Search Results (18,601)

Automated guided vehicles (AGVs) operating in uneven environments are typically designed with an elevated chassis to enhance obstacle-crossing. In inductive power transfer (IPT) systems for such AGVs, a long transmission distance along with limited installation space for coils leads to a medium distance-to-diameter ratio (DDR) (1 < DDR ≤ 2), which reduces coupling efficiency and degrades misalignment tolerance. To address this issue, this paper proposes a compact dual-receiver IPT system for medium DDR conditions. The system adopts a flat U-shaped solenoid (FUS) coil as both the transmitter and the primary receiver, and a square solenoid (SS) coil as the secondary receiver, forming the FUSS dual-receiver structure. The FUS coil is optimized through finite element analysis to improve coupling, while the SS coil captures vertical flux to compensate for misalignment losses, thereby enhancing misalignment tolerance. A hybrid rectifier integrating a full-bridge and voltage doubler topology is used to suppress output voltage fluctuation, reduce the number of receiver coil turns, and minimize system volume. A 300 W/100 kHz prototype with a coupler size of 183 × 126 × 838 mm³ achieves 83.51% efficiency under medium DDR and a 185 mm air gap. Voltage fluctuation remains within 5% under ±51.4% X-axis and ±51.7% Y-axis misalignment, confirming the stable power delivery and improved misalignment tolerance of the system. Full article

To explore a direct predictive model for the tensile strength of ultra-high-performance concrete (UHPC), machine learning (ML) algorithms are presented. Initially, a database comprising 178 samples of UHPC tensile strength with varying parameters is established. Then, feature engineering strategies are proposed to optimize the robustness of ML models under a small-sample condition. Further, the performance and efficiency of algorithms are compared under default hyperparameters and hyperparameter tuning, respectively. Moreover, the utilization of SHapley Additive exPlanations (SHAP) enables the analysis of the relationships between UHPC tensile strength and its influencing factors. The quantitative analysis results indicate that ensemble algorithms exhibit superior performance, indicated by R² values of above 0.92, under default hyperparameters. After hyperparameter tuning, both conventional and ensemble models achieve R² values exceeding 0.94. However, Bayesian ridge regression (BRR) consistently demonstrates a suboptimal performance, irrespective of hyperparameter tuning. Notably, Categorical Boosting (CatBoost) requires a substantial duration of 1208 s, which is notably more time-consuming than that of other algorithms. The most influential feature identified is fiber reinforcement index with a contribution of 37.5%, followed by the water-to-cement ratio, strain rate, and cross-sectional size. The nonlinear relationship between UHPC tensile strength and the top four factors is visualized, and the critical thresholds are identified. Full article

Microchip electrophoresis (ME) has been recognized as a promising analytical technique in life sciences, disease diagnostics, and environmental monitoring due to advantages such as minimal reagent consumption, rapid analysis, and compact size. While extensive efforts have been made to enhance ion analysis performance, the influence of solution properties—such as zeta potential, diffusion coefficient, ionic charge, and dynamic viscosity—has not been fully explored. In this study, the influence of solution properties on the performance of ion separation via ME was systematically evaluated through numerical simulations. A finite element method (FEM) model was established, in which multiple physical fields were considered. To verify the model, ion analysis experiments were conducted under corresponding conditions. Based on the validated model, a series of simulations were carried out to evaluate the effects of solution properties on separation performance. It was demonstrated that solution properties significantly affect the separation behavior, including ion arrival time, concentration-peak height, and separation resolution. These findings suggest that solution properties should not be overlooked in the design and optimization of ME systems. The simulation approach presented in this work is expected to provide valuable insights into the improvement of ion analysis using ME. Full article

This study established a biological fermentation process using Saccharomyces cerevisiae and Lactobacillus plantarum to deodorize squid cartilage homogenate. The optimal fermentation conditions for S. cerevisiae were determined as follows: fermentation time 105 min, temperature 34 °C, and inoculum size 0.85%. For L. plantarum, the optimum conditions were 79 min, 34.5 °C, and 4.5% inoculum. Based on electronic nose and HS-SPME-GC-MS analyses, S. cerevisiae outperformed L. plantarum in eliminating key offensive odor compounds, especially sulfur-containing compounds and aldehydes, while promoting the formation of pleasant aroma compounds such as esters and ketones (e.g., carvone and δ-pentenol). Mechanistic insights suggest that the enhanced deodorization efficiency of S. cerevisiae may be attributed to its multi-pathway synergistic metabolism, involving enzymes like dioxygenases and sulfide oxidases that facilitate the conversion of malodorous substances into odorless or pleasantly aromatic compounds. These findings provide a valuable theoretical and practical foundation for the high-value utilization of squid processing by-products and propose a promising bio-deodorization strategy for aquatic products. Full article

The survival status of Lumnitzera littorea is near threatened globally and critically endangered in China. Clarifying its global distribution pattern and its changing trends under different future climate models is of great significance for the protection and restoration of its endangered status. To build a model for this purpose, this study selected 73 actual distribution points of Lumnitzera littorea worldwide, combined with 12 environmental factors, and simulated its potential suitable habitats in six periods: the Last Interglacial (130,000–115,000 years ago), the Last Glacial Maximum (27,000–19,000 years ago), the Mid-Holocene (6000 years ago), the present (1970–2000), and the future 2050s (2041–2060) and 2070s (2061–2080). The results show that the optimal model parameter combination is the regularization multiplier RM = 4.0 and the feature combination FC (Feature class) = L (Linear) + Q (Quadratic) + P (Product). The MaxEnt model has a low omission rate and a more concise model structure. The AUC values in each period are between 0.981 and 0.985, indicating relatively high prediction accuracy. Min temperature of the coldest month, mean diurnal range, clay content, precipitation of the warmest quarter, and elevation are the dominant environmental factors affecting its distribution. The environmental conditions for min temperature of the coldest month at ≥19.6 °C, mean diurnal range at <7.66 °C, clay content at 34.14%, precipitation of the warmest quarter at ≥570.04 mm, and elevation at >1.39 m are conducive to Lumnitzera littorea’s survival and distribution. The global potential distribution areas are located along coasts. Starting from the paleoclimate, the plant’s distribution has gradually expanded, and its adaptability has gradually improved. In China, the range of potential highly suitable habitats is relatively narrow. Hainan Island is the core potential habitat, but there are fragmented areas in regions such as Guangdong, Guangxi, and Taiwan. The modern centroid of Lumnitzera littorea is located at (109.81° E, 2.56° N), and it will shift to (108.44° E, 3.22° N) in the later stage of the high-emission scenario (2070s (SSP585)). Under global warming trends, it has a tendency to migrate to higher latitudes. The development of the aquaculture industry and human deforestation has damaged the habitats of Lumnitzera littorea, and its population size has been sharply and continuously decreasing. The breeding and renewal system has collapsed, seed abortion and seedling establishment failure are common, and genetic variation is too scarce. This may indicate why Lumnitzera littorea is near threatened globally and critically endangered in China. Therefore, the protection and restoration strategies we propose are as follows: strengthen the legislative guarantee and law enforcement supervision of the native distribution areas of Lumnitzera littorea, expanding its population size outside the native environment, and explore measures to improve its seed germination rate, systematically collecting and introducing foreign germplasm resources to increase its genetic diversity. Full article

Chinese coalbed methane (CBM) reservoirs exhibit characteristically low recovery rates due to adsorbed gas dominance and “three-low” properties (low permeability, low pressure, and low saturation). CO₂ thermal drive (CTD) technology addresses this challenge by leveraging dual mechanisms—thermal desorption and displacement to enhance production; however, its effectiveness necessitates uniform fracture networks for temperature field homogeneity—a requirement unmet by conventional long-fracture fracturing. To bridge this gap, a coupled seepage–heat–stress–fracture model was developed, and the temperature field evolution during CTD in coal under non-uniform fracture networks was determined. Integrating multi-cluster fracture propagation with stress barrier and intra-stage stress differential characteristics, a stress-barrier-responsive diverting fracturing technology meeting CTD requirements was established. Results demonstrate that high in situ stress and significant stress differentials induce asymmetric fracture propagation, generating detrimental CO₂ channeling pathways and localized temperature cold islands that drastically reduce CTD efficiency. Further examination of multi-cluster fracture dynamics identifies stress shadow effects and intra-stage stress differentials as primary controlling factors. To overcome these constraints, an innovative fracture network uniformity control technique is proposed, leveraging synergistic interactions between diverting parameters and stress barriers through precise particle size gradation (16–18 mm targeting toe obstruction versus 19–21 mm sealing heel), optimized pumping displacements modulation (6 m³/min enhancing heel efficiency contrasted with 10 m³/min improving toe coverage), and calibrated diverting concentrations (34.6–46.2% ensuring uniform cluster intake). This methodology incorporates dynamic intra-stage adjustments where large-particle/low-rate combinations suppress toe flow in heel-dominant high-stress zones, small-particle/high-rate approaches control heel migration in toe-dominant high-stress zones, and elevated concentrations (57.7–69.2%) activate mid-cluster fractures in central high-stress zones—collectively establishing a tailored framework that facilitates precise flow regulation, enhances thermal conformance, and achieves dual thermal conduction and adsorption displacement objectives for CTD applications. Full article

The ocean harbors vast potential for oil and gas resources, positioning offshore drilling as a critical approach for future energy exploration. However, high-temperature and high-pressure offshore reservoirs present formidable challenges, as conventional water-based drilling fluids are prone to thermal degradation and rheological instability, leading to wellbore collapse and stuck-pipe incidents. Offshore oil-based drilling fluids (OBDFs), typically water-in-oil emulsions, offer advantages in wellbore stability, lubricity, and contamination resistance, yet their stability under extreme high-temperature conditions remains limited. This study reveals the enhancement of offshore OBDFs performance in harsh conditions by employing nano-SiO₂ to synergistically improve emulsion film stability and non-Newtonian rheological behavior while systematically elucidating the underlying mechanisms. Nano-SiO₂ forms a composite film with emulsifiers, reducing droplet size, enhancing mechanical strength, and increasing thermal stability. Optimal stability was observed at an oil-to-water ratio of 7:3 with 2.5% nano-SiO₂ dispersion and 4.0% emulsifier. Rheological analyses revealed that nano-silica enhances electrostatic repulsion, reduces plastic viscosity, establishes a network structure that increases yield stress, and promotes pronounced shear-thinning behavior. Macroscopic evaluations, including fluid loss, rheological performance, and electrical stability, further confirmed the improved high-temperature stability of offshore OBDFs with nano-SiO₂ at reduced emulsifier concentrations. These findings provide a theoretical basis for optimizing offshore OBDFs formulations and their field performance, offering breakthrough technological support for safe and efficient drilling in ultra-high-temperature offshore reservoirs. Full article

Background/Objectives: Double ureter kidney grafts raise concerns about increased urologic complications. Limited data exist on optimal surgical management due to small sample sizes in previous reports. This study evaluated outcomes using pantaloon ureteroneocystostomy in the largest reported cohort worldwide. Research Questions: Does pantaloon ureteroneocystostomy achieve comparable outcomes to single ureter transplants? Are long-term graft survival and function equivalent? Should this technique be adopted as standard practice? Methods: This retrospective matched cohort study involves 2210 kidney transplantations (2010–2024). Twenty-six double ureter grafts underwent pantaloon ureteroneocystostomy with dual stenting. Controls matched 1:1 for donor type, era, and recipient characteristics. The primary outcome was urologic complications. Statistical analysis included Kaplan–Meier survival curves and Mann–Whitney U tests. Results: Groups were well matched (median age: 51 vs. 52 years, 50% living donors each). Urologic complications occurred in 3.8% double ureter versus 7.7% control grafts (p = 1.000), markedly lower than 15.4% reported in recent literature. The single complication was early urinary leak, surgically repaired. No late strictures developed. The 5-year graft survival was 96.0% vs. 92.3% (p = 1.000). The final creatinine was comparable (1.25 vs. 1.28 mg/dL, p = 0.891). Conclusions: The pantaloon technique achieves superior outcomes in the largest reported double ureter cohort, with complication rates lower than previously published series. These findings support adopting this standardized approach globally to expand donor criteria while maintaining excellent outcomes. Full article

This study investigated the effects of the foliar application of sodium silicate (SS) on phytolith formations in Dendrocalamus brandisii (Munro) Kurz. leaves by analyzing the phytolith concentration, morphological parameters, and assemblage compositions across leaves of varying ages and different phenological stages. The results showed that SS significantly increased the phytolith concentration in D. brandisii leaves, showing a trend of old leaves > mature leaves > young leaves. The concentration of phytoliths was the highest at the late shooting stage (November) and the lowest at the dormancy stage (January). August (shooting stage) and May (branching and leafing stage) were the critical periods for phytolith formation and the size and morphological variation. Sodium silicate significantly increased the proportion of saddle, bilobate, and stomatal phytoliths, which might help optimize the silicified structure of leaf epidermal cells and enhance the leaf resistance and light energy utilization efficiency. The results help clarify the mechanism of phytolith formation in different phenological periods of D. brandisii and provide a theoretical basis for the efficient use of silicon fertilizers in bamboo cultivation. Full article

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

Drowning constitutes the leading cause of injury-related fatalities among adolescents. In swimming pool environments, traditional manual surveillance exhibits limitations, while existing technologies suffer from poor adaptability of wearable devices. Vision models based on YOLO still face challenges in edge deployment efficiency, robustness in complex water conditions, and multi-scale object detection. To address these issues, we propose YOLO11-LiB, a drowning object detection model based on YOLO11n, featuring three key enhancements. First, we design the Lightweight Feature Extraction Module (LGCBlock), which integrates the Lightweight Attention Encoding Block (LAE) and effectively combines Ghost Convolution (GhostConv) with dynamic convolution (DynamicConv). This optimizes the downsampling structure and the C3k2 module in the YOLO11n backbone network, significantly reducing model parameters and computational complexity. Second, we introduce the Cross-Channel Position-aware Spatial Attention Inverted Residual with Spatial–Channel Separate Attention module (C2PSAiSCSA) into the backbone. This module embeds the Spatial–Channel Separate Attention (SCSA) mechanism within the Inverted Residual Mobile Block (iRMB) framework, enabling more comprehensive and efficient feature extraction. Finally, we redesign the neck structure as the Bidirectional Feature Fusion Network (BiFF-Net), which integrates the Bidirectional Feature Pyramid Network (BiFPN) and Frequency-Aware Feature Fusion (FreqFusion). The enhanced YOLO11-LiB model was validated against mainstream algorithms through comparative experiments, and ablation studies were conducted. Experimental results demonstrate that YOLO11-LiB achieves a drowning class mean average precision (DmAP50) of 94.1%, with merely 2.02 M parameters and a model size of 4.25 MB. This represents an effective balance between accuracy and efficiency, providing a high-performance solution for real-time drowning detection in swimming pool scenarios. Full article

Introduction: Overuse injuries are a growing concern among adolescent soccer players, with the repetitive nature of the sport placing significant physical demands on young athletes. These injuries can have long-term implications for physical development, performance, and overall well-being. This narrative synthesis aimed to evaluate the existing literature on the epidemiology, risk factors, and management strategies for overuse injuries in adolescent soccer players. Methods: A comprehensive literature search was conducted using PubMed and Embase. A total of 123 articles were identified, 27 of which met the inclusion criteria after screening. Studies focusing on overuse injuries in adolescent soccer players aged 10–18 years were included, while those addressing acute injuries, non-soccer populations, or adult athletes were excluded. Relevant quantitative and qualitative data were extracted and evaluated. Due to heterogeneity in study designs and outcomes, findings were narratively synthesized rather than meta-analyzed. Results: The period around peak height velocity (PHV: 11.5 years in girls, 13.5 years in boys) was consistently identified as a high-risk window, with seven studies demonstrating a significantly increased incidence of overuse injuries. Additional risk factors included leg length asymmetry, truncal weakness, early sport specialization, high ratios of organized-to-free play, and increased body size. Injury burden was greatest for hamstring and groin injuries, often leading to prolonged time lost from play. Preventive interventions such as plyometric training, trunk stabilization, and structured load monitoring demonstrated reductions in injury incidence in several prospective studies, though protocols varied widely. Conclusion: This narrative synthesis highlights PHV as the most consistent risk factor for overuse injuries in adolescent soccer players, alongside modifiable contributors such as training load, sport specialization, and free play balance. Evidence supports neuromuscular training and structured monitoring as promising preventive strategies, but there remains a lack of standardized, evidence-based protocols. Future research should focus on optimizing and validating interventions, integrating growth and load monitoring, and leveraging emerging approaches such as machine learning-based risk prediction. Full article

Background: Whole-slide images (WSIs) are crucial in pathology for digitizing tissue slides, enabling pathologists and AI models to analyze cancer patterns at gigapixel scale. However, their large size incorporates artifacts and non-tissue regions that slow AI processing, consume resources, and introduce errors like false positives. Tissue detection serves as the essential first step in WSI pipelines to focus on relevant areas, but deep learning detection methods require extensive manual annotations. Methods: This study benchmarks four thumbnail-level tissue detection methods—Otsu’s thresholding, K-Means clustering, our novel annotation-free Double-Pass hybrid, and GrandQC’s UNet++ on 3322 TCGA WSIs from nine cancer cohorts, evaluating accuracy, speed, and efficiency. Results: Double-Pass achieved an mIoU of 0.826—very close to the deep learning GrandQC model’s 0.871—while processing slides on a CPU in just 0.203s per slide, markedly faster than GrandQC’s 2.431s per slide on the same hardware. As an annotation-free, CPU-optimized method, it therefore enables efficient, scalable thumbnail-level tissue detection on standard workstations. Conclusions: The scalable, annotation-free Double-Pass pipeline reduces computational bottlenecks and facilitates high-throughput WSI preprocessing, enabling faster and more cost-effective integration of AI into clinical pathology and research workflows. Comparing Double-Pass against established methods, this benchmark demonstrates its novelty as a fast, robust and annotation-free alternative to supervised methods. Full article

Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact. Full article

Analog and mixed-signal integrated circuit (AMS-IC) designs remain a critical yet challenging aspect within electronic design automation (EDA), primarily due to the inherent complexity, nonlinear behavior, and increasing variability associated with advanced semiconductor technologies. Traditional manual and intuition-driven methodologies for AMS-ICs design, which rely heavily on iterative simulation loops and extensive designer experience, face significant limitations concerning efficiency, scalability, and reproducibility. Recently, machine learning (ML) techniques have emerged as powerful tools to address these challenges, offering significant enhancements in modeling, abstraction, optimization, and automation capabilities for AMS-ICs. This review systematically examines recent advancements in ML-driven methodologies applied to analog circuit design, specifically focusing on modeling techniques such as Bayesian inference and neural network (NN)-based surrogate models, optimization and sizing strategies, specification-driven predictive design, and artificial intelligence (AI)-assisted design automation for layout generation. Through an extensive survey of the existing literature, we analyze the effectiveness, strengths, and limitations of various ML approaches, identifying key trends and gaps within the current research landscape. Finally, the paper outlines potential future research directions aimed at advancing ML integration in AMS-ICs design, emphasizing the need for improved explainability, data availability, methodological rigor, and end-to-end automation. Full article