Search Results (48,871)

Urban green spaces increasingly serve as sites of human–tick contact, yet long-term data on tick activity and host associations in urban recreational areas remain limited. This study investigated the seasonal activity patterns of Ixodes ricinus and Dermacentor reticulatus and the host spectrum of juvenile tick stages in an urban park in eastern Poland over a five-year period (2015–2019). Questing ticks were collected from vegetation using the flagging method, while small mammals were live-trapped to assess tick infestation of juvenile stages. The effects of air temperature, relative humidity, and seasonality on tick activity were analysed using generalized additive models (GAMs). D. reticulatus was the dominant tick species throughout the study, exhibiting pronounced autumn activity peaks, whereas I. ricinus occurred at lower densities with peak activity in late spring and early summer. GAM analyses revealed that apparent temperature effects observed in uncorrected models disappeared after accounting for seasonality, while seasonal timing remained a strong and consistent predictor of tick activity across species, developmental stages, and sexes. Juvenile ticks of both species were most frequently associated with Apodemus agrarius, indicating that urban-adapted rodent hosts play a key role in sustaining tick life cycles in simplified urban ecosystems. These findings demonstrate that urban recreational areas can function as persistent foci of tick hazard, with tick activity driven primarily by intrinsic seasonal dynamics rather than short-term weather variation. Full article

Cricket fast bowlers rely on the front leg as a mechanical lever during front foot contact, yet the underlying mechanisms that govern front leg behaviour remain unclear. This study examined front leg mechanics in 18 junior fast bowlers (17.2 ± 1.7 years) using a 14-camera 3D motion capture system and force platforms. Joint power and angular impulse analyses were performed to quantify hip and knee extension–flexion mechanics from front foot contact to ball release, enabling the classification of joint function as active (concentric), controlled (eccentric), or negligible. Power and angular impulse profiles revealed that front leg motion was dominated by controlled (eccentric) power at both the hip and knee, indicating that the regulation of knee angle occurred primarily through eccentric braking rather than concentric quadriceps extension. These findings suggest that achieving a “braced leg” position via isolated knee extensor strengthening may be ineffective. To evaluate whether kinematics and anthropometry contributed to performance, a multiple linear regression model was used. Run-up speed at back foot contact emerged as the strongest predictor of ball speed, whereas knee angle at front foot contact showed only a small and non-significant effect. Overall, the results indicate that front leg behaviour reflects coordinated whole-body dynamics, and performance interventions should prioritise momentum generation and timing across the kinetic chain rather than isolated joint actions. Full article

The development of urban underground spaces has led to an increasing number of projects involving super-large-diameter shield tunnels, making research on their impact on existing structures particularly significant. This paper investigated the numerical simulation on deformation and damage mechanism of existing underground structures induced by adjacent construction of super-large-diameter tunnels. A 3D finite element model using ABAQUS (version 2022) software incorporating the Concrete Damaged Plasticity (CDP) constitutive model was established, and this paper was used to systematically analyze the deformation, internal force response, and damage evolution of existing tunnels. The results showed the following: (1) The double-line tunnel excavation intensified settlement superposition, increasing the maximum settlement from −19.70 mm (single-line) to −24.51 mm (double-line) and transforming the settlement trough from a V shape to a W shape. (2) The vertical bending moment evolved from a single peak to double peaks being the dominant loading mode, with the maximum horizontal moment only about 1/8 of the vertical value. (3) During the construction, the peak tensile stress at the tunnel bottom reached 2.655 MPa, exceeding the C50 concrete tensile strength, but later decreased to 2.097 MPa. Damage was primarily caused by bending-induced tension. (4) Tunnel damage was triggered by the historical peak stress and accumulated irreversibly, resulting in a final state of low-stress and high-damage, with a maximum tensile damage of 92.4%. This research can provide a theoretical basis for safety control in similar adjacent engineering projects. Full article

The 2022 global mpox outbreak represented a turning point in the Monkeypox virus (MPXV) epidemiology, highlighting the incredible capability of this virus to adapt to different conditions, also in a non-endemic context. To investigate the genomic dynamics of MPXV 2022 strains, we performed whole-genome sequencing of 40 clinical samples from 16 Italian patients across multiple anatomical sites and timepoints between May and December 2022. Combining single-nucleotide analysis with detailed investigation of short tandem repeats (STRs), we explored inter- and intra-host viral dynamics. We identified 19 STR loci located near or within genes involved in immune modulation and virion morphogenesis. While most STRs remained stable across patients, a subset displayed locus- or matrix-specific variation. Among these, STR-VII—embedded within the coding sequence of OPG153, an envelope-associated protein implicated in viral attachment—showed a 12-nucleotide in-frame deletion, resulting in the loss of four aspartic acid residues (Δ4D variant). Structural modeling indicated that this deletion slightly alters a disordered acidic loop without affecting the global fold, potentially modulating surface charge and immune recognition. Integrating STR profiling into genomic surveillance may enhance resolution in outbreak reconstruction and reveal subtle adaptive processes underlying poxvirus–host interaction and immune escape. Full article

Rock mechanical parameters can provide fundamental data for the numerical simulation of hydraulic fracturing, aiding in the construction of hydraulic fracturing models. Due to the laminated nature of shale, constructing a hydraulic fracturing model requires obtaining the rock mechanical parameters of each lamina and the bedding planes. However, acquiring the mechanical parameters of individual shale laminas through physical experiments demands that, after rock mechanics testing, cracks propagate along the centre of the laminae without connecting additional bedding planes, which imposes extremely high requirements on shale samples. Current research on the rock mechanics of the Minfeng subsag shale is relatively limited. Therefore, to obtain the rock mechanical parameters of each lamina and the bedding planes in the Minfeng subsag shale, a numerical simulation approach can be employed. The model, built using PFC2D, is based on prior X-ray diffraction (XRD) analysis, conventional thin-section observation, scanning electron microscopy (SEM), Brazilian splitting tests, and triaxial compression tests. It replicates the processes of the Brazilian splitting and triaxial compression experiments, assigning initial parameters to different bedding planes based on lithology. A trial-and-error method is then used to adjust the parameters until the simulated curves match the physical experimental curves, with errors within 10%. The model parameters for each lamina at this stage are then applied to single-lithology Brazilian splitting, biaxial compression, and three-point bending models for simulation, ultimately obtaining the tensile strength, uniaxial compressive strength, Poisson’s ratio, Young’s modulus, brittleness index, and Mode I fracture toughness for each lamina. Simulation results show that the Minfeng subsag shale exhibits strong heterogeneity, with all obtained rock mechanical parameters spanning a wide range. Calculated brittleness indices for each lamina mostly fall within the “good” and “medium” ranges, with carbonate laminae generally demonstrating better brittleness than felsic laminae. Fracture toughness also clearly divides into two ranges: mixed carbonate shale laminae have overall higher fracture toughness than mixed felsic laminae. Full article

This study constructs a time-dependent model to predict the nighttime suspended sediment concentration near Wenzhou based on the convolutional neural network U-Net, which integrates the high-resolution Delft3D (version 4.03.01) hydrodynamic model and GOCI satellite observation data. The model’s prediction accuracy is significantly improved by replacing the original tide level with the tide level variation and increasing the temporal resolution of the flow field to 15 min via sensitivity analysis of the model’s input parameters. The validation results show that the model can maintain high consistency with GOCI observations in short-term prediction, with a structural similarity index (SSIM) of 0.82. For multi-hour continuous nighttime predictions, while quantitative uncertainty increases with the forecast horizon, the model successfully captures the spatial evolution patterns and maintains stable structural characteristics. The model effectively provides missing remote sensing nighttime observations as well as a new method for full-cycle prediction of nearshore SSC. Full article

Background/Objectives: Studying the role of UV-induced metabolic changes in skin physiology, and especially skin diseases, has gained importance in both medicine and cosmetics. With the development of new technologies, a variety of approaches have been implemented to model these metabolic effects. In this study, we explore the reproducibility of the UVA1-induced metabolic changes observed in different in vitro, ex vivo, and in vivo systems with escalating complexity. Our aim is to elaborate on the role of experimental setups in the reliable representation of in vivo data in other systems. Methods: Metabolic profiles post UVA1 treatment were assessed in skin cell culture, skin explants, and intact skin. For cell culture and explants, the metabolites from the culture medium were assessed via 1D-CPMG NMR. Intact skin samples were collected via microdialysis and the resulting dialysate was measured with GC–TOF-MS. Results: Data show that, despite great metabolic variations between the systems, several metabolites, such as glutamic acid, succinic acid, and threonine, change in a similar manner across multiple systems after UVA1 irradiation, including in vivo settings. Some metabolites, like phenylalanine, citric acid, and pyruvic acid, show similar UVA-mediated metabolic patterns between corresponding in vitro and ex vivo systems, but do not overlap well with in vivo data. Conclusions: Our findings emphasize the need for a metabolite-by-metabolite approach when deciding on the proper experimental system to perform UV irradiation experiments with regard to cutaneous physiology. Full article

Accurate estimation of Leaf Area Index (LAI) is essential for monitoring vegetation structure and ecosystem services in urban and peri-urban environments, particularly in small, heterogeneous patches typical of semi-arid cities. This study presents a comparative assessment of four empirical LAI estimation methods—NDVI-based, NDVI-advanced, SAVI-based, and EVI-based methods—applied to atmospherically corrected Sentinel-2 Level-2A imagery (10 m spatial resolution) over a 0.045 km² urban–agricultural polygon in the Tashkent region, Uzbekistan. Multi-temporal observations acquired during the 2023 growing season (June–August) were used to examine intra-seasonal vegetation dynamics. In the absence of field-measured LAI, a Random Forest regression model was implemented as an inter-method consistency analysis to assess agreement among index-derived LAI estimates rather than to perform external validation. Statistical comparisons revealed highly systematic and practically significant differences between methods, with the EVI-based approach producing the highest and most dynamically responsive LAI values (mean LAI = 1.453) and demonstrating greater robustness to soil background and atmospheric effects. Mean LAI increased by 66.7% from June to August, reflecting irrigation-driven crop phenology in the semi-arid study area. While the results indicate that EVI provides the most reliable relative LAI estimates for small urban–agricultural patches, the absence of ground-truth data and the influence of mixed pixels at 10 m resolution remain key limitations. This study offers a transferable methodological framework for comparative LAI assessment in data-scarce urban environments and provides a basis for future integration with field measurements, higher-resolution imagery, and LiDAR-based 3D vegetation models. Full article

Carburizing process optimization requires accurate prediction of multiple interrelated outcomes, yet existing models either oversimplify the physics or require prohibitively large datasets. Here, we present a physics-informed machine learning (PIML) cascade model for vacuum carburizing of 20Cr2Ni4 gear steel that predicts surface carbon content, maximum hardness, and effective case depth through a three-stage sequential architecture. The model integrates Fick’s diffusion law and empirical carbon–hardness relationships with ensemble learning using physics-derived features to reduce data requirements while maintaining interpretability. Validation against experimental data yields coefficient of determination values of 0.968 (surface carbon, RMSE = 0.0023 wt%), 0.963 (maximum hardness, RMSE = 1.27 HV), and 0.999 (case depth, RMSE = 0.0053 mm) on physics-augmented test data; leave-one-out cross-validation (LOOCV) on original experimental data yields $R^2$ = 0.87–0.95, representing true generalization capability. Feature importance analysis reveals that physics-derived features collectively account for over 70% of the prediction power, with the characteristic diffusion length ($\sqrt{Dt}$) contributing 42.2%, followed by temperature-related features (22.4%) and time-related features (14.8%). Compared to pure physics-based and data-driven approaches, the proposed framework achieves superior accuracy for case depth prediction while preserving physical consistency. The methodology demonstrates potential for adaptation to other vacuum-carburizing applications with similar Cr-Ni steel compositions, although extension to fundamentally different processes (e.g., gas carburizing and nitriding) would require process-specific recalibration. Full article

We present FingerType, a one-handed text input method based on thumb-to-finger gestures. FingerType detects tap events from 3D hand data using a Temporal Convolutional Network (TCN) and decodes the tap sequence into words with an n-gram language model. To inform the design, we examined thumb-to-finger interactions and collected comfort ratings of finger regions. We used these results to design an improved T9-style key layout. Our system runs at 72 frames per second and reaches 94.97% accuracy for tap detection. We conducted a six-block user study with 24 participants and compared FingerType with controller input and touch input. Entry speed increased from 5.88 WPM in the first practice block to 10.63 WPM in the final block. FingerType also supported more eyes-free typing: attention on the display panel within $\pm {15}^{\circ }$ of head-gaze was 84.41%, higher than touch input (69.47%). Finally, we report error patterns and WPM learning curves, and a model-based analysis suggests improving gesture recognition accuracy could further increase speed and narrow the gap to traditional VR input methods. Full article

Monocular depth estimation is one of the key tasks in autonomous driving, which derives depth information of the scene from a single image. And it is a fundamental component for vehicle decision-making and perception. However, approaches currently face challenges such as visual artifacts, scale ambiguity and occlusion handling. These limitations lead to suboptimal performance in complex environments, reducing model efficiency and generalization and hindering their broader use in autonomous driving and other applications. To solve these challenges, this paper introduces a Neural Radiance Field (NeRF)-based monocular depth estimation method for autonomous driving. It introduces a Gaussian probability-based ray sampling strategy to effectively solve the problem of massive sampling points in large complex scenes and reduce computational costs. To improve generalization, a lightweight spherical network incorporating a fine-grained adaptive channel attention mechanism is designed to capture detailed pixel-level features. These features are subsequently mapped to 3D spatial sampling locations, resulting in diverse and expressive point representations for improving the generalizability of the NeRF model. Our approach exhibits remarkable performance on the KITTI benchmark, surpassing traditional methods in depth estimation tasks. This work contributes significant technical advancements for practical monocular depth estimation in autonomous driving applications. Full article

Accurate segmentation of ischemic stroke lesions from multimodal magnetic resonance imaging (MRI) is fundamental for quantitative assessment, treatment planning, and outcome prediction; yet, it remains challenging due to highly heterogeneous lesion morphology, low lesion–background contrast, and substantial variability across scanners and protocols. This work introduces Tri-UNetX-2D, a large-kernel and scale-aware 2D convolutional network with explicit boundary refinement for automated ischemic stroke lesion segmentation from DWI, ADC, and FLAIR MRI. The architecture is built on a compact U-shaped encoder–decoder backbone and integrates three key components: first, a Large-Kernel Inception (LKI) module that employs factorized depthwise separable convolutions and dilation to emulate very large receptive fields, enabling efficient long-range context modeling; second, a Scale-Aware Fusion (SAF) unit that learns adaptive weights to fuse encoder and decoder features, dynamically balancing coarse semantic context and fine structural detail; and third, a Boundary Refinement Head (BRH) that provides explicit contour supervision to sharpen lesion borders and reduce boundary error. Squeeze-and-Excitation (SE) attention is embedded within LKI and decoder stages to recalibrate channel responses and emphasize modality-relevant cues, such as DWI-dominant acute core and FLAIR-dominant subacute changes. On the ISLES 2022 multi-center benchmark, Tri-UNetX-2D improves Dice Similarity Coefficient from 0.78 to 0.86, reduces the 95th-percentile Hausdorff distance from 12.4 mm to 8.3 mm, and increases the lesion-wise F1-score from 0.71 to 0.81 compared with a plain 2D U-Net trained under identical conditions. These results demonstrate that the proposed framework achieves competitive performance with substantially lower complexity than typical 3D or ensemble-based models, highlighting its potential for scalable, clinically deployable stroke lesion segmentation. Full article

Optimizing photovoltaic (PV) installations requires precise understanding of the annual energy yield, which depends heavily on geographical location, panel technology, tilt, and azimuth. This study establishes the framework for a “European Photovoltaic Atlas”. In this pilot phase, the dynamic tool is applied to representative European climatic zones to compare diverse latitudes and technologies. Consequently, we aim to create a robust database and interactive visualization tool that allows users to analyze technology-specific yields based on variable orientation parameters. The study employs a large-scale simulation campaign using EnergyPlus coupled with a PVWatts model. Two photovoltaic technologies (PERC and TOPCon monocrystalline) have been simulated in seven European reference cities: Naples, Madrid, Berlin, Paris, London, Stockholm, and Warsaw. For each city and technology, simulations have been performed for a complete grid of orientations. The tilt was varied from 0° to 90° in 5° increments, and the azimuth was varied from 0° to 360° in 5° increments. All panels have been simulated at a height of 15 m to represent typical rooftop installations. The main result is a comprehensive database that links location, technology, tilt, azimuth, and normalized annual energy yield. This database feeds an interactive application developed in Python. This tool generates 2D heatmaps showing the surface orientation factor of any selected city–technology pair, 3D surface plots comparing performance across multiple technologies or locations simultaneously, and 2D charts estimating hourly annual productivity by varying technology efficiency values. The “Photovoltaic Atlas” serves as a practical decision support tool for architects and engineers by enabling the rapid optimization of photovoltaic systems and clearly illustrating performance in the European context. Full article

Water-scarce river basins face the dual challenge of sustaining development progress while maintaining water resources carrying capacity (WRCC), yet city-scale evidence remains limited on how New Quality Productive Force (NQPF)-driven high-quality development reshapes WRCC through coupled coordination and development–pressure decoupling processes. Using a balanced panel of 15 cities in the Weihe River Basin (WRB) during 2014–2023, an integrated analytical framework was implemented by combining composite index evaluation (WRCC and the high-quality development index (HQDI)), the Coupling Coordination Degree (CCD) model, Tapio decoupling diagnosis between HQDI and total water use (TWU), and logarithmic mean Divisia index (LMDI) decomposition. The results indicate that: (1) both the HQD index and WRCC exhibited sustained growth, with their CCD improving significantly from mild imbalance to primary coordination, while a distinct spatial pattern of “Guanzhong leading, northern Shaanxi improving, and eastern Gansu stabilizing” emerged; (2) the HQDI–WRCC linkage was further supported by pooled statistical tests and a two-way fixed effects specification with city-clustered robust standard errors, confirming a significant positive association (Pearson = 0.517, p < 0.01; Spearman = 0.183, p < 0.05) and a stable positive effect of HQDI on WRCC (β = 0.194, p = 0.0088); (3) Tapio results reveal an overall transition from earlier volatility toward a later-period regime dominated by Weak Decoupling (WD) and Strong Decoupling (SD), implying that development progress became less dependent on rising TWU, although pronounced inter-city heterogeneity persisted; (4) LMDI decomposition further identified water use intensity and industrial structure as primary inhibitors of water consumption, whereas the R&D scale effect increased nearly 60-fold, emerging as a major driver of water demand. This study provides a mechanistic basis for coordinating ecological protection and high-quality development under rigid water constraints in water-scarce basins. Full article

Depression represents a major global health challenge, yet traditional clinical diagnosis faces limitations, including high costs, limited coverage, and low patient willingness. Social media platforms provide new opportunities for early depression screening through user-generated content. However, existing methods often lack systematic integration of clinical knowledge and fail to leverage multi-modal information comprehensively. We propose a DSM-5-guided methodology that systematically maps clinical diagnostic criteria to computable social media features across three modalities: textual semantics (BERT-based deep semantic extraction), behavioral patterns (temporal activity analysis), and topic distributions (LDA-based cognitive bias identification). We design a hierarchical architecture integrating BERT, Bi-LSTM, hierarchical attention, and multi-task learning to capture both character-level and post-level importance while jointly optimizing depression classification, symptom recognition, and severity assessment. Experiments on the WU3D dataset (32,570 users, 2.19 million posts) demonstrate that our model achieves 91.8% F1-score, significantly outperforming baseline methods (BERT: 85.6%, TextCNN: 78.6%, and SVM: 72.1%) and large language models (GPT-4 few-shot: 86.9%). Ablation studies confirm that each component contributes meaningfully with synergistic effects. The model provides interpretable predictions through attention visualization and outputs fine-grained symptom assessments aligned with DSM-5 criteria. With low computational cost (~50 ms inference time), local deployability, and superior privacy protection, our approach offers significant practical value for large-scale mental health screening applications. This work demonstrates that domain-specialized methods with explicit clinical knowledge integration remain highly competitive in the era of general-purpose large language models. Full article