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The maritime sector is undergoing rapid change due to stricter environmental regulations, shifting policies, and growing concerns about energy security and operational resilience. In this landscape, hybrid and all-electric ships (AES) are gaining recognition as effective solutions for reducing emissions, boosting efficiency, and harnessing alternative energy sources. Nonetheless, many electrification initiatives mainly target propulsion, while the entire onboard energy infrastructure, including generation, storage, distribution, power conversion, and energy management, is often developed separately. This paper investigates hybrid and AES systems linking maritime decarbonisation goals to the development of integrated onboard energy architectures. It starts with an overview of key international, European, and Portuguese policies that impact maritime decarbonisation, emphasising their influence on both the commercial and military sectors. The literature review then charts the evolution of onboard power systems, showing the shift from mechanical propulsion to integrated power solutions. Finally, it analyses different architectural configurations, energy storage technologies, alternative energy sources, and energy management strategies found in current research. Our analysis shows that adopting hybrid and AES systems requires a comprehensive system-level strategy that links power generation, storage, distribution, operational profiles, and energy management. By combining policy initiatives, technological progress, and current research, our review highlights the crucial role of integrated energy system architectures in the development of future hybrid and AES systems. We also identify key research gaps, such as multi-domain modelling, consistent performance metrics, mission-oriented energy management, and assessments of long-term robustness and operational sustainability. Full article

Large university campuses need timely, privacy-conscious information about how indoor spaces are used in order to improve space management, energy efficiency, and operational responsiveness. Yet many indoor positioning solutions still depend on additional hardware such as Bluetooth Low Energy beacons, ultra-wideband anchors, or camera-based sensing, which increases deployment cost and maintenance complexity. This paper presents a lightweight campus occupancy analysis and indoor localization framework that reuses an existing Cisco Wireless LAN Controller (WLC) infrastructure as a sensing layer. The system retrieves received signal strength indicator (RSSI) observations from access points over secure SSH sessions, converts these observations into approximate distance estimates through a calibrated log-distance path loss model, and computes user positions using weighted non-linear least-squares multilateration (Mlat). In addition to point localization, the framework generates occupancy heatmaps, cumulative reliability curves, and access-point-density sensitivity analyses based on a 50 m × 50 m evaluation scenario with 500 randomized samples. To support privacy-preserving deployment, the service layer exposes only zone-level occupancy information and omits personally identifiable network identifiers. The results indicate that Wi-Fi-based localization can provide cost-effective and scalable occupancy intelligence with sufficient accuracy for campus-wide density monitoring and smart building applications.$\times$ Full article

The supply chain ecosystem (SCE) operating within an urban environment is characterised by continuous interactions among manufacturing, logistics, service, information, and energy flows across multiple smart city manufacturing-service clusters (SCMSCs). Within the SCE, intelligent production buildings (IPBs) emerge as multifunctional multistorey production-service infrastructures developed under conditions of limited urban land availability and increasing demand for localised manufacturing and service integration. These buildings operate under heterogeneous and dynamically changing energy-demand conditions, substantially complicating energy consumption management. This study develops a knowledge-driven coordination approach for the energy consumption management of IPBs operating within SCMSCs from the perspective of the urban SCE. IPBs are conceptualised as distributed environments with finite building-level power supply system capacity, where manufacturing, logistics, service, and digital processes dynamically compete for shared energy resources. A hierarchical representation of the SCMSC energy environment is proposed, capturing distributed interactions and heterogeneous electricity-demand profiles across interconnected clusters. An information-analytical system integrating monitoring, data acquisition, analysis, ML-based demand prediction, planning, and decision-support functions is developed to support predictive electricity-demand coordination. The proposed framework combines IoT-enabled monitoring with digital-twin-supported synchronisation of energy states for distributed coordination among IPBs. The proposed approach is evaluated through scenario-based analysis of an IPB operating within an urban manufacturing-service environment. The results indicate the potential of knowledge-driven coordination to improve energy-capacity utilisation, mitigate peak-load formation, and enhance operational stability within SCMSCs. Full article

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort. Full article

The operation and management of urban dynamic systems often rely on information, including holiday and event calendars, to anticipate deviations from routine demand. However, such calendars cannot capture irregularly announced holidays or undocumented physical disruptions and require continuous maintenance. This study proposes Structural Equilibrium, a reference state for interpreting urban dynamic systems without external calendars. Temporal variation is represented as Concentration–Dispersion–Relative Balance (CDR) structural states under two representative Structural Equilibria: Information-oriented and Stability-oriented Equilibria. Applying the two Equilibria to Jeju Airport electricity consumption and Gangnam Station subway passenger-flow data showed that structural representations diverged at specific times, producing structural Gaps. Moving-block bootstrap analysis showed that these divergences exceeded the range typically expected under the dataset’s own temporal dependence structure. Without calendar information, the divergences corresponded to the 2024 Chuseok holiday period in the subway data and a government-designated temporary public holiday and major snowstorm in the airport data. One high-Gap observation at Jeju Airport had no external cause despite an unremarkable raw magnitude, showing that structural analysis can reveal unusual conditions overlooked by magnitude-based monitoring. These findings indicate that Structural Equilibrium supports calendar-independent interpretation and adaptive monitoring under changing conditions, contributing to resilient and sustainable urban infrastructure operation. Full article

Ports are progressively evolving from traditional logistics nodes into integrated energy ecosystems, characterised by increasing electrification of maritime and land-based operations, the deployment of renewable energy sources, and the emergence of new and highly variable energy demand profiles. In this context, the main challenge is not only the availability of renewable energy but also the capacity of port energy systems to provide sufficient electrical power, flexibility, and resilience under increasing operational constraints. These issues are particularly relevant in Mediterranean ports, where limited grid capacity, infrastructure constraints, load variability, and interactions with surrounding urban areas strongly influence energy planning strategies. This paper proposes an integrated framework for the development of sustainable port energy hubs based on renewable generation, energy storage, green hydrogen systems, port microgrids, and intelligent energy management strategies (EMS). The main novelty lies in the integration of these energy vectors within a unified framework that explicitly accounts for the specific operational and infrastructure constraints of Mediterranean ports. The proposed approach aims to optimise the interaction between energy production, distribution, storage, and consumption, with particular attention to the role of hydrogen as a long-duration energy storage vector and as an energy carrier for selected port logistics applications. Through a data-driven Port Energy Baseline Assessment (PEBA), port operational characteristics are translated into quantified energy demand and power requirements, providing the basis for power adequacy assessment and the evaluation of alternative transition pathways. An illustrative application to a representative Mediterranean port, characterized by a peak electricity demand of 42 MW, illustrates how the framework quantifies power requirements, assesses power adequacy under infrastructure constraints, and compares alternative transition pathways based on renewable generation, battery storage, and hydrogen. Full article

Construction supply chains are pivotal to circular economy transitions but remain structurally fragmented, limiting the scalability of resource-efficient solutions. At the same time, digital technologies and life cycle assessment are often deployed in isolation, constraining their ability to enable system-level circularity. Using a theory-building literature synthesis of 141 publications across circular economy, sustainable supply chain management, digitalization, and life cycle sustainability assessment, this study develops an integrated conceptual framework that explains how circular performance may be shaped by AI-enabled decision capabilities, lifecycle sustainability constraints operationalized through PESI-LCA, and system-level alignment conceptualized through DCAM. AI is conceptualized as a dynamic capability for prediction and optimization, while PESI-LCA is positioned as an operationalized LCSA-based constraint system that embeds environmental, social, and economic criteria into decision architectures. DCAM defines the alignment conditions required across digital infrastructure, circular strategies, business models, and institutional enablers. The framework advances a non-additive logic: circular outcomes depend on how sustainability constraints shape AI-driven decision-making and how alignment enables coordinated implementation across supply chains. A key theoretical contribution is the identification of structural distortion as a failure mode in which digital optimization reinforces linear resource flows. The study advances sustainable supply chain theory and offers testable propositions and governance implications for scaling circular construction systems. Full article

The management and consultation of heterogeneous spectral datasets present significant challenges in multidisciplinary research, where records from different campaigns, instruments, and scientific domains must remain linked to consistent metadata, geographic provenance, and analytical tools within a unified framework. This paper presents the design, implementation, and operational use of the INGV Spectral Library, a web-based spatial data infrastructure integrating structured metadata management, georeferenced Web-GIS consultation, and browser-native spectral preprocessing within a single environment. The system relies on a three-tier architecture and organizes spectral records through a domain-aware metadata model associating each entry with geographic, thematic, and domain-specific descriptors. Consultation is supported through two complementary access modes: an interactive map enabling spatial exploration and direct map-to-record navigation, and a metadata-driven filtered table supporting progressive retrieval across domains including geology, mineralogy, environmental surveys, and cultural heritage. Integrated preprocessing tools—first derivative, continuum removal, and sensor spectral response function-based resampling—operate directly within the environment without export to external software. A controlled ingest and harmonization workflow ensures metadata consistency and spectral validity prior to record exposure. The paper discusses the platform’s geoinformation design principles, spatial consultation model, and current limitations, contributing a practical example of georeferenced spatial data management applied to multidisciplinary spectral archives. Full article

Hydrogen innovation requires coordination across scientific research, engineering development, infrastructure deployment, market application, and public governance. However, heterogeneous incentives can destabilize collaborative participation even under strong policy support. This study examines how science-push and market-pull shape collaborative innovation in hydrogen technology. We develop a tripartite evolutionary game involving enterprises, universities, research institutions, and government under bounded rationality. The model incorporates collaborative returns and proportional downside-risk allocation, participation costs, spillover-based outside payoffs, market demand sensitivity, technological-foresight capability, and government governance strategies. The analytical results and numerical scenario illustrations identify three equilibrium configurations: independent R&D, one-sided participation, and full bilateral participation. Their occurrence depends on collaborative returns, participation costs, downside-risk penalties, external driving intensity, and governance conditions. Under matched driving intensity, the relative effectiveness of science-push and market-pull is actor-specific and determined by the balance among collaborative returns, costs, and outside opportunities. These findings clarify the strategic conditions shaping participation in hydrogen innovation systems and provide scenario-contingent implications for benefit allocation, risk management, and collaborative governance. Full article

Polymer Additive Manufacturing (AM) offers substantial opportunities for material-efficient and distributed production, yet its transition towards genuine circularity remains constrained by cumulative material degradation, fragmented recovery strategies, and the limited integration of lifecycle, environmental, economic, and industrial considerations. This critical systematic review examines circularity in polymer AM beyond conventional end-of-life recycling by integrating material behaviour, manufacturing-induced evolution, degradation mechanisms, lifecycle performance and value retention, recovery pathways, and sustainability assessment within a unified systems perspective. Following a PRISMA-based selection process, 3214 records were progressively screened to a final corpus of 175 peer-reviewed studies published between 2015 and 2025. The evidence demonstrates that polymer circularity is not an intrinsic material property, but an emergent lifecycle outcome governed by polymer chemistry, manufacturing history, cumulative degradation, functional-value retention, waste-stream quality, recovery technology, infrastructure, and environmental and economic conditions. Based on this synthesis, the review introduces Intelligent Circular Polymer Additive Manufacturing (ICPAM), a lifecycle-wide framework integrating Design for Circularity, degradation-aware manufacturing, adaptive recovery, digital intelligence, industrial implementation, and continuous feedback. ICPAM further incorporates multi-criteria decision support for selecting context-dependent circular strategies and a qualitative/semi-quantitative maturity assessment for identifying lifecycle bottlenecks and implementation priorities. The resulting framework shifts polymer AM circularity from reactive waste management towards proactive lifecycle engineering, while recognizing that emerging regenerative materials and digital technologies still require substantial industrial validation before their full circular potential can be realized. Full article

The integration of unmanned aerial vehicles (UAVs) as aerial base stations has emerged as a key enabler for next-generation wireless networks, particularly in disaster recovery, temporary events, and infrastructure-deficient regions. However, multi-UAV deployments introduce severe co-channel interference due to spectrum reuse and overlapping coverage areas, while existing spectrum allocation methods either rely on centralized optimization with limited scalability or on reinforcement learning frameworks that lack spatial awareness of interference sources. To address these challenges, this paper proposes a Hybrid DeepMUSIC-assisted Cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for intelligent spectrum allocation and interference management in multi-UAV 6G networks. The proposed framework integrates a hybrid interference localization module, which fuses the classical MUltiple SIgnal Classification (MUSIC) algorithm with a deep neural network to accurately estimate the direction of arrival (DoA) of interference sources, into a DeepMUSIC-enhanced state representation used by cooperative Deep Q-Network (DQN) agents trained under a Centralized Training and Decentralized Execution (CTDE) paradigm, enabling coordinated yet fully distributed spectrum allocation decisions. Extensive simulations demonstrate that the proposed Hybrid DeepMUSIC module reduces the mean DoA estimation error to approximately 0.105°, more than an order of magnitude better than classical MUSIC and standalone DeepMUSIC estimators. Compared with seven baseline algorithms spanning heuristic, optimization-based, single-agent, and cooperative multi-agent reinforcement learning approaches, the proposed framework achieves the highest network throughput, SINR, spectrum efficiency, and energy efficiency, together with the fastest and most stable training convergence, reaching a stable cooperative reward of 76.246 within approximately 371 training epochs. The framework further maintains near-linear computational scaling with the number of UAV agents, confirming its suitability for real-time deployment in dense, AI-native multi-UAV 6G wireless communication systems. Full article

The rapid expansion of artificial intelligence has intensified the demand for high-performance data centers, leading to unprecedented pressures on energy, water, and material resources. This critical review examines the emerging challenges associated with resource management in AI-driven data centers by focusing on the interplay between computational growth and environmental constraints. The analysis integrates recent advances in energy efficiency, cooling technologies, and hardware design while highlighting the increasing water footprint of thermal management systems and the material implications linked to semiconductor manufacturing and infrastructure scaling. Particular attention is given not only to the trade-offs between performance optimization and sustainability but also to the limitations of current metrics used to assess resource efficiency. The review identifies key gaps in the literature, including the lack of integrated frameworks that simultaneously address energy, water, and material flows. Finally, this review provides insights into pathways for a more sustainable AI infrastructure by synthesizing present-day knowledge, critically evaluating existing strategies, and emphasizing the need for systemic approaches that align technological innovation with resource conservation and long-term environmental resilience. Full article

Coastal zones are subject to a range of natural and anthropogenic processes that result in coastal erosion and accretion, threatening essential infrastructure and straining livelihoods. Analysis of shoreline changes is thus crucial for informing coastal zone planning and management to avert the ramifications of erosion and accretion. Despite a range of literature examining coastline changes worldwide, there is a paucity of literature focusing on Southern Africa, particularly within small harbours. The paper, therefore, aims to analyse shoreline changes at four small harbour zones along the Atlantic Ocean in the Western Cape province, South Africa, over the period from 1985 to 2025. To acquire an accurate shoreline position, four spectral criteria were applied simultaneously: the Automated Water Extraction Index (AWEI), the Modified Normalised Difference Water Index (MNDWI), the Normalised Difference Vegetation Index (NDVI), and the Near Infrared (NIR). Four statistics were then used to measure shoreline changes in the USGS Digital Shoreline Analysis System (DSAS): Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Weighted Linear Regression (WLR). Considerable variability was observed within and among the four small harbour study areas, with several erosion and accretion hotspots identified. The 20-year forecast indicated that future shoreline positions would largely maintain the 2025 curvature. Although the study did not reveal significant threats, authorities are encouraged to pay particular attention to erosion and accretion hotspots through appropriate mitigation and adaptation efforts. Full article

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021. Full article

Agentic artificial intelligence (AI) systems are increasingly deployed across distributed cloud–edge–radio infrastructures, where autonomous agents make decisions with direct operational and economic impact. Smart contracts (SCs) enforce business rules and policies, constraining autonomous actions according to predefined operational intent. As agent autonomy expands across heterogeneous networks, ensuring accountability requires transparency, verifiability, and compliance with SC-defined requirements. To address these challenges, this paper proposes the Agent Governance Framework (AGF), which integrates SC-based governance into agentic AI systems, enabling verifiable accountability through traceable autonomous decisions. Built on European Telecommunications Standards Institute (ETSI) and TM Forum principles, AGF comprises six components: (i) a TM Forum-aligned business support system (BSS); (ii) an agentic AI-enhanced operations support system (OSS); (iii) a Network Resource Operations subsystem; (iv) an Identity and Access Role Manager; (v) a Distributed Marketplace; and (vi) a Traceability and Auditability Registry. Together, these components provide an end-to-end (E2E) traceable architecture, linking each action to the responsible agent and SC. A prototype on a local test network with the evaluation of two complementary network test cases demonstrates the framework’s feasibility, confirms its full traceability and immutability, and highlights AGF’s potential as a foundation for reliable, large-scale agent-based systems in telecommunications networks. The results demonstrate 100% traceability across the E2E governance loop and a minimal latency overhead of 2–4% due to the Traceability and Auditability registry. Full article