Search Results (2,088)

The vortex throw, similar to the javelin throw, requires exceptional mastery of technique and specific motor abilities to ensure success. This study examines the anthropometric and motoric status of young female track-and-field athletes and investigates their relationship with vortex throwing performance. This research included 63 young female athletes; the results of 14 motor tests, three anthropometric measures, and training experience were compared with vortex throwing distance and vortex release velocity. Pearson’s correlation analysis revealed that the most valuable strong correlation was between the release velocity and the throwing distance (r > 0.75), indicating that they almost equally contributed to throwing performance. The most valuable moderate correlations were those between the leg tapping test, the overhead medicine ball throw, and the chest medicine ball launch and the performance of both forms of throwing. Accounting for shared variance among predictors, multivariable models explained 43% of the variance in vortex release velocity and 58% in vortex throwing distance, with the standing long jump uniquely predicting release velocity and the overhead 1-kg medicine ball throw uniquely predicting throwing distance. Finally, the motor abilities recognized in athletes in this research are valuable indicators of quality throwing performance and could play a crucial role in throwing success, which supports previous evidence on similar topics. Collectively, these results support using release velocity alongside distance to evaluate youth vortex throwers and highlight simple field tests (leg tapping, medicine ball throws, and long jumps) as practical markers for training prescription and early talent identification. Full article

This study investigates stress detection in dairy cattle by integrating circadian rhythm analysis and deep learning. Behavioral biomarkers, including feeding, resting, and rumination, were continuously monitored using Nedap CowControl sensors over a 12-month period to capture seasonal variability. Circadian features were extracted using the Fast Fourier Transform (FFT), and deviations from expected 24 h patterns were quantified using Euclidean distance. These features were used to train a Long Short-Term Memory (LSTM) neural network to classify stress into three levels: normal, mild, and high. Expert veterinary observations of anomalous behaviors and environmental records were used to validate stress labeling. We continuously monitored 10 lactating Holstein cows for 365 days, yielding 87,600 raw hours and 3650 cow-days (one day per cow as the analytical unit). The Short-Time Fourier Transform (STFT, 36 h window, 1 h step) was used solely to derive daily circadian characteristics (amplitude, phase, coherence); STFT windows are not statistical samples. A 60 min window prior to stress onset was incorporated to anticipate stress conditions triggered by management practices and environmental stressors, such as vaccination, animal handling, and cold stress. The proposed LSTM model achieved an accuracy of 82.3% and an AUC of 0.847, outperforming a benchmark logistic regression model (65% accuracy). This predictive capability, with a one-hour lead time, provides a critical window for preventive interventions and represents a practical tool for precision livestock farming and animal welfare monitoring. Full article

Long-runout landslides usually cause a significant loss of life and property because of their hypermobility and immense energy to travel long distances at high velocities, attracting a global focus on the dynamics and mechanism of long-runout landslides. In the past few decades, a great number of past studies on long-runout landslides have seen a surge in a range of innovative ideas and vigorous debates contributing to the advancement of understanding the dynamics and mechanism of the hypermobility of long-runout landslides. As a consequence, a review of the dynamics of long-runout landslides has been conducted by comprehensively and systematically summarizing the data and achievements of long-runout landslides over the past few decades in terms of the phenomenon and characteristics, mobility, dynamic process, dynamic mechanism, and models of long-runout landslides. This review would be of great significance in providing a comprehensive reference in understanding the dynamics of long-runout landslides. Full article

To accurately assess the development potential of a high-speed rail (HSR) express in the logistics system, this study constructs a forecasting method for HSR express volume. Grey relational analysis is used to identify key influencing factors, and a multiple regression model is established to predict intercity express volume. A generalized cost model for road, HSR, and air express is developed, considering infrastructure availability and delivery timeliness. Cost differences between supply and demand sides are analyzed, and a Logit model is applied to quantify mode share, deriving HSR express volume. A gravity model allocates the volume between cities. The method is validated in Henan Province, China. Results show that: (1) Intercity express volume in China will continue growing over the next decade, with HSR forming a stable share, and Henan playing a significant role as a central hub; (2) Suppliers prefer HSR for medium-to-long distances with lower timeliness demands, while consumers prefer it for shorter, time-sensitive deliveries; (3) Lower consumer prices significantly increase HSR mode share, urging suppliers to balance cost and infrastructure investment. This method supports HSR express forecasting and promotes sustainable logistics. Full article

This article develops a method for detecting malware based on the multi-scale recurrent architecture (time-aware multi-scale LSTM) with salience gating, multi-headed attention, and a sequential statistical change detector (CUSUM) integration. The research aim is to create an algorithm capable of effectively detecting malicious activities in behavioural data streams of executable files with minimal delay and ensuring interpretability of the results for subsequent use in forensic audit and cyber defence systems. To implement the task, deep learning methods (training LSTM models with dynamic consideration of time intervals and adaptive attention mechanisms) and sequence statistical analysis (CUSUM, Kulback–Leibler divergence, and Wasserstein distances), as well as regularisation approaches to improve the model stability and explainability, were used. Experimental evaluation demonstrates the proposed approaches’ high efficiency, with the neural network model achieving competitive indicators of accuracy, recall, and classification balance with a low level of false positives and an acceptable detection delay. Attention and salience profile analysis confirmed the possibility of interpreting signals and early detection of abnormal events, which reduces the experts’ workload and reduces the number of false positives. This study introduces the new hybrid architecture development that combines the advantages of recurrent and statistical methods, the theoretical properties formalisation of gated cells for long-term memory, and the proposal of a practical approach to the model solutions’ explainability. The developed method implementation, implemented in the specialised software product form, is shown in a forensic audit. Full article

Reinforced soil retaining walls (RSWs) for railways are key subgrade structures that bear cyclic loads from trains, and their long-term durability directly affects railway operation safety. The mechanical behavior of RSWs under cyclic loading has been extensively investigated in previous studies, primarily focusing on seismic conditions or conventional structural configurations. While these works have established fundamental understanding of load transfer mechanisms and deformation patterns, research on their responses to long-term train-induced vibrations, particularly for concrete-block-panel-wrapped RSWs, an improved structure based on traditional concrete-block-panel RSWs, remains limited. To investigate the dynamic responses of the concrete-block-panel-wrapped RSW, a model test was conducted under cyclic loading conditions where the amplitude was 30 kPa and the frequency was 10 Hz. The model size was 3.0 m in length, 1.0 m in width, and 1.8 m in height, incorporating six layers of geogrid. Each layer of geogrid was 2.0 m in length with a vertical spacing of 0.3 m or 0.15 m. The results indicate that as the number of load cycles increases, deformation, acceleration, static and dynamic stresses, and geogrid strain also increase and gradually stabilize, exhibiting only marginal increments thereafter. The maximum horizontal displacement reaches 0.08% of the wall height (H), with horizontal displacement increasing uniformly along the height of the wall. The vertical acceleration in the non-reinforced soil zone is lower than that in the reinforced soil zone. The horizontal dynamic stress acting on the back of the panel remains minimal and is uniformly distributed along the height of the wall. The maximum geogrid strain was found to be 0.88%, corresponding to a tensile stress amounting to 20.33% of its ultimate tensile strength. The predicted failure surface approximates a bilinear configuration, consisting of one line parallel to the wall face at a distance of 0.3H from the back of the soil bags and another line inclined at an angle equal to the soil’s internal friction angle (φ) relative to the horizontal plane. This study has important reference significance for the application of concrete-block-panel-wrapped RSWs in railways. Full article

In order to study the impact on the shock effect when a high-speed train passes over a concrete-filled steel tube (CFST) tied-arch bridge, a dynamic load test was carried out in the background of the Qinjiang River Bridge in Qinzhou, Guangxi Province, to test the bridge displacements, accelerations, and dynamic stresses. The bridge finite element model was coupled with a CRH2 train model developed in SIMPACK to perform ANSYS–SIMPACK co-simulation of vehicle–bridge interactions. Model reliability was verified by comparing simulated results with field measurements under matched operating conditions. On this basis, a parametric study was conducted for single-line operation with a mainline spacing of 4.2–5.4 m (0.4 m increments) and train speeds of 80–270 km/h (10 km/h increments), yielding 80 working conditions to evaluate hanger impact responses. The results indicate that the ANSYS–SIMPACK co-simulation provides reliable predictions. Compared with long hangers, short hangers exhibit larger stress impact coefficients. As train speed increases, the hanger impact effect shows a wavelike increasing trend. When the speed approaches 180–200 km/h, the excitation nears the bridge’s dominant natural frequency, and impact effects on bridge components peak, identifying a critical speed range that is more prone to inducing vehicle–bridge resonance; the impact coefficient of the shock effect on both sides of the train is different: the coefficient on the far side of the bridge is about 2 times of that on the near side of the bridge, so when the impact coefficient is regulated, the unevenness of the impact of the shock effect on both sides can be taken into account. Single-line operation can introduce a lateral load bias on the train, and the distance of the train from the center line is positively correlated with the impact size of the shock effect, with the stress impact coefficient of the shock effect on both sides of the bridge and span deflection increasing as the spacing of the main line increases. Full article

Extracellular vesicles (EVs) are bilayer-membrane cellular components that deliver protected cargo to the extracellular environment and can mediate long-distance signaling. We have previously reported that EVs isolated from the virulent fungal pathogen Paracoccidioides brasiliensis Vpb18 can revert the expression, in the attenuated variant Apb18, of stress-related virulence traits. We presently show that the Vev and Aev, respectively, produced by these variants display distinct proteomes, with prevalent functional enrichment in Vev related to oxidative stress response, signal transduction, transport, and localization, in addition to richer protein–protein interaction. Proteome sequences were obtained by nanoflow liquid chromatography coupled with tandem mass spectrometry (nano LC-ESI-MS/MS). The Vev and corresponding Vpb18 proteomes also differed, suggesting a selective bias in vesicle protein cargo. Moreover, sublethal oxidative (VevOxi) and nitrosative (VevNO) stress modulated the Vev proteome and a positive correlation between VevOxi/VevNO-enriched and Vev-enriched (relative to Aev) proteins was observed. Out of 145 fungal virulence factors detected in Vev, 64% were enriched, strongly suggesting that molecules with virulence roles in Paracoccidioides are selectively concentrated in Vev. Our study significantly advanced the field by exploring protein N-terminal acetylation to a dimension rarely investigated in fungal EV proteomics. The proportion of N-terminally acetylated proteins in Vev was higher than in Vpb18 and the presence of Nt-acetylation in Vev-enriched virulence factors varied across the samples, suggesting that it might interfere with protein sorting into EVs and/or protein functionality. Our findings highlight the relevance of our fungal model to unraveling the significance of fungal EVs in pathogenesis and phenotypic transfer. Full article

During long-range imaging, the turbid medium in the atmosphere absorbs and scatters light, resulting in reduced contrast, a narrowed dynamic range, and obscure detail information in remote sensing images. The prior-based method has the advantages of good real-time performance and a wide application range. However, few of the existing prior-based methods are applicable to the dehazing of panchromatic images. In this paper, we innovatively propose a prior-based dehazing method for panchromatic remote sensing images through statistical histogram features. First, the hazy image is divided into plain image patches and mixed image patches according to the histogram features. Then, the features of the average occurrence differences between adjacent gray levels (AODAGs) of plain image patches and the features of the average distance to the gray-level gravity center (ADGG) of mixed image patches are, respectively, calculated. Then, the transmission map is obtained according to the statistical relation equation. Then, the atmospheric light of each image patch is calculated separately based on the maximum gray level of the image patch using the threshold segmentation method. Finally, the dehazed image is obtained based on the physical model. Extensive experiments in synthetic and real-world panchromatic hazy remote sensing images show that the proposed algorithm outperforms state-of-the-art dehazing methods in both efficiency and dehazing effect. Full article

This study uses the case of substituting natural river sand with molybdenum tailings in concrete production in China to propose a methodological framework for evaluating the life cycle environmental impact of concrete materials. This approach addresses the mechanical performance adaptability and environmental friendliness, as well as the resource utilization of solid waste. The resource consumption, environmental impact, and economic costs are systematically analyzed using a life cycle assessment (LCA) approach, and the circular economy potential of tailings-based concrete is explored. A three-dimensional evaluation framework is constructed, encompassing raw material production, transportation, and construction stages. The environmental impacts of concrete with different molybdenum tailings replacement rates and strength grades are quantified using a willingness-to-pay (WTP) model. The results indicate that increasing the dosage of molybdenum tailings can significantly reduce environmental indicators such as global warming potential and acidification potential value. Specifically, C30 concrete with a 100% replacement rate shows an 8.5% reduction in total WTP compared to ordinary concrete, with a 2.85% reduction in energy consumption during the production stage. High-strength concrete further optimizes the environmental cost per unit strength through the “strength dilution effect,” with a 44.9% reduction in carbon footprint for 60 MPa concrete compared to 30 MPa concrete. Regional analysis reveals that the environmental contribution of the production stage dominates in short-distance transportation scenarios, while logistics optimization has a significant emission reduction effect in long-distance transportation scenarios. The study demonstrates that the proposed LCA methodology provides a scientific approach for the development of green building materials and the sustainable resource utilization of solid waste through case-informed generalization. Full article

The rapid expansion of global shipping has led to continuously increasing vessel traffic density, making high-accuracy ship trajectory prediction particularly critical for navigational safety and traffic management optimization in complex waters such as ports and narrow channels. However, existing methods still face challenges in medium-to-long-term prediction and nonlinear trajectory modeling, including insufficient accuracy and low computational efficiency. To address these issues, this paper proposes an enhanced Informer model (WOA-Informer) based on the Whale Optimization Algorithm (WOA). The model leverages Informer to capture long-term temporal dependencies and incorporates WOA for automated hyperparameter tuning, thereby improving prediction accuracy and robustness. Experimental results demonstrate that the WOA-Informer model achieves outstanding performance across three distinct trajectory patterns, with an average reduction of 23.1% in Root Mean Square Error (RMSE) and 27.8% in Haversine distance (HAV) compared to baseline models. The model also exhibits stronger robustness and stability in multi-step predictions while maintaining a favorable balance in computational efficiency. These results substantiate the effectiveness of metaheuristic optimization for strengthening deep learning architectures and present a computationally efficient, high-accuracy framework for vessel trajectory prediction. Full article

Liquefied natural gas plays a crucial role in global energy transitions due to its high efficiency and low emissions, especially in long-distance transportation. However, the thermal management of LNG storage tanks remains a significant challenge due to temperature fluctuations, which impact both efficiency and safety. Traditional methods rely on thermodynamic models or computational fluid dynamics simulations but are computationally expensive and time-consuming. This study proposes a hybrid approach that integrates machine learning techniques with CFD data to predict temperature variations inside LNG storage tanks. Several ML models, including Random Forest, XGBoost, and deep learning-based models like CNN and TCN, were tested. Results indicate that CNN and TCN models offer the best performance in predicting temperature changes, showing superior accuracy and computational efficiency. This approach significantly enhances the real-time prediction capability, offering a promising solution for improving LNG tank thermal management, ensuring both operational safety and efficiency. Full article

Sea level variability in the East Sea (Sea of Japan) and the Northwest Pacific poses challenges for coastal risk management due to the scarcity of long-term observations at remote locations such as Dokdo (Dok Island). This study reconstructs a continuous monthly sea level record at Dokdo from 1993 to 2023 by imputing gaps in 13 nearby Permanent Service for Mean Sea Level tide gauge stations using eight machine learning models and geospatial interpolation methods. The ensemble mean of Machine Learning-based imputations produced physically realistic and temporally coherent timeseries, preserving both seasonal and interannual variability. Sea level at Dokdo, estimated via inverse distance weighting, aligned well with satellite altimetry from Copernicus Marine Service and exhibited strong regional coherence with nearby stations. These results demonstrate that a hybrid framework combining statistical imputation, Machine Learning, and inverse barometric correction can effectively reconstruct sea level in data-sparse marine regions. The methodology provides a scalable tool for monitoring long-term trends and validating satellite and model products in marginal seas like the East Sea. Full article

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS³Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article

Flexible pipes have significant application potential in deep-sea mineral resource exploitation. As the innermost barrier of flexible pipes, the liner directly withstands abrasive wear from mineral particles. The extrusion quality of the liner is a decisive factor for the service life of the pipe and requires optimization of process parameters for improvement. However, the extrusion process of wear-resistant liners made of ultra-high molecular weight polyethylene (UHMWPE) involves complex thermo-mechanical coupling behavior, which creates major challenges in developing accurate numerical models that represent the entire process. To precisely simulate the extrusion process and guide process parameter optimization, this paper establishes a numerical simulation model for flexible pipe liner extrusion based on the Eulerian–Lagrangian coupling method. Simulations under various outlet temperature and screw speed conditions were carried out to reveal the evolution of mechanical behavior during extrusion and clarify the influence of key process parameters. The main conclusions can be summarized as follows. An increase in extrusion temperature reduces the maximum stress and promotes better molecular orientation and crystallinity in UHMWPE material, while the maximum heat flux remains essentially unchanged. An increase in screw speed has little effect on maximum material stress but leads to a significant increase in maximum heat flux. In addition, significant stress appears in the UHMWPE material at the extrusion die exit and is mainly concentrated in the unextruded material section. The numerical model effectively addresses the challenges of simulating material phase transition, large deformation and long-distance flow, which are difficult to capture with traditional methods. The findings offer a theoretical basis and technical guidance for optimizing extrusion process parameters and strengthening quality control in flexible pipe liner extrusion. Full article