Search Results (280)

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

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

The imbalance between water supply and demand is intensified by population growth and economic development. While water diversion projects are capable of mitigating water shortages, multiple ecological and environmental risks, such as accidental pollution and impairment of ecosystem structure, are introduced by their long-distance water transport and complex corridor environments. The reduction in potential losses hinges on the accurate assessment of these risks. This study integrates the Driving Force–Pressure–State–Impact–Response (DPSIR) model with a projection pursuit model optimized by an improved Sparrow Search Algorithm (SSA) based on seagull optimization and whale optimization operators. A comprehensive risk assessment model was constructed and validated using data from the Chuhe Main Canal for the period 2015 to 2024 as a case study. The results indicate that “water resource utilization rate”, “biodiversity index”, and “public satisfaction” are key factors; project risks have gradually escalated from “relatively low risk” to “relatively high risk”. By this model, the key risk factors and evolutionary patterns of ecological and environmental risks in water diversion projects are able to be scientifically identified, thereby providing a quantitative basis for risk early warning and differentiated management strategies, as well as serving as a reference for the ecological risk assessment of similar inter-basin water diversion projects. Full article

Quantifying the relative contributions of local emissions and regional transport is critical for urban air quality management. Chemical transport models (CTMs) are widely applied for source apportionment, but they require detailed emission inventories, extensive input data, and substantial computational resources, which limit their operational use. In contrast, urban monitoring networks provide continuous and readily available observations. This study develops an observation-based framework that estimates regional contribution ratios (RCs) from inter-station concentration variability, quantified by the coefficient of variation (CV), using WRF–CAMx results as a reference. Using Linyi as the primary case, with Xi’an and Beijing for comparison, concentration-stratified regression was applied to establish CV–RC relationships. Results show a consistent nonlinear relationship between CV and RC, with coefficients of determination (R²) up to 0.86 for PM₁₀ (daily), 0.81 for NO₂ (hourly), and 0.78–0.79 for O₃. CV decreases markedly with increasing concentration; for PM_2.5, values decline from ~0.17–0.18 to 0.05–0.06 (≈65–70%), indicating enhanced spatial homogeneity under regional influence. The relationship is most stable within a 10–15 km spatial scale. Application-based evaluation for January 2022 shows moderate agreement between estimated and modeled RC (R = 0.55–0.65), reflecting pollutant-dependent uncertainties, partly associated with biases in the model-derived reference RC. These results demonstrate that inter-station concentration variability provides a first-order, computationally efficient indicator of the balance between local emissions and regional transport. Full article

Rapid urbanization in Saudi Arabia puts increasing pressure on energy, water, mobility, and waste-management systems, strengthening the need for evidence-based smart-city policy under Vision 2030. Rather than offering a descriptive inventory of projects, this paper develops a verification-oriented framework for assessing smart-city initiatives in the Kingdom. The framework is built on four principles: (i) distinguishing national contextual indicators from city-level evidence, (ii) separating stated ambitions from observed outcomes, (iii) applying an evidence-grading rubric that prioritizes publicly verifiable mechanisms and performance indicators over anecdotal or promotional claims, and (iv) introducing a readiness–impact matrix adapted to Saudi climatic, infrastructural, and institutional conditions. The framework is applied to major Saudi smart-city cases, including NEOM, KAEC, Riyadh, Jeddah, Makkah, and Madinah. The analysis shows that the strongest publicly documented evidence is concentrated in selected sectoral applications, particularly demand response and smart-building control in electricity systems, leak detection and pressure management in water networks, and intelligent traffic management in urban transport. These cases indicate plausible pathways for improving service efficiency and reducing resource waste; however, publicly verifiable city-level outcome data remain limited, fragmented, and uneven across cases. In response, the paper proposes a policy playbook centered on KPI transparency, interoperable data governance, cybersecurity safeguards, and public–private partnership templates to improve the measurability, comparability, and scalability of smart-city outcomes. By formalizing verification and cross-case assessment, the study contributes a reproducible methodological basis for evaluating smart-city progress and prioritizing future investments in Saudi Arabia. Full article

Digital Twins (DTs) are increasingly deployed in Industry 4.0 to enable real-time monitoring, analysis, and control, yet the transition from isolated DT instances to plant-wide ecosystems across cloud and edge infrastructures introduces fragmentation and coordination challenges among heterogeneous assets, data sources, and services. This paper addresses this gap by proposing a cloud-native Digital Twin Computing Layer (DTCL) that provides a unified control and orchestration plane for composing and operating DT applications in Smart Manufacturing. The DTCL is designed as a three-tier architecture comprising a developer-facing user interface, a Deploy Engine for automated deployment and lifecycle management, and a Service Catalog of reusable, independently deployable microservices. Standardized interaction is supported through semantic DT models and API- and message-based communication mechanisms. A governance workflow, based on service discovery and validation, is introduced to support non-redundant integration and controlled evolution of services. The approach is demonstrated through a Smart Manufacturing predictive maintenance case study and further extended with a Smart Mobility scenario for urban public transport planning, highlighting the flexibility of the DTCL across different application domains. Overall, the DTCL supports modular composition, interoperability, and lifecycle governance across heterogeneous Digital Twin applications, providing a scalable foundation for both industrial and urban data-driven scenarios. Full article

Maritime transport is under increasing pressure to cut greenhouse gas and pollutant emissions to meet global decarbonization goals and tighter environmental standards. Ship electric propulsion systems offer a promising solution for short-range maritime operations, particularly for small vessels and coastal activities. Full-electric vessels can significantly reduce operational emissions; however, a key challenge is the extensive charging time for onboard energy storage, which can affect operational continuity and logistical efficiency. This study examines mission planning and energy management for a hybrid multi-source electric mail boat operating in the Aeolian archipelago. It evaluates the viability and performance of a daily inter-island route powered by a high-temperature methanol fuel cell, batteries, and photovoltaic panels. A routing and simulation framework was developed to model the boat’s itinerary among seven islands, accounting for realistic navigation speeds, scheduled stops, solar energy availability, and battery state-of-charge constraints. The study analyzes distance, travel time, energy consumption, solar power generation, and fuel–electric usage with high temporal resolution, enabling detailed analysis of power flows during sailing and docking. Several operational strategies were assessed, including periods of increased speed supported by battery assistance and fuel–electric cell output, combined with coordinated energy management to keep battery levels above a lower acceptable threshold while completing the route in a single day. The methodology provides a practical tool for planning low-emission island networks and supports the integration of innovative energy systems into small electric workboats operating in specific maritime regions. Full article

Infrared photodetectors are important for military, medical, and environmental applications. Dual-color quantum well infrared photodetectors (QWIPs) are attractive because they can provide multi-spectral information, but their performance is often limited by high dark current. In this study, we designed and fabricated two dual-color QWIPs. Sample A exhibits rectification-like dark-current behavior, whereas Sample B shows a nearly symmetric current–voltage characteristic together with an approximately two-order-of-magnitude reduction in dark current under the same operating condition. By combining secondary ion mass spectrometry (SIMS), scanning spreading resistance microscopy (SSRM), energy-band simulations, and optoelectronic characterization, we show that Sample B exhibits a larger disparity in effective carrier distribution between the two quantum-well groups than Sample A. The experimental results and simulations consistently indicate that this disparity, together with the higher barrier design, is associated with a redistribution of the internal potential and a stronger voltage drop across the lightly doped region, which is consistent with reduced thermally activated carrier transport. Although the lower carrier concentration in the lightly doped wells is accompanied by reduced blackbody responsivity, the stronger suppression of dark current leads to a higher peak blackbody detectivity under identical blackbody-illumination conditions. At 50 K and −1.5 V, the peak blackbody detectivity of Sample B is approximately four times that of Sample A. These results support the conclusion that combining barrier-height design with controlled inter-group carrier disparity is an effective strategy for tuning carrier transport and improving the peak blackbody detectivity trade-off in dual-color QWIPs within the conditions examined here. Full article

Growing mobility demand and declining vehicle utilization motivate dual-use vehicles that can alternately transport passengers and freight. This work presents an early-stage utility value analysis to select a baseline concept and integrates it into model-based systems-engineering architecture development of an autonomous dual-use shuttle. Existing dual-use-capable shuttle concepts were screened and comparatively assessed using a utility value analysis with exclusion criteria and weighted evaluation criteria, including operational versatility, module exchange flexibility, infrastructure effort, battery positioning, and technology readiness. Criterion weights were derived by pairwise preference analysis, emphasizing the versatility of use scenarios. The highest-ranking concept, 101 Modular Mobility, was selected as the reference architecture. Subsequently, a SysML system model was developed in a MagicGrid-structured model-based systems-engineering (MBSE) process, covering stakeholder needs, key use cases such as transport service usage, module exchange, and automated charging, and the resulting system context and interfaces. The system model is augmented by a tailored Grey Box structural viewpoint within the MagicGrid workflow to make module boundaries and inter-module interfaces explicit for the modular dual-use shuttle architecture. The resulting model provides a traceable early architectural baseline for further refinement and subsequent verification activities. Full article

Geopolitical volatility and conflict are increasingly altering the operating conditions of maritime transport by affecting route feasibility, service reliability, port operations, regulatory compliance, and energy-related decisions. However, the relevant literature remains fragmented across smart port studies, shipping optimization research, cybersecurity analysis, and resilience-oriented discussions. This review addresses that fragmentation by examining smart port and shipping optimization as interdependent components of maritime resilience rather than as separate efficiency-oriented domains. Methodologically, the paper adopts a structured, semi-systematic review design combining bibliometric mapping and thematic synthesis to identify the evolution, thematic structure, and major research gaps of the field. The review shows that smart port research highlights the resilience value of real-time visibility, interoperable data exchange, dynamic terminal control, digital twins, and cyber-secure infrastructure, while shipping-optimization research emphasizes conflict-aware routing, schedule recovery, network redesign, capacity reallocation, and fuel-related decision support. At the same time, the literature provides only limited integration across the port–shipping interface, where resilience is actually produced through coordination between nodes, networks, and governance arrangements. Based on this synthesis, the paper argues that future research should move beyond isolated technical solutions and develop more integrated approaches that jointly address digitalization, operational adaptation, security, and decarbonization under geopolitical stress. The review contributes by clarifying the intellectual structure of this emerging field and by proposing a more system-oriented perspective on maritime resilience. Full article

With the increasing integration of transportation and energy systems, highway energy replenishment facilities are gradually evolving into hybrid refueling stations that integrate photovoltaic generation, energy storage, battery charging, and hydrogen refueling. However, due to differences in resource conditions across stations, independently operated hybrid refueling stations find it difficult to simultaneously improve overall economic performance and renewable energy utilization. To address this issue, this paper investigates the coordinated operation and distributed optimization of highway hybrid refueling stations. First, an inter-station hydrogen–carbon–green certificate trading framework is established, and a trading model for a cluster of hybrid refueling stations is then developed on this basis. Then, the inter-station trading problem is decomposed into two subproblems: symmetric trading volume determination and asymmetric Nash bargaining-based price determination. These two subproblems are solved in a distributed manner using the alternating direction method of multipliers. In addition, a hydrogen transportation model is developed to translate trading decisions into feasible transportation arrangements under highway network and hydrogen tube trailer scheduling constraints. Finally, the case study demonstrates that the proposed model enables multi-resource sharing among hybrid refueling stations, reduces the overall system cost by 21.30%, and achieves a fairer distribution of benefits among stations. Full article

Never has the analysis of bus travel times been so essential to transit planning: travelers complain about a decline in service quality, urban congestion is on the rise, and public transport companies struggle with a structural driver shortage. This research paper aims to address the urgent need to explore new tools to increase commercial speed on transit lines while avoiding costly, potentially inefficient technological investments. A data-driven, cost-neutral, and replicable methodological framework is proposed to provide a first-order estimation of the potential benefits of Transit Signal Priority (TSP) at signalized intersections. The approach is based on Automatic Vehicle Monitoring (AVM) data analysis, which is underpinned by a lean network representation logic built from origin/destination pairs of stops located upstream and downstream of signalized intersections. Bus travel inter-times across network arcs are compared between peak and off-peak periods through a two-level analytical process that progressively refines the estimation of recoverable delay. The methodology is applied to the urban bus network of Pavia (Italy), operated by Autoguidovie S.p.A. (one of the most important Local Public Transport companies in Italy), with a specific focus on the high-frequency PV3 line. Results indicate a potential reduction of up to approximately 6 h and 45 min of operating time per day at the line level (−13.5% of total driving time), and up to 2 min per trip along a 2 km corridor (−6% along the single corridor selected). The procedure integrates both infrastructural and operational perspectives, supporting preliminary decision-making on TSP implementation using only data already collected by transit agencies. Full article

The transition to low-emission road transport in Europe depends not only on the growth of plug-in electric vehicle (PEV) uptake, but also on the timely expansion of publicly accessible charging infrastructure. This article provides a descriptive and diagnostic assessment of the relationship between electrification dynamics and public charging infrastructure development in Europe. The analysis combines a long-run descriptive window (2015–2024, with 2025 treated separately as a scenario observation) and a core diagnostic window (2020–2024) for which a consistent proxy of potential infrastructure strain—plug-in vehicles per public recharging point (VPP)—is available. The results show a strong increase in PEV share in new registrations, from 1.0% in 2015 to 20.92% in 2024, while the number of public recharging points rose from 67,064 to 900,000 over the same period. In the core sample, VPP declined from 15.24 in 2020 to 13.92 in 2024, which is consistent with a catch-up phase in infrastructure deployment after 2021. At the same time, the short-window relationship between PEV share, infrastructure scale and average CO₂ emissions of newly registered cars remains weak and unstable, indicating the role of additional structural factors. The article contributes a transparent, replicable indicator-based framework for describing infrastructure strain in aggregate European data. In policy terms, the findings support a shift from simple point-count targets toward functionally and spatially differentiated infrastructure planning, including interoperability, power structure, and accessibility in underserved areas. Full article

Machine condition monitoring increasingly depends on distributed sensing, edge intelligence, and cloud analytics, yet timely and trustworthy health assessment remains constrained by latency, bandwidth, privacy, and reliability requirements. Cloud-only architectures provide scalable computation and historical data integration but often fail to satisfy real-time industrial needs, whereas edge-only deployments are limited by restricted computing resources and fragmented local knowledge. Edge–cloud collaboration has, therefore, emerged as a practical architecture for distributing perception, inference, learning, and coordination across hierarchical industrial systems. This review examines 147 publications on edge–cloud collaboration for machine condition monitoring published between 2019 and February 2026. A four-dimensional taxonomy is developed to organize the literature into model-centric, data-centric, resource and task-centric, and architecture and trust-centric mechanisms, while 13 survey and review papers are considered separately for contextual comparison. On this basis, the review analyzes representative collaboration mechanisms and enabling technologies, with particular attention to federated learning, transfer learning, knowledge distillation, digital twins, and deep reinforcement learning, and surveys their deployment in manufacturing, energy, transportation, and infrastructure monitoring scenarios. The literature remains dominated by model-centric collaboration, while architecture and trust-centric studies increasingly provide the system foundations required for practical deployment. The review further identifies major open challenges, including robust generalization under changing operating conditions, efficient data transmission, real-time resource coordination, interoperability, and trustworthy large-scale deployment, and outlines future directions in foundation-model-based edge–cloud collaboration, continual learning, dual digital twins, trustworthy collaboration, and privacy-preserving industrial ecosystems. Full article

The accelerated development of Smart Cities globally, driven by rapid urbanization and urgent climate challenges, underscores the critical role of advanced energy infrastructures integrated with emerging digital technologies. This article explores the evolution of smart cities from an energy-centric viewpoint, emphasizing the interdependence among energy systems, digitalization and cutting-edge communication technologies. Adopting a system-of-systems perspective, we examine how different urban subsystems, including energy grids, transportation networks and data management systems, interact to improve overall urban functionality and long-term viability. Through a structured analysis of recent literature, we highlight the transformative potential of renewable energy integration, intelligent energy management systems and the crucial transition from 5G to 6G communication infrastructures, which collectively promise significant enhancements in urban sustainability, efficiency and resilience. Additionally, we address key challenges such as cybersecurity vulnerabilities, fragmented standardization frameworks and the need for comprehensive data governance. Viewing smart cities as a complex system of systems, this article argues for a holistic and interdisciplinary approach, emphasizing enhanced interoperability, robust cybersecurity protocols and inclusive participatory governance frameworks. Full article