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Application of electromagnetic metallurgy in continuous casting: A review

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Progress in Natural Science:Materials International 2024年第1期34卷 1-11页

作者： Jiale Li Yi nian Xiang Liu Youcheng Zong Xinyu Tang Chaojie Zhang Liqiang Zhang The Intelligent Metallurgy Research Institute School of Metallurgical Engineering Anhui University of Technology

The standards within the global steel industry are constantly advancing, necessitating increasingly rigorous quality control protocols during the continuous casting process. Central to the challenges faced in this domain are persistent defects such as central segregation, V-shaped segregation, and center porosity, which significantly impede the production of high-caliber steel products. The discipline of electromagnetic metallurgy has emerged as a critical solution in reducing these defects, thereby enhancing the efficiency and stability of continuous casting operations. Its demonstrated effectiveness has led to its widespread implementation across global and domestic steel production industries. This paper aims to deliver a comprehensive examination of electromagnetic metallurgy's impact on the final stages of solidification in continuous casting, along with its contributions to the heating and purification processes within the tundish. Additionally, it will explore the potential advancements and applications of this technology in the evolving sphere of continuous casting.

关键词： continuous casting Quality control Final electromagnetic stirring Induction heating Electromagnetic purification

Experimental and numerical study on protective effect of RC blast wall against air shock wave

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Defence Technology（防务技术） 2024年第1期31卷 567-579页

作者： Xin-zhe nian Quan-min Xie Xin-li Kong Ying-kang Yao Kui Huang State Key Laboratory of Precision Blasting Jianghan UniversityWuhan430056China Hubei Key Laboratory of Blasting Engineering Jianghan UniversityWuhan430056China Hubei(Wuhan)Institute of Explosion and Blasting Technology Jianghan UniversityWuhan430056China College of Defense Engineering Army Engineering University of PLANanjing210007China Institute of Defense Engineering AMSPLALuoyang471023China

Prototype experiments were carried out on the explosion-proof performance of the RC blast *** mass of TNT detonated in the experiments is 5 kg and 20 kg *** shock wave overpressure was tested in different *** above experiments were numerically simulated,and the simulated shock wave overpressure waveforms were compared with that tested and given by CONWEP *** results show that the numerically simulated waveform is slightly different from the test waveform,but similar to CONWEP *** dimensional analysis and numerical simulation under different working conditions,the equation for the attenuation rate of the diffraction overpressure behind the blast wall was *** to the corresponding standards,the degree of casualties and the damage degree of the brick concrete building at a certain distance behind the wall can be determined when parameters are *** above results can provide a reference for the design and construction of the reinforced concrete blast wall.

关键词： Blast wall Shock wave Diffraction Overpressure Protection

Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna

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Journal of Oceanology and Limnology 2024年第3期42卷 972-987页

Bigeye tuna Thunnus obesus is an important migratory species that forages deeply,and El Niño events highly influence its distribution in the eastern Pacific *** sea surface temperature is widely recognized as the main factor affecting bigeye tuna(BET)distribution during El Niño events,the roles of different types of El Niño and subsurface oceanic signals,such as ocean heat content and mixed layer depth,remain *** conducted A spatial-temporal analysis to investigate the relationship among BET distribution,El Niño events,and the underlying oceanic signals to address this knowledge *** used monthly purse seine fisheries data of BET in the eastern tropical Pacific Ocean(ETPO)from 1994 to 2012 and extracted the central-Pacific El Niño(CPEN)indices based on Niño 3 and Niño ***,we employed Explainable Artificial Intelligence(XAI)models to identify the main patterns and feature importance of the six environmental variables and used information flow analysis to determine the causality between the selected factors and BET ***,we analyzed Argo datasets to calculate the vertical,horizontal,and zonal mean temperature differences during CPEN and normal years to clarify the oceanic thermodynamic structure differences between the two types of *** findings reveal that BET distribution during the CPEN years is mainly driven by advection feedback of subsurface warmer thermal signals and vertically warmer habitats in the CPEN domain area,especially in high-yield fishing *** high frequency of CPEN events will likely lead to the westward shift of fisheries centers.

关键词： bigeye tuna central-Pacific El Niño(CPEN) ocean heat content Argo explainable artificial intelligence(XAI) information flow

A Comprehensive Survey of Recent Transformers in Image,Video and Diffusion Models

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Computers, Materials & Continua 2024年第7期80卷 37-60页

作者： Dinh Phu Cuong le Dong Wang Viet-Tuan le College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Faculty of Information Technology Yersin University of Da LatDa Lat66100Vietnam Faculty of Information Technology Ho Chi Minh City Open UniversityHo Chi Minh City722000Vietnam

Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention *** study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion *** begin by discussing existing surveys of vision transformers and comparing them to this ***,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,*** the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for *** also provide a comprehensive overview of recent transformer models for video tasks and diffusion *** compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark ***,we explore some future research directions to further improve the field.

关键词： Transformer vision transformer self-attention hierarchical transformer diffusion models

Boundless love:a German Professor Uli Schwarz in China

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Protein & Cell 2024年第8期15卷 553-558页

作者： le Kang State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of ZoologyChinese Academy of SciencesBeijing 100101China

More than 20 years ago,an old German man embarked on a quest to explore the suburbs of Shanghai and the villages of jiangsu Province in search of antique Chinese *** was not a mere antique collector or aficionado of ancient furmiture,but a German scientist motivated by his profound love of Chinese *** name was Uli Schwarz(Fig.1),a distinguished German scientist who had dedicated the majority of his life to promoting cross-cultural scientific exchanges,an effort that left an indelible mark on the landscape of research collaborations between China and Germany.

关键词： Schwarz German landscape

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang nian-Tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network least squares support vector machine Feature optimization Gas-bearing distribution prediction

Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy le Duy-Hung NGUYEN Thanh SANG-TO Samir KHATIR Hoang le-MINH Amir H.GANDOMI Thanh CUONG-le Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

Honeycomb-like biochar framework coupled with Fe_(3)O_(4)/FeS nanoparticles as efficient heterogeneous Fenton catalyst for phenol degradation

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Journal of Environmental Sciences 2024年第2期136卷 390-399页

作者： Aihua Cheng Yi he Xiaohe Liu Chi he Department of Environmental Engineering College of Geology and EnvironmentXi’an University of Science and TechnologyXi’an 710054China State Key Laboratory of Multiphase Flow in Power Engineering School of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an 710049China National Engineering Laboratory for VOCs Pollution Control Material and Technology University of Chinese Academy of SciencesBeijing 101408China

Achieving an efficient and stable heterogeneous Fenton reaction over a wide pH range is of great significance for wastewater ***,a pollen-derived biochar catalyst with a unique honeycomb-like structure,coupled with the dispersion of magnetic Fe_(3)O_(4)/FeS(Fe/S)nanoparticles,was synthesized by simple impregnation precursor,followed by *** prepared Fe/S-biochar catalyst demonstrated outstanding phenol degradation efficiency across a wide pH range,with 98%of which eliminated even under neutral conditions(pH 7.0).The high catalytic activity was due to the multilevel porous structure of pollenderived biochar provided enough active sites and allowed for better electron transfer,then increases oxidation ability to promote the ***,the acid microenvironment formed by SO_(4)^(2-)group from Fe/S composite extended the pH range for Fenton reaction,and S^(2-)facilitated the conversion of≡Fe^(3+)to≡Fe^(2+),resulting in remarkable degradation ***,biochar can effectively promote cycling stability by limiting Fe *** work may provide a general strategy for designing 3D framework biochar-based Fe/S catalysts with excellent performance for heterogeneous Fenton reactions.

关键词： Biomass Biochar Fenton reaction Fe_(3)O_(4)/FeS(Fe/S) Phenol degradation

Energy band modulation of N-doped rutile/anatase TiO_(2) photoanode promoting charge separation toward prominent photoelectrochemical performance

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Rare Metals 2024年第8期43卷 3784-3797页

作者： Juan Gao Shi-Xuan Wang Jia-le Deng Ping-Ping Wei Fu-Zhang Chen Ling-Cheng Zheng Yue-Qin Wang Yang Li Yin Liu Gang he School of Mechanics and Photoelectric Physics Anhui University of Science and TechnologyHuainan 232001China School of Materials Science and Engineering Anhui UniversityHefei230039China Tianneng Battery(Wuhu)Co. Ltd.Wuhu 241000China School of Materials Science and Engineering Anhui University of Science and TechnologyHuainan 232001China

To solve the problem of high photogenerated carrier recombination rate and low photoelectric conversion efficiency of TiO_(2)-based materials,a simple N-doped anatase/rutile TiO_(2) heterophase nanorod film was designed by a low-temperature hydrothermal method in this *** enhanced separation and transport of photogenerated charges were facilitated by the smaller contact barrier and appropriate band matching between anatase TiO_(2)nanoparticles and rutile TiO_(2) *** introduction of N doping in anatase TiO_(2) resulted in an upward shift of the valence band and a narrowing of the band gap,consequently enhancing the efficiency of visible light *** combination of the heterophase junction and N-doping exhibited a synergistic effect,effectively suppressing the recombination of photogenerated charges and enhancing the photoelectric conversion efficiency of the *** AM 1.5G irradiation,the photocurrent density(J)of the A-TO(N)@R-TONR photoanode reached2.19 mA·cm^(-2)(V_(Rhe,1.23 eV)).Additionally,the incident photon-electron conversion efficiency(IPCE)and the charge injection efficiency(η)reached 81.4%and 51.6%at320 ***,the J,IPCE,andηvalues of the A-TO(N)@R-TONR photoanode were 2.96,2.1 and 3.2times those of pure R-TONR photoanode,*** work presents a rational strategy for designing efficient TiO_(2)-based photoanodes.

关键词： TiO_(2) heterophase junction Photoanode