Search Results (12,582)

The cosmic microwave background (CMB) contains key information about the early Universe, particularly through its polarization structure. This work proposes a Fuzzy and Explainable Artificial Intelligence framework (FAS-XAI) for the regional analysis of CMB polarization using Planck SMICA data. From the Stokes components Q and U, the polarization amplitude P and the scalar polarization modes E and B are derived. Regional features are then extracted over a HEALPix grid, considering only polarization-valid regions defined by the Planck polarization mask. Fuzzy C-Means identifies four interpretable polarization regimes: high-polarization structured regions, E-dominated medium-polarization regions, B-enhanced medium-polarization regions, and low-polarization regions. An XGBoost-SHAP layer is used to explain the resulting fuzzy memberships. XGBoost accurately reproduces the memberships, with R² > 0.98 for all clusters, while SHAP confirms the physical relevance of amplitude-related features and the log(B/E) balance. Finally, controlled perturbations in P and log(B/E) reveal a globally robust fuzzy structure with localized sensitivity. The proposed framework provides an interpretable methodology for studying regional CMB polarization patterns and their stability under controlled perturbations. Full article

Snakeflies (Raphidioptera) are generally assumed to have the most gradual (and plesiomorphic) type of holometabolous metamorphosis, often including saproxylic larvae. Herein we investigate the diversity of snakeflies over time. We explore morphological details that have rarely been in focus of scientific studies such as the clavate organs of the legs. In total, 165 new immature snakefly specimens, mostly from 100 million-year-old (Cretaceous) Kachin amber, are reported. Combined with data from the literature, we assembled a dataset of 550 specimens, including immatures and adults from Cretaceous (over 200 immatures) and Eocene amber and from the extant fauna. From these, we extracted shape data of different body regions—ten subsets in total with over 2500 analysed shapes. Our analysis supports earlier observations (based on relative lengths) that snakefly larvae were much more diverse in their morphology in the past compared to their modern representatives. Furthermore, we recognise a strong morphological separation of modern larvae and adults (with pupae being intermediate), while in fossils the overlap of representatives of both life phases is quite strong. This supports earlier qualitative observations that the ontogeny of Cretaceous snakeflies was even more gradual (and likely plesiomorphic for Raphidioptera and presumably Holometabola) than in extant snakeflies. The analyses revealed that some Cretaceous and Eocene snakeflies had a slender head and prothorax morphology that is absent nowadays. This supports a difference between the modern and Eocene fauna. Additionally, a gap analysis was performed for the best-sampled subsets to explore morphological constraints in snakefly morphology. Full article

To address the difficulty of road-constrained weed extraction and operational assessment in orchard road regions under weed encroachment, background interference, and complex illumination, this study developed a vision-based framework integrating road segmentation, in-road weed extraction, spatial quantification, and workload evaluation. A joint image enhancement strategy combining LAB-based luminance correction, HSV-based color gain adjustment, ExG enhancement, and morphological refinement was first applied to improve the separability of green vegetation targets. An improved YOLOv11 with an SE attention mechanism was then used for robust orchard road segmentation. On this basis, road-region constraints and a dual-threshold HSV–ExG strategy were combined to extract in-road weeds and calculate global weed coverage. Furthermore, a geometry-adaptive grid based on actual road boundaries was constructed to quantify grid-cell coverage, aggregation, spatial heterogeneity, and workload index. Results showed that the proposed enhancement method increased the mean and standard deviation of ExG by 21.30% and 19.22%, respectively. The improved YOLOv11 achieved 91.28% precision, 87.52% recall, 93.37% AP₅₀, 68.31% mAP_@0.5:0.95, and 89.36% F1-score. Across five sample groups, global weed coverage ranged from 0.6123 to 0.6471, and the workload index ranged from 0.6403 to 0.6859. Overall, the proposed method establishes an integrated image-based analytical pipeline that may support future variable-rate weeding and decision-making after further operational validation. Full article

Automated mapping of vertical traffic signs and horizontal road markings is essential for road safety and Intelligent Transportation Systems (ITS). Traditional methods are labor-intensive, while existing automated solutions often lack a unified approach or are proprietary, limiting research accessibility and reproducibility. This paper presents a comprehensive framework for identifying these assets using a low-cost, vehicle-mounted action camera. A distance-aware frame extraction strategy is introduced to minimize data redundancy and ensure high spatial diversity. Specific strategies address the class imbalance inherent in real-world driving, ensuring robust detection for infrequent sign categories. Deep learning models handle the distinct geometries of vertical and horizontal assets, employing segmentation-based annotation for irregular road markings. Experimental results show high performance, with leading YOLO-based architectures achieving an F1-score of 0.92 for vertical signage and 0.96 for horizontal markings. By transforming raw visual data into structured georeferenced information, this framework facilitates the generation of High-Definition (HD) maps and digital inventories, supporting road authorities in proactive maintenance planning and regional road safety assessments. Full article

Between 1 July and 30 November 2022, four spatially adjacent shallow M_W ≥ 5.7 earthquakes successively struck the Hormozgan province in southern Iran. This earthquake sequence offers a vital opportunity to clarify the subsurface seismogenic structure and rupture evolution in the eastern segment of the Zagros Fold-and-Thrust Belt (ZFTB). In the paper, we apply multi-temporal archived SAR images from the Sentinel-1 satellite to extract the high-precision coseismic surface deformation covering the July and November earthquake events, respectively, and further investigate the related seismogenic fault structure and slip distribution. Geodetic inversion results reveal that the cumulative coseismic slip of the three M_W ≥ 5.7 earthquakes in July is distributed at a downdip depth of 5.5 to 8 km on a SW-dipping thrust seismogenic fault plane, while the coseismic slip of the November M_W 5.7 earthquake is concentrated in the shallow downdip range of 1.5 to 6 km on the same fault, finely characterizing a partially overlapping depth-segmented rupture. According to a joint analysis of the regional topography and geomorphology, active fault distribution, and coseismic inversions, we conclude that this earthquake sequence nucleated on a secondary blind back-thrust fault of the Zagros Frontal Fault (ZFF). Coseismic Coulomb stress changes reveal that the July earthquake sequence triggered the occurrence of the November earthquake and that the shallow eastern segment of the Mountain Frontal Fault (MFF) and the eastern segment of the ZFF exhibit significant stress loading, indicating a high risk of future rupture. Full article

Accurate extraction of transmission towers from Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) point clouds is a prerequisite for overhead transmission line (OTL) acceptance. This task remains challenging because tower points are heavily entangled with ground, vegetation, conductors, and insulators, especially in complex terrain. To address this issue, we propose a shape prior-guided coarse-to-fine framework for tower extraction from UAV LiDAR point clouds. First, candidate tower regions are localized from the scene point cloud through preprocessing, near-ground suppression, and density-based clustering. Second, the least-disturbed central body of each candidate tower is identified in a slice-wise manner and used to estimate the tower orientation and four principal structural axes. Third, side-view and front-view structural envelopes are progressively inferred to suppress non-tower points around the tower body and tower head. Finally, a base-constrained filtering strategy is introduced to remove residual ground and low-vegetation points within the tower footprint. Experiments conducted on multiple OTL datasets acquired in different regions of China, including plains and mountainous areas, demonstrate that the proposed method achieves robust and efficient tower extraction across diverse scenarios. The results indicate that explicit structural priors offer a promising complement to feature-driven and data-intensive approaches, particularly in scenarios with limited annotated data and strict real-time requirements. The proposed method processes scene point clouds containing tens to hundreds of millions of points, with an average extraction time of approximately 100 to 300 s per tower depending on scene density. Full article

With the rapid growth of urban motorization, personalized travel modes, including taxis and private cars, have expanded considerably. However, conventional public transportation systems, constrained by fixed routes and limited service flexibility, often struggle to satisfy residents’ increasingly diversified and high-quality commuting needs. To address this issue, this study proposes an integrated planning framework for customized bus services using taxi trajectory data. First, passenger origin–destination (OD) information is extracted by detecting changes in the taxi passenger-status field. The extracted OD records are then used to identify potential commuting demand by jointly considering peak-hour travel characteristics and regional OD stability. Second, the identified potential commuting demand is used to generate candidate boarding and alighting stops through an improved DBSCAN-based clustering method, namely IDK-SG. For route planning among the candidate stops, a bi-objective optimization model is developed to simultaneously account for passenger travel-time costs and bus operating costs, and the model is solved using a genetic algorithm. Finally, timetable optimization is formulated as a Markov decision process and solved using a Deep Q-Network (DQN) algorithm. Case studies using taxi GPS trajectory data from Chongqing demonstrate that the proposed framework can effectively identify stable commuting demand, optimize stop layouts and route schemes, and improve vehicle occupancy and service quality. These findings provide practical decision-making support for the operation and dynamic scheduling of customized bus services in urban peak-hour commuting corridors. Full article

Reconstructing building models from urban SAR tomography (TomoSAR) point clouds is often constrained by limited resolution, low positioning accuracy in elevation, as well as data incompleteness and artifacts caused by microwave imaging mechanisms. These challenges seriously restrict the extraction of high-accuracy building models with structural details from TomoSAR point clouds. This paper proposes a refined urban building modeling method that effectively utilizes structural priors, including directionality, orthogonality, and potential symmetry. First, a piecewise fitting strategy integrated with density-based segmentation is employed to iteratively estimate the main directions of the buildings and capture finer geometric variations of complex façade footprints than simple-plane approximations. Second, a roof extraction algorithm combining an adaptive Doug-las–Peucker approach with symmetry evaluation and constraints is developed to regularize roof outlines and repair data defects. Crucially, to handle extreme cases where roof data are entirely missing, a novel building width estimation method based on building shadow analysis is proposed. Experiments conducted on the SARMV3D-1.0 and SARMV3D-3.0 point cloud datasets demonstrate that the proposed method significantly enhances reconstruction accuracy and geometric fidelity in urban regions compared to state-of-the-art approaches. Full article

Diagnosing tilapia diseases in complex aquaculture environments is severely hindered by noisy backgrounds and limited high-quality pathological data. To overcome these bottlenecks, this study presents a two-stage diagnostic framework integrating an enhanced Segment Anything Model (SAM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the first stage, SAM is augmented with a Convolutional Block Attention Module (CBAM) feature adapter and a Region Proposal Network (RPN)-based prompt encoder. This design enables the automated and precise extraction of irregular disease lesions by self-generating spatial prompts, thereby isolating water background noise. In the second stage, clinical color features extracted from the lesion masks are classified using ANFIS. To optimize performance on small-scale datasets, ANFIS parameters are trained via Particle Swarm Optimization (PSO) under a numerically stable One-vs-Rest (OvR) binary cross-entropy loss. Validated on the public dataset “Enhancing Disease Detection in Nile Tilapia”, our method delivers an average segmentation Dice coefficient of 86.2% and a classification accuracy of 93.5%. This hybrid approach demonstrates strong potential as a foundational baseline for the automated monitoring of aquaculture diseases. Full article

Background: Suicide is a major mental health concern, particularly among university students facing unique stressors. Understanding their attitudes toward suicide is essential for effective prevention, yet the existing literature lacks a systematic review on this population. This review synthesises and evaluates the literature on attitudes toward suicide among university students. Methods: A systematic search was conducted on four databases (MEDLINE, Web of Science, Scopus, and PubMed) using Medical Subject Headings terms and keywords identified from previous studies. The search, conducted in February 2024, included studies published between 2014 and 2024. One researcher screened the titles and abstracts, while two independent researchers extracted the data. Twenty-one articles (N participants = 13,424) were selected for further assessment. Quantitative designs were the most common (n = 18), followed by qualitative (n = 2) and mixed-method designs (n = 1), spanning multiple regions. Themes were derived by organising findings into thematic categories based on recurring patterns across the studies. Results: Two core themes emerged: Factors associated with attitudes toward suicide and their associations with suicide outcomes. Conclusions: The review underscores the need for culturally sensitive approaches to address negative attitudes toward suicide and promote help-seeking among university students, highlighting the importance of further research in this area. Full article

Water scarcity in semi-arid regions threatens seed germination and early crop establishment, driving the development of biodegradable Nature-based Solutions to replace synthetic plastic mulches. Porous cellulose membranes were fabricated from rice husk (RH), banana pseudostem (BP), and sugarcane bagasse (SB) by thermo-chemical extraction and high-shear homogenization (n = 5 replicates per membrane type). Membranes were characterized by ATR-FTIR and scanning electron microscopy, confirming removal of non-cellulosic components and biogenic silica preservation in RH, and revealing biomass-dependent porous architectures linked to mechanical and transport behavior. RH produced the most compact fibrillar matrix (compressive strength: 8.16 ± 0.24 MPa; WVT: 170 ± 60 g m⁻² day⁻¹), BP an open interconnected network with superior deformability (9.83 ± 0.25% elongation) and moisture transport (WVT: 400 ± 100 g m⁻² day⁻¹), and SB the highest moisture-retention capacity (215.7 ± 15.8%). Germination assays with Brassica oleracea var. botrytis under water stress showed SB achieved the highest germination rate (90.5 ± 0.99%), confirming that sustained moisture availability governs germination more decisively than transport rate alone. Soil burial tests confirmed biodegradable behavior across all membranes (R² ≥ 0.995; k = 0.043–0.046 day⁻¹). These findings establish a hydrogen-bond-mediated structure–property–function framework for designing biomass-specific cellulose membranes as biodegradable solutions for water-limited agricultural systems. Full article

Hyperspectral remote sensing enables precise identification of alteration mineral through spectral–image integration and high-resolution capabilities. However, vegetation interference significantly hinders the extraction of alteration information in vegetated areas, thereby posing challenges to the reliable identification of alteration minerals. This study employs GF-5B satellite AHSI imagery acquired in the Huzhou region of Zhejiang Province, China, to address this challenge via a novel Zonal Adaptive Vegetation Suppression Technique (ZAVST). By constructing segmented statistical models that links reflectance characteristics across multiple spectral bands to NDVI values, ZAVST demonstrates an enhanced capability to mitigate vegetation obscuration effects on subsurface lithological features while substantially improving the identification of subtle spectral signatures characteristic of mineralization. Results reveal distinct spatial patterns: Fe-bearing alteration minerals (hematite, pyrite) align along NE-trending faults and volcanic basin margins; Al-OH alterations (montmorillonite, kaolinite) cluster near intrusive contacts; Mg-OH alterations (chlorite, epidote) occur at interfaces between carbonate sequences and concealed intrusions. Composite alteration anomalies exhibiting stacked mineral signatures (up to four distinct types) were identified across the region, demonstrating a strong spatial correlation with known mineralization centers. By integrating alteration zonation, structural lineaments, stratigraphy, geochemical anomalies, and orebody records, this study delineated four priority targets: Lijiaxiang Town, eastern Meixi Town, Miaoxi Town, and the central Moganshan Volcanic Basin. Full article

The Amazon Basin and its rivers play a vital role in regional biodiversity, the carbon cycle, and socio-economic security. Through erosion and deposition, river planforms change over time, affecting local infrastructure, food security, and changes to ecosystems. Long-term monitoring is essential for observing these dynamics. Synthetic Aperture Radar (SAR) provides a method to consistently map river planform dynamics across large areas because it is largely independent of atmospheric conditions. This study presents an approach for deriving river planform metrics across the entire Amazon Basin using Sentinel-1 C-band SAR data. This approach followed three main steps: water mask generation, validation of the data and river metrics extraction. Sentinel-1 imagery from 2017 to 2025 was composited into quarterly mean images, after which Otsu thresholding was applied to derive water classifications. Additional post-processing steps were applied to reduce terrain- and seasonal effects. The final water masks were divided into water-change classes, validated using stratified sampling and achieved an overall accuracy of 98.5%. Quarterly river planform metrics, including sinuosity, mean channel width and migration rate, were derived using channel centerline extraction, but due to a lack of in situ validation data the river metric values have not been validated. The resulting time series provide insights into how river planform changes across all Amazon sub-basins from 2017 to 2025 can be monitored using SAR-based methods. The results reveal spatial differences in river dynamics between tributaries, mostly depending on flow pattern, up- or downstream path and location in the upper, middle or lower Amazon Basin. These findings demonstrate the potential of SAR time series for monitoring large-scale river planform dynamics. Full article

Saltwater intrusion (SI) threatens groundwater sustainability in nearshore regions, particularly in Bangladesh, where over-extraction and sea-level rise accelerate aquifer salinization. Accurate prediction of SI dynamics is therefore critical for effective groundwater management. This study developed and evaluated several deep learning and hybrid models, including CNN, DNN, LSTM, CNN–DNN, CNN–LSTM, DNN–LSTM, and CNN–DNN–LSTM, to predict SI in a nearshore aquifer system. Predictor–response datasets were generated using the three-dimensional density-dependent flow and solute transport model FEMWATER. This study presents the first comprehensive benchmarking of standalone and hybrid CNN–DNN–LSTM models for SI prediction in a Bangladesh nearshore aquifer, supported by CRITIC–EDAS-based model ranking. Model performance was assessed using RMSE, MAE, MAD, R, IOA, a-20, NRMSE, along with CRITIC weighting and EDAS ranking. Results indicate that hybrid models integrating LSTM outperformed standalone models. The CNN–LSTM model achieved the best performance at OW1 (RMSE = 1.57 mg/L, MAE = 1.26 mg/L, R = 0.99, IOA = 0.99). The DNN–LSTM model performed best at OW2 (RMSE = 2.87 mg/L, IOA = 0.98, R = 0.97) and OW3 (RMSE = 1.95 mg/L, IOA = 0.99, R = 0.99). In contrast, the DNN model showed poor performance, while the CNN model demonstrated moderate performance and the LSTM model underperformed. Overall, the hybrid CNN–LSTM and DNN–LSTM models demonstrated superior accuracy and robustness for reliable SI prediction and sustainable groundwater management. Full article

The efficiency of airport resource allocation is improved through the establishment of a scientific multi-granularity configuration scheme for flight slot coordination parameters. In this study, a collaborative configuration method for hourly and 15 min coordination parameters is proposed, with Beijing Capital International Airport serving as a case study. Short-term traffic clusters are frequently omitted by traditional hourly parameters, thereby leading to sudden delay surges. First, local delays were extracted from March 2024 Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data. Subsequently, a delay prediction model was constructed through the integration of a non-stationary queuing model and a gradient boosting regression tree. Second, simulated timetables were generated via a Monte Carlo method under various parameter combinations. With a constant daily flight volume utilized as the experimental baseline, a mapping relationship was established between parameter combinations and expected local delays. Finally, feasible delay regions were delineated and interpretable configuration rules were extracted via a decision tree to maximize schedule flexibility. It was indicated by the results that at an hourly parameter of 70 flights, the target delay is maintained below 8 min by tightening the 15 min parameter to 19 flights. The findings suggest that average load is controlled by hourly parameters, while traffic clustering in high-load scenarios is effectively suppressed by 15 min parameters. A quantitative reference is provided by this method for the configuration of multi-granularity time parameters at hub airports. Full article