Search Results (6,113)

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision fault diagnosis. A fixed-threshold wavelet denoising method with the scene-specific pre-optimized parameter a (0 < a ≤ 1.3) is proposed: the parameter a is determined via offline grid search using the feature retention rate (FRR) as the objective function for typical wind farm operating scenarios. A multi-scale depthwise separable CNN (MS-DSCNN) captures multi-scale spatial features via 3 × 1 and 5 × 1 kernels, reducing computational complexity by 73.4% versus standard CNNs. An attention-based minimal peephole LSTM (AttMPLSTM) enhances temporal feature measurement, using minimal peephole connections for long-term dependencies and channel attention to weight fault-relevant signals. Joint L1–L2 regularization mitigates overfitting and environmental interference, improving model robustness. Validated on a WT test bench, the Adams simulation dataset, and the CWRU benchmark, the model achieves a 90.2 ± 1.4% feature retention rate (FRR) in signal processing, an over 98% F1-score for fault classification, and over 99% accuracy. With 2.5 s single-epoch training and a 12.8 ± 0.5 ms single-sample inference time, the reduced parameters enable real-time deployment in embedded systems, advancing signal processing for rotating machinery fault diagnosis. Full article

With accelerating urbanisation, extreme rainfall events have become increasingly frequent, leading to rising urban flooding risks that threaten city operation and infrastructure safety. The rapid expansion of impervious surfaces reduces infiltration capacity and accelerates runoff responses, making cities more vulnerable to short-duration, high-intensity storms. Although the SWMM is widely used for urban stormwater simulation, its application is often constrained by the lack of detailed drainage network data, such as pipe diameters, slopes, and node connectivity. To address this limitation, this study focuses on the main built-up area within the Second Ring Expressway of Chengdu, Sichuan Province, in southwestern China. As a regional core city, Chengdu frequently experiences intense short-duration rainfall during the rainy season, and the coexistence of rapid urbanisation with ageing drainage infrastructure further elevates flood risk. Accordingly, a technical framework of “open road data substitution–automated modelling–SWMM-based assessment” is proposed. Leveraging the spatial correspondence between road layouts and drainage pathways, open road data are used to construct a virtual drainage system. Combined with DEM and land-use data, Python-based automation enables sub-catchment delineation, parameter extraction, and network topology generation, achieving efficient large-scale modelling. Design storms of multiple return periods are generated based on Chengdu’s revised rainfall intensity formula, while socioeconomic indicators such as population density and infrastructure exposure are normalised and weighted using the entropy method to develop a comprehensive flood-risk assessment. Results indicate that the virtual drainage network effectively compensates for missing pipe data at the macro scale, and high-risk zones are mainly concentrated in densely populated and highly urbanised older districts. Overall, the proposed method successfully captures urban flood-risk patterns under data-scarce conditions and provides a practical approach for large-city flood-risk management. Full article

The intensifying pressures of urbanization and climate change on coastal zones necessitate a holistic understanding of the interplay between human activity and ecological integrity for sustainable development. However, prevailing methods for assessing coastal vibrancy often overlook direct measures of human presence and fail to quantitatively capture its complex relationship with ecological vulnerability. To address these gaps, this study develops a novel multi-dimensional assessment framework for Coastal Landscape Vibrancy (CLV) and empirically examines its interaction with ecological vulnerability factors in Beihai, China. Moving beyond built-environment-centric approaches, our framework integrates the ‘Crowd’ dimension, directly quantified using Baidu Heat Index data, with the ‘Place’ dimension, characterized by urban features, natural attributes, and visual experience. Principal Component Analysis (PCA) was employed to objectively weight these indicators and construct a composite CLV index. We then applied multiple linear regression to analyze the influence of ecological factors constructed based on the Sensitivity-Resilience-Pressure (SRP) model. The results revealed that vibrancy was highly concentrated in urban cores and exhibited significant spatiotemporal variations. Regression analysis revealed that while ecological quality factors like green coverage (β = 0.236, p < 0.001) positively influenced vibrancy, anthropogenic stressors such as slope (β = −0.457, p < 0.001) and the impervious surface index (β = −0.092, p < 0.001) had significant negative impacts, highlighting a critical trade-off between human activity and ecological conditions. The findings provide a quantitative, evidence-based foundation for spatial planning, demonstrating that sustainable coastal vibrancy is achieved through a balanced integration of human activity and ecological conservation, rather than through unchecked development. This framework offers critical insights for formulating strategies that simultaneously enhance ecological resilience and optimize human service facilities. Full article

Assessment of water resource spatial equilibrium (WRSE) is crucial for regional sustainable development, yet traditional methods always face difficulties in quantifying systemic differences and resolving their internal uncertainties. Accordingly, this study proposes a novel multi-attribute decision-making (MADM) model that integrates symmetric interaction entropy (SIE) with connection numbers (CNs) within a variable-weight framework. Firstly, information differences between alternatives and an ideal state were quantified by SIE, then these differences were decomposed into certain and uncertain components through the “identity–difference–opposition” (IDO) idea of CNs. In addition, a variable-weight mechanism was incorporated to enhance the model’s adaptability to regional characteristics. Applied to evaluate the WRSE in the Beijing–Tianjin–Hebei (BTH) region from 2014 to 2023, the model reveals that Hebei maintains the most favorable equilibrium state, with a partial identity potential or equal potential, followed by Beijing, while Tianjin predominantly exhibits partial opposite potential due to pronounced conflicts between its resource endowment and industrial structure. The proposed model not only enhances the sensitivity and interpretability of evaluation results but also facilitates the identification of key vulnerable indicators, thereby providing a scientific basis for formulating differentiated regional water governance strategies. Full article

Geodiversity assessment has become an important tool for understanding the spatial heterogeneity of abiotic elements and supporting conservation and land-use planning in protected areas. This study presents a comprehensive geodiversity assessment of the Mount Cangshan Global Geopark in Dali, Yunnan Province, China. The primary objective was to develop a quantitative geodiversity evaluation model based on spatial density metrics, addressing existing gaps in subjective and non-reproducible assessment methods. The study integrates four key dimensions of geodiversity: geological units, structural geomorphology, hydrogeology, and soils and land cover. By employing a hybrid AHP-CRITIC method to assign both subjective and objective weights to indicators, the study computes the Geodiversity Index (GDI) to quantify and map geodiversity across the geopark. Results show significant spatial heterogeneity, with high-geodiversity areas concentrated in the central and northern regions, primarily driven by tectonic and geological complexity and glacial, fluvial, and hydrological processes. The results indicate that the GDI can be used as a reliable tool for geosite delineation, heritage management, and sustainable tourism development. The findings provide a framework for geodiversity assessment and support landscape-level land-use zoning, conservation prioritization and sustainable land management in mountain geoparks. Full article

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems. Full article

Deploying high-precision deep learning models on resource-constrained edge devices remains a challenge for agricultural disease detection. This study introduces CropHealthyNet, a lightweight hybrid architecture optimized for both accuracy and computational efficiency. The architecture incorporates three key components: the ExGhostConv module, which integrates FReLU and SimAM attention for enhanced feature utilization; a Universal Position Encoding mechanism that adaptively captures spatial information to address variable lesion scales; and a MemoryEfficientTransformer employing chunked attention to mitigate global modeling memory overhead. Experiments on CDC, AGD_256, and CornLeafDisease datasets indicate that CropHealthyNet achieves a weighted average accuracy of 90.55% with 0.47 million parameters. The model outperforms several state-of-the-art lightweight architectures and achieves accuracy comparable to DenseNet121, with approximately 15 times fewer parameters. These results position CropHealthyNet as a viable solution for real-world deployment in resource-limited agricultural environments. Full article

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

As heritage museums shift toward more experience-oriented development, fragmented layouts and discontinuous visitor flows can reduce both spatial efficiency and the coherence of on-site experience. This study proposes an immersive experience-centred evaluation framework for exhibition layout in heritage museums, intended to translate experience goals into practical and diagnosable criteria for spatial optimization. An indicator system was refined through two rounds of Delphi consultation with an interdisciplinary expert panel, resulting in a hierarchical framework comprising five dimensions and multiple indicators. To support intervention prioritization in design and operations, weights were derived using the Group Analytic Hierarchy Process (GAHP), with Aggregation of Individual Judgments (AIJs) and consistency checks applied to control group judgement quality. A CV–entropy procedure was further used to support prioritization at the third-indicator level. Importance–Performance Analysis (IPA) was then employed to convert “importance–fit” assessments into an actionable sequence of optimization priorities. The results indicate that narrative and scene design carries the greatest weight (0.2877), followed by circulation and spatial organization (0.2281), sensory experience and atmosphere (0.1981), authenticity and sense of place (0.1644), and interactivity and participation (0.1217), suggesting that a “narrative–circulation–atmosphere” chain forms the core support for immersive layout design. A feasibility application using the Yinxu Museum demonstrates the framework’s value for benchmarking and diagnosis, helping decision-makers enhance visitor experience while respecting conservation constraints and more precisely target spatial investment priorities. Full article

Existing studies on urban public space vitality predominantly focus on single temporal scales or macro-urban levels, lacking a systematic understanding of day–night and weekday–weekend differentiation patterns at the meso-scale. This study examines 149 public spaces in the Yuexiu District, Guangzhou, employing Baidu heatmap data and the geographically weighted random forest (GWRF) model to analyze built environment impacts across four temporal scenarios. The SHAP interaction analysis is incorporated to quantitatively evaluate factor interdependencies and their temporal variations. Findings reveal significant spatiotemporal heterogeneity. Building density shows greater night-time importance while residential density exhibits enhanced daytime importance, particularly on weekend. Weekday–weekend comparison demonstrates contrasting spatial reorganization patterns, with weekday showing divergence and weekend showing convergence in factor importance distributions. The factor interaction analysis highlights stable synergistic relationships between density and diversity, alongside temporal transitions in density–residential density interactions from competitive to synergistic during night-time. Low-vitality public spaces are concentrated in peripheral areas with high building density but insufficient commercial facilities and functional mix. These findings deepen our understanding of the spatiotemporal mechanisms underlying public space vitality generation and the interaction effects among built environment factors, thereby providing an empirical foundation for the formulation of temporally adaptive planning strategies. Full article

Accurately quantifying the ecological functions of small and micro green spaces in high density urban environments supports urban ecological planning and management. This study assessed 271 pocket parks in the main urban area of Fuzhou, China, using multi-source remote sensing data from the growing seasons of 2019 to 2024. Six indicators were derived, including NDVI, NPP, WET, NDBSI, ISI, and LST. A composite Eco-environmental Index (EEI) was constructed using the entropy weight method. We combined the coefficient of variation, Theil–Sen slope estimation, the Mann–Kendall test, and the Hurst exponent to quantify spatial heterogeneity, interannual stability, and short-term persistence. We also examined climatic associations using correlation analysis. Pocket parks consistently outperformed their surrounding 500 m buffers across all indicators, and park buffer contrasts increased for most indicators. The mean EEI significantly increased from 0.563 in 2019 to 0.650 in 2024, with a pronounced step increase around 2022. At the site level, 261 of 271 parks (96.3%) exhibited an upward trend in EEI, indicating widespread ecological improvement. Specifically, park vegetation greenness (NDVI) rose from 0.413 to 0.578, widening the gap with surrounding areas. Parks consistently maintained a lower land surface temperature (LST) than their buffers, with a cooling magnitude ranging from 3.5 °C to 4.6 °C. Precipitation was positively associated with NDVI and NPP, while LST was positively associated with air temperature and negatively associated with precipitation. These findings support the planning and adaptive management of pocket parks to strengthen urban ecological resilience. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

Tau proteins are microtubule-associated proteins that regulate axonal structure, dynamics, and transport, and their dysregulation underlies several neurodegenerative diseases. The MAPT gene produces multiple tau isoforms through alternative splicing, including the high-molecular-weight isoform known as Big tau, which contains an insert of the large 4a exon of approximately 250 amino acids. Big tau is predominantly expressed in neurons of the peripheral nervous system (PNS), cranial motor nuclei, and select neurons of the central nervous system (CNS) such as the cerebellum and brainstem. Developmental expression studies indicate a switch from low-molecular-weight isoforms of tau to Big tau during axonal maturation, suggesting that Big tau optimizes cytoskeletal dynamics to accommodate long axonal projections. Comparative sequence and biophysical analyses show that the exon-4a insert is highly acidic, intrinsically disordered, and evolutionarily conserved in its length but not its primary sequence, implying a structural role. Emerging modeling and in vitro assays suggest that the extended projection domain provided by the exon-4a insert spatially and electrostatically shields the aggregation-prone PHF6 and PHF6* motifs in tau’s microtubule-binding domain, thereby reducing β-sheet driven aggregation. This mechanism may explain why tauopathies that involve aggregation of tau have little effect on the PNS and specific regions of the CNS such as the cerebellum, where Big tau predominates. Transcriptomic and proteomic data further suggest that alternative Big tau variants, including 4a-L, are expressed in certain cancerous tissues, indicating broader roles in cytoskeletal remodeling beyond neurons. Despite its putative anti-aggregation properties, the physiological regulation, interaction partners, and in vivo mechanisms of Big tau remain poorly defined. This review summarizes what is known about Big tau and what is missing toward a better understanding of how expansion via inclusion of exon 4a modifies tau’s structural and functional properties. Our purpose is to inspire future studies that could lead to novel therapeutic strategies to mitigate tau aggregation in neurodegenerative diseases. Full article

This paper presents a novel deep learning framework for traffic crash prediction that leverages graph-based representations to model complex interactions among road users. At its core is a dynamic Graph Attention Network (GAT), which abstracts road users and their interactions as evolving nodes and edges in a spatiotemporal graph. Each node represents an individual road user, characterized by its state as features, such as location and velocity. A node-wise Long Short-Term Memory (LSTM) network is employed to capture the temporal evolution of these features. Edges are dynamically constructed based on spatial and temporal proximity, existing only when distance and time thresholds are met for modeling interaction relevance. The GAT learns attention-weighted representations of these dynamic interactions, which are subsequently used by a classifier to predict the risk of a crash. Experimental results demonstrate that the proposed GAT-based method achieves 86.1% prediction accuracy, highlighting its effectiveness for proactive collision risk assessment and its potential to inform real-time warning systems and preventive safety interventions. Full article