Search Results (180)

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Against the backdrop of the accelerated development of the low-altitude economy and the structural transformation of modern logistics systems, systematically elucidating the key driving factors and their interaction structure is paramount for optimizing operational efficiency, promoting sustainable industry growth, and enhancing policy effectiveness. Integrating an extensive literature review with expert consultations, this study constructs a comprehensive indicator system of influencing factors for the coordinated development of the low-altitude economy and sustainable modern logistics. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to characterize the causal relationships and influence directions among the factors. Empowered by these findings, an Analytic Network Process (ANP) model is established to calculate refined weights, forming a hybrid DEMATEL–ANP analytical framework. The results indicate that technological factors and institutional factors constitute the primary driving layer of the system. Specifically, System Integration and Operational Technology, Flight Control and Scheduling Capability, as well as the Standardisation of Airspace Management and the Completeness of the Regulatory and Standards Framework, exert pivotal influences on the systemic evolution. Social factors and infrastructure factors primarily function as the outcome and feedback layers, with their effectiveness contingent upon the maturity of the core driving elements. Further hybrid weight analysis demonstrates that the ranking of key influencing factors exhibits high stability and robustness. The coordinated development process presents a progressive transmission characteristic from “technology–institution” to “market–application” providing targeted practical guidance for promoting the sustainable and high-quality synergy between the low-altitude economy and modern logistics. Full article

Hyperconnected IoT ecosystems have become crucial for organizational operations; yet, existing governance structures remain fragmented, are technology-centric, and not well-equipped to manage the risks, compliance pressures, and resilience needs of IoT. This paper presents an integrated, theory-based information security governance model that is tailored for IoT-driven organizations. A conceptual synthesis is performed through integrating five theoretical anchors: governance theory, socio-technical systems theory, risk governance theory, institutional/compliance theory, and resilience/adaptive capacity theory. These theoretical lenses are used to derive essential governance constructs and to develop a modular architecture tailored to IoT security needs. The model’s validity is grounded in theoretical integration rather than empirical testing, consistent with the nature of conceptual research. The integrated model provides six interdependent governance dimensions: strategic governance, operational governance, technical oversight, compliance alignment, risk governance, and resilience/adaptation, anchored by an ecosystem coordination layer. It provides structured decision rights, continuous risk monitoring, regulatory legitimacy, and native adaptive capabilities toward dynamic cyber-physical threats. This research addresses a known gap in the literature on IoT governance by providing an integrated, theoretically validated governance model that systematically connects the rationale and operational mechanisms of governance for resilient, future-proof IoT adoption. The model is further operationalized through a five-level maturity structure, enabling organizations to assess and progressively enhance governance capabilities. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

Grid-based 3D object detection methods effectively leverage mature point cloud processing techniques and convolutional neural networks for feature extraction and object localization. However, unlike the 2D object detection domain, the unique characteristics of point cloud data being unevenly and sparsely distributed in space necessitate that detection networks possess a certain level of spatial structural perception. Learning spatial information such as point cloud density and distribution patterns can significantly benefit 3D detection networks. This paper proposes a Spatial-aware Network for 3D object detection (SpaA). Based on the 3D sparse convolution network, we designed a Variable Sparse Convolution network (VS-Conv) capable of perceiving the importance of locations. To address the issue of set abstraction operations completely ignoring spatial structure during local feature aggregation, we proposed a Spatial-aware Density-based Local Aggregation (SDLA) method. Experiments demonstrate that enhancing the spatial-awareness capability of detection networks is crucial for complex 3D object detection. Detection results on the KITTI dataset validate the effectiveness of our method. The test set results of SpaA achieved 3D AP values of 82.20%, 44.04%, and 70.34% for the Car, Pedestrian, and Cyclist categories, respectively, and a competitive 3D mAP of 67.23%, outperforming several published methods. Full article

The growing digitalization of supply chains and increasing sustainability requirements create the need for structured tools that assess organizational readiness for Cognitive Twin (CT) adoption. However, existing digital twin and sustainability maturity models rarely integrate technological architecture, governance, and circularity within a unified framework. To address this gap, the study proposes the Supply Chain Twin Sustainability–Cognitive Maturity Model (SCT-SCMM), a novel framework that explicitly integrates governance structures, sustainability objectives, and a hierarchical system architecture into the assessment of Cognitive Twin readiness. Unlike existing models, the proposed framework captures the interdependencies between technological capabilities, decision intelligence, and governance mechanisms across multiple system layers, providing a systemic perspective on sustainable digital transformation. The framework structures organizational readiness through five interdependent layers: Physical, Control, Communication, Decision-making, and Governance, and defines staged maturity levels reflecting progression toward sustainable cognitive autonomy. This layered architecture enables the simultaneous evaluation of operational automation, digital intelligence, and institutional governance as co-evolving dimensions of Cognitive Twin adoption. The model was developed through a structured literature review and operationalized using a hybrid multi-criteria and fuzzy-based evaluation approach, enabling the evaluation of complex socio-technical systems under uncertainty. The framework was applied in an automated product-to-human warehouse case study to evaluate technological, sustainability, and governance readiness. The results demonstrate the model’s ability to identify maturity gaps, reveal inter-layer dependencies, and prioritize transformation pathways toward more resilient and circular logistics systems. By integrating governance, sustainability, and system architecture into a single maturity model, SCT-SCMM extends existing digital twin maturity approaches and provides a transparent decision-support tool for guiding staged Cognitive Twin adoption in next-generation sustainable supply chains. Full article

Manufacturing firms face intensifying pressure to achieve sustainability while remaining competitive under environmental stress, rapid technological change, and institutional uncertainty—challenges that are particularly acute in developing economies. Although Industry 5.0 has emerged as a human-centric and sustainability-oriented industrial paradigm, limited research explains how it can be systematically operationalized to enhance sustainable business performance. This study addresses this gap by developing an integrative conceptual framework linking Industry 5.0, Smart Circular Economy (SCE), and Green Human Resource Management (GHRM) within manufacturing contexts. Drawing on resource-based, dynamic capability, and institutional perspectives, the framework conceptualizes Industry 5.0 as a strategic digital orientation that enables circular resource orchestration and sustainability-aligned human capital systems. SCE and GHRM are positioned as complementary operational mechanisms that translate Industry 5.0 principles into organizational capabilities. Innovation capability is introduced as a mediating dynamic capability explaining how technological and human resource investments generate environmental, social, and economic performance outcomes. Digital maturity and policy support are incorporated as contextual moderators shaping transformation pathways in developing economies. The proposed model advances sustainability-oriented industrial transformation theory by integrating previously fragmented research streams into a coherent socio-technical capability architecture. It also offers actionable insights for managers and policymakers seeking to align digital industrial development with long-term sustainability objectives under conditions of institutional heterogeneity. Full article

This study develops and empirically grounds the Human-Centered Transformation Framework (HCTF), an integrative model explaining how talent management (TM) functions as a dynamic capability aligning digital transformation (DT) and sustainability (SUS) within traditional manufacturing small- and medium-sized enterprises (SMEs) in the European Union. Integrating the Resource-Based View, dynamic capabilities theory, and Organizational Culture Theory, the framework was constructed through structured theory-building and validated using a mixed-methods sequential explanatory design. Quantitative data from 203 manufacturing SMEs across Poland, the Czech Republic, and Slovakia (78-item survey; Cronbach’s α = 0.84–0.91 across six constructs) provide statistical support for the framework’s core propositions, while qualitative interviews with 18 senior executives offer explanatory depth on the mechanisms through which TM enables transformation integration. Findings indicate that TM practice intensity is positively associated with both digital readiness (β = 0.42; p < 0.001) and sustainability maturity (β = 0.36; p < 0.001), with transformational leadership and learning-oriented organizational culture operating as significant mediating and moderating variables respectively. The study contributes a context-specific theoretical synthesis extending prior integrative TM models to the twin transitions context, while acknowledging limitations including the cross-sectional design and Central European sample. Full article

Organisations increasingly face complex cybersecurity threats shaped not only by internal capabilities but also by external regulatory, institutional, and environmental conditions. While existing cybersecurity standards and maturity models provide valuable guidance, they often offer limited support for assessing organisational readiness in a manner that is both context-sensitive and diagnostically meaningful. This paper presents a context-aware cybersecurity readiness assessment framework designed to support organisational evaluation of cybersecurity readiness while explicitly accounting for external environmental influences. The framework adopts a two-tier architecture. Tier 1 assesses organisational awareness of and engagement with the external cybersecurity environment, including national regulatory obligations, institutional support mechanisms, and international collaboration. Tier 2 evaluates internal organisational cybersecurity readiness across governance, operational controls, awareness and culture, and external collaboration practices. The two tiers are designed to operate independently, enabling complementary interpretation without assuming deterministic relationships between external context and internal capability. The framework is developed and evaluated using a Design Science Research approach and is operationalised through a structured assessment instrument and an interpretable scoring model. Empirical validation is conducted across multiple organisational contexts operating in developing and emerging environments, with qualitative case study evidence where available. The results demonstrate that the framework differentiates meaningfully across readiness domains, avoids artificial score inflation or compression, and supports interpretable diagnosis of alignment gaps between external expectations and internal practices. The study contributes a validated assessment artefact that extends cybersecurity awareness research into a broader organisational readiness perspective. From a practical standpoint, the framework provides organisations, policymakers, and researchers with a structured tool to support incremental improvement, informed decision-making, and reflective engagement with both internal cybersecurity practices and external environmental conditions. Full article

This paper focuses on the modeling and design of an adaptive vortex generator (AVG). The device is actuated through shape memory alloy (SMA) elements. The interest of the research community in these devices is due to their ability to improve the performance of the aircraft, directly altering and controlling the boundary layer. Their action consists of energizing the flow, thereby hindering separation. The peculiarity of the presented AVG architecture lies in its compactness and adaptability, which allows for its activation just for some specific phases that are not adequately covered by the conventional. This system can enable load alleviation in the cruise phase when a gust occurs (spoiler modality) and stall prevention in high-lift conditions (vane modality). These two working capabilities can be obtained by mounting the AVGs at different angles of incidence, with respect to the direction of the flow. The present paper is structured as follows. First, the project of RADAR, hosting the activities, is presented with specific focus on the main objectives and on the strategy of maturation of the technologies. Then, attention is paid to the simulations of the aerodynamic field produced by the AVG. These outcomes have driven the next part of the work, focusing on the identification of the architecture of the AVG. A dedicated finite element modeling approach was implemented to address the design task, even in the presence of SMA non-linear elements. Three main operational phases were simulated: (1) the stretching of the springs up to their connection to the architecture (pre-load phase); (2) the elastic recovery of the springs and the achievement of equilibrium with the hosting structure; and (3) the activation of the springs through heating to deflect the AVG. The simulations proved the capability of the system to produce the required deflection/deployment, even under the most severe load conditions. In particular, the simulations highlighted the capability of the system to produce a deflection of the vortex generator of 83.5 deg under the most severe load conditions, against the required value of 80 deg. This result was obtained by also keeping the structural safety factor at a value of four, in line with the wind tunnel facility requirement. Another key outcome of the dynamic analysis was the absence of coupling with vortex shedding, since the system resonance frequencies (135 and 415 Hz) are well outside the vortex-shedding frequency range (500–1400 Hz). Full article

Digital Twin (DT) technologies are widely discussed in the context of Industry 4.0 and advanced manufacturing; however, their role in supporting the sustainability and survival of academic spin-offs remains underexplored. This paper argues that, particularly in peripheral and resource-constrained innovation ecosystems, Digital Twins should be understood not merely as optional technological enhancements but as strategic capabilities that support sustainable technology commercialization in early-stage, research-driven ventures. Building on literature on academic entrepreneurship, technology commercialization, digital innovation, and regional innovation systems, the study develops a conceptual framework that positions Digital Twins as entrepreneurial infrastructures linking scientific outputs to market readiness through three interrelated mechanisms: the reduction in technological uncertainty, the acceleration of market validation, and the enhancement of organizational learning and strategic adaptability. Extending beyond conceptual development, the paper proposes a staged Digital Twin adoption roadmap aligned with Technology Readiness Levels, offering a practical pathway for integrating DT capabilities across venture maturation phases while strengthening investor readiness and commercialization outcomes. The analysis further connects DT-enabled experimentation with sustainability objectives by demonstrating how virtual testing, digital validation, and data-driven learning support capital-efficient, resource-conscious, and resilient innovation processes. By integrating theoretical insights with operational guidance, this conceptual study contributes to research on technology transfer, deep-tech entrepreneurship, and sustainability-oriented innovation by proposing a framework that may guide future empirical investigations of Digital Twin adoption in academic spin-offs. The framework also offers actionable implications for spin-off founders, university technology transfer offices, and policymakers seeking to foster resilient and inclusive innovation ecosystems. Full article

Urban blue–green infrastructure (BGI), comprising vegetation and aquatic elements, is fundamental to city resilience and climate adaptation. Effective BGI management necessitates high-resolution, spatially accurate data for which Unmanned Aerial Vehicles (UAVs) have emerged as versatile monitoring tools. This study provides a critical synthesis and analytical evaluation of UAV-based technologies for BGI management from 2018 to 2025. Following a PRISMA-guided methodology, the review evaluates dominant research themes, sensor technologies (RGB, multispectral, thermal, LiDAR, and water and air quality sensors), and analytical methods. Departing from traditional descriptive reviews, this study appraises the operational maturity of these technologies using an adapted Technology Readiness Level (TRL) framework. The analysis identifies a significant “maturity gap” between standardized structural mapping (TRL 9) and experimental functional assessments of environmental conditions (TRL 4–6). Notably, the article includes a detailed analysis of specific UAV platforms and sensors, providing specifications of technological capabilities. By identifying critical technical, regulatory, and economic bottlenecks, this review provides a robust, evidence-based foundation for the deployment of drones in enhancing urban resilience and sustainable environmental governance. Full article

Direct air capture (DAC) is a carbon removal technology that selectively extracts CO₂ from ambient air, where it exists at trace concentrations of approximately 400 ppm (0.04%), using chemical or physical separation processes. As the only CO₂ capture approach capable of delivering negative net emissions, DAC has emerged as a critical CO₂ removal (CDR) strategy for achieving global net-zero targets. However, its operation requires substantial electrical energy to drive large air flows and significant thermal energy for sorbent regeneration, which remains a major barrier to large-scale deployment. Coupling DAC with nuclear power has been proposed as a promising approach because nuclear systems can provide stable, carbon-free electricity and heat. This review summarizes recent studies on the integration of DAC with nuclear power plants and analyzes the current technological maturity of nuclear–DAC systems. In particular, the paper compares different DAC configurations, evaluates their energy requirements and integration strategies with nuclear heat and power sources, and identifies key technical and economic challenges for future deployment. Full article

Maritime decarbonization has shifted from a long-term aspiration to an engineering and systems-integrated problem under near-term compliance pressure. International regulatory bodies, governments, and a wide array of private-sector coalitions will tighten greenhouse-gas fuel-emission standards from 2028, translating climate targets into enforceable cost signals and accelerating interest in alternative-fuel and retrofit pathways. This review synthesizes the state of the art (SoA) of maritime decarbonization by mapping where technological bottlenecks concentrate along the well-to-wake (WtW) value chain for the main candidate pathways: biofuels, LNG/bio-LNG, hydrogen, ammonia, e-methanol, and electrification, and by benchmarking them side-by-side using a unified framework designed to compare their realizable well-to-wake GHG-reduction potential under maritime operating constraints. Building on that comparative lens, this work aims to connect pathway readiness to the near-term market and regulatory reality, while the alternative-fuel-capable fleet is projected to expand rapidly, creating a structural capability vs. supply gap, in which, for example, ship readiness can outpace low-GHG fuel availability and bunkering rollout. The merged evidence indicates that near-term abatement will be dominated by scalable drop-in biofuels, whereas deep-sea options (ammonia/hydrogen and e-fuels) remain gated by upstream low-GHG production, port infrastructure, and safety/regulatory maturation. Nevertheless, mid-term deployment of low-GHG fuels can act as a system “relief valve”, reducing infrastructure lock-in and accelerating emissions reductions while zero-carbon fuel supply chains scale up. Full article

This paper presents a taxonomy-based survey of AI-driven network optimization mechanisms relevant to the transition from fifth generation (5G) to sixth generation (6G) mobile communication systems. In contrast to earlier generational shifts that are often described as technology replacement cycles, the 5G-to-6G evolution is increasingly characterized in the literature as a prolonged period of coexistence, hybrid operation, and progressive integration of new capabilities across radio, edge, core, and service layers. To structure this transition, the paper organizes prior work into a transition-oriented taxonomy covering migration strategies, AI-enabled closed-loop control, RAN disaggregation and edge intelligence, core virtualization and slice orchestration, spectrum-aware coexistence, service-driven requirements, and security-aware governance. Rather than introducing a new optimization algorithm or an experimentally validated architecture, the contribution of this survey is analytical and integrative. Specifically, it consolidates fragmented research directions into a reference view of how AI-driven control mechanisms are distributed across spectrum, RAN, edge, and core domains during hybrid 5G–6G operation. In addition, the paper includes a structured evidence synthesis of performance trends, deployment maturity signals, and recurring methodological limitations reported across the literature. The review indicates that meeting anticipated 6G objectives, including ultra-low latency, high reliability, scalability, and improved energy efficiency, depends less on isolated enhancements at individual protocol layers and more on coordinated cross-layer optimization supported by AI-native control loops. At the same time, the surveyed literature reveals persistent gaps in service-to-control mapping, security-aware orchestration, interoperability across heterogeneous domains, and reproducible evaluation methodologies for hybrid 5G–6G environments. The survey is intended to provide researchers, network operators, and standardization stakeholders with a structured analytical basis for assessing how AI-driven optimization can support the staged evolution from 5G systems toward 6G-ready infrastructures. Full article