Search Results (3,453)

We present a unified GPU-accelerated framework for real-time Smoothed Particle Hydrodynamics (SPH) fluid simulation with two-way rigid body coupling, secondary particle effects, and volumetric rendering, implemented entirely within the Unity game engine. The framework employs a weakly compressible SPH formulation with $O\left(n\right)$ count sort-based spatial hashing and introduces a signed distance field (SDF) coupling system that evaluates three representative geometric primitives, sphere, cylinder, and torus, of increasing topological complexity directly on the GPU. Bidirectional force exchange is achieved through lock-free atomic compare-and-swap impulse accumulation, enabling thousands of fluid particles to interact simultaneously with each rigid body without serialization. A GPU stream compaction–based secondary particle system generates and classifies foam, spray, and bubble effects in real time, while a volumetric rendering pipeline samples fluid density into a 3D texture for SDF-composited volume rendering without surface mesh extraction. A conditional kernel dispatch strategy eliminates GPU cycles for disabled subsystems, and dynamic buffer management reduces memory pressure through runtime allocation. The system sustains above 54 frames per second at four million particles on a consumer-grade GPU, with sub-linear frame time scaling and a $1.70\times$ speedup from dynamic buffer allocation over static pre-allocation. Full article

Galloping of overhead transmission lines is a low-frequency, large-amplitude vibration hazard that poses a severe threat to power grid safety, yet existing monitoring approaches fail to simultaneously provide flexible deployment, quantitative measurement, and robustness under severe weather conditions. This paper makes three primary contributions. First, we propose a novel line-structure center adsorption algorithm that converts a single operator touch-point into a sub-pixel-precision conductor prompt, achieving prompt accuracy above 95% with one round of interactive correction. Second, we introduce—for the first time—SAM2’s streaming memory architecture for continuous zero-shot pixel-level tracking of galloping conductors under complex outdoor backgrounds including snow, ice, and poor illumination, achieving a segmentation IoU of 93.8% and zero identity switches over 500 consecutive frames, outperforming XMem (87.4%) and DeAOT (88.9%). Third, we develop a two-stage spatial correction framework combining vanishing-point-based inverse perspective mapping (IPM) with equidistant linear transformation (ELT), which eliminates perspective distortion inherent in non-orthogonal field imaging and enables quantitative measurement of galloping amplitude (error < 0.5 m), frequency (error < 0.1 Hz), and inter-phase spacing (ranging error < 1 m). The complete pipeline is implemented on a portable, tripod-mounted device (≤15 kg) integrating a monocular camera, laser rangefinder, and high-precision PTZ gimbal. Field validation at three 110/500 kV sites in Jiangsu Province under extreme winter conditions ($-4$ °C, Level 5 wind, continuous snowfall) confirms engineering-grade accuracy and practical robustness, providing a viable technical pathway for real-time non-contact galloping monitoring and disaster early warning. Full article

Detecting previously unseen malware remains a critical challenge for modern cybersecurity systems due to the rapid evolution of malicious software and the limitations of traditional supervised detection models. This paper proposes a Dual-Channel Mamba-Based Semantic–Behavioral Feature Learning framework for zero-day malware detection that jointly models static malware artifacts and dynamic execution traces within a unified representation space. The proposed architecture employs two parallel encoders that extract semantic features from executable structures and behavioral features from API call sequences. These features are integrated through a cross-channel fusion mechanism and processed using a Mamba-based selective state space architecture, which efficiently captures long-range dependencies in malware behavior while maintaining linear computational complexity. To address the zero-day detection problem, a prototype-guided inference strategy is introduced that enables similarity-based classification of previously unseen malware families within the learned embedding space. Extensive experiments conducted on multiple malware datasets demonstrate that the proposed framework consistently outperforms strong deep learning baselines. The model achieves an average classification accuracy of 96.01% ± 0.35 and an F1-score of 95.56% ± 0.36, while the zero-day detection rate reaches 88.93% ± 0.98, significantly improving detection performance compared with transformer and recurrent architectures. Runtime analysis further shows that the proposed framework achieves efficient inference with an average latency of approximately 8 ms per sample, making it suitable for real-time malware analysis systems. These results indicate that combining dual-channel feature learning with Mamba-based sequential modeling provides an effective and scalable solution for detecting both known and previously unseen malware threats. Full article

Background: Inguinal hernia repair is a high-volume procedure frequently performed in Day Surgery settings. While local anesthesia is often considered the gold standard, its feasibility is limited in complex cases or due to patient refusal, necessitating alternatives like general (GA) or spinal anesthesia (SA). This study evaluates the impact of these techniques on acute pain, complications, and chronic postoperative inguinal pain (CPIP). Methods: A retrospective observational study was conducted on 73 adult patients undergoing unilateral Lichtenstein hernioplasty (GA = 24; SA = 49). Pain was assessed using the Numeric Rating Scale (NRS) at discharge (T0), 24 h (T1), 7 days (T2), and 180 days (T3). Postoperative complications, rescue analgesic consumption, and perceived time to recovery were recorded. A multivariable linear regression analysis was performed to adjust pain outcomes for age, sex, and ASA status. Results: GA patients reported significantly lower median NRS scores at T0, T1, and T2 in univariate analysis (p < 0.05). However, the multivariable model did not show statistical significance for anesthetic technique as an independent predictor. Constipation was the most frequent complication (35.6%), while nausea occurred only in the SA group (10.2%). Descriptive data showed a trend toward lower rescue analgesic needs and a faster perceived time to recovery in the GA group compared to SA. CPIP incidence was remarkably low (2.7%). Conclusions: GA is a valid alternative to SA in Day Surgery, showing a clinical trend toward better early pain control, lower analgesic consumption, and improved recovery perception, although multivariable analysis did not reach statistical significance. Full article

This article focuses on the research and development of innovative polymer-concrete composites for the manufacture of precision machine tool frames and critical mechanical engineering components. The relevance of this work stems from the need to replace traditional cast iron and cement concrete with materials with superior damping properties and thermal stability. The polymer matrix used in this study was ED-20 epoxy-diane resin, modified with (FAM) furan resin and cured with polyethylenepolyamine (PEPA), which together ensured minimal linear shrinkage (less than 0.5–1%) during polymerization. The focus was on the effect of multimodal filler distribution, including quartz sand, gabbro, and basalt, as well as reinforcing additives such as silicon carbide and fiberglass, on the final performance characteristics of the material. Experimental studies determined the key physical and mechanical parameters of the obtained samples. The results showed that the optimized composition (Smp_001) exhibited compressive strength up to 92.3 MPa, significantly exceeding that of standard high-strength concrete. It was established that the use of silicon carbide and glass fiber promotes the formation of a dense heterogeneous microstructure characterized by extremely low porosity (1.2–2.5%) and record-low water absorption (less than 0.05%). These characteristics guarantee high dimensional stability of the frames during prolonged contact with process fluids and cutting fluids. The scanning electron microscopy (SEM) and (EDS) energy dispersive X-ray spectroscopy methods confirmed the dense packing and high degree of interaction of the polymer matrix with the crystalline phases of the filler. This condition of the interfacial boundaries guarantees stable stress transfer throughout the entire volume of the material, which minimizes the risk of local damage during operation. The study confirmed that the developed material has vibration damping properties 6–10 times more effective than gray cast iron, a critical factor in improving machining accuracy on modern metal-cutting machines. The scientific novelty of the study lies in its substantiation of the synergistic effect of the combined use of basalt fillers and silicon carbide to achieve the precision properties of a structural material. Its practical significance is confirmed by the possibility of producing large-scale parts by casting without the need for complex finishing, opening up new prospects for modernizing the machine tool industry. Full article

Multimodal image fusion has emerged as a core technology for complex perception systems—such as autonomous driving, remote sensing monitoring, and medical diagnosis—by integrating complementary information from heterogeneous sensors. Given the rapid technological evolution within this field, particularly driven by the emergence of Mamba architectures, Generative Diffusion Models, and Vision Foundation Models (VFMs), traditional classification methods no longer fully encompass the ongoing paradigm shifts. Following the PRISMA guidelines to ensure the objectivity and reproducibility of the findings, this paper provides a systematic literature review and data extraction for multimodal image feature fusion. Under this standardized framework, a five-dimensional decoupling classification architecture is proposed to deconstruct models across fusion hierarchy, backbone architecture, fusion operator, supervision paradigm, and deployment constraints. Specifically, the analysis highlights the linear computational efficiency of Mamba in long-sequence modeling, the high-fidelity reconstruction capabilities of diffusion models via generative priors, and the universal semantic alignment achieved by VFMs . Furthermore, this study summarizes qualitative and quantitative evaluation metrics alongside cross-domain public datasets for performance benchmarking while discussing critical future directions, including cross-modal alignment in complex environments, parameter-efficient fine-tuning of large models, and real-time inference at the edge. Full article

Subadult stress is an important bioarchaeological indicator of the health status of archaeological populations, and its interpretation requires consideration of biological, environmental, and social factors. This paper examines the impact of cribra orbitalia (CO) and linear enamel hypoplasia (LEH) on adult mortality in Late Antique Southern Pannonia. A sample of 400 adult individuals from the sites of Mursa (Osijek), Cibalae (Vinkovci), Certissa (Štrbinci), and Incerum (Tekić) was analyzed. The results show that CO has a statistically significant negative impact on age-at-death in both sexes, whereas LEH shows a non-significant to weak impact with a statistically non-significant trend. The cumulative effect of multiple indicators of subadult stress could particularly negatively affect females, which is associated with reproductive burden and socio-cultural factors. The results confirm the complex interaction between environment and humans—the importance of living conditions and health stressors on the health and mortality of individuals and the entire observed population—at the same time in the analyzed sample. Full article

To address the problems of blurred lesion boundaries, noise interference, and the lack of lightweight design in segmentation models for breast ultrasound images, this paper proposes a lightweight, high-real-time segmentation model, MV-UNet, based on Mamba architecture. The model employs an improved MambaVision encoder paired with a UNetMamba decoder. This architecture, augmented by a Local Supervision Module (LSM) during training, effectively integrates global context with local details while maintaining linear computational complexity, thereby enhancing boundary delineation capability. The experimental results on the BUSI_WHU dataset show that the MV-UNet achieves 90.51% in mIoU, 90.85% in Recall, and 4.59 pixels in ASSD, surpassing most of the existing advanced models in multiple metrics. At the same time, the number of parameters is only 14.7% of the EMGANet, and the inference speed is increased by 3.2 times. Furthermore, an independent benchmark test on the BUSI dataset demonstrates the model’s practical efficiency, achieving an ASSD of 14.94 pixels while maintaining its clear advantages in model lightness and inference speed. In summary, the excellent balance between segmentation accuracy and model efficiency achieved by MV-UNet provides a novel and effective approach for breast ultrasound image segmentation. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

This paper investigates the thermal buckling behavior of a four-edge simply supported bimodular functionally graded rectangular thin plate subjected to thermal loads. Unlike existing studies, this work introduces the bimodular effect into the thermal buckling analysis of functionally graded thin plates for the first time, accounting for the influence of tension–compression modulus on the critical temperature difference. The problem is challenging due to the complexity of materials and the nonlinearity of structural thermal buckling. For the theoretical analysis, we propose a simplified mechanical model which contains the four important assumptions: there exists a neutral plane in bending; the influence of shear stresses may be neglected; the membrane effect and bending effect are considered separately; and there are two different buckling regimes: a compression-dominated pre-buckling state and a bending-dominated post-buckling state. Three types of thermal loading cases are considered, including uniform temperature rise, linear temperature gradient through the thickness, and nonlinear temperature distribution satisfying Fourier’s law of heat conduction. Within the framework of the simplified mechanical model, the pre-buckling membrane forces, equilibrium equations, and stability equations are derived, thus obtaining a closed-form analytical expression for the critical buckling temperature difference under three different temperature rise modes. The reliability of the present analytical model is validated through comparison with finite element results. Furthermore, a detailed parametric study is conducted to reveal the influences of aspect ratio, width-to-thickness ratio of plate, bimodular indices, and gradient parameters of materials on the critical temperature difference. The results provide a theoretical basis for the thermal stability design of bimodular functionally graded plates operating in high-temperature environments. Full article

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

Imbalanced classification remains a fundamental challenge in machine learning, where the Area Under the ROC Curve (AUC) is widely used for threshold-independent ranking evaluation, especially in AUC maximization studies. Existing AUC maximization methods suffer from two critical limitations: they rely on single fixed kernels that fail to capture complex data structures, and they incur prohibitive computational costs due to pairwise constraint construction. To address these issues, we propose the Generalized AUC Maximization Core Vector Machine (GAM-CVM), a fast imbalanced classification framework integrating multi-kernel learning with core vector machine optimization. Multiple affinity graphs are constructed from complementary perspectives and fused via cross-diffusion into a unified kernel matrix that respects the intrinsic data manifold. This fused kernel is embedded into a generalized AUC objective with a flexible ranking margin. Given the fused kernel matrix, the optimization stage of GAM-CVM achieves asymptotic linear time complexity with respect to the number of sample pairs under a fixed approximation accuracy by reformulating the learning objective as a center-constrained minimum enclosing ball problem. Extensive experiments demonstrate that GAM-CVM achieves the best overall average ranking and significantly outperforms most competing methods while maintaining the lowest optimization-stage running time. Full article

Mastication and swallowing are complex physiological processes involving the coordinated activity of multiple tissues in the oral cavity, facial region, and laryngeal system. Some detection methods suffer from limitations such as insufficient information acquisition and inadequate temporal feature analysis. To address these issues, this study proposes a conceptual method for analyzing the state of masticatory and swallowing movements. It integrates maxillofacial electromyographic (EMG) signals with laryngeal movement signals. The goal is to preliminarily explore state analysis of masticatory and swallowing movements over time. A designed gain-adjustable conditioning circuit processes and acquires these signals: maxillofacial EMG signals from EMG electrodes and laryngeal movement signals from flexible PVDF piezoelectric sensors. These two signal streams complement each other’s missing information, enabling comprehensive detection of the state of masticatory and swallowing movements. To address time-point labeling in mastication and swallowing, a sliding-window-based dispersion calculation method was employed to extract characteristic signal nodes, which were then accurately associated with their corresponding physiological motion states. We combined temporal features such as the zero point, onset of fluctuations, characteristic peaks, and baseline recovery from electromyographic (EMG) signals and laryngeal movement signals. This allowed us to establish a correspondence between key time points in the mastication and swallowing processes. The coefficient of determination (R²) for the pressure–voltage linear fit of the PVDF flexible piezoelectric sensor was 0.99446. The pressure resolution was approximately 0.08 kPa. Response times were no more than 15 ms for the EMG channel and no more than 10 ms for the PVDF pressure channel. These results indicate that this method is feasible for extracting oral movement time parameters in healthy subjects. Full article

Accurate and efficient vehicle detection from unmanned aerial vehicle (UAV) imagery is essential for intelligent transportation, urban monitoring, and public safety, yet this task remains challenging due to high target density, extreme scale variation, complex backgrounds, and stringent onboard computational constraints. Existing DETR-based detectors model global context through self-attention but incur quadratic $\mathcal{O}\left(N^2\right)$ complexity that is prohibitive for high-resolution UAV images, while CNN-based methods lack the long-range contextual awareness needed for dense small-object scenarios. We propose RSMamDet, an efficient end-to-end detection framework built upon RT-DETR that replaces quadratic self-attention with linear $\mathcal{O}\left(N\right)$ State Space Model scanning. The framework integrates a MobileMamba backbone with a Selective Feature Scanning module for efficient global context modeling, a Dimension-Aware Selective Integration module for adaptive cross-scale feature fusion, a Poly Kernel Inception Network encoder for multi-receptive-field feature enrichment, and an Adaptive Multi-Level Feature Fusion module for content-aware dynamic upsampling, complemented by an Uncertainty-Minimal Composite loss for stable query selection in cluttered aerial scenes. Experiments on DroneVehicle and VisDrone2019 demonstrate that RSMamDet achieves mAP₅₀ of 72.6% and 40.2%, surpassing state-of-the-art methods by 4.1% and 2.2%, respectively, while maintaining real-time inference at 186.2 FPS with only 19.8M parameters and 42.3 GFLOPs, representing a $6.14\times$ reduction in computational cost and a $3.86\times$ reduction in model parameters compared to the strongest baseline. Full article

This study utilized three controlled Sitika spruce beam specimens and established a parameterized transfer-function model based on force–acceleration frequency response functions (FRFs) to characterize and reconstruct the frequency-domain modal response of beam specimens. The specimens were tested using non-contact magnetic swept-sine excitation, laser Doppler vibration measurement, and synchronous FFT analysis methods under free–free boundary conditions. In the experiment, one specimen was used for modeling and the other two specimens were used for consistency verification. Based on the measured complex FRF, a 1st–5th order modal transfer-function model was established in the frequency range of 0–1000 Hz. The experiment identified five resonance frequencies of the specimen, which were 65.0, 198.5, 370.5, 620.0, and 930.0 Hz, respectively. The model can reconstruct the measured magnitude and phase responses, with magnitude residuals within ±5 dB, resonance-peak magnitude errors of 0.03–0.73 dB, and wrapped-phase deviation around the poles of 0.20–5.08°. The Nyquist trajectory was continuous and smooth, with all poles located in the left half-plane, indicating that the model has stable pole behavior. The research results support the specimen vibration response as an approximate linear time-invariant system under small-magnitude and controlled testing conditions. The model can provide a physically interpretable and reconstructable modal-parameter expression for evaluating frequency-domain vibration responses of controlled wooden beam specimens. Full article