Laser-induced plasma imaging for quantitative analysis of intergranular corrosion susceptibility of aluminum alloys.

Magnesium plays an important role in the hardening mechanism of aluminum alloys, but sensitisation-induced intergranular corrosion cracking limits the widespread use of aluminum alloy in equipment. For on-site quantitative assessment of sensitisation in 5-series aluminum alloys, a laser-induced plasma imaging technique is proposed, which evaluates the degree of aluminum alloy sensitisation by obtaining images of the plasma formed by laser ablation of aluminum alloys, and then classifying and quantifying the images using a residual network. Compared to EMAT, XRD, ECT and LIBS techniques, the sample surface only needs to be polished, does not consume chemical reagents and is not affected by the shape and thickness of the workpiece, which provides higher quantitative accuracy, stability and detection efficiency.

Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing.

The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image.

Chatbots for breast cancer education: a systematic review and meta-analysis.

Background: Effective education and awareness regarding breast cancer are critical. Traditional educational methods often fail to meet the diverse information needs of patients. Patients should be provided with tailored, accessible information to improve their retention and understanding of disease-related information.

A study on the detection of conductor quantity in cable cores based on YOLO-cable.

The quantity of cable conductors is a crucial parameter in cable manufacturing, and accurately detecting the number of conductors can effectively promote the digital transformation of the cable manufacturing industry. Challenges such as high density, adhesion, and knife mark interference in cable conductor images make intelligent detection of conductor quantity particularly difficult. To address these challenges, this study proposes the YOLO-cable model, which is an improvement made upon the YOLOv10 model.

Plasma-Assisted Preparation of Reduced Graphene Oxide and Its Applications in Energy Storage.

Reduced graphene oxide (rGO) exhibits mechanical, optoelectronic, and conductive properties comparable to pristine graphene, which has led to its widespread use as a method for producing graphene-like materials in bulk. This paper reviews the characteristics of graphene oxide and the evolution of traditional reduction methods, including chemical and thermal techniques. A comparative analysis reveals that these traditional methods encounter challenges, such as toxicity and high energy consumption, while plasma reduction offers advantages like enhanced controllability, the elimination of additional reducing agents, and reduced costs.

Hamstring activation deficits during Double-Leg Jump-Landing tasks in athletes with hamstring strain injuries using EMG Time-Frequency analysis.

This study aimed to investigate the hamstring activation in athletes with hamstring strain injuries in vertical jump tasks using an EMG time-frequency analysis. Fifteen injured athletes and 15 matched controls were recruited. The EMG signals of the lateral hamstring (LH) and medial hamstring (MH) were assessed during countermovement, squat, and drop vertical jump tasks.

Time-hybrid OSAformer (THO): A hybrid temporal sequence transformer for accurate detection of obstructive sleep apnea via single-lead ECG signals.

Background And Objective: Obstructive Sleep Apnea (OSA) is among the most sleep-related breathing disorders, capable of causing severe neurological and cardiovascular complications if left untreated. The conventional diagnosis of OSA relies on polysomnography, which involves multiple electrodes and expert supervision. A promising alternative is single-channel Electrocardiogram (ECG) based diagnosis due to its simplicity and relevance.

Leader-follower method-based formation control for snake robots.

This paper proposes a leader-follower control method for multiple snake robot formation. Based on the simplified snake robot model, this work improves the traditional Serpenoid gait mode to a time-varying frequency form. Combined with the line-of-sight (LOS) method, a snake robot trajectory tracking controller is designed to enable the leader to track the desired trajectory at the ideal velocity.

Dynamic parallel traction theoretical model for the application and validation in femoral neck fractures - a finite element analysis.

Study Objective: This study aims to validate the application effects of a novel theoretical model of dynamic parallel traction in the treatment of femoral neck fractures through three-dimensional finite element analysis. By simulating the femoral neck fracture model, we explore the promotional effect of dynamic parallel traction on fracture healing.

Method: A digital 3D femur model was constructed using high-resolution computed tomography data of the lower limbs of a 70-year-old elderly subject.

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks.

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees.

Astragalus polysaccharide/carboxymethyl chitosan/sodium alginate based electroconductive hydrogels for diabetic wound healing and muscle function assessment.

Natural polysaccharides with excellent biocompatibility are considered ideal materials for repairing diabetic foot ulcer. However, diabetic foot ulcer is often accompanied by decreased muscle function, even resulting in muscle atrophy. During wound repair, monitoring muscle function at the wound site in real time can identify the decreased muscle strength timely, which is crucial for precise wound rehabilitation.

Bifurcation analysis and control of the full velocity difference model with delayed velocity difference.

With the increase in the number of urban vehicles, various traffic problems have gradually emerged. Studying the causes of traffic congestion and proposing effective mitigation strategies have important practical significance. This paper proposes a macroscopic traffic flow model that considers the delayed speed difference.

Multiple loci for foveolar vision in macaque monkey visual cortex.

In humans and nonhuman primates, the central 1° of vision is processed by the foveola, a retinal structure that comprises a high density of photoreceptors and is crucial for primate-specific high-acuity vision, color vision and gaze-directed visual attention. Here, we developed high-spatial-resolution ultrahigh-field 7T functional magnetic resonance imaging methods for functional mapping of the foveolar visual cortex in awake monkeys. In the ventral pathway (visual areas V1-V4 and the posterior inferior temporal cortex), viewing of a small foveolar spot elicits a ring of multiple (eight) foveolar representations per hemisphere.

Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network.

The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults.

Autoencoder-Based System for Detecting Anomalies in Pelletizer Melt Processes.

Effectively identifying and preventing anomalies in the melt process significantly enhances production efficiency and product quality in industrial manufacturing. Consequently, this paper proposes a study on a melt anomaly identification system for pelletizers using autoencoder technology. It discusses the challenges of detecting anomalies in the melt extrusion process of polyester pelletizers, focusing on the limitations of manual monitoring and traditional image detection methods.

SLAM Algorithm for Mobile Robots Based on Improved LVI-SAM in Complex Environments.

The foundation of robot autonomous movement is to quickly grasp the position and surroundings of the robot, which SLAM technology provides important support for. Due to the complex and dynamic environments, single-sensor SLAM methods often have the problem of degeneracy. In this paper, a multi-sensor fusion SLAM method based on the LVI-SAM framework was proposed.

Intraoperative interaction modeling between surgical instruments and soft tissues in neurosurgery based on energy functions.

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas.

Bifurcation control and analysis of traffic flow model based on driver prediction effect.

Based on the measured data, in this paper, we find that there are significant differences in the ideal speed of drivers with different attributes during their driving process. Through wavelet analysis, it is proved that the regularity of the car-following behavior of the driver is poor in a short time, and it cannot be predicted and analyzed at a small time scale. As one of the important participants in the traffic system, it is necessary to consider the driving behavior factors in the traffic flow model.

Simple preparation of PVDF composite flexible film with transparent, self-cleaning and radiative cooling properties.

Daytime radiative cooling is a technique that relies on reflecting sunlight and radiating heat through mid-infrared wavelengths to cool objects. However, most daytime radiative cooling materials are not transparent, cannot be used for vehicle windows or other objects that need to retain their original color, and are susceptible to rain, dust, and other contamination, resulting in reduced cooling performance. Here, we developed a transparent, self-cleaning, radiative cooling, highly flexible PVDF composite film (PPF film), which was prepared by solvent evaporation phase conversion method and scraping coating method.

An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms.

Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding.

UMS-ODNet: Unified-scale domain adaptation mechanism driven object detection network with multi-scale attention.

Unsupervised domain adaptation techniques improve the generalization capability and performance of detectors, especially when the source and target domains have different distributions. Compared with two-stage detectors, one-stage detectors (especially YOLO series) provide better real-time capabilities and become primary choices in industrial fields. In this paper, to improve cross-domain object detection performance, we propose a Unified-Scale Domain Adaptation Mechanism Driven Object Detection Network with Multi-Scale Attention (UMS-ODNet).

The Enhanced Hydrophobic, Photothermal, and Anti-Icing/Ice-Melting Performance of C/TiN/WC/PDMS Composite Coating by Inserting a Thermal Insulation Layer.

Enhancing the hydrophobic and photothermal characteristics of the coating can significantly boost its anti-icing/ice-melting capabilities. In this study, an epoxy resin thermal insulation layer is interposed between the aluminum sheet substrate and the C/TiN/WC/PDMS photothermal composite coating. This method not only equips the coating with exceptional superhydrophobic properties but also markedly elevates its photothermal and anti-icing/ice-melting performance.

Efficient Music Genre Recognition Using ECAS-CNN: A Novel Channel-Aware Neural Network Architecture.

In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an adaptive channel attention mechanism (ECA module) within the convolutional layers, the network significantly improves the extraction of key musical features.