Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics.

Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks.

Classification of pain expression images in elderly with hip fractures based on improved ResNet50 network.

The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function.

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks.

The recognition of DNA-binding proteins (DBPs) is the crucial step to understanding their roles in various biological processes such as genetic regulation, gene expression, cell cycle control, DNA repair, and replication within cells. However, conventional experimental methods for identifying DBPs are usually time-consuming and expensive. Therefore, there is an urgent need to develop rapid and efficient computational methods for the prediction of DBPs.

Improved XLNet modeling for Chinese named entity recognition of edible fungus.

Introduction: The diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem.

Investigations of Laser-Assisted Renal Denervation for Treatment of Resistant Hypertension.

Background And Objectives: Renal denervation (RDN) is an emerging surgical treatment for resistant hypertension. However, the current RDN using radiofrequency can cause undesirable thermal damage to the medial and luminal layers due to direct contact between the arterial lumen and energy source. The aim of this study is to evaluate the feasibility of the new laser-assisted RDN by exploring the potential treatment conditions.

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images.

A Series of Endohedral Metallo-BN Fullerene Superatomic Structures.

Superatoms are crucial in the assembly of functional and optoelectronic materials. This study investigates the endohedral metallo-boron nitride [boron nitride (BN)] fullerenes U@BN, Cm@BN, and U@BN in theory. Our findings confirm that U@BN, Cm@BN, and U@BN are superatoms and their electronic configurations are 1P1S1D1F2P2S2D2F3P, 1P1S1D1F1G1H2S2P2D2F, and 1P1S1D1F2P2S2D2F, respectively.

Efficient Quasi-Two-Dimensional Perovskite Light-Emitting Diodes Achieved through the Passivation of Multi-Fluorine Phosphate Molecules.

The surface morphology of perovskite films significantly influences the performance of perovskite light-emitting diodes (PeLEDs). However, the thin perovskite thickness (~10 nm) results in low surface coverage on the substrate, limiting the improvement of photoelectric performance. Here, we propose a molecular additive strategy that employs (FDPP) molecules as additives.

Advances in Host-Free White Organic Light-Emitting Diodes Utilizing Thermally Activated Delayed Fluorescence: A Comprehensive Review.

The ever-growing prominence and widespread acceptance of organic light-emitting diodes (OLEDs), particularly those employing thermally activated delayed fluorescence (TADF), have firmly established them as formidable contenders in the field of lighting technology. TADF enables achieving a 100% utilization rate and efficient luminescence through reverse intersystem crossing (RISC). However, the effectiveness of TADF-OLEDs is influenced by their high current density and limited device lifetime, which result in a significant reduction in efficiency.

The Diels-Alder Cross-Linked Gelatin/Dextran Nanocomposite Hydrogels with Silver Nanoparticles for Wound Healing Applications: Synthesis, Characterization, and In Vitro Evaluation.

Wound healing involves a sophisticated biological process that relies on ideal conditions to advance through various stages of repair. Modern wound dressings are designed to imitate the natural surroundings around cells and offer properties such as moisture regulation, strength, and antimicrobial defense to boost healing. A recent research project unveiled a new type of gelatin (Gel)/dextran (Dex) hydrogels, linked through Diels-Alder (D-A) reactions, loaded with silver nanoparticles (Ag-NPs) for cutting-edge wound treatment.

Electrochemical detection of nalbuphine drug using oval-like ZnO nanostructure-based sensor.

Monitoring pharmaceutical drugs in various mediums is crucial to mitigate adverse effects. This study presents a chemical sensor using an oval-like zinc oxide (ZnO) nanostructure for electrochemical detection of nalbuphine. The ZnO nanostructure, produced via an efficient sol-gel technique, was extensively characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), UV-visible spectrophotometry, and fourier transform infrared spectroscopy (FTIR).

Multifunctional Microneedle Patch with Diphlorethohydroxycarmalol for Potential Wound Dressing.

Background: Treatment of skin wounds with diverse pathological characteristics presents significant challenges due to the limited specific and efficacy of current wound healing approaches. Microneedle (MN) patches incorporating bioactive and stimulus materials have emerged as a promising strategy to overcome these limitations and integrating bioactive materials with anti-bacterial and anti-inflammatory properties for advanced wound dressing.

Methods: We isolated diphlorethohydroxycarmalol (DPHC) from Ishige okamurae and assessed its anti-inflammatory and anti-bacterial effects on macrophages and its antibacterial activity against Cutibacterium acnes.

Transforming performance: The impact of an 8-week complex training program on strength, power, and change of direction in female basketball athletes.

Background: This study aimed to investigate the effect of complex training on the strength, power, and change of direction (COD) performance of college female basketball athletes.

Methods: This design used experimental and randomized studies. A total of 32 female basketball players volunteered to participate in this study and were randomly allocated to a complex training group (CT group: n = 16) and a resistance training group (RT group: n = 16).

κ-Carrageenan masking bitterness perception in surimi gels containing potassium chloride-based salt substitutes: Gel properties, oral processing, and sensory evaluation.

κ-Carrageenan (CG) was employed to mask the bitterness induced by 50% KCl in surimi gels to achieve salt reduction and gel performance improvement. The combination of KCl and CG (KCl + CG) yielded the increased textural characteristics and water-holding capacity (WHC) of surimi gels and facilitated the transition of free water to immobilized water. In addition, the KCl + CG supplement increased the turbidity and particle size of myofibrillar protein (MP) sols but decreased the surface hydrophobicity in a dose-dependent manner.

ST-D3DDARN: Urban traffic flow prediction based on spatio-temporal decoupled 3D DenseNet with attention ResNet.

Urban traffic flow prediction plays a crucial role in intelligent transportation systems (ITS), which can enhance traffic efficiency and ensure public safety. However, predicting urban traffic flow faces numerous challenges, such as intricate temporal dependencies, spatial correlations, and the influence of external factors. Existing research methods cannot fully capture the complex spatio-temporal dependence of traffic flow.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms.

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler's laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data.

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.