Feature decoupling integrated domain generalization network for bearing fault diagnosis under unknown operating conditions.

In real engineering scenarios, the complex and variable operating conditions of mechanical equipment lead to distributional differences between the collected fault data and the training data. This distribution difference can lead to the failure of deep learning-based diagnostic models. Extracting generalized diagnostic knowledge from the source domain in scenarios where the target domain is not visible is the key to solving this problem.

An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention.

In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.

Recent Developments in Photofunctional Nanomaterials and Nanostructures for Emitting, Manipulating, and Harvesting Light.

Photofunctional nanomaterials and nanostructures that can emit, manipulate, convert, and utilize photons in diverse forms have profound meanings, from fundamental understandings to applications [...

Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.

Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology.

Skin Microbiota, Circulating Metabolic Biomarker, Epilepsy: A Mendelian Randomization Analysis.

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Currently, the associations among skin microbiota, circulating metabolites, and epilepsy are still not well studied. In this study, we applied univariate and two-step Mendelian randomization analysis using single nucleotide polymorphisms as instrumental variables to analyze the possible associations.

Deep Neural Networks for Accurate Depth Estimation with Latent Space Features.

Depth estimation plays a pivotal role in advancing human-robot interactions, especially in indoor environments where accurate 3D scene reconstruction is essential for tasks like navigation and object handling. Monocular depth estimation, which relies on a single RGB camera, offers a more affordable solution compared to traditional methods that use stereo cameras or LiDAR. However, despite recent progress, many monocular approaches struggle with accurately defining depth boundaries, leading to less precise reconstructions.

A Multiform Heterogeneity Framework for Alzheimer's Disease Based on Multimodal Neuroimaging.

Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity, yet translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity.

Safe controller design for circular motion of a bicycle robot using control Lyapunov function and control barrier function.

This paper investigates the safety control problem of a bicycle robot with front-wheel drive and without a trail or mechanical regulator during circular motion. Constraints on the drive angular speed necessary for the bicycle to achieve circular motion are proposed. In practical robot systems, bounded input disturbances are inevitable.

[Drift Correction Method of Wastewater Treatment Model Based on Transfer Learning Across Time Scales].

Data is the core foundation of intelligent operation and maintenance, but currently, there is generally insufficient data for wastewater treatment plants, and the status of wastewater treatment systems dynamically evolves with the changes in the internal and external environment. The intelligent operation and maintenance of wastewater plants face difficulties in modeling and model drift caused by system evolution. In response to this issue, the summer and winter seasons with significant differences in wastewater temperature, wastewater quality, and microbial status were selected as typical comparison scenarios.

The power of sound: Exploring the auditory influence on visual search efficiency.

In a dynamic visual search environment, a synchronous and meaningless auditory signal (pip) that corresponds with a change in a visual target promotes the efficiency of visual search (pop out), which is known as the pip-and-pop effect. We conducted three experiments to investigate the mechanism of the pip-and-pop effect. Using the eye movement technique, we manipulated the interval rhythm (Exp.

Graphene Mitigates Nanoscale Tribochemical Wear of Silica Glass in Water.

Despite the ubiquitous use of glasses, their simultaneous susceptibility toward scratch-induced defects and atmospheric hydration deteriorates their mechanical and chemical durability. Here, it is demonstrated that the deposition of a few-layer graphene provides unprecedented wear resistance to silica glass in aqueous conditions. To this extent, nanoscale scratch tests are carried out on graphene-glass surfaces via contact-mode atomic force microscopy with chemically inert and reactive tips.

Identification of Multi-functional Therapeutic Peptides Based on Prototypical Supervised Contrastive Learning.

High-throughput sequencing has exponentially increased peptide sequences, necessitating a computational method to identify multi-functional therapeutic peptides (MFTP) from their sequences. However, existing computational methods are challenged by class imbalance, particularly in learning effective sequence representations. To address this, we propose PSCFA, a prototypical supervised contrastive learning with a feature augmentation method for MFTP prediction.

Decoding of movement-related cortical potentials at different speeds.

The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals.

Mapping rapeseed () aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery.

Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass.

Intermittent social isolation enhances social investigation but impairs social memory in adult male mice.

Social isolation profoundly impacts motivated behavior and neural plasticity. While the effects of acute and chronic social isolation have been extensively studied, the consequences of intermittent isolation during adulthood, particularly relevant to modern lifestyles, remain poorly understood. This study investigated the impact of intermittent social isolation (ISI) on social behavior and brain activation in adult male mice.

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods.

Similarity-based context aware continual learning for spiking neural networks.

Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environments. However, existing spiking neural network-based continual learning algorithms treat each task equally, ignoring the guiding role of different task similarity associations for network learning, which limits knowledge utilization efficiency. Inspired by the context-dependent plasticity mechanism of the brain, we propose a Similarity-based Context Aware Spiking Neural Network (SCA-SNN) continual learning algorithm to efficiently accomplish task incremental learning and class incremental learning.

Association between hemoglobin and in-hospital mortality in critically ill patients with sepsis: evidence from two large databases.

Background: The relationship between baseline hemoglobin levels and in-hospital mortality in septic patients remains unclear. This study aimed to clarify this association in critically ill patients with sepsis.

Methods: Patients with sepsis were retrospectively identified from the Medical Information Mart for Intensive Care-IV (MIMIC-IV 2.

Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval.

The goal of Text-to-Image Person Retrieval (TIPR) is to retrieve specific person images according to the given textual descriptions. A primary challenge in this task is bridging the substantial representational gap between visual and textual modalities. The prevailing methods map texts and images into unified embedding space for matching, while the intricate semantic correspondences between texts and images are still not effectively constructed.

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning models have not sufficiently focused on the importance of active atoms and adsorbates, instead placing more emphasis on the overall structure of the catalytic materials.

SPW-TransUNet: three-dimensional computed tomography-cone beam computed tomography image registration with spatial perpendicular window Transformer.

Background: Current medical image registration methods based on Transformer still encounter challenges, including significant local intensity differences and limited computational efficiency when dealing with three-dimensional (3D) computed tomography (CT) and cone beam CT (CBCT) images. These limitations hinder the precise alignment necessary for effective diagnosis and treatment planning. Therefore, the aim of this study is to develop a novel method that overcomes these challenges by enhancing feature interaction and computational efficiency in 3D medical image registration.

Functional-Structural Plant Model "GreenLab": A State-of-the-Art Review.

It is crucial to assess the impact of climate change on crop productivity and sustainability for the development of effective adaptation measures. Crop models are essential for quantifying this impact on crop yields. To better express crops' intrinsic growth and development patterns and their plasticity under different environmental conditions, the functional-structural plant model (FSPM) "GreenLab" has been developed.

Core reference ontology for individualized exercise prescription.

"Exercise is medicine" emphasizes personalized prescriptions for better efficacy. Current guidelines need more support for personalized prescriptions, posing scientific challenges. Facing those challenges, we gathered data from established guidelines, databases, and articles to develop the Exercise Medicine Ontology (EXMO), intending to offer comprehensive support for personalized exercise prescriptions.

Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model.

Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset.