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.

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 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.

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.

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.

: 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.