Recent research has extensively investigated the rheological and thermal behaviors of nanofluids. The present work provides a detailed numerical investigation of heat transfer and pressure drop characteristics in sinusoidal and straight tubes using two types of nanofluids: water-based and ethylene glycol-water-based suspensions with nanoparticles. The simulations were conducted under laminar flow conditions using ANSYS CFX software with a constant and uniform heat flux boundary condition. Different nanoparticle sizes and concentrations were examined to evaluate their impact on heat transfer enhancement.
The research gap addressed in this work lies in the limited number of studies that have simultaneously integrated sinusoidal tube geometry and nanofluid properties to enhance heat exchanger performance. This paper bridges that gap by numerically comparing the heat transfer characteristics of sinusoidal tubes against traditional straight tubes under identical operating conditions.

The results demonstrate that increasing Reynolds number and nanoparticle concentration significantly enhances the heat transfer coefficient. Overall, sinusoidal tubes combined with nanofluids achieved up to 25–30% improvement in the Nusselt number compared to straight tubes, highlighting their potential for compact and energy-efficient heat exchangers.

The research gap lies in the limited studies that simultaneously integrate sinusoidal tube geometry and nanofluid properties to enhance heat exchanger performance.

The results clearly demonstrate that increasing the Reynolds number and nanoparticle volume concentration significantly enhances the heat transfer coefficient. Overall, sinusoidal tubes combined with nanofluids provided up to 25% improvement in Nusselt number compared to straight tubes, highlighting their potential for compact and efficient heat exchangers.

The study is aimed at identifying and analyzing key determinants that influence the propensity of youth to prefer one social network over another. The methodological basis of the work consisted of a comprehensive systems analysis and synthesis of scholarly publications combined with the processing of statistical datasets from recognized industry sources. As a result, a close relationship was established between user choices and three interrelated factors: the specifics of algorithmic content ranking mechanisms, the dominant formats of materials and the sociocultural constitution features of each network’s community. Thus, TikTok emerges as an ecosystem stimulating authentic self-expression, interactive engagement and the consumption of predominantly entertainment media products, whereas Instagram retains the status of a gallery of visually refined idealized content serving as a means of social validation and personal brand maintenance. The scientific novelty of the study lies in the proposal of an adaptive model of PR strategies that accounts for the unique psychographic and behavioral characteristics of each platform’s users. The findings demonstrate the impossibility of effectively applying universal communication solutions and justify the need to design targeted tactics customized to the specifics of TikTok’s and Instagram’s functioning. The presented materials will be useful to both media communications researchers and PR and marketing practitioners seeking to optimize interactions with youth audiences.

This article presents information on the structure of the structural crystalline and amorphous regions of the fibroin protein in natural silk fiber. The study of the crystalline and amorphous regions in silk fiber provides valuable information on the relationship between molecular structure and macroscopic properties.

Production and manufacturing processes are witnessing a significant increase in the reliance on 3D printers in this field, especially in complex and rare forms. The aim of this study is to demonstrate the capability and efficiency of a 3D printer in the production of unique and complex spare parts. A small damaged gear (a damaged spare part for the coffee machine) was selected as a case study due to the difficulty of obtaining such a spare part. Reverse engineering was used to take the necessary measurements of the gear and redraw it using the SolidWorks program, and then convert it to the printer's program and print it. A Creality Ender 3 Pro printer and polylactic acid (PLA) were used for the purpose of gear printing, and the printing time took about 4 hours. The measurements of the printed piece showed a great match as the accuracy reached in the range of ±0.15mm. The results of the comparison also showed that the cost of printing the gear is estimated at $1, while the cost of producing the same gear ranges from (10-25) dollars by traditional methods such as CNC milling or machining, while the molds are less expensive but require the purchase of large quantities (+1000), which contradicts the goal of the study and increases the cost significantly. Laboratory tests have also proven the efficiency of the material after printing, as good mechanical properties have been obtained and suitable for the intended purpose. Finally, the study concluded that the use of a 3D printer is an excellent and cheap solution for the purpose of making spare parts, especially unique and rare.

This paper discusses how large language models are being integrated into recommender systems. The paper attempts to carry out a broad evaluation of the capability of LLM embeddings and generative mechanisms in increasing ranking accuracy as well as decreasing inference latency, ensuring robustness toward the cold-start effect. An application such as this has enormous economic consequences since recommendations drive the central portion of viewing hours on Netflix and purchases on Amazon—over 80% and about 35%, respectively, initiated by recommendation algorithms. The novelty here is that the study applies a unified vector space setup for converting heterogeneous signals—textual descriptions, identifiers, and multilingual metadata—in a comparative analysis of classical and LLM-oriented schemes based on Recall@k, nDCG, and inference-latency metrics. Also included is the hybrid architectures systematization scope that ranges from feature-enrichment pipelines up to fully agent-based solutions. Vocabulary Expansion Techniques, Compressed alongside zero-shot ranking with GPT-4 in the loop, return a dramatic leap in recommendation accuracy at orders of magnitude less latency. Takeaways: LLM helps reduce manual feature engineering, increases ranking accuracy up to 62 per cent, remains stable in multilingual and low data scenarios, and generative and agent components make possible conversational interfaces and multi-step service orchestration. Hybrid solutions offer an optimal trade-off between recommendation quality and computational cost in industrial deployment. This article will be helpful to machine-learning researchers and practitioners, recommender-system developers, and personalization-service architects.

This article presents an overview of methods for analyzing and classifying errors in automated tests using modern language models. The research is based on a systematization of international publications that examine the solutions RCACopilot, LogLLM, FlakyDoctor, and LogGPT. It is shown that these approaches differ in their architectural solutions and task formulations: classification of incident root causes, anomaly detection in logs, repair of flaky tests, and real-time log interpretation. The study identifies specific data preparation and training strategies that determine the models' effectiveness. The presented metrics demonstrate high accuracy and practical applicability but also point to significant limitations. Among them are a dependence on monitoring infrastructure and computational resources, sensitivity to prompt parameters, and weak results in repairing NOD-type tests. The analysis showed that integrating the models into existing pipelines with filtering and validation allows for minimizing risks and increasing the reliability of the solutions. Practical implementation experience is noted, which confirmed an increase in the stability of test runs and a reduction in regression time. The article will be useful for researchers and practitioners in the fields of software engineering, automated testing, and quality assurance.

The rapid integration of digital technology into everyday life has significantly reshaped the developmental environment of adolescents. This paper investigates the psychological effects of digital overexposure on emotional development, drawing from a synthesis of secondary data and empirical research. Focusing on adolescents aged 14 to 18, the study analyzes how excessive and emotionally immersive use of digital platforms, particularly social media, influences self-esteem, depressive symptoms, emotional regulation, and gender-based responses.

The research reveals that emotional outcomes are not solely determined by the amount of screen time, but by the type of engagement and the user’s emotional investment. Girls, in particular, demonstrate heightened vulnerability to emotional distress linked to digital behaviors, especially during periods of societal disruption like the COVID-19 pandemic. This study also integrates theoretical frameworks such as Social Cognitive Theory and the Socio-Technical Interaction Networks model to explain behavioral patterns and digital norms. Visual representations of data further illustrate key patterns in screen time and mental health, gender disparities, and pandemic-specific outcomes. The paper concludes with recommendations for educators, policymakers, and families to support healthy digital habits and outlines critical directions for future interdisciplinary and inclusive research. Ultimately, the goal is to inform the development of responsive strategies that foster emotional well-being in the digital age.

This article examines the architecture of pipelines, embedded systems, also real-time systems when they are implemented via an event-driven approach, using the Godzilla vs. Kong escape room works as an empirical case study. The study is relevant as the location-based entertainment industry rapidly advances and technological installations escalate in complexity, where numerous heterogeneous hardware and software components reliably integrate. Customary monolithic architectures, also cloud-centered solutions, are often insufficient under stringent requirements for modularity, fault tolerance, and low interaction latency. For identifying universal principles for designing distributed real-time systems, the study will deconstruct and analyze the system architecture through a real commercial project. This work formalizes practical solutions derived from applied engineering within the context of event-driven architecture (EDA) and distributed-systems theory, contributing to scientific novelty. It becomes possible when elevating of the empirical COGS bus links nodes to a canonical architectural pattern. The principal results show the proposed model achieves extremely low response latency of about 30 ms as well as scalability plus loose coupling of modules. This model enables efficient computational load redistribution between peripheral devices and a central system. The article will help out researchers and also engineers that work for embedded systems and real-time systems as well as develop engaging interactive environments.

The study systematically presents the fundamental principles for constructing end-to-end pipelines for duplicate detection using machine learning methods. The objective is to analyze and subsequently formalize an architectural schema that unifies stream processing, adaptive ML mechanisms, and scalable cloud components. The methodological foundation is based on a review of existing entity resolution approaches and the design of an integrated architectural solution derived with consideration of the key concepts embedded in patent US11995054B2. The technological core comprises the following components: Apache Kafka for stream orchestration, Apache Spark for distributed processing, Amazon SageMaker for model development and management, and NoSQL stores for flexible and scalable persistence of intermediate and final data. As a result, a fault-tolerant, horizontally scalable architecture is proposed, intended for operation in near real-time conditions. The central mechanism is a machine learning system with a continuous feedback loop, in which user verdicts on ambiguous duplicate cases are employed for dynamic retraining and improvement of detection quality. The findings of the study offer practical value for data architects, machine learning engineers, and researchers focused on data quality management in the design of high-throughput analytical systems.

The article examines methods for optimizing PL/SQL queries in distributed banking databases, emphasizing the transition from static rule-based mechanisms to adaptive, learning-driven architectures. The study’s relevance is defined by the increasing complexity of financial data environments that require real-time consistency, fault tolerance, and intelligent workload distribution. The research synthesizes results from seven recent works published between 2021 and 2025, covering neural cost modeling, heuristic algorithms, hybrid plan enumeration, and visualization-based diagnostics. Special attention is devoted to learned cost models and metaheuristic strategies that enhance selectivity estimation, reduce latency, and stabilize throughput in distributed ledger systems. The methodological framework integrates comparative analysis, systematization, and critical evaluation of hybrid, heuristic, and learning-based optimizers. The findings reveal a multi-layered optimization model that combines probabilistic inference, robust plan selection, and heuristic refinement. The conclusions underscore the practical applicability of adaptive PL/SQL optimization for high-volume banking infrastructures and data-intensive financial analytics.

The rapid integration of digital technology into everyday life has significantly reshaped the developmental environment of adolescents. This paper investigates the psychological effects of digital overexposure on emotional development, drawing from a synthesis of secondary data and empirical research. Focusing on adolescents aged 14 to 18, the study analyzes how excessive and emotionally immersive use of digital platforms, particularly social media, influences self-esteem, depressive symptoms, emotional regulation, and gender-based responses.

The research reveals that emotional outcomes are not solely determined by the amount of screen time, but by the type of engagement and the user’s emotional investment. Girls, in particular, demonstrate heightened vulnerability to emotional distress linked to digital behaviors, especially during periods of societal disruption like the COVID-19 pandemic. This study also integrates theoretical frameworks such as Social Cognitive Theory and the Socio-Technical Interaction Networks model to explain behavioral patterns and digital norms. Visual representations of data further illustrate key patterns in screen time and mental health, gender disparities, and pandemic-specific outcomes. The paper concludes with recommendations for educators, policymakers, and families to support healthy digital habits and outlines critical directions for future interdisciplinary and inclusive research. Ultimately, the goal is to inform the development of responsive strategies that foster emotional well-being in the digital age.

In the context of higher education’s transition to hybrid learning–campus formats, this study addresses the gap between the operational optimization of infrastructure and the psychological and pedagogical outcomes of the student experience. The aim is to develop and theoretically substantiate an integrated resource management model for the hybrid campus that draws on digital traces from heterogeneous sources (LMS, Wi-Fi, access control systems) not only to increase the efficiency of classroom utilization and timetable construction, but also to design a learning environment that supports growth in academic achievement and student well-being. Methodologically, the work is based on a systematic literature review and content analysis of industry reports, covering publications from the Scopus/WoS databases and materials from leading analytical centers. The results show that incorporating data on students’ digital activity into multicriteria optimization models, including genetic algorithms, reveals latent relationships between behavioral patterns, social engagement, and academic achievement. A conceptual framework is proposed that integrates data collection, analytical processing, resource allocation, and the resulting educational and psychological metrics into a single feedback loop. It is concluded that such an integrated approach, despite ethical and organizational constraints, dissolves the efficiency–experience dichotomy and fosters more adaptive, student-centered educational ecosystems. The work is addressed to university administrators, educational technology specialists, and researchers in learning analytics.

The healthcare insurance sector processes over $4.3 trillion annually in global transactions, with artificial intelligence (AI) adoption increasing from 23% in 2019 to 78% of major insurers by 2024. This study presents a novel multi-layered security framework designed to address critical vulnerabilities inherent in AI-augmented healthcare transactions. Through a comprehensive analysis of 2,847 security incidents recorded between 2019 and 2024, real-world data from major breach databases, and an evaluation of 93 health insurers' AI implementations, we identify three primary threat vectors: data-centric attacks (47% of incidents), model-centric vulnerabilities (31%), and ethical-compliance breaches (22%). With healthcare data breaches costing an average of $9.77 million per incident in 2024—the highest across all industries for the 14th consecutive year—the need for robust security is paramount. AI-specific security incidents have grown exponentially from 7 incidents (1.8% of total) in 2019 to 219 incidents (29.8% of total) in 2024. Our proposed framework integrates Zero Trust Architecture, privacy-enhancing technologies, blockchain immutability, and AI governance protocols. Empirical validation across three pilot organizations demonstrated a 74% reduction in security incidents, a 26% improvement in compliance metrics, and a 28% enhancement in transaction processing efficiency, with an average return on investment (ROI) timeline of 16 months. Statistical analysis reveals significant threat pattern distributions (χ² = 273.98, p < 0.001), supporting the framework's targeted approach to mitigating emerging AI vulnerabilities.

: Background: The escalating global demand for protein, coupled with the significant environmental footprint of conventional livestock farming, necessitates the exploration of sustainable alternatives. Edible insects, particularly the house cricket (Acheta domesticus), have emerged as a highly promising source of nutrition to enhance global food security. However, its integration into mainstream food systems, especially in Western societies, is fraught with challenges.

Scope and Approach: This paper provides a comprehensive review of the current state of the Acheta domesticus food industry. It synthesizes existing literature on cricket farming systems, processing technologies, nutritional composition, and sustainability metrics. Furthermore, it critically examines the primary technological, regulatory, and socio-cultural challenges that hinder its widespread adoption as a human food source.

Key Findings: Acheta domesticus presents an excellent nutritional profile, rich in high-quality protein, essential amino acids, fatty acids, and vital micronutrients. Environmentally, cricket farming is significantly more sustainable than traditional livestock, demonstrating a higher feed conversion efficiency and lower greenhouse gas emissions, as well as reduced land and water requirements. Key processing techniques, such as drying and milling, transform crickets into versatile food ingredients like protein powder for use in various products. However, the industry's growth is constrained by several barriers. These include a lack of large-scale, automated farming technologies; complex and evolving regulatory landscapes, such as the European Union's Novel Food framework; safety concerns related to allergenicity; and profound socio-cultural resistance, or 'neophobia,' among consumers.

Conclusion: The house cricket holds substantial potential to contribute to a more sustainable and secure global food system. Realizing this potential hinge on concerted efforts in research and development to advance farming and processing technologies, establish clear international regulatory standards, and implement effective marketing and educational strategies to overcome consumer hesitancy.