Search Results (550)

The rapid growth of UAV use in urban logistics, infrastructure inspection, emergency response, and campus services has made low-altitude flights more common in residential environments. Previous studies have mainly examined aircraft acoustics, flight conditions, building transmission, or listener responses. Repeated field evidence on UAV noise under contrasting canopy-site configurations remains limited. This study compared UAV noise under low-canopy (<8 m) and tall-canopy (>15 m) conditions at a six-storey campus residential area with a regular row-type layout. The two focal sites had similar building layouts, façade conditions, measurement positions, flight altitudes, and source–receiver geometry. They differed mainly in tree height and canopy density. We tested a DJI Mavic 2 Pro and a DJI Matrice 200 V2 in three independent hovering trials for each condition. We measured sound levels using Class 1 sound level meters. We also compared indoor and outdoor exposure. The analysis included one-third-octave-band spectra from representative recordings, descriptive psychoacoustic indicators, 118 valid questionnaires, principal component analysis, and exploratory empirical Bayesian kriging based on 15 measurement points. At the outdoor focal sites, the Matrice 200 showed a L_ASmax 10.0 dB higher under the tall-canopy condition. Its L_Aeq was 2.7 dB higher, whereas its L₉₀ was 10.5 dB lower. This pattern indicates a wider, more intermittent sound-level distribution rather than a uniform increase. The Mavic 2 outdoor L_Aeq was 5.0 dB higher under the tall-canopy condition. Indoor differences varied by UAV model, window condition, and acoustic indicator. Higher transient sound levels and stronger psychoacoustic responses were associated with greater annoyance. EBK interpolation was used to visualize spatial heterogeneity. The assessment framework may also support studies in campus dormitory areas and row-layout multi-storey residential compounds with similar spatial features. The findings suggest that canopy-site configuration, building enclosure, UAV characteristics, and flight geometry should be assessed together. Full article

Highland vernacular dwellings face multiple environmental stresses, but existing studies often examine either individual passive strategies or qualitative cultural interpretations, leaving limited evidence on how regional climate, settlement wind environments, and indoor microclimates are connected. This study investigates Tibetan vernacular dwellings in northwestern Sichuan, China, across a climatic gradient from warm–humid low altitude to dry–cool mid altitude and cold–arid high altitude. Three settlements—Zhonglu Township, Sergu Town, and Gemo Township—were examined through field surveys, settlement mapping, and measured drawings of 24 dwellings. Climate Consultant, WindNinja, GBSware CFD simulations, and Radiance daylighting analysis were integrated to assess climatic stresses, outdoor wind environments, indoor ventilation, daylighting, and envelope responses. The results show that patchy, dense, and linear settlement morphologies produce different wind-protection and ventilation performances. Building form factors decrease with altitude from 0.65 to 0.54 and 0.44, while daylighting compliance remains below 10% in all cases. The findings demonstrate the value of multi-scale simulation for diagnosing climate-responsive strategies in vernacular dwellings and provide evidence for highland settlement conservation and climate-responsive building renewal. Full article

Hybrid environmental monitoring systems combining fixed and mobile sensing platforms are increasingly attracting attention for the characterization of complex outdoor environments. However, the practical integration of heterogeneous sensing infrastructures remains challenging because measurement consistency, sensor calibration, data fusion, and spatial reconstruction are often addressed separately rather than within a unified monitoring methodology. This work presents a hybrid environmental monitoring framework that integrates professional fixed monitoring stations with an autonomous ground vehicle equipped with low-cost environmental sensors. The proposed methodology combines reference-based calibration, temporal alignment, heterogeneous data fusion, and spatial interpolation to generate spatially consistent environmental information from complementary sensing platforms. The proposed methodology is experimentally validated through a monitoring campaign conducted within the IRCCS Policlinico San Martino hospital campus (Genoa, Italy), where repeated mobile measurements were integrated with two professional monitoring stations along a 350 m outdoor route characterized by heterogeneous environmental conditions. The calibration procedure significantly improved the agreement between fixed and mobile observations, while the comparative analysis of four interpolation techniques demonstrated that interpolation performance depends on the spatial distribution of measurements and the characteristics of the monitored environmental field rather than on the intrinsic superiority of a specific algorithm. The results further demonstrate the feasibility of integrating fixed and mobile sensing into a coherent and reproducible environmental monitoring workflow. Although validated in a hospital environment, the proposed methodology is applicable to other complex outdoor scenarios featuring distributed infrastructures and repeated operational routes, including industrial campuses, logistics hubs, freight terminals, airports, and port facilities. Full article

High-density commercial areas in northern monsoon coastal cities face dual microclimate challenges: hot summers and harsh winters. This study aims to optimize year-round outdoor thermal comfort (Physiological Equivalent Temperature, PET) by exploring morphological optimization strategies in Qingdao. Combining field observations with ENVI-met simulations, we evaluated the impact of building density, street aspect ratio (H/W), and orientation. The results reveal a fundamental “seasonal mechanism conflict.” In summer, a 45% density point-tower configuration with a deep canyon profile (H/W ≈ 2.5) maximizes shade and sea-breeze ventilation. Conversely, in winter, a 35% density enclosed topology with an open profile (H/W ≈ 1.6) optimally blocks cold northwesterly monsoons and increases solar gain. To resolve these conflicting seasonal requirements, we propose a dynamic morphological equilibrium. We recommend adjusting the H/W ratio dynamically between 1.6 and 2.5 and adopting a southeast block orientation of 15–30° to synergistically guide summer breezes while blocking winter winds. Ultimately, this study shifts the design paradigm from static parameters to dynamic seasonal trade-offs, offering actionable morphological guidelines for climate-resilient built environments. Full article

Wind turbine towers operate in complex outdoor environments, where visible surface anomalies such as cracks, pitting, and honeycombing can develop. Field-acquired visible-light images are commonly affected by illumination variation, shadows, local reflections, surface textures, and structural joints, which makes pixel-level anomaly segmentation difficult. This study proposes a task-oriented lightweight U-Net, termed LPAU-Net, that combines DWConv–PWConv feature extraction, parallel channel–spatial attention with learnable scalar fusion, grouped multi-level feature aggregation, and multi-scale decoding. Defect-free images are included during training, and defective and defect-free samples are evaluated separately to distinguish anomaly segmentation from false-positive suppression. The dataset contains 762 original field images collected from the same nine wind turbine towers at one wind farm during five time-separated acquisition campaigns. Campaigns 1–3 were used for training, Campaign 4 for validation, checkpoint selection, and threshold determination, and Campaign 5 for final evaluation. Thus, Campaign 5 is a later acquisition batch from the same towers and site, rather than unseen-tower or cross-wind-farm validation. Across three independent random seeds, LPAU-Net achieved 89.84 ± 0.08% Precision, 89.24 ± 0.08% Recall, 89.54 ± 0.08% F1-score, and 81.07 ± 0.13% Defect IoU on defective Campaign 5 images, with 4.34 M parameters, 14.8 G FLOPs, and 32.81 FPS under the reported desktop-GPU benchmark. On the 30 defect-free Campaign 5 images, the average false-positive area ratio was 0.42 ± 0.03%, and the image-level false-alarm rate was 10.00 ± 3.33%. The results indicate a balanced accuracy–complexity trade-off within the evaluated cross-time-campaign setting. However, strong light, low light, shadows, and reflections were not evaluated as independent subsets, so condition-specific robustness improvement cannot be quantified. Because all visible anomalies were merged into one binary defect class, the model localizes anomalous regions but does not classify cracks, pitting, honeycombing, or other defect types. Full article

Path planning for cargo unmanned aerial vehicles (UAVs) in continuous indoor–outdoor heterogeneous environments poses a critical challenge: promoting payload stability under sharp turns and abrupt altitude variations while maintaining navigational efficiency. To address this issue, this paper proposes a hybrid A*–APF path planning framework that embeds trajectory smoothness optimization directly into the planning process rather than treating it as a post-processing step. An improved A* algorithm is developed by incorporating a trajectory smoothness term into its cost function to penalize sharp turns during global path generation. The resulting path is further refined using an enhanced artificial potential field (APF) method with virtual target points and multi-field force synthesis to mitigate local minima. In addition, the Ramer–Douglas–Peucker algorithm is employed to remove redundant waypoints, and a trajectory generation module based on B-spline interpolation and minimum snap optimization is introduced to produce smooth and dynamically feasible trajectories. Numerical simulation results demonstrate that, in indoor warehouse environments, the proposed method reduces the average turning angle by 88.4% (to 23.1°) compared with the standard A* algorithm while maintaining a comparable path length of 135.11 m. In large-scale outdoor urban scenarios, it achieves a path smoothness of 0.0124 with an average turning angle of 40.0°, substantially outperforming the Genetic Algorithm (104.6°) and Particle Swarm Optimization (83.5°) on turning angle while delivering competitive computation times of 0.52–1.51 s. An ablation study confirms that the improved A* and enhanced APF components each contribute independently to turning angle reduction and local minima avoidance, respectively, and that their integration yields the optimal balance across all metrics. These results indicate the proposed framework’s effectiveness for UAV-based last-mile delivery in scenarios requiring seamless indoor–outdoor transitions under payload stability constraints. Full article

Achieving scalable swarm autonomy in Global Navigation Satellite System (GNSS)-denied and communication-constrained environments remains an open challenge at the intersection of robotics, distributed optimization, and reinforcement learning. Existing unmanned aerial vehicle (UAV) autonomy frameworks typically decouple navigation, perception, and distributed learning, while assuming centralized coordination or reliable global positioning. This paper introduces a unified federated deep reinforcement learning architecture that enables GNSS-independent multi-UAV autonomy through the principled integration of multi-modal perception, decentralized policy optimization, energy-aware control, and edge-compliant inference. The proposed framework formulates joint navigation and dynamic target tracking as a partially observable Markov decision process optimized via Proximal Policy Optimization (PPO) over structured motion primitives. A communication-efficient federated learning mechanism enables distributed policy convergence under non-independent and identically distributed (non-IID) agent experiences without sharing raw data, establishing a scalable alternative to centralized training. To address sim-to-real discrepancies, the architecture incorporates domain randomization, structured sensor noise modeling, and curriculum-based training to promote robust zero-shot deployment. Multi-agent simulation experiments evaluate the swarm-level and federated-learning behavior of the proposed framework, while single-UAV field deployment evidence using a DJI Matrice 100 platform supports the feasibility of the onboard sensing, perception, and edge-inference pipeline under realistic outdoor conditions. The evaluation demonstrates stable decentralized convergence, improved energy efficiency relative to centralized baselines, robust target-tracking performance under GNSS-denied conditions, and real-time edge-compliant inference. The results establish that federated reinforcement learning can serve as a viable systems-level foundation for resilient, energy-aware, and scalable aerial swarm intelligence, advancing the state of the art in distributed autonomous robotics. Full article

Individual differences in outdoor thermal comfort (OTC) are critical to the healthy use of urban public spaces, yet whether thermal preference can shape OTC independently of demographic characteristics remains largely unexamined. Using residential outdoor spaces in Fuzhou, a representative hot-humid city in China, as a case, this study combines field measurements and questionnaire data from 296 respondents (72.6% aged 60 or above) with explainable machine learning and K-Modes clustering to examine how thermal preference drives individual differences in OTC. Three stable preference profiles were identified—heat-sensitive (56.4%), wind-seeking (20.3%), and heat-tolerant (23.3%)—which exhibit markedly different thermal responses. The neutral globe temperature ranges from 29.90 °C for the heat-sensitive profile to 35.85 °C for the heat-tolerant profile, a difference of 5.95 °C, whereas the comfort bandwidth is widest for the heat-sensitive profile (9.03 °C) and narrowest for the heat-tolerant profile (4.13 °C), the former being 2.2 times the latter. The profiles are independent of sex and BMI and only weakly correlated with age, yet their explanatory power for the variance in thermal comfort vote (TCV) (η² = 0.254) is 4.9 to 23.1 times that of the demographic variables. The thermal environment contributes far more to TCV than the visual environment (74.4% versus 25.6%), with globe temperature (Tg) as the strongest single factor. Overall, differentiated design that adopts the most heat-sensitive profile as the constraint boundary covers the comfort needs of a broad population more effectively than demographic stratification. The novelty of this study lies in introducing psychologically grounded thermal-preference profiles as an operational stratification dimension for architectural design, offering age-friendly hot-humid residential environments a preference-oriented pathway toward refined, human-centered outdoor space design. Full article

Winter underuse of urban parks in severe-cold cities limits year-round outdoor activity, especially for cold-sensitive users. This study developed a comfort–risk–adaptation framework integrating thermal perception, model-estimated cold-stress risk, and behavioral responses. Field microclimate measurements and synchronous questionnaires were conducted in Nanhu Park, Changchun, China, under clear winter conditions, yielding 386 paired human–environment samples. The Universal Thermal Climate Index (UTCI), Required Clothing Insulation (IREQ), wind chill temperature (WCT), and contact cooling indicators were used to quantify thermal exposure and cold-stress risk. Results showed significant spatial differences in wind speed, solar radiation, mean radiant temperature, and UTCI, while air temperature and humidity varied little. The neutral UTCI was 3.14 °C (unweighted) and 3.70 °C (weighted), and the 80% thermal acceptability threshold was −15.24 °C (95% CI: −16.14 to −14.22 °C). Despite acceptable thermal perception, physiological cold-stress risks remained under certain conditions. The findings highlight the need to integrate solar access, wind mitigation, low-conductivity materials, and moderate activity routes to improve winter usability in severe-cold urban parks. Results are condition-specific and reflect observed users under clear to partly cloudy winter daytime conditions rather than universal thresholds. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article

Within the subway systems of megacities, individual passenger transfer experiences have long been marginalized due to an overemphasis on macro-level, systemic, and functional performance, positioning low transfer efficiency as a pervasive bottleneck in enhancing the overall network efficacy. Adopting an individual passenger perspective, this study takes the Changping Line of the Beijing Subway as an empirical case. By using walking speed to evaluate transfer efficiency and through field survey, behavioral experiment, and quantitative model analysis, this paper reveals the key built environment factors influencing transfer efficiency and their underlying impact mechanisms and also provides empirical evidence for the synergistic optimization of transfer efficiency and the built environment in megacity subway systems. The findings indicate that the built environment impacts transfer efficiency in macro-non-linear and micro-linear ways, specifically manifesting across six specific mechanisms: the geographic location mechanism, the pressure mechanism of high-density development, the spatial exclusivity mechanism of regional transport hubs, the topological penalty mechanism of transfer paths, the bottleneck constraint mechanism of node facilities, and the compensatory mechanism of information guidance. Furthermore, as a medium affecting transfer efficiency, the shaping of the built environment is essentially determined by the city’s subway planning and construction institutions, the external technical conditions of the particular stations, and localized tactical governance to manage the dynamic daily traffic mobility. Based on these findings, this study suggests that improving the transfer efficiency of megacity metro systems like the Changping Line should implement systemic strategies from four aspects: tailored TOD at the macro-spatial planning phase, the micro-spatial integration of indoor and outdoor built environments during the station design phase, differentiated collaborative governance to alleviate station-external intermodal transfer competitions during the operation phase, and digitally empowered transfer guidance to proactively manage transfer demand across three scenarios. 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

Cold-region community outdoor spaces are not only everyday activity settings for older adults in winter, but also public-space types that need to be translated into design evidence for architecture and healthy human-settlement research. Existing restorative-environment studies usually treat improved mood, perceived restoration, and environmental appraisal as evidence of health benefits. The key finding of this study is that subjective restoration and physiological recovery are not always synchronized after outdoor exposure in cold-region communities. This discordance reveals a design risk and an innovative value that can be overlooked when restoration is evaluated only through perception-based indicators. Based on a winter field exposure experiment with 345 older adults in a community in Shenyang, China, this study compared staged changes in systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), POMS, ROS, and ENPQ across an activity plaza, a greenway walkway, and a street corridor. It further developed a psychophysiological concordance classification and a residual cardiovascular risk indicator for the recovery period. The greenway walkway showed the most stable concordant recovery, with 86.84% of women and 79.35% of men showing concordant recovery. The activity plaza showed a clear pattern of emotional recovery: the proportions of women and men whose psychological state improved without a synchronized SBP decrease were 61.58% and 50.32%, respectively. The street corridor had the highest recovery-failure rates, at 92.63% for women and 91.61% for men. Among women, 90.53% reached SBP values of 140 mmHg or higher during the walking phase in the street corridor, and 59.47% remained above this risk threshold during recovery. These results show that health evaluation of cold-region community outdoor spaces should not rely only on subjective restoration indicators, but should also include psychophysiological concordance and residual risk after exposure. The study translates site health effects into three architectural design judgments: concordant-restoration spaces, emotional-restoration spaces, and recovery-failure spaces, providing a testable evidence framework for age-friendly community renewal, path organization, green buffering, and winter wind-protection design. Full article

In the context of global climate change and rapid urbanization, urban outdoor thermal environment issues in summer have become increasingly severe. Shading has been widely recognized as an effective strategy for improving outdoor thermal comfort, yet existing evaluation methods still suffer from limitations in adaptability and accuracy. Taking Chongqing, a typical hot-humid city in China, as a case study, this paper proposes an evaluation method that accounts for human thermal adaptation, introducing three complementary indicators, namely Universal Thermal Climate Index Load (UTCIL), cumulative UTCIL (cUTCIL), and Heat Stress Duration (HSD). Focusing on four shading-related urban canyon morphological factors—orientation, aspect ratio (H/W), building asymmetry, and leaf area index (LAI) of street trees—a series of simulation scenarios was designed to quantitatively explore their impacts on summer outdoor thermal comfort. The applicability and reliability of the ENVI-met model for block-scale outdoor thermal environment simulation were validated by comparing field-measured microclimate data with simulation results. The findings demonstrate that all four morphological factors substantially influence the outdoor thermal environment. Canyon orientation considerably affects thermal comfort, with a 30° clockwise deviation from the north–south yielding optimal conditions, whereas the east–west (90°) orientation produces the poorest thermal environment, with a maximum UTCI of approximately 48.9 °C. For aspect ratio, thermal comfort improves continuously as H/W increases, with the benefit stabilizing beyond H/W = 3.5. Building asymmetry also plays a notable role: raising building height on one side can effectively reduce outdoor thermal stress, and canyons with taller west-side buildings show better thermal performance under the same asymmetry ratio. Furthermore, street tree shading and aspect ratio exhibit a synergistic cooling effect, where high LAI (e.g., 4.77) reduces UTCI_max by approximately 1.8 °C at H/W = 1, but this benefit diminishes as H/W increases. The optimal outdoor thermal environment is achieved through the combination of a high aspect ratio and high LAI. These findings provide a quantitative basis and design references for optimizing outdoor thermal comfort in Chongqing. In addition, the quantitative evaluation proposed method can offer a methodological reference for other hot-humid regions. Full article

In the severely cold regions of northern China, large-scale clean heating retrofits in rural areas face critical problems, including substandard indoor thermal environments, excessive energy consumption, and prohibitive operating costs. To address these challenges, this study focuses on rural residences in Hohhot as the research subject. Field measurements were conducted throughout the heating season in a typical rural house in Hohhot, a representative city with severe cold weather, to collect indoor/outdoor thermal parameters and real-time operational data of an air-source heat pump (ASHP). A dynamic simulation platform was established using TRNSYS 18. The optimization scheme integrates passive envelope retrofitting (ground insulation improvement and energy-efficient windows) with the active optimized control of the ASHP system. Indoor thermal comfort was evaluated using the Predicted Mean Vote (PMV) index. The results show that the ASHP exhibits excellent heating effectiveness and economic viability, making it the preferred technology for rural residences in Hohhot and similar regions. After implementing the active–passive scheme, the proportion of time with comfortable indoor conditions in rural houses surges from 34.1% to 84.1%, while during the severe cold period, this proportion increases from 16.97% to 61%. The indoor thermal comfort index shifts from its previous state to the baseline comfort range of −1.0 to 0. The total heating energy consumption decreased from 18,646 kWh to 15,861 kWh, and the seasonal operating cost dropped from 3207 to 2579.3 RMB, achieving an overall reduction of 19.6% in both energy and costs. The proposed active–passive synergistic optimization scheme simultaneously improves the indoor thermal environment and reduces heating energy consumption, overcoming the limitations of single-measure retrofits. This study fills the research gap on the quantitative evaluation of active–passive synergy for rural clean heating in severely cold regions, providing a theoretical basis and technical support for clean heating retrofits in Hohhot and Inner Mongolia, facilitating low-carbon and efficient rural clean heating in northern China. Full article