Search Results (14,444)

To address the challenge of determining the borehole layout scheme in the practical application of high-pressure gas expansion rock breaking, this study takes the excavation of the safety passage at Kaixuan Road Station on the North Extension Line 2 of Chongqing Metro Line 18 as the engineering background. The rock-breaking capacity was evaluated by analyzing the damaged zone volume caused by gas expansion using FLAC3D 6.0 numerical simulation software, and vibration monitoring was conducted for the historical buildings on the surface. This study revealed the following: (1) When the borehole depth is 1.2 m and the charge length is 0.6 m, the optimal angle is 70°, with the optimal vertical and horizontal spacing between holes being 1200 mm and 2000 mm, respectively. (2) The numerical simulations indicated that by adjusting the charge density, the optimized sandstone borehole network parameters could be applied to mudstone strata, and the rock-breaking effect was similar. The difference in the volume of the damaged zones obtained in the two strata was less than 3%. (3) The vibration analysis demonstrated that the peak particle velocity generated by high-pressure gas expansion rock fracturing at the ancient building directly above was 0.06316 cm/s, which was lower than the threshold value of 0.1 cm/s and approximately 67.95% lower than that of explosive blasting. Furthermore, when the tunnel depth exceeded 29 m, the vibration velocity of surface structures remained within the safety range. The results verified the feasibility of applying the same borehole network parameters to different strata, providing theoretical support for the practical application of high-pressure gas expansion rock fracturing technology in engineering projects. Full article

Multiple-hops performed horizontally in series effectively assess return-to-play readiness, as they mimic the propulsive and decelerative demands of sports. Movement strategy variables (kinetic variables) offer more insight into injury recovery than outcome-based measures (kinematic variables) like hop distance alone. This study focused on kinematic and kinetic variables to assess asymmetries during triple-hop (3-Hop) and quintuple-hop (5-Hop) tests with 44 male athletes from university sports clubs and teams. The aim was to determine the magnitude and potential direction of asymmetry and compare the sensitivity of kinematic and kinetic variables. Results showed mean kinematic asymmetries below 7.1% (range: 0.00 to 28.9%), while average kinetic asymmetries were as high as 38.8% (range: 0.0% to 95.4%). These findings suggest that kinetic variables are more sensitive in assessing movement strategy, providing more detailed insight into rehabilitation and return-to-play decisions. The study emphasizes the importance of considering both outcome and movement strategy variables in injury recovery. These results have practical applications for clinicians and coaches supporting those in return-to-play scenarios, as well as those addressing performance deficits, therefore offering valuable information to refine exercise prescriptions and athletic program design. Full article

Drilling fluid losses into formation voids are among the major issues that lead to increases in the costs and nonproductive time of operations. Lost circulation materials have been widely used to stop or mitigate losses. In most cases, the size of the loss zone is not known, making conventional lost circulation materials unsuitable for plugging the loss zone. In this study, novel temperature-sensitive LCM (TS-LCM) particles composed of diglycidyl ether of bisphenol A (DGEBA) and 4,4′-diaminodiphenyl methane were prepared. It is a thermal-response shape-memory polymer. The molecular structure was analyzed by Fourier transform infrared spectroscopy. The glass transition temperature (T_g) was tested by Different scanning calorimetry (DSC). The shape-memory properties were evaluated by a bend-recovery test instrument. The expansion and mechanical properties of particles were investigated under high temperature and high pressure. Fracture sealing testing apparatus was used to evaluate sealing performance. The mechanism of sealing fracture was discussed. Research results indicated that the T_g of the TS-LCM was 70.24 °C. The shape fixation ratio was more than 99% at room temperature, and the shape recovery ratio was 100% above the T_g. The particle was flaky before activation. It expanded to a cube shape, and the thickness increased when activated. The rate of particle size increase for D90 was more than 60% under 120 °C and 20 MPa. The activated TS-LCM particles had high crush strength. The expansion of the TS-LCM particles could self-adaptively bridge and seal the fracture without knowing the width. The addition of TS-LCM particles could seal the tapered slot with entrance widths of 2 mm, 3 mm and 4 mm without changing the lost circulation material formulation. The developed TS-LCM has good compatibility with local saltwater-based drilling fluid. In field tests in the Yan’an area of the Ordos Basin, 15 shale oil horizontal wells were plugged with excellent results. The equivalent circulating density of drilling fluid leakage increased by an average of 0.35 g/cm³, and the success rate of plugging malignant leakage increased from 32% to 82.5%. The drilling cycle was shortened by an average of 14.3%, and the effect of enhancing the pressure-bearing capacity of the well wall was significant. The prepared TS-LCM could cure fluid loss in a fractured formation efficiently. It has good prospects for promotion. Full article

The mobile tunnel scanning system (MTSS) enables efficient tunnel inspection; however, motion blur (MB) generated at high travel speeds remains a major factor undermining the reliability of deep-learning-based crack detection. This study focuses on investigating how horizontally oriented MB in MTSS imagery affects the crack-detection performance of convolutional neural networks (CNNs) and proposes a data-centric quality-assurance framework that leverages no-reference image quality assessment (NR-IQA) to optimize model performance. By intentionally applying MB to both public and real-world MTSS datasets, we analyzed performance changes in ResNet-, VGG-, and AlexNet-based models and established the correlations between four NR-IQA metrics (BRISQUE, NIQE, PIQE, and CPBD) and performance (F1 score). As the MB intensity increased, the F1 score of ResNet34 dropped from 89.43% to 4.45%, confirming the decisive influence of image quality. PIQE and CPBD exhibited strong correlations with F1 (−0.87 and +0.82, respectively), emerging as the most suitable indicators for horizontal MB. Using thresholds of PIQE ≤ 20 and CPBD ≥ 0.8 to filter low-quality images improved the AlexNet F1 score by 1.46%, validating the effectiveness of the proposed methodology. The proposed framework objectively assesses MTSS data quality and optimizes deep learning performance, enhancing the reliability of intelligent infrastructure maintenance systems. Full article

This paper studies $(p,q)$-harmonic maps by unified geometric analytic methods. First, we deduce variation formulas of the $(p,q)$-energy functional. Second, we analyze weakly conformal and horizontally conformal $(p,q)$-harmonic maps and prove Liouville results for $(p,q)$-harmonic maps under Hessian and asymptotic conditions on complete Riemannian manifolds. Finally, we define the $(p,q)$-$SSU$ manifold and prove that non-constant stable $(p,q)$-harmonic maps do not exist. Full article

The spatial and seasonal characteristics of submesoscales in the Northwest Pacific Subtropical Ocean are thoroughly investigated here using a submesoscale-permitting model within a localized multiscale energetics framework, in which three scale windows termed background, mesoscale, and submesoscale are decomposed. It is found that submesoscale energetics are highly geographically inhomogeneous. In the Luzon Strait, baroclinic and barotropic instabilities are the primary mechanisms for generating submesoscale available potential energy (APE) and kinetic energy (KE), and they exhibit no significant seasonal variations. Although buoyancy conversion experiences pronounced seasonal cycles and serves as the main sink of submesoscale APE in winter and spring, its contribution to submesoscale KE is negligible. The major sinks of submesoscale KE are advection, horizontal pressure work, and dissipation. In the Western Boundary Current transition and Subtropical Countercurrent (STCC) interior open ocean zone, submesoscales undergo significant seasonality, with large magnitudes in winter and spring. In spring and winter, baroclinic instability dominates the generation of submesoscale APE via forward cascades, while KE is mainly energized by buoyancy conversion and dissipated by the residual term. Meanwhile, in summer and autumn, submesoscales are considerably weak. Additionally, submesoscale energetics in the Western Boundary Current transition zone are slightly greater than those in the STCC interior open ocean zone, which is attributed to the strengthened straining of the Western Boundary Current and mesoscale eddies. Full article

This study experimentally investigated the influence of vegetation integrity, vertical architecture and morphology, flexibility, and patch length on tsunami bore attenuation and structural force reduction, using Pandanus odoratissimus (screwpine) as a model species. A key aspect of the experimental design was the simulation of vegetation breakage, defined as occurring when the tsunami water depth exceeded 80% of tree height, a critical threshold for structural failure. Results showed that vegetation under non-breaking conditions significantly attenuated water levels and hydrodynamic forces, with maximum tsunami force reductions of up to 70% for rigid and 66.5% for flexible vegetation, particularly when the patch extended further inland (i.e., longer vegetation length). In contrast, vegetation breakage led to a notable decline in protective performance, with horizontal and uplift force reductions dropping between 10.1–45.2% and 10.7–16.7%, respectively, in short patches. Flexible vegetation exhibited the greatest loss of effectiveness due to structural collapse. However, longer vegetation patches played a compensatory role, maintaining higher force reduction even under breaking conditions. Notably, broken P. odoratissimus still contributes to energy dissipation through its intact dense aerial roots that resist flow near the bed. These findings highlight the importance of maintaining vegetation integrity for effective tsunami mitigation, while also recognizing that partially damaged vegetation retains some protective function, particularly when configured in extended patches. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly conceptual and speculative, rather than grounded in empirical research. To address this gap, this study contributes to defining L4.0 through the sub-area of Warehouse 4.0 (W4.0), focusing on the challenges of adopting I4.0 technologies in warehouses. Methods: Through the I4.0 and L4.0 literature, an initial classification of W4.0 technologies in third-party logistics (3PL) was developed. This was refined using a case study of a global logistics service provider (LSP) in the 3PL industry, through semi-structured interviews with stakeholders. Results: The empirical findings identify new application areas for I4.0 technology in 3PL warehouses, including horizontal and vertical system integration, big data, and cybersecurity, technologies that can enhance 3PL competitiveness. Conclusions: This study offers a structured classification of W4.0 technologies and insights into the application areas of W4.0 in 3PLs. It contributes practical insights into which I4.0 technologies are relevant for the 3PL warehouse industry and their potential application areas. Full article

In this study, the PM_2.5 pollution transport budget in the atmospheric boundary layer (ABL) of Beijing–Tianjin–Hebei (BTH) and Chengdu–Chongqing (CY) was quantitatively evaluated from the perspective of horizontal and vertical exchange. Based on the aircraft meteorological data relay (AMDAR) observation data, the study found that the vertical exchange process of pollutants is mainly influenced by the combined effects of meteorological conditions and topographical factors. Meteorological factors determine the direction and intensity of the vertical exchange, while the complexity of the terrain affects the exchange pattern through local circulation and air flow convergence. The characteristics of the pollution transport budget between the BTH and CY regions show that the BTH region has a net output of pollutants throughout the year, while the CY region has a net input of pollutants. The total transport budget of the four typical representative seasons in BTH is negative. It indicated that BTH, as the region with the highest intensity of air pollution emission in China, is dominated by outward transport of air pollutants to surrounding regions. Due to the influence of topographic and meteorological conditions in the CY region, the air pollutants tend to accumulate in the basin rather than diffuse. The transport budget relationship of the four seasons is positive and the input of air pollutants can be obviously simulated. Combined with the results of the vertical wind profile, Beijing is more vulnerable to the prevailing cold air sinking in the northwest in winter, which is characterized by the inflow of the free troposphere (FT) into the ABL. As for Chongqing, it is blocked by mountains so that the gas convection at the top of the ABL is obvious. This horizontal convergence phenomenon induces upward vertical movement, which makes Chongqing show a strong characteristic of the ABL transport to the FT. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

The energy potential of sea waves has gained relevance, leading to extensive research on converters. The present work analyzes the contribution of Constructal Design to the development of wave energy converters. Constructal Design utilizes performance indicators to enhance system efficiency by varying the degrees of freedom where flow occurs. Thus, the systematic literature review methodology was applied to gather a collection of documents focused on the research topic. This study identified articles published between 2014 and 2024 by 40 authors affiliated with institutions in Brazil, Italy, and Portugal. The oscillating water column (OWC) converter received the most research attention, followed by the overtopping converter. Analyzing the documents collected for this study, the performance indicators revealed improvements ranging from 1.19 to 839 times, indicating the lowest and highest enhancements observed, respectively. The Constructal Design method has proven highly effective in identifying specific architectures or geometric arrangements that enhance flow configuration and improve the performance of wave energy converters. However, relatively few studies have applied the Constructal Design method to wave energy converters in comparison to other methodologies, presenting a significant opportunity for future research. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

A multidisciplinary investigation was conducted in southwestern Sicily, near the seismically active Belice Valley, based on the analysis of morphostructural features. These were observed as open fractures between 2014 and 2017; they were subsequently filled anthropogenically and then reactivated during a seismic swarm in 2019. We generated a seismic event distribution map to analyze the location, magnitude, and depth of earthquakes. This analysis, combined with multitemporal satellite imagery, allowed us to investigate the spatial and temporal relationship between seismic activity and fracture evolution. To investigate the spatial variation in thickness of the superficial cover and to assess the depth to the underlying bedrock or stiffer substratum, 45 Horizontal-to-Vertical Spectral Ratio (HVSR) ambient noise measurements were conducted. This method, which analyzes the resonance frequency of the ground, produced maps of the amplitude, frequency, and vulnerability index of the ground (K_g). By inverting the HVSR curves, constrained by Multichannel Analysis of Surface Waves (MASW) results, a subsurface model was created aimed at supporting the structural interpretation by highlighting variations in sediment thickness potentially associated with fault-controlled subsidence or deformation zones. The surface investigation revealed depressed elliptical deformation zones, where mainly sands outcrop. Grain-size and morphoscopic analyses of sediment samples helped understand the processes generating these shapes and predict future surface deformation. These elliptical shapes recall the liquefaction process. To investigate the potential presence of subsurface fluids that could have contributed to this process, Electrical Resistivity Tomography (ERT) was performed. The combination of the maps revealed a correlation between seismic activity and surface deformation, and the fractures observed were interpreted as inherited tectonic and/or geomorphological structures. Full article

Understanding neuromuscular adaptations resulting from specific training modalities is crucial for optimizing athletic performance and injury prevention. This in silico proof-of-concept study aimed to computationally model and predict neuromuscular adaptations induced by strength and plyometric training, integrating musculoskeletal simulations and machine learning techniques. A validated musculoskeletal model (OpenSim 4.4; 23 DOF, 92 musculotendon actuators) was scaled to a representative athlete (180 cm, 75 kg). Plyometric (vertical jumps, horizontal broad jumps, drop jumps) and strength exercises (back squat, deadlift, leg press) were simulated to evaluate biomechanical responses, including ground reaction forces, muscle activations, joint kinetics, and rate of force development (RFD). Predictive analyses employed artificial neural networks and random forest regression models trained on extracted biomechanical data. The results show plyometric tasks with GRF 22.1–30.2 N·kg⁻¹ and RFD 3200–3600 N·s⁻¹, 10–12% higher activation synchrony, and 7–12% lower moment variability. Strength tasks produced moments of 3.2–3.8 N·m·kg⁻¹; combined strength + plyometric training reached 3.7–4.2 N·m·kg⁻¹, 10–16% above strength only. Machine learning predictions revealed superior neuromuscular gains through combined training, especially pairing back squats with high-intensity drop jumps (50 cm). This integrated computational approach demonstrates significant practical potential, enabling precise optimization of training interventions and injury risk reduction in athletic populations. Full article