systems engineering

To address interdisciplinary interoperability, variant configuration governance, end-to-end traceability, and cross-organizational collaboration challenges arising from the networked evolution of Systems of Systems (SoS), this paper proposes a lifecycle management framework for Network-Centric Development (NCD). Methodologically, it grounds the framework in Model-Based Systems Engineering (MBSE) semantics and integrates Product Lifecycle Management (PLM) governance, CAD-CAE model synchronization, and closed-loop digital thread/digital twin capabilities. Its core contributions are four foundational principles: (1) reference architecture with a unified data model; (2) end-to-end configuration sovereignty; (3) review-driven model gating; and (4) quantifiable value contribution assessment. Empirical validation across transportation, healthcare, and public-sector domains demonstrates significant improvements in change robustness and model reuse rate, reduced delivery cycles, and enhanced support for sustainability-oriented decision-making.

Cyber-physical systems of systems (CPSoS) in Industry 4.0 and smart homes face escalating cyber threats and dynamic environmental perturbations, undermining their resilience and operational continuity. Method: This paper proposes an enhanced cyber-resilience lifecycle framework that integrates systems engineering principles, multi-layered risk assessment, adaptive control, and closed-loop lifecycle management—distinguishing itself from conventional static models through disturbance-aware sensing, dynamic reconfiguration, and continuous evolution capabilities. Contribution/Results: Empirical validation across multiple representative CPSoS scenarios demonstrates that the framework significantly improves post-attack or post-perturbation recovery speed (average 37% improvement) and operational stability (52% reduction in fault recovery time). It establishes a novel, integrable, and scalable resilience assurance paradigm for large-scale, interconnected CPSoS deployments.

This study addresses structural and conceptual gaps in AI safety governance—particularly regulatory fragmentation, insufficient global coordination, and limited participation from the Global South—that hinder cross-jurisdictional interoperability. Through a comparative analysis of China, South Korea, Singapore, and the United Kingdom across three high-risk domains—autonomous vehicles, education, and cross-border data flows—the research systematically integrates ethical, legal, and technical governance dimensions. It identifies convergences and divergences across seven key aspects: governance objectives, regulatory authorities, ethical principles, enforcement mechanisms, domain-specific frameworks, technical standards, and critical risks. Aligning interoperability pathways with the UN-endorsed Global Digital Compact and relevant UN resolutions, the study offers actionable policy recommendations that balance local contexts with global cooperation, thereby advancing an inclusive, effective, and trustworthy AI safety governance framework.

This paper addresses governance challenges in AI systems arising from inherent complexity—specifically, synthetic-data-driven feedback loops, AI–critical-infrastructure coupling inducing cascading failures, and nonlinear evolution with emergent behaviors. Drawing on complexity science, public health, and climate governance, the study employs cross-domain analogy and mechanistic analysis to formulate a novel governance framework for complex adaptive systems. It establishes three core principles: (1) identification of optimal timing for dynamic interventions, (2) design of resilient institutional architectures, and (3) adaptive calibration of risk thresholds. Its key contribution is the first systematic integration of complexity science into AI governance, yielding an actionable “complexity-compatible” framework. The framework explicitly targets two high-risk scenarios—synthetic-data feedback cycles and AI–infrastructure interdependence—and provides theoretical grounding and practical pathways for mitigating emergent, path-dependent, and cross-domain propagating risks. (149 words)

Safety-critical small Unmanned Aircraft Systems (sUAS) lack systematic, standardized testing processes that are tightly integrated with safety analysis. Method: This paper proposes a requirement-driven coupled testing framework, introducing the novel triadic paradigm of “requirements–simulation testing–safety analysis.” It employs formal requirement modeling with bidirectional traceability, a simulation–hardware-in-the-loop cooperative testing architecture, scenario-driven test case generation, and deep integration of safety analysis methods (e.g., Fault Tree Analysis and System-Theoretic Process Analysis). Contribution/Results: Evaluated on an sUAS case study, the framework significantly improves simulation fidelity coverage and requirement coverage, enables end-to-end safety evidence generation, fills the gap in standardized sUAS testing procedures, and delivers reproducible, verifiable testing assets to support airworthiness certification.

This study addresses the inadequacy of current IT compliance–oriented cybersecurity policies in safeguarding the physical safety of cyber-physical systems, as digital failures often precipitate real-world harm. By coding 292 critical infrastructure policies (2000–2025) and aligning them with the NIST SP 800-160 Vol. 2 resilience lifecycle, the research reveals a significant misalignment between prevailing policy approaches—overreliant on IT control catalogs during resistance and recovery phases—and actual physical risks. The work proposes a modernized “duty of reasonable care” standard centered on hazard-specific traceability, structured assurance cases, and cyber resilience engineering. It identifies three critical disconnects: misaligned delegation of standards, reduction of recovery mechanisms to mere incident reporting, and uneven sectoral adaptability. The study further outlines a viable pathway for federal policy that integrates engineering implementation with targeted incentives.

Critical infrastructure increasingly incorporates embodied AI for monitoring, predictive maintenance, and decision support. However, AI systems designed to handle statistically representable uncertainty struggle with cascading failures and crisis dynamics that exceed their training assumptions. This paper argues that Embodied AIs resilience depends on bounded autonomy within a hybrid governance architecture. We outline four oversight modes and map them to critical infrastructure sectors based on task complexity, risk level, and consequence severity. Drawing on the EU AI Act, ISO safety standards, and crisis management research, we argue that effective governance requires a structured allocation of machine capability and human judgement.

This study addresses the accelerating fragmentation of global AI governance, which exacerbates the divergence between technological development and regulatory frameworks, thereby impeding systemic interoperability and cross-jurisdictional compliance. To tackle this challenge, the work proposes a “technology–regulation dual interoperability” framework, systematically analyzing the root causes and interaction mechanisms underlying governance fragmentation through policy analysis, cross-jurisdictional comparison, and evaluation of standardization systems. The research reveals a trend of rapid yet dispersed growth in AI governance initiatives, identifies critical barriers to coordination, and offers strategic pathways and practical recommendations for fostering a compatible and coherent global AI governance ecosystem.

Static architectural approaches exhibit insufficient adaptability in Systems of Systems (SoS) due to highly uncertain and dynamically evolving mission environments. Method: This paper proposes a novel mission engineering paradigm integrating digital engineering with deep reinforcement learning (DRL). It constructs a high-fidelity digital mission model, formalizes tactical mission management as a Markov Decision Process (MDP), and trains adaptive mission coordination policies via the Proximal Policy Optimization (PPO) algorithm within an agent-based simulation sandbox. Contribution/Results: The work pioneers deep coupling between digital engineering and DRL, enabling mission-agnostic online task allocation and dynamic system reconfiguration. Evaluated on an aerial wildfire suppression case study, the framework significantly improves mission completion stability and reduces performance volatility—overcoming the adaptability bottleneck of conventional static architectures in dynamic operational settings.

This work addresses the challenge of integrating human values—characterized by their ambiguity, diversity, and context-dependence—into requirements engineering for ethically aware autonomous systems. The authors propose a goal-oriented requirements engineering approach that formalizes human values into five actionable categories: Social, Legal, Ethical, Empathic, and Cultural (SLEEC), aligning them with functional and adaptive system goals. Through normative modeling, conflict detection algorithms, and a design-time negotiation mechanism, the method enables structured integration of value-based requirements, automated validation of their well-formedness, and early identification of conflicts. The feasibility and effectiveness of this approach are demonstrated in a case study on medical body-area sensor networks, where it significantly enhances the system’s ethical alignment during early design phases.