Molecular nitrogen induced structural evolution of single transition metal atoms supported by B/N co-doped graphene for enhanced nitrogen electroreduction performance.

The structural evolution of local coordination environments of single-atom catalysts (SACs) under reaction conditions plays an important role in the catalytic performance of SACs. Using density functional theory calculations, the possible structural evolution of transition metal single atoms supported by B/N codoped-graphene (TM-BN/G) under nitrogen reduction reaction (NRR) conditions is explored and the catalytic performance based on reconstructed SACs is theoretically evaluated. A novel nitrogen adsorption mode on TM-BN/G is discovered and the protonation of one of the N atoms results in the TM atoms binding with three N atoms, among which one associates with two B atoms (TM-NB/G).

An investigation into the response of the soil ecological environment to tourist disturbance in Baligou.

The purpose of this study is to understand the response patterns of the soil ecological environment of the Macau Wetland Park to different levels of tourist interference and to provide a scientific basis for the rational development of the Bali Gou ecological tourism plan and the protection and management of the scenic area's ecological environment. Combine the methods of field collection and laboratory physical and chemical data analysis to analyze the impact of the strength of tourism disturbance on the soil ecological environment of Baligou. During the tourist activities in Baligou, the human factors in the process have an impact on the physical aspects of the scenic area's soil, such as soil bulk density, color tone, porosity, compactness, capacity, and leaf litter.

Tunable electronic and optical properties of ferroelectric WS/GaOheterostructures.

To integrate two-dimensional (2D) materials into van der Waals heterostructures (vdWHs) is regarded as an effective strategy to achieve multifunctional devices. The vdWHs with strong intrinsic ferroelectricity is promising for applications in the design of new electronic devices. The polarization reversal transitions of 2D ferroelectric GaOlayers provide a new approach to explore the electronic structure and optical properties of modulated WS/GaOvdWHs.

transcription factor family identification, phylogeny, and its response to abiotic stress in .

The second-largest transcription factor superfamily in plants is that of the basic helix-loop-helix (bHLH) family, which plays an important complex physiological role in plant growth, tissue development, and environmental adaptation. Systematic research on the bHLH family will enable a better understanding of this species. Herein, authors used a variety of bioinformatics methods and quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) to explore the evolution and function of the 218 genes identified.

A series of two-dimensional carbon allotropes with Dirac cone structure.

By combining a hexagon and square carbon ring, a series of two-dimensional (2D) carbon allotropes, named (HS)-graphene, can be obtained. Based on the first-principles calculations, the energetic, dynamical and mechanical stability were evaluated. Importantly, we predicted that some carbon allotropes possess the Dirac cone structure.

Micro-Shaping of Pure Aluminum in Long-Duration Wire Electrochemical Micromachining Using Bipolar Nanosecond Pulses.

With the increasing application of three-dimensional pure aluminum microstructures in micro-electromechanical systems (MEMS) and for fabricating terahertz components, high-quality micro-shaping of pure aluminum has gradually attracted attention. Recently, high-quality three-dimensional microstructures of pure aluminum with a short machining path have been obtained through wire electrochemical micromachining (WECMM), owing to its sub-micrometer-scale machining precision. However, machining accuracy and stability decrease owing to the adhesion of insoluble products on the surface of the wire electrode in long-duration WECMM, which limits the application of pure aluminum microstructures with a long machining path.

COVID-19 disease identification network based on weakly supervised feature selection.

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet).

Assessing financial performance through green bond markets and energy reliance in Asian economies.

This article's overarching goal is to learn more about the effects of financial performance on the reliance upon or migration to energy efficiency sources in Asian countries using data envelopment analysis (DEA) and system GMM from 2017 to 2022. The results demonstrated the importance of relying on renewable energy sources when expanding the electricity sector effectively in an Asian environment. This same influence of green bond financing on energy investment during an eco-friendly improving economy is in addition to the proportion of renewable energy demands, power usage to gross domestic product, power manufacturing stretchability, electricity usage stretchability, or the overall impact of renewable energy transformation.

Single-Atom Anchored g-CN Monolayer as Efficient Catalysts for Nitrogen Reduction Reaction.

Electrochemical N reduction reaction (NRR) is a promising approach for NH production under mild conditions. Herein, the catalytic performance of 3d transition metal (TM) atoms anchored on s-triazine-based g-CN (TM@g-CN) in NRR is systematically investigated by density functional theory (DFT) calculations. Among these TM@g-CN systems, the V@g-CN, Cr@g-CN, Mn@g-CN, Fe@g-CN, and Co@g-CN monolayers have lower ΔG(*NNH) values, especially the V@g-CN monolayer has the lowest limiting potential of -0.

Effect of Final Thermomechanical Treatment on the Mechanical Properties and Microstructure of T Phase Hardened Al-5.8Mg-4.5Zn-0.5Cu Alloy.

The effect of final thermomechanical treatment (FTMT) on the mechanical properties and microstructure of a T-Mg(Al Zn) phase precipitation hardened Al-5.8Mg-4.5Zn-0.

Improving the Reaction Kinetics by Annealing MoS/PVP Nanoflowers for Sodium-Ion Storage.

Under the ever-growing demand for electrochemical energy storage devices, developing anode materials with low cost and high performance is crucial. This study established a multiscale design of MoS/carbon composites with a hollow nanoflower structure (MoS/C NFs) for use in sodium-ion batteries as anode materials. The NF structure consists of several MoS nanosheets embedded with carbon layers, considerably increasing the interlayer distance.

Hydrogel of HEMA, NVP, and Morpholine-Derivative Copolymer for Sulfate Ion Adsorption: Behaviors and Mechanisms.

SO-containing compounds are widely present in wastewater generated from various industries and mining industries, such as slag leachate, pulp and paper wastewater, modified starch wastewater, etc. When the concentration of SO is too high, it will not only be corrosive to metal equipment but also accumulate in the environmental media. Based on this, a novel cationic hydrogel HNM was synthesized in this study by introducing morpholine groups into the conventional hydrogel HEMA-NVP system for the adsorption of SO in aqueous solutions.

Automatic polyp image segmentation and cancer prediction based on deep learning.

The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network.

Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data.

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN).

Levy Equilibrium Optimizer algorithm for the DNA storage code set.

The generation of massive data puts forward higher requirements for storage technology. DNA storage is a new storage technology which uses biological macromolecule DNA as information carrier. Compared with traditional silicon-based storage, DNA storage has the advantages of large capacity, high density, low energy consumption and high durability.

A novel and ultrasensitive fluorescent probe derived from labeled carbon dots for recognitions of copper ions and glyphosate.

Labeling materials with special functional groups are very valuable for the creation of novel probes. Hence, a novel fluorescent probe was constructed by conjugating 4-butyl-3-thiosemicarbazide (BTSC) with carbon dots (CDs). The CDs labeled by BTSC (BTSC-CDs) displayed a strong capability for recognition of Cu and Cu could quench the emission of BTSC-CDs significantly.

Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature.

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins.

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.