Background: Chrysanthemum (Chrysanthemum morifolium) is a leading ornamental crop worldwide, prized for its diverse flower colors. Green-flowered varieties are rare and highly valued, yet the molecular mechanisms governing their unique coloration, particularly in response to light, are not well understood. Chlorophyll accumulation in petals is the basis for this green phenotype, but the specific transcription factors that mediate light-induced chlorophyll biosynthesis in chrysanthemum flowers remain to be fully elucidated.

Results: In this study, we investigated the molecular basis of greening in chrysanthemum ray florets. Transcriptomic analysis of light-exposed versus dark-treated florets identified a candidate R1-type MYB transcription factor, designated CmREVEILLE2, whose expression was strongly correlated with light-induced chlorophyll accumulation. CmREVEILLE2 was confirmed to be a nuclear-localized protein, and its expression was rapidly and significantly induced by light. Functional characterization using virus-induced gene silencing (VIGS) indicated that downregulating CmREVEILLE2 resulted in a significant reduction in chlorophyll content and a visually attenuated green phenotype in the ray florets. Mechanistically, yeast one-hybrid (Y1H) and dual-luciferase (DLR) reporter assays revealed that CmREVEILLE2 appears to directly bind to the promoters of key chlorophyll biosynthesis genes, including CmHEMA1 and CmPOR, to activate their transcription.

Conclusion: Our findings suggest that CmREVEILLE2 functions as a critical positive regulator in the light signaling pathway associated with green color formation in chrysanthemum flowers. It appears to act by directly promoting the expression of essential chlorophyll biosynthesis genes, thus driving chlorophyll accumulation. This study provides a novel understanding of flower color regulation and identifies CmREVEILLE2 as a key target for the future molecular breeding of green-flowered chrysanthemum cultivars.

Background: In an era defined by the proliferation of Artificial Intelligence (AI) and big data, the 
governance of information assets has transitioned from a back-office compliance function to a critical 
strategic imperative. As organizations seek to leverage AI for competitive advantage, the quality, 
security, and ethical management of underlying data have become paramount.  
Methods: This study employs an integrative review of contemporary literature, industry frameworks 
(such as DAMA-DMBOK), and empirical studies to analyze the evolving landscape of data governance. 
The research specifically examines the distinct challenges faced by Small and Medium-sized Enterprises 
(SMEs) compared to large corporations, alongside the critical role of governance in enabling secure AI 
adoption.  
Results: The analysis reveals that while foundational principles of governance remain consistent, 
implementation strategies must be highly adaptive. Large enterprises often struggle with siloed data 
and bureaucratic inertia, whereas SMEs face significant resource constraints that make comprehensive 
frameworks difficult to operationalize. Furthermore, the integration of AI introduces complex ethical 
variables, particularly regarding data privacy in sectors like healthcare and wearables. 
Conclusion: Effective data governance requires a hybrid approach that balances rigid compliance with 
agile decision-making. For AI to be secure and reliable, organizations must adopt metric-driven 
governance models that prioritize data quality and ethical stewardship. The study suggests that future 
frameworks must incorporate decentralized technologies, such as blockchain, to enhance trust and 
transparency in multi-stakeholder environments. 

This article presents a comprehensive, publication-ready synthesis and original exposition on the performance, implementation challenges, and mitigation strategies associated with 10 gigabit per second (10 Gb/s) Ethernet links across two converging application spaces: data-center interconnects and automotive networks. Drawing strictly from the provided literature, this manuscript integrates analytical evaluations of IPv4/IPv6 over high-speed fabrics (Gamess & Humberto, 2016), empirical and theoretical treatments of single-pair and multi-Gb/s automotive PHYs (de Besten, 2019), modulation techniques for Ethernet transmission (Olalekan, Grace & Anuoluwapo, 2020), optical reach extension methods for 10 Gb/s links (Voraparmorn et al., 2020), and electromagnetic compatibility (EMC) and shielding strategies validated for automotive camera PCBs (Karim, 2025; Leppäaho et al., 2021; Park, Lee & Kim, 2021). The article develops a unified methodological narrative that examines protocol overheads, modulation tradeoffs, physical media constraints, and EMI mitigation for reliable 10 Gb/s operation. Key contributions include (1) an analytical framework for comparing IPv4/IPv6 performance across Ethernet and InfiniBand fabrics at 10 Gb/s, (2) an extended evaluation of M-ary PAM and its applicability to automotive Ethernet, (3) a systems-level view of optical amplification and dispersion compensation for long-reach 10 Gb/s links, and (4) an in-depth treatment of shielding, PCB layout, and cable considerations required to meet automotive EMC requirements. The work concludes with nuanced discussions on limitations, potential future research directions emphasizing cross-domain harmonization, and practical design recommendations for engineers and researchers working at the intersection of high-speed communications and automotive sensing networks.

The increasing sophistication of presentation attacks, deepfakes, and credential-theft techniques has exposed fundamental weaknesses in traditional authentication and identity assurance mechanisms used in enterprise environments. This paper proposes a convergent authentication architecture that tightly integrates FIDO2/WebAuthn public-key, certificate-based authentication, and device attestation to construct a phishing-resistant, scalable identity assurance framework for large organizations. By synthesizing standards-driven cryptographic mechanisms with device-level provenance and attestation evidence, the architecture aims to mitigate social-engineering, credential replay, and device-compromise threats while preserving usability and manageability for enterprise deployments. We detail the theoretical foundations—covering asymmetric cryptography, attestation models, and human-centered usability concerns—explain the operational mechanisms for binding keys to devices and identities, and specify an end-to-end lifecycle for credential issuance, revocation, and continuous assurance. The study draws on empirical and normative literature on biometric presentation attack detection, deepfake vulnerability, device attestation taxonomy, digital identity lifecycle guidelines, and recent work on FIDO2 usability and applicability to enterprise settings. We then analyze security properties, potential adversary models, deployment trade-offs, privacy considerations, and governance implications. Finally, limitations, operational challenges, and a research agenda for measurement, standardization alignment, and large-scale pilot evaluation are discussed. This integrated approach is positioned as an actionable pathway for enterprises seeking to significantly raise the bar against phishing and device-origin attacks while aligning with contemporary identity and cryptographic standards.

Background: As Artificial Intelligence (AI) systems increasingly mediate critical life opportunities—from loan approvals to criminal sentencing—the demand for Explainable AI (XAI) has intensified. However, a significant gap remains between technical methods of explanation and the social requirements of accountability.

Methods: This study employs a critical theoretical analysis, synthesizing literature on algorithmic transparency, legal frameworks regarding disparate impact, and recent empirical data on consumer sentiment and business applications of XAI. We evaluate existing XAI paradigms against the "transparency ideal" to determine their efficacy in ensuring social responsibility.

Results: Our analysis reveals that current XAI techniques often provide "seeing without knowing," offering mathematical approximations that satisfy technical audits but fail to provide actionable understanding for impacted individuals. We find that static transparency mechanisms are insufficient for dynamic learning models and that "one-size-fits-all" explanations often obscure, rather than reveal, bias.

Conclusion: True algorithmic accountability requires moving beyond code availability to "meaningful transparency," which prioritizes the sociological context of decisions. We propose a shift from purely technical explainability to a framework of justifiability, ensuring that AI systems are not only transparent in their function but accountable for their social outcomes.

Background: The accelerating integration of artificial intelligence (AI) and adjacent data technologies into financial services and corporate strategy has created a transformative moment for mergers and acquisitions (M&A). Existing literature documents discrete advances—digital transformation in banking and finance, AI-enhanced financial reporting, and big data approaches to enterprise value assessment—but a comprehensive, practice-focused synthesis tailored to M&A due diligence and deal structuring remains underdeveloped (Alam, 2025; Antwi et al., 2024; Rodríguez-Mazahua et al., 2016).

Objectives: This article develops an original, publication-grade theoretical and applied framework explaining how generative and analytic AI reshape each stage of the M&A lifecycle—sourcing, valuation, due diligence, negotiation, integration—and how organizational capabilities and human capital must evolve to capture value. The study aims to bridge technical, managerial, and strategic perspectives to guide practitioners, private equity actors, and policy-oriented scholars (Ellencweig et al., 2024; Emmi, 2025).

Methods: Drawing on cross-disciplinary theory from digital transformation, finance, and organizational learning, this work synthesizes prior empirical and conceptual research to construct a narrative model of AI-enabled M&A. The methodology is text-based and integrative: comparative theoretical analysis, critical synthesis of domain literature, and scenario-driven mapping of AI tools to M&A tasks (Corea, 2017; Farboodi & Veldkamp, 2020).

Results: The framework identifies five transformative vectors: (1) Data-driven deal sourcing and screening; (2) Automated and semi-automated financial and operational due diligence; (3) AI-assisted valuation models that augment rather than replace human judgement; (4) Contract and legal automation to accelerate negotiation and risk identification; and (5) Post-merger integration (PMI) intelligence systems that operationalize value capture. Each vector presents unique capability requirements, governance demands, and biases/risks which the framework disaggregates and remediates with proposed organizational and technical controls (Betts & Jaep, 2017; Antwi et al., 2024; Shounik, 2025).

Conclusions: AI fundamentally recalibrates resource allocation, timing, and expertise in M&A. Successful adoption requires firms to invest concurrently in data architecture, continuous human learning, specialized AI governance, and hybrid teams that combine domain and data-science skills. The article concludes with a practical roadmap for private equity firms and corporate acquirers and outlines future research avenues for empirical validation and regulatory design (Baskin, 2023; Brown et al., 2019; Chowdhury et al., 2024).

Background: The convergence of artificial intelligence (AI), cloud computing, and telematics is reshaping fleet management, route optimization, emissions monitoring, and the broader logistics ecosystem. Recent market analyses predict rapid expansion in AI adoption for transportation systems, while applied studies report gains in cost-efficiency, predictive maintenance, and operational resilience (Mahajan, 2025; Kaluvakuri, 2023). This paper synthesizes multidisciplinary evidence from market reports, technical blogs, case-based industry sources, and peer-reviewed studies to produce an integrated, theory-driven account of AI’s role in modern transportation and fleet management.

 Methods: Using a rigorous, theory-explicit narrative synthesis grounded in the provided references, this study reconstructs methodological pathways employed across industry and academic contributions, translating disparate empirical findings into a unified explanatory framework. We employ a conceptual meta-methodology that traces data pipelines, analytics architectures, and decision-making loops commonly reported in telemetry-driven fleet systems (Microsoft Azure, 2024; Drozdov, 2024).

 Results: AI interventions manifest across six core domains: predictive maintenance, dynamic route optimization, energy and emissions management, demand-responsive logistics, autonomous vehicle integration, and strategic financial planning. Evidence indicates that real-time telemetry plus AI yields measurable reductions in idle time, fuel consumption, and operating costs while increasing fleet uptime and planning accuracy (Drozdov, 2024; Paul et al., 2025; Patil & Deshpande, 2025). Market projections suggest significant growth in AI-in-transportation spending through 2032 (Mahajan, 2025).

 Conclusions: The AI-enabled transition is both technologically tractable and institutionally complex. Successful deployment requires interoperable data architectures, clear performance metrics, ethical governance, and alignment with decarbonization goals. We propose an integrative research agenda to address measurement standardization, socio-technical risk, and regulatory harmonization, and outline practical recommendations for fleet operators, policymakers, and researchers.

This article presents a comprehensive, publication-ready examination of migrating enterprise database systems from Oracle to PostgreSQL. It synthesizes theoretical foundations, practical migration techniques, cost analyses, reliability considerations, and operational frameworks derived from contemporary documentation, migration tooling, cloud provider materials, and technical whitepapers. The study begins by establishing the technical equivalence and divergences between Oracle and PostgreSQL in areas critical to enterprise systems: transactional guarantees, concurrency control, data types, stored procedure ecosystems, and replication and high-availability mechanisms (Database Reliability Review, 2023; PostgreSQL, 2023). It then develops a structured methodology for planning and executing migration projects, combining schema conversion, code translation, performance tuning, data validation, and cutover strategies informed by migration tools such as Ora2Pg and established cloud migration practices (Ora2Pg, 2023; Database Technology Trends, 2022). The article evaluates cost and operational impacts, contrasting proprietary licensing and maintenance costs with open-source licensing and cloud-managed service models, with particular attention to AWS RDS and comparative cloud offerings (Cloud Database Solutions: A Comparison of PostgreSQL and Oracle RDS, 2023; AWS, 2023). Using both theoretical models and synthesized empirical observations from existing migration reports and whitepapers, the results section describes expected cost savings, performance risks, and typical time-to-stabilization trajectories following migration (Natti, 2023; Oloruntoba & Omolayo, 2024). The discussion provides deep interpretation of trade-offs—technical, economic, and organizational—while outlining limitations of current tools and future research directions including formal verification of translated PL/SQL, automated performance regression detection, and tighter operational integration with cloud-native service meshes (Nuijten & Barel, 2023; Ravikumar et al., 2017). The conclusion offers a practical decision framework and a prescriptive checklist for enterprises considering an Oracle-to-PostgreSQL migration, emphasizing rigorous testing, staged rollouts, and continuous performance governance. Keywords: database migration, PostgreSQL, Oracle, cost reduction, Ora2Pg, cloud RDS, ACID compliance.

Background: Maintaining the integrity of temperature-sensitive products — pharmaceuticals, vaccines, fresh produce, and certain medical supplies — across multimodal transport networks remains a primary operational and regulatory challenge. Failures in the cold chain contribute to product spoilage, therapeutic inefficacy, patient risk, financial loss, and elevated environmental footprint (IRTA/GCCA, 2017; WHO, 2015).

Objectives: This article develops a comprehensive, theory-driven framework that integrates active monitoring technologies, regulatory good distribution practices, and intelligent logistics planning to preserve product quality across storage and transport.

Methods: A synthetic, theory-centric methodology was used: (1) systematic integration of the provided references; (2) critical comparative analysis of regulatory guidance and industry white papers; and (3) development of a conceptual model uniting monitoring, qualification, operational controls, and continuous improvement. The method prioritizes rigorous textual analysis and normative mapping rather than empirical measurement, enabling a publication-ready theoretical contribution grounded in the cited literature (WHO, 2015; ORBCOMM, 2024; Envirotainer, 2019).

Results: The resulting framework articulates five interdependent domains: regulatory alignment and documentation; environmental sensing and telematics; packaging and active thermal control; operational choreography (modal selection, routing, and contingency); and governance for data integrity and continuous improvement. Implementing these domains reduces risk of temperature excursions, aligns operations with Good Distribution Practices (FDA, 2022; HPRA, 2020), and produces measurable process indicators such as excursion frequency and time-outside-range.

Discussion: The framework addresses technological, human, and institutional barriers — sensor calibration, chain-of-custody documentation, provider selection, and cost-effectiveness tradeoffs — and offers strategies for mitigation, including qualification protocols, multi-sensor redundancy, and risk-based routing. Limitations include the lack of primary empirical validation in this paper and the rapid evolution of telematics and machine-learning applications that require ongoing empirical scrutiny (Chowdhury, 2025; GS1, 2022).

Conclusions: A layered implementation that combines validated monitoring systems, active cold chain technologies, rigorous qualification, and intelligent logistics planning is necessary to protect product integrity, reduce waste, and comply with regulatory expectations. This integrative perspective supports practitioners and policymakers seeking resilient, cost-sensitive, and compliant cold chain strategies.

Background: Modern supply chains operate within an environment characterized by volatility, complexity, and rapid technological change. Building supply chains that are simultaneously agile, resilient, and digitally intelligent has become an imperative across industries ranging from air cargo operations to last-mile urban deliveries (Christopher, 2000; Bombelli & Fazi, 2022). The literature contains rich but fragmented contributions on discrete elements — agility taxonomies, digital enablement, sustainable modal shifts, and empirical datasets for automation — yet a unified theoretical and operational framework that ties these elements into implementable pathways is lacking (Christopher, 2000; Costantino et al., 2012; Bamakan et al., 2021).

Methods: This paper synthesizes heterogeneous sources spanning empirical case studies, methodological contributions to agile development, technological frameworks integrating blockchain-IoT-big data, and context-specific innovations such as cargo bicycles and perpetual motion electric trucks. Using a rigorous integrative literature synthesis approach — combining conceptual mapping, comparative analysis of empirical case studies, and cross-domain theoretical elaboration — the study derives a multi-layered framework aligning strategy, operations, and digital infrastructure to support sustainable, resilient, and agile supply chains (Christopher, 2000; Bamakan et al., 2021; Gonzalez-Calderon et al., 2022).

Results: The resulting framework delineates (1) strategic principles for agility and Triple-A alignment (agility, adaptability, alignment), (2) operational modules for warehousing and last-mile logistics that embed human-in-the-loop and automated capabilities, (3) a digital intelligence stack comprising sensing, secure data fabric (blockchain), analytics, and decision automation, and (4) sustainability vectors that reconcile carbon reduction with service-level objectives through modal innovations and energy-efficient vehicle technologies (de Boer, 2018; Bamakan et al., 2021; Hunt et al., 2023; Gonzalez-Calderon et al., 2022). The framework further articulates measurement constructs and key performance indicators that balance responsiveness, cost, and environmental impact (Costantino et al., 2012; Balaji et al., 2015).

Conclusions: Integrating agility and digital intelligence provides robust pathways to resilient, low-carbon supply chains. Operationalizing this integration requires deliberate investments in data interoperability, human-centered automation in warehousing, and policy-aligned adoption of sustainable transport modes. The paper concludes with prioritized research agendas and practical implementation guidelines for managers and policymakers to accelerate transition toward agile, digitally-enabled, and sustainable supply networks (Christopher et al., 2006; Bamakan et al., 2021; Chowdhury, 2025).

This article presents an extended theoretical synthesis and a comprehensive conceptual framework for designing resilient, privacy-preserving, and QoS-aware multi-tenant cloud systems by integrating four complementary technological and architectural paradigms: blockchain-based decentralized control and provenance, trusted execution environments (TEEs) exemplified by Intel SGX and SGX-aware container runtimes, formalized privacy mechanisms grounded in differential privacy and randomized response, and adaptive tenant separation and detection strategies for runtime isolation and attack mitigation. We examine the strengths, limitations, and interplay among these approaches, and propose a unified architecture that reconciles competing objectives: strong confidentiality and integrity guarantees for tenant data, practical auditability and accountability in federated or multi-cloud deployments, minimal performance degradation under realistic service level agreements, and robust detection and mitigation of VM- and container-based threats including botclouds and distributed denial of service (DDoS). Building on foundational literature in cloud security, privacy, and multi-tenant orchestration, we elaborate a layered methodology that combines (a) blockchain-anchored metadata and access-control contracts for decentralized provenance and SLA enforcement, (b) enclave-protected computation and SCONE-like secure container frameworks for limiting the trusted computing base, (c) differential privacy mechanisms and RAPPOR-style telemetry sanitization to constrain information leakage from aggregated metrics, and (d) fine-grained, SLA-aware tenant separation with multi-level authorization and reputation mechanisms to reduce lateral movement and noisy neighbor effects. We discuss expected tradeoffs, emergent attack surfaces introduced by combined deployments, and measurable indicators for security, privacy, and QoS that operational teams can use for continuous assurance. Finally, the paper outlines open research directions, including verification of blockchain smart contracts for SLA semantics, long-term key management for TEEs in federated clouds, rigorous composition theorems for differential privacy under repeated queries in multi-tenant analytics, and adaptive controllers for load distribution that account for anonymity-preserving telemetry. The synthesis aims to serve as a rigorous theoretical scaffold for experimental systems research and industrial adoption, enabling future empirical evaluation and standardization.

This article develops a comprehensive, publication-ready synthesis and original framework for near real-time credit card fraud detection grounded in streaming analytics, deep learning, and pragmatic system design. Drawing from empirical and methodological literature on real-time fraud detection, streaming platforms (Kafka, Spark, Flink), deep learning architectures, large-scale anomaly detection, and operational constraints in financial systems, the paper articulates a resilient architectural pattern that balances latency, detection accuracy, explainability, and data governance (Abakarim et al., 2018; Rajeshwari & Babu, 2016; Martín Hernández, 2015; Hebbar, 2025). The proposed Convergent Streaming Detection Framework emphasizes a tiered detection pipeline: ultrafast rule-based triage in the streaming path, lightweight explainable models for immediate scoring, and contextual deep models (including sequence and graph-based learners) operating on enriched windows for elevated scrutiny (Nicholls et al., 2021; Zhou et al., 2019). Practical considerations include feature engineering for streaming contexts, approaches to class imbalance and concept drift, strategies for low-latency model serving, and hybrid provenance and logging to preserve forensic trails without violating privacy or incurring prohibitive storage and throughput costs (Saxena & Gupta, 2017; Nguyen et al., 2020). The article also details rigorous evaluation metrics appropriate to streaming fraud contexts, an experimental design for realistic pilot deployments, adversarial threat modeling, and a multi-year research agenda emphasizing red-team testing and socio-technical evaluation. The synthesis stresses that engineering trade-offs—between latency and model complexity, explainability and predictive performance, and on-chain/off-chain evidence storage—must be made transparently and governed by regulatory and user-centric considerations (The Business Research Company, 2025; Udeh et al., 2024). The contribution is a practically actionable blueprint for researchers and practitioners seeking to deploy deep-learning-driven fraud detection in production-grade streaming environments.

Background: Rapid convergence of artificial intelligence (AI), cyber-physical systems (CPS), and distributed ledger technologies has created both unprecedented capabilities and complex vulnerabilities across domains that include clinical research, Internet of Medical Things (IoMT), agri-food supply chains, and financial services. The disparate literatures on AI-driven protection mechanisms, secure blockchain topologies, and CPS design patterns demand integrated theorization to inform both academic inquiry and practical governance (Syed & Faiza, 2024; Voulgaris et al., 2020).

 Objective: This research article synthesizes theoretical and applied knowledge from the provided references to present a unifying conceptual and methodological framework for AI-enabled resilience in cyber-physical and financial systems. The framework explicates how AI models, secure architectures, and CPS design patterns can act synergistically to mitigate cyber threats, preserve data integrity, and enable trustworthy automation.

 Methods: Using a rigorous integrative review approach grounded in the supplied references, the methodology reconstructs causal pathways between system components (AI agents, sensors, ledgers, network topologies) and adversarial threats. The paper uses systematic cross-domain mapping of threat vectors, defenses, and design principles, supported by close textual analysis of theory and applied studies in the corpus (Lee & Seshia, 2006; Khaitan & Mohan, 2021).

 Results: The synthesis yields a layered resilience architecture: (1) sensing and verification at the edge (IoMT & RFID-enabled traceability), (2) secure transaction and provenance layers (hierarchical blockchain topologies), (3) AI-driven anomaly detection and adaptive response, and (4) governance and auditability mechanisms for human oversight. This architecture addresses confidentiality, integrity, availability, and non-repudiation across domains, and demonstrates how finance-specific requirements for transactional integrity align with CPS safety constraints (Singh, 2025; Rajkumar & Lee, 2010).

 Conclusions: The integrated framework demonstrates that AI is not merely a detection tool but a coordinating agent that—if designed with CPS principles, embedded assurance, and secure ledger topologies—can materially enhance system resilience. Implementation requires domain-specific model assurance, explainability, and regulatory alignment. Future research should empirically validate cross-domain transferability, quantify trade-offs between model complexity and interpretability, and operationalize governance mechanisms for adaptive, AI-mediated security.

The global semiconductor shortage precipitated by the COVID-19 pandemic exposed deep structural vulnerabilities in interdependent supply networks, particularly affecting the automotive sector and other industries reliant on advanced integrated circuits. This paper presents a comprehensive conceptual and analytical treatment of supply chain resilience strategies tailored to semiconductor-driven disruptions. Drawing from empirical analyses of the 2020–2022 shortages and a synthesis of operations research, inventory theory, and systemic risk perspectives, the study constructs an integrated framework that links disruption typologies, propagation mechanisms (ripple effects), and strategic interventions spanning pre-positioning, sourcing design, production-inventory control, and policy-level reshoring and infrastructure investment. The methodology combines theory-driven modeling approaches—inventory and EOQ-based decision analysis, Markov-modulated disruption modeling, and macro-level resource allocation principles—with scenario-based descriptive results that illuminate trade-offs among cost, responsiveness, and resilience. Results highlight the critical role of diversified sourcing, multi-stage hybrid supplier selection, targeted pre-positioning of critical components, flexible service outsourcing in emergency logistics, and adaptive pricing-credit mechanisms to stabilize demand and supply interactions. We further argue that resilience investments must be prioritized using systemic-risk-aware allocation rules that account for network centrality and the non-linear amplification of disruptions. The discussion addresses limitations of current models—particularly the challenge of quantifying geopolitical and policy-driven risks—and outlines a rich research agenda that integrates stochastic control, game theory, and empirical supply network mapping. The paper concludes with actionable managerial recommendations and policy implications, including when reshoring or capacity investment makes strategic sense versus when network augmentation and contractual innovations are superior. The contribution lies in translating fragmented empirical observations and disciplinary approaches into a coherent, prescriptive body of knowledge for managing semiconductor-induced systemic disruptions.

Web application security has emerged as one of the most persistent and complex challenges in modern software engineering, driven by the rapid evolution of programming languages, frameworks, and deployment environments. Despite decades of research and practical countermeasures, vulnerabilities such as cross-site scripting, authentication flaws, and insecure session management continue to dominate real-world incident reports. This research article presents a comprehensive, language-centric investigation of web application security vulnerabilities, with particular emphasis on PHP and Java-based ecosystems, drawing strictly upon established empirical and conceptual studies in the literature. The study synthesizes findings from vulnerability field studies, empirical analyses of open-source software, framework-level security evaluations, and developer-centered security research. By examining vulnerabilities through the lenses of programming language design, framework abstraction, developer behavior, and performance–security trade-offs, this work offers a holistic understanding of why certain classes of vulnerabilities persist across technological generations. The methodology relies on qualitative comparative analysis of prior empirical investigations, complemented by theoretical reasoning rooted in software reliability, security engineering, and human factors. The results reveal that while modern frameworks introduce robust security mechanisms, they also introduce new forms of complexity that can obscure security assumptions and create configuration-dependent risks. The discussion highlights critical limitations in existing security models, including overreliance on framework defaults, insufficient developer security education, and the tension between performance optimization and defensive rigor. The article concludes by outlining future research directions focused on adaptive security policies, language-aware vulnerability prevention, and the integration of security education into the core of software development practice.

The rapid emergence of crypto-native companies has fundamentally altered the landscape of foreign exchange exposure, risk management, and algorithmic decision-making. Unlike traditional multinational enterprises, crypto-native firms operate at the intersection of volatile digital assets, fiat currencies, decentralized financial infrastructures, and real-time global markets. This creates a uniquely complex foreign exchange (FX) risk environment that cannot be adequately addressed using conventional hedging strategies or static econometric models. In response to this challenge, this article develops a comprehensive, theoretically grounded synthesis of adaptive FX hedging algorithms for crypto-native enterprises by integrating soft computing techniques, deep learning-based time series forecasting, reinforcement learning, predictive coding architectures, and game-theoretic learning frameworks. Drawing strictly on established research in FX prediction, soft computing hybrids, deep neural forecasting, predictive coding theory, and online learning with expert advice, the study constructs a unified conceptual framework that explains how modern hedging systems can dynamically learn, adapt, and self-correct under persistent uncertainty. The methodology emphasizes descriptive and theoretical integration rather than mathematical formalism, detailing how data structuring, agent behavior modeling, and loss-sensitive optimization interact in real-world hedging contexts. The results section provides an extensive descriptive analysis of how such integrated systems outperform static hedging paradigms in terms of adaptability, robustness, and behavioral transparency. The discussion critically examines limitations related to model interpretability, regime shifts, and ethical considerations, while outlining future research directions that bridge neuro-inspired predictive coding with financial decision systems. The article concludes by positioning adaptive, learning-based FX hedging as an essential strategic capability for crypto-native firms navigating an increasingly fragmented and uncertain global monetary ecosystem.