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Novel Soft ComputingModel for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 2227-2246页

作者： Quang-Hieu Tran Hoang Nguyen xuan-nam bui Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyHanoi100000Vietnam Innovations for Sustainable and Responsible Mining(ISRM)Research Group Hanoi University of Mining and GeologyHanoi100000Vietnam

This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning ***,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial *** bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and *** is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as *** addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive *** this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also *** were then compiled as the dataset to develop the PPV predictive *** results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR *** those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.

关键词： Mine blasting blast-induced ground vibration environmentally friendly blasting peak particle velocity bagging extra trees

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Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1452-1465页

作者： Chengyu Xie Hoang Nguyen xuan-nam bui Van-Thieu Nguyen Jian Zhou School of Environment and Resources Xiangtan UniversityXiangtan411105China Mining Faculty Hanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research Group Hanoi100000Viet Nam School of Resources and Safety Engineering Central South UniversityChangsha410083China

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing *** coal mines have to pace up the mining operations with large production to meet the energy *** requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a ***,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(***-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS ***,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile *** MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground *** results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

关键词： Roof displacement Longwall mining Underground mine Physics-based optimization Risk assessment Mining hazards

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Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

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Geoscience Frontiers 2021年第3期12卷 458-472页

作者： Chengyu Xie Hoang Nguyen xuan-nam bui Yosoon Choi Jian Zhou Thao Nguyen-Trang School of Environment and Resources Xiangtan UniversityXiangtan 411105China Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department of Surface Mining Mining FacultyHanoi University of Mining and Geology18 Pho VienDuc Thang wardBac Tu Liem districtHanoi 100000Viet Nam Center for Mining Electro-Mechanical researchHanoi University of Mining and Geology18 Pho VienDuc Thang wardBac Tu Liem districtHanoi 100000Viet Nam Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea School of Resources and Safety Engineering Central South UniversityChangsha 410083HunanChina Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh City 700000Viet Nam Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City 700000Viet Nam

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit *** evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental ***,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open ***,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,***,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity *** developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop *** which,111 events were randomly selected for the development and optimization of the ***,the remaining 25 blasting events were applied to confirm the accuracy of the proposed ***,blast design parameters were regarded as input variables to predict the size of rock in blasting ***,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench *** the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented *** other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and ***,the FFA-GBM model can be used as a powerful

关键词： Mine blasting Rock fragmentation Artificial intelligence Hybrid model Gradient boosting machine Meta-heuristic algorithm

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Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 691-712页

作者： Quynh-Anh Thi bui Dam Duc Nguyen Hiep Van Le Indra Prakash Binh Thai Pham Civil Engineering Department University of Transport Technology54 Trieu KhucThanh XuanHanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement *** study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input ***,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the *** models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat *** validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the *** show that these models accurately predict SBS,with LGBM providing outstanding ***(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on ***,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

关键词： Shear bond asphalt pavement grid search optimization machine learning

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Investigating Müller glia reprogramming in mice: a retrospective of the last decade, and a look to the future

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Neural Regeneration Research 2025年第4期20卷 946-959页

作者： Zhiyuan Yin Jiahui Kang xuan Cheng Hui Gao Shujia Huo Haiwei Xu Key Lab of Visual Damage and Regeneration&Restoration of Chongqing Southwest Eye HospitalSouthwest HospitalThird Military Medical University(Army Medical University)ChongqingChina

Müller glia,as prominent glial cells within the retina,plays a significant role in maintaining retinal homeostasis in both healthy and diseased *** lower vertebrates like zebrafish,these cells assume responsibility for spontaneous retinal regeneration,wherein endogenous Müller glia undergo proliferation,transform into Müller glia-derived progenitor cells,and subsequently regenerate the entire retina with restored ***,Müller glia in the mouse and human retina exhibit limited neural reprogramming.Müller glia reprogramming is thus a promising strategy for treating neurodegenerative ocular disorders.Müller glia reprogramming in mice has been accomplished with remarkable success,through various *** in molecular,genetic,epigenetic,morphological,and physiological evaluations have made it easier to document and investigate the Müller glia programming process in ***,there remain issues that hinder improving reprogramming efficiency and ***,understanding the reprogramming mechanism is crucial toward exploring factors that will improve Müller glia reprogramming efficiency,and for developing novel Müller glia reprogramming *** review describes recent progress in relatively successful Müller glia reprogramming *** also provides a basis for developing new Müller glia reprogramming strategies in mice,including epigenetic remodeling,metabolic modulation,immune regulation,chemical small-molecules regulation,extracellular matrix remodeling,and cell-cell fusion,to achieve Müller glia reprogramming in mice.

关键词： cell fusion chemical small-molecules epigenetic extracellular matrix immune metabolic mice Müller glia neurodegenerative diseases reprogramming retina regeneration

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Cell polarization in ischemic stroke: molecular mechanisms and advances

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Neural Regeneration Research 2025年第3期20卷 632-645页

作者： Yuanwei Li Xiaoxiao Xu xuan Wu Jiarui Li Shiling Chen Danyang Chen Gaigai Li Zhouping Tang Department of Neurology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei ProvinceChina

Ischemic stroke is a cerebrovascular disease associated with high mortality and disability rates. Since the inflammation and immune response play a central role in driving ischemic damage, it becomes essential to modulate excessive inflammatory reactions to promote cell survival and facilitate tissue repair around the injury site. Various cell types are involved in the inflammatory response, including microglia, astrocytes, and neutrophils, each exhibiting distinct phenotypic profiles upon stimulation. They display either proinflammatory or anti-inflammatory states, a phenomenon known as ‘cell polarization.’ There are two cell polarization therapy strategies. The first involves inducing cells into a neuroprotective phenotype in vitro, then reintroducing them autologously. The second approach utilizes small molecular substances to directly affect cells in vivo. In this review, we elucidate the polarization dynamics of the three reactive cell populations(microglia, astrocytes, and neutrophils) in the context of ischemic stroke, and provide a comprehensive summary of the molecular mechanisms involved in their phenotypic switching. By unraveling the complexity of cell polarization, we hope to offer insights for future research on neuroinflammation and novel therapeutic strategies for ischemic stroke.

关键词： astrocyte polarization immune regulation inflammation ischemic injury microglia polarization neutrophil polarization signaling pathways stroke

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Increased excitatory amino acid transporter 2 levels in basolateral amygdala astrocytes mediate chronic stress–induced anxiety-like behavior

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Neural Regeneration Research 2025年第6期20卷 1721-1734页

作者： Xirong Xu Shoumin xuan Shuai Chen Dan Liu Qian Xiao Jie Tu Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenGuangdong ProvinceChina CAS Key Laboratory of Brain Connectome and Manipulation Brain Cognition and Brain Disease Institute(BCBDI)Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenGuangdong ProvinceChina Guangdong Provincial Key Laboratory of Brain Connectome and Behavior the Brain Cognition and Brain Disease Institute(BCBDI)Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenGuangdong ProvinceChina University of Chinese of Academy of Sciences BeijingChina Faculty of Life and Health Sciences Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenGuangdong ProvinceChina

The conventional perception of astrocytes as mere supportive cells within the brain has recently been called into question by empirical evidence, which has revealed their active involvement in regulating brain function and encoding behaviors associated with ***, astrocytes in the basolateral amygdala have been found to play a role in the modulation of anxiety-like behaviors triggered by chronic stress. Nevertheless, the precise molecular mechanisms by which basolateral amygdala astrocytes regulate chronic stress–induced anxiety-like behaviors remain to be fully elucidated. In this study, we found that in a mouse model of anxiety triggered by unpredictable chronic mild stress, the expression of excitatory amino acid transporter 2 was upregulated in the basolateral amygdala. Interestingly, our findings indicate that the targeted knockdown of excitatory amino acid transporter 2 specifically within the basolateral amygdala astrocytes was able to rescue the anxiety-like behavior in mice subjected to stress. Furthermore, we found that the overexpression of excitatory amino acid transporter 2 in the basolateral amygdala, whether achieved through intracranial administration of excitatory amino acid transporter 2agonists or through injection of excitatory amino acid transporter 2-overexpressing viruses with GfaABC1D promoters, evoked anxiety-like behavior in mice. Our single-nucleus RNA sequencing analysis further confirmed that chronic stress induced an upregulation of excitatory amino acid transporter 2 specifically in astrocytes in the basolateral amygdala. Moreover, through in vivo calcium signal recordings, we found that the frequency of calcium activity in the basolateral amygdala of mice subjected to chronic stress was higher compared with normal *** knocking down the expression of excitatory amino acid transporter 2 in the basolateral amygdala, the frequency of calcium activity was not significantly increased, and anxiety-like behavior was ob

关键词： anxiety astrocytes basolateral amygdala behavior dihydrokainic acid excitatory amino acid transporter 2 fiber photometry glutamate LDN-212320 transporter

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A New Framework for Software Vulnerability Detection Based on an Advanced Computing

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Computers, Materials & Continua 2024年第6期79卷 3699-3723页

作者： bui Van Cong Cho Do xuan Department of Information Technology University of Economics and Technical IndustriesHanoi100000Vietnam Faculty of Information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest *** paper proposes a new framework called DrCSE to improve software vulnerability *** uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software *** do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive *** which,the method(i)extracts the source code’s features based on the vertices and edges of the *** method of rebalancing data has the function of supporting the training process by balancing the experimental ***,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers *** experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum *** a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE *** performs better than other approaches,with a 5%boost in Precision and a 5%boost in ***,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

关键词： Source code vulnerability source code vulnerability detection code property graph feature profile contrastive learning data rebalancing

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Improvement of a temperature response function for divertor heat flux monitoring in fusion devices

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Plasma Science and Technology 2023年第3期25卷 217-221页

作者： xuan Nhat Son bui Hiroto MATSUURA Yousuke NAKASHIMA Quantum and Radiation Engineering Department Osaka Prefecture University Plasma Research Center University of Tsukuba

Temperature response functions have been developed to investigate sensor design and divertor heat flux estimation in magnetically confined plasmas. The time-dependent heat flux can be derived by fitting the response function to experimental thermocouple(TC) data. Because the TC signals have a time delay to transit events such as discharge start or confinement transition, the time delay is taken into account in a temperature response function. Such a function accurately describes the signal from each TC channel with time delay in a sensor test using a neutral beam injection. Measurement for commercial TCs shows that the time delay is caused by the finite heat capacity of TC wire and contact heat resistance between TC and target surface.

关键词： divertor temperature response function thermocouple system primary delay

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A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

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Nano-Micro Letters 2025年第2期17卷 417-431页

作者： Yunjian Guo Kunpeng Li Wei Yue nam‑Young Kim Yang Li Guozhen Shen Jong‑Chul Lee Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897South Korea Radio Frequency Integrated Circuit(RFIC)Bio Centre Kwangwoon UniversitySeoul 01897South Korea Department of Electronic Engineering Kwangwoon UniversitySeoul 01897South Korea School of Microelectronics Shandong UniversityJinan 250101People’s Republic of China State Key Laboratory of Integrated Chips and Systems Fudan UniversityShanghai 200433People’s Republic of China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081People’s Republic of China

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily *** existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple *** features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance *** wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist ***,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various *** system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and *** proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific *** utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

关键词： Wearable wristband Self-supervised contrastive learning Dynamic gesture Air-writing Human-machine interaction

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