Search Results (110)

Aim: To investigate the vestibulosympathetic reflex (VSR) in humans by comparing the hemodynamic responses to air-conducted sound stimulus (ACSS) of the vestibular system between healthy individuals and participants with vestibular neuritis (VN). Methods: Twenty-one healthy controls and seven participants with VN were enrolled. Each autonomic test was first conducted without and then with ACSS of the vestibular system. The following autonomic tests were performed: heart rate response to Valsalva maneuver; heart rate response to deep breathing; and heart rate and blood pressure response to a supine position, passive tilt, and active standing. Results: In healthy participants, there was a difference between respiratory sinus arrhythmia values without and with otolithic stimulation (26.63 ± 6.16 vs. 24.67 ± 7.34, p = 0.02). During passive tilt, the average heart rate throughout ACSS was lower than immediately before ACSS (88.63 ± 14.68 vs. 90.96 ± 14.93, p = 0.001). In participants with VN, no such differences were observed. Conclusions: This study demonstrated a significant effect of otolithic stimulation with ACSS on heart rate during passive tilt in healthy participants. These findings suggest that ACSS of the vestibular system could be a valuable method for future research on the VSR. Full article

National parks are key tools for safeguarding ecosystem health, yet their conservation performance remains unclear. Comprehensive evaluations are crucial for guiding targeted and effective conservation strategies. This study employed the Vigor–Service–Resilience (VSR) framework together with causal inference models to assess the role of Huangshan National Park (HNP) in promoting ecosystem health and to examine the heterogeneity of its ecological outcomes from 2010 to 2020. The results indicate that (1) ecosystem health improved significantly across the region, with 69.5% of pixels showing positive change, particularly in ecosystem services and vigor; (2) compared with matched unprotected sites, HNP enhanced EH by 5.7% in 2010, 3.4% in 2015, and 6.5% in 2020, and also generating positive spillover effects within 30 km of its boundaries; (3) conservation impacts differed notably across socio-ecological conditions, with greater benefits observed in areas of lower elevation, gentle slopes, and reduced precipitation. These findings provide robust causal evidence of the protective value of HNP and underscore the importance of targeted and cost-efficient management strategies to optimize conservation outcomes and support sustainable regional development. Full article

This study proposes Dual-Stream Former, a novel architecture that integrates a Video Swin Transformer and Conformer designed to address the challenges of visual speech recognition (VSR). The model captures spatiotemporal dependencies, achieving a state-of-the-art character error rate (CER) of 3.46%, surpassing traditional convolutional neural network (CNN)-based models, such as 3D-CNN + DenseNet-121 (CER: 5.31%), and transformer-based alternatives, such as vision transformers (CER: 4.05%). The Video Swin Transformer captures multiscale spatial representations with high computational efficiency, whereas the Conformer back-end enhances temporal modeling across diverse phoneme categories. Evaluation of a high-resolution dataset comprising 740,000 utterances across 185 classes highlighted the effectiveness of the model in addressing visually confusing phonemes, such as diphthongs (/ai/, /au/) and labio-dental sounds (/f/, /v/). Dual-Stream Former achieved phoneme recognition error rates of 10.39% for diphthongs and 9.25% for labiodental sounds, surpassing those of CNN-based architectures by more than 6%. Although the model’s large parameter count (168.6 M) poses resource challenges, its hierarchical design ensures scalability. Future work will explore lightweight adaptations and multimodal extensions to increase deployment feasibility. These findings underscore the transformative potential of Dual-Stream Former for advancing VSR applications such as silent communication and assistive technologies by achieving unparalleled precision and robustness in diverse settings. Full article

Background and Objectives: The impact of sarcopenia and myosteatosis on renal transplantation (RT) outcomes has yet to be explained, certainly due to differences in assessment methods. The role of visceral adiposity is also not clearly defined. This retrospective study aimed to evaluate pretransplant body composition—including sarcopenia, myosteatosis, and visceral adiposity ratio (VSR)—using computed tomography (CT) and analyze their relationship with short- and long-term graft outcomes. Materials and Methods: A total of 94 patients who underwent RT between 2019 and 2023 and had pretransplant non-contrast abdominal CT scans were included. Skeletal muscle area (SMA) was assessed at the L3 vertebral level, including multiple muscle groups. Sarcopenia was defined by a low skeletal muscle index (SMI), while myosteatosis was defined by high intramuscular adipose tissue content (IMAC). Visceral adiposity was evaluated by the visceral-to-subcutaneous adipose tissue ratio (VSR). These parameters were compared with post-transplant outcomes. Results: The mean age was 42.69 ± 12.47 years, with 54.3% male patients. High IMAC was significantly associated with early graft failure (p = 0.026), delayed graft function (p = 0.005), death-censored graft failure (p = 0.036), and overall graft failure (p = 0.047). One-year mortality was also higher in the high IMAC group (14.8% vs. 0.0%, p = 0.012). SMI and VSR were not significantly associated with outcomes. Myosteatosis emerged as a significant risk factor in univariate analysis but was not independently predictive in multivariate analysis. Among the established risk factors identified in the study, recipient age was found to be a significant predictor for overall graft failure, donation type (cadaveric vs. living) for death-censored graft failure, and cold ischemia time for delayed graft function (OR: 1.068, 95% CI: 1.001–1.141, p = 0.049; OR: 147.7, 95% CI: 2.1—10,427.0, p = 0.021; OR: 1.003, 95% CI: 1.001–1.006, p = 0.023). Conclusions: Myosteatosis correlates with worse graft outcomes and higher mortality, but its independent prognostic value requires further investigation. Full article

An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management. Full article

In this study, to address the problems of high feedback latency and redundant codebook traversal in traditional Reconfigurable Intelligent Surface (RIS) beam tracking systems, two novel experimental schemes are proposed: the Edge-Integrated RIS Control Mechanism (EIR-CM) and the Visually Steered RIS Control Mechanism (VSR-CM). The EIR-CM eliminates the feedback latency of the remote server and optimizes the local computation by integrating the RIS control system and the User Equipment (UE) into the same edge server to reduce the beam tuning time by 50%. The VSR-CM realizes beam tracking based on visual perception, and directly maps the UE position to the optimal RIS codebook with a response speed as low as milliseconds. Experimental results show that the EIR-CM reduces the RIS feedback latency to 1–2 s, and the VSR-CM can be further optimized to less than 0.5 s. The two mechanisms are applicable to 6G communications, smart transport, and drone networks, providing feasibility verification for low-latency and efficient RIS deployment. Full article

Speech recognition approaches typically fall into three categories: audio, visual, and audio–visual. Visual speech recognition, or lip reading, is the most difficult because visual cues are ambiguous and data is scarce. To address these challenges, we present a new multi-task audio–visual speech recognition, or MultiAVSR, framework for training a model on all three types of speech recognition simultaneously primarily to improve visual speech recognition. Unlike prior works which use separate models or complex semi-supervision, our framework employs a supervised multi-task hybrid Connectionist Temporal Classification/Attention loss cutting training exaFLOPs to just 18% of that required by semi-supervised multitask models. MultiAVSR achieves state-of-the-art visual speech recognition word error rate of 21.0% on the LRS3-TED dataset. Furthermore, it exhibits robust generalization capabilities, achieving a remarkable 44.7% word error rate on the WildVSR dataset. Our framework also demonstrates reduced dependency on external language models, which is critical for real-time visual speech recognition. For the audio and audio–visual tasks, our framework improves the robustness under various noisy environments with average relative word error rate improvements of 16% and 31%, respectively. These improvements across the three tasks illustrate the robust results our supervised multi-task speech recognition framework enables. Full article

We review the $Sim(2)$ invariant infrared regularization of Very Special Relativity models that we have proposed recently and apply it to compute loop corrections in quantum electrodynamics with VSR masses for neutrino and photon. Then, we compute the axial anomaly. Finally, we study the Gross–Neveu model with a VSR mass in the large N limit uncovering a new phase of the model. Full article

To address the challenge of optimizing system adaptability, disturbance rejection, control precision, and convergence speed simultaneously in four-wheel steering (4WS) stability control, a 4WS controller with a variable steering ratio (VSR) strategy and fast adaptive super-twisting (FAST) sliding mode control is proposed to control and output the steering angles of four wheels. The ideal VSR strategy is designed based on the constant yaw rate gain, and a cubic quasi-uniform B-spline curve fitting method is innovatively used to optimize the VSR curve, effectively mitigating steering fluctuations and obtaining precise reference front wheel angles. A controller based on FAST is designed for active rear wheel steering control using a symmetric 4WS vehicle model. Under double-lane change conditions with varying speeds, the simulations show that, compared with the constant steering ratio, the proposed VSR strategy enhances low-speed sensitivity and high-speed stability, improving the system’s adaptability to different operating conditions. Compared with conventional sliding mode control methods, the proposed FAST algorithm reduces chattering while increasing convergence speed and control precision. The VSR-FAST controller achieves optimization levels of more than 7.3% in sideslip angle and over 41% in yaw rate across different speeds, achieving an overall improvement in the stability control performance of the 4WS system. Full article

The increasing adoption of high-resolution imaging sensors across various fields has led to a growing demand for techniques to enhance video quality. Video super-resolution (VSR) addresses this need by reconstructing high-resolution videos from lower-resolution inputs; however, directly applying single-image super-resolution (SISR) methods to video sequences neglects temporal information, resulting in inconsistent and unnatural outputs. In this paper, we propose FDI-VSR, a novel framework that integrates spatiotemporal dynamics and frequency-domain analysis into conventional SISR models without extensive modifications. We introduce two key modules: the Spatiotemporal Feature Extraction Module (STFEM), which employs dynamic offset estimation, spatial alignment, and multi-stage temporal aggregation using residual channel attention blocks (RCABs); and the Frequency–Spatial Integration Module (FSIM), which transforms deep features into the frequency domain to effectively capture global context beyond the limited receptive field of standard convolutions. Extensive experiments on the Vid4, SPMCs, REDS4, and UDM10 benchmarks, supported by detailed ablation studies, demonstrate that FDI-VSR not only surpasses conventional VSR methods but also achieves competitive results compared to recent state-of-the-art methods, with improvements of up to 0.82 dB in PSNR on the SPMCs benchmark and notable reductions in visual artifacts, all while maintaining lower computational complexity and faster inference. Full article

To improve the safety and service life of carbon steel used in marine structures, appropriate regulation of residual stress in carbon steel is required. This paper investigates the effects of high-frequency vibratory stress relief (VSR) and tempering on the residual stress, microstructure, and surface hardness of 45 steel. After the high-frequency VSR and tempering at 200 °C for 30 min treatment, the microstructure is still tempered martensite. When the 45 steel experimental specimens were tempered at 600 °C for 30 min, the microstructure changed from tempered martensite to tempered sorbite, and the residual stress regulation effect of 45 steel experimental specimens was significantly improved. However, its surface hardness decreased significantly, which reduces the mechanical properties of marine structural components. Comparatively, high-frequency VSR is an effective method to regulate residual stress while ensuring that the microstructure of marine structural components does not undergo drastic changes. This study provides technical and theoretical support for the residual stress regulation treatment of 45 steel in marine engineering. Full article

Video super-resolution aims to generate high-resolution video sequences with realistic details from existing low-resolution video sequences. However, most existing video super-resolution models require substantial computational power and are not suitable for resource-constrained devices such as smartphones and tablets. In this paper, we propose a lightweight video super-resolution (LightVSR) model that employs a novel feature aggregation module to enhance video quality by efficiently reconstructing high-resolution frames from compressed low-resolution inputs. LightVSR integrates several novel mechanisms, including head-tail convolution, cross-layer shortcut connections, and multi-input attention, to enhance computational efficiency while guaranteeing video super-resolution performance. Extensive experiments show that LightVSR achieves a frame rate of 28.57 FPS and a PSNR of 39.25 dB on the UDM10 dataset and 36.91 dB on the Vimeo-90k dataset, validating its efficiency and effectiveness. Full article

Japan faces a significant labor shortage due to an aging population, particularly in the agricultural sector. The rising average age of farmers and the declining participation of younger individuals threaten the sustainability of farming practices. These trends reduce the availability of agricultural labor and pose a risk to lowering Japan’s food self-sufficiency rate. The reliance on food imports raises concerns regarding price fluctuations and sanitation standards. Moreover, the challenging working conditions in agriculture and a lack of technological innovation have hindered productivity and increased the burden on the existing workforce. To address these challenges, “smart agriculture” presents a promising solution. By leveraging advanced technologies such as sensors, drones, the Internet of Things (IoT), and automation, smart agriculture aims to optimize farm operations. Real-time data collection and AI-driven analysis play a crucial role in monitoring crop growth, assessing soil conditions, and improving overall efficiency. This study proposes enhancements to the YOLO (You Only Look Once) object detection model to develop an automated tomato harvesting system. This system uses a camera to detect tomatoes and assess their ripeness for harvest. Our objective is to streamline the harvesting process through AI technology. Our improved YOLO model integrates two novel loss functions to enhance detection accuracy. The first, “VSR”, refines the model’s ability to classify tomatoes and determine their harvest readiness. The second, “SBCE”, enhances the detection of small tomatoes by training the model to recognize a range of object sizes within the dataset. These improvements have significantly increased the system’s detection performance. Our experimental results demonstrate that the mean Average Precision (mAP) of YOLOv7-tiny improved from 61.81% to 70.21%. Additionally, the F1 score increased from 0.61 to 0.71 and the mean Intersection over Union (mIoU) rose from 65.03% to 66.44% on the tomato dataset. These findings underscore the potential of our proposed system to enhance efficiency in agricultural practices. Full article

Dissimilar steel welded structures are commonly used in the marine engineering field. Owing to the scarcity of in-depth investigation into the intricate pattern of residual stress distribution in welding within 316L/Q345 dissimilar steel welded joints and methods for reducing this stress, a platform-based vibratory stress relief (VSR) experimental system was established to comprehensively study the effects of VSR on the mechanical properties and microstructure of 316L/Q345 welded structures. Scanning electron microscopy (SEM) was used to examine the fracture morphology and explore the intrinsic mechanisms by which VSR enhances the mechanical properties of welded joints. The findings suggest that VSR is capable of significantly homogenizing and diminishing the welding residual stress within the heat-affected area of 316L/Q345 mismatched steel welded specimens. The significant reduction in residual stress after VSR can primarily be attributed to the combination of alternating stress applied by the VSR platform and the welding residual stress, which exceeded the yield limit of the metal materials. Furthermore, the significant reduction in residual stress, refinement of second-phase particles, and changes in fracture mechanisms are the main reasons for the increased strength observed after VSR. This study has significant engineering application value, providing a theoretical basis for the use of VSR treatment to enhance the reliability of the safe operation of marine engineering equipment. Full article

As an important part of intelligent traffic, vehicle recognition plays an irreplaceable role in traffic management. Due to the complexity and occlusion of various objects in the traffic scene, the accuracy of vehicle target recognition is poor. Therefore, based on the distribution features of vehicle components, this paper proposes a two-stage VSRS-VCFM net occlusion vehicle target recognition method. Based on the U-net codec structure, combining multi-scale detection and double constraints loss to improve the visual region segmentation under complex background (VSRS) performance. At the same time, to establish the vehicle component feature mask (VCFM) module, based on the Swin Transformer backbone unit, combined with the component perception enhancement unit and the efficient attention unit, the extraction of the low-contrast component area of the vehicle target and the filtering of the irrelevant area are realized. Then, the component mask recognition unit is introduced to remove the occlusion component feature area and realize the accurate recognition of the occluded vehicle. By labeling the public data set and the collected data set, six types of vehicle component data sets are constructed for training, as well as design ablation experiments and comparison experiments to verify the trained network, which prove the superiority of the recognition algorithm. The experimental results show that the proposed recognition method effectively solves the problem of misrecognition and missing recognition caused by interference and occlusion in vehicle recognition. Full article