Biocompatible autonomous self-healing PVA-CS/TA hydrogels based on hydrogen bonding and electrostatic interaction.

The biocompatible autonomous self-healing hydrogels have great potential in biomedical applications. However, the fairly weak tensile strength of the hydrogels seriously hinders their application. Here, we introduced chitosan (CS) into the polyvinyl alcohol (PVA)-tannic acid (TA) hydrogel and investigated the effects of the CS content, as CS can not only form reversible H bonds with PVA and TA but also form reversible electrostatic interactions with TA.

Robust Multi-Subtype Identification of Breast Cancer Pathological Images Based on a Dual-Branch Frequency Domain Fusion Network.

Breast cancer (BC) is one of the most lethal cancers worldwide, and its early diagnosis is critical for improving patient survival rates. However, the extraction of key information from complex medical images and the attainment of high-precision classification present a significant challenge. In the field of signal processing, texture-rich images typically exhibit periodic patterns and structures, which are manifested as significant energy concentrations at specific frequencies in the frequency domain.

Scale-Consistent and Temporally Ensembled Unsupervised Domain Adaptation for Object Detection.

Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.

Effect of Si Addition on Structure and Corrosion Resistance of FeCoNiCr High-Entropy Alloy Coating.

In this study, FeCoNiCrSi (x = 0, 4, and 8) powders were successfully prepared using the aerosol method and employed to produce high-entropy coatings on Q235 steel via laser cladding. The microstructure and phase composition of the coatings were analyzed using scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. Corrosion resistance and potential were evaluated through electrochemical analysis and Kelvin probe force microscopy.

Improved Mechanical Performances of Hastelloy C276 Composite Coatings Reinforced with SiC by Laser Cladding.

Composite coatings reinforced with varying mass fractions of SiC particles were successfully fabricated on 316 stainless steel substrates via laser cladding. The phase compositions, elemental distribution, microstructural characteristics, hardness, wear resistance and corrosion resistance of the composite coatings were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Vickers hardness testing, friction-wear testing and electrochemical methods. The coatings have no obvious pores, cracks or other defects.

Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment.

Introduction: Alzheimer's disease (AD) is a common neurological disorder. Based on clinical characteristics, it can be categorized into normal cognition (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia (AD). Once the condition begins to progress, the process is usually irreversible.

G-RCenterNet: Reinforced CenterNet for Robotic Arm Grasp Detection.

In industrial applications, robotic arm grasp detection tasks frequently suffer from inadequate accuracy and success rates, which result in reduced operational efficiency. Although existing methods have achieved some success, limitations remain in terms of detection accuracy, real-time performance, and generalization ability. To address these challenges, this paper proposes an enhanced grasp detection model, G-RCenterNet, based on the CenterNet framework.

Research on Tool Wear Monitoring Technology Based on Variational Mode Decomposition and Back Propagation Neural Network Model.

Accurately predicting tool wear during the machining process not only saves machining time and improves efficiency but also ensures the production of good-quality parts and automation. This paper proposes a combined variational mode decomposition (VMD) and back propagation (BP) neural network model (VMD-BP), which maps spindle power to tool wear. The model is trained using both historical and real-time data.

Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin.

Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces.

Research on Downhole MTATBOT Positioning and Autonomous Driving Strategies Based on Odometer-Assisted Inertial Measurement.

In response to the current situation of backward automation levels, heavy labor intensities, and high accident rates in the underground coal mine auxiliary transportation system, the mining trackless auxiliary transportation robot (MTATBOT) is presented in this paper. The MTATBOT is specially designed for long-range, space-constrained, and explosion-proof underground coal mine environments. With an onboard perception and autopilot system, the MTATBOT can perform automated and unmanned subterranean material transportation.

Fabrication of Polytetrafluoroethylene-Reinforced Fluorocarbon Composite Coatings and Tribological Properties Under Multi-Environment Working Conditions.

Currently, few studies have been conducted on the use of fluorocarbon resin (FEVE) and polytetrafluoroethylene (PTFE) as adhesive substrates and lubricating and anti-corrosion fillers, respectively, for the fabrication of PTFE-reinforced fluorocarbon composite coatings. In this paper, the tribological properties of polytetrafluoroethylene-reinforced fluorocarbon composite coatings were investigated through orthogonal tests under various operating conditions. The optimal configuration for coating preparation under dry friction and aqueous lubrication was thus obtained: the optimal filler particle size, mass ratio of FEVE to PTFE, spraying pressure, and curing agent content were 50 μm, 3:4.

Residual Stress Analysis at the Conductor-Insulator Interface During the Curing Process of Hair-Pin Motors.

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor's lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies.

Finite Element Numerical Simulation and Repair Process of Laser Cladding Repair of Surface Cracks on Mechanical Parts.

This study focuses on the planetary gear reducer and employs ANSYS 13.0 software to perform thermo-mechanical coupled simulations for the laser cladding repair process, aiming to address gear failure caused by cracks. The optimal theoretical repair parameters were determined based on temperature and stress field analyses, and performance testing of the cladding layer was conducted to validate the feasibility of the selected parameters.

Research on Stability of Removal Function in Figuring Process of Mandrel of X-Ray-Focusing Mirror with Variable Curvature.

Over the past 30 years, researchers have developed X-ray-focusing telescopes by employing the principle of total reflection in thin metal films. The Wolter-I focusing mirror with variable-curvature surfaces demands high precision. However, there has been limited investigation into the removal mechanisms for variable-curvature X-ray mandrels, which are crucial for achieving the desired surface roughness and form accuracy, especially in reducing mid-spatial frequency (MSF) errors.

Detection of Rat Pain-Related Grooming Behaviors Using Multistream Recurrent Convolutional Networks on Day-Long Video Recordings.

In experimental pain studies involving animals, subjective pain reports are not feasible. Current methods for detecting pain-related behaviors rely on human observation, which is time-consuming and labor-intensive, particularly for lengthy video recordings. Automating the quantification of these behaviors poses substantial challenges.

Size-specific clonidine-loaded liposomes: Advancing melanoma microenvironment suppression with safety and precision.

The immunosuppressive tumor microenvironment (TME) plays a crucial role in the progression and treatment resistance of melanoma. Modulating the TME is thus a key strategy for enhancing therapeutic outcomes. Previousstudies have identified clonidine (CLD), an α2-adrenergic receptor agonist, as a promising agent that enhances T lymphocyte infiltration and reduces myeloid-derived suppressor cells within the TME, thereby promoting antitumor immune responses.

SegRap2023: A benchmark of organs-at-risk and gross tumor volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma.

Radiation therapy is a primary and effective treatment strategy for NasoPharyngeal Carcinoma (NPC). The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Despite that deep learning has achieved remarkable performance on various medical image segmentation tasks, its performance on OARs and GTVs of NPC is still limited, and high-quality benchmark datasets on this task are highly desirable for model development and evaluation.

Development of a portable gas chromatograph-mass spectrometer embedded with a low-temperature adsorption thermal desorption module for enhanced detection of volatile organic compounds.

A portable gas chromatograph-mass spectrometer (GC-MS) is an effective instrument for rapid on-site detection of volatile organic compounds (VOCs). Current instruments typically adsorb samples at ambient temperature, challenging the detection of low-boiling VOCs. In this study, a low-temperature adsorption thermal desorption method is proposed for sample enrichment in a portable GC-MS.

DCP-YOLOv7x: improved pest detection method for low-quality cotton image.

Introduction: Pests are important factors affecting the growth of cotton, and it is a challenge to accurately detect cotton pests under complex natural conditions, such as low-light environments. This paper proposes a low-light environments cotton pest detection method, DCP-YOLOv7x, based on YOLOv7x, to address the issues of degraded image quality, difficult feature extraction, and low detection precision of cotton pests in low-light environments.

Methods: The DCP-YOLOv7x method first enhances low-quality cotton pest images using FFDNet (Fast and Flexible Denoising Convolutional Neural Network) and the EnlightenGAN low-light image enhancement network.

Salient object detection with non-local feature enhancement and edge reconstruction.

The salient object detection task based on deep learning has made significant advances. However, the existing methods struggle to capture long-range dependencies and edge information in complex images, which hinders precise prediction of salient objects. To this end, we propose a salient object detection method with non-local feature enhancement and edge reconstruction.

Innovative equipment for lower limb muscle strength measurement: Design and application in sarcopenia screening.

Background: Grip Strength has been established as a practical and efficient method for screening and diagnosing sarcopenia. It is recognized that with advancing age, there is a more significant decline in lower limb muscle mass compared to the upper limb. However, due to the inherent complexity of assessing lower limb muscle strength compared to measuring Grip Strength, these assessments have not been universally adopted for sarcopenia screening.

Effect of human papillomavirus (HPV) vaccination on HPV infection and recurrence of HPV related disease after local surgical treatment: A systematic review and meta-analysis.

Background: The prophylactic vaccines available to protect against infections by human papillomavirus (HPV) are well tolerated and highly immunogenic. This systematic review and meta-analysis aimed to explore the efficacy of HPV vaccination on the risk of HPV infection and recurrent diseases related to HPV infection in individuals undergoing local surgical treatment.

Methods: A literature search was performed using PubMed/MEDLINE, Embase, the Cochrane Library, Scopus, Web of Science, and bioRxiv/medRxiv from inception to July 15, 2024.

Study on jet dynamic impact performance under the influence of standoff.

Due to the sensitivity of the shaped charge jet to standoff and the complexity of its impact under lateral disturbances, this study aims to investigate the dynamic impact evolution of the jet influenced by standoff and lateral disturbances. A finite element model for the dynamic impact of shaped charge jets was established. Dynamic impact experiments were designed and conducted to validate the effectiveness of the numerical simulations.

Data-driven assessment of well stimulation in unconventional gas reservoirs.

Unconventional gas reservoirs, characterized by their complex geologies and challenging extraction conditions, demand innovative approaches to enhance gas production and ensure economic viability. Well stimulation techniques, such as hydraulic fracturing and acidizing, have become indispensable tools in unlocking the potential of these tight formations. However, the effectiveness of these techniques can vary widely depending on the specific characteristics of the reservoir.

A new prediction model based on deep learning for pig house environment.

A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.