Improved marine predators algorithm for engineering design optimization problems.

The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA).

Enhanced classification of left ventricular hypertrophy in cardiac patients using extended Siamese CNN.

Left ventricular hypertrophy (LVH) is the thickening of the left ventricle wall of the heart. The objective of this study is to develop a novel approach for the accurate assessment of LVH) severity, addressing the limitations of traditional manual grading systems.We propose the Multi-purpose Siamese Weighted Euclidean Distance Model (MSWED), which utilizes convolutional Siamese neural networks and zero-shot/few-shot learning techniques.

Speech preprocessing and enhancement based on joint time domain and time-frequency domain analysis.

Speech enhancement aims to make noisy speech signals clearer. Traditional time-frequency domain methods struggle to differentiate between speech and noise, leading to a risk of speech distortion. This paper introduces an approach that combines the time domain and time-frequency domain using the W-net module to suppress noise at the front end.

A Novel Deep Learning Model for Drug-drug Interactions.

Introduction: Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.

Methods: In this paper, we propose a novel approach for DDI prediction using two separate message-passing neural network (MPNN) models, each focused on one drug in a pair. By capturing the unique characteristics of each drug and their interactions, the proposed method aims to improve the accuracy of DDI prediction.

A maize seed variety identification method based on improving deep residual convolutional network.

Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network's learning process and enabled more efficient information propagation across the network layers.

Photocatalytic degradation of antibiotic sulfamethizole by visible light activated perovskite LaZnO.

In this work, the perovskite LaZnO was synthesized via sol-gel method and applied for photocatalytic treatment of sulfamethizole (SMZ) antibiotics under visible light activation. SMZ was almost completely degraded (99.2% ± 0.

Simulation Study and Optimization Strategies for Vacuum Infusion of GFRP Hoses Based on Resin Time-Viscosity Variables.

During the infusion process of a glass-fiber-reinforced thermosetting composite hose, the viscosity of its resin matrix undergoes temporal variations. Consequently, if the impact of resin viscosity changes over time on the internal resin fluidity is not considered during the infusion process, this may result in the incomplete impregnation of the hose, characterized by the presence of numerous voids. This phenomenon adversely affects the quality of the pipe's curing and forming process.

Exploring the Progress of Hyaluronic Acid Hydrogels: Synthesis, Characteristics, and Wide-Ranging Applications.

This comprehensive review delves into the world of hyaluronic acid (HA) hydrogels, exploring their creation, characteristics, research methodologies, and uses. HA hydrogels stand out among natural polysaccharides due to their distinct features. Their exceptional biocompatibility makes them a top choice for diverse biomedical purposes, with a great ability to coexist harmoniously with living cells and tissues.

Skip connection information enhancement network for retinal vessel segmentation.

Many major diseases of the retina often show symptoms of lesions in the fundus of the eye. The extraction of blood vessels from retinal fundus images is essential to assist doctors. Some of the existing methods do not fully extract the detailed features of retinal images or lose some information, making it difficult to accurately segment capillaries located at the edges of the images.

Continuous estimation of respiratory system compliance and airway resistance during pressure-controlled ventilation without end-inspiration occlusion.

Background: Assessing mechanical properties of the respiratory system (C) during mechanical ventilation necessitates an end-inspiration flow of zero, which requires an end-inspiratory occlusion maneuver. This lung model study aimed to observe the effect of airflow obstruction on the accuracy of respiratory mechanical properties during pressure-controlled ventilation (PCV) by analyzing dynamic signals.

Methods: A Hamilton C3 ventilator was attached to a lung simulator that mimics lung mechanics in healthy, acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) models.

Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types.

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)-Angstrom exponent (AE) method (AA1M), and AOD-AE method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea.

Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.

Background: Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face.

Roles of Pseudomonas aeruginosa siderophores in interaction with prokaryotic and eukaryotic organisms.

Pseudomonas aeruginosa is an opportunistic pathogen that produces two types of siderophores, pyoverdine and pyochelin, that play pivotal roles in iron scavenging from the environment and host cells. P. aeruginosa siderophores can serve as virulence factors and perform various functions.

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.

Background: The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets.

Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification.

The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal role in facilitating communication for individuals with physical limitations through Brain-Computer Interface (BCI) systems. Recent strides in Attention-Based Networks (ATN) have showcased remarkable performance in EEG signal classification, presenting a promising alternative to conventional Convolutional Neural Networks (CNNs). However, while CNNs have been extensively analyzed for their resilience against adversarial attacks, the susceptibility of ATNs in comparable scenarios remains largely unexplored.

Classification and transfer learning of sleep spindles based on convolutional neural networks.

Background: Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science.

Methods: This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles.

Detection of maize stem diameter by using RGB-D cameras' depth information under selected field condition.

Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter.

Segmentation of liver CT images based on weighted medical transformer model.

Deep convolutional neural networks have made significant strides in the field of medical image segmentation. Although existing convolutional structures enhance performance by leveraging local image information, they often lose the interdependence information between contexts. Therefore, the article utilizes the multi-attention mechanism of the Transformer structure to more comprehensively express relationships between contexts and introduced the Transformer network architecture into the field of medical image segmentation.

Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models.

Identification of druggable proteins can greatly reduce the cost of discovering new potential drugs. Traditional experimental approaches to exploring these proteins are often costly, slow, and labor-intensive, making them impractical for large-scale research. In response, recent decades have seen a rise in computational methods.

An Electroanalytical Enzymeless α-FeO-ZnO Hybrid Nanostructure-Based Sensor for Sensitive Quantification of Nitrite Ions.

Nitrite monitoring serves as a fundamental practice for protecting public health, preserving environmental quality, ensuring food safety, maintaining industrial safety standards, and optimizing agricultural practices. Although many nitrite sensing methods have been recently developed, the quantification of nitrite remains challenging due to sensitivity and selectivity limitations. In this context, we present the fabrication of enzymeless iron oxide nanoparticle-modified zinc oxide nanorod (α-FeO-ZnO NR) hybrid nanostructure-based nitrite sensor fabrication.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages.

AI-based tool for early detection of Alzheimer's disease.

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease.

Predictive performance of machine learning compared to statistical methods in time-to-event analysis of cardiovascular disease: a systematic review protocol.

Background: Globally, cardiovascular disease (CVD) remains the leading cause of death, warranting effective management and prevention measures. Risk prediction tools are indispensable for directing primary and secondary prevention strategies for CVD and are critical for estimating CVD risk. Machine learning (ML) methodologies have experienced significant advancements across numerous practical domains in recent years.

Multifunctional hydrogel dressing based on fish gelatin/oxidized hyaluronate for promoting diabetic wound healing.

Non-healing chronic diabetic wound treatment remains an unsolved healthcare challenge and still threatens patients' lives. Recently, hydrogel dressings based on natural biomaterials have been widely investigated to accelerate the healing of diabetic wounds. In this study, we introduce a bioactive hydrogel based on fish gelatin (FG) as a candidate for diabetic wound treatments, which is a recently emerged substitute for mammalian derived gelatin.

Redox-Responsive Gold Nanoparticles Coated with Hyaluronic Acid and Folic Acid for Application in Targeting Anticancer Therapy.

Methotrexate (MTX) has poor water solubility and low bioavailability, and cancer cells can become resistant to it, which limits its safe delivery to tumor sites and reduces its clinical efficacy. Herein, we developed novel redox-responsive hybrid nanoparticles (NPs) from hyaluronic acid (HA) and 3-mercaptopropionic acid (MPA)-coated gold NPs (gold@MPA NPs), which were further conjugated with folic acid (FA). The design of FA-HA-ss-gold NPs aimed at enhancing cellular uptake specifically in cancer cells using an active FA/HA dual targeting strategy for enhanced tumor eradication.