Search Results (1,915)

Urban PV is commonly connected through individual inverters, causing low utilization, uncoordinated reverse power flow, and overvoltage at high penetration. This paper addresses where to site DC aggregation points that collect nearby PV systems on a common DC bus and connect through centralized inverters. This paper formulates a bi-level model under urban land-use constraints as follows: the upper level jointly selects point locations, capacities, and PV allocations to minimize annualized cost and maximum voltage deviation; the lower level routes collectors along roads and verifies AC/DC power flows. PV uncertainty is represented by clustered scenarios, and an improved NSGA-II is coupled with SOCP DistFlow evaluation. On a modified IEEE 33-bus feeder at 122% penetration, the method reduces annualized cost by 8.3% versus two-stage siting and 14.6% versus K-means siting, with maximum voltage deviation reduced from 5.5% to 4.8%. Against conventional AC connection, it eliminates overvoltage and reduces active loss by 40.3% from the no-PV base. Tests on a 69-bus system plus sensitivity, extreme-scenario, and reliability analyses confirm scalability and robustness. Full article

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. This gap is corroborated by a limited but growing body of empirical studies on governance in deployed DT settings. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. Consistent with design science research, the framework is delivered and evaluated at design time; empirical implementation and outcome evaluation are planned across multi-actor digital twin infrastructure contexts, including smart city governance, port logistics, and energy networks, where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research. Full article

Rapid urban expansion in arid intermediate cities often produces fragmented urban structures that exacerbate inequalities in public transport accessibility and hinder sustainable mobility. However, the relationships between urban fragmentation, transport accessibility, and travel behavior remain insufficiently explored in pre-Saharan cities. This study investigates these interactions through the case of El Bayadh, Algeria, using an integrated methodology combining urban morphological analysis, Geographic Information Systems (GIS), a household survey of 577 households, traffic counts, institutional interviews, and spatial equality assessment based on the Gini coefficient. Public transport accessibility was evaluated using a 300 m walking threshold, complemented by sensitivity analyses at 400 m and 500 m. The results indicate that approximately 65% of the population lives within 300 m of a bus stop, although coverage is strongly concentrated in central districts. A Gini coefficient of 0.41, provided by the El Bayadh Directorate of Transport and computed across 20 urban zones, indicates moderate-to-high spatial inequalities in accessibility that persist despite increasing the service threshold, suggesting that these disparities are more closely associated with urban fragmentation than with the specific walking-distance threshold used to define service coverage. The transport network comprises 11 bus routes, approximately 160 bus stops, and 90 km of routes; however, limited service quality and uneven spatial distribution contribute to a high dependence on private transport, with 45% of trips made by private car, 35% by taxi, and only 20% by bus. These findings are consistent with accessibility inequalities arising from the mismatch between fragmented urban morphology, centralized urban functions, and the configuration of the public transport network. The proposed framework provides transferable insights for planning more equitable and sustainable mobility systems in rapidly urbanizing arid intermediate cities. Full article

Air pollution in urban and coastal regions is increasingly shaped by interactions among port operations, freight logistics, urban mobility systems, spatial development patterns, and governance arrangements. Although emission reduction technologies and regulatory measures have advanced significantly, their implementation frequently remains distributed across sector-specific institutional and operational domains. This paper introduces the concept of Air-Aware Port–City–Logistics Systems, proposing an integrated governance framework that treats air quality as a system-level outcome of linked maritime, logistics, urban transport, spatial, technological, and adaptive processes. Drawing on a structured interdisciplinary synthesis of literature in transport planning, maritime economics, logistics, environmental governance, air-quality management, and resilience, the study identifies key gaps in cross-sectoral coordination and in the governance of interdependencies influencing air-quality outcomes. The framework maps relationships among emission sources, transport and logistics flows, spatial configurations, population exposure, institutional coordination, technological and data systems, and adaptive capacity, thereby identifying potential intervention points across the port–city–logistics system. Its distinctive contribution lies not merely in combining these dimensions, but in organising them through a governance-oriented architecture in which monitoring, operational decisions, institutional responses, and adaptive learning are treated as interconnected processes. The framework also incorporates resilience and risk perspectives, emphasising adaptive governance approaches capable of responding to disruptions, changing demand patterns, climatic pressures, and other external environmental influences. By advancing a systems-based perspective, the paper contributes to air quality management and policy development through the integration of port–city interfaces, freight distribution networks, mobility planning, exposure considerations, and institutional decision-making within a unified analytical framework. As a conceptual framework, AAPCLS provides a transferable basis for future empirical application, contextual adaptation, and policy-oriented analysis rather than a validated operational model. Full article

This study presents a comprehensive spatial analysis of the public transport network across the Almaty agglomeration, encompassing 211 routes within eight urban districts and seven adjacent administrative divisions. Drawing on digitized geodata from 3634 unique bus stops, the research methodology integrates quantitative and qualitative spatial metrics, including distribution density, average stop spacing, elevation gradients, and topological connectivity. The analysis highlights significant territorial imbalances: while 70.53% of all unique stops are concentrated within Almaty’s city limits, the surrounding regional network is highly fragmented, with an average stop spacing of 2579.53 m. Furthermore, topographic assessments confirm substantial operational challenges for north–south routing due to steep elevation changes, reaching 984.48 m within the city and 1243.23 m regionally. A critical evaluation of topological connectivity reveals that 44.63% of suburban bus stops lack transfer intersections, underscoring severe deficits in peripheral public transport provision. By assessing 56 distinct connection types, the study categorizes administrative districts based on their route integration levels. Ultimately, the derived spatial parameters offer a robust evaluation of the current transport framework. These insights establish a crucial scientific and empirical foundation for optimizing route geometries, bridging infrastructural gaps, and guiding sustainable transit planning in alignment with Almaty’s transition toward a polycentric urban model. Furthermore, the developed 3D spatial-topological framework provides a scalable, data-driven blueprint for municipal authorities to prioritize infrastructure investments, deploy multimodal hubs and enhance transit equity in other rapidly growing and topographically complex agglomerations worldwide. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

Reliable operating-state recognition of gas pressure regulators is essential for pressure stability, operational safety, and supply continuity in urban natural gas distribution networks. However, SCADA pressure–flow signals from regulating stations are often affected by non-stationary noise, impulsive disturbances, limited fault-state samples, and short-window temporal fluctuations, which reduce the reliability of data-driven recognition. To address these issues, this study proposes K²-TLNet, a noise-state-guided fault-state recognition framework for gas pressure regulation processes. The framework integrates adaptive Kalman filtering, training-only KMeans-SMOTE, parallel temporal convolutional network–long short-term memory feature extraction, and a Noise-Aware Gated Fusion mechanism. Adaptive Kalman filtering is used to generate denoised pressure–flow sequences and extract innovation-residual-based noise descriptors. These descriptors guide the fusion module to adaptively balance local transient features from the temporal convolutional network and contextual temporal features from long short-term memory. A field-SCADA-background-based semi-synthetic dataset was constructed using real operating records and mechanism-informed fault-state emulation rules. Experimental results demonstrate that K²-TLNet achieves 98.50% accuracy and 98.20% Macro-F1, while maintaining strong robustness under Gaussian and impulsive noise disturbances. Full article

Underground cables are critical infrastructure for electrical power and communication transmission, and their reliable operation is of paramount importance to urban public safety. Although Distributed Acoustic Sensing (DAS) enables wide-range, continuous, and real-time monitoring, traditional DAS signal processing methods suffer from poor intrusion discrimination and weak anti-interference capability. To address these limitations, we propose a dual-branch network based on multi-domain feature fusion, integrating a Multilayer Perceptron, a Long Short-Term Memory network (LSTM), and an attention mechanism. Vibration signals corresponding to four representative high-risk intrusion events were acquired through controlled field experiments, and a standardized, category-balanced dataset was constructed accordingly. Time-domain, frequency-domain and joint time-frequency features were extracted and mapped through a time-frequency weighting transformation to form one branch of the network, while the parallel branch employed an LSTM to capture long-range temporal dependencies. A multi-head attention mechanism enables deep adaptive fusion of two types of modal information and overcomes the limitations of conventional simple feature concatenation. Comparative experiments against KNN, 1D-CNN and LSTM baselines demonstrate that the proposed model achieves a test accuracy of 98.89%, outperforming all reference methods. Ablation studies further validate the necessity and effectiveness of each constituent module within the proposed architecture. The results indicate that this approach provides reliable support for DAS-based online monitoring of power cables against external damage. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

Urban–rural governance and planning systems, as viewed through the lens of online public attention, are reshaping approaches to achieving coordinated development of cultural heritage conservation and urban space. Taking Nanchang as a case study, this paper investigates the application mechanism of online public attention in the evaluation of heritage corridor nodes and the formulation of optimization strategies. Employing multi-source data collection, we extracted textual content from online travelogues. We quantified the online attention level of each cultural heritage unit and analyzed its spatial distribution using kernel density estimation and standard deviational ellipse methods. We then applied a minimum resistance model, incorporating geographic data on land use and transportation networks, to identify potential heritage corridor routes. Based on attention classification, we propose a three-tier optimization strategy: core-leading, marginal-activating, and gradient-linking. Specifically, high-attention heritage units are positioned as central nodes that reinforce corridor connectivity; low-attention nodes are targeted for activation through corridor links to overcome spatial isolation; and attention gradients serve as the basis for hierarchical integration across the network. Our results reveal a pronounced spatial pattern of “core agglomeration and peripheral dispersion” in Nanchang’s heritage corridors. High-attention resources are heavily concentrated in the historic urban core, anchoring the main corridor axes, whereas low-attention resources are scattered across peripheral counties, exhibiting a clear urban–rural attention gradient. Importantly, attention levels show a positive correlation with corridor hierarchy. These findings provide a novel perspective for heritage corridor spatial planning and offer practical insights for sustainable cultural heritage governance and culture–tourism integration in an era of pervasive internet and urban expansion. Full article

Artificial Intelligence-driven detection mechanisms present both opportunities and challenges in modern systems, particularly within smart cities that rely on complex operational technologies. In the context of Saudi urban infrastructure, rapidly evolving and multidimensional cyber threats require advanced, energy-efficient security solutions. This research proposes an Artificial Intelligence-based Threat Detection Mechanism designed to proactively identify and mitigate cyber threats while maximizing energy efficiency and minimizing cost and system complexity. Purpose: The proposed theoretical framework focuses on securing sustainable smart cities by integrating Artificial Intelligence-based anomaly detection with quantum-enhanced algorithms to address high-dimensional and emerging cyber threats across interconnected urban systems. The Artificial Intelligence-based Threat Detection Mechanism enables early and proactive threat detection across sustainable smart city networks, including connections to external and global infrastructures, ensuring continuous monitoring, resilience, and service continuity. Methods: The methodology emphasizes the development of energy-efficient Artificial Intelligence models and quantum protocols, incorporating intelligent risk assessment, adaptive calibration, and automated response mechanisms. In addition, the framework introduces distributed security hubs to enhance cybersecurity robustness and scalability. Anticipated Results and Conclusions: Anticipated outcomes include improved security management policies, automated threat detection and response, and adaptive protection against evolving cyber risks. The proposed framework provides a scalable and cost-effective solution aligned with sustainability objectives. Ultimately, this research contributes a proactive and intelligent framework for securing smart city ecosystems, supporting long-term development goals and aligning with Saudi Vision 2030. Full article

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems. Full article

Mediterranean agriculture is increasingly constrained by severe and recurrent water scarcity. Rising temperatures, declining precipitation, and more frequent droughts are driving higher crop water demand and reducing rainfall reliability. Combined with growing competition for limited freshwater resources, these climatic pressures pose a major challenge to the long-term sustainability and productivity of farming systems in the region. In this context, urban treated wastewater (UTWW) is increasingly recognized as a strategic alternative resource. Regulation (EU) 2020/741 establishes minimum quality requirements for agricultural water reuse based on the fit-for-purpose principle. The Italian regulatory framework requires regional authorities to plan water reuse by mapping reclamation facilities, water demand, and distribution networks. From this perspective, to support the planning process, this study describes the application of a Geographic Information System (GIS)-based methodology to assess the potential for UTWW reuse in Sicily by integrating national datasets (regularly updated) and comparing reclaimed water supply with irrigation demand from both qualitative and quantitative perspectives. UTWW supply was estimated by integrating data from the Italian National Institute of Statistics (ISTAT) Census of Water for Civil Use and the European Environment Agency (EEA) dataset, while irrigation demand was derived by combining Copernicus crop data, ISTAT irrigated-to-total UAA ratios and crop-specific irrigation demand (Seasonal Specific Volumes) derived from the SIGRIAN database. Two scenarios were analyzed for grasslands, vineyards, olive groves, and orchards. Scenario 1 included only wastewater treatment plants (WWTPs) located inside irrigation areas, whereas Scenario 2 also included selected WWTPs located outside irrigation areas where orographic and distance constraints allowed potentially feasible connections. Under Regulation (EU) 2020/741, potential compatibility with the required water quality classes was identified at the screening level for all crop categories considered, which require classes B, C or D. From a quantitative perspective, Scenario 1 identified 19 WWTPs serving 11 out of 38 irrigation areas with an aggregated uncapped demand coverage of 53%; only two areas reached full local demand satisfaction. Scenario 2 added 23 external WWTPs, increased potentially served areas to 20, and raised the aggregate uncapped coverage to 73%. When surplus volumes were capped within each irrigation area, effective demand satisfaction was 12% and 22% in Scenarios 1 and 2, respectively, highlighting the importance of storage, conveyance and inter-area transfer. The results indicate that treated wastewater reuse could contribute to agricultural water security and climate-resilient water management in Mediterranean regions. The framework supports the first-order identification of candidate reuse areas, but site-specific assessments of effluent quality, risk management, costs, and seasonal storage remain necessary before implementation. Full article

In the context of China’s shift from incremental urban expansion to stock-based regeneration, the adaptive reuse of industrial heritage is no longer limited to physical transformation but increasingly involves spatial reintegration, cultural memory, and public value reconstruction. Taking Tianjin as a case study, this paper introduces Scene Theory into an urban-regeneration framework and combines kernel density analysis, field observation, case comparison, and online public-perception text mining. The study first examines the spatial distribution, accessibility conditions, and renewal typologies of industrial heritage sites in Tianjin. It then uses online comments from Xiaohongshu and Ctrip as supplementary perception data to analyze how different renewal models are experienced in relation to neighborhood environment, material facilities, activities, authenticity, theatricality, and legitimacy. The results show that creative industrial parks, museum-oriented renewal, and commercial renewal differ significantly in spatial location, activity organization, cultural expression, and public recognition. Common problems include uneven accessibility, superficial representation of industrial symbols, excessive consumption orientation, weak continuity of labor memory, limited community participation, and the potential exclusion of local users. Based on these findings, the paper proposes an integrated renewal strategy that emphasizes spatial network repair, authenticity-oriented design, everyday public use, community participation, and inclusive governance. The study provides an analytical framework and practical references for the regeneration of industrial heritage in Tianjin and other post-industrial cities. Full article