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.

Background: The exponential growth of data across industrial and healthcare sectors has necessitated a paradigm shift from traditional data processing to advanced Business Intelligence (BI) and multimodal analytics. While BI has established itself as a cornerstone of competitive advantage in the corporate sector, its integration with clinical informatics and emerging artificial intelligence methodologies—specifically prompt engineering—remains a complex, evolving frontier.

Methods: This study employs a systematic qualitative meta-synthesis of literature ranging from 1994 to 2025. We analyze 25 key sources spanning business management, medical informatics, and computational linguistics to construct a unified framework for modern data utility.

Results: The analysis reveals that successful BI implementation relies heavily on organizational readiness and ethical data governance rather than software capabilities alone. In healthcare, the convergence of Electronic Health Records (EHR) with predictive data mining is accelerating the shift toward precision medicine. Furthermore, the emergence of few-shot prompt learning and multimodal approaches offers a solution to the "label scarcity" problem, enabling richer feature extraction without extensive manual annotation.

Conclusion: The future of analytics lies in the symbiotic relationship between structured BI frameworks and unstructured, multimodal AI interpretations. Organizations must prioritize privacy-preserving technologies, such as k-anonymity, while adopting agile prompt engineering techniques to maintain competitive viability and clinical safety.

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.

This article presents a comprehensive, publication-ready synthesis and original theoretical elaboration on the contemporary design, optimization, and future directions of automotive electrical/electronic (E/E) architectures. Building strictly on the provided literature, the paper articulates the motivations driving architectural change, evaluates prevailing approaches — including AUTOSAR, virtualization, Time-Sensitive Networking (TSN)/Ethernet integration, zonal and domain-controller topologies, and fault-tolerant hardware strategies — and develops an integrated framework for resilient, secure, and scalable vehicle E/E systems. The work juxtaposes engineering objectives (latency bounds, determinism, resource efficiency) with stakeholder concerns (supply-chain complexity, maintainability, lifecycle cost) and highlights methods for tool-chain engineering, timing analysis, and electromagnetic and signal-injection threat mitigation. Methodology is descriptive and analytic: it synthesizes referenced empirical and conceptual findings, extrapolates design trade-offs, and frames a set of prescriptive guidelines and research hypotheses for future validation. Results are presented as a detailed mapping of architectural patterns to system-level properties, a taxonomy of risks and mitigations, and recommended engineering practices spanning specification, modeling, verification, and runtime adaptation. The discussion interrogates limitations of current approaches, particularly around virtualization overheads, TSN integration complexity, electromagnetic interference, cybersecurity of actuator/communication channels, and the challenges of achieving industry-wide interoperability. The conclusion presents an agenda for applied research, standardization priorities, and industrial adoption strategies to meet the demands of advanced driver assistance and autonomous driving while keeping costs and energy budgets tractable

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.

The accelerating integration of artificial intelligence into financial systems has fundamentally altered the epistemological, operational, and governance foundations of credit scoring, risk management, and regulatory compliance. Contemporary loan platforms increasingly rely on real-time data processing architectures, advanced machine learning models, and automated decision-making systems to evaluate borrower creditworthiness, predict default probabilities, and align lending practices with complex compliance regimes. This transformation is neither linear nor uncontroversial. It raises persistent questions regarding transparency, explainability, ethical accountability, institutional risk, and the evolving role of human judgment in financial intermediation. Against this backdrop, the present research develops a comprehensive, theoretically grounded, and empirically informed analysis of AI-driven real-time credit scoring systems and their intersection with broader risk and compliance frameworks.

Drawing extensively on interdisciplinary literature spanning financial risk management, artificial intelligence governance, legal theory, and compliance studies, the article situates real-time credit scoring within a historical continuum of quantitative risk assessment practices while emphasizing the qualitative rupture introduced by adaptive, self-learning systems. Particular attention is given to the operational logic of real-time credit scoring models, including data ingestion pipelines, algorithmic learning processes, and feedback mechanisms that continuously recalibrate risk assessments. The study critically examines how these systems reshape traditional notions of credit risk by privileging behavioral, transactional, and alternative data over static financial indicators, thereby enabling dynamic and context-sensitive lending decisions. This analysis is anchored in contemporary scholarship on AI-enabled credit platforms, including recent empirical contributions that document the performance, scalability, and governance challenges of real-time risk analytics in digital lending ecosystems (Modadugu et al., 2025).

Methodologically, the research adopts a qualitative-analytical design grounded in systematic literature synthesis, conceptual modeling, and comparative institutional analysis. Rather than presenting new quantitative datasets, the article constructs interpretive insights by triangulating findings from financial risk literature, AI governance frameworks, and compliance-oriented policy analyses. This approach enables a nuanced examination of both the capabilities and limitations of AI-driven credit scoring, particularly in relation to model opacity, bias propagation, regulatory alignment, and systemic risk amplification. The results reveal that while real-time AI systems significantly enhance predictive accuracy and operational efficiency, they simultaneously intensify governance complexity and compliance burdens, especially in jurisdictions with evolving regulatory expectations around algorithmic accountability.

The discussion advances the argument that effective integration of AI into credit scoring and risk management requires a reconceptualization of governance structures, moving from static control mechanisms toward adaptive, lifecycle-oriented oversight models. The article further explores the implications of AI-driven risk analytics for compliance leadership, legal accountability, and organizational culture, emphasizing the need for interdisciplinary collaboration between technologists, risk managers, legal experts, and regulators. By synthesizing insights across domains, this research contributes to ongoing scholarly debates on the future of financial risk governance in the age of artificial intelligence and offers a robust conceptual foundation for future empirical and policy-oriented studies.

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.

The accelerating complexity of global supply chains, particularly in high‑technology sectors such as semiconductor and GPU manufacturing, brings profound challenges in managing risk, uncertainty, and regulatory disruption. This paper develops a comprehensive conceptual framework for building “viable supply chains,” defined as supply networks capable of sustaining performance under geopolitical turbulence, regulatory constraints, trade‑policy shifts, and demand volatility. Drawing upon established literature on supply chain risk management (Fan & Stevenson, 2018; Ho et al., 2015), supply chain resilience and agility (Gligor et al., 2019; Han, Chong & Li, 2020; Hosseini, Ivanov & Dolgui, 2019), and recent analyses of supply‑chain strategies in the semiconductor industry (Bernstein, 2023; Lulla, 2025; BIS, 2023; Flamm & Bonvillian, 2025), the framework synthesizes prior conceptualizations and extends them to address contemporary challenges such as reshoring, capacity reservation under disruption, and elasticity of substitution between domestic and imported goods (Ahmad & Riker, 2020; Devarajan, Go & Robinson, 2023). Using a systematic literature-based methodology, we analyze key dimensions—risk identification and mitigation, supply chain agility, resilience capacities, contractual and sourcing strategies, regulatory compliance, and design for adaptability. The results highlight critical capabilities required for supply‑chain viability in high‑tech manufacturing: diversified sourcing including backup and reshored suppliers; dynamic coordination and information flows; contractual mechanisms for revenue sharing under uncertainty; and alignment with regulatory and trade policy frameworks. The discussion elaborates theoretical implications, limitations, and proposes directions for empirical validation and extension, including digital‑twin simulations (Ivanov & Dolgui, 2021) and performance metric benchmarking (Han, Chong & Li, 2020). This integrative framework provides a roadmap for academics and practitioners seeking to design, analyze, and adapt supply chains for strategic robustness in a rapidly evolving geopolitical landscape.

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.

Background: The rise of agentic AI — systems that autonomously perform tasks, make decisions, and interact with other systems and humans — has created novel identity, authentication, and authorization challenges that traditional IAM paradigms were not designed to address (Kumar, 2023; Hasan, 2024). Agentic systems blur the line between human-driven access and machine-driven actions, requiring architectures that treat intent, provenance, and runtime context as first-class identity attributes (Bhushan et al., 2025; Syros et al., 2025). Zero Trust principles and emerging decentralized identity standards offer complementary tools, but integrating them into a coherent, scalable, and auditable architecture for agentic AI remains an open problem (Cloud Native Computing Foundation, 2024; W3C, 2023).

 Methods: This article develops a comprehensive Intent-Aware Zero-Trust Identity Architecture (IA-ZTIA) tailored for agentic AI workloads. The methodology synthesizes canonical Zero Trust concepts, SPIFFE/SPIRE runtime identity primitives, decentralized identifiers (DIDs), intent modeling approaches, credential lifecycle management, behavioral anomaly detection, and cryptographically verifiable logging. The architecture is specified in layered components (bootstrapping, identity provisioning, intent-aware policy, runtime enforcement, telemetry and assurance) and evaluated by qualitative threat mapping against OWASP agentic threat categorizations and by comparative analysis with existing proposals (Huang et al., 2025; Syros et al., 2025; OWASP, 2024).

 Findings: IA-ZTIA clarifies the identity and intent semantics required for agentic interactions, proposes practical mappings to SPIFFE/SPIRE identities and DID-based attestations, and prescribes intent-based conditional access policies that extend conditional access paradigms to machine agency (Microsoft, 2024; Li & Zhao, 2025). The design demonstrates improved auditability through cryptographically signed logs, reduces attack surface by least-privilege intent scoping, and supports credential lifecycle management for IIoT and edge agents (Nishida, 2024; Reyes & Nakamoto, 2025). The architecture aligns with OWASP multi-agent threat modeling guidance and mitigates classes of attacks identified in agent risk taxonomies (OWASP, 2024; OWASP, 2025).

 Conclusions: Intent awareness is essential for next-generation Zero Trust applied to agentic AI. IA-ZTIA shows that combining ephemeral SPIFFE identities, DID attestations, intent-aware policy, and cryptographic assurance yields a practical, auditable, and scalable architecture that unifies human and machine access. Remaining challenges include standardizing intent representations, scaling high-fidelity behavioral detection without false positives, and operationalizing cross-organization attestation ecosystems. The article closes with a research agenda for protocol work, governance models, and empirical evaluation in industrial settings.

 (Word count: abstract ≤ 400 words)

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.

 The Java ecosystem has undergone profound structural, architectural, and operational transformations over the last decade, driven by the introduction of the Java Platform Module System (JPMS), rapid long-term support (LTS) release cycles, increasing dependency complexity, and the need for highly reliable continuous integration and delivery pipelines in enterprise environments. Large-scale Java systems, particularly those rooted in legacy architectures, face compounded challenges when attempting to modernize while preserving operational stability. These challenges span multiple layers, including runtime performance optimization, garbage collection tuning, dependency and transitive risk management, modularization of monolithic codebases, and the orchestration of CI/CD pipelines across heterogeneous Java versions. This research presents an integrated, theory-driven analysis of enterprise Java evolution, synthesizing empirical findings and practitioner-oriented insights from established literature. By grounding the discussion strictly in existing scholarly and industrial references, the article develops a holistic framework that explains how modularity adoption, garbage collection optimization, dependency governance, and Jenkins-based CI/CD pipelines interact within non-containerized and mixed-version enterprise environments. The study emphasizes descriptive and interpretive analysis rather than experimental quantification, offering deep theoretical elaboration on why certain modernization strategies succeed or fail in practice. Key findings indicate that successful enterprise Java modernization depends less on isolated technical upgrades and more on systemic alignment between language features, module boundaries, dependency policies, runtime observability, and delivery automation. The article concludes by outlining strategic implications for software architects, DevOps engineers, and organizational decision-makers seeking to sustain Java-based platforms in an era of continuous change.

: 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.