Search Results (1,557)

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example to evaluate the sustainability of historical official routes in achieving economically cost-efficient operation and maintenance. Official ways in the Qiantang River Basin connected the Jiangnan region, the economic center of China, with surrounding provinces were assessed. During the past six hundred years, the official road network in this area gradually simplified, evolving from valley roads to river banks, which covered longer distances. However, this transformation lacks a systematic explanation. By applying the analytic hierarchy process (AHP) with geographic information system (GIS), quantitative analysis was gained and the results are as follows: (1) Among the influencing factors, the weights of transportation cost and population related to economic needs are 39.54% and 29.52% respectively, with a combined total of 69.06%. (2) The official road network is often designed for governing the people, but in places such as the Qiantang River Basin, economic logic superseded political imperatives, becoming the dominant factor in reshaping the official ways. (3) In the pre-industrial era characterized by limited technological capacity, the physical environment had a greater impact on economic costs, ultimately reshaping the spatial configuration of official route networks. Overall, the evolution of official routes reflects the decline in their military-political function, driven by sustained peace and long-term decline in strategic position. The evolution of the official ways in the Qiantang River Basin reveals the importance of economic benefits in road selection. Full article

This paper investigates the performance of drone-to-ground (D–G) communication links under generalized composite fading conditions in urban environments. A unified analytical framework based on single-scattering single-shadowing (SS–SS), double-scattering single-shadowing (DS–SS), and double-scattering double-shadowing (DS–DS) fading models is adopted to accurately characterize the combined effects of multipath scattering, shadowing, and propagation nonlinearity, while also encompassing experimentally validated D–G channel models as special cases. Novel closed-form and integral-form expressions for end-to-end SNR statistics are derived, enabling the evaluation of outage probability (OP), average bit error rate (BER), and ergodic capacity (C). The analysis further provides physical insights into the influence of fading severity, nonlinearity, and shadowing parameters through a comparative investigation of the SS–SS, DS–SS, and DS–DS models. All analytical results are verified through extensive Monte Carlo simulations. Numerical results confirm the accuracy and flexibility of the proposed framework, highlighting its potential application in the analysis, optimization, and design of beyond-5G and future 6G drone-assisted wireless networks. Full article

In this paper, we introduce SPADE, a framework engineered for building Digital Twins through Multi-Agent Systems. The architecture is inherently scalable and distributed, aligning perfectly with the demands of modern Digital Twin environments. We implement the Agents and Artifacts meta-model via the SPADE Artifacts extension, which serves as a structured interface connecting autonomous agents with their physical system counterparts. To demonstrate the framework’s efficacy, we detail a case study involving urban traffic management in Valencia, Spain. In this implementation, we model 386 street segments as individual agents responsible for managing traffic flows and coordinating redistribution efforts. The research delineates a MAS-based communication strategy spanning the entire network and introduces a consensus algorithm specifically designed to manage traffic rerouting when a street is closed. Finally, we present results from a series of experimental trials and evaluate the system’s broader potential. By synthesizing diverse data sources and providing an interactive dashboard for visualizing network conditions, this work demonstrates how SPADE can serve as a robust foundation for Digital Twin development, illustrating its potential for real-world urban applications through a conceptual implementation grounded in open sensor data. Full article

The rapid growth of heterogeneous IoT devices in 5G environments has created stringent requirements for low-latency edge-based protocol processing. Existing static parsing frameworks lack adaptability to dynamic multi-protocol traffic, resulting in increased processing delays and quality-of-service (QoS) violations under bursty workloads. This paper presents HeteroEdge, a latency-aware adaptive protocol parsing framework for 5G Multi-access Edge Computing (MEC) environments. HeteroEdge integrates four tightly coupled components: (i) a lightweight machine-learning-based Heterogeneous Protocol Parsing Layer (HPPL) built on gradient-boosted decision trees (XGBoost); (ii) a Network Digital Twin (NDT) that maintains a compressed and continuously updated representation of IoT endpoint states; (iii) a Real-Time Inference Engine (RTIE) that dynamically reallocates parsing resources at 50 ms intervals; and (iv) a What-If Simulation (WIS) module that proactively evaluates resource-allocation strategies under hypothetical traffic scenarios. Experimental evaluation on a physical 5G MEC testbed comprising four Intel Xeon Silver 4316 edge nodes and 2000 emulated IoT endpoints spanning twelve protocol classes demonstrates the effectiveness of the proposed framework. HeteroEdge reduces median edge parsing latency (including parsing, classification, and queuing delays, but excluding the 5G radio component) by up to 44.7% compared with static MEC baselines, achieves a macro-averaged protocol classification accuracy of 97.8%, and sustains sub-7 ms edge parsing latency at a line-rate NIC injection throughput of 18 Gbps. Furthermore, latency spikes under bursty traffic are reduced by 39% at the 95th percentile, while SLA violation rates decrease by a factor of 3.9 relative to static resource allocation. These results demonstrate that HeteroEdge provides an effective and scalable solution for latency-critical IoT applications, including smart manufacturing, connected vehicles, and urban sensing. Full article

Healthcare and urban infrastructure are increasingly supported by Internet of Things-based sensing systems, in which heterogeneous physiological, environmental, and transmission-level data require reliable, low-latency processing. Existing works typically treat medical IoT sensing, smart-city anomaly detection, or edge-cloud offloading as isolated problems, thereby failing to support integrated sensing scenarios in shared smart environments. This paper introduces a Hybrid Edge–Cloud Intelligence Framework (HECIF) for reliable sensing and data fusion in smart healthcare and urban IoT environments. HECIF introduces modality-specific feature extraction, adaptive offloading to the edge cloud, an attention mechanism for multimodal fusion, and a reliability-weighted decision layer that incorporates sensor quality and transmission delay. The framework was tested on three publicly available datasets: the Multi-Sensor Medical IoT dataset for physiological signal classification, the UrbanIoT Anomaly dataset for urban anomaly detection, and the IoT Sensor Cloud Data Transmission dataset for offloading decision modeling, all from Kaggle. It achieved a 92.1% accuracy, 91.3% F1-score, 93.8% AUC, and 0.821 Matthews correlation coefficient in a simulated edge cloud environment, outperforming the baselines (logistic regression, random forest, XGBoost, MLP, CNN/LSTM). The framework also reduced the mean inference time to 29 ms, down from 142 ms in the cloud-only configuration, while achieving a throughput of 1150 samples per second. The results show that reliability-aware edge cloud fusion is feasible for cross-domain IoT sensing with a simulated edge cloud. However, physical device validation and real-world IoT network validation are still required before practical deployment. Full article

Gathering spaces within urban parks serve as primary outdoor leisure venues, playing a critical role in facilitating social interaction and restoring the physical and mental well-being of this demographic. This study uses the example of Pipa Mountain Park in Chongqing, China to explore the psychological and physiological perceptual effects of spatial environmental characteristics on young adults in four typical gathering spaces: path platform, elevated point, viewing boundary, and key node. To this end, we employed onsite experimental methods using wearable ergonomic devices to collect participants’ physiological data, including electrophysiological, electroencephalogram (EEG), and eye-tracking data. Visual and auditory psychological perception evaluation data were obtained through on-site questionnaires. Descriptive statistical analysis revealed differential trends in participants’ psychological perceptions and physiological responses across distinct gathering spaces. The elevated point demonstrated the most favorable ratings for the psychological dimension “comfort” (M = 1.63, SD = 2.09). Subsequent principal component analysis elucidated key psychological perception indicators in mountainous settings, while Friedman test, Kruskal–Wallis tests, and random forest modeling quantified the effects of specific spatial environmental indicators on perceptual responses. Results indicated significant differences in psychological perceptions and physiological responses across gathering space typologies (p < 0.05). Influenced by the preferences and behavioral habits of young adults, environmental element complexity significantly enhanced attentional engagement (χ² = 68.428, p < 0.01) and facilitated positive perceptual responses. The synergistic effects of the visual and auditory elements significantly enhance the restorative benefits of space; however, poor accessibility weakens this advantage. This study provides evidence for the in-depth analysis of the intrinsic mechanisms between the spatial environment and multisensory perception in urban mountain parks. Full article

Amid growing global instability and the escalating impacts of climate change, there is an increasing need to develop resilient human habitats, particularly underground environments. At the same time, resource-extraction activities have left behind extensive underground voids in abandoned mines, presenting a valuable opportunity to expand multifunctional spaces that can serve daily needs as well as emergency shelter functions (dual-use), while also supporting urban–rural transformation and sustainable development goals. Due to their geological conditions and mining methods, underground goafs offer inherent advantages for dual-use development. In light of this, this study proposes a theoretical approach to address the three fundamental challenges associated with the dual-use of underground goafs in gypsum mines from the perspective of architectural space creation. This study does not present a completed empirical validation; instead, it develops a conceptual and interdisciplinary methodological framework intended to guide future empirical research and engineering implementation. Specifically, the framework is as follows: (1) defining escape safety capacity under disaster impacts by constructing a dynamic assessment model integrating disaster physics, behavior simulation, and VR-calibrated experiments; (2) elucidating the correlation mechanism between spatial topological features and human response patterns using space syntax and multi-modal psychological experiments to reveal how spatial morphology influences orientation, emotion, and behavior; and (3) moving beyond the traditional notion that space should be adapted to functional requirements, proposing an innovative strategy involving adapting predefined functions to the space. Full article

Physical activity during recess can provide an important opportunity for secondary school students to accumulate health-enhancing movement within the school day. However, in high-density urban schools, limited campus land and uneven spatial conditions may constrain students’ physical activity during recess. Although previous studies examined the role of the school environment in shaping students’ physical activity, little is known about how different types of campus public spaces and students’ perception of such spaces are associated with recess physical activity. In this study, such associations are examined in three high-density urban schools in Guangzhou, China among 900 students in grades 10–12. The students’ moderate-to-vigorous physical activity (MVPA) during recess is measured by using Huawei Band 8 wearable devices, and their primary activity spaces and perception of their spatial environment are determined by using a structured questionnaire. The campus public spaces are classified as sports field, courtyard, plaza, undercroft space and corridors, and the students’ perception of their environment is assessed across four dimensions: usability, accessibility, safety and comfort. Descriptive statistics, Pearson correlation analysis and multiple linear regression are used for the data analysis. Results show differences in recess MVPA levels across the three schools, with high activity levels observed in the school with superior spatial resources and public space conditions, as well as the use of and MVPA behaviour in the different public spaces. Sports fields were generally associated with high use and higher MVPA levels, whereas corridors mainly supported students’ movement between destinations and brief resting and were associated with relatively low MVPA levels. Courtyards, plazas and undercroft spaces show varied patterns, in which activity is related to specific spatial conditions, such as the scale, openness, paving and shading conditions and facility availability. The perception analysis indicates that usability, comfort, accessibility and safety are significantly and positively associated with recess MVPA, with usability showing the strongest association. The regression model can explain 54.7% of the variance in recess MVPA levels. The findings suggest that in similar high-density urban secondary school contexts, spatial support for recess physical activity depends on not only the amount of available space but also the activity-supportive characteristics and perceived environmental quality of the campus public spaces. Improvement of the usability, comfort and accessibility of campus public spaces may provide favourable spatial conditions for students’ physical activity during recess. Full article

Understanding the spatial generation mechanisms of ride-hailing demand is crucial for sustainable urban mobility. However, existing literature largely assumes monocentric urban layouts and globally stationary spatial scales, often overlooking the severe topographical constraints inherent in “strip cities”. To bridge this gap, the present study proposes a novel dual-level analytical framework coupling the Spatially Embedded Laplacian Graph Partition (SE-LGP) algorithm with a Log-Gaussian Multiscale Geographically Weighted Regression (MGWR) model. Taking Jinan, China, as a quintessential strip city, we incorporate spatial penalties to decode its mobility dynamics. Macroscopically, we reveal that substantial topographic friction fragments the workday mobility network into a chain of 23 highly localized micro-circulations. This anisotropic friction results in a notable 41.70% intra-community retention rate, demonstrating that flexible mobility operates within confined functional basins rather than a unified citywide market. Microscopically, the MGWR uncovers significant multiscale spatial heterogeneity: the jobs–housing mismatch is strongly associated with demand at a global macro scale (bandwidth = 1335), whereas public transit integration operates predominantly at a localized micro scale (bandwidth = 44). Crucially, the interaction between topographical friction and infrastructure capacity unveils a highly localized pressure-valve effect (bandwidth = 46), indicating that physical road networks mitigate natural barriers strictly at a micro scale. Comparative analysis quantifies a “spatial fusion effect” during weekends; the relaxation of rigid tidal commuting reveals a structural invariance in built-environment scales (bandwidth = 1335), while the impact intensity of natural topographical friction undergoes a marked spatial inversion. This behavioral elasticity merges fragmented micro-circulations into larger regional communities ($k=20$). The findings indicate that flexible transit is strongly associated with scale-dependent and temporally elastic mechanisms. It provides insights for planners to transition from uniform city-wide fleet dispatching toward region-customized, temporally dynamic mobility management in topographically constrained metropolises. Full article

Optimal node deployment in Wireless Sensor Networks (WSNs) is crucial for maximizing monitoring coverage. However, traditional metaheuristics like the Sparrow Search Algorithm (SSA) often suffer from premature convergence and redundant clustering, creating severe coverage holes. To address this, we introduce the Density-Aware Repulsive Sparrow Search Algorithm (DAR-SSA). Integrating electrostatic principles, DAR-SSA calculates a local density-based repulsive force vector to actively disperse nodes from high-density clusters. This physics-guided approach, combined with a dynamic explorer-exploiter allocation rule, ensures a computationally efficient balance between global and local search phases. Evaluated via a probabilistic sensing model, DAR-SSA significantly outperforms standard SSA, its variants (EFSSA, EASOA), and classical algorithms (PSO, GWO). In high-density urban deployments, DAR-SSA achieves a 95.25% effective coverage rate, compared to SSA’s 76.74%. In low-density environments, coverage reaches 97.12%. Validated by Wilcoxon rank-sum tests, DAR-SSA proves to be a robust, efficient framework for mitigating spatial redundancy and maximizing WSN sensing coverage. Full article

Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, purely data-driven models, though efficient, lack physical consistency and generalization capability. This review systematically examines Physics-Informed Neural Networks (PINNs), a hybrid paradigm in which physical prior knowledge is embedded directly into the neural network training process. A structured keyword search of the Web of Science Core Collection was performed, and 94 peer-reviewed journal articles were analyzed. The evolution from numerical simulations and data-driven surrogate models to PINNs is outlined. PINN methods are classified according to the stage at which physical prior information is integrated (i.e., dataset development, model construction, or loss function formulation). Current research remains heavily focused on loss function constraints, whereas systematic integration into data augmentation and model construction remains limited. Application domains span indoor environments, outdoor environments, and building systems, with each domain exhibiting unique prior integration strategies tailored to specific problems. Future PINN modeling should evolve toward multi-physics coupling, adaptive loss balancing, cross-scenario transfer learning, and unified evaluation benchmarks. PINNs in this field are promising but remain at an early stage, especially for complex urban-scale deployment. This review synthesizes existing research around the three stages of dataset development, model construction, and loss function formulation, summarizes the prior integration strategies adopted in the domain of building thermal environments, and provides a practical workflow for embedding physical prior knowledge at different stages of model development. Full article

Under the growing threat of urban heat stress, street canyons play a critical role in shaping the pedestrian thermal environment. While street greenery is an effective mitigation strategy, its performance varies substantially with physical characteristics—such as aspect ratio, street width, and sidewalk width—highlighting the need for spatially adaptive design. This study evaluates the effects of sidewalk width, street orientation, and planting structure on thermal conditions in a humid subtropical climate in Daegu Metropolitan City, Republic of Korea. The analysis focuses on open low-aspect-ratio street canyons (H/W = 0.86 for E–W and 0.43 for N–S orientations). Using a validated ENVI-met (Version 5.6.1) model based on field measurements from Daegu, Republic of Korea, 56 street-greening scenarios were simulated by systematically varying sidewalk width, street orientation, planting rows, spacing, and planting structure. Results show that multi-row planting served as the primary structural framework governing thermal performance. Optimal configurations varied with sidewalk width, with two-row planting for 6 m sidewalks and three-row planting for 10 m sidewalks providing the most effective cooling. The greatest cooling (−2.02 °C) was achieved when optimized multi-row configurations were combined with multi-layer planting. Once optimal multi-row configurations were established, the presence of understory vegetation had a greater influence on thermal improvement than its specific composition, allowing flexibility in understory design. Clear spatial asymmetries were identified, with the highest thermal stress occurring on the north-side sidewalk in E–W streets and the west-side sidewalk in N–S streets. Targeted planting in these locations produced greater cooling benefits than uniform strategies. These findings provide a spatially grounded framework for climate-responsive street greenery and offer practical design guidance, highlighting the need for context-specific, optimized multi-row planting strategies adapted to local urban and climatic conditions. Full article

Domestic cats (Felis catus) are ubiquitous companion animals that provide substantial psychological and social benefits to children and adults alike, but they also serve as reservoirs and vectors for a wide range of zoonotic pathogens. Close physical contact between cats and children, frequent use of shared environments such as homes, playgrounds, and sandboxes, and still-developing hygiene behaviours increase opportunities for exposure to protozoa, helminths, bacteria, fungi, and ectoparasite-borne agents. This review synthesizes current evidence on key feline-associated zoonoses of pediatric concern—including Toxoplasma gondii, Toxocara cati, Ancylostoma spp., Dipylidium caninum, Bartonella henselae, Salmonella enterica, Campylobacter jejuni, Pasteurella multocida, Microsporum canis, flea-borne Rickettsia species, and rabies—with emphasis on transmission routes, clinical manifestations, and risk modifiers in children, pregnant women, and immunocompromised individuals. Within a One Health framework, we also summarize global publication trends on feline zoonoses, discuss how urban cat ecology and management (including free-ranging cats in child-frequented environments) may shape pediatric risk, and outline practical prevention strategies centred on hygiene, veterinary care, and targeted education for caregivers and children. Full article

Leftover spaces, often associated with neglected urban corners, bear physical and conceptual similarities to ignored parts of designed wholes. This study proposes an analytical approach to develop resilient intervention strategies by analyzing the production of leftover spaces through users’ emotional experiences. An experimental pilot study was conducted along Söğütlüçeşme Street in Kadıköy, Istanbul, where all corner points were typologically classified based on morphological characteristics. To measure the impact of these configurations on spatial emotional characters, a survey was implemented using Plutchik’s wheel of emotions. Following a quantitative analysis of emotion frequencies and intensities, findings were visualized via radar charts and spatialized using QGIS 3.40 to generate an emotional map. The resulting emotional maps were further used to identify spatial vulnerabilities and resilience priorities across the study area. By making the gaps between point-based emotional clusters continuous through the IDW interpolation method, the emotional topography of the study area was modeled, thereby presenting an analytical framework that identifies emotional thresholds, spatial vulnerabilities, and resilience priorities. Results indicate that as the physical boundaries of corner voids expand, influenced by angling and massing decisions, public diversity increases, creating a positive emotional atmosphere. Conversely, compressed voids demonstrate a higher potential for producing leftover spaces. This study reveals that mapping user emotions as a data layer is critical for constructing more inclusive and resilient urban environments. Full article

Helicopter operations are often underrepresented in environmental assessments due to their relatively low number of movements and the use of aggregated indicators that do not capture their localised impacts. At the same time, rotorcraft activity typically occurs at low altitude within urban environments, where noise and emissions are directly perceptible and spatially concentrated. This creates a need for assessment approaches based on observed operations and capable of providing spatially resolved results. Automatic Dependent Surveillance-Broadcast (ADS-B) data provide high-resolution observations of aircraft trajectories and are increasingly used to analyse real-world aviation activity. However, existing approaches to ADS-B data processing have largely been developed for fixed-wing operations and do not address the specific challenges of rotorcraft activity, including low-altitude signal loss, positional artefacts, and incomplete trajectories. As a result, ADS-B data for helicopters are generally not suitable for direct use in applications requiring physically consistent and operationally defined inputs. This study proposes a methodology to condition ADS-B helicopter trajectories into a physically consistent and operationally characterised dataset suitable for downstream analysis. The approach integrates trajectory correction, reconstruction of incomplete operations, and the derivation of flight modes and associated parameters. The resulting dataset provides a complete, operation-level description of helicopter activity derived from observed data. The methodology is demonstrated through its application to helicopter operations in the Zurich area and its integration with established environmental modelling approaches, including a rotorcraft-specific noise model (NORAH2) and a flight-mode-based emissions estimation method (Rindlisbacher and Chabbey). The results produce spatially resolved maps and tabulated outputs describing environmental impacts over a defined period, enabling the identification of localised hotspots. The contribution of this work lies in providing a reproducible and integrated framework that bridges the gap between raw ADS-B rotorcraft observations and application-ready datasets for spatially explicit environmental assessment. Full article