Search Results (3,869)

In recent decades, residential mobility has emerged as a fundamental interpretative key lens for understanding contemporary urban transformations, particularly in polycentric and fragmented urban contexts. Movements between different residential settings reflect economic, social and cultural changes, impacting the organisation of urban spaces, the demand for services and mobility systems. In territories characterised by dispersed settlement patterns and strong functional polarisation, these dynamics tend to promote the intensive use of private means, with consequent negative impacts on environmental sustainability, social equity and economic efficiency. In response to these critical issues, there is growing interest in sustainable mobility models based on proximity and on the integration between daily travel, access to services and the quality of public space. Within this perspective, greenways are configured as hybrid infrastructures, capable of reorganising mobility while contributing to the regeneration of urban spaces. In the Caserta area, in the Campania region, the disused route of the former Alifana railway represents a topic of great interest, both for research and planning. Its potential strategic conversion into a greenway opens a broader perspective than that so far considered at the regional level, which has mainly focused on the infrastructure dimension. The paper analyses the strengths and weaknesses of an approach limited to infrastructural mobility, proposing a comparative evaluation of project scenarios—including the non-intervention hypothesis—both through the application of the MACBETH approach and preliminary parametric estimation of construction costs, in order to emphasise the importance of integrating social and environmental benefits, as well as quality of life, into decision-making processes. Full article

The rapid reshoring of semiconductor manufacturing in the United States has introduced large-scale, energy-intensive industrial facilities into metropolitan regions increasingly exposed to climate-related infrastructure pressures. While existing research on industrial development often emphasizes agglomeration-driven economic spillovers, less attention has been given to how the early operational period of such facilities corresponds with surrounding commercial activity, particularly in peri-urban and greenfield suburban contexts. This study examines the spatiotemporal dynamics of localized commercial vitality surrounding semiconductor fabrication facilities in Phoenix, Arizona, and Austin, Texas. High-frequency point-of-interest (POI) mobility data are used to measure localized commercial activity, while regional electricity load records provide contextual information on metropolitan-scale demand conditions. Using a comparative Difference-in-Differences (DiD) framework combined with distance-band analysis and sectoral-temporal stratification, the study evaluates activity patterns between 2020 and 2025. The results indicate that the early operational period of the Phoenix facility is associated with a sustained relative divergence in mobility-derived commercial activity compared with the Austin benchmark trajectory. Spatial analysis identifies a clear distance-dependent gradient, with the largest relative reductions concentrated in intermediate suburban zones rather than immediately adjacent to the facility. Sectoral and temporal analyses further show larger reductions in dining and nighttime activity than in routine retail and daytime activity. Overall, the findings suggest that the early operational period of large industrial mega-projects may be associated with differentiated commercial activity trajectories across surrounding suburban environments. More broadly, the study demonstrates how high-frequency mobility data can be used to examine spatiotemporal variation in commercial vitality around major industrial developments. Full article

The representativeness of point measurements in the atmospheric boundary layer is a fundamental challenge for interpreting observations and evaluating numerical models. In this study, we quantify the representativeness of near-surface wind measurements using a dense network of 13 meteorological towers from the Army Research Laboratory’s Meteorological Sensor Array. These towers are distributed over an approximately 3 × 3 km domain at the U.S. Department of Agriculture Jornada Experimental Range in southern New Mexico. The analyzed domain consists of relatively flat terrain within a broader region of more complex topography. Representativeness is assessed using pairwise differences between towers and deviations from the array mean. Spatial variability decreases with temporal averaging, with the largest reductions occurring between 1 and 10 min and diminishing improvements beyond 10–30 min. Wind measurements become progressively less similar with increasing separation distance, particularly at separations approaching 1 km. Representativeness errors are larger under unstable conditions due to enhanced turbulence and spatial variability, while stronger winds increase wind speed variability but enhance directional coherence. Deviations from domain-averaged conditions are comparable among towers, indicating that no single location is uniquely representative. These results quantify the extent to which temporal averaging, spatial separation, and atmospheric conditions influence representativeness, providing practical estimates of the associated spatial scales and residual errors. The results are useful for interpreting observations, evaluating models, and designing sampling strategies using fixed and mobile platforms, including Uncrewed Aircraft Systems. Full article

This study focuses on 91 human individuals from the Western Zhou period excavated from the Jucun cemetery in Jiang County, southern Shanxi Province, and examines their dietary structure and its changes within the context of population movements in early China. Stable carbon and nitrogen isotope analysis was employed, combined with archaeological phase divisions, to compare dietary patterns across different periods. The results show that the Jucun population exhibits a diet dominated by C₄ resources, with a mean δ¹³C value of −8.0 ± 0.7‰ and a mean δ¹⁵N value of 8.6 ± 0.9‰, indicating a relatively low level of animal protein intake. Diachronic analysis indicates that δ¹³C values remain generally stable throughout the Western Zhou period, whereas δ¹⁵N values show a decreasing trend. Regional comparison further shows that populations of different origins all fall within the isotopic range characterized by millet-based agriculture in southern Shanxi. Overall, the dietary structure of this population exhibits a convergence toward an agriculture-based pattern centered on millet. This study provides bioarchaeological evidence for subsistence transformation and cultural integration among mobile populations in the Central Plains during the Western Zhou period. Full article

Rift Valley fever virus (RVFV) and Crimean–Congo hemorrhagic fever virus (CCHFV) are endemic zoonotic pathogens in Senegal, transmitted by mosquitoes and ticks, respectively. Understanding the seasonal and spatial dynamics of their vectors is essential to improve targeted surveillance. This study investigated the abundance, diversity, and viral infection status of vector populations in a transboundary region (Matam) and a non-transboundary region (Thiès) over two seasons from September 2022 to March 2024. We collected mosquitoes using CO2-baited CDC light traps and sampled ticks directly from domestic small ruminants. A total of 6558 mosquitoes across 23 species and 1904 ticks representing seven species were morphologically identified. Mosquito abundance peaked significantly during the rainy season. Conversely, tick diversity increased during the dry season, with Hyalomma rufipes emerging as the predominant species. Crucially, RVFV was detected exclusively in Aedes vexans mosquito pools from the transboundary Matam region, emphasizing the epidemiological risk associated with cross-border livestock mobility. Viral RNA of CCHFV was detected in multiple tick species across both regions and seasons, confirming a sustained, multi-vector enzootic cycle. These findings demonstrate persistent RVFV and CCHFV circulation in Senegal and highlight the critical need for integrated, season-specific vector surveillance frameworks. Full article

Glaucoma, the leading cause of irreversible vision loss, often goes undetected in early stages due to its asymptomatic behaviour. Early diagnosis typically involves visual analysis of the optic disc (OD) in eye fundus images. Machine and deep learning techniques have emerged as valuable tools for automating this process; however, their integration into clinical practice still faces limitations. These challenges include the presence of image regions that are not directly related to glaucoma assessment, such as retinal vasculature, the macula, and background structures, which may introduce irrelevant information and negatively affect classification performance, as well as a general lack of transparency in the decision-making process. This article proposes a methodology that enhances both the accuracy and interpretability of glaucoma detection by focusing solely on the OD region. First, a metaheuristic-based strategy is employed for precise OD detection and cropping, generating an OD-centric dataset with glaucoma-labeled images, which is composed of different public datasets. Four convolutional neural networks (CNNs), namely VGG-19, MobileNet-V2, ResNet-50, and DenseNet-161, are trained on this dataset using transfer learning. To address the need for model explainability, Grad-CAM, Score-CAM, and Eigen-CAM are applied to the trained models to generate post hoc visual explanations of their predictions. The experimental results showed that DenseNet-161 achieved the best overall performance on the assembled public dataset, using an 80%-10%-10% training, validation, and testing split, with a test accuracy of 0.9369 and an AUC of 0.9831. By isolating the OD region and incorporating explainability techniques, the methodology provides a robust and interpretable second opinion, supporting more accurate and efficient glaucoma screening. Full article

The role of pedestrian movement in urban environments is often overlooked, despite its critical importance in supporting effective city functioning and long-term sustainability. While there has been growing scholarly interest in this area, research on pedestrian mobility remains fragmented across various disciplines and lacks a unified framework. For urban planners and designers to collaborate more effectively, a clearer understanding of the key themes shaping pedestrian mobility is needed. This paper addresses that gap by organizing and analysing existing research through a bibliometric review of 1934 articles published between 1994 and 2023 in the Web of Science database. This article explores the evolution of pedestrian mobility research between 1994 and 2023, highlighting key topics and potential future directions. The bibliometric analysis draws on a range of indicators, including published papers, citation data, journal impact factors, h-index scores, top-cited authors and papers, and regional trends in research output. Most importantly, science mapping was conducted using the SciMAT software, with co-occurrence networks helping to reveal how research themes have evolved over time. The extensive body of work on pedestrian mobility made it possible to develop a conceptual map that traces the field’s intellectual development. From this analysis, five key thematic areas were identified: health, methods, environmental–social, city, and mobility. Full article

This study examines the structural determinants of behavioral intention to use a City Airport Terminal (CAT) in Ulsan using a structural equation modeling (SEM) framework. Whereas prior literature has predominantly explained CAT adoption in terms of accessibility, this study investigates whether usage intention can be sufficiently explained by accessibility alone or whether it reflects a broader multi-factor structure involving service quality and safety, economic efficiency, infrastructure convenience, and perceived public value. To this end, five latent constructs were specified, and a survey of 500 Ulsan residents was conducted. The confirmatory factor analysis indicated an acceptable measurement structure for the five latent constructs. The structural model results show that perceived public value and regional development was the only construct with a statistically significant direct path to CAT usage intention, whereas the baseline accessibility-only model provided a statistically insufficient explanation. A nested model comparison further indicated that non-accessibility constructs collectively contributed additional explanatory value beyond what accessibility alone could provide. These findings suggest that CAT usage intention is not adequately explained by accessibility alone but is better understood through a multi-factor conceptualization of CAT adoption. This study contributes to the literature by providing structural evidence that public value—encompassing regional development expectations and community-level benefits—should be explicitly considered in sustainable airport infrastructure planning. The results highlight the importance of a multi-dimensional approach to CAT implementation policy, integrating service quality and safety, economic efficiency, infrastructure convenience, and community-level value perceptions alongside physical accessibility. From a sustainable mobility perspective, the findings offer useful implications for sustainable airport access planning and air transport management. Full article

Maintaining reliable communication while ensuring collision-free motion is a central challenge for mobile relay robots operating in indoor disaster environments, where abrupt non-line-of-sight (NLOS) degradation and narrow structural bottlenecks can severely disrupt multi-hop connectivity. To address this problem, this paper proposes a Gaussian Process-Driven Adaptive Tube Model Predictive Control (GP-ATMPC) framework for communication-preserving relay navigation. Gaussian process regression (GPR) is used to construct a probabilistic spatial radio map from sparse received signal strength indicator (RSSI) measurements, providing both the predicted channel mean and its uncertainty over unvisited regions. Motion uncertainty is represented by an adaptive ellipsoidal error tube whose radius varies with translational motion, angular motion, and localization uncertainty. Based on this tube model, both obstacle and communication constraints are tightened over the full closed-loop state tube via a tube-tightened lower confidence bound (LCB) that jointly accounts for radio-prediction and motion-tracking uncertainty. Across two indoor disaster environments and 50 Monte Carlo runs each, the proposed method attains the highest connectivity satisfaction rate among controllers that preserve a safe motion margin, with significantly fewer end-to-end connectivity violations than nominal and heuristic adaptive-margin MPC by a paired Wilcoxon test, while maintaining millisecond-level online solve times. A reactive connectivity-first baseline reaches slightly higher raw connectivity but at three to four times the near-collision rate and without feasibility or stability guarantees. The radio-prediction layer is further validated in a higher-fidelity Gazebo environment and on real indoor RSSI measurements, where it reconstructs the measured channel with a mean absolute error of about 2.1 dB. These results indicate that coupling spatial radio prediction with adaptive tube-based robust control provides an effective framework for resilient communication-aware relay navigation in degraded indoor environments. Full article

This article reconsiders the emergence of the “military martyr” figure in late antique Christianity, not through the hagiographical narratives alone, but along the axes of cult formation, ritual practice, relic circulation, and public space. Modern scholarship has tended to focus either on whether early Christians served in the Roman army or on the later development of military saint iconography. This study reframes the question, asking instead through what processes the military martyr became a distinct cultic category. At the center of the analysis are two key figures in the fourth-century preaching tradition of Cappadocia: Theodore the Recruit and the Forty Martyrs of Sebaste. The texts attributed to Basil of Caesarea and Gregory of Nyssa construct these figures not merely as witnesses of faith, but as agents who protect communities and intercede and whose sacred power circulates through relics, martyrial spaces, and liturgical practices. When epigraphic evidence, transitional spaces, networks of mobility and lodging, and early visual transformations are considered together, the “military martyr” emerges not as a fixed identity, but as a model of sanctity that intensifies across different regional contexts. Military identity thus becomes embedded within a late antique Christian discourse shaped around protection, mobility, and belonging. Full article

In recent years, shrubland is an important land cover type in alpine regions, while accurate segmentation of shrubs using remote sensing data remain challenging. To address these issues, this study proposes an alpine shrub segmentation method based on an improved lightweight DeepLabV3+ network, in which MobileNetV2 is used to replace the original backbone to reduce model complexity while maintaining feature representation capability, a channel squeeze-and-excitation (cSE) attention module is introduced to enhance the response to key shrub features and boundary details, and Ghost convolution is incorporated to reduce computational redundancy while preserving segmentation accuracy. Experimental results from both ablation and comparative studies demonstrate that the proposed model achieves a mean intersection over union (MIoU) of 88.47%, mean pixel accuracy (mPA) of 92.93%, F1-score of 91.80%, and overall accuracy of 94.52%, representing improvements of 3.53%, 2.64%, 2.96%, and 1.69%, respectively, over the original DeepLabV3+ model, while also significantly reducing the number of parameters and model size. In addition, independent cross-year validation using unmanned aerial vehicle (UAV) imagery acquired in 2025 suggests that the proposed model has good applicability under similar UAV sensor and acquisition conditions. Overall, this study provides an effective lightweight semantic segmentation approach for alpine shrub segmentation from high-resolution UAV imagery and offers useful technical support for vegetation monitoring in alpine regions such as the Qinghai–Tibet Plateau. Full article

Medical infrared thermography, which involves the use of infrared thermal cameras for the non-invasive assessment of skin surface temperature distribution, has gained increasing interest in recent years as a tool supporting diagnosis and treatment monitoring. The aim of this article is to present the historical background and critically reassess the current role of infrared thermography in medicine, with particular emphasis on standardization as a key determinant of its clinical utility. This Perspective highlights the fundamental impact of methodological variability on diagnostic performance and reproducibility. A structured framework for standardization is proposed, encompassing patient preparation, environmental conditions, device parameters and calibration, image acquisition protocols, region-of-interest definition and analysis, as well as reporting and clinical interpretation. The analysis demonstrates how inconsistencies at each of these levels reduce measurement reliability, limit inter-study comparability, and weaken clinical confidence in infrared thermography. The article also addresses the growing availability of mobile thermal imaging systems and their integration with artificial intelligence, while emphasizing the need for stronger evidence-based support across all methodological domains. The presented analysis suggests that, despite existing limitations, medical infrared thermography holds considerable potential as a supportive clinical tool. However, its broader clinical implementation remains limited by several factors, with the lack of standardized protocols constituting a major and practically addressable translational barrier. Wider adoption will require standardization efforts alongside rigorous validation studies and application-specific interpretative guidelines. Addressing these challenges through technological advances and coordinated international standardization may facilitate meaningful progress over the next decade. Full article

The humid coastal Lingnan region of South China, including the Chaoshan area of eastern Guangdong, is frequently exposed to disaster-prone weather conditions such as high humidity, typhoon-related winds, heavy rainfall, and salt-laden coastal air. These long-term environmental exposures may contribute to surface deterioration risks of architectural heritage. Located in Shantou, Yingchuan Shijia has shown five visible surface deterioration types—cracks, staining, saltpetering, plants, and spalling—under the combined influence of environmental exposure, material aging, previous disturbance, and insufficient maintenance. To address the limitations of manual inspection, this study explores a conservation-oriented intelligent workflow integrating YOLO-based detection, digital documentation, and screening-level conservation interpretation. Digital documentation used UAV imagery, mobile LiDAR scanning, measured drawings, and SketchUp-based three-dimensional modeling. The dataset was built in three stages: a 99-image preliminary dataset, where YOLOv8 showed only basic learning capability with low performance metrics, including Precision of 33.0 ± 3.0%, Recall of 28.0 ± 1.0%, mAP50 of 25.0 ± 1.0%, and mAP50-95 of 11.0 ± 1.0%; a 362-image non-augmented case-study dataset, where YOLOv8 still showed limited performance, with mAP50 of 20.0 ± 1.0% and mAP50-95 of 8.0 ± 1.0%; and a final YOLO-format case-study dataset of 2000 images after training-set-only augmentation using 11 geometric and photometric transformation methods. After augmentation, YOLOv8 mAP50 increased to 62.0 ± 2.0%. Under the same augmented-data condition, YOLOv13 showed Precision of 89.0 ± 1.0%, Recall of 77.0 ± 1.0%, mAP50 of 84.0 ± 1.0%, and mAP50-95 of 65.0 ± 1.0%, indicating relatively higher validation performance than YOLOv8. In the normalized confusion matrix, the background missed-detection values for cracks and saltpetering were 0.29 and 0.22, respectively, indicating that weak-feature and low-contrast deterioration types remained challenging. Based on YOLOv13, a mini program was developed to organize detection outputs and provide field-oriented preliminary conservation hints. Overall, this study provides a preliminary workflow linking digital collection, image-based deterioration detection, Grad-CAM visualization, and assisted field recording for the preventive conservation of Chaoshan overseas-Chinese residences in humid coastal heritage environments. Full article

Object detection in complex industrial environments is prone to being affected by insufficient dynamic weighting of local and global features, as well as illumination variations and impurities. Moreover, existing models suffer from excessive model complexity, which directly impairs computational efficiency. To more accurately distinguish Chinese herbal materials with diverse morphologies, this paper proposes the MobileAttn module. Drawing on the idea of token representation in the Transformer architecture, this module extracts contextual information through global feature compression, fuses it with tokens to generate a spatial attention map, and realizes dynamic recalibration of convolutional features. This process enhances the feature weights of key semantic regions, suppresses redundant background information, and improves feature discriminability. To address illumination interference, brightness-aware weights are combined with dual-path (channel and spatial) attention for global control, dynamically reducing the impact of illumination; this component is named LightAttn. When Chinese herbal materials contain common industrial unknown impurities (e.g., small stones and weeds), an impurity detection auxiliary module, a post-processing step independent of the main detection network, is proposed. This module refines Non-Maximum Suppression (NMS) logic to distinguish target Chinese herbal materials from interfering impurities. Subsequently, it accurately locates and marks impurities on the conveyor belt, thereby achieving effective unknown impurity detection. Experimental results demonstrate that, compared with the original YOLOv11 on the Chinese herbal materials detection task, the optimized model achieves a 1.7% improvement in the overall mean Average Precision (mAP@0.5:0.95). On a per-class basis, gains are particularly pronounced for certain challenging high-aspect-ratio Chinese herbal materials. Prunella vulgaris and orange peel achieve respective AP improvements of 5.8% and 4.1%. Meanwhile, the model parameter count is reduced by 23.1% and the computational complexity by 20.3%. The F1-Score of the impurity detection results is 86.38%, verifying the effectiveness of the impurity detection auxiliary module. Full article

This study presents a transient computational fluid dynamics (CFD) analysis of dielectric flow behaviour and debris transport in micro-EDM milling, considering the effects of dielectric properties, inter-electrode gap (IEG) size (20–30 µm), and tool rotational speed (400–850 rpm). Four dielectric media, nitrogen gas, deionized water, HEDMA111 EDM oil, and sunflower seed oil, were investigated using a two-dimensional FEM-based model coupled with particle tracking simulations to evaluate debris mobility within the machining region. The results demonstrate that dielectric properties, particularly viscosity, strongly influence hydrodynamic behaviour and particle transport within the IEG. Under the adopted equal mass flow rate condition, nitrogen gas exhibited the highest flow velocities and the fastest debris evacuation due to the combined effects of its low viscosity and the resulting higher inlet velocity. Deionized water and HEDMA111 oil exhibit comparable intermediate behaviour, indicating that moderate viscosity variations within liquid dielectrics do not significantly alter the overall flow regime. In contrast, sunflower seed oil generates the most damped flow conditions, with reduced velocities and prolonged particle residence due to increased viscous resistance. Variations in IEG size produce only minor changes in evacuation efficiency compared with the dominant influence of dielectric properties, while tool rotational speed primarily affects velocity magnitude without altering qualitative transport behaviour. Full article