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Stoichiometric Ti_(3)C_(2)T_(x)Coating for Inhibiting Dendrite Growth in Anode-Free Lithium Metal Batteries

Energy & Environmental Materials 2024年第4期7卷 153-162页

作者： Xiangrong Zeng Manmatha Mahato Woong Oh Hyunjoon Yoo van hiep nguyen Saewoong Oh Geetha Valurouthu Soon-Ki Jeong Chi Won Ahn Yury Gogotsi Il-Kwon Oh National Creative Research Initiative for Functionally Antagonistic Nano-Engineering Department of Mechanical EngineeringKorea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon 34141Korea Department of Materials Science&Engineering and A.J.Drexel Nanomaterials InstituteDrexel UniversityPhiladelphiaPennsylvania 19104USA Department of Energy Engineering Soonchunhyang University22Soonchunhyang-roSinchang-myeonAsan-siChungcheongnam-do 31538Korea National Nanofab Center(NNFC) Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon 34141Korea

Lithium metal batteries(LMBs)and anode-free LMBs(AFLMBs)present a solution to the need for batteries with a significantly superior theoretical energy ***,their adoption is hindered by low Coulombic efficiency(CE)and rapid capacity fading,primarily due to the formation of unstable solid electrolyte interphase(SEI)layer and Li dendrite growth as a result of uneven Li ***,we report on the use of a stoichiometric Ti_(3)C_(2)T_(x)(S-Ti_(3)C_(2)T_(x))MXene coating on the copper current collector to enhance the cyclic stability of an anode-free lithium metal *** S-Ti_(3)C_(2)T_(x)coating provides abundant nucleation sites,thereby lowering the overpotential for Li nucleation,and promoting uniform Li ***,the fluorine(-F)termination of S-Ti_(3)C_(2)T_(x)participates in the SEI formation,producing a LiF-rich SEI layer,vital for stabilizing the SEI and improving cycle *** equipped with S-Ti_(3)C_(2)T_(x)@Cu current collectors displayed reduced Li consumption during stable SEI formation,resulting in a significant decrease in capacity *** with S-Ti_(3)C_(2)T_(x)@Cu current collectors achieved a high initial capacity density of 4.2 mAh cm^(-2),70.9%capacity retention after 50 cycles,and an average CE of 98.19%in 100 *** innovative application of MXenes in the energy field offers a promising strategy to enhance the performance of AFLMBs and could potentially accelerate their commercial adoption.

关键词： anode-free lithium metal batteries stoichiometric MXene solid electrolyte interphase surface terminations

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Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 467-500页

作者： Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models Duc-Dam nguyen nguyen Viet Tiep Quynh-Anh Thi Bui hiep van Le Indra Prakash Romulus Costache Manish Pandey Binh Thai Pham Faculty of Civil Engineering University of Transport TechnologyThanh XuanHa Noi100000Vietnam Faculty of Hydraulic Engineering Hanoi University of Civil EngineeringHa Noi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India Research Institute of the University of Bucharest Bucharest050663Romania National Institute of Hydrology and Water Management Bucharest013686Romania Department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Marwadi University Research Center(MURC) Marwadi UniversityRajkotGujaratBharat360003India Department of Civil Engineering Faculty of Engineering&TechnologyMarwadi UniversityRajkotGujaratBharat360003India

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and ***,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and *** models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to *** factors were instrumental in predicting the output variable,which was the probability of landslide *** analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other *** AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of *** results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

关键词： Landslide susceptibility map spatial analysis ensemble modelling information values(IV)

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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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Applying Wide & Deep Learning Model for Android Malware Classification

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Computer Systems Science & Engineering 2023年第6期45卷 2741-2759页

作者： Le Duc Thuan Pham van Huong Hoang van hiep nguyen Kim Khanh Ha Noi University of Science and Technology Ha Noi100000Viet Nam Academy of Cryptography Techniques Ha Noi100000Viet Nam

Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile *** the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android *** learning technology is designed to handle a large amount of raw and continuous data,such as image content ***,it is incompatible with discrete features,i.e.,features gathered from multiple ***,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine *** the other hand,a wide learning model can expand the feature set to enhance the classification *** maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning *** feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep *** proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw *** accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model.

关键词： Wide and deep(W&D)learning convolutional neural network image feature raw features generalized features

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

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Frontiers of Structural and Civil Engineering 2023年第5期17卷 812-826页

作者： Long Hoang nguyen Dung Quang VU Duc Dam nguyen Fazal E.JALAL Mudassir IQBAL Vinh The DANG hiep van LE Indra PRAKASH Binh Thai PHAM Faculty of Civil Engineering University of Transport TechnologyHanoi 10000Vietnam Department of Civil Engineering State Key Laboratory of Ocean EngineeringShanghai Jiao Tong UniversityShanghai 200025China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of *** this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid *** performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)*** models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR ***,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms

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Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

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Geoscience Frontiers 2021年第5期12卷 54-68页

作者： Hoang Phan Hai Yen Binh Thai Pham Tran van Phong Duong Hai Ha Romulus Costache hiep van Le Huu Duy nguyen Mahdis Amiri nguyen van Tao Indra Prakash Department of Geography School of Social Sciences EducationVinh University182 Le DuanVinhNghe AnVietnam University of Transport Technology Hanoi 100000Viet Nam Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)84 Chua LangDong DaHanoiViet Nam Institute for Water and Environment Hanoi 100000Viet Nam Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharestRomania National Institute of Hydrology and Water Management București-Ploiești Road97E1st District013686 BucharestRomania Faculty of Geography VNU University of ScienceVietnam National University334 Nguyen TraiHanoi 100000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan ***Iran DDG(R)Geological Survey of India Gandhinagar 382010India

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like *** this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in *** this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the *** statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of *** show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the *** the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

关键词： Locally weighted learning Hybrid models Groundwater potential GIS Vietnam

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An Evaluation of Ramp Control Signal Head Placement Based on Motorist’s Cone of Visions

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International Journal of Transportation Science and Technology 2014年第3期3卷 265-276页

作者： Zhongren Wang hiep nguyen Division of Traffic Operations California Department of Transportation1120 N StreetMS-36SacramentoCA 95814

Roadside mounted ramp control signals have one upper head,and one lower head in *** upper head is intended for use by the approaching motorists,while the lower one for use by the stopped *** have been received regarding the visibility of these signal *** evaluation presented in this paper is to mathematically analyze the visibility of these signal heads on a tangent roadway alignment based on satisfactory motorists’cones of *** design,signal placement,and driver characteristics are integrated into one analytic *** results indicated that the horizontal satisfactory cone of vision controls the placement *** meet the 20 degree satisfactory horizontal cone of vision as specified by the Manual on Uniform Traffic Control Devices(MUTCD),a ramp control signal head has to be placed a minimum of 13.3 m downstream of the limit line.

关键词： mounted approaching specified

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A noncoding secret to stay young—protecting T_(reg)cells to keep the balance in the liver

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Science China(Life Sciences) 2023年第11期66卷 2694-2696页

作者： hiep Tai nguyen Pedro J.Batista Laboratory of Cell Biology Center for Cancer ResearchNational Cancer InstituteNational Institutes of HealthBethesda MD 20892 USA

The liver plays a crucial role in maintaining whole-body metabolic homeostasis and harbors a distinct immune environment(Robinson et al.,2016).However,during the aging process,the liver undergoes structural and functional changes that can disrupt the delicate balance between the immune system and liver *** age-related alterations can lead to increased susceptibility to metabolic diseases,including non-alcoholic fatty liver disease(NAFLD),and impairments in immune cell regulation and tissue repair(Albillos et al.,2022;Anstee et al.,2019).

关键词： al. alterations homeostasis

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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1431-1449页

作者： nguyen Thanh Hoan nguyen van Dung Ho Le Thu Hoa Thuy Quynh Nadhir Al-Ansari Tran van Phong Phan Trong Trinh Dam Duc nguyen hiep van Le Hanh Bich Thi nguyen Mahdis Amiri Indra Prakash Binh Thai Pham Institute of Geography Vietnam Academy of Science and TechnologyHanoi10000Viet Nam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)Dong DaHanoi10000Viet Nam University of Transport Technology Thanh XuanHa Noi10000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran DDG(R)Geological Survey of India Gandhinagar382010India

Water level predictions in the river,lake and delta play an important role in flood *** year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high *** subsidence may also aggravate flooding problems in this ***,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and *** are so many methods available to predict the water levels based on historical data but nowadays Machine Learning(ML)methods are considered the best tool for accurate *** this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study *** of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML *** data of 19 years period was divided into 70:30 ratio for the *** data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the *** of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study ***,the Bagging-based hybrid models are slightly better than another model such as ***,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

关键词： Computational techniques bagging water level time series algorithms

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Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-induced landslides

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International Journal of Digital Earth 2021年第5期14卷 575-596页

作者： Binh Thai Pham Abolfazl Jaafari Trung nguyen-Thoi Tran van Phong Huu Duy nguyen Neelima Satyam Md Masroor Sufia Rehman Haroon Sajjad Mehebub Sahana hiep van Le Indra Prakash Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam Research Institute of Forests and Rangelands Agricultural ResearchEducationand Extension Organization(AREEO)TehranIran Institute of Geological Sciences Vietnam Academy of Sciences and TechnologyHanoiViet Nam Faculty of Geography V.N.U.University of ScienceVietnam National UniversityHanoiViet Nam Discipline of Civil Engineering Indian Institute of Technology IndoreMadhya PradheshIndia Department of Geography Jamia Millia IslamiaNew DelhiIndia School of Environment Education and DevelopmentUniversity of ManchesterManchesterUK Institute of Research and Development Duy Tan UniversityDa NangViet Nam DDG(R)Geological Survey of India GujaratIndia

In this paper,we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree(REPT)as a base classifier with the Bagging(B),Decorate(D),and Random Subspace(RSS)ensemble learning techniques for spatial prediction of rainfallinduced landslides in the Uttarkashi district,located in the Himalayan range,*** do so,a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation *** Mean Square Error(RMSE)and Area Under the receiver operating characteristic Curve(AUC)were used to evaluate the training and validation performances of the *** results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of *** D-REPT model with RMSE=0.351 and AUC=0.907 was identified as the most accurate model,followed by RSS-REPT(RMSE=0.353 and AUC=0.898),B-REPT(RMSE=0.396 and AUC=0.876),and the single REPT model(RMSE=0.398 and AUC=0.836),*** prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.

关键词： Machine learning ensemble modeling Bagging Decorate Random Subspace

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