Search Results (205)

Aramid fibre has become a critical material for individual soft body armour due to its lightweight nature and exceptional impact resistance. To investigate its energy absorption mechanism, quasi-static and dynamic tensile experiments were conducted on Kevlar^® 29 plain-woven fabric using a universal material testing machine and a Split Hopkinson Tensile Bar (SHTB) apparatus. Tensile mechanical responses were obtained under various strain rates. Fracture morphology was characterised using scanning electron microscopy (SEM) and ultra-depth three-dimensional microscopy, followed by an analysis of microstructural damage patterns. Considering the strain rate effect, a viscoelastic constitutive model was developed. The results indicate that the tensile mechanical properties of Kevlar^® 29 plain-woven fabric are strain-rate dependent. Tensile strength, elastic modulus, and toughness increase with strain rate, whereas fracture strain decreases. Under quasi-static loading, the fracture surface exhibits plastic flow, with slight axial splitting and tapered fibre ends, indicating ductile failure. In contrast, dynamic loading leads to pronounced axial splitting with reduced split depth, simultaneous rupture of fibre skin and core layers, and fibrillation phenomena, suggesting brittle fracture characteristics. The modified three-element viscoelastic constitutive model effectively captures the strain-rate effect and accurately describes the tensile behaviour of the plain-woven fabric across different strain rates. These findings provide valuable data support for research on ballistic mechanisms and the performance optimisation of protective materials. Full article

The effect of periodical heatwaves and related thermal stratification in freshwater aquatic ecosystems has been a hot research issue. A large dataset of samples was generated from samples exposed to temporary thermal stratification in mesocosms mimicking shallow eutrophic freshwater lakes. Temperature regimes were based on IPCC climate warming scenarios, enabling simulation of future warming conditions. Surface oxygen levels reached 19.37 mg/L, while bottom layers dropped to 0.07 mg/L during stratification. Analysis by FlowCAM revealed dominance of Cyanobacteria under ambient conditions (up to 99.2%), while Cryptophyta (up to 98.9%) and Chlorophyta (up to 99.9%) were predominant in the A2 and A2+50% climate scenarios, respectively. We identified temperature changes and shifts in nutrient concentrations, particularly phosphate, as critical factors in microbial community composition. Furthermore, five distinct Microcystis morphospecies identified by FlowCAM-based analysis were associated with different microbial clusters. The combined use of imaging flow cytometry, which differentiates phytoplankton based on morphological parameters, and nanopore long-read sequencing analysis has shed light into the dynamics of microbial communities associated with different Microcystis morphospecies. In our observations, a peak of algicidal bacteria abundance often coincides with or is followed by a decline in the Cyanobacteria. These findings highlight the importance of species-level classification in the analysis of complex ecosystem interactions and the dynamics of algal blooms in freshwater bodies in response to anthropogenic effects and climate change. Full article

The icebreaking process of water-exiting vehicles involves complex nonlinear interactions as well as multi-physical field coupling effects among ice, solids, and fluids, which poses enormous challenges for numerical calculations. Addressing the low solution accuracy of traditional grid methods in simulating large deformation and destruction of ice layers, a numerical model was established based on the FEM-SPH-SALE coupling algorithm to study the dynamic characteristics of the water-exiting vehicle on the icebreaking process. The FEM-SPH adaptive algorithm was used to simulate the damage performance of ice, and its feasibility was verified through the four-point bending test and vehicle breaking ice experiment. The S-ALE algorithm was used to simulate the process of fluid/structure interaction, and its accuracy was verified through the wedge-body water-entry test and simulation. On this basis, numerical simulations were performed for different ice thicknesses and initial velocities of vehicles. The results show that the motion characteristics of the vehicle undergoes a sudden change during the ice-breaking. The head and middle section of the vehicle are subject to greater stress, which is related to the transmission of stress waves and inertial effect. The velocity loss rate of the vehicle and the maximum stress increase with the thickness of ice. The higher the initial velocity of the vehicle, the larger the acceleration and maximum stress in the process of the vehicle breaking ice. The acceleration peak is sensitive to the variation in the vehicle’s initial velocity but insensitive to the thickness of the ice. Full article

This study examines pantograph aerodynamic lift at 400 km/h, and uncovers the dynamic behaviors and mechanisms that influence pantograph–catenary performance. Using computational fluid dynamics (CFD) with a compressible fluid model and an SST k-ω turbulence model, aerodynamic characteristics were analyzed. Simulation data at 300, 350, and 400 km/h showed lift fluctuation amplitude increases with speed, peaking near 50 N at 400 km/h. Power spectral density (PSD) energy, dominated by low frequencies, peaked around 10 dB/Hz in the low-frequency band, highlighting exacerbated lift instability. Component analysis revealed the smallest lift-to-drag ratio and most significant fluctuations at the head, primarily due to boundary-layer separation and vortex shedding from its non-streamlined design. Turbulence energy analysis identified the head and base as main turbulence sources; however, base vibrations are absorbed by the vehicle body, while the head causes pantograph–catenary vibrations due to direct contact. These findings confirm that aerodynamic instability at the head is the main cause of contact force fluctuations. Optimizing head design is necessary to suppress fluctuations, ensuring safe operation at 400 km/h and above. Results provide a theoretical foundation for aerodynamic optimization and improved dynamic performance of high-speed pantographs. Full article

To address the issue of poor film–soil separation in traditional subsoil residual film recovery machines, which leads to recovered film containing excessive soil, a film–soil conveying and separation device was designed. By establishing a mechanical model for the balanced conveyance of the film–soil composite, the range of conveyor chain inclination angles enabling stable transport was determined. Using RecurDyn 2023 simulation software, a sensitivity analysis was conducted on the effects of vibrating wheel speed, vibrating wheel mounting distance, and conveyor chain inclination angle on vibration characteristics. This analysis revealed that vibrating wheel speed and mounting distance have a significant impact on the vibrating mechanism. Based on the DEM–MBD (Discrete Element Method—Multi-Body Dynamics) coupling approach, a discrete element simulation model was built for the film–soil vibrating conveyor device, residual film, and soil. Using the primary conveyor chain speed, vibrating wheel speed, and mounting distance as experimental factors, and soil content rate and film leakage rate as experimental indicators, single-factor tests and a three-factor, five-level orthogonal rotational composite design test were performed. The results showed that, at a primary conveyor chain speed of 1.61 m/s, a vibrating wheel speed of 186.2 r/min, and a mounting distance of 688.2 mm, the soil content rate was 18.11% and the film leakage rate was 7.61%. The film–soil conveying and separation process was also analyzed via simulation. Field validation tests using the optimal parameter combination yielded relative errors of 3.43% and 5.51%, respectively, demonstrating effective film–soil separation. This research provides a theoretical foundation and equipment support for addressing residual film pollution in the cultivated layer of Xinjiang region. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article

The capillary force driving the water penetration process in the coal pore network is the key factor affecting the effect of coal seam water injection. The resistivity method can be used to determine the migration characteristics of water in coal. In order to study the relationship between the resistivity of gas-bearing coal and the migration of water in the process of imbibition, the self-generated imbibition tests of coal under different external water conditions were carried out by using the self-developed gas-bearing coal imbibition experimental platform and the dynamic response characteristics of coal resistivity with external water were obtained. The results show that the water injected into the coal body migrates from bottom to top under the driving of capillary force, and the resistivity of the wetted coal body shows a sudden decline, slow decline, and gradually stable stage change. Through the slice drying method, it is found that the moisture in the coal body is almost uniform after imbibition, and the resistivity method can be used to accurately and quantitatively characterize the moisture content of the coal body. In the axial direction, as water infiltrates layer by layer, the sudden change time of resistivity is delayed with the deepening of the layer. The resistivity of each layer first drops sharply then slows down and tends to stabilize. The stable value of resistivity increases gradually with the depth of the layer. In the radial direction, within the same plane, water first migrates to the centre of the coal body and then begins to spread outwards. The average mutation time and stable value of coal resistivity during spontaneous imbibition decrease with increasing water content. When the water content reaches 10%, the stable value of resistivity tends to be constant, and the relationship between the stable value of coal resistivity and water content conforms to an exponential function. Full article

Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly regarding interactions with meteorological, topographical, and socio-economic factors. According to the urban-suburban air temperature difference, this study classified the city into non-, weak, and strong CUHI regions. We integrated causal inference, machine learning and a geographical detector (Geodetector) to model and interpret PCI dynamics across CUHI gradients. The results reveal that surrounding impervious surface coverage is a universal driver of PCI by enhancing thermal contrast at park boundaries. However, the dominant drivers of PCI varied significantly across CUHI gradients. In non-CUHI regions, surrounding imperviousness dominated PCI and exhibited bilaterally enhanced interaction with intra-park patch density. Weak CUHI regions relied on intra-park green coverage with nonlinear synergies between water body proportion and park area. Strong CUHI regions involved systemic urban fabric influences mediated by surrounding imperviousness, evidenced by a validated causal network. Crucially, causal inference reduces model complexity by decreasing predictor counts by 79%, 25% and 71% in non-, weak and strong CUHI regions, respectively, while maintaining comparable accuracy to full-factor models. This outcome demonstrates the efficacy of causal inference in eliminating collinear metrics and spurious correlations from traditional feature selection, ensuring retained predictors reside within causal pathways and support process-based interpretability. Our study highlights the need for context-adaptive cooling strategies and underscores the value of integrating causal–statistical approaches. This framework provides actionable insights for designing climate-resilient blue–green spaces, advancing urban sustainability goals. Future research should prioritize translating causal diagnostics into scalable strategies for sustainable urban planning. Full article

We address the problem of humanoid locomotion in 3D environments consisting of planar regions with arbitrary inclination and elevation, such as staircases, ramps, and multi-floor layouts. The proposed framework combines an offline randomized footstep planner with an online control pipeline that includes a model predictive controller for gait generation and a whole-body controller for computing robot torque commands. The planner efficiently explores the environment and returns the highest-quality plan it can find within a user-specified time budget, while the control layer ensures dynamic balance and adequate ground friction. The complete framework was evaluated via dynamic simulation in MuJoCo, placing the JVRC1 humanoid in four scenarios of varying complexity. Full article

The accurate identification of water bodies in hyperspectral images (HSIs) remains challenging due to hierarchical representation imbalances in deep learning models, where shallow layers overly focus on spectral features, boundary ambiguities caused by the relatively low spatial resolution of satellite imagery, and limited detection capability for small-scale aquatic features such as narrow rivers. To address these challenges, this study proposes Heuristic–Adaptive Spectral–Spatial Neural Architecture Search with Dynamic Cell Evaluation (HASSDE-NAS). The architecture integrates three specialized units; a spectral-aware dynamic band selection cell suppresses redundant spectral bands, while a geometry-enhanced edge attention cell refines fragmented spatial boundaries. Additionally, a bidirectional fusion alignment cell jointly optimizes spectral and spatial dependencies. A heuristic cell search algorithm optimizes the network architecture through architecture stability, feature diversity, and gradient sensitivity analysis, which improves search efficiency and model robustness. Evaluated on the Gaofen-5 datasets from the Guangdong and Henan regions, HASSDE-NAS achieves overall accuracies of 92.61% and 96%, respectively. This approach outperforms existing methods in delineating narrow river systems and resolving water bodies with weak spectral contrast under complex backgrounds, such as vegetation or cloud shadows. By adaptively prioritizing task-relevant features, the framework provides an interpretable solution for hydrological monitoring and advances neural architecture search in intelligent remote sensing. Full article

This study focuses on the energy absorption and wearer comfort attributes of regular lattice structures fabricated by laser powder bed fusion from two elastomeric materials, namely TPU1301 and TPE300, for use in personal protective equipment (PPE). This study compares Body-Centered Cubic (BCC), Face-Centered Cubic (FCC) and Kelvin (KE) lattice structures with density varying from 0.15 to 0.25 g/cm³, cell size varying from 10 to 14 mm and feature size varying from 1 to 3 mm. Quasi-static and dynamic compression testing confirmed that among the studied geometries, KE structures printed with TPE300 powders provide the best combination of reduced peak acceleration and increased compliance, thereby improving both safety and comfort. Using the protection–comfort maps built on the basis of this study enables the design of lightweight and compact protective structures. For example, if a safety layer protecting a 100 mm² surface area can be manufactured from either TPE300 or TPU1100 powders using either KE or FCC structures, the KE TPE300 layer will be 1.5 times thinner and 2.5 times lighter than its FCC TPU1301 equivalent. The results of this study thus provide a basis for the optimization of lattice structures in 3D-printed PPE to meet both service and manufacturing requirements. Full article

This paper examines the philosophy of “Dao follows nature” (道法自然) and investigates how Daoist clothing transforms abstract cosmological concepts into a “wearable interface for spiritual practice” through the use of materials, colors, and patterns. By integrating symbol system analysis, material culture theory, and the philosophy of body practice, this study uncovers three layers of symbolic mechanisms inherent in Daoist attire. First, the materials embody the tension between “nature and humanity”, with the intentional imperfections in craftsmanship serving as a critique of technological alienation. Second, the color coding disrupts the static structure of the Five Elements system by dynamically shifting between sacred and taboo properties during rituals while simultaneously reconstructing symbolic meanings through negotiation with secular power. Third, the patterns (such as star constellations and Bagua) employ directional arrangements to transform the human body into a miniature cosmos, with dynamic designs offering a visual path for spiritual practice. This paper introduces the concept of a “dynamic practice interface”, emphasizing that the meaning of Daoist clothing is generated through the interaction of historical power, individual experience, and cosmological imagination. This research fills a critical gap in the symbolic system of Daoist art and provides a new paradigm for sustainable design and body aesthetics, framed from the perspective of “reaching the Dao through objects”. Full article

This paper explores the impact of the Israel–Palestine conflict on the stock performance of U.S. companies and their public positions on the conflict. In an era where corporate positions on geopolitical issues are increasingly scrutinized, understanding the market implications of such statements is critical. This research aims to capture the complex, non-linear relationships between corporate actions, media coverage, and financial outcomes by integrating traditional statistical techniques with advanced machine learning models. To achieve this, we constructed a novel dataset combining public corporate announcements, media sentiment (including headline and article body tone), and philanthropic activities. Using both classification and regression models, we predicted whether companies had affiliations with Israel and then analyzed how these affiliations, combined with other features, affected their stock returns over a 30-day period. Among the models tested, ensemble learning methods such as stacking and boosting achieved the highest classification accuracy, while a Multi-Layer Perceptron (MLP) model proved most effective in forecasting abnormal stock returns. Our findings highlight the growing relevance of machine learning in financial forecasting, particularly in contexts shaped by geopolitical dynamics and public discourse. By demonstrating how sentiment and corporate stance influence investor behavior, this research offers valuable insights for investors, analysts, and corporate decision-makers navigating sensitive political landscapes. Full article

The intestinal health and functionality of animals play pivotal roles in nutrient digestion and absorption, as well as in maintaining defense against pathogenic invasions. These biological processes are modulated by various determinants, including husbandry conditions, dietary composition, and gut microbial ecology. The excessive use of anthropogenic antibiotics may disrupt intestinal microbiota composition, potentially leading to dysbiosis that directly compromises host homeostasis. While Lactobacillus species are recognized for their immunomodulatory properties, their precise mechanisms in regulating host anti-inflammatory gene expression and influencing mucosal layer maturation, particularly regarding E. coli colonization resistance, require further elucidation. To investigate the regulatory mechanisms of Lactobacillus in relation to intestinal architecture and function during E. coli infection, we established a colonic infection model using Bal b/c mice, conducting systematic analyses of intestinal morphology, inflammatory mediator profiles, and microbial community dynamics. Our results demonstrate that Lactobacillus supplementation (Pediococcus acidilactici) effectively mitigated E. coli O78-induced enteritis, with co-administration during infection facilitating the restoration of physiological parameters, including body mass, intestinal histoarchitecture, and microbial metabolic functions. Microbiome profiling revealed that the Lactobacillus intervention significantly elevated Lactococcus abundance while reducing Weissella populations (p < 0.05), concurrently enhancing metabolic pathways related to nutrient assimilation and environmental signal processing (including translation mechanisms, ribosomal biogenesis, amino acid transport metabolism, and energy transduction systems; p < 0.05). Mechanistically, Lactobacillus administration attenuated E. coli-induced intestinal pathology through multiple pathways: downregulating pro-inflammatory cytokine expression (IL-1β, IL-1α, and TNF-α), upregulating epithelial junctional complexes (Occludin, Claudin-1, and ZO-1), and stimulating mucin biosynthesis (MUC1 and MUC2; p < 0.05). These modifications collectively enhanced mucosal barrier integrity and promoted epithelial maturation. This investigation advances our comprehension of microbiota–host crosstalk during enteropathogenic infections under probiotic intervention, offering valuable insights for developing novel nutritional strategies and microbial management protocols in animal husbandry. Full article

Moisture ingress in power distribution cable bodies can lead to insulation degradation, jeopardizing the operational safety of power grids. However, current cable maintenance technologies lack effective diagnostic methods for identifying moisture defects in cable bodies. To address this gap, this paper proposes a dynamic frequency domain reflectometry (D-FDR) method for moisture localization and diagnosis in power distribution cables. Leveraging the temperature-sensitive nature of moisture defects—in contrast to the temperature-insensitive characteristics of other defects—the method involves the application of thermal excitation to induce differential dynamic changes in the distributed capacitance of moisture-affected cable segments compared to normal segments, enabling the precise identification and diagnosis of moisture ingress. Simulations and experiments confirm that moisture ingress in cable bodies increases the distributed capacitance, generating reflection peaks at corresponding distances on frequency domain localization plots. Under thermal excitation, the reflection peak amplitude of moisture defects exhibits a temperature-dependent decrease, distinct from the behavior of intact cables (amplitude increase) and copper shielding layer damage (negligible variation). By utilizing the dynamic characteristics of reflection peak amplitudes as diagnostic criteria, this method is able to accurately localize and diagnose moisture defects in cable bodies. Full article