Search Results (15,033)

Slab-replacement operations are crucial for restoring deteriorated CRTS I slab ballastless tracks to operational standards. This study investigates the structural implications of the operation by evaluating the strength characteristics and material properties of track components both prior to and following replacement. Apparent strength was measured using rebound hammer tests on three categories of slabs: retained, deteriorated, and newly installed track slabs. In addition, samples of old and new filling resins were collected and tested to determine their elastic moduli. These empirical data were subsequently used to develop a refined finite-element model that captures both pre- and post-replacement conditions. Under varying temperature loads, disparities in component performance were found to significantly affect stress distribution. Specifically, before replacement, deteriorated track slabs exhibited 10.74% lower strength compared to adjacent retained slabs, whereas, after replacement, new slabs showed a 25.26% increase in strength over retained ones. The elastic modulus of old filling resin was measured at 5.19 kN/mm, 35.13% below the minimum design requirement, while the new resin reached 10.48 kN/mm, exceeding the minimum by 31.00%. Although the slab-replacement operation enhances safety by addressing structural deficiencies, it may also create new weak points in adjacent areas, where insufficient stiffness results in stress concentrations and potential damage. This study offers critical insights for optimizing maintenance strategies and improving the long-term performance of ballastless track systems. Full article

Currently, various pruning strategies including different methods and distribution types are commonly used to reduce the number of FLOPs and parameters in deep learning models. However, their impact on actual energy savings remains insufficiently studied, particularly in resource-constrained settings. To address this, we introduce PruneEnergyAnalyzer, an open-source Python tool designed to evaluate the energy efficiency of pruned models. Starting from the unpruned model, the tool calculates the energy savings achieved by pruned versions provided by the user, and generates comparative visualizations based on previously applied pruning hyperparameters such as method, distribution type (PD), compression ratio (CR), and batch size. These visual outputs enable the identification of the most favorable pruning configurations in terms of FLOPs, parameter count, and energy consumption. As a demonstration, we evaluated the tool with 180 models generated from three architectures, five pruning distributions, three pruning methods, and four batch sizes, using another previous library (e.g. FlexiPrune). This experiment revealed the significant impact of the network architecture on Energy Reduction, the non-linearity between FLOPs savings and energy savings, as well as between parameter reduction and energy efficiency. It also showed that the batch size strongly influences the energy consumption of the pruned model. Therefore, this tool can support researchers in making pruning policy decisions that also take into account the energy efficiency of the pruned model. Full article

Background/Objectives: Perceived physical literacy (PPL) appears to be a relevant strategy for combating the prevalent sedentary lifestyle among young people. Therefore, understanding their PPL levels will facilitate the implementation of appropriate strategies for this purpose. Therefore, this study aimed to analyze the differences in PPL considering the sex dimension (i.e., males and females) and the age-group (i.e., early compulsory secondary education, late compulsory secondary education and baccalaureate). Methods: Seven-hundred-and-four Spanish students (age = 14.3 ± 1.6 years old) belonging to three different Spanish secondary schools voluntarily participated in this study. To assess adolescents’ perceptions of their physical literacy, the Spanish Adolescents’ Perceived Physical Literacy Assessment (S-PPLI) was used. This instrument consists of nine items equally distributed across three categories: self-perception and self-confidence, self-expression and communication with others, and knowledge and understanding. Results: Males obtained higher scores in all the indicators of PPL, except for items 1, 8 and 9, compared to their female counterparts (p < 0.05), while the oldest age-group reported higher scores in the indicators of knowledge and understanding category compared to students in the early years of compulsory secondary education (p < 0.01). Conclusions: Programs based on increasing the PPL should be implemented specifically for females. Also, similar programs must be included into scholar curriculums from the beginning of secondary education, with the aim of promoting improvements in the health and physical condition of Spanish adolescents. Full article

A modular strategy for the molecular design of silicone-based antifoaming agents was developed by precisely controlling the architecture of poly (methylhydrosiloxane) (PMHS). Sixteen PMHS variants were synthesized by systematically varying the siloxane chain length (L1–L4), backbone composition (D₃T₁ vs. D₃₀T₁), and terminal group chemistry (H- vs. M-type). These structural modifications resulted in a broad range of Si-H functionalities, which were quantitatively analyzed and correlated with defoaming performance. The PMHS matrices were integrated with high-viscosity PDMS, a nonionic surfactant, and covalently grafted fumed silica—which was chemically matched to each PMHS backbone—to construct formulation-specific defoaming systems with enhanced interfacial compatibility and colloidal stability. Comprehensive physicochemical characterization via FT-IR, ¹H NMR, GPC, TGA, and surface tension analysis revealed a nonmonotonic relationship between Si-H content and defoaming efficiency. Formulations containing 0.1–0.3 wt% Si-H achieved peak performance, with suppression efficiencies up to 96.6% and surface tensions as low as 18.9 mN/m. Deviations from this optimal range impaired performance due to interfacial over-reactivity or reduced mobility. Furthermore, thermal stability and molecular weight distribution were found to be governed by repeat unit architecture and terminal group selection. Compared with conventional EO/PO-modified commercial defoamers, the PMHS-based systems exhibited markedly improved suppression durability and formulation stability in high-viscosity environments. These results establish a predictive structure–property framework for tailoring antifoaming agents and highlight PMHS-based formulations as advanced foam suppressors with improved functionality. This study provides actionable design criteria for high-performance silicone materials with strong potential for application in thermally and mechanically demanding environments such as coating, bioprocessing, and polymer manufacturing. Full article

This paper introduces a knowledge–data dual-driven method for predicting groundwater conditions during tunnel construction. Unlike existing methods, our approach effectively integrates trend characteristics of apparent resistivity from detection results with geological distribution characteristics and expert insights. This dual-driven strategy significantly enhances the accuracy of the prediction model. The intelligent prediction process for tunnel groundwater conditions proceeds in the following steps: First, the apparent resistivity data matrix is obtained from transient electromagnetic detection results and standardized. Second, to improve data quality, trend characteristics are extracted from the apparent resistivity data, and outliers are eliminated. Third, expert insights are systematically integrated to fully utilize prior information on groundwater conditions at the construction face, leading to the establishment of robust predictive models tailored to data from various construction surfaces. Finally, the relevant prediction segment is extracted to complete the groundwater condition forecast. Full article

As China’s first systematic assessment of high-risk Amaranthaceae invaders, this study addresses a critical knowledge gap identified in the National Invasive Species Inventory, in which four invasive Amaranthaceae species (Dysphania ambrosioides, Celosia argentea, Amaranthus palmeri, and Amaranthus spinosus) are prioritized due to CNY 2.6 billion annual ecosystem damages in China. By coupling multi-species comparative analysis with a parameter-optimized Maximum Entropy (MaxEnt) model integrating climate, soil, and topographical variables in China under Shared Socioeconomic Pathways (SSP) 126/245/585 scenarios, we reveal divergent expansion mechanisms (e.g., 247 km faster northward shift in A. palmeri than D. ambrosioides) that redefine invasion corridors in the North China Plain. Under current conditions, the suitable habitats of these species span from 92° E to 129° E and 18° N to 49° N, with high-risk zones concentrated in central and southern China, including the Yunnan–Guizhou–Sichuan region and the North China Plain. Temperature variables (Bio: Bioclimatic Variables; Bio6, Bio11) were the primary contributors based on permutation importance (e.g., Bio11 explained 56.4% for C. argentea), while altitude (e.g., 27.3% for A. palmeri) and UV-B (e.g., 16.2% for A. palmeri) exerted lower influence. Model validation confirmed high accuracy (mean area under the curve (AUC) > 0.86 and true skill statistic (TSS) > 0.6). By the 2090s, all species showed net habitat expansion overall, although D. ambrosioides exhibited net total contractions during mid-century under the SSP126/245 scenarios, C. argentea experienced reduced total suitability during the 2050s–2070s despite high-suitability growth, and A. palmeri and A. spinosus expanded significantly in both total and highly suitable habitat. All species shifted their distribution centroids northward, aligning with warming trends. Overall, these findings highlight the critical role of temperature in driving range dynamics and underscore the need for latitude-specific monitoring strategies to mitigate invasion risks, providing a scientific basis for adaptive management under global climate change. Full article

Efflux is one of the key mechanisms used by Gram-negative bacteria to reduce internal antibiotic concentrations. These active transport systems recognize and expel a wide range of toxic molecules, including antibiotics, thereby contributing to reduced antibiotic susceptibility and allowing the bacteria to acquire additional resistance mechanisms. To date, unlike other resistance mechanisms such as enzymatic modification or target mutations/masking, efflux is challenging to detect and counteract in clinical settings, and no standardized methods are currently available to diagnose or inhibit this mechanism effectively. This review first outlines the structural and functional features of major efflux pumps in Gram-negative bacteria and their role in antibiotic resistance. It then explores various strategies used to curb their activity, with a particular focus on efflux pump inhibitors under development, detailing their structural classes, modes of action, and pharmacological potential. We discuss the main obstacles to their development, including the structural complexity and substrate promiscuity of efflux mechanisms, the limitations of current screening methods, pharmacokinetic and tissue distribution issues, and the risk of off-target toxicity. Overcoming these multifactorial barriers is essential to the rational development of less efflux-prone antibiotics or of efflux pump inhibitors. Full article

With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks. Full article

Microplastic (MP) pollution is an emerging concern in ruminant production, as animals are exposed to MPs through air, water, and feeds. Ruminants play a key role in MP transmission to humans via animal products and contribute to MP return to agricultural soil through excreta. Identifying effective strategies to mitigate MP pollution in the ruminant sector is crucial. A promising yet understudied approach involves the potential ability of rumen microbiota to degrade MPs. This study investigated the in vitro ruminal degradation of three widely distributed MPs—low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polyamide (PA)—over 24, 48, and 72 h. PET MP exhibited the highest degradation rates (24 h: 0.50 ± 0.070%; 48 h: 0.73 ± 0.057%; and 72 h: 0.96 ± 0.082%), followed by LDPE MP (24 h: 0.03 ± 0.020%; 48 h: 0.25 ± 0.053%; and 72 h: 0.56 ± 0.066%) and PA MP (24 h: 0.10 ± 0.045%; 48 h: 0.02 ± 0.015%; and 72 h: 0.14 ± 0.067%). These findings suggest that the ruminal environment could serve as a promising tool for LDPE, PET, and PA MPs degradation. Further research is needed to elucidate the mechanisms involved, potentially enhancing ruminants’ natural capacity to degrade MPs. Full article

High penetration of photovoltaic (PV) generation presents new challenges for voltage regulation in distribution networks (DNs), primarily due to output intermittency and constrained reactive power capabilities. This paper introduces a distributed voltage control method leveraging reactive power compensation from PV inverters. Instead of relying on centralized computation, the proposed method allows each inverter to make local decisions using real-time voltage measurements and delayed communication with neighboring PV nodes. To account for practical asynchronous communication and feedback delay, a Distributed Online Primal–Dual Push–Sum (DOPP) algorithm that integrates a fixed-step delay model into the push–sum coordination framework is developed. Through extensive case studies on a modified IEEE 123-bus system, it has been demonstrated that the proposed method maintains robust performance under both static and dynamic scenarios, even in the presence of fixed feedback delays. Specifically, in static scenarios, the proposed strategy rapidly eliminates voltage violations within 50–100 iterations, effectively regulating all nodal voltages into the acceptable range of [0.95, 1.05] p.u. even under feedback delays with a delay step of 10. In dynamic scenarios, the proposed strategy ensures 100% voltage compliance across all nodes, demonstrating superior voltage regulation and reactive power coordination performance over conventional droop and incremental control approaches. Full article

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators—including dew point depression, critical temperature–humidity zones, and damp effect indices—were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials. Full article

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing magnetic flux concentrators (MFCs) offers an effective approach to enhance TMR sensitivity. In this study, the finite element method was employed to analyze the effects of different MFC geometric structures on the uniformity of the magnetic field in the air gap and the magnetic circuit gain (MCG). It was determined that the MCG of the MFC is not directly related to the absolute values of its parameters but rather to their ratios. Simulation analyses evaluated the impact of these parameter ratios on both the MCG and its spatial distribution uniformity, leading to the formulation of MFC design optimization principles. Building on these simulation-derived principles, several MFCs were fabricated using the 1J85 material, and an experimental platform was established to validate the simulation findings. The fabricated MFCs achieved an MCG of 7.325 times. Based on the previously developed TMR devices, a detection sensitivity of 2.46 nT/$\sqrt{\mathrm{Hz}}$ @1Hz was obtained. By optimizing parameter configurations, this work provides theoretical guidance for further enhancing the performance of TMR sensors in magnetic field measurements. Full article

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

Stainless steel is recognized for its excellent durability and anti-corrosion properties, which are essential qualities across various industrial applications. The machining of stainless steel, particularly under a dry environment to attain sustainability, poses several challenges. The poor heat conductivity and high ductility of stainless steel results in poor heat distribution, accelerating tool wear and problematic chip formation. To mitigate these challenges, the implementation of surface texturing has been identified as a beneficial strategy. This study investigates the impact of wave-type texturing patterns, developed on the flank surface of tungsten carbide ceramic tool inserts, on the machinability of AISI 316 stainless steel under dry cutting conditions. In this investigation, chip morphology and surface roughness were used as key indicators of machinability. Analysis of Variance (ANOVA) was conducted for chip thickness, chip thickness ratio, and surface roughness, while Taguchi mono-objective optimization was applied to chip thickness. The ANOVA results showed that linear models accounted for 71.92%, 83.13%, and 82.86% of the variability in chip thickness, chip thickness ratio, and surface roughness, respectively, indicating a strong fit to the experimental data. Microscopic analysis confirmed a substantial reduction in chip thickness, with a minimum observed value of 457.64 µm. The corresponding average surface roughness Ra value 1.645 µm represented the best finish across all experimental runs, highlighting the relationship between thinner chips and enhanced surface quality. In conclusion, wave textures on the cutting tool’s flank face have the potential to facilitate the dry machining of AISI 316 stainless steel to obtain favorable machinability. Full article

To address freshwater scarcity and the underutilization of low-saline water in the North China Plain, a field study was conducted to evaluate the effects of alternating sprinkler irrigation using saline and fresh water on soil water–salt dynamics and corn growth. Two salinity levels (3 and 5 g·L⁻¹, representing S1 and S2, respectively) and three irrigation strategies—saline–fresh–saline–fresh (F1), saline–fresh (F2), and mixed saline–fresh (F3)—were tested, resulting in six treatments: S1F1, S1F2, S1F3, S2F1, S2F2, and S2F3. S1F1 significantly improved soil water retention at a 30–50 cm depth and reduced surface electrical conductivity (EC) and Na⁺ concentration (p < 0.05). S1F1 also promoted more uniform Mg²⁺ distribution and limited Ca²⁺ loss. Under high salinity (5 g·L⁻¹), surface salt accumulation and ion concentration (Na⁺, Mg²⁺, and Ca²⁺) increased, particularly in S2F3. Corn growth under alternating irrigation (F1/F2) outperformed the mixed mode (F3), with S1F1 achieving the highest plant height, leaf area, grain number, and 100-grain weight. The S1F1 yield surpassed others by 0.4–3.0% and maintained a better ion balance. These results suggest that alternating irrigation with low-salinity water (S1F1) effectively regulates root-zone salinity and improves crop productivity, offering a practical strategy for the sustainable use of low-saline water resources. Full article