Background: The convergence of Information Technology (IT) and Operational Technology (OT) in Industrial Automation and Control Systems (IACS) has expanded the cyber-attack surface, creating critical risks where security failures can propagate into physical safety hazards. Traditional, static risk assessment methods are inadequate for this complex, converged environments, and the application of standards like IEC 62443 remains a significant challenge.
Objective: This paper designs and validates a novel, hybrid cybersecurity risk assessment (CRA) methodology that integrates Model-Driven Engineering (MDE), explicit safety-security interdependency analysis, and dynamic attack path modeling. The objective is to provide a systematic, semi-automated framework to operationalize the IEC 62443 standard within a "Safety-Security by Design" paradigm.
Methods: We propose a four-phase methodology: (1) automated system modeling and asset identification using MDE principles; (2) integrated threat analysis mapping cyber-threats to physical safety hazards; (3) dynamic risk modeling using attack path analysis to identify critical vulnerability chains; and (4) risk evaluation and mitigation alignment with IEC 62443 Security Levels (SLs). The methodology was validated using a case study of a modular manufacturing testbed.
Results: The application of the methodology successfully identified critical attack paths exploiting IT-OT boundaries that were missed by traditional static analyses. The MDE approach automated the discovery of safety-critical assets, and the interdependency analysis (Phase 2) explicitly linked specific cyber-vulnerabilities to high-priority safety hazards.
Conclusion: The proposed hybrid methodology offers a more robust, dynamic, and integrated approach to IACS cybersecurity. By embedding risk assessment within a model-driven framework, it enables the systematic identification of safety-critical risks and provides a clear roadmap for implementing IEC 62443 controls.

This article investigates the nexus between private equity ownership, leverage decisions, and the dynamics of financial distress in leveraged buyouts (LBOs). Building on a rich empirical and theoretical literature, the study synthesizes evidence on why buyouts employ high leverage, how capital structure influences operational incentives and governance, and the consequences of distress for firm value and creditor outcomes. The manuscript integrates classical views on the role of debt as a disciplinary device with modern perspectives emphasizing contractual design, reputation, and strategic default. It examines the trade-offs implicit in high leverage—between tax shields and bankruptcy costs—while also detailing how debt composition, payment-in-kind (PIK) amendments, and covenant structures shape bankruptcy thresholds and renegotiation outcomes. The study further explores reverse leveraged buyouts and the channels through which private equity exits are realized, assessing value realization under different macroeconomic and industry conditions. Methodologically, the article adopts a rigorous, text-based synthesis approach: theoretical reasoning is combined with exhaustive narrative analysis of principal empirical contributions to derive implications for practitioners and policymakers. Empirical generalizations are drawn primarily from seminal and recent working papers and peer-reviewed studies on buyouts, distress costs, and private equity contracting. The results synthesize consistent patterns: (1) leverage amplifies both returns and downside risk, (2) governance improvements and strategic restructuring under private equity ownership can mitigate a portion of financial distress costs but do not eliminate them, and (3) contractual design—especially debt composition and covenants—plays a decisive role in shaping the path and severity of financial distress. The discussion elaborates on nuanced mechanisms such as reputation effects, information asymmetries between sponsors and creditors, and the role of banks and bondholders as strategic agents in distress. Limitations of the synthesis approach are acknowledged and directions for future empirical and theoretical research are articulated. This comprehensive review offers a coherent framework for understanding how private equity sponsors deploy leverage to create value and how capital-structure choices mediate the transition from leverage-induced discipline to debilitating financial distress. (Word count: 312)

This article provides a comprehensive analysis of governmental regulation and ethical considerations for the development and deployment of artificial intelligence (AI). It begins by examining the evolution of AI governance frameworks, from early self-regulatory approaches and ethical codes of conduct to more formal regulatory acts introduced by national and transnational bodies. Drawing on current policy discussions, the paper highlights the legislative “lag” in addressing AI’s rapidly evolving applications and underscores the need for dynamic, risk-based, or iterative regulatory models. The discussion then moves to ethical imperatives, notably explainability, accountability, fairness, and security, emphasizing how these principles can be integrated into corporate social responsibility (CSR) strategies. Particular attention is devoted to the role of transparency and inclusiveness as crucial elements in mitigating algorithmic biases and preserving human rights. The paper also presents examples of hybrid governance mechanisms that combine legal instruments with voluntary industry standards, and illustrates case studies such as Singapore’s AI Verify framework, the EU’s proposed Artificial Intelligence Act, and the U.S. AI Bill of Rights. Finally, it elaborates on the long-term challenges posed by continuously advancing technologies, diverse cultural values, and geopolitical interests. The conclusion argues that effective AI governance requires an interdisciplinary, multi-stakeholder approach to ensure legal certainty, encourage innovation, and maintain public trust. It calls for ongoing collaborative efforts, policy updates, and empirical research to refine governance tools that can respond to AI’s unprecedented rate of change.

Background: The digitization of the financial services sector has shifted operational risk from physical trading floors to distributed digital interfaces. Historical precedents of massive financial losses, such as the JP Morgan "London Whale" incident and the Société Générale rogue trading scandal, highlight catastrophic failures in governance and monitoring. As the web browser becomes the primary workspace for modern enterprises, it represents a critical, often unmanaged, vector for data exfiltration and unauthorized activity.

Methods: This study proposes a novel evaluation framework for selecting Secure Enterprise Browser (SEB) solutions to mitigate these risks. Recognizing the complexity and ambiguity inherent in cybersecurity decision-making, we employ a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, we integrate Single-Valued Neutrosophic Sets (SVNS) with the Analytic Network Process (ANP) to determine criteria weights, followed by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank technology alternatives.

Results: The analysis reveals that within the financial context, "Granular Policy Enforcement" and "Auditability" significantly outweigh "Implementation Cost." The Neutrosophic analysis demonstrates that specialized SEBs provide a superior governance capability compared to traditional browsers patched with extensions.

Conclusion: The study establishes that adopting Secure Enterprise Browsers is not merely an IT upgrade but a strategic imperative for risk containment. The proposed Neutrosophic framework offers a robust mathematical tool for CISOs to justify security investments amidst uncertainty.

The integration of computer vision into real-world applications is often hindered by the high computational demands of deep learning models and the associated cost of hardware. This paper presents a novel, comprehensive solution for the real-time digitization of a physical chessboard, specifically designed for low-cost embedded platforms. We introduce a complete system that combines efficient classical computer vision algorithms for chessboard localization with a custom-designed, lightweight Convolutional Neural Network (CNN) for piece recognition. The core of our innovation lies in the use of a low-cost, open-source RISC-V platform, which is augmented with a custom, tightly coupled hardware accelerator to offload the most computationally intensive tasks. Our system demonstrates a chessboard localization accuracy of 99.5% and a piece recognition accuracy of 98.2%. Critically, it achieves an average processing latency of 150 milliseconds per frame, enabling true real-time operation on hardware that is an order of magnitude more affordable and power-efficient than conventional GPU-based systems. This work validates a crucial hardware-software co-design paradigm for deploying complex AI tasks on resource-constrained devices, paving the way for a new generation of affordable, intelligent edge applications.

The cold chain—temperature-controlled logistics that preserve perishable goods across storage, transport, and distribution—has become central to global food security, pharmaceutical delivery, and pandemic resilience. This paper presents a comprehensive, publication-ready synthesis and original theoretical framework that integrates technological, managerial, and policy perspectives on cold chain performance and innovation. Drawing strictly on the provided body of literature spanning reviews of monitoring technologies, refrigeration engineering, logistics service quality, sustainability performance metrics, hybrid distribution optimization, and machine learning applications, the article articulates a multidisciplinary research agenda and proposes a layered framework for resilient, intelligent cold chain systems. The structured abstract summarizes objectives, methods, key findings, and implications. Methodologically the paper adopts a rigorous integrative-review approach combined with constructive theoretical synthesis: it systematically reinterprets empirical findings and technological trends from existing studies to derive a unified conceptual model and testable propositions. Results synthesize evidence that: (1) recent advances in sensor, telemetry, and data analytics enable real-time, distributed monitoring that materially reduces temperature excursions (Badia-Melis et al., 2018; Zhao et al., 2020); (2) intelligent routing and machine learning can optimize scheduling and reduce spoilage in last-mile and urban distribution (Yu, 2022; Wang et al., 2022); (3) performance measurement and sustainability metrics must be hybridized to capture both service quality and carbon/cost trade-offs (Kilibarda et al., 2016; Liao et al., 2023; Babagolzadeh et al., 2020); and (4) institutional and regulatory guidance—especially for pharmaceuticals and vaccines—remains a binding constraint that shapes operational design (WHO, 2015; WHO, 2021). The discussion interprets these findings, articulates limitations and critical uncertainties, and maps out a future research agenda with prioritized empirical and modeling challenges. The conclusion offers clear managerial recommendations for practitioners seeking to transform legacy refrigerated transport architectures into resilient, data-driven cold chains that balance quality, sustainability, and cost. This work synthesizes extant knowledge while providing specific theoretical contributions: a layered conceptual model of cold-chain intelligence, a set of propositions linking monitoring fidelity to service outcomes, and an interdisciplinary roadmap for integrating refrigeration engineering, data analytics, and policy compliance.

The convergence of development, security, and operations (DevSecOps) has become essential in modern software engineering, emphasizing the integration of security practices directly into continuous integration and continuous deployment (CI/CD) pipelines. As software systems evolve toward cloud-native architectures and increasingly complex deployment environments, traditional security testing methods have proven insufficient to detect sophisticated vulnerabilities in real-time. This research investigates the implementation of automated security mechanisms within DevSecOps pipelines, focusing on static and dynamic testing, AI-assisted vulnerability detection, big data-driven threat intelligence, and heuristic optimization algorithms. By synthesizing insights from contemporary research, the study identifies critical gaps in current DevSecOps practices, including latency in vulnerability detection, limited integration of predictive analytics, and insufficient alignment of automated security testing with rapid deployment cycles. A methodology emphasizing end-to-end automation, leveraging genetic algorithms for heuristic optimization, and integrating cloud-native security frameworks is proposed. The findings reveal that multi-layered automation enhances security posture, reduces detection latency, and ensures compliance with contemporary security standards. Moreover, the study highlights the strategic role of AI and big data analytics in real-time anomaly detection and predictive threat mitigation. The implications extend to software development organizations, cloud service providers, and security operations centers, providing a roadmap for achieving resilient, scalable, and proactive DevSecOps environments. This research contributes to the ongoing discourse on security automation by offering comprehensive theoretical insights and practical guidance for implementing advanced DevSecOps frameworks in complex, cloud-centric ecosystems.