REDT: a specialized transformer model for the respiratory phase and adventitious sound detection.

Background And Objective: In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones.

A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.

Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.

Herb-disease association prediction model based on network consistency projection.

A growing number of biological and clinical reports indicate the usefulness of herbs in the treatment of complex human diseases, giving an essential supplement for modern medicine. Similar to drugs, the use of experimental validation to identify related diseases of given herbs is both expensive and time-consuming. Such validation is even more difficult because each herb always contains several components.

Low-resource MobileBERT for emotion recognition in imbalanced text datasets mitigating challenges with limited resources.

Modern dialogue systems rely on emotion recognition in conversation (ERC) as a core element enabling empathetic and human-like interactions. However, the weak correlation between emotions and semantics poses significant challenges to emotion recognition in dialogue. Semantically similar utterances can express different types of emotions, depending on the context or speaker.

Non-Rigid Cycle Consistent Bidirectional Network with Transformer for Unsupervised Deformable Functional Magnetic Resonance Imaging Registration.

Background: In neuroscience research about functional magnetic resonance imaging (fMRI), accurate inter-subject image registration is the basis for effective statistical analysis. Traditional fMRI registration methods are usually based on high-resolution structural MRI with clear anatomical structure features. However, this registration method based on structural information cannot achieve accurate functional consistency between subjects since the functional regions do not necessarily correspond to anatomical structures.

Gene regulatory network inference based on modified adaptive lasso.

Gene regulatory networks (GRNs) reveal the regulatory interactions among genes and provide a visual tool to explain biological processes. However, how to identify direct relations among genes from gene expression data in the case of high-dimensional and small samples is a critical challenge. In this paper, we proposed a new GRN inference method based on a modified adaptive least absolute shrinkage and selection operator (MALasso).

Characterize Firmness Changes of Nectarine and Peach Fruit Associated With Harvest Maturity and Storage Duration Using Parameters of Force-Displacement Curves.

Fruit firmness is a critical attribute for evaluating the quality of peaches and nectarines. The precise measurement of fruit firmness plays a key role in maturity assessment, determining harvest periods, and predicting shelf-life. Texture analyzers are increasingly employed for accurate fruit firmness measurement, offering advantages in reducing operator errors compared to the traditional Magness-Taylor test.

Preparation and photoelectric properties of Si:B nanowires with thermal evaporation method.

We have successfully prepared a significant number of nanowires from non-toxic silicon sources. Compared to the SiO silicon source used in most other articles, our preparation method is much safer. It provides a simple and harmless new preparation method for the preparation of silicon nanowires.

Exploring Brain Imaging and Genetic Risk Factors in Different Progression States of Alzheimer's Disease Through OSnetNMF-Based Methods.

Alzheimer's disease (AD) is a neurodegenerative disease with no effective treatment, often preceded by mild cognitive impairment (MCI). Multimodal imaging genetics integrates imaging and genetic data to gain a deeper understanding of disease progression and individual variations. This study focuses on exploring the mechanisms that drive the transition from normal cognition to MCI and ultimately to AD.

A novel wearable device integrating ECG and PCG for cardiac health monitoring.

The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions. Traditional methods, often costly, bulky, and prone to subjectivity, fall short of meeting the need for daily monitoring. Digital and portable wearable monitoring devices have emerged as a promising research frontier.

Evaluation of predictions of disordered binding regions in the CAID2 experiment.

A large portion of the Intrinsically Disordered Regions (IDRs) in protein sequences interact with proteins, nucleic acids, and other types of ligands. Correspondingly, dozens of sequence-based predictors of binding IDRs were developed. A recently completed second community-based Critical Assessments of protein Intrinsic Disorder prediction (CAID2) evaluated 32 predictors of binding IDRs.

Probiotics and Food Bioactives: Unraveling Their Impact on Gut Microbiome, Inflammation, and Metabolic Health.

This review paper delves into the role of probiotics and food bioactives in influencing gut health and overall well-being, within the context of probiotics and food bioactives, emphasizing their roles in modulating inflammation, gut microbiota, and metabolic health. Probiotics are defined as live microorganisms that confer health benefits to the host, primarily through their impact on the gut microbiome; a complex community of microorganisms crucial for maintaining health. The review aims to elucidate how probiotics, incorporated into both traditional and modern food systems, can enhance gut health and address metabolic disorders.

Prob-sparse self-attention extraction of time-aligned dynamic functional connectivity for ASD diagnosis.

Dynamic functional connectivity (DFC) has shown promise in the diagnosis of Autism Spectrum Disorder (ASD). However, extracting highly discriminative information from the complex DFC matrix remains a challenging task. In this paper, we propose an ASD classification framework PSA-FCN which is based on time-aligned DFC and Prob-Sparse Self-Attention to address this problem.

Epilepsy Prediction and Detection Using Attention-CssCDBN with Dual-Task Learning.

Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens of millions of people worldwide. Currently, the most effective diagnostic method employs the monitoring of brain activity through electroencephalogram (EEG). However, it is critical to predict epileptic seizures in patients prior to their onset, allowing for the administration of preventive medications before the seizure occurs.

Data-model interactive Rul prediction of stochastic degradation devices with multiple uncertainty quantification and multi-sensor information fusion.

This paper proposes an improved remaining useful life (RUL) prediction method for stochastic degradation devices monitored by multi-source sensors under data-model interactive framework. Firstly, the interrelationships among sensors are established using k-nearest neighbor (KNN), and the composite health index (CHI) is constructed by aggregating the multi-source sensor information through the graph convolutional network (GCN). Secondly, a stochastic degradation model with triple uncertainty at any initial degradation level is established to improve the matching degree between the stochastic degradation model and the actual degradation process.

Integrated transcriptome and metabolome analysis revealed molecular regulatory mechanism of saline-alkali stress tolerance and identified bHLH142 in winter rapeseed (Brassica rapa).

Soil salinization is one of the main problems leading to a reduction in arable land area. In the present study, strongly salt-tolerant lines were screened for germination rates and physiological indices. The mechanism of saline-alkali stress tolerance in winter rapeseed was examined using transcriptome and metabolome analyses.

Automated gradation design of natural waste gravel soil stabilized by composite soil stabilizer based on a novel DNNSS-APDM-PFC model.

The utilization of natural waste gravel soil as base course material contributes to environmental protection and carbon emission reduction. The purpose of this research is to establish a new model for automated gradation design of the composite soil stabilizer-stabilized waste gravel soil (CSSWGS). A gradation range of CSSWGS has been proposed.

On Security Performance of SWIPT Multi-User Jamming Based on Mixed RF/FSO Systems with Untrusted Relay.

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker.

Infrared Aircraft Detection Algorithm Based on High-Resolution Feature-Enhanced Semantic Segmentation Network.

In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features.

Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8.

This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases.

Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks.

Natural disasters can severely disrupt conventional communication systems, hampering relief efforts. High-altitude tethered balloon base stations (HATBBSs) are a promising solution to communication disruptions, providing wide communication coverage in disaster-stricken areas. However, a single HATBBS is insufficient for large disaster zones, and limited resources may restrict the number and energy capacity of available base stations.

A Real-Time Lightweight Behavior Recognition Model for Multiple Dairy Goats.

Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed.

A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.

Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition.

Methods: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study.

Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits.

Soil salinization seriously affects the efficiency of crops in absorbing soil nutrients, and the cotton production in southern Xinjiang accounts for more than 60% of China's total. Therefore, it is crucial to monitor the dynamic changes in the salinity of the soil profile in cotton fields in southern Xinjiang, understand the status of soil salinization, and implement effective prevention and control measures. The drip-irrigated cotton fields in Alaer Reclamation Area were taken as the research objects.

A recurrent sigma pi sigma neural network.

In this paper, a novel recurrent sigma‒sigma neural network (RSPSNN) that contains the same advantages as the higher-order and recurrent neural networks is proposed. The batch gradient algorithm is used to train the RSPSNN to search for the optimal weights based on the minimal mean squared error (MSE). To substantiate the unique equilibrium state of the RSPSNN, the characteristic of stability convergence is proven, which is one of the most significant indices for reflecting the effectiveness and overcoming the instability problem in the training of this network.