International Bulletin of Applied Science and Technology
International Bulletin of Applied Sciences and Technology - (ISSN-2750-3402) is an open access international peer reviewed german journal. IBAST publishes research monthly frequency basis. we explore your research worldwide by international Database indexings. we provide all articles crossref metadata link, google scholar, microsoft academic, Metadata Indexing and much more worldwide international indexings.
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DIOSPYROS KAKI L. — CHEMICAL COMPOSITION OF ORIENTAL PERSIMMON LEAVES
This article examines the chemical composition of the leaves of Diospyros kaki L. — Oriental persimmon, cultivar “Hyakume”, cultivated in the Republic of Uzbekistan, with particular emphasis on phenolic and flavonoid compounds. Chloroform, ethanol, and ethyl acetate were used to extract biologically active compounds from the plant material. Each stage of the extraction process was repeated three times. The total polyphenol content of the obtained extracts was determined, and qualitative and quantitative analyses of the ethyl acetate fraction were performed using high-performance liquid chromatography (HPLC). According to the study results, the total polyphenol content in D. kaki leaves was 1.33% of the dry plant mass. HPLC analysis revealed the presence of dihydroquercetin, luteolin, quercetin, rutin, sineroside, apigenin, and salidroside in the ethyl acetate fraction. Their contents were 2.13, 1.12, 0.58, 36.18, 0.572, 4.50, and 1.50 mg/g, respectively. Among the identified compounds, the high content of rutin is of particular interest, indicating that it is one of the major identified flavonoid components of the studied fraction. The obtained results demonstrate the significance of D. kaki leaves as plant material rich in phenolic compounds and support their consideration as an important object for further phytochemical studies.
Diospyros kaki L., Oriental persimmon, persimmon leaves, phenolic compounds, polyphenols, flavonoids, rutin, apigenin, quercetin, luteolin, HPLC.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
A LINGUOPRAGMATIC STUDY OF TRADITION AND RITUAL DISCOURSE IN CONTEMPORARY ENGLISH SOCIETY
This article explores the linguopragmatic features of tradition and ritual discourse in contemporary English society. It examines how customs and rituals are represented through language and how tradition-related lexical units express cultural values, social relations, and communicative intentions. The study focuses on the pragmatic functions of linguistic expressions used in greetings, celebrations, weddings, funerals, and other ritual contexts.
tradition, ritual, discourse, linguopragmatics, English society, speech act, politeness, cultural values, communicative strategy, pragmatic meaning, linguoculture.
DEVELOPMENT OF A TECHNOLOGY FOR PRODUCING MOISTURE- AND BACTERIA-RESISTANT PAPER FROM PAULOWNIA STEMS GROWN UNDER THE CLIMATIC CONDITIONS OF SURKHANDARYA
The development of environmentally friendly and functional paper materials from locally available renewable biomass is an important research direction in the pulp and paper industry. This study focuses on the development of a technology for producing moisture- and bacteria-resistant paper from the stems of Paulownia trees grown under the climatic conditions of the Surkhandarya region of Uzbekistan.
Paulownia, cellulose, paper, moisture resistance, antibacterial paper, hydrophobic modification, lignin, hemicellulose, alkaline pulping, Surkhandarya, renewable biomass, functional materials.
PEDAGOGICAL TECHNOLOGIES FOR DEVELOPING STUDENTS’ INTERCULTURAL COMPETENCE THROUGH A COMPARATIVE APPROACH
This article identifies and describes a set of concrete pedagogical technologies that operationalize the comparative approach to intercultural competence development in higher education. Whereas much of the existing literature discusses comparative pedagogy at the level of general principle, this study focuses on the technological level of implementation, detailing four specific instructional technologies — the comparative case-study technology, the cultural-mapping technology, the critical-incident simulation technology, and the reflective comparative portfolio technology — together with their procedural stages, assessment criteria, and classroom management requirements. The proposed technologies were piloted with 76 university students over one academic semester and evaluated through structured observation, expert assessment of student work, and a competence rating scale. The results indicate that combining these four technologies within a single instructional module produces balanced development across the cognitive, affective, and behavioral components of intercultural competence, and the article offers a stage-by-stage model that instructors can adapt to their own disciplinary context.
pedagogical technology, comparative approach, intercultural competence, case-study technology, cultural mapping, critical incidents, reflective portfolio, instructional design.
Resilient and Scalable Data Lake Management Using Intelligent Orchestration for AI and Big Data
The rapid convergence of artificial intelligence (AI), multimodal analytics, and large-scale data processing has intensified the need for data lake environments that can accommodate heterogeneous workloads while maintaining scalability, resilience, fairness, and operational efficiency. Conventional data lake management approaches often treat storage, processing, workload scheduling, and governance as relatively independent functions, which can create bottlenecks when AI workloads become dynamic and resource-intensive. This paper proposes a conceptual framework for resilient and scalable data lake management based on intelligent orchestration, in which workload characteristics, data-processing requirements, fairness constraints, and system resilience are jointly considered during resource and task coordination. The study develops its theoretical foundation by synthesizing the provided literature on fairness-aware learning, multimodal datasets, bias reduction, and representation learning, while connecting these principles to intelligent data-lake orchestration. Particular attention is given to multitenant environments in which concurrent AI and big-data workloads compete for computational and storage resources. The proposed approach emphasizes adaptive workload classification, fairness-aware resource allocation, resilience-oriented task management, and continuous orchestration. The analysis indicates that intelligent orchestration can provide a stronger conceptual basis for balancing scalability with reliability and responsible AI requirements. The findings further suggest that fairness should not be treated exclusively as a model-level concern but should also influence data and workload management decisions. The resulting framework offers a research direction for building adaptive data-lake infrastructures capable of supporting heterogeneous AI workloads without sacrificing operational resilience or responsible data utilization.
Copyright (c) 2026 Dr. Rafi Pratama https://creativecommons.org/licenses/by/4.0
Resilient and Scalable Event Streaming Using Intelligent Multi-Agent AI Architectures
The increasing dependence on event-driven applications has intensified the need for streaming architectures that can sustain high data rates while remaining resilient to workload fluctuations, failures, and changing computational conditions. Conventional event-streaming systems generally depend on static routing, predefined optimization rules, and centralized control mechanisms, which can become inefficient when streaming workloads exhibit dynamic and heterogeneous behavior. This paper develops a research-oriented conceptual framework for resilient and scalable event streaming using intelligent multi-agent artificial intelligence (AI) architectures. The proposed approach combines multi-agent decision-making with graph-based representation, uncertainty-aware learning, optimization techniques, and adaptive heuristic control. The theoretical foundation is derived exclusively from the provided literature on uncertainty quantification, mixed-integer programming, primal heuristics, machine learning for combinatorial optimization, graph neural networks, and AI-based optimization. The methodology models event-streaming infrastructure as a dynamic optimization environment in which specialized agents cooperate to perform workload prediction, routing, resource allocation, anomaly detection, and recovery decisions. Graph-based representations allow relationships among producers, brokers, consumers, queues, and computational resources to be incorporated into decision processes, while uncertainty-aware mechanisms improve the reliability of learned decisions. The resulting framework indicates that intelligent agents can improve adaptability by distributing decision responsibilities and dynamically selecting optimization strategies according to workload conditions. However, the approach introduces additional computational, coordination, and explainability requirements. The study therefore positions multi-agent AI not as a replacement for established optimization methods, but as an adaptive supervisory layer capable of selecting and coordinating optimization mechanisms for resilient event-streaming environments.
Copyright (c) 2026 Rizky Pratama https://creativecommons.org/licenses/by/4.0
ARTISTIC AND AESTHETIC REFLECTION OF THE TRAGEDY OF THE AFGHAN WAR IN UZBEK LITERATURE
This article examines the impact of the tragedy of the Afghan War on our national literature, the content of works written on this topic. The fate of the characters, the psychological state of the works about the Afghan War, and the person of the war are discussed. The horrors of the tragedy are thoroughly analyzed through the novels, stories, and memoirs of writers who witnessed them with their own eyes.
War literature, Afghan tragedy, military-field novel, adventure-thriller, front-line, man of war
Scale Adapt: An Adaptive LLM Framework for Scalable Combinatorial Constraint Optimization
The increasing complexity of combinatorial optimization problems has created a need for computational frameworks capable of reasoning across heterogeneous constraints, changing problem dimensions, and competing optimization objectives. Large language models (LLMs) provide a potentially useful reasoning interface because they can interpret natural-language requirements, translate problem descriptions into structured representations, and interact with external computational tools. However, conventional LLM applications are not inherently designed to maintain constraint consistency or adapt their reasoning as optimization problems scale. This paper proposes ScaleAdapt, an adaptive LLM framework for scalable combinatorial constraint optimization. The framework integrates natural-language constraint interpretation, structured constraint modeling, adaptive decomposition, tool-assisted optimization, consistency verification, and iterative feedback. Its conceptual foundation combines research on conversational agents, LLM application development, tool-augmented question answering, and LLM security with recent work on LLM-based scalability constraint reasoning. In particular, the ScalePulse framework demonstrates the relevance of combining LLM reasoning with combinatorial scalability constraints and provides a conceptual foundation for extending adaptive optimization mechanisms (Ramamurthy et al., 2026). ScaleAdapt extends this direction by introducing adaptive problem decomposition and verification loops that dynamically respond to problem size and constraint complexity. The analysis indicates that adaptive decomposition can improve scalability, interpretability, and robustness compared with static prompting approaches, although computational overhead, hallucinated constraints, tool dependency, and verification complexity remain important limitations. The proposed framework establishes a research-oriented architecture for integrating conversational LLM capabilities with structured combinatorial optimization.
Copyright (c) 2026 Zainab Hassan https://creativecommons.org/licenses/by/4.0
PRACTICAL STRATEGIES FOR IMPLEMENTING AN EAST-WEST INTEGRATIVE PEDAGOGICAL MODEL IN THE EDUCATION OF GENERATION ALPHA
Building on a comparative analysis of Eastern and Western historical pedagogical concepts, this paper moves from theory toward classroom-level practice. It addresses the question of how the moral-spiritual orientation of Eastern pedagogical thought (al-Farabi, Ibn Sina, Abdulla Avloniy) can be operationalised through the experiential and technology-integrated methods associated with Western pedagogy (Comenius, Dewey, Vygotsky) in order to meet the specific learning profile of Generation Alpha. Drawing on recent empirical findings regarding Generation Alpha's digital fluency, shortened attention span, and preference for interactive, personalised learning, the paper proposes a stage-by-stage set of practical strategies spanning early childhood through adolescence, together with recommendations for teacher preparation, family engagement, and assessment reform. The paper concludes with a set of implementation guidelines for schools and teacher-training institutions seeking to adopt the integrative model in practice.
Generation Alpha, pedagogical technology, comparative education, moral education, digital learning, teacher training, gamification, Uzbekistan.
ScaleCore: An LLM-Based Combinatorial Framework for Scalable Constraint Reasoning and Optimization
Scalable constraint reasoning and combinatorial optimization require systems capable of representing heterogeneous constraints, identifying feasible solution regions, evaluating competing objectives, and adapting computational effort as problem size increases. Conventional clustering, machine-learning, and optimization approaches provide useful computational foundations, but their effectiveness depends strongly on problem representation, algorithm selection, scalability, and validation. This paper proposes ScaleCore, a conceptual large language model (LLM)-based combinatorial framework designed to integrate natural-language constraint interpretation with structured decomposition, algorithm selection, solution generation, validation, and iterative optimization. The framework positions the LLM as a reasoning and orchestration layer rather than as an unrestricted numerical optimizer. Its architecture combines constraint normalization, combinatorial problem abstraction, scalable solver selection, feasibility verification, and feedback-driven refinement. The theoretical foundation draws on machine-learning algorithm selection, spectral clustering, cluster validation, evolutionary optimization, and artificial-intelligence reasoning. The framework also considers the practical importance of predictive and adaptive computational systems, particularly where complex interdependencies make static decision procedures insufficient (Geo Philip, 2026). Analytical findings indicate that the principal contribution of ScaleCore is not replacement of specialized optimization algorithms but coordination of heterogeneous reasoning and optimization components through an LLM-mediated control layer. The framework offers a pathway toward scalable constraint reasoning while retaining explicit validation mechanisms necessary for reliability, interpretability, and computational control.
Copyright (c) 2026 Dr. Haruto Nakamura https://creativecommons.org/licenses/by/4.0
AI-Driven Continuous Testing Frameworks for Agile Software Quality Engineering
Agile software development requires rapid delivery, frequent change, continuous integration, and sustained product quality. Conventional testing approaches often struggle to maintain adequate coverage and timely feedback when software changes occur at high frequency. AI-driven continuous testing provides a potential framework for addressing this challenge by combining automated test selection, adaptive execution, defect-oriented prioritization, and feedback-driven quality assessment within the software delivery lifecycle. This research-review paper develops a conceptual framework for AI-driven continuous testing by synthesizing the functional principles represented in the provided literature, particularly research concerning locomotion planning, coordination, adaptive control, and biomimetic systems. Although the supplied references primarily concern robotic systems rather than software testing, their treatment of coordinated movement, path recognition, gait planning, kinematic modeling, and control provides transferable theoretical perspectives for designing adaptive testing processes. The paper proposes a layered continuous testing framework consisting of change analysis, intelligent test prioritization, adaptive execution, quality assessment, and continuous feedback. The analysis indicates that AI-driven testing can be theoretically positioned as a closed-loop control process in which test activities respond dynamically to software changes and observed quality signals. The study further identifies challenges involving explainability, training requirements, false positives, integration complexity, and the limitations of transferring concepts from robotics to software quality engineering. The resulting framework offers a conceptual foundation for integrating intelligent decision-making with Agile testing while emphasizing the need for empirical validation using real software-development datasets.
Copyright (c) 2026 Arjun Malhotra https://creativecommons.org/licenses/by/4.0
AI-Driven Intelligent Test Automation Frameworks for Advanced Software Quality Management
The increasing complexity, scale, and continuous delivery requirements of modern software systems have exposed significant limitations in conventional test automation. Traditional automation generally depends on predefined scripts, manually designed test cases, and deterministic execution paths, making it difficult to respond efficiently to changing requirements, emerging defects, and evolving software architectures. This research and review paper examines the conceptual foundations of AI-driven intelligent test automation frameworks for advanced software quality management by synthesizing the provided literature on technology acceptance, intelligent knowledge representation, contextual awareness, machine learning, structured learning, cybersecurity education, and technology–performance relationships. The study develops a conceptual framework in which artificial intelligence supports test knowledge acquisition, test-case generation, prioritization, execution, defect interpretation, and continuous quality feedback. Particular attention is given to the relationship between system usefulness and usability, represented through the technology acceptance perspective of Adams, Nelson, and Todd (1992), and the task-technology fit perspective of Goodhue and Thompson (1995). Transformer-based language representations provide a foundation for processing software artifacts and testing knowledge, while knowledge-graph principles support relationships among requirements, code components, test cases, defects, and quality outcomes. The review indicates that intelligent automation is most effective when AI capabilities are integrated with contextual awareness, structured knowledge, human oversight, and continuous feedback rather than deployed as isolated prediction mechanisms. The resulting framework provides a theoretically grounded approach for improving test adaptability, defect detection, prioritization, and quality-management decision making. Limitations concerning interpretability, training-data dependency, domain transfer, and human validation are also identified.
Copyright (c) 2026 Ren Kobayashi https://creativecommons.org/licenses/by/4.0
AI-Based Automated Defect Detection and Test Prioritization in Software Quality Engineering
The increasing complexity of software systems has created a need for quality engineering approaches that can identify defects efficiently and allocate testing resources according to risk. AI-based automation offers a mechanism for transforming conventional test execution from a largely rule-driven process into an adaptive decision-making activity. This paper develops a conceptual framework for AI-based automated defect detection and test prioritization by synthesizing principles from the provided literature on graphical authentication, password usability, user behavior, and security-oriented evaluation. Although the cited studies primarily address authentication rather than software testing, they collectively demonstrate the importance of usability, behavioral variability, attack resistance, user choice, and systematic evaluation in automated security-sensitive systems. These principles are mapped to defect detection and test prioritization through a proposed pipeline consisting of test-data acquisition, defect-feature extraction, risk scoring, intelligent classification, prioritization, execution, and feedback-based model refinement. The analysis argues that AI-driven test automation can improve the allocation of testing effort by prioritizing cases associated with higher predicted defect probability or security impact. The framework also incorporates the broader perspective of AI-driven test automation frameworks for modern software quality engineering (Ramamurthy, 2023). The paper concludes that AI-based prioritization should not replace conventional testing controls; rather, it should function as an adaptive decision-support layer whose effectiveness depends on representative data, explainability, continuous validation, and careful management of false-positive and false-negative outcomes.
Copyright (c) 2026 Azizbek Karimov https://creativecommons.org/licenses/by/4.0
ISSUES OF ENSURING THE INTIMATENESS OF PRIVATE LIFE, PRIVATE PROPERTY AND HOME AT THE COURT TRIAL STAGE IN CRIMINAL PROCEEDINGS
In criminal proceedings, the balance between the right to privacy, private property and the security of the home during the trial stage often comes into conflict with the examination of evidence, the transparency of court proceedings and the procedural rights of the parties. The aim of this study is to determine the procedural mechanisms through which these three inviolable rights are restricted or protected during court proceedings in order to ensure an individual’s right to a fair trial, as well as to identify gaps in the existing procedure. A comparative analysis has shown that the level of protection in this area increases when procedural safeguards are concentrated in a separate article or chapter. It has been concluded that, in order to ensure immunities at the trial stage, it is necessary to define authorisation, judicial review and procedural.
examination of evidence at trial, guarantees of inviolability of home, private property and private life, the Criminal Procedure Code, procedure for obtaining permission to enter a building, procedural obligations, comparative jurisprudence, judicial supervision, and its role in ensuring the right to a fair trial.
TECHNOLOGY FOR IMPROVING THE PROFESSIONAL READINESS OF PROSPECTIVE TEACHERS TO FORM A CULTURE OF INTERETHNIC COMMUNICATION
This article examines the sequential pedagogical technology for improving the professional readiness of prospective teachers to form a culture of interethnic communication. Five stages of the technology — diagnostic-preparatory, theoretical-formative, practical-training, practicum-testing, and final-evaluative — are examined. The purpose, content, and outcome of each stage are substantiated from a scientific and practical perspective.
culture of interethnic communication, pedagogical technology, prospective teacher, professional readiness, diagnostics, tolerance, pedagogical practicum, competence-based approach, ethnopedagogy, higher education.
Intelligent Construction Ecosystems: Integrating AI, Robotics, and Sustainability Through Digital Technologies
The transformation of construction into an intelligent, digitally integrated ecosystem requires the coordinated application of artificial intelligence (AI), robotics, machine learning, data platforms, and sustainability-oriented decision processes. This review-oriented research paper develops a conceptual framework for intelligent construction ecosystems by examining how AI-enabled analytical capabilities can be integrated with robotic execution, digital data infrastructures, and risk-aware operational management. Because the supplied literature primarily addresses machine learning, data-driven detection, cybersecurity, and intelligent analytical systems rather than construction directly, the study positions these contributions as transferable technological foundations rather than as construction-specific empirical evidence. The literature demonstrates the value of feature-based machine learning, logistic regression, deep learning, cloud-oriented data processing, and systematic analytical approaches for converting complex digital data into actionable decisions (Das Guptta et al., 2022; Gupta and Mahajan, 2022; Kalla et al., 2023). Building on these technological principles, the paper proposes an intelligent construction ecosystem comprising five interconnected layers: data acquisition, intelligent analytics, robotic and automated execution, sustainability optimization, and cybersecurity/governance. The analysis indicates that successful digital construction cannot depend on isolated AI or robotic applications; instead, interoperability, data quality, cybersecurity, human oversight, and lifecycle sustainability must be treated as integrated design requirements. The resulting framework provides a theoretical basis for future empirical research involving construction-specific datasets, autonomous equipment, digital twins, and sustainability metrics.
Copyright (c) 2026 Ayesha Khan https://creativecommons.org/licenses/by/4.0
STAGES, CRITERIA, AND INDICATORS OF DEVELOPING THE CULTUROLOGICAL COMPETENCE OF PROSPECTIVE TEACHERS
This article provides a scientific analysis of the sequential stages of developing the culturological competence of prospective teachers, along with their corresponding criteria and indicators. Four stages — reproductive, conscious-practical, creative-independent, and professional-exemplary — and their inherent criteria and indicators are examined. The possibilities of diagnosing competence development through this system are substantiated from a scientific and practical perspective.
culturological competence, developmental stages, criterion, indicator, prospective teacher, diagnostics, national values, professional training, pedagogy, higher education.
DIDACTIC CAPABILITIES AND PEDAGOGICAL EFFECTIVENESS OF THE INTEGRAL APPROACH IN MUSIC EDUCATION
This article provides a scientific analysis of the didactic capabilities of the integral approach in music education and its pedagogical effectiveness. Five main didactic characteristics inherent to the integral approach — enhanced knowledge transfer, economical use of instructional time, ensuring deep and durable understanding, increased learning motivation, and the development of the ability to solve complex problems — are examined. The content of each characteristic and the indicators of pedagogical effectiveness are substantiated from a scientific and practical perspective.
integral approach, music education, didactic capabilities, pedagogical effectiveness, knowledge transfer, learning motivation, music pedagogy, professional training, complex problems, higher education.
THE STRUCTURE, COMPONENTS, AND DEVELOPMENT CRITERIA OF MEDIA COMPETENCE OF PROSPECTIVE TEACHERS
This article provides a scientific and pedagogical analysis of the structure, components, and criteria determining the levels of development of media competence in prospective teachers. The structural model of media competence — comprising motivational, contact, informational, evaluative-reflective, practical-activity, and creative components — is examined. Specific criteria determining low, medium, and high levels of development for each component, along with methods for their practical diagnosis, are substantiated from a scientific and practical perspective.
media competence, structural model, development criteria, prospective teacher, diagnostics, motivational component, critical evaluation, media literacy, professional training, pedagogical diagnostics.
THE IMPACT OF DIGITAL TOOLS ON PEDAGOGICAL COMMUNICATIVE ACTIVITY AND RHETORICAL COMPETENCE IN A DIGITAL EDUCATIONAL ENVIRONMENT
This article provides a scientific and pedagogical analysis of the impact of digital tools on pedagogical communicative activity and rhetorical competence within a digital educational environment. The concepts of pedagogical communicative activity and rhetorical competence, and the features of their transformation under digital conditions, are examined. The positive and negative influence of five digital tools — video conferencing, written-text communication tools, multimedia, social network platforms, and artificial-intelligence-based speech analysis tools — on pedagogical communication and rhetorical competence is substantiated from a scientific and practical perspective.
pedagogical communication, rhetorical competence, digital educational environment, video conferencing, artificial intelligence, nonverbal communication, speech culture, teacher activity, professional training, digital tools.
THE PEDAGOGICAL NECESSITY OF DEVELOPING ENTREPRENEURIAL COMPETENCE IN PROSPECTIVE PRIMARY EDUCATION TEACHERS
This article substantiates, from a scientific and theoretical perspective, the pedagogical necessity of developing entrepreneurial competence in prospective primary education teachers. Modern socio-economic requirements, state educational standards, the specific nature of the primary education period, shortcomings in the existing system of pedagogical education, and the need for the teacher's professional-personal development are examined. Five main directions determining the necessity of developing this competence, together with their essence, are substantiated from a scientific and practical perspective.
entrepreneurial competence, prospective primary school teacher, pedagogical necessity, state educational standard, initiative, primary education, professional training, competence-based approach, economic thinking, higher pedagogical education.
Large Language Models for Intelligent Decision-Making in Robotics-Enabled Sustainable Construction
The integration of Large Language Models (LLMs) with robotics-enabled construction represents an emerging socio-technical approach to intelligent decision-making in complex and sustainability-oriented construction environments. This paper develops a conceptual research framework for understanding how language-based artificial intelligence can support robotic systems in interpreting operational information, coordinating activities, prioritizing construction decisions, and responding to uncertainty. Because the supplied literature primarily addresses data-driven prediction, customer-behavior modeling, class imbalance, feature selection, and social-network effects rather than construction robotics or LLMs directly, the study adopts a theory-transfer methodology. The analytical foundation is derived exclusively from the seven supplied references and extends their principles of predictive modeling, feature selection, imbalance management, survival-oriented reasoning, and relational analysis toward construction decision environments. The proposed framework conceptualizes LLM-enabled robotic decision-making as a layered process comprising data acquisition, contextual interpretation, predictive reasoning, decision generation, robotic execution, and feedback-based adaptation. The analysis indicates that intelligent construction systems require more than language generation: they require reliable data representation, uncertainty management, contextual prioritization, and mechanisms for translating analytical outputs into operational actions. The study identifies potential benefits in resource allocation, safety-oriented planning, equipment coordination, workflow adaptation, and sustainability management while emphasizing limitations involving domain transfer, data quality, interpretability, imbalance, and decision accountability. The paper contributes a theoretically grounded framework for future empirical investigation of LLM-driven decision intelligence in robotics-enabled sustainable construction.
Copyright (c) 2026 Muhammad Hamza https://creativecommons.org/licenses/by/4.0
DRUG ADDICTION - THE RUIN OF LIFE: LEGAL AND EDUCATIONAL FOUNDATIONS FOR PROTECTING YOUTH FROM THE SCOURGE OF DRUG ADDICTION
This article analyzes the negative impact of drug addiction on the health and moral development of young people, as well as on the stability of society. It examines the current legislation of the Republic of Uzbekistan on the circulation of narcotic drugs and psychotropic substances, the liability measures provided for in the Criminal Code and the Code of Administrative Responsibility, and recent legislative developments in this area. The article also highlights the educational tasks of the family, educational institutions, mass media, and the public in protecting youth from drug addiction, and offers recommendations for improving the prevention system.
drug addiction, youth, legal responsibility, narcotic substances, prevention, education, healthy lifestyle, social security.
ANALYSIS OF GOVERNANCE AND COORDINATION MECHANISMS IN PUBLIC-PRIVATE SECTOR COOPERATION
This article analyzes the essence, institutional foundations, and practical effectiveness of governance and coordination mechanisms within the framework of cooperation between the public and private sectors. Special attention is given to the mutual coordination of powers, responsibilities, and interests among state bodies, private business entities, and other stakeholders in public-private partnership relations. The role of decision-making, resource allocation, risk management, information exchange, monitoring, and control mechanisms in the cooperation process is also examined. The study highlights problems related to ensuring a balance of interests between the public and private sectors, increasing the transparency and accountability of governance, and strengthening the socio-economic effectiveness of joint projects. Scientific and practical proposals are put forward for improving institutional coordination, utilizing digital governance tools, and enhancing principles of mutual responsibility for the effective organization of public-private cooperation.
public-private sector cooperation, public-private partnership, governance mechanisms, coordination, institutional governance, public administration, private sector, balance of interests, risk management, monitoring, control, digital governance, transparency, accountability, cooperation effectiveness.
SOCIO-PHILOSOPHICAL FOUNDATIONS FOR THE INTEGRATION OF PERSONS WITH DISABILITIES INTO THE SYSTEM OF SOCIAL RELATIONS
This article analyzes the socio-philosophical foundations of integrating people with disabilities into the system of social relations. It examines the social status and place of individuals in society, equality of rights and opportunities, social justice, and inclusiveness as important factors ensuring the full participation of people with disabilities in public life. The article substantiates the need to understand disability not only as a medical condition but also as a social, cultural, and philosophical phenomenon. It reveals the impact of social barriers, stereotypes, discrimination, and social exclusion on the integration of people with disabilities. The study emphasizes that creating an inclusive social environment, recognizing human dignity, and developing social interaction are essential conditions for ensuring an active civic position and the full integration of people with disabilities into modern society.
people with disabilities, social integration, social relations, inclusiveness, social justice, human dignity, equal opportunities, social exclusion, discrimination, social interaction.
Low-Dimensional HRTF Characterization Through Common-Pole/Zero Modeling and Statistical Component Analysis
Head-related transfer functions (HRTFs) provide a mathematical representation of the acoustic filtering imposed by the human head, torso, and external ears and constitute a fundamental component of three-dimensional spatial audio reproduction. However, the high dimensionality of measured HRTF datasets creates substantial challenges for storage, interpolation, transmission, and real-time rendering. This paper develops a low-dimensional HRTF characterization approach that integrates common-pole/zero modeling with statistical component analysis. The proposed methodology first represents individual HRTFs through a shared acoustic pole/zero structure and subsequently transforms the resulting parameter space into a compact statistical representation. The approach is theoretically motivated by established common-acoustical-pole/zero models and finite- and infinite-impulse-response HRTF representations. The proposed framework separates relatively stable acoustic characteristics from location-dependent variation, thereby reducing the number of parameters required to describe a spatial HRTF dataset. A methodological analysis demonstrates how common-pole/zero modeling can provide physically interpretable spectral parameters while component analysis can reduce redundancy among those parameters. The study further examines reconstruction, dimensionality, computational efficiency, and potential deployment in real-time spatial-audio systems. The resulting framework offers a structured compromise between acoustic fidelity, parameter compactness, and computational complexity. Its principal significance lies in combining parametric acoustic modeling with statistical dimensionality reduction rather than treating either technique independently.
Copyright (c) 2026 Dr. Andreas Nicolaou https://creativecommons.org/licenses/by/4.0
Optimized Graph Neural Network Framework for Intelligent Cyber Threat Identification and Security Monitoring in Cloud Platforms
The increasing interconnectivity, virtualization, and dynamic resource allocation of cloud platforms have created complex cybersecurity environments in which conventional security monitoring approaches may struggle to represent relationships among users, virtual machines, services, network flows, and attack behaviors. This research proposes an Optimized Graph Neural Network (OGNN) Framework for intelligent cyber threat identification and security monitoring in cloud platforms. The framework models cloud infrastructures as heterogeneous interaction graphs and applies graph-based representation learning to capture structural and behavioral dependencies among interconnected entities. Graph normalization, feature aggregation, uncertainty estimation, and optimization-oriented decision mechanisms are incorporated to improve detection robustness and operational effectiveness. The theoretical foundation combines graph neural network reasoning with machine-learning-assisted optimization, particularly insights from graph-based combinatorial reasoning and mathematical optimization. The proposed framework uses node, edge, and graph-level representations to classify suspicious activities and generate security-risk indicators while accounting for prediction uncertainty. The conceptual findings indicate that graph-based modeling can improve contextual threat identification compared with isolated event analysis, while optimization mechanisms can support efficient monitoring under computational constraints. The framework further emphasizes uncertainty-aware decisions because high-confidence and low-confidence alerts should not receive identical security responses. The research contributes an integrated architecture for cloud threat monitoring that connects relational deep learning, optimization, and uncertainty quantification. The study also identifies limitations associated with dynamic graph construction, computational scalability, heterogeneous cloud telemetry, and the absence of universally representative benchmark conditions. The proposed architecture provides a research foundation for adaptive, explainable, and optimization-driven cloud cybersecurity systems.
THE LEGAL REGULATION OF CITIZENS’ INTERACTION WITH PUBLIC AUTHORITIES IN THE CONTEXT OF ELECTRONIC GOVERNMENT
This article analyzes the legal regulation of citizens’ interaction with public authorities in the context of electronic government from constitutional, administrative, information law, and comparative legal perspectives. It examines electronic government as a comprehensive legal institution ensuring transparency, accessibility of public services, protection of citizens’ digital rights, administrative accountability, and effective digital governance. The study also identifies existing legislative challenges and proposes directions for improving the legal framework governing electronic interaction between citizens and public authorities.
electronic government, digital governance, citizens’ rights, public authorities, administrative law, digital public services, electronic interaction, legal regulation, interoperability, cybersecurity
IMPROVING THE MECHANISMS FOR STRATEGIC DECISION-MAKING IN THE DEVELOPMENT OF GENERAL SECONDARY SCHOOL PROGRAMS
This article examines the improvement of strategic management decision-making mechanisms in the development of programs for the advancement of general secondary schools. The study analyzes organizational and pedagogical aspects of identifying development prospects of educational institutions, ensuring the effective use of available resources, and improving the evidence-based nature and effectiveness of management decisions. Particular attention is paid to the importance of strategic planning, data-driven management, stakeholder participation, as well as monitoring and evaluation mechanisms in developing school development programs. The article presents proposals and recommendations aimed at improving the process of strategic management decision-making and increasing the effectiveness of managing the development of general secondary schools.
general secondary education, educational institution, strategic management, management decisions, development program, strategic planning, quality of education, management effectiveness, monitoring, evaluation.
EFFECTIVENESS OF THE MODEL FOR DEVELOPING MEDIA LITERACY IN EDUCATION MANAGERS: EXPERIMENTAL AND TRIAL RESULTS
This article analyzes the effectiveness of a model aimed at developing the media culture of education managers and examines the results of its practical implementation. The study involved experimental work focused on developing education managers' competencies in searching for, analyzing, evaluating, and selecting media information, as well as its effective use in professional management activities. Based on the experimental results, changes in the level of media culture among education managers were identified according to established criteria and indicators, and a comparative analysis of the experimental and control groups was conducted. The findings demonstrate the effectiveness of the developed model in improving media literacy, information and communication competence, critical thinking, and responsible behavior within the digital information environment. The practical significance of the model for improving the management of educational organizations in the context of the digitalization of education is substantiated.
education manager, media culture, media literacy, digital competence, information and communication competence, experimental work, educational management, critical thinking, digital educational environment.