Search Results (8,064)

Housing markets worldwide have undergone major disruptions during the COVID-19 period, raising questions about how systemic shocks reshape housing preferences and spatial structures. This study develops an integrated spatial framework to examine multi-dimensional housing market restructuring, combining global and local modelling with network-based submarket delineation. Using Shanghai as a case study, we compare pre- and post-pandemic conditions (2019 and 2023) to explore fluctuations in housing prices, shifts in attribute effects, and reconfiguration of submarkets. The results reveal highly differentiated market responses across space. A dual restructuring is observed: decentralisation within the urban core and reinforced integration of outer-peripheral areas into the metropolitan centre, suggesting a gradual transition from a monocentric system towards a more polycentric and context-dependent housing landscape. Methodologically, the study proposes a transferable framework for analysing spatial restructuring under systemic shocks. Empirically, it provides fine-grained evidence of housing market reconfiguration across spatial scales, offering practical insights for spatially informed urban planning and housing market management. Full article

Coastal areas are under intense pressure worldwide because diverse stakeholders rely on coastal resources, and the supply of land is highly limited. Coast-dependent economic activities like transportation and logistics infrastructure in the Jade Bay, Germany, have experienced extensive demand for land. The situation is more interesting because national parks encircle the seaport. Understanding the complex seaside–landside dynamics following the development of Jade Weser Port is crucial for promoting sustainability, as massive development exceeds existing spatial capacity. However, a comprehensive framework to assess land use conflicts when dealing with infrastructure development in sensitive coastal areas is often missing. We analyze the origin of land use developments and the planning process at different administrative levels by retracing land use changes from 1970 to 2015 using a time series of satellite images, analyzing planning documents, and examining realized activities. We look for an embedding of transport infrastructure development and its feedback on land use. As a consequence of land use conflicts, these land system dynamics create winners and losers across multidisciplinary aspects. Our findings reflect interdisciplinary aspects which discuss both societal changes and the constellation of inadequate planning approaches to address the complexity of coastal land use. The degree to which these activities cause land use conflicts depends on institutional settings, especially the consistency of ICZM and infrastructure planning. Full article

This paper proposes an innovative hybrid optimization framework for the optimal installation and operation of thyristor-switched capacitors (TSCs) within medium-voltage distribution networks, targeting both energy losses reduction and cost efficiency. The core of the approach combines the exploratory capabilities of the atan-sinc optimization algorithm (ASOA), a recent metaheuristic inspired by mathematical functions, with the local refinement power of the IPOPT solver within a master–slave architecture. This integrated method addresses the inherent complexity of a multi-objective, mixed-integer nonlinear programming problem that seeks to balance conflicting goals: minimizing annual system losses and investment costs. Extensive testing on IEEE 33- and 69-bus systems under fixed and dynamic reactive power injection scenarios demonstrates that our framework consistently delivers superior solutions when compared to traditional and state-of-the-art algorithms. Notably, the variable operation case yields energy savings of up to 12%, translating into annual monetary gains exceeding USD 1000 in comparison with the fixed support scenario.The solutions produce well-distributed Pareto fronts that illustrate valuable trade-offs, allowing system planners to make informed decisions. The findings confirm that the proposed strategy constitutes a scalable, and robust tool for reactive power planning, supporting the deployment of smarter and more resilient distribution systems. Full article

Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article

The UK electricity market is changing to adapt to Net Zero targets and respond to disruptions like the Russia–Ukraine war. This requires strategic planning to decide on the construction of new electricity generation plants for a resilient UK electricity grid. Such planning is based on forecasting the UK electricity demand long-term (from 1 year and beyond). In this paper, we propose a long-term predictive model by identifying the main components of the UK electricity demand, modelling each of these components, and combining them in a multiplicative manner to deliver a single long-term prediction. To the best of our knowledge, this study is the first to apply a multiplicative decomposition model for long-term predictions at both monthly and hourly resolutions, combining neural networks with Fourier analysis. This approach is extremely flexible and accurate, with a mean absolute percentage error of 4.16% and 8.62% in predicting the monthly and hourly electricity demand, respectively, from 2019 to 2021. Full article

Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning. Full article

Deep brain stimulation (DBS) is an established neurosurgical treatment for Parkinson’s disease (PD), mainly targeting motor symptoms resistant to pharmacological therapy. This review examines strategies to optimize DBS using advanced anatomical, functional, and imaging approaches. The subthalamic nucleus (STN) remains the principal target for alleviating bradykinesia and rigidity, while recent evidence highlights the dentato-rubro-thalamic tract (DRTt) as an additional promising target, especially for tremor control. Clinical data demonstrate that co-stimulation of both STN and DRTt via electrode electric fields results in superior motor outcomes, including greater reductions in UPDRS-III scores and lower levodopa requirements. The review highlights the use of high-resolution MRI and diffusion tensor imaging tractography in visualizing STN and DRTt with high precision. These methods support accurate targeting and individualized treatment planning. Electric field modelling is discussed as a tool to quantify stimulation overlap with target structures and predict clinical efficacy. Anatomical variability in DRTt positioning relative to the STN is emphasized, supporting the need for patient-specific DBS approaches. Alternative and emerging DBS targets—including the pedunculopontine nucleus, zona incerta, globus pallidus internus, and nucleus basalis of Meynert—are discussed for their potential in treating axial and cognitive symptoms. The review concludes with a forward-looking discussion on network-based DBS paradigms, the integration of adaptive stimulation technologies, and the potential of multimodal imaging and electrophysiological biomarkers to guide therapy. Together, these advances support a paradigm shift from focal to network-based neuromodulation in PD management. Full article

Land cover mapping is essential for territorial management due to its links with ecological, hydrological, climatic, and socioeconomic processes. Traditional methods use discrete classes per pixel, but this study proposes estimating cover fractions with Sentinel-2 imagery (20 m) and AI. We employed the French Land cover from Aerospace ImageRy (FLAIR) dataset (810 km² in France, 19 classes), with labels co-registered with Sentinel-2 to derive precise fractional proportions per pixel. From these references, we generated training sets combining spectral bands, derived indices, and auxiliary data (climatic and temporal variables). Various machine learning models—including XGBoost three deep neural network (DNN) architectures with different depths, and convolutional neural networks (CNNs)—were trained and evaluated to identify the optimal configuration for fractional cover estimation. Model validation on the test set employed RMSE, MAE, and R² metrics at both pixel level (20 m Sentinel-2) and scene level (100 m FLAIR). The training set integrating spectral bands, vegetation indices, and auxiliary variables yielded the best MAE and RMSE results. Among all models, DNN2 achieved the highest performance, with a pixel-level RMSE of 13.83 and MAE of 5.42, and a scene-level RMSE of 4.94 and MAE of 2.36. This fractional approach paves the way for advanced remote sensing applications, including continuous cover-change monitoring, carbon footprint estimation, and sustainability-oriented territorial planning. Full article

Urbanization, characterized by rapid construction land expansion, has transformed natural landscapes and significantly altered river networks in emerging metropolitan areas. Understanding the historical and current conditions of river networks is crucial for policy-making in sustainable urban development planning. Based on the topographic maps and remote sensing images, this study employs a multi-metric framework to investigate river network variations in the Suzhou-Wuxi-Changzhou metropolitan area, a rapidly urbanized plain with high-density river networks in the Yangtze River Delta, China. The results indicate a significant decline in the quantity of rivers, with the average river density in built-up areas falling from 2.70 km·km⁻² in the 1960s to 1.95 km·km⁻² in the 2010s, along with notable variations in the river network’s structure, complexity and its storage and regulation capacity. Moreover, shifts in the structural characteristics of river networks reveal that urbanization has a weaker impact on main streams but plays a dominant role in altering tributaries. The analysis demonstrates the extensive burial and modification of rivers across the metropolitan plains. These findings underscore the essence of incorporating river network protection and restoration into sustainable urban planning, providing insights for water resource management and resilient city development in rapidly urbanizing regions. Full article

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems. Full article

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article

Rapid climate change has exacerbated global ecosystem degradation, leading to habitat fragmentation and landscape connectivity loss. Constructing ecological networks (EN) with resilient conduction functions and conservation priorities is crucial for maintaining regional ecological security and promoting sustainable development. However, the spatiotemporal modeling and dynamic resilience assessment of EN under the combined impacts of future climate and land use/land cover (LULC) changes remain underexplored. This study focuses on the Central Yunnan Urban Agglomeration (CYUA), China, and integrates landscape ecology with complex network theory to develop a dynamic resilience assessment framework that incorporates multi-scenario LULC projections, multi-temporal EN construction, and node-link disturbance simulations. Under the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) scenarios, we quantified spatiotemporal variations in EN resilience and identified resilience-based conservation priority areas. The results show that: (1) Future EN patterns exhibit a westward clustering trend, with expanding habitat areas and enhanced connectivity. (2) From 2000 to 2040, EN resilience remains generally stable, but diverges significantly across scenarios—showing steady increases under SSP1-2.6 and SSP5-8.5, while slightly declining under SSP2-4.5. (3) Approximately 20% of nodes and 40% of links are identified as critical components for maintaining structural-functional resilience, and are projected to form conservation priority patterns characterized by larger habitat areas and more compact connectivity under future scenarios. The multi-scenario analysis provides differentiated strategies for EN planning and ecological conservation. This framework offers adaptive and resilient solutions for regional ecosystem management under climate change. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article