Volume 7.
Issue 10.

The article discusses the event-driven API integration of delivery aggregators with restaurant CRM as a core foundation of digital transformation within the gastronomic sector. The purpose is to design and test empirically an end-to-end event loop comprising a cloud message bus, implemented in a Go/Node.js microservices architecture orchestrated by Kubernetes, and versioned using blockchain data to minimize latency and increase fault tolerance, such that each transaction becomes a stream of training signals. Topicality is justified in reference to the widening gap between customer expectations for immediacy and sequential REST call practices today. It becomes strategically indispensable to frame possible and envisaged latency reduction figures of 73.8% and a 284% throughput increase around an event model. The novelty of this work lies in the complex synergy of three layers: (1) a unified JavaScript/TypeScript stack that eliminates cognitive and serialization overhead; (2) serverless functions with autoscaling to zero, aligning the cost of infrastructure with actual peak traffic; (3) a layer-two blockchain that reduces the cost of an immutable ledger by 94% and makes events legally binding. In this way, ordering, logistics, inventory, and loyalty programs merge into a self-learning fabric, wherein idempotency, type-safe schemas, and observability are intrinsic rather than bolt-on mechanisms. To that end, moving integration onto events transforms a restaurant from a static system into a reactive cyber-physical system that discovers demand, learns to price dynamically, and cryptographically records every action. The article will be helpful to architects and digital product managers in the HoReCa sector, as well as researchers of distributed systems and applied AI.

The modern digital ecosystem is characterized by an unprecedented volume of information and a lightning-fast speed of its dissemination, which renders the forecasting of trends in social networks the cornerstone of effective strategic communications. Within the scope of the study, a critical-systemic evaluation of methodological tools of artificial intelligence (AI) employed for predictive analytics in social media was conducted, followed by their adaptation to the tasks of public relations specialists. The aim of the research is to develop a conceptual framework that integrates various algorithmic AI approaches—from natural language processing (NLP) and computer vision to network analysis—for constructing proactive strategies in corporate reputation management and communication campaigns. The empirical foundation of the study is formed through a systematic review of leading scientific publications dedicated to the described technologies and their applied capabilities in the context of detecting and predicting public sentiment. The scientific novelty of the work lies in the formulation of a unified methodological paradigm that shifts the emphasis of PR activities from passive response to informational challenges to active shaping of media discourse. The conclusions obtained will be useful both to researchers in the fields of communications, data processing, and computational linguistics, and to practitioners — PR directors and heads of digital agencies.

The contemporary food industry is undergoing a paradigm shift driven by consumer demand for health-promoting functional foods and the necessity for robust New Product Development (NPD) frameworks. This research article synthesizes two critical trajectories in food science: the optimization of sensory and chemical monitoring in fruit-based beverages, specifically pomegranate and apple juices, and the fortification of cereal-based products using water chestnut (Trapa natans) flour. By examining the efficacy of electronic tongues alongside traditional sensory panels, this study evaluates the reliability of instrumental measurements in predicting consumer preference. Furthermore, the article explores the rheological and nutritional implications of incorporating non-traditional tubers into bakery formulations. The methodology adheres to ISO standards for sensory analysis, ensuring a rigorous environment for hedonic testing. Results indicate that while instrumental tools provide high precision in detecting chemical variance, human sensory perception remains the ultimate arbiter of market success. The integration of market-oriented development models, such as the Stage-Gate process, is highlighted as essential for bridging the gap between laboratory innovation and consumer acceptance. This comprehensive analysis provides a theoretical and practical roadmap for food technologists aiming to balance nutritional fortification with palatability in a competitive global market.

The study examines the issue of insufficient physical activity among female students and explores the effectiveness of handball training as a compensatory mechanism for improving their physical fitness, health, and psycho-emotional stability. In the context of modern higher education, sedentary learning processes and academic stress significantly reduce students’ physical activity, leading to a decline in adaptive capacity and an increased risk of functional disorders. This research, conducted at Gulistan State University between 2019 and 2022, assessed 102 first-year female students aged 17–19 who had no prior sports training but expressed an interest in handball. Using questionnaires, professionographic analysis, physical and functional assessments, and health diagnostics based on the G.L. Apanasenko method, the study revealed a pronounced deficit of motor activity in the majority of participants.

The experiment demonstrated that systematic handball training significantly enhanced the students’ aerobic endurance, motor coordination, and cardiovascular efficiency while improving their social engagement and teamwork skills. The intervention also contributed to the normalization of vital indicators such as lung capacity, muscle strength, and body composition. Data analysis indicated a positive correlation between regular handball training and improvements in physical and mental well-being. Consequently, handball was shown to be an effective pedagogical and recreational tool for counteracting hypokinesia and fostering the holistic development of female students.

The findings emphasize the necessity of integrating innovative handball-based training programs into university curricula as a structured approach to promoting health and preventing lifestyle-related diseases among young women. The study highlights that regular participation in sports not only compensates for the lack of physical activity but also supports students’ academic performance and overall quality of life.

The transformation of credit markets through artificial intelligence-driven analytics and real-time data processing has redefined the epistemic foundations of credit risk assessment, financial inclusion, and regulatory oversight. Over the past two decades, credit scoring has evolved from static, historically grounded statistical models toward dynamic, continuously updated systems capable of integrating heterogeneous data streams at unprecedented temporal resolutions. This evolution has been accelerated by advances in machine learning, big data infrastructures, and digital financial platforms, which together have enabled real-time credit scoring architectures that promise enhanced predictive accuracy, operational efficiency, and market responsiveness. At the same time, these developments have raised fundamental theoretical, methodological, ethical, and regulatory questions that remain insufficiently resolved in contemporary scholarship. This article develops a comprehensive and integrative research framework for understanding real-time credit scoring and risk analysis within digital loan platforms, grounding the analysis in interdisciplinary literature spanning finance, data science, regulatory theory, and socio-technical systems research.

Drawing extensively on existing scholarship, the study situates real-time credit scoring within broader debates on fintech-driven financial inclusion, algorithmic governance, and systemic financial stability. Particular attention is given to the conceptual shift from periodic, backward-looking credit evaluations to continuous, forward-looking risk monitoring, as articulated in recent research on AI-enabled loan platforms (Modadugu et al., 2025). The article elaborates the theoretical underpinnings of machine learning-based credit analytics, including learning paradigms, feature construction, temporal modeling, and interpretability challenges, while critically engaging with counter-arguments concerning opacity, bias, and regulatory compliance (Alhaddad, 2018; Bodo et al., 2017). Methodologically, the paper adopts a qualitative, literature-driven analytical design that synthesizes insights from empirical studies, conceptual models, and policy-oriented analyses to derive an integrated explanatory narrative.

The results section presents a structured interpretation of how real-time credit scoring systems reshape risk classification, borrower–lender relationships, and institutional decision-making processes. These findings are not presented as numerical outputs but as analytically grounded patterns emerging across the reviewed literature, highlighting convergences and tensions among competing scholarly perspectives (Arner et al., 2016; Brummer & Yadav, 2018). The discussion extends this analysis by interrogating the implications of real-time risk analytics for financial regulation, ethical accountability, and long-term financial stability, while identifying persistent gaps related to governance, transparency, and socio-economic impact. By offering an expansive theoretical and methodological synthesis, this article contributes to ongoing academic and policy debates on the role of artificial intelligence in credit markets and provides a foundation for future empirical and normative research.

The article examines the adaptive control as well as the compelled reverse engineering phenomenon of an autonomous solar power station in conditions of infrastructural and geopolitical crises precipitated by large-scale blackouts. The study stresses energy resilience is important, noting a rise in proprietary black boxes and absent documentation about safety-critical parts. Conflict along with disaster zones especially highlight this issue. In Odesa, engineers independently deconstructed an undocumented inverter protocol and developed a monitoring system to minimise life-safety risks. This occurred at a time of wartime outages so the research objective is for a systematic analysis of this case study. This work is novel in that it conceptualizes a Resilience, Vulnerability Nexus model, elucidating a paradox: reverse engineering locally for improvement of resilience inextricably links to globalizing vulnerabilities that were previously hidden within closed technologies. The principal findings show two cardinal engineering trade-offs converge, feasibility opposes rigour, trust opposes accuracy, as well as they also recognize that, under extreme constraints, immediate functionality with solution transparency supersedes technical perfection. Alternative algorithms for estimating battery state-of-charge are also assessed. The article will be of use to researchers in energy and cybersecurity, practicing engineers and policymakers, and system designers when they are developing decentralized and resilient energy systems.

The exponential growth of data-driven business models has fundamentally transformed organizational decision-making, particularly in analytics-intensive and finance-oriented sectors. While data analytics enables efficiency, predictive accuracy, and strategic advantage, it simultaneously introduces significant risks related to data privacy, regulatory non-compliance, and ethical misuse. This research article provides an extensive theoretical and empirical examination of data privacy enhancement methods, regulatory impacts, and financial integrity challenges within modern business analytics. Drawing strictly on established scholarly literature, this study integrates perspectives from data privacy engineering, regulatory economics, accounting ethics, surveillance theory, blockchain security, and financial misreporting research.

The article investigates how privacy-preserving mechanisms, regulatory compliance frameworks, and governance structures influence organizational behavior, risk exposure, and trust formation in financial and analytical environments. It critically examines how historical accounting scandals, corporate misreporting, and dataveillance practices inform contemporary regulatory responses and data protection mandates. The discussion further explores the tension between transparency and confidentiality, particularly in financial reporting and advanced analytics systems.

Through a qualitative synthesis of prior empirical findings and theoretical constructs, the study identifies recurring patterns linking weak data governance to financial misconduct, regulatory penalties, and reputational damage. It also evaluates the emerging role of secure digital infrastructures, including cryptographic and decentralized systems, in mitigating privacy risks while maintaining analytical utility. The findings underscore that data privacy is not merely a technical challenge but a multidimensional governance issue intersecting law, ethics, economics, and organizational culture.

This research contributes a unified conceptual framework that connects data privacy practices with financial integrity and regulatory accountability. It offers nuanced insights for scholars, policymakers, and practitioners seeking to balance innovation with responsible data stewardship. By situating modern analytics within broader socio-economic and regulatory contexts, the article advances understanding of how robust privacy governance can reinforce trust, reduce misconduct, and support sustainable business performance.

Background: The development of dexterous and intuitive prosthetic hands remains a significant challenge in rehabilitation engineering. Myoelectric control systems, which interpret surface electromyography (sEMG) signals from residual muscles, offer a promising avenue for non-invasive human-machine interfacing. However, traditional systems often suffer from limited accuracy, slow response times, and a lack of robustness to real-world conditions, hindering their clinical viability and user adoption. This article provides a comprehensive review of recent advancements aimed at overcoming these limitations.

Methods: We conducted a systematic review of contemporary literature focused on sEMG-based prosthetic control. The analysis covers the full spectrum of the control pipeline, including signal acquisition, pre-processing, and, most critically, feature extraction and pattern recognition. A special emphasis is placed on the transition from traditional machine learning classifiers to advanced deep learning architectures, particularly Convolutional Neural Networks (CNNs), for decoding hand gestures. The performance of these models is evaluated based on key metrics such as classification accuracy, computational latency, and robustness.

Results: The synthesis of recent findings reveals a clear trend: deep learning models, especially CNNs, consistently outperform traditional methods in hand gesture recognition accuracy, often exceeding 95% in controlled settings. Studies demonstrate that CNNs can automatically learn discriminative features from raw or minimally processed sEMG signals, eliminating the need for complex manual feature engineering. Furthermore, hybrid models and optimized network architectures have shown significant progress in achieving the low latency required for real-time prosthetic control.

Conclusion: Advanced signal analysis, powered by deep learning, represents a paradigm shift in myoelectric prosthetic control. These techniques are paving the way for more natural, reliable, and dexterous artificial limbs. Despite this progress, challenges related to inter-user variability, long-term stability, and clinical translation remain. Future research should focus on developing more generalizable models, integrating sensory feedback, and conducting extensive real-world usability studies to bridge the gap between laboratory breakthroughs and practical application.

The evolution of heating, ventilation, and air conditioning (HVAC) systems toward intelligent, data-driven frameworks has necessitated the integration of advanced machine learning methodologies for fault detection, diagnosis, prognostics, and system optimization. This research investigates contemporary and emergent techniques in smart HVAC operations, emphasizing convolutional neural networks, semi-supervised learning, transfer learning, and predictive control strategies. Through critical synthesis of literature, the study highlights the importance of interpretability, limited data handling, and occupant-centered adaptive controls in achieving enhanced energy efficiency and operational reliability. Key contributions include a detailed exposition of fault diagnosis frameworks that leverage 2-D convolutional neural networks for multizone HVAC environments (Elnour & Meskin, 2022), the utilization of semi-supervised and transfer learning paradigms for rooftop units and air-handling systems (Albayati et al., 2023; Martinez-Viol et al., 2022), and the integration of physiological and environmental signals for real-time adaptive control (Deng & Chen, 2020). The study also examines the economic and practical implications of early adoption of intelligent fault detection and predictive maintenance systems in commercial and residential contexts. Emphasis is placed on bridging the gap between high-fidelity data-driven models and real-world deployment constraints, including data scarcity, false alarms, and heterogeneous system configurations. The findings demonstrate the transformative potential of harmonizing IT/OT infrastructure in HVAC systems for achieving sustainable energy performance while maintaining occupant comfort. The research provides a comprehensive theoretical and applied framework for future developments in predictive HVAC control, energy optimization, and intelligent fault management.

Objective: This study evaluates the impact of maternal and infant mortality on healthcare system efficiency in Sub-Saharan Africa (SSA) using a directional Data Envelopment Analysis (DEA) model.

Method: The analysis incorporates desirable outputs, such as healthcare coverage, and undesirable outputs, specifically maternal and infant mortality rates, to assess the relative efficiency of 36 SSA countries over 12 years (2006–2017).

Findings: Findings reveal that healthcare expenditure significantly influences efficiency, accounting for over 50% of the variation in performance scores, followed by the availability of nurses and midwives. Countries with lower maternal and infant mortality rates consistently ranked higher in efficiency, highlighting the dual role of mortality as both an outcome and a determinant of health system performance. Sensitivity analysis further confirmed that strategic investments in workforce and financing yield substantial efficiency gains.

Conclusion: The study offers empirical evidence supporting the integration of mortality indicators into performance assessments and presents a robust methodological framework for evaluating health systems in resource-constrained settings. It contributes to the literature by addressing methodological gaps and providing actionable insights for policymakers. The results underscore the urgency of prioritizing maternal and child health in healthcare reform agendas to achieve equity and efficiency across SSA.

This study investigates the role of instant analytical interfaces in enhancing managerial judgments and organizational flexibility. In contemporary organizational environments characterized by high uncertainty and rapid technological change, decision-makers are increasingly reliant on real-time data and interactive analytical tools to support complex strategic and operational choices. Despite the proliferation of dashboard technologies and visualization interfaces, the empirical understanding of their impact on managerial decision quality and organizational adaptability remains limited.

Drawing on both cognitive and organizational theories, this research examines how instant analytical interfaces facilitate improved information processing, reduce cognitive load, and enable rapid scenario evaluation. The study employs a synthesis of prior empirical and theoretical work, particularly focusing on resource-based views of strategy, core organizational competencies, and the integration of technological tools into managerial processes (Alvarez &Busenitz, 2001; Deng & Chen, 2006; Garcia-Morales et al., 2006; Singh, 2024). Key outcomes include enhanced decision accuracy, reduced latency in managerial responses, and strengthened organizational flexibility, which together contribute to the capacity of firms to adapt proactively to dynamic environments.

The paper presents a structured analytical framework linking real-time visualization, cognitive facilitation, and organizational performance. By critically examining both theoretical foundations and practical implementations, the study identifies best practices, design considerations, and potential limitations associated with instant analytical interfaces. Moreover, the research integrates cross-contextual insights from high-tech, small business, and enterprise-level case studies to provide comprehensive guidance for managers and organizational designers.

Findings indicate that well-designed instant analytical interfaces act as strategic enablers, extending human cognitive capacities while promoting agile organizational structures. The study further highlights the critical role of interface design, usability, and data accuracy in maximizing decision-making effectiveness. Overall, this research contributes both to the theoretical understanding of technology-mediated managerial cognition and to the practical optimization of real-time analytical tools for enhanced organizational performance (Singh, 2024)

This article presents the scientific conclusions related to the natural science hypotheses found in the intellectual correspondence (Q&A letters) between Abu Rayhan Beruni and Abu Ali Ibn Sina — two key figures considered vital sources in the development of socio-philosophical thought during the first Renaissance period in Uzbekistan. The article also explores the social and philosophical aspects of natural philosophy as reflected in the views of Beruni and Ibn Sina.

The rapid digital transformation of global financial markets has introduced unprecedented levels of complexity, uncertainty, and interconnectedness, challenging the foundations of classical portfolio theory and conventional risk management paradigms. Traditional optimization frameworks, while mathematically elegant, are increasingly unable to adapt to the nonstationary, nonlinear, and highly volatile environments that characterize modern asset markets. Within this evolving context, intelligent cloud-based deep reinforcement learning has emerged as a promising computational paradigm capable of learning, adapting, and optimizing financial decision-making under uncertainty. This study develops a comprehensive theoretical and methodological framework for intelligent cloud-driven dynamic portfolio risk prediction and optimization by synthesizing insights from classical portfolio theory, stochastic control, hierarchical risk models, and contemporary deep reinforcement learning systems.

Building upon foundational theories such as mean variance optimization, dynamic programming, information theoretic portfolio selection, and continuous-time asset allocation, this research situates deep reinforcement learning within a broader lineage of financial decision science. At the same time, it integrates recent advances in computational intelligence, including policy-based and value-based reinforcement learning, graph-based representation learning, and hierarchical risk parity architectures, to address limitations inherent in static and parametric financial models. Central to the conceptual architecture developed in this study is the role of intelligent cloud infrastructures that enable distributed learning, real-time data ingestion, adaptive model updating, and scalable simulation of market environments.

The results demonstrate that deep reinforcement learning, when deployed within intelligent cloud frameworks, offers a fundamentally different epistemology of portfolio risk than classical models. Risk is no longer treated as a static distributional property of returns but as a dynamic, learned, and context-sensitive construct that evolves as agents interact with markets. By comparing hierarchical risk parity methods, graph-based learning architectures, and meta-policy reinforcement learning systems, this study shows how intelligent cloud-based models can internalize diversification, downside protection, and long-term growth objectives within their learning dynamics.

In the study, an analysis was carried out of the impact of the implementation of unified standards on production parameters and clinical outcomes of ART laboratories. The aim was to elucidate how the systematic application of detailed standard operating procedures (SOP), multilevel quality control (QC) mechanisms and regular staff training programs is reflected in key performance indicators (KPI). The results demonstrate that the standardized approach ensures a reduction in procedural variability and a decrease in the frequency of operational errors which, in turn, leads to an increase in the fertilization rate, an increase in the proportion of formed blastocysts and a growth in the probability of achieving clinical pregnancy. On the basis of the obtained data it is concluded that standardization serves not as a formal administrative measure but as a fundamental basis for guaranteeing a high level of quality and safety of embryological manipulations and is also a necessary condition for the integration of prospective innovative technologies including artificial intelligence systems. The presented materials will be useful for directors of ART clinics, embryologists, quality control specialists and researchers in the field of reproductive medicine.

Aquaculture has emerged as one of the fastest-growing food production sectors globally, playing a critical role in meeting rising protein demand amid stagnating capture fisheries. However, the rapid expansion of aquaculture systems has generated complex economic, environmental, and technological challenges that threaten long-term sustainability. This research article develops an integrated analytical framework that connects global fish supply–demand dynamics, aquaculture productivity, environmental externalities, and water treatment technologies, with particular emphasis on natural coagulants and aquatic monitoring systems. Drawing strictly from authoritative international reports and peer-reviewed studies, the article synthesizes economic projections of fish demand up to 2030, examines structural productivity differences among aquaculture producers, and explores how wastewater treatment innovations can mitigate ecological degradation. Extensive theoretical elaboration is used to analyze how effluent management, water quality monitoring, and natural coagulation technologies intersect with economic efficiency, human nutrition outcomes, and ecosystem health. The findings suggest that sustainability in aquaculture cannot be achieved through isolated technological fixes or market mechanisms alone, but rather through systemic integration of environmental monitoring, low-impact treatment technologies, and informed policy frameworks aligned with global food security goals. By bridging economic analysis with environmental engineering and aquatic science, this study contributes a comprehensive, multidisciplinary perspective on the future of aquaculture under intensifying global pressures.

Traditional attribution models struggle to accurately represent the intricate, multi-touchpoint customer journeys typ- ical of contemporary influencer marketing campaigns, especially in the Fast-Moving Consumer Goods (FMCG) sector, where brief purchase cycles and impulsive buying behaviors present distinct measurement difficulties. Three major problems with current methods are: they can’t tell the difference between real campaign impact and random correlations, they don’t handle cross-platform customer journey fragmentation well, and they don’t find a balance between measuring short-term sales and building long-term brand equity. This research introduces a comprehensive five-layer artificial intelligence architecture that integrates Long Short-Term Memory (LSTM) neural networks with attention mechanisms for sequential customer journey modeling, causal inference engines for distinguishing genuine campaign effects from external factors, and multi-objective optimization algorithms that concurrently maximize return on investment while maintaining brand-building objectives. The suggested method combines real-time data from many sources, such as social media APIs, e-commerce transaction logs, brand perception surveys, and competitive intelligence systems, with advanced machine learning processing layers that use computer vision and natural language processing to analyze content per- formance, graph neural networks to group influencers, and real-time scoring engines and budget allocation logic to make decisions automatically. Validation via synthetic control methods and counterfactual analysis guarantees measurement precision while mitigating the endogeneity bias seen in conventional attri- bution methodologies. The architecture offers substantial benefits over traditional models by supplying detailed, touchpoint-level attribution insights with temporal dependency modeling, facilitat- ing automated campaign optimization through real-time budget reallocation based on performance thresholds, and merging quantitative conversion metrics with qualitative brand equity indicators. This all-encompassing method fills the important gap between academic attribution theory and the needs of real-world FMCG marketing, providing a scalable framework for optimizing influencer marketing based on evidence that balances short-term performance with long-term brand building goals.

Against the backdrop of the rapid, essentially exponential, expansion of the global segment of eyelash aesthetic interventions, a scientific and practical gap is widening between the pursuit of neuroaesthetically grounded ideals of attractiveness and the documented biochemical and toxicological constraints of prevailing technologies. This study aims to conduct an integrated comparative analysis of eyelash extension and lamination procedures from the standpoint of their influence on perceptual mechanisms and on the structural integrity of the keratin fiber, as well as to perform scientific validation of innovative, biocompatible approaches. The methodological basis includes a systematic review of the specialized literature, a comparative chemical-toxicological analysis, and a case study of the author’s solutions. The data obtained indicate that traditional methods based on cyanoacrylate adhesives and thioglycolate-containing formulations are associated with substantial risks of sensitization, immunosuppressive effects, and persistent degradation of hair structure. In contrast, lamination systems based on cysteamine, a derivative of a natural amino acid, demonstrate comparable aesthetic outcomes with a markedly more favorable safety profile. In conclusion, it is asserted that overcoming the neuro-toxicological dissonance is achieved through a systemic approach that combines biocompatible chemical compositions, individualized clinical protocols, and stringent educational standards. The material presented in this article will be of interest to cosmetologists, dermatologists, chemical technologists, regulatory specialists in the cosmetics industry, and researchers in the fields of neuroaesthetics and perceptual psychology.

The rise of serverless and event-driven architectures is changing how we design and implement distributed systems. These approaches eliminate the need for server management and use event-based execution. They create systems that are more scalable, easy to deploy, resilient, and cost-effective. This paper looks at how serverless computing shifts the focus from managing infrastructure to focusing on application logic. At the same time, event-driven models improve responsiveness through asynchronous and independent communication. Together, they enhance the design of distributed systems by increasing fault tolerance, lowering operational costs, and supporting real-time, data-heavy applications. The discussion covers architectural principles, design choices, and new best practices that help integrate serverless and event-driven methods in current distributed computing. Ultimately, this study shows how these approaches lead to next-generation cloud-native systems that are flexible, quick, and tailored for changing workloads.