Investigation on the Atomic Mechanism for Grain Refinement of Magnesium Alloys by Mg-Zr Master Alloy.

The valence electron structure, bond energy, and cohesive energy of Mg, Zr, and α-Mg containing Zr, and α-Zr containing Mg crystals were calculated using the empirical electron theory of solids and molecules (EET). The calculation results show that the bond and cohesive energies of Zr were much greater than those of Mg, so Zr particles could precipitate ahead of α-Mg in general magnesium alloy melts or insoluble Zr particles exist when the magnesium melt temperature is relatively low. The bond energy of α-Zr decreases with the increase in Mg content; therefore, at the end of the growth of Zr particles, the remaining Zr atoms in the melt exist in the form of Mg-Zr clusters.

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions.

Research on Integral Fabrication and Inner Surface Metallization of the High-Frequency Terahertz Hollow-Core Metal Rectangular Waveguide Cavity by a Combined Process Based on Wire Electrochemical Micromachining and Electrochemical Deposition.

With the development of fabrication technology for terahertz rectangular cavity devices, the fabrication process of integral terahertz waveguide cavities has received much attention because of its beneficial effect on improving the transmission of terahertz signals. However, smaller feature sizes, higher dimensional accuracy, and more stringent requirements for cavity surface roughness and edge radius make it difficult to manufacture terahertz waveguide cavities with a high operating frequency by using existing micro-manufacturing technology. At the same time, the smaller feature size also makes it more difficult to realize uniform metallization on the inner surface of a terahertz waveguide cavity.

Evaluation Model of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Improved BP Neural Network.

Entrepreneurship education activities in colleges and universities play an important role in improving students' innovation ability. Therefore, this paper has important practical value to evaluate the innovation and entrepreneurship ability of college students. At present, most studies use qualitative research methods, which is inefficient.

Mitigations pathways towards sustainable development: assessing the influence of higher education on environmental quality in BRICS economies.

Education plays a significant role in the development of economies. This study empirically contributes to the literature by examining the impacts of higher education on CO emissions of BRICS economies over the period 1998-2020. For empirical analysis, we used the ARDL bound testing approach.

PseAraUbi: predicting arabidopsis ubiquitination sites by incorporating the physico-chemical and structural features.

We makes three kinds of important features from Arabidopsis thaliana: protein secondary structure based on the Chou-Fasman parameter, amino acids hydrophobicity and polarity information, and analyze their properties. Ubiquitination modification is an important post-translational modification of proteins, which participates in the regulation of many important life activities in cells. At present, ubiquitination proteomics research is mostly concentrated in animals and yeasts, while relatively few studies have been carried out in plants.

One step synthesis of ultra-high quantum yield fluorescent carbon dots for "on-off-on" detection of Hg and biothiols.

In this paper, the carbon dots (CDs) with strong blue fluorescence were synthesized through hydrothermal method, which using folic acid, ammonium citrate and ethylenediamine as precursors. The prepared CDs with a high absolute quantum yield of 81.94% and showed excellent stability in high concentration salt solution and different pH conditions.

Comprehensive secondary prevention of lateral epicondylitis in young tennis players.

The research aims to increase the efficiency of the training process for young tennis players old based on lateral epicondylitis secondary prevention methods. Tennis players with apparent residual effects of lateral epicondylitis were selected for the study and divided into experimental (18 boys and 14 girls) and control groups (18 boys and 13 girls). The experimental group was trained according to the developed technique, and the control group as per standard one.

Electrochemical Deposition of Pure-Nickel Microstructures with Controllable Size.

Pure nickel microstructures have been widely used in MEMS and have great application potential as a sacrificial mandrel for fabricating terahertz micro-cavity components. The performance of MEMS and terahertz micro-cavity components can be significantly improved through the use of high-quality pure nickel microstructures. Up to now, microfabrication techniques, such as laser micromachining, wire electrical-discharge machining, and cold-spray additive manufacturing, have been used to machine various types of such microstructures.

Investigating the Role of English as a Foreign Language Learners' Academic Motivation and Language Mindset in Their Grit: A Theoretical Review.

This review made a critical attempt to examine the studies on the role of English as a foreign language (EFL) learners' academic motivation and growth mindsets in their grit. Some investigations have been done on the role of academic motivation in learner grit. However, a significant positive correlation between academic motivation and grit has been approved in related studies.

SGEResU-Net for brain tumor segmentation.

The precise segmentation of tumor regions plays a pivotal role in the diagnosis and treatment of brain tumors. However, due to the variable location, size, and shape of brain tumors, the automatic segmentation of brain tumors is a relatively challenging application. Recently, U-Net related methods, which largely improve the segmentation accuracy of brain tumors, have become the mainstream of this task.

Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.

Identification of protein-ligand binding sites plays a critical role in drug discovery. However, there is still a lack of targeted drug prediction for DNA-binding proteins. This study aims at the binding sites of DNA-binding proteins and drugs, by mining the residue interaction network features, which can describe the local and global structure of amino acids, combined with sequence feature.

White-noise-induced double coherence resonances in reduced Hodgkin-Huxley neuron model near subcritical Hopf bifurcation.

Coherence resonance (CR) describes a counterintuitive phenomenon in which the optimal oscillatory responses in nonlinear systems are shaped by a suitable noise amplitude. This phenomenon has been observed in neural systems. In this research, the generation of double coherence resonances (DCRs) due to white noise is investigated in a three-dimensional reduced Hodgkin-Huxley neuron model with multiple-timescale feature.

Topological insulators on the square-hexagon lattice driven by next-nearest-neighbor hopping.

We investigate the topological phase transition of the square-hexagon lattice driven by the next-nearest-neighbor (NNN) hopping. By means of the Fukui-Hatsugai method, the topological invariantcan be determined. The phase diagrams in the (,) plane for different filling fractions are displayed, together with the size of the bulk band gap.

A Rumor Detection Method from Social Network Based on Deep Learning in Big Data Environment.

Aiming at the lack of feature extraction ability of rumor detection methods based on the deep learning model, this study proposes a rumor detection method based on deep learning in social network big data environment. Firstly, the scheme of combining API interface and third-party crawler program is adopted to obtain Weibo rumor information from the Weibo "false Weibo information" public page, so as to obtain the Weibo dataset containing rumor information and nonrumor information. Secondly, the distributed word vector is used to encode text words, and the hierarchical Softmax and negative sampling are used to improve the training efficiency.

Drug repositioning of COVID-19 based on mixed graph network and ion channel.

Research on the relationship between drugs and targets is the key to precision medicine. Ion channel is a kind of important drug targets. Aiming at the urgent needs of corona virus disease 2019 (COVID-19) treatment and drug development, this paper designed a mixed graph network model to predict the affinity between ion channel targets of COVID-19 and drugs.

Theoretical Investigation on the Hydrogen Evolution, Oxygen Evolution, and Oxygen Reduction Reactions Performances of Two-Dimensional Metal-Organic Frameworks Fe(CX) (X = NH, O, S).

Two-dimensional metal-organic frameworks (2D MOFs) inherently consisting of metal entities and ligands are promising single-atom catalysts (SACs) for electrocatalytic chemical reactions. Three 2D Fe-MOFs with NH, O, and S ligands were designed using density functional theory calculations, and their feasibility as SACs for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) was investigated. The NH, O, and S ligands can be used to control electronic structures and catalysis performance in 2D Fe-MOF monolayers by tuning charge redistribution.

Characterization and phylogenetic analysis of the mitochondrial genome of (Coleoptera: Scarabaeidae: Melolonthinae).

(Motschulsky, 1854) is an important pest for peanut, potato, and soybean in China, and it causes great economic losses. In this study, we sequenced and analyzed the complete mitochondrial genome (mitogenome) of . This mitogenome was 16,975 bp long and encoded 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and two ribosomal RNA genes (rRNAs).

Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression.

PPDTS: Predicting potential drug-target interactions based on network similarity.

Identification of drug-target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs.

First-principles study of pressure-induced phase transitions, mechanical and thermodynamic properties of ThBC.

First-principles calculations and particle swarm optimization algorithm are combined to predict the crystal structures in the pressure range from 0 to 100 GPa. Four phases of ThBC are determined, including the422,,andphases, in which the,andphases are newly predicted structures. The mechanical, electronic and thermodynamic properties of the four phases are investigated.

Second-order ResU-Net for automatic MRI brain tumor segmentation.

Tumor segmentation using magnetic resonance imaging (MRI) plays a significant role in assisting brain tumor diagnosis and treatment. Recently, U-Net architecture with its variants have become prevalent in the field of brain tumor segmentation. However, the existing U-Net models mainly exploit coarse first-order features for tumor segmentation, and they seldom consider the more powerful second-order statistics of deep features.

Research on the Fastest Detection Method for Weak Trends under Noise Interference.

Trend anomaly detection is the practice of comparing and analyzing current and historical data trends to detect real-time abnormalities in online industrial data-streams. It has the advantages of tracking a concept drift automatically and predicting trend changes in the shortest time, making it important both for algorithmic research and industry. However, industrial data streams contain considerable noise that interferes with detecting weak anomalies.

Global potential distribution prediction of Xanthium italicum based on Maxent model.

Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum.

Synovial mesenchymal stem cell-derived extracellular vesicles containing microRN555A-26a-5p ameliorate cartilage damage of osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease characterized by cartilage damage. We aimed to improve the understanding of the protective mechanism of synovial mesenchymal stem cell (SMSC)-derived extracellular vesicles (EVs) in cartilage damage of OA.

Methods: SMSCs and SMSC-EVs were isolated from synovial biopsies of patients without OA and then identified.