Search Results (92,423)

With the development of inspection robot control technology, wheel-legged robots are increasingly used in complex underground space inspection. To address low stability during obstacle crossing in unstructured terrains, a motion control strategy integrating an improved CPG algorithm and a biological reflex mechanism is proposed. It introduces an adaptive coupling matrix, augmented with the Lyapunov function, and vestibular/stumbling reflex models for real-time motion feedback. Simulink–Adams virtual prototypes and single-wheeled leg experiments (on the left front leg) were used to verify the system. Results show that the robot’s turning oscillation was ≤±0.00593 m, the 10° tilt maintained a stable center of mass at 10.2° with roll angle fluctuations ≤±5°, gully-crossing fluctuations ≤±0.01 m, and pitch recovery ≤2 s. The experiments aligned with the simulations, proving that the strategy effectively suppresses vertical vibrations, ensuring stable and high-precision inspection. Full article

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Let \( \mathcal{Q} \) be the finite classical polar space of rank \( \nu\geq 1 \) over \( \mathbb{F}_q \), and \( \mathcal{Q}_m \) be the set of all m-dimensional subspaces of \( \mathcal{Q} \). In this paper, we introduce the \( (m,2) \)-graph with \( \mathcal{Q}_m \) as its vertex set, and two vertices \(P,Q\) are adjacent if and only if \( P+Q \) is an \( (m+2) \)-dimensional subspace of \( \mathcal{Q} \). The full automorphism group of \( (m,2)\)-graph is determined. Full article

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

In tool wear monitoring, the efficient fusion of multi-source sensor signals poses significant challenges due to their inherent heterogeneous characteristics. In this paper, we propose a Multi-Sensor Multi-Domain feature fusion Transformer (MSMDT) model that achieves precise tool wear prediction through innovative feature engineering and cross-modal self-attention mechanisms. Specifically, we first develop a physics-aware feature extraction framework, where time-domain statistical features, frequency-domain energy features, and wavelet packet time–frequency features are systematically extracted for each sensor type. This approach constructs a unified feature matrix that effectively integrates the complementary characteristics of heterogeneous signals while preserving discriminative tool wear signatures. Then, a position-embedding-free Transformer architecture is constructed, which enables adaptive cross-domain feature fusion through joint global context modeling and local feature interaction analysis to predict tool wear values. Experimental results on the PHM2010 demonstrate the superior performance of MSMDT, outperforming state-of-the-art methods in prediction accuracy. Full article

The blended system of solid waste micro-powders is of great significance for the efficient utilization of recycled micro-powder. In this study, a ternary blended cementitious system composed of fly ash, slag, and recycled micro-powder was constructed, and its effects on the workability, mechanical properties, shrinkage performance, and microstructure of recycled mortar were systematically investigated. The experimental results show that with the increasing dosage of slag and recycled micro-powder (partially replacing cement and fly ash), the standard consistency water demand of the cementitious system decreases and the setting time is prolonged. When the replacement levels of recycled micro-powder and slag are both 10%, the 3-day, 7-day, and 28-day mechanical strengths of the mortar specimens are comparable to those of the reference group, with an increased flexural-to-compressive strength ratio and improved brittleness. SEM and mercury intrusion porosimetry (MIP) analyses revealed that systems incorporating low addition levels of recycled micro powder and slag powder exhibit calcium silicate hydrate (C-S-H) gel, acicular ettringite crystals, and a denser pore structure. However, at higher dosages (>10%), the porosity increases significantly and the pore structure deteriorates, resulting in reduced shrinkage performance. Overall, when the replacement rate of cement–fly ash by recycled micro-powder and slag is 10%, the ternary blended system exhibits optimal macroscopic performance and microstructure, providing a scientific basis for the resource utilization of solid waste. Full article

As direct detection methods of Borrelia burgdorferi are limited, serology plays an important role in diagnosing Lyme disease (LD). There are various types of Lyme serological tests with varying diagnostic accuracy, so it is necessary to compare and rank them. The aim of this study is to compare the accuracy of various serological diagnostic methods for LD using network meta-analysis (NMA). We searched the Cochrane Library and PubMed databases for all serological diagnostic accuracy studies published from the discovery of LD until June 2024. After screening, we assessed the quality of the included studies with QUADAS-C and extracted relevant data. We calculated the Q* index of the receiver operating characteristic curve for each diagnostic test. Meta-disc 2.0 and Stata 15.0 were used to perform traditional meta-analysis and NMA with the gold standard (the comprehensive evaluation) as a reference. We then compared the Q* index values between different methods using two-by-two comparisons and ranked them accordingly. A total of 52 studies with 181,032 participants, including 5318 patients with LD, were included. These studies covered 14 diagnostic methods. The results of the NMA suggest that modified two-tiered testing (MTTT), C6 enzyme immunoassay (EIA), and standard two-tiered testing (STTT) rank in the top three among the 14 methods in terms of Q* index, with MTTT being the highest, followed by C6 EIA and STTT. MTTT and C6 EIA have higher overall diagnostic performance, and their accuracy is not inferior to that of the widely used STTT (PROSPERO CRD42022378326). Full article

In recent years, most proposed digital image encryption algorithms have primarily focused on encrypting raw pixel data, often neglecting the integration with image compression techniques. Image compression algorithms, such as JPEG, are widely utilized in internet applications, highlighting the need for encryption methods that are compatible with compression processes. This study introduces an innovative color image encryption algorithm integrated with JPEG compression, designed to enhance the security of images susceptible to attacks or tampering during prolonged transmission. The research addresses critical challenges in achieving an optimal balance between encryption security and compression efficiency. The proposed encryption algorithm is structured around three key compression phases: Discrete Cosine Transform (DCT), quantization, and entropy coding. At each stage, the algorithm incorporates advanced techniques such as block segmentation, block replacement, DC coefficient confusion, non-zero AC coefficient transformation, and RSV (Run/Size and Value) pair recombination. Extensive simulations and security analyses demonstrate that the proposed algorithm exhibits strong robustness against noise interference and data loss, effectively meeting stringent security performance requirements. Full article

Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexpected bugs may occasionally pass analysis, underlining the necessity for automated safe and unsafe code detection. This paper presents Rust-IR-BERT, a machine learning approach to detect security vulnerabilities in Rust code by analyzing its compiled LLVM intermediate representation (IR) instead of the raw source code. This approach offers novelty by employing LLVM IR’s language-neutral, semantically rich representation of the program, facilitating robust detection by capturing core data and control-flow semantics and reducing language-specific syntactic noise. Our method leverages a graph-based transformer model, GraphCodeBERT, which is a transformer architecture pretrained model to encode structural code semantics via data-flow information, followed by a gradient boosting classifier, CatBoost, that is capable of handling complex feature interactions—to classify code as vulnerable or safe. The model was evaluated using a carefully curated dataset of over 2300 real-world Rust code samples (vulnerable and non-vulnerable Rust code snippets) from RustSec and OSV advisory databases, compiled to LLVM IR and labeled with corresponding Common Vulnerabilities and Exposures (CVEs) identifiers to ensure comprehensive and realistic coverage. Rust-IR-BERT achieved an overall accuracy of 98.11%, with a recall of 99.31% for safe code and 93.67% for vulnerable code. Despite these promising results, this study acknowledges potential limitations such as focusing primarily on known CVEs. Built on a representative dataset spanning over 2300 real-world Rust samples from diverse crates, Rust-IR-BERT delivers consistently strong performance. Looking ahead, practical deployment could take the form of a Cargo plugin or pre-commit hook that automatically generates and scans LLVM IR artifacts during the development cycle, enabling developers to catch vulnerabilities at an early stage in the development cycle. Full article

In this study, the impact of moderate and high CO₂ and O₂ levels was compared to low and moderate gas combinations during prolonged storage on the quality of Regina sweet cherries harvested in different maturity stages, particularly in terms of decreasing internal browning. Fruits were harvested in two different maturity stages (Light and Dark Mahogany skin color) and stored in CA of 15% CO₂ + 10% O₂; 10% CO₂ + 10% O₂; 10% CO₂ + 5% O₂; 5% CO₂ + 5% O₂ and MA of 4 to 5% CO₂ + 16 to 17% O₂ for 30 and 40 days at 0 °C and 90% RH, followed by a marketing period. After the storage, both maturity stages significantly reduced internal browning, decay, and visual quality losses in CA with 10–15% CO₂ and 10% O₂. In addition, it preserved luminosity, total soluble solids (TSSs), titratable acidity (TA), and bioactive compounds such as anthocyanins and phenols. This treatment also maintained the visual appearance of the sweet cherries, favoring their market acceptance. At the same time, the light red fruits showed a better general quality compared to darker color after the storage. In conclusion, a controlled atmosphere with optimized CO₂ and O₂ concentrations, together with harvesting with a Light Mahogany external color, represents an effective strategy to extend the shelf life of Regina sweet cherries up to 40 days plus the marketing period, maintaining their physical and sensory quality for export markets. Full article

Rhabditis (Rhabditella) axei is a predominantly free-living nematode commonly found in sewage systems and decomposing organic matter. While primarily saprophytic, it has been documented as an opportunistic pathogen in human urinary and gastrointestinal tracts. The Chinese red panda (Ailurus styani), a rare and protected species in China, has not previously been reported as a host for Rhabditis (Rhabditella) spp. infections. This study reports the first documented occurrence of R. axei in red panda feces, unambiguously confirmed through integrative taxonomic approaches combining morphological and molecular analyses. The nematodes exhibited key morphological features consistent with R. axei, including a cylindrical rhabditiform esophagus, sexually dimorphic tail structures, and diagnostic spicule morphology. Molecular analysis based on 18S-ITS-28S rDNA sequencing confirmed their identity, showing >99% sequence similarity to R. axei reference strains (GenBank: PP135624.1, PP135622.1). Phylogenetic reconstruction using 18S rDNA and ITS rDNA sequences placed the isolate within a well-supported R. axei clade, clearly distinguishing it from related species such as R. blumi and R. brassicae. The findings demonstrate the ecological plasticity of R. axei as a facultative parasite capable of infecting non-traditional hosts and further highlight potential zoonotic risks associated with environmental exposure in captive wildlife populations. Our results emphasize the indispensable role of molecular diagnostics in accurately distinguishing morphologically similar nematodes within the Rhabditidae family, while providing essential baseline data for health monitoring in both in situ and ex situ conservation programs for this endangered species. Full article

Antibiotic misuse accelerates resistance dissemination via plasmid conjugation, but quorum sensing (QS) regulatory mechanisms remain undefined. Using Escherichia coli (E. coli) MG1655 conjugation models (RP4-7/EC600 plasmids), we demonstrate that long-chain acyl-homoserine lactones (C10/C12-HSL) enhance transfer frequency by up to 7.7-fold (200μM C12-HSL; p < 0.001), while quorum-quenching by sub-inhibitory vanillin suppressed this effect by 95% (p < 0.0001). C12-HSL compromised membrane integrity via ompF upregulation (4-fold; p < 0.01) and conjugative pore assembly (trbBp upregulated by 1.38-fold; p < 0.05), coinciding with ROS accumulation (1.5-fold; p < 0.0001) and SOS response activation (recA upregulated by 1.68-fold; p < 0.001). Crucially, rpoS and rmf deletion mutants reduced conjugation by 65.5% and 55.8%, respectively (p < 0.001), exhibiting attenuated membrane permeability (≤65.5% reduced NPN influx; p < 0.0001), suppressed ROS (≤54% downregulated; p < 0.0001), and abolished transcriptional induction of conjugation/stress genes. Reciprocal RpoS–RMF (ribosomal hibernation factor) crosstalk was essential for AHL responsiveness, with deletions mutually suppressing expression (≤65.9% downregulated; p < 0.05). We establish a hierarchical mechanism wherein long-chain AHLs drive resistance dissemination through integrated membrane restructuring, stress adaptation, and RpoS–RMF-mediated genetic plasticity, positioning QS signaling as a viable target for curbing resistance spread. Full article

This study focuses on optimizing track and grouser structural parameters to enhance UATV drawbar pull, particularly under soft soil conditions. A numerical soil thrust model for single-track shoes was developed based on track–soil interaction mechanics, revealing distinct mechanistic roles: track structural parameters (length/width) govern pressure–sinkage relationships at the track base, while grouser structural parameters (height, spacing, V-shaped angle) dominate shear stress–displacement dynamics on grouser shear planes. A novel DEM-MBD coupling simulation framework was established through soil parameter calibration and multi-body dynamics modeling, demonstrating that soil thrust increases with grouser height and V-shaped angle, but decreases with spacing, with grouser height exhibiting the highest sensitivity. A soil bin test validated the numerical model’s accuracy and the coupling method’s efficacy. Parametric optimization via the Whale Optimization Algorithm (WOA) achieved a 55.86% increase in drawbar pull, 40.38% reduction in ground contact pressure and 57.33% improvement in maximum gradability. These advancements substantially improve the tractive performance of UATVs in soft beach terrains. The proposed methodology provides a systematic framework for amphibious vehicle design, integrating numerical modeling, high-fidelity simulation, and experimental validation. Full article

Understanding the migratory strategies of plateau-endemic species is essential for informing effective conservation, especially under climate change. The Black-necked Crane (Grus nigricollis), a high-altitude specialist, has shown notable population growth in recent years. We analysed satellite tracking data from 16 individuals of a western subpopulation in the lake basin region of northern Tibet (2021–2024), focusing on migration patterns, stopover use, and habitat selection. This subpopulation exhibited short-distance (mean: 284.21 km), intra-Tibet migrations with low reliance on stopover sites. Autumn migration was shorter, more direct, higher in altitude, and slower in speed than spring migration. Juveniles used smaller, more fragmented habitats than subadults, and their spatial range expanded over time. Given these patterns, we infer that the short-distance migration strategy may reduce energetic demands and mortality risks while increasing route flexibility—characteristics that may benefit population growth. We refer to this as a low-energy, high-efficiency migration strategy, which we hypothesise could support faster population growth and enhance resilience to environmental change. We recommend prioritizing the conservation of short-distance migration corridors, such as the typical lake basin area in northern Tibet–Yarlung Tsangpo River system, which may help sustain plateau-endemic migratory populations under future climate scenarios. Full article

For high-voltage signal detection applications, an auto-zero and chopper operational amplifier (OPA) is proposed in this paper. With the auto-zero and chopper technique, the OPA adopts an eight-channel Ping-Pong mechanism to reduce the high-frequency ripple and glitch generated by chopper modulation. The main transconductor effectively suppresses low-frequency noise and offset by combining input coarse and output fine auto-zero. A common-mode voltage tracking circuit is presented to ensure constant gate-source and gate-substrate voltages of the chopper, which reduces the charge injection caused by threshold voltage drift of their transistors and improves output signal resolution. The OPA is implemented using CMOS 180 nm BCD process. The post-simulation results show that the unit gain bandwidth (UGB) is 2.5 MHz and common-mode rejection ratio (CMRR) is 137 dB when the power supply voltage is 5–55 V. The noise power spectral density (PSD) is 6.6 nV/√Hz, and the offset is about 47 µV. The overall circuit consumes current of 960 µA. Full article