Search Results (689)

Driven by the digital transformation of territorial spatial governance, traditional urban planning is irreversibly shifting towards a data-driven empirical paradigm. However, constrained by mimetic isomorphism and path dependence, many geography-based regional universities remain trapped in an educational dilemma: they overemphasize morphological representation while marginalizing quantitative decision-making, fostering a structural mismatch between graduate competencies and industry demands. To explore a systematic pathway out of this dilemma, this study chronicles a three-year pedagogical intervention utilizing a mixed-methods design with a historical control cohort (N = 275) within the urban planning program of Gansu Agricultural University—a regional institution situated in a less-developed frontier where territorial renewal demands macro-spatial synthesis over aesthetic forms. The intervention strategically redefined the graduate competency profile as “spatial data analysts”, constructing a pedagogical model comprising foundational algorithmic training, cross-disciplinary faculty collaboration, and real-world Project-Based Learning (PBL), coupled with a restructured, evidence-based evaluation system. Longitudinal tracking and quantitative analyses indicate a structural alignment with elevated educational efficacy. At the macro level of employment trajectories, the proportion of graduates securing knowledge-intensive data positions experienced a structural shift, rising from a baseline of 14.5% to 42.5%, reflecting an enhanced capacity to capitalize on expanding societal demands. At the meso level of practical competence, the award rate in high-level professional competitions increased by 35.4%. At the micro cognitive level, the new evaluation mechanism is associated with a successful redirection of students’ cognitive resources toward algorithmic logic and policy translation (p < 0.001) while highly significantly enhancing their self-efficacy in tackling complex, wicked engineering problems (p < 0.001). Rather than isolating pure causal mechanics, this study interprets these systemic gains as a contextual realignment of academic supply. It provides a context-sensitive, reproducible methodological reference for cultivating professional distinctiveness and reshaping the spatial planning education system in the digital era. Full article

In the current Industrial Internet of Things (IIoT) environment, data security for product lifecycle management is greatly challenged, particularly in scenarios involving vertical multi-level Bill of Materials (BOM) deep nesting and lifecycle dynamic evolution. The traditional case-bounding model, in large-scale deployment, easily leads to rule expansion and an increase in database I/O overhead, thus causing authorization lag, authority boundary ambiguity and other problems. To address these limitations, this paper proposes a Decoupled Hybrid Access Resolution (DHAR) framework. The framework separates static organizational roles from dynamic lifecycle constraints, and the complexity of authorization configuration is reconstructed from case-dependent growth into an object-instance-independent bounded structure; combined with the state-based pre-filtering mechanism and memory cache strategy, redundant recursive query is reduced. Experiments on increasing BOM depths show that, under a 20-layer topology, DHAR reduces average access latency from 285.8 ms to 1.3 ms. Under a 20-layer BOM with 1000 concurrent requests, DHAR maintains an average latency of 5.2 ms, while compressing the authorization rule set from millions to hundreds. These results indicate that, within the studied vertical multi-level BOM setting, DHAR improves response performance while preserving data consistency and strengthening protection against unauthorized modification. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article

Industrial automation systems are increasingly cyber-physical, interconnected, and software-dependent, which expands both their operational capability and their cybersecurity exposure. This article reports a systematic literature review, conducted following the PRISMA 2020 guidelines, of cybersecurity requirements and certification standards in industrial automation, with emphasis on Industrial Control Systems (ICS), Supervisory Control and Data Acquisition (SCADA), Programmable Logic Controllers (PLCs), and Industry 4.0 contexts. From 3570 records identified across five academic databases, 75 studies were retained after duplicate removal, title and abstract screening, and full-text eligibility assessment. The included studies were analyzed along three dimensions: cybersecurity requirements, standards and certification, and application context. Quantitative synthesis shows that network segmentation, intrusion detection, secure communication, access control, lifecycle security, and safety–security coordination are the six most frequently emphasized requirement categories, and that ISA/IEC 62443, ISO/IEC 27001, NIST SP 800-82, and NERC-CIP are the four dominant certification frameworks. The review identifies four critical gaps between technical cybersecurity requirements and certification practice and proposes an integrated mapping framework linking requirement categories, standards, and application contexts. The findings indicate that effective industrial cybersecurity assurance depends on a layered compliance architecture rather than on dependence on any single framework. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions. Full article

Critical infrastructures are increasingly Internet-connected cyber–physical systems whose recovery after cyber incidents must satisfy safety, timing, regulatory, and interdependency constraints. Yet, the use of large language models (LLMs) for generating recovery plans remains fragmented across cybersecurity, industrial control, digital twins, and AI assurance research. This review synthesizes that emerging field through a structured critical survey of studies on LLMs in incident response, OT/ICS resilience, and cyber–physical recovery, with a focused perspective on grounding, trust, and assurance mechanisms relevant to recovery-plan generation. It develops an architecture-centric taxonomy spanning prompt-only assistants, retrieval-augmented copilots, graph-aware planners, multi-agent systems, and hybrid verification/simulation pipelines; maps realistic applications across energy, water, manufacturing, transportation, healthcare, and telecommunications; and organizes limitations into technical, security, governance, and human-factor categories. Based on this synthesis, the paper proposes the Grounded Recovery Planning Stack as a reference architecture and outlines a staged roadmap from human-in-the-loop copilots to bounded orchestration. The main conclusion is that near-term value lies in grounded, auditable, compliance-aware copilots, whereas autonomous recovery execution remains premature without stronger validation, state-aware grounding, sector-specific benchmarks, and formal safeguards. Full article

Modern cybersecurity challenges span multiple layers, from human behavior and identity management to network communication and device security. This paper proposes a unified multi-layered security framework that integrates human-centric, identity-centric, and communication-centric defenses into a coherent architecture. Drawing on insights from diverse domains (industrial control systems, IoT, healthcare, blockchain, and quantum communications), we identify common defense-in-depth principles and interdependencies across layers. The study highlights the persistent gaps in current research, which often focuses on isolated layers or domain-specific models, and addresses these gaps by synthesizing a cross-domain framework. We develop a mixed-method methodology to compare and integrate multi-layer security mechanisms, and we implement a proof-of-concept risk assessment engine to evaluate the framework’s effectiveness. Preliminary results from this implementation demonstrate that combining layers yields significantly improved detection performance and resilience compared to single-layer baselines. The framework’s contributions include a comprehensive literature-driven model, an operational validation in a simulated environment, and guidelines for deploying multi-layer defenses in complex, interconnected infrastructures. Empirical findings confirm that an integrated multi-layer approach can adapt to varied threat scenarios and reduce vulnerabilities, underscoring the value of coordinated controls across technical and human factors. The proposed framework lays a foundation for future work on scalable, cross-layer cybersecurity architectures that better protect contemporary cyber–physical systems. Full article

The standard for effective communication between Internet of Things (IoT) devices has been demonstrated by the increasing demand for IoT technologies in Industry 5.0, along with the growing use of actuators, sensors, and automated processes in these settings. De-vice-to-device interactions controlled by communication protocols that specify data sharing are essential to effective operation. By establishing a single standard that permits plug-and-play integration and improves flexibility across various IoT devices, the IEEE 1451 standard represents an approach. This standard ensures interoperability and enables smooth communication with devices from various companies, regardless of their features. By addressing major obstacles to system integration, the IEEE 1451 standard enables IoT technologies to reach their full potential. By integrating information technology (IT) through automation and industrial control systems (ICSs), the Industrial IoT (IIoT) is transforming many industries, especially essential sectors such as energy, chemicals, oil and gas, and water plants. Although drinking water is an essential resource for life and an aspect of technological progress, little is known about the potential for cyberattacks, including the disastrous consequences they could have for water treatment plants. This re-view identifies and documents several adversarial cyberattacks targeting the water distribution and purification sector. Understanding the range of risk factors in this sector is our primary objective. This study presents a technical assessment from an IIoT perspective that addresses attack scenarios, real-world instances of cyberattacks in the water industry, a range of security challenges, and security measures. The contribution is an informative, up-to-date resource that benefits both prospective scholars and industrial practitioners. By integrating key findings to build a secure and reliable digital future, this work will advance a comprehensive understanding of the cybersecurity environment in water plants in Industry 5.0 and smart cities. Full article

Operational Technology (OT) and Industrial Control Systems (ICSs) along the NATO-EU eastern flank face escalating hybrid threats, yet existing cyber-resilience metrics remain IT-centric, lacking OT-specific constraints and geopolitical exposure dimensions. This paper presents a Design Science Research contribution: the development and simulation-based feasibility demonstration of two interconnected artefacts. The first is the AI-enabled Cyber-Resilience Index (ACRI)—a composite 0–100 metric operationalized through 16 indicators across four domains (detection performance, operational continuity, governance maturity, supply-chain risk), aggregated as a three-term convex combination of capability domains with a linear subtractive supply-chain exposure penalty, weighted via AHP-based illustrative sector-reference profiles. The second is the Unified Compliance Framework (UCF), a structured R → C → E → SLO mapping linking 47 atomic regulatory requirements (NIS2, DORA, CER, AI Act, CRA) to standards (IEC 62443, ISO/IEC 27001) and auditable evidence artifacts, with a Continuous Assurance Loop operationalizing continuous control monitoring. Feasibility is demonstrated through digital twin simulation under three OT-representative threat scenarios (energy SCADA APT, railway supply-chain compromise, manufacturing ransomware). Results in simulated environments show ACRI improvement from Moderate-Risk baselines (45–61) to Adequate-Resilience thresholds (65–73); the proposed federated autoencoder–LSTM detector attains a composite $D_{perf}$ of 0.883 versus 0.510 for a static ±3$\sigma$ threshold baseline (a 73% relative improvement at the domain level). Sensitivity analysis confirms classification robustness ($\pm 7.3\%$ weight perturbation; coefficient of variation below 9.1% across 10,000 Monte Carlo iterations). Critical limitations are explicit: simulation-only evidence ($n=12$ scenario instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. The contribution is positioned as a proof-of-concept design artifact establishing methodological foundations for OT-centric resilience assessment and compliance-to-engineering traceability, not as a field-validated operational system. Full article

Artificial intelligence is increasingly embedded in cyber–physical systems that coordinate sensing, computation, communication, and control across critical and semi-critical physical environments. Within the European Union, however, its adoption is shaped not only by technological maturity and economic value, but also by an increasingly dense regulatory landscape governing data processing, cybersecurity, product security, accountability, traceability, interoperability, and safety-relevant deployment. A PRISMA ScR-informed scoping review is used to examine how European Union regulation influences artificial intelligence adoption across four representative domains: energy and smart grids, smart buildings, mobility and transport systems, and industrial and manufacturing environments. The analysis draws on primary legal sources, the peer-reviewed literature, and policy and standards-related materials, and is structured around three dimensions: regulatory barriers, technological and architectural challenges, and cross-sector implications for governance, innovation, and competitiveness. The results show that regulation functions simultaneously as a constraint and an enabling condition. It increases compliance burden, raises integration complexity, and slows deployment in higher risk settings, while promoting trustworthy artificial intelligence, stronger cybersecurity, lifecycle governance, clearer accountability, and more interoperable digital infrastructures. The central finding is that regulation is not external to artificial intelligence adoption in cyber–physical systems, but actively shapes the design space within which such systems can be developed, integrated, validated, and scaled. Future progress therefore depends on regulation-aware systems engineering, stronger implementation guidance, and cross-sector reference architectures capable of aligning legal compliance with technical architecture and operational value creation. Full article

As a core component of power system reform, the transmission and distribution price reform plays a critical role in optimizing the grid regulation model and promoting efficient allocation of power resources by establishing an independent pricing mechanism based on “permitted cost plus reasonable return”. This study evaluates the provincial transmission and distribution price reform effect in China. First, an evaluation index system is constructed from four dimensions, namely, economic efficiency, security guarantee, market mechanism and social welfare. Second, a comprehensive evaluation model is developed using a multi-attribute decision-making model consist of the Best–Worst Method (BWM), entropy weight method (EWM) and cloud model. Of these, the BWM and EWM are employed to determine the indicator weights, and the cloud model is utilized to rank the transmission and distribution price reform effect. Third, an empirical assessment and analysis are conducted on three typical provinces in China. Empirical analysis reveals significant regional heterogeneity in reform effectiveness. Based on the comprehensive cloud expectation (Ex) values, Province B (eastern coastal) ranks first with an Ex of 82.10 (on a 0–100 scale), falling into the “good” grade; Province C (northern) ranks second with an Ex of 81.05, also “good”; and Province A (central-western) ranks third with an Ex of 78.70, likewise “good”. Province B’s leading position is attributed to synergistic outcomes in cost control, market vitality, and social welfare. The study can provide references for the sustainable development of electric power companies and the electricity industry. Full article

China’s steel decarbonization is a key sustainability challenge because cleaner production routes must be evaluated not only by their mitigation potential, but also by their implications for industrial continuity, cost affordability, resource security, and transition manageability. This study develops a national-scale soft-linked sustainability assessment framework that translates policy-conditioned macro signals into a multi-period, multi-objective optimization model of steelmaking-route transition from 2025 to 2050. Three policy environments are examined: carbon-control pressure, electricity-cost support for electrified routes, and their combined application. The model evaluates route portfolios by cumulative system cost, emissions, and transition adjustment intensity, linking mitigation with affordability and implementation feasibility. Results show that policy environments do not shift pathways uniformly; instead, they reshape the feasible trade-off frontier and alter which route combinations emerge as plausible compromise solutions. Across scenarios, scrap-based electric arc furnace steelmaking (Scrap-EAF) becomes the central medium-term route, while blast furnace–basic oxygen furnace steelmaking (BF-BOF) contracts but remains residual. Hydrogen-based direct reduced iron–electric arc furnace steelmaking (H₂-DRI-EAF) expands under favorable conditions, but does not become dominant by 2050 under the baseline national-scale parameterization. Overall, this study contributes to sustainability-oriented industrial transition analysis by showing how policy-conditioned environments reshape route feasibility, transition sequencing, affordability–mitigation trade-offs, and the practical manageability of China’s steel-sector decarbonization. Full article

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article

Advances in quantum computing have created a serious threat to modern asymmetric cryptosystems protecting heterogeneous critical information infrastructures (CIIs). During this transition period, the primary threat is the “Harvest Now, Decrypt Later” (HNDL) temporal strategy of attackers, which requires the forced migration of CIIs to post-quantum cryptography (PQC) algorithms. However, such migration is associated with nonlinear “technological friction.” This will manifest as a drop in the performance of legacy systems, such as SCADA. In the context of deep cross-industry integration, this can trigger avalanche-like cascading CII failures. This article presents a model of a zero-sum differential game between a CII defender and an attacker (APT group). Using Pontryagin’s maximum principle and the Forward–Backward Sweep Method (FBSM) iterative algorithm, a saddle point was found that determines the equilibrium trajectories of limited resource allocation over a given planning horizon for the CII transition to PQC. The results of the computational experiment demonstrated that isolated sectoral migration is ineffective. It is shown that optimal control requires cross-sector synchronization to prevent cascading degradation of the CII. The proposed mathematical framework provides a practical toolkit for strategic IT budget planning and national security risk management in anticipation of quantum supremacy (Q-Day). Full article