Search Results (1,394)

This paper presents an experimental study validating the feasibility of Radio Frequency Identification (RFID) marker systems as a complementary solution for autonomous vehicle (AV) localization in Global Navigation Satellite System (GNSS)-degraded urban environments. A novel synchronized dynamic testbed featuring hardware-level integration with wheel revolution tracking enables precise correlation of RFID marker reads with vehicle angular position. Experimental results demonstrate that multi-antenna configurations achieve consistently high read success rates (up to 99.6% at 0.5 m distance), sub-meter localization accuracy (~55 cm marker spacing), and reliable performance at average urban speeds (36 km/h simulated velocity). Spatial diversity from four strategically positioned antennas overcomes multipath interference and orientation challenges inherent to high-speed RFID reading. Processing latency remains well within the 58 ms time budget critical for autonomous navigation. These findings validate RFID’s potential for smart road infrastructure integration and demonstrate a scalable, cost-effective solution for enhancing AV safety and decision-making capabilities through contextual information transmission. Full article

Transitioning from traditional electrical grids to smart grids is currently an ongoing process that many nations are striving for due to their access to renewable resources. Energy management is one of the key parameters that decides the performance of such complex systems. Distributed Model Predictive Control (DMPC) is a promising technique that can be used to improve the energy management of grid-connected systems. This paper analyzes a grid-connected inverter system with DMPC that exchanges key operating parameters with the grid to optimize coordinated power sharing between its respective loads. The state-space model for the inverter is derived and verified to ensure controllability and observability. A state observer for an inverter system is then developed to estimate the nominal states in the derived state-space model. The system performance is evaluated with MATLAB simulation by implementing load disturbances, which validate the effectiveness of the proposed power management control algorithm. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

Perceived safety strongly shapes how residents use and experience their neighborhoods, yet evidence on how spatial and social factors interact in rapidly urbanizing Asian cities remains limited. This study investigates the role of natural surveillance, spatial connectivity, and perceived incivilities in shaping residents’ perceived safety in Bayan Baru, Malaysia, with fear of crime examined as a key mediating factor. A face-to-face survey of 300 adults measured five constructs: natural surveillance, spatial connectivity, perceived incivilities, fear of crime, and perceived safety. Data were analyzed using PLS-SEM in SmartPLS 4.0, supported by bootstrapping and predictive relevance tests. Results showed that natural surveillance and spatial connectivity increased perceived safety both directly and indirectly by reducing fear, while perceived incivilities undermined perceived safety through heightened fear. Additional interdependencies indicated that spatial connectivity strengthened natural surveillance, which in turn reduced perceived incivilities and reinforced perceived safety, though connectivity alone did not directly reduce incivilities. Mediation analysis confirmed fear of crime as a central psychological bridge linking environmental cues to safety evaluations. These findings highlight how the interplay of visibility, connectivity, and disorder shape perceived safety in Malaysian neighbourhood settings. Interventions should combine design improvements, maintenance of public space, and community engagement to reduce fear and strengthen everyday confidence in neighborhood safety. Full article

Street livability is widely recognized as a fundamental indicator of urban livability. Despite growing global agendas advocating human-centered, sustainable, and smart cities, the microscale implementation of streetscape interventions remains limited and non-integrated. This gap is particularly evident in developing cities’ contexts where policy-level frameworks fail to translate into tangible street-level transformations. Responding to this challenge, this paper investigates how streetscape components can enhance everyday street livability. The study aims to explore opportunities for improving street livability through the utilization of three core streetscape components: vegetation, street furniture, and lighting. The discourse on street livability identifies vegetation, street furniture, and lighting as the primary drivers of high-quality urban spaces. Scholarly research suggests that these micro-interventions are most effective when viewed through the combined lenses of human-centered design, environmental sustainability, and smart city technology. While the literature indicates that integrating climate-responsive greenery and renewable energy systems can enhance social interaction and safety, it also highlights significant implementation hurdles. Specifically, researchers point to policy limitations, technical feasibility in developing nations, and the socio-economic threat of green gentrification. Despite these complexities, microscale streetscape improvements remain a vital strategy for fostering inclusive and resilient cities. Full article

Context: Optimizing nitrogen application and planting density is critical for achieving high yields and increasing lodging resistance in crops. However, the agronomic mechanisms underlying these benefits remain unclear. Objectives: This study aimed to elucidate the relationships among light distribution within the canopy, photosynthetic capacity, root architecture, yield, and lodging resistance in sweet corn. Methods: A two-year field experiment (2024–2025) was conducted using a split-plot design with two factors: nitrogen application levels as main plots (namely, N150 and N200; 150 kg/ha and 200 kg/ha, respectively) and three planting densities as sub-plots (D20, D25, and D30, representing plant spacing of 20 cm, 25 cm, and 30 cm, respectively, with a fixed row spacing of 80 cm). Results: At a given planting density, N150-treated plants exhibited significantly enhanced basal stem node strength and root architecture compared to those treated with N200. These improvements were closely associated with the increase in light interception rate (IR) into the lower canopy under N150. Consequently, root-lodging resistance increased, reducing the root lodging rate by 80.82% (7.32% vs. 13.21% under N200). Due to these advantages, the average yield of N150-treated plants was higher than that of N200-treated plants (+3.16%). Notably, increasing planting density emerged as the primary factor driving ear yield improvement, with the highest yield observed under the N150D20 group plants, which can reach ~29 t/ha. Conclusion: Coordinating nitrogen input with appropriate planting density improves vertical light distribution, particularly in the middle and lower canopy, thereby strengthening the basal stem and root systems and enhancing root lodging resistance and yield. Implication: These findings offer practical guidance for achieving high sweet corn yields by integrating canopy light management with optimized nitrogen application and planting density, and provide scientific guidance on “smart canopy” selection for sweet corn breeding. Full article

The paper examines the phenomenon of urban envelopes, a conceptual parallel to building envelopes, which is considered an emerging theme in studies of the built environment. The term ‘envelope’ refers to various physical and non-physical occurrences in the built environment that delimit, enclose, or demarcate spatial configurations. In the first part of the paper, six distinct types of urban envelopes are identified: physical, programmatic, technological, ecological, environmental, and representational. These are defined based on a systematic literature review to clarify their form, role, and meaning in the context of contemporary cities. All six urban envelope types are formalised using ontology-building methods in Protégé and visualised through WebVOWL, producing domain-agnostic RDF/OWL models that support semantic interoperability. The results provide a concise definition of urban envelopes, which are becoming increasingly relevant in their non-physical representations, such as spaces of control (surveillance of public urban spaces), dynamic environmental and ecological phenomena (pollution, heat islands, and more), temporal or dynamic definitions of space use, and many others in the context of contemporary smart city development. The analysis of possible alignment with existing smart city-related ontologies is presented. By providing the methodology for linking urbanistic principles with data-driven smart city frameworks, the paper provides a unified methodological foundation for incorporating such emerging spatial phenomena into formal urban models. Full article

Urban Green Spaces (UGS) are integral components of the built environment, significantly contributing to its ecological, social, and performance dimensions, including microclimate regulation, occupant well-being, and energy efficiency. This decadal review (2015–2025) systematically analyzes 70 high-impact studies to propose a “Symbiotic Intelligence” framework. This framework integrates Generative AI, ethical algorithms, and innovations from the Global South to revolutionize the planning, design, and management of UGS within building landscapes and urban fabrics. Our analysis reveals that Generative AI can optimize participatory design processes and generate efficient planning schemes, increasing public satisfaction by 41% and achieving fivefold efficiency gains. Metaverse digital twins enable high-fidelity simulation of UGS performance with a mere 3.2% error rate, providing robust tools for building environment analysis. Ethical algorithms, employing fairness metrics and SHAP values, are pivotal for equitable resource distribution, having been shown to reduce UGS allocation disparities in low-income communities by 67%. Meanwhile, innovations from the Global South, such as lightweight federated learning and low-cost sensors, offer scalable solutions for building-environment monitoring under resource constraints, reducing model generalization error by 18% and decreasing data acquisition costs by 90%. However, persistent challenges-including data heterogeneity, algorithmic opacity (with only 23% of studies adopting interpretability tools), and significant data gaps in the Global South (coverage < 15%)-hinder equitable progress. Future research should prioritize developing UGS-climate-building coupling models, decentralized federated frameworks for building management systems, and blockchain-based participatory planning to establish a more robust foundation for sustainable built environments. This study provides an interdisciplinary roadmap for integrating intelligent UGS into building practices, contributing to the advancement of green buildings, occupant-centric design, and the overall sustainability and resilience of our built environment. Full article

Distribution terminal units (DTUs) play critical roles in smart grid for supporting data acquisition, remote monitoring, and fault management. A single DTU generates continuous data streams, imposing new challenges on data processing. To tackle these issues, a cloud-edge collaboration-based data processing method is introduced for DTU edge clusters. First, considering the load imbalance degree of DTU data queues, a cloud-edge integrated data processing architecture is designed. It optimizes edge server selection, the offloading splitting ratio, and edge-cloud computing resource allocation in a collaboration mechanism. Second, an optimization problem is formulated to maximize the weighted difference between the total data processing volume and the load imbalance degree. Next, a cloud-edge collaboration-based data processing method is proposed. In the first stage, cloud-edge collaborative data offloading based on the load imbalance degree, and a data volume-aware deep Q-network (DQN) is developed. A penalty function based on load fluctuations and the data volume deficit is incorporated. It drives the DQN to evolve toward suppressing the fluctuation of load imbalance degree while ensuring differentiated long-term data volume constraints. In the second stage, cloud-edge computing resource allocation based on adaptive differential evolution is designed. An adaptive mutation scaling factor is introduced to overcome the gene overlapping issues of traditional heuristic approaches, enabling deeper exploration of the solution space and accelerating global optimum identification. Finally, the simulation results demonstrate that the proposed method effectively improves the data processing efficiency of DTUs while reducing the load imbalance degree. Full article

This paper proposes the integration of the phase-field method (PFM) with deep neural networks (DNNs) for UAV navigation in smart city environments. Using the proposed approach, simulations of an intelligent navigation and obstacle avoidance framework for drones in complex urban environments have been presented. Within the unified PFM-DNN model, phase-field modeling provides a continuous spatial representation, allowing for highly accurate characterization of boundaries between free space and obstacles. In parallel, the deep neural network component offers semantic perception and intelligent classification of environmental features. The proposed model was tested using the 3DCity dataset, which comprises 50,000 urban scenes under diverse environmental conditions, including fog, low light, and motion blur. The results demonstrated that the proposed system achieves high performance in classification and segmentation tasks, outperforming modern models such as DeepLabV3+, Mask R-CNN, and HRNet, while exhibiting high robustness to sensor noise and partial obstructions. The framework was evaluated within a simulated environment, and no real-world UAV drone tests were performed. This framework proves its effectiveness as a promising solution for intelligent drone navigation in future cities thanks to its ability to adapt and respond in dynamic environments. Full article

Recognizing human actions in low-light industrial environments remains a significant challenge for safety-critical applications in power systems. In this paper, we propose a Low-Light Pose-Action Collaborative Network (LPAC-Net), an integrated framework specifically designed for monitoring scenarios in underground electrical vaults and smart power stations. The pipeline begins with a modified Zero-DCE++ module for reference-free illumination correction, followed by pose extraction using YOLO-Pose and a novel rotation-invariant encoding of keypoints optimized for confined industrial spaces. Temporal dependencies are captured through a bidirectional LSTM network with attention mechanisms to model complex operational behaviors. We evaluate LPAC-Net on the newly curated ARID-Fall dataset, enhanced with industrial monitoring scenarios representative of electrical infrastructure environments. Experimental results demonstrate that our method outperforms state-of-the-art models, including DarkLight-R101, DTCM, FRAGNet, and URetinex-Net++, achieving 95.53% accuracy in recognizing worker activities and safety-critical events. Additional studies confirm LPAC-Net’s robustness under keypoint noise and motion blur, highlighting its practical value for intelligent monitoring in challenging industrial lighting conditions typical of underground electrical facilities and automated power stations. Full article

Fulgides are a class of organic compounds that exhibit photochromic behavior in both the solid state and in solution. These compounds have attracted considerable research interest due to their wide range of potential applications, including photochromic eyewear, smart windows, optical switches, data storage, and chemical and biological sensors. Here, we report the synthesis and crystal structures of fulgides bearing four different para-substituents on the phenyl moiety. All four molecules crystallize in space groups containing an inversion center. The distances between the two carbon atoms that would form the single C–C bond in the cyclized products fall within the range of 3.301–3.475 Å. The observed structural variations are attributed to intermolecular interactions based on Hirshfeld surface analysis. The fulgides exhibit photochromism, but they are not expected to display ferroelectric behavior due to their crystallization in centrosymmetric space groups. Full article

Urban lighting is evolving from a basic technical infrastructure to a strategic tool for sustainable regeneration, energy efficiency, and public space reactivation. This paper explores the potential of smart and adaptive lighting systems as enablers of 24 h services, equitable access, and environmental resilience. By integrating lighting strategies with urban planning instruments (PRIC, PEC, PMU), cities can reduce energy consumption, limit light pollution, and foster new urban centralities. The study outlines regulatory gaps, technical solutions, and cultural shifts needed to transform lighting into a key asset for livable, inclusive, and digitally enabled urban futures. Full article

Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective urban green space management. However, existing methods often struggle with noise, crown overlap, and the complexity of street environments. To address these challenges, this paper introduces a multi-constraint and shortest path optimization method for individual urban street tree segmentation from point clouds. In this paper, object primitives are first generated using multi-constraints based on graph segmentation. Subsequently, trunk points are identified and associated with their corresponding crowns through structural cues. To further improve the robustness of the proposed method under dense and cluttered conditions, the shortest-path optimization and stem-axis distance analysis techniques are proposed to further refine the individual tree extraction results. To evaluate the performance of the proposed method, the WHU-STree benchmark dataset is utilized for testing. Experimental results demonstrate that the proposed method achieves an average F1-score of 0.768 and coverage of 0.803, outperforming superpoint graph structure single-tree classification (SSSC) and nyström spectral clustering (NSC) methods by 17.4% and 43.0%, respectively. The comparison of visual individual tree segmentation results also indicates that the proposed framework offers a reliable solution for street tree detection in complex urban scenes and holds practical value for advancing smart city ecological management. Full article

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article