribute to the development of the universality of professional skills and constant changes in society.

Particular attention is paid to the formation of competencies as a new modern method of educational development. It is clarified that assessing the ability of students to solve problems based on knowledge and skills, such as self-education, interconnection of knowledge, information search, decision making, teamwork, helps to develop skills and competencies.

In conclusion, the article emphasizes the importance of developing and cultivating universal competencies necessary for successful activities in the field of vocational education. The need for continuous updating of teaching methodology is discussed, as well as the presence of two categories of competencies for students: general and special.

Thus, the main idea of the article is the need to adapt educational programs to the new requirements of modern society and develop in students the competencies necessary for successful work in the field of their professional activities.

Assessment Of The Potential For Zoning Water-Saving Irrigation Technologies Based On The Aridity Index

2025-11-23T15:38:32+00:00

This article presents the results of assessing aridity indicators by districts of Jizzakh region for the period 2021–2024. Table 1 provides calculations of M.I. Budyko’s radiation aridity index for total and irrigated areas, showing that the values vary within a very narrow range across districts, which indicates a relatively uniform background of natural radiation aridity in the region. Table 2 presents a comparative analysis of an improved aridity index that accounts for irrigation-specific factors and components of the water balance; it is shown that the aridity index for total areas generally ranges from 1.2 to 1.6, while for irrigated areas in some districts it reaches 2.0–3.0. This reflects a deficit of water resources and a high drought potential during the vegetation period, and confirms the urgency of introducing water-saving and innovative irrigation technologies, differentiating crop structure, and implementing adaptive water resources management, especially in the districts of Zomin, Zafarabad, Arnasay and Mirzachul.

Green Chemistry Principles In Biopolymer Synthesis

2025-11-23T15:38:35+00:00

The transition to sustainable polymer production is urgent in response to global plastic pollution and fossil resource depletion. Biopolymers — derived from renewable resources or produced biologically — offer promising alternatives to conventional plastics, but their synthesis and processing must adhere to green chemistry principles to realize environmental benefits. This review synthesizes core green chemistry strategies applied in biopolymer synthesis, including renewable feedstocks, benign solvents, enzyme and heterogeneous catalysis, energy-efficient processes, and waste-minimization. Case studies on polylactic acid (PLA), chitosan, and bacterial cellulose illustrate practical implementations and techno-economic considerations. Metrics and life cycle assessment (LCA) approaches used to quantify “greenness” are discussed, as are challenges for scale-up and regulatory acceptance. Recommendations highlight integrated process design, circularity through end-of-life planning, and interdisciplinary research needs to accelerate adoption.

Antidiabetic Properties Of Oxovanadium (Iv) Ion Complexes: Analysis And Prospects

2025-12-22T04:51:31+00:00

This article provides an analytical overview of the antidiabetic properties of oxovanadium (IV) complexes, focusing on their structural characteristics, molecular mechanisms of action, and pharmacological prospects. The influence of ligand nature and coordination environment on the biological activity of VO²⁺ complexes is examined, along with their ability to modulate insulin signaling pathways, suppress gluconeogenesis, and reduce oxidative stress. Available studies indicate the significant therapeutic potential of these compounds in disorders of carbohydrate metabolism. Evaluation of pharmacokinetic and toxicological parameters highlights the necessity of designing new, selective, and safe oxovanadium-based therapeutic formulations for potential clinical use.

Dual-Loop P/Pi Control Of A Dc Servodrive: Modeling, Analysis

2025-12-22T04:51:33+00:00

This paper presents a dual-loop P/PI control strategy for a DC servodrive, focusing on the modeling, analysis, and numerical demonstration of system performance. The control structure consists of two cascaded loops: the inner current loop, which ensures fast electromagnetic torque generation, and the outer velocity loop, responsible for dynamic tracking of motor speed. The inner loop is designed using a proportional (P) controller to obtain a fast current response, while the outer loop utilizes a proportional-integral (PI) controller to achieve zero steady-state error in speed control. A mathematical model of the DC motor is developed, including the electrical and mechanical subsystems, and the transfer functions for each control loop are derived. Stability and transient performance of the closed-loop system are evaluated based on classical control theory methods. Finally, a numerical example is provided to demonstrate the tuning approach and illustrate the effectiveness of the dual-loop P/PI control architecture. The results confirm that the cascaded control structure improves dynamic performance and robustness, making it suitable for industrial servodrive applications.

Study Of Methods For Obtaining Potassium Sulfate

2025-11-23T15:38:33+00:00

This article describes methods for obtaining potassium sulfate. Information on the physicochemical properties of potassium sulfate and some raw materials is also provided. Modern progress in agriculture, along with the introduction of new high-yielding varieties, increased mechanization of field work and irrigation, is largely determined by the degree of its chemicalization and, above all, the use of mineral fertilizers, plant growth and development stimulants, and chemical pesticides.

SUSTAINABLE SUPPLY CHAIN MANAGEMENT: THEORETICAL SYNTHESIS, MODELING APPROACHES, AND PRACTICAL PATHWAYS TOWARD LOW-WASTE, RESILIENT NETWORKS

2025-12-22T04:51:30+00:00

Background: Sustainable supply chain management (SSCM) has matured from normative prescriptions to an interdisciplinary field that blends ethics, operations research, risk management, and market mechanisms. Foundational work established definitional boundaries and conceptual frameworks; subsequent quantitative modeling and empirical studies have advanced our ability to design, manage, and evaluate supply chains that account for economic, environmental, and social dimensions (Linton et al., 2007; Seuring & Müller, 2008; Carter & Rogers, 2008). Despite progress, persistent gaps remain in integrating heterogeneous theoretical perspectives, operationalizing social criteria, and aligning market incentives with system-level sustainability objectives (Touboulic & Walker, 2015; Seuring & Müller, 2008).

Objective: This article synthesizes extant theoretical foundations and modeling approaches for SSCM, proposes an integrated conceptual architecture for designing and evaluating sustainable supply chains, and articulates prescriptive insights for managers and policy makers. The work strictly draws on the provided reference corpus and elaborates theoretical implications, counter-arguments, and future research directions.

Methods: A structured interpretive synthesis of seminal and contemporary works was performed: conceptual frameworks (Seuring & Müller, 2008; Carter & Rogers, 2008), structured literature reviews (Touboulic & Walker, 2015; Seuring, 2012), and quantitative model surveys (Brandenburg et al., 2014). The methodology section describes how theory integration, taxonomy construction, and model selection heuristics are derived from these sources and used to generate normative guidance.

Results: The synthesis identifies three organizing logics for SSCM: (1) normative-ethical logic emphasizing social responsibility and legitimacy (Anner, 2012; Buchanan, 2000); (2) market-incentive logic focusing on the role of demand, regulation, and transparency (Vermeulen & Seuring, 2009; Chowdhury, 2025); and (3) analytic-technical logic emphasizing modeling, optimization, and measurement (Seuring, 2012; Brandenburg et al., 2014). From this, an integrated architecture is proposed that brings together governance mechanisms, metric systems, quantitative models, and digital enablers. Practical recommendations center on robust decision rules, layered metrics, and stakeholder-aligned governance.

Conclusions: Progress in SSCM requires co-evolution of theory and application: richer social metrics, hybrid quantitative methods, and market structures that internalize externalities. The article concludes with a research agenda and policy recommendations that prioritize measurement, transparency, and resilience while acknowledging trade-offs and limitations.

Synthesis Of A Mixed Heteroligand Complex Of Copper Salts With Acetylacetone And Acetamid

2025-12-22T04:51:32+00:00

In recent years, scientists worldwide have been conducting research on the synthesis of heteroligand complex compounds, identifying coordination centers in heteroligand compounds, studying their geometric structure, composition, and properties, elucidating mechanisms of complex formation reactions, investigating complex compounds in solutions, and isolating them in solid form. This article discusses the results of synthesis and analysis of such complex compounds.

Security Challenges In The Era Of Cryptography And Quantum Technologies

2025-12-22T04:51:54+00:00

This article analyzes the profound impact of quantum computing on modern cryptographic systems. It explores the computational complexity theory framework to assess the vulnerability of current cryptographic mechanisms, which rely on complex mathematical problems. The analysis confirms that symmetric cryptography and hash functions remain relatively secure against quantum attacks, primarily threatened by Grover's algorithm. In contrast, widely used public-key cryptosystems are critically vulnerable to Shor's algorithm. The study concludes by examining the transition to post-quantum cryptography, highlighting the challenges of efficiency, trust, and usability that must be overcome before these new algorithms can be widely deployed.

Mapping The Computational And Memory-Bound Characteristics Of Image Filtering Operations Using The Roofline Model

2025-11-23T15:38:35+00:00

The Roofline model offers a compact way to reason about whether an image-processing kernel is limited by floating-point throughput or by memory bandwidth. This article applies the Roofline framework to canonical filters—generic 3×3 convolution, Sobel edge detection with gradient magnitude, and separable Gaussian blurs (5×5 and 7×7)—to map their computational intensity and predict performance on representative CPU and GPU profiles. We formalize arithmetic intensity under transparent assumptions for single-channel 32-bit images and two-pass separable pipelines, and we derive bandwidth-bound ceilings and compute-bound plateaus for each architecture. Using an illustrative device pair (CPU with 500 GFLOP/s peak and 50 GB/s bandwidth; GPU with 10 TFLOP/s peak and 300 GB/s bandwidth), we show that the studied filters occupy the bandwidth-limited region with intensities between ≈0.38 and ≈1.25 FLOPs/byte, which implies that improving blocking, reuse, and data movement often dominates raw FLOP optimization. A Roofline chart and a comparative table report predicted ceilings in GFLOP/s and converted pixel-throughput ceilings for each kernel. We discuss implications for schedule design, separability, and pipeline organization and argue that Roofline-guided reasoning helps prioritize cache tiling, fusion, and memory-traffic reduction before micro-optimizing scalar FLOPs.

Control Algorithms For Robot Manipulators To Optimize Paper Flow In Printing Processes

2025-12-22T04:51:31+00:00

The article examines the problem of optimizing the processes of transferring, sorting, and packaging paper sheets along a conveyor in printing enterprises using a robot manipulator. The main issue highlighted is the lack of mathematically grounded control algorithms that properly link conveyor speed, paper flow intensity, and the robot manipulator’s operational cycle. As a result, paper jams, robot idle time, and defect rates increase. In the article, the movement of paper on the conveyor is expressed using basic differential equations, while the flow intensity is modeled through the M/M/1 queueing theory framework. Based on the kinematic and dynamic models of the robot manipulator, a jerk-limited trajectory planning algorithm is proposed. Simulation results conducted in the MATLAB/Simulink environment show that the proposed algorithm keeps the load coefficient of the conveyor–robot system around ρ = 0.73, reduces the queue length from 20 to 2.7 items, and decreases the frequency of paper jams by up to 40%.

Mathematical Induction In Proving Theorems

2025-12-22T04:51:33+00:00

This article presents the proof of a number of theorems using the convenient method of mathematical induction.

Method For Assessing The Operating Mode Of Pump Stations In The Karshi Machine Channel Cascade And Calculating Their Hydraulic Parameters

2025-11-23T15:38:33+00:00

First of all, the method for improving the pumping station and the anti-machine channel cascade is based on the design mode of the pumping station and the parameters of the water and channel flow, as well as the development of a hydraulic model. which characterizes the mutual dynamics of the hydraulic parameters of the pump unit and the flow of water and channel, as well as the energy characteristics of the motor pump.

Performance‑Aware Fine‑Tuning and Inference Optimization for Large Language Model Code Generation: A Unified Framework

2025-12-22T04:51:30+00:00

As Large Language Models (LLMs) become increasingly central to code generation, there arises a critical need to not only improve the syntactic and semantic correctness of generated code but also to optimize for performance metrics such as execution efficiency, inference latency, and overall responsiveness in practical deployment scenarios. This article presents a unified framework that integrates two complementary approaches: performance‑aware fine‑tuning of LLMs for code generation and system‑level inference optimization through scheduling and firmware‑level enhancements. Drawing from recent empirical advances in learning performance‑improving code edits (Shypula et al., 2023) and efficiency‑aware fine‑tuning methods (Huang et al., 2025), we design a fine‑tuning pipeline that emphasizes generation of code optimized for run-time performance, while avoiding degradation of correctness. Concurrently, inspired by scheduling and preemption strategies for inference serving (Kim et al., 2024) and firmware‑level optimization approaches (2025), we incorporate an inference serving infrastructure that reduces latency and improves throughput. Through a series of controlled experiments, we demonstrate that our approach yields code that runs 15–25% faster on common benchmarks compared to baseline models, without sacrificing functional correctness, and cuts average end‑user latency by up to 30% in batch inference settings. We analyze trade‑offs, limitations, and outline a research agenda for broader adoption. The results underscore the importance of co‑designing model fine‑tuning and system‑level serving strategies to achieve real‑world performance gains.

Lightweight and High-Performance Encryption for Real-Time Multimedia and Embedded Systems: Theory, Implementation Considerations, and Comparative Analysis

2025-12-22T04:51:32+00:00

This article presents a comprehensive, publication-ready examination of lightweight and high-performance encryption strategies for real-time multimedia transmission and constrained embedded systems. Beginning with an expansive theoretical framing of the unique security requirements of streaming video, voice, and sensor data, the work synthesizes decades of prior engineering practice and academic study to articulate clear design objectives for encryption schemes in latency-sensitive environments (Aly, 2004; Shi, Changgui & Wang, 2004). We review the cryptographic primitives most relevant to such contexts—symmetric block ciphers and stream ciphers—and discuss implementation trade-offs across software, hardware (FPGA), and mobile environments (Atul et al., 2011; Hoang & Nguyen, 2012; Rouaf & Yousif, 2021). Performance metrics are examined in depth: throughput, latency, computational overhead, memory footprint, energy consumption, and compatibility with compression pipelines (Andriani, Wijayanti & Wibowo, 2018; Advani & Gonsai, 2019). Building on standards and canonical references for AES/Rijndael and guidance on governmental cryptographic deployment (Daemen & Rijmen, 2009; Daemen & Rijmen, 2010; Lee, 2009), we analyze specific MPEG video encryption approaches and lightweight real-time variants with respect to security, perceptual impact, and computational cost (Shi, Changgui & Wang, 2004; Aly, 2004). Case studies and hypotheticals address FPGA acceleration, embedded secure modules for sensor streams, and voice-over-IP protections, drawing on practical implementations and comparative performance evaluations (Atul et al., 2011; Chalermwat et al., 2011; Bassil et al., 2005). Limitations, attack surfaces, and countermeasures—covering chosen-plaintext vulnerabilities, key-management issues, and side-channel leakage—are elaborated with citations to standards, analysis pages, and implementation reports (Cole et al., 2005; Gladman, 2012; CSOR/NIST). The article closes with concrete recommendations for architects of real-time secure transmission systems: when to prefer lightweight cipher variants, how to partition cryptographic tasks between hardware and software, and how to integrate encryption with compression to maintain both confidentiality and streaming performance (Aly, 2004; Shi, Changgui & Wang, 2004; Andriani et al., 2018). This synthesis is intended to guide implementers and researchers toward designs that balance robust security with the demanding performance constraints of modern multimedia and embedded deployments.

The Impact Of Antioxidant Enzymes On Oxidative Stress

2025-12-22T04:51:34+00:00

Oxidative stress represents a critical imbalance between the production of reactive oxygen species (ROS) and the antioxidant defense systems in living organisms. Antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) play a vital role in neutralizing oxidative damage. This study explores the biochemical mechanisms, experimental evaluation, and therapeutic implications of antioxidant enzymes in mitigating oxidative stress in human and animal systems. Using spectrophotometric assays, enzyme kinetics, and comparative analysis, the research highlights the potential of natural antioxidants to enhance enzyme efficiency. The results demonstrate a significant correlation between elevated enzyme activity and reduced oxidative biomarkers, suggesting the crucial role of enzymatic regulation in maintaining cellular homeostasis.

Beyond Confidentiality: A Dynamic Framework for Systemic Risk Management and Enhanced Resilience in Critical Infrastructure Operations

2025-11-23T15:38:34+00:00

Purpose: Cyber-Physical Systems (CPS) underpinning critical infrastructure face unique security challenges due to the interconnected nature of their computational and physical components. Traditional Information Technology (IT) risk assessment methods prove inadequate, as they fail to quantify the kinetic impact—the physical and safety consequences—of a cyber breach. This study addresses this critical gap by proposing and defining a novel Unified Cyber-Physical Risk Management (UCPRM) Framework.

Methodology: The UCPRM Framework is built upon three core principles: holistic IT/OT integration, real-time dynamism, and quantitative consequence mapping. The framework introduces a Cyber-Physical Attack Graph (CPAG) to model complex, cascading failures and a Kinetic Impact Score (KIS) metric to translate cyber likelihoods into measurable physical and financial risk. The methodology integrates established international standards (ISO, NIST) with continuous operational telemetry data for dynamic risk updates.

Findings: Application of the UCPRM Framework to a simulated critical infrastructure environment demonstrated that traditional IT-centric risk models significantly underestimate the actual risk profile of CPS by failing to account for the KIS. The framework's real-time risk score enabled predictive alerting and superior resource allocation decisions, aligning security investments with actual physical safety and continuity concerns.

Originality: The UCPRM Framework is the first to seamlessly integrate real-time operational data with a structured, quantitative mechanism for assessing the physical consequences of cyber-attacks, offering a necessary paradigm shift for managing the security and resilience of critical CPS.

Evaluation Of Production Technologies And Market Potential Of Magnesial Binders In Uzbekistan

2025-12-22T04:51:31+00:00

This paper provides a comprehensive assessment of the current condition and development trajectory of the magnesial binder industry in the Republic of Uzbekistan. The study examines the country’s raw material potential, including reserves of magnesite- and dolomite-based minerals, and analyzes the technological processes used in the production of caustic magnesite and caustic dolomite. Special attention is given to the quality parameters of locally produced binders, their competitiveness, and their applicability in various sectors of construction, chemical production, and environmental technologies. Existing challenges related to resource utilization, energy efficiency, and modernizations of production facilities are identified. The paper also outlines strategic opportunities for advancing the industry through innovation, improved processing technologies, and expansion of domestic and export markets. Overall, the study highlights the importance of magnesial binders as a promising segment within Uzbekistan’s construction material industry and underscores the need for further research and investment to ensure sustainable sectoral growth.

Dermatological Patterns In Patients During The Post-Covid Period

2025-12-22T04:51:33+00:00

At the beginning of the pandemic, dermatological changes were rarely documented, possibly due to the lack of conditions for conducting a full skin examination; subsequent studies showed a significantly greater number of cases of skin lesions and the emergence of dermatological manifestations. The true cause of dermatological changes after a previous coronavirus infection, the uncertainty as to whether they appear in connection with the direct effect of the infection or are associated with systemic diseases (for example, reactive or drug-induced), and how best to manage them, are pressing issues that complicate the understanding of these dermatological conditions.

Opportunities For Organizing Counterflow (Reversion) Traffic For Motor Vehicles

2025-11-23T15:38:33+00:00

Managing traffic flows in modern urban infrastructure shapes the efficiency of the transport system. In recent years, the rapid growth of vehicles in major cities of Uzbekistan, especially in Samarkand, has strained existing road networks and increased congestion. Along with building new roads, the introduction of intelligent control systems and reversible traffic methods has become an urgent priority.

Molecular Mechanisms Of Catabolism And Their Significance For Cellular Metabolism

2025-11-23T15:38:36+00:00

This study examines the molecular mechanisms of catabolism as the foundation of energy and plastic metabolism in the cell. Particular attention is given to the sequence of biochemical reactions that ensure the breakdown of complex organic compounds into simple metabolites. The oxidative pathways of carbohydrate, lipid, and protein degradation, as well as their interrelations within the overall metabolic system, are analyzed. It is demonstrated that catabolic processes play a crucial role in regulating the cell’s energy balance and maintaining homeostasis. The specific features of enzymatic control and intermolecular interactions determining the efficiency of catabolic reactions have been identified. The obtained results may serve as a theoretical basis for further research in the field of cellular biochemistry and metabolic regulation.

Environmental Impacts Of Chitosan Extraction From Marine Resources: Towards Greener Alternatives

2025-12-22T04:51:32+00:00

Chitosan, a biopolymer derived from chitin, is primarily obtained from marine crustaceans such as shrimp and crab shells. While its biodegradability and biocompatibility have positioned it as a sustainable material in biomedical, agricultural, and environmental fields, the conventional extraction processes present significant ecological drawbacks. The chemical-intensive demineralization, deproteinization, and deacetylation steps rely heavily on hydrochloric acid (HCl) and sodium hydroxide (NaOH), resulting in high wastewater salinity, sludge accumulation, and carbon emissions. This paper evaluates the environmental consequences of marine-based chitosan production and highlights emerging green alternatives including enzymatic, ionic liquid, and deep eutectic solvent (DES)-assisted extraction. Life Cycle Assessment (LCA) comparisons reveal that insect- and fungal-derived chitosan offer the lowest environmental footprints, aligning with circular economy and sustainable development goals. Future research directions emphasize hybrid extraction technologies, scalable bioreactor systems, and the integration of chitosan production into zero-waste bioeconomy models.

Integrated Framework For Robust, Responsible, And Evaluative Deployment Of Large Language Models: A Comprehensive Methodological And Theoretical Treatise

2025-12-22T04:51:34+00:00

Background: The rapid evolution of large language models (LLMs) has outpaced standardized frameworks for their evaluation, deployment, and governance. Diverse evaluation protocols, emergent alignment techniques, and domain adaptation strategies have been proposed, yet a unified, theoretically grounded, and practically applicable framework that connects evaluation, fine-tuning, bias assessment, and end-to-end testing remains underdeveloped.

Objective: This article proposes and elaborates an integrated framework that synthesizes state-of-the-art evaluation methodologies, bias and truthfulness assessments, domain-specific fine-tuning practices, and automation frameworks for end-to-end testing, grounded in existing literature and empirical benchmarks. Methods: Drawing strictly on the provided literature, we construct a conceptual pipeline where standardized evaluation metrics (including human-aligned LLM-based evaluators), bias and safety assays, and domain adaptation workflows interlock to produce responsible deployment. We analytically extend evaluation taxonomies, compare model families (closed versus open foundation models), and propose best-practice procedural guidelines for automated testing.

Results: The framework clarifies relationships among intrinsic metrics (e.g., perplexity and proxy linguistic measures), extrinsic metrics (task performance), human-aligned automated evaluation (G-Eval and OmniEvalKit principles), and qualitative safety/bias tests (StereoSet, CrowS-Pairs, TruthfulQA). We articulate methodological choices for domain fine-tuning and provide an automation blueprint for continuous validation and regression testing in production settings.

Conclusions: The proposed integrated framework operationalizes evaluation and governance for LLMs, balancing performance optimization with societal risk mitigation. Adoption of this framework can reduce deployment failures, improve alignment with human judgments, and create a replicable pipeline for domain-specialized LLMs. Limitations include dependence on evolving evaluation tools and the need for empirical calibration in diverse application domains. The article closes with prioritized avenues for future research, including benchmark harmonization and adaptive testing regimes.

The Production Of Organic Reagents From Renewable Raw Materials

2025-11-23T15:38:34+00:00

This comprehensive article deeply analyzes the production of organic reagents from renewable raw materials, focusing on technological, environmental, and economic aspects. It emphasizes the significance of renewable feedstocks such as lignocellulosic biomass, plant oils, and algae as sustainable alternatives to fossil-based sources. Moreover, it examines recent innovations, industrial applications, and future directions in green chemistry.

A Comprehensive Analysis of SQL Code Quality, Performance Optimization, and Multi-Model Database Integration

2025-12-22T04:51:31+00:00

The evolving landscape of database systems has prompted a comprehensive examination of SQL code quality, relational and non-relational paradigms, and emerging hybrid frameworks. This study synthesizes insights from foundational relational database theory, object-relational metrics, code quality assessments, and contemporary advances in multi-model database integration. Emphasis is placed on identifying structural inefficiencies, commonly referred to as “code smells,” their impact on database performance, and the practical approaches to remediation in both transactional and analytical contexts. The paper further investigates normalization strategies in nested relational databases, materialized view selection for query optimization, and the emergent convergence of graph and relational query frameworks. By leveraging theoretical models alongside empirical studies of high-performance computing failures and PostgreSQL latency optimization, this research provides a holistic understanding of both classical and modern database systems. Implications for database schema design, query performance, and cross-platform interoperability are explored, highlighting future directions for database engineering, especially in scenarios where NoSQL and NewSQL architectures intersect with traditional relational models.

Teaching Complex Biochemical Reactions Through Simplified Algorithms

2025-12-22T04:51:33+00:00

This article discusses the effectiveness of teaching complex metabolic processes and multi-step reactions in biochemistry using simplified algorithms. Since topics explained through complex schemes in traditional methods often cause difficulties for students, it is emphasized that the algorithmic approach helps to master the processes step by step, in a logical sequence. During the research, it was observed that when algorithmic blocks, the “if-then” principle, and visual flowcharts were used, the level of students' understanding of the topic, the effectiveness of memorizing the sequence of processes, and systematic thinking skills significantly increased. It was also noted that interactive tasks made the lesson more interesting and increased student activity. The results obtained allow us to recommend the algorithmic approach as an innovative and effective pedagogical method in teaching biochemical processes.

Studying The Importance Of Processes Performing A Useful Function Due To Movement-Based Vibratory Movement

2025-11-23T15:38:32+00:00

This article studies the physical and mathematical models of technological processes based on induced vibrational motions and their significance in industrial sectors. The influence of vibrational (vibrational) action on mass transfer, energy distribution and structure stabilization in liquid, solid and mixed media is analyzed. Also, the energy-saving and eco-technological advantages of using vibrating systems in chemical and building materials production processes are shown.

Comparative Analysis Of Bio-Composites Derived From Animal Chitosan And Fungal Chitosan

2025-11-23T15:38:36+00:00

Chitosan, a biopolymer derived from chitin, is widely recognized for its biodegradability, biocompatibility, and non-toxic characteristics. Animal-derived chitosan, extracted primarily from crustacean shells, has been the conventional source for decades. However, fungal-derived chitosan has recently gained traction as a sustainable and hypoallergenic alternative. This extended article explores structural, physicochemical, and biological differences between animal and fungal chitosan, analyzing their advantages, limitations, and potential applications in medicine, agriculture, food packaging, nanotechnology, and environmental science. The paper further highlights recent research developments (2018–2025), sustainability implications, and future perspectives. Comparative graphs, tables, and literature insights are included to provide a comprehensive understanding of bio-composites based on both sources.

Increasing Learning Motivation Through The Use Of Clinical Examples In Biochemistry

2025-12-22T04:51:32+00:00

This article examines the pedagogical value of using clinical examples in teaching biochemistry and their impact on learning motivation. Real-life situations in a clinical context help students connect theoretical knowledge with practice, develop analytical thinking and independent work skills. The article also highlights the possibilities of effectively organizing lessons through interactive methods, virtual laboratories, and situational games. The results of the study show that the use of clinical examples increases learning motivation, makes lessons more interesting, and makes biochemistry more understandable for students.

Integration Of Artificial Intelligence In Environmental Protection Technologies

2025-12-22T04:51:33+00:00

This research explores the integration of artificial intelligence (AI) into modern environmental protection technologies. It highlights how AI-driven systems—such as machine learning, computer vision, and data analytics—enhance environmental monitoring, pollution control, waste management, and climate modeling. The paper discusses applications of AI in predicting natural disasters, optimizing energy use, and supporting sustainable resource management. Furthermore, ethical challenges, data reliability, and policy implications are examined to ensure responsible and effective deployment of AI technologies in environmental protection. The study concludes that AI has transformative potential to accelerate global sustainability efforts and improve decision-making for environmental governance.