American Journal of Applied Science and Technology

Opportunities For Using Interdisciplinary Relationships In Teaching Mathematics In Technical Higher Educational Institutions

2026-01-15T09:45:34+00:00

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Credible, Privacy-Preserving, And Maintainable Machine Learning Systems: An Integrated Framework Grounded In Data Quality, Underspecification, And Software Engineering Principles

2026-01-01T02:35:20+00:00

The rapid institutionalization of machine learning systems across scientific, commercial, and public-sector domains has elevated concerns regarding credibility, privacy, robustness, and long-term maintainability. While advances in model architectures and learning paradigms have attracted significant scholarly and industrial attention, foundational challenges related to data quality, system underspecification, privacy leakage, and engineering rigor remain insufficiently integrated into a unified conceptual framework. This article develops a comprehensive, theoretically grounded analysis that synthesizes insights from data cleaning systems, data integration research, differential privacy theory, adversarial machine learning, underspecified model behavior, and classical software engineering methodologies. Drawing strictly on the provided references, the study articulates how data defects propagate through learning pipelines, how underspecification undermines empirical credibility, and how privacy and security threats exploit both data and model artifacts. The methodology adopts a qualitative, analytical synthesis approach, treating established systems and theories as conceptual instruments rather than empirical datasets. Results are presented as a structured descriptive analysis identifying recurring patterns, tensions, and complementarities across the literature. The discussion interprets these findings through the lens of system-level accountability, arguing that credibility in modern machine learning emerges not from isolated technical fixes but from coordinated design principles spanning data preprocessing, algorithm selection, privacy guarantees, verification techniques, and disciplined software development practices. Limitations related to empirical generalization and evolving technological contexts are acknowledged, and future research directions emphasize automated workflow validation, deductive reasoning verification, and institutional governance mechanisms. The article concludes that a credible machine learning system must be understood as an engineered socio-technical artifact, whose reliability depends equally on data hygiene, theoretical guarantees, and sustainable engineering processes.

In Uzbekistan, The Press Is The Main Symbol Of A Democratic Society

2026-01-14T18:30:08+00:00

This article analyzes the role and significance of the press in Uzbekistan's democratic society. Particular attention is paid to the role of mass media in implementing public oversight within the framework of the Law "On Public Oversight" adopted in 2018. The press is viewed as an important tool for fostering freedom of speech, openness, and civil society.

Technologies Of Artificial Intelligence in Optical Communication

2026-01-11T15:05:02+00:00

This article explores the application of artificial intelligence (AI) in optical communication technologies. Optical communication, as a backbone of high-speed data transmission, requires optimization methods to reduce noise, minimize errors, and ensure adaptive control. AI techniques such as deep learning, Bayesian algorithms, and ant colony optimization are widely employed for signal processing and adaptive modulation in optical networks. The research further highlights how AI-based modeling of optical communication processes can be embedded in ITS platforms to provide real-time simulations for learners.

Solute Transport In A Porous Medium With Piecewise Homogeneous Salinization

2026-01-08T09:43:56+00:00

In the paper a problem of salt transport in a porous medium with picewise salinization is numerically solved. Two cases are considered: 1) bed with immoile liquid zone saturated with salt solution; 2) dry bed with picewise salinization. Influence of salt adsorption on salt transport characteristics is established.

Polyphenolic Compounds Of Pomegranate Peel: Extraction, Characterization, And Nutritional Potential

2026-01-16T09:34:22+00:00

Background: Pomegranate peel, a by-product of juice production, contains bioactive compounds with significant nutritional and therapeutic properties.

Objective: This study aimed to isolate and characterize polyphenolic compounds from Punica granatum L. peel and to evaluate their nutritional, functional, and mineral composition.

Methods: Extraction was performed using distilled water at 60 °C, followed by precipitation with gelatin. High-performance liquid chromatography (HPLC) and atomic absorption spectroscopy (AAS) were used for compound identification.

Results: The tannin content in pomegranate peel was 77.8%, with rutin, isorhamnetin, gallic acid, and hyperoside being the main flavonoids identified. Mineral analysis revealed the presence of Ca, Na, Mg, Fe, Cu, and Al in the tannin powder.

Conclusion: Pomegranate peel extract demonstrated high polyphenolic content with potential applications as functional food ingredients and nutraceuticals.

Reconceptualizing Intelligent Financial Operations: A Theoretical and Applied Examination of Hyperautomation through Generative Artificial Intelligence and Process Mining

2026-01-05T13:34:11+00:00

The accelerating convergence of artificial intelligence, advanced analytics, and enterprise automation has catalyzed a profound transformation in how financial workflows are designed, executed, and governed across organizational contexts. Hyperautomation, as an emergent paradigm, transcends traditional automation by integrating robotic process automation, machine learning, generative artificial intelligence, process mining, and data-driven orchestration into cohesive, adaptive systems. This article develops a comprehensive, publication-ready scholarly investigation into hyperautomation as a foundational architecture for intelligent financial operations. Drawing rigorously and exclusively from the provided body of literature, the study synthesizes theoretical foundations, historical evolutions, and applied perspectives to articulate how generative artificial intelligence and process mining jointly redefine financial workflow optimization, resilience, and strategic value creation.

The research is grounded in a qualitative, theory-driven methodology that critically examines extant academic and practitioner-oriented contributions on artificial intelligence evolution, business process optimization, data analytics, sustainability, ethical design, and sector-specific automation, with particular attention to financial and insurance ecosystems. Central to the analysis is the framework proposed by Krishnan and Bhat (2025), which conceptualizes hyperautomation as an integrative, intelligence-amplifying system for financial workflows. This framework is positioned within broader debates on digital transformation, cognitive augmentation, and socio-technical system design, allowing for an expansive interpretation of hyperautomation not merely as a technological toolkit but as an organizational capability and governance challenge.

The findings reveal that hyperautomation-driven financial workflows exhibit enhanced process transparency, decision accuracy, compliance robustness, and adaptive learning when generative AI and process mining are synergistically deployed. However, the results also surface critical tensions related to ethical accountability, data governance, workforce displacement, and sustainability, underscoring the necessity of human-centered and policy-aligned automation strategies. The discussion extends these insights by engaging deeply with competing scholarly viewpoints, articulating limitations of current frameworks, and proposing future research directions that emphasize hybrid human–AI collaboration, explainability, and sectoral contextualization. By offering an exhaustive theoretical elaboration and critical discourse, this article contributes a robust academic foundation for understanding hyperautomation as a transformative force in financial operations and beyond.

Green Energy And Economic Growth Dynamics

2026-01-15T07:13:13+00:00

The global transition to green energy has transformed renewable technologies into fundamental drivers of economic growth, industrial modernization, and national resilience. This study examines the economic mechanisms through which green energy contributes to macroeconomic performance by integrating a modified Cobb–Douglas production function, Leontief input–output analysis, and comparative institutional assessment. Using multi-source data from the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the OECD, the World Bank, and Bloomberg New Energy Finance, the study evaluates the impacts of renewable deployment on capital formation, employment dynamics, supply chain complexity, green finance expansion, and energy security.

The results demonstrate that renewable energy adoption reinforces total factor productivity and stimulates high-value industrial clusters, including photovoltaic systems, wind turbines, hydrogen production, battery energy storage systems (BESS), and smart microgrids. These clusters exhibit significant backward and forward linkages, generating manufacturing spillovers, skilled job creation, and technological innovation. The findings also indicate that green finance instruments—particularly green bonds, ESG-compliant investment, and carbon pricing frameworks—play a central role in mobilizing private capital and accelerating renewable deployment. Moreover, the diversification of the energy mix reduces dependence on imported hydrocarbons, thereby enhancing strategic autonomy and macroeconomic stability.

Overall, the study concludes that green energy should be interpreted not merely as an environmental asset but as a strategic economic catalyst of the twenty-first century. Its contribution extends across economic, financial, industrial, and geopolitical domains, suggesting that countries capable of successfully integrating renewable energy into their industrial and financial architectures will secure stronger competitive advantages, more diversified economic structures, and more resilient development trajectories.

Innovative Art-Supported Techniques for Teaching English to Primary School Students

2026-01-11T15:13:23+00:00

The article examines innovative technologies for teaching English to young learners through the integration of art-based methods, with a particular emphasis on drama and its related forms - such as simulation, role-play, puppetry, and pantomime. These interactive and expressive techniques are explored as effective tools for enhancing communicative competence, emotional intelligence, and cultural awareness among young learners. The study discusses how drama-based technologies foster creativity, motivation, and linguistic confidence by engaging children in authentic communicative situations. It also analyzes the methodological and psychological foundations of incorporating drama into the English language classroom, demonstrating that art-infused instruction-supported by modern digital tools - creates a multisensory and learner-centered environment conducive to holistic language development.

Optimization Of Thermal-Electric Transport Of The Interface In The Cdte/Cds – Bi₂Te₃/Sb₂Te₃ Monolithic System

2026-01-08T09:46:21+00:00

This article is aimed at an in-depth analysis of the thermal-electrical transport processes that occur when CdTe/CdS-based photovoltaic (PV) layers and Bi₂Te₃/Sb₂Te₃ thermoelectric (TE) modules are combined into a single monolithic structure. Excess heat generated in PV elements can be converted into electrical energy directly through the TE module, however, factors such as diffusion between materials, thermomechanical stress, and high thermal resistance of the interface reduce the efficiency of such integration. Therefore, it is proposed to use nano-thick, thermally conductive and electrically insulating layers such as h-BN or AlN as interfaces. During the study, the thickness, thermal resistance, diffusion-limiting properties, and energy bandgap compatibility of these nano-interfaces were evaluated through computer modeling. The results obtained showed that the correct choice of interface can reduce heat losses in PV–TE systems, increase the power generation capacity of the TE module, and improve the overall efficiency of the hybrid system. Thus, the study scientifically confirms that interface engineering is one of the key factors for high efficiency and stable operation in monolithic PV–TE energy systems.

Change In Shrinkage Of Aluminum Alloy During Crystallization As A Result Of Alloy Modification

2026-01-07T08:14:14+00:00

Aluminum alloys differ from other non-ferrous alloys due to their high casting properties. This article analyzes the effect of modification of aluminum alloys on their shrinkage. The studies were carried out on alloys of the aluminum–silicon and aluminum–copper systems. These alloys were modified with lithium fluoride, and changes in their shrinkage were investigated. Based on the conducted experiments and tests, a relationship between the shrinkage of aluminum alloys and the amount of modifying additive was established. The article also presents the authors’ conclusions drawn from the obtained results.