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 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 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.

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.

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 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.

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.

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.

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.