Search Results (319)

Accurate extraction of impervious surface areas (ISA) is essential for urban environmental monitoring, yet severe spectral confusion among complex urban land-cover types limits the performance of classifications based solely on optical imagery. To address this issue within a localized context, this study proposes a multi-source framework integrating UAV-based LiDAR (UAV-LiDAR) and high-resolution visible imagery for fine-scale ISA extraction. An improved segmentation optimization strategy, termed EGS-Optimizer, is developed to enhance boundary delineation within the object-based image analysis (OBIA) framework by coupling edge detection with global segmentation quality evaluation. A comprehensive feature set including spectral, index, texture, geometric, and terrain features is constructed, and Shapley Additive Explanations (SHAP) is applied to select the most informative variables while reducing dimensionality. The proposed framework is validated in a typical 1.45 km² built-up area in Deyang City, Sichuan Province. Experimental results demonstrate that, within this specific study area, multi-source data fusion improves classification accuracy by 3.59–5.79% compared with single-source data, while feature selection reduces the feature dimension from 45 to 21. Among the evaluated classifiers, the random forest (RF) model achieves the highest performance, with an overall accuracy of 97.24% (Kappa = 0.96). While the high accuracy highlights the efficacy of synergizing spectral and structural information for micro-landscape mapping, these findings are constrained to the demonstrated fine-scale local environment. The results provide an effective, interpretable solution for detailed neighborhood-level ISA mapping, though further validation is required before the framework can be generalized to larger or more heterogeneous urban scenarios. Full article

This study examines how high-speed rail (HSR) hubs shape public space vitality in central-city station areas, using Shapingba Station (Chongqing, China) as a representative case of station–city integration. We delineated pedestrian catchments using Baidu Map walking isochrones (300–1200 s) and integrated multi-source data, including Public Space Public Life (PSPL) field observations (eight monitoring points, 07:00–24:00), Baidu heat maps, point-of-interest (POI) records, streetscape semantic segmentation, and a perception questionnaire. Indicators were synthesized via entropy weighting, and multivariate associations between perceived vitality and environmental variables were examined using Mantel tests. Pedestrian flow exhibits a clear double-peak pattern (09:00–11:00 and 15:00–16:00), averaging 42,248 pedestrians per day (2347 per hour) and showing strong spatial heterogeneity across monitoring points. POIs show a pronounced core–periphery structure: totals increase from 803 (300 s) to 4365 (600 s) and 7539 (1200 s), while overall density declines from 7477 to 2492 POIs/km², highlighting a 600 s core where accessibility and functional agglomeration are most strongly coupled. Overall, this study contributes a replicable multi-source evaluation framework and quantitative evidence on accessibility–function coupling and micro-scale design effects in HSR station areas, enabling theory-informed comparisons across station typologies and urban contexts. Full article

Noise in long highway tunnels and underground interchanges poses a significant environmental concern, affecting both drivers and nearby residents. This research develops an acoustic finite element model of a long tunnel in Leuven Measurement Systems (LMS) Virtual Lab to characterize the tunnel noise field, and the effectiveness of different noise mitigation measures was also evaluated and optimized accordingly. The model is validated against in situ monitoring data, with deviations controlled within 3 dB(A) and strong agreement confirmed by the Kappa consistency test. Both simulations and measurements show that sound pressure levels (SPLs) are generally highest near the tunnel center and lower toward the portal, exhibiting periodic fluctuations rather than a monotonic decrease. The dominant noise energy is concentrated between 125 Hz and 500 Hz. SPLs at 1.8 m above the road surface are noticeably higher than at 1.2 m and 1.5 m, indicating greater noise exposure for drivers of large vehicles compared with smaller vehicles. Noise reduction performance is further assessed for different lining materials and pavement types. Installing sound-absorbing panels in the tunnel midsection provides effective attenuation, with expanded perlite panels, single-layer metal micro-perforated panels, and FC quiet perforated panels (FC-PP) performing best, while porous asphalt shows superior noise reduction compared with conventional dense-graded asphalt pavements. Full article

Titanium alloys exhibit exceptional strength-to-density ratios, high hardness, and outstanding resistance to elevated temperatures, making them indispensable structural materials in aerospace engineering, marine construction, and biomedical applications. In aerospace systems specifically, fatigue failure represents the predominant failure mode for titanium alloy components. This review systematically examines prevalent surface treatment techniques for titanium alloys—including shot peening, ultrasonic rolling treatment, hot isostatic pressing (HIP), physical vapor deposition (PVD), micro-arc oxidation (MAO), and thermal spray processes—and critically evaluates their respective effects on fatigue performance. The underlying mechanisms of each technique are concisely outlined, with emphasis on stress state evolution, near-surface microstructural refinement, and interfacial integrity. Building upon the characteristic surface-dominated fatigue fracture behavior of titanium alloys, this work focuses on how coating composition, architecture (e.g., graded, multilayer, or nanocomposite designs), and interfacial bonding strength govern fatigue resistance. A unified analysis is presented on the distinct yet complementary roles of substrate deformation strengthening (e.g., residual compression, grain refinement) and coating-mediated protection (e.g., barrier function, crack deflection, stress redistribution) during fatigue crack initiation and propagation. Key determinants of fatigue performance, including residual stress distribution, coating/substrate adhesion, thermal mismatch, and environmental degradation susceptibility, are rigorously assessed. Finally, emerging research frontiers are identified, including intelligent process–structure–property mapping, in situ monitoring of fatigue damage at coated interfaces, and design of multifunctional gradient coatings that synergistically enhance strength, wear resistance, and fatigue endurance of titanium alloy components. Full article

The sustainable renewal of old residential communities faces increasing challenges in addressing the diverse environmental needs of older residents while respecting spatial constraints. Conventional approaches often treat older adults as a homogeneous group, overlooking how functional and social heterogeneity shape spatial perception. To address this gap, this study examines perceptual priorities in micro-public spaces of old residential communities in Qingdao, China, by classifying 60 community-dwelling older adults into four profiles using the Successful Aging framework. Participants performed free-viewing tasks using eye-tracking to observe 18 areas of interest (AOIs). Results reveal a clear perceptual hierarchy structured by individual profiles. Older adults with lower functional ability (Q3, Q4) allocate significant visual resources to safety-critical elements as a form of compensatory monitoring. Conversely, a systematic perceptual shift from survival-oriented assessment to quality-oriented evaluation was observed as functional and participatory reserves increased. High-participation groups (Q1, Q3) prioritized comfort facilities, while esthetic features attracted sustained attention primarily among the high-function/high-participation group (Q1). These findings provide empirical evidence for differentiated micro-renewal strategies that prioritize perceptual stress reduction and affordance enrichment in old residential communities. Full article

The growing demand for intelligent environmental monitoring is driving the advancement of high-performance, low-cost, and highly integrated gas sensors. Silicon-compatible semiconductor gas sensors provide a promising platform to achieve this goal by leveraging their compatibility with complementary metal–oxide semiconductor (CMOS) processes. The established mass-manufacturing capabilities of micro-electromechanical systems (MEMS) and the high sensitivity and signal amplification characteristics of field effect transistors (FETs) in recent years have made the development of next-generation sensing devices feasible. In this review, we systematically summarize the latest advances in silicon-compatible gas sensors, with a focus on MEMS and FET technologies. We discuss their sensing mechanisms and performance optimization strategies, and further highlight the evolution of gas sensor technology toward on-chip intelligent olfactory systems that integrate sensing, computing, and storage capabilities. Full article

This study introduces a portable piezoelectric thin-film microbalance platform that combines acoustic signal analysis with deep learning for point-of-care mass detection. The system employs a flexible polyvinylidene fluoride sensor, a smartphone for acoustic signal acquisition, and three deep learning models: convolutional neural network, long short-term memory network, and Transformer. Experimental findings indicate that the Transformer achieves the highest classification accuracy of 99.5%, outperforming the convolutional neural network at 96.9% and the long short-term memory network at 97.3%, attributed to its enhanced capability to capture long-range temporal dependencies. The platform facilitates real-time, label-free detection without the necessity for bulky instrumentation, providing a cost-effective and scalable solution for decentralized diagnostics. This research establishes a foundational framework for intelligent portable micro-mass sensing with significant potential applications in precision medicine, environmental monitoring, and personalized healthcare. Full article

Micro- and nanoplastics (MNPs) are being found, with growing frequency, in agroecosystems, where soils function as major sinks and direct interfaces with food crops. This review shows an integrated soil–plant–food analytical framework and synthesizes evidence on MNPs behavior in soils (dispersion, aging, aggregation), plant uptake pathways (root vs. foliar, including atmospheric deposition), tissue translocation, and plant physiological responses. Across crop species and exposure conditions, convergent patterns included oxidative stress, disruption of nutrient homeostasis, impaired photosynthesis, and growth penalties, with magnitude modulated by particle size, polymer type, and surface chemistry within specific soil–plant contexts. Occurrence of MNPs in edible tissues of leafy, root, and fruit vegetables is critically appraised, as well as its implications for food safety and potential dietary exposure. Key uncertainties persist, including heterogeneous analytical methods, scarce long-term field datasets, and limited alignment between laboratory doses and environmental concentrations. These constraints translate into priorities for exposure assessment and risk governance, including the need for standardized metrics, harmonized quality criteria, and field-scale monitoring aligned with agronomic practices. By re-centering the analysis on crops and food systems while acknowledging human exposure implications, the review provides a decision-oriented basis for research and mitigation. Full article

Listeria monocytogenes poses a significant risk in meat-processing plants, especially in micro and small businesses, where structural, organisational and operational limitations make it difficult to control. Although there is evidence of its environmental distribution and recurrence, this information does not always translate into clear operational criteria for risk management. To design an intervention framework for mitigating the risk associated with L. monocytogenes in micro and small meat-processing plants, based on the integration of previously published microbiological and operational evidence, the study integrated results on environmental distribution, recurrence of isolates and risk factors identified in eight plants. Functional prioritisation criteria were defined considering hygienic zoning, the function of sites in the process flow, proximity to the ready-to-eat product, and environmental conditions favourable to “persistence”. Differentiated risk scenarios and a functional hierarchy of priority intervention points were detected, prioritising site types recurrently associated with the presence of Listeria spp. and L. monocytogenes. Based on this hierarchy, the proposed intervention formulation aimed at prevention, control and environmental monitoring, adapted to the operating conditions of micro- and small-scale meat-processing plants. The proposed framework offers a transferable tool to support decisions in the management of L. monocytogenes risk in small-scale plants. Full article

Two major global trends shaping 21st-century society are population aging and urbanization. Consequently, the living conditions of older adults have become an increasing focus of societal attention. Social–Emotional Responses play a crucial role in the mental health, emotional well-being, and social identity of older adults. Urban streets, as key sites for walking and social activity among older adults, can be seen as extensions of their homes—places where they regularly interact with neighbors and build new connections. Compared to built environments often termed “gray spaces,” exposure to green spaces has been shown to offer greater benefits to residents’ well-being. Among streetscape features, the Spatial Openness Level is closely associated with the psychological well-being of elderly individuals. Visual-spatial features correlate with an EEG-derived proxy for emotional state during exposure to street scenes. The Gray-Green space Exposure Ratio (GER) and Spatial Openness Level (SOL) serve as key indicators for evaluating streetscape quality. Designing age-friendly streets requires evidence-based tools that link visual features to emotional well-being. This study provides such a tool by combining EEG measurements with configurational analysis of street visual dimensions: SOL and GER. In this study, conducted in Wuhan City, objective physiological monitoring of brainwave activity was employed to examine the responses of older adults to variations in GER and SOL. The results indicate that SOL significantly influences the emotional states of older adults (correlation coefficient R² = 0.7262, p < 0.01). The results indicate that the effect of GER on the emotional states of older adults was moderated by gender. Specifically, GER exerted a significant effect on the emotional states of females (correlation coefficient R² = 0.6262, p < 0.01), whereas no significant effect was observed in males (p > 0.01). These results allow us to rank the nine tested scenes. For example, Scene L-3 (open space with abundant vegetation) scored highest on emotional well-being, while Scene H-1 (enclosed gray space) scored lowest. The difference is explained by the configurational logic: greenery delivers emotional benefits only when combined with sufficient openness. The findings will enable EEG data to extend beyond serving as a unique standalone outcome and integrate into a more comprehensive explanatory model. This model aims to elucidate how urban morphology influences the micro-foundations of social activity in later life. Furthermore, it seeks to equip urban designers and policymakers with an evidence-based tool for creating age-friendly environments, facilitating a shift from intuition-driven to evidence-based design. Future research should incorporate additional environmental factors to establish a more comprehensive assessment framework for age-friendly urban spaces. Full article

Reliable and real-time environmental monitoring is essential for controlling pollution and protecting public health. However, conventional station-based measurements are expensive and often lack spatial and temporal resolution. This paper proposes a low-cost multimodal environmental monitoring system. Experiments verified that thin-film thermocouples exhibit near-linear voltage–temperature characteristics ($R^2>0.99$). Integration of the AI data pipeline substantially enhances monitoring accuracy: the proposed fusion strategy reduces relative error to approximately 2.3% under typical noise conditions, with a correlation coefficient of 0.79 between predicted and observed PM2.5 values. This research provides a scalable blueprint for edge-deployable environmental monitoring. A thin-film thermocouple with a fast response time is used as a temperature sensor and is statically calibrated against a K-type reference. To improve dynamic tracking and reduce measurement noise, a Kalman filter-based fusion strategy is employed, which is then compared with weighted averaging and Bayesian fusion. Simulation-driven validation is performed for thermocouple linearity, PID-based temperature control, micro-signal filtering and system-level latency and robustness. The results demonstrate that thin-film thermocouples exhibit near-linear voltage–temperature characteristics (R² > 0.99) with Seebeck coefficients ranging from 40.92 to 42.08 μV/°C, close to the theoretical K-type value of 42.87 μV/°C. The proposed fusion strategy reduces relative error to ~2.3% under typical noise conditions, enabling stable, real-time processing with near-second latency for 10,000-point batches. This study summarizes the design considerations for selecting and calibrating sensors and for achieving AI robustness in the presence of drift and faults. It provides a scalable blueprint for edge-deployable environmental monitoring. Full article

Remote sensing-based Earth observation provides essential spatial data for analyzing and monitoring both natural and urban environments. Precise characterization of objects in these scenes is vital for environmental management, land-use planning, and monitoring global change. Semantic segmentation of remote sensing imagery (RSI) is a fundamental yet complex task due to significant variability in object shape, scale, and distribution, as well as the complexity of multiscale landscapes captured by advanced sensors. Convolutional neural networks, especially the U-Net architecture, have achieved notable success in segmentation tasks. However, their application in remote sensing is often impeded by persistent issues such as loss of spatial detail, substantial intra- and inter-class variability, and high sensitivity to hyperparameter settings. Manual tuning of hyperparameters is typically inefficient and error-prone, which highlights the importance of heuristic methods for automated optimization. Genetic Algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are metaheuristics that provide systematic approaches for exploring large hyperparameter spaces. This study investigates an evolutionary framework for the automated optimization of four critical U-Net hyperparameters—learning rate, number of training epochs, optimizer, and loss function—using micro-evolutionary algorithms. Specifically, micro Genetic Algorithms (micro-GAs), micro Differential Evolution (micro-DE), and micro Particle Swarm Optimization (micro-PSO) are employed to efficiently explore the hyperparameter search space under reduced population settings. The experimental results demonstrate that the proposed micro-evolutionary optimization framework consistently enhances segmentation performance, achieving improvements in Mean Intersection over Union (MIoU) ranging from 3% to 35%, along with systematic gains in overall accuracy across different datasets and configurations. Full article

Recurrent massive strandings of pelagic Sargassum have severely impacted Caribbean and Gulf of Mexico coastlines over the past decade, generating major environmental, sanitary, and socioeconomic consequences. Accurate monitoring of Sargassum dynamics in nearshore waters remains challenging, as most existing satellite products rely on moderate-resolution sensors that inadequately resolve coastal processes. Here, we present a high-spatial- and -temporal-resolution Sargassum detection dataset derived from the VENµS (Vegetation and Environment New Micro-Satellite) mission, providing daily observations at 4 m resolution for five coastal zones in Guadeloupe, Martinique, and the Yucatán Peninsula over the 2022–2024 period. VENµS imagery consists of 12 multispectral bands, and the analysis specifically uses the red, the red-edge/near-infrared and the short-wave infrared bands. Detection is based on the Alternative Floating Algae Index (AFAI), combined with land and cloud masking, background estimation, and adaptive thresholding. We demonstrate the capability of this dataset to resolve fine-scale Sargassum raft dynamics, characterize the seasonal influx of Sargassum along the coastline, and assess exposure across different coastal typologies. By offering the highest combined spatial and temporal resolution currently available for these regions, this dataset provides a novel resource for coastal impact assessment, nearshore drift analysis, and validation of Sargassum transport and stranding models. Full article

The widespread application of glyphosate—the world’s most used herbicide—presents a significant environmental challenge due to its persistence and mobility within interconnected land–soil–water systems. This study addresses the limitations of traditional, discrete water monitoring by developing a predictive framework for glyphosate and its primary metabolite, aminomethylphosphonic acid (AMPA), in the agricultural context of Apulia, Southern Italy. The methodology integrates high-sensitivity analytical chemistry with advanced spatial intelligence. Water samples were analyzed using an optimized UHPLC–MS/MS framework with pre-column derivatization (FMOC-Cl), achieving an ultra-trace Limit of Quantification (LOQ) of 0.025 μg/L. To transition from point data to continuous spatial profiles, a hybrid Machine Learning (ML) architecture was implemented. The model utilized a suite of geospatial predictors, including land use (Corine Land Cover), Digital Elevation Models (DEMs), and slope characteristics extracted from river offset lines. A dual-modeling strategy was employed: Global Models (Random Forest, Gradient Boosting, and KNN) for regional trends and Individual Models for river segments exhibiting sufficient internal variability. Analytical findings (2018–2024) revealed that AMPA consistently exhibited higher mean concentrations than glyphosate, reaching peaks of 9.27 μg/L. This trend is primarily attributed to its superior environmental persistence and a half-life of up to 240 days, compared to the parent compound. Spatiotemporal analysis identified critical peaks in the second quarter for glyphosate and extreme surges in the fourth quarter for AMPA, particularly in the Cervaro basin. The Random Forest Regressor emerged as the most robust predictive tool, achieving a coefficient of determination (R²) of approximately 0.68 at the global scale and up to 0.75 for localized models where data density was sufficient. The integration of ML frameworks allows for the identification of contamination “micro-hotspots” and the mapping of probabilistic pollutant distribution along entire river reaches without additional sampling costs. This high-fidelity diagnostic tool provides a cost-effective strategy for environmental agencies to implement targeted mitigation and proactive water resource protection in Mediterranean agroecosystems. Full article

The transition from cycling to electric micro-mobility, such as e-scooters, introduces distinct safety risks. While physiological sensing is established for monitoring cyclist exertion, its transferability to high-vibration e-scooter environments remains unclear. This study systematically reviews wearable sensors used to detect stress, fatigue, and exertion in cycling and micro-mobility to identify gaps preventing active safety systems. A PRISMA-guided search of IEEE Xplore, Web of Science, PubMed, Scopus, and ScienceDirect was performed on 2 October 2025 for studies published in 2015–2025. From 273 records, 11 publications representing nine unique studies met the inclusion criteria. Laboratory studies ($n=4$) utilizing deep learning (CNN-LSTM) achieved high exertion prediction accuracy (F1 86.3–91.7%) but relied on a single redundant dataset ($N=27$), lacking independent validation. Field studies ($n=7$) relied on statistical associations between heart rate variability and environmental stress but lacked real-time predictive capabilities. Notably, evidence for automated physiological safety classification in e-scooters is critically underdeveloped. Current models are overfitted to cycling biomechanics and fail to account for e-scooter constraints, such as whole-body vibration. Future research must shift toward Unsupervised Domain Adaptation (UDA) and noise-resilient edge AI architectures to bridge the technological lag in micro-mobility safety. Full article