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Chinese Journal of Cancer Research 2009年第3期21卷 207-216页

作者： hai Sun Long-bang Chen Department of Medical Oncology Jinling Hospital Nanjing University School of Medicine Nanjing 210002 China

Objective： To investigate the mechanism of resistance to docetaxel in human lung cancer. Methods： Human lung carcinoma SPC-A1/Docetaxel cells were derived from parental SPC-A1 cells by continuous exposure to increasing concentration of docetaxel. The drug sensitivity was measured by MTT assay in vitro. The cDNA microarray identified a set of differentially expressed genes, and some genes were confirmed by RT-PCR. P-glycoprotein level was measured by flow cytometry analysis. Results： The results of drug sensitivity measured by MTT assay showed that SPC-A1/Docetaxel cells were 13.2-fold resistant to docetaxel and cross-resistant at varying levels to other drugs. The cDNA microarray results identified a set of differentially expressed genes, which showed 428 genes that were up-regulated and 506 genes that were down-regulated in SPC-A1/Docetaxel ceils, and some genes were confirmed by RT-PCR. Flow cytometry analysis suggests expression of P-glycoprotein （P-gp） was more abundant in SPC-A1/Docetaxel cells than in the parental cells and docetaxel selection reduces the apoptotic response. Conclusion： The results suggest that docetaxel selection led to changes in gene expression that contribute to the multidrug resistance phenotype.

关键词： Lung cancer Multidrug resistance Docetaxel cDNA mieroarray

Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

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Frontiers of Structural and Civil Engineering 2022年第10期16卷 1267-1286页

作者： Thuy-Anh NGUYEN hai-bang LY Van Quan TRAN Department of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately *** primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams(SB).A database encompassing 1118 experimental findings from the relevant literature was compiled,containing eight distinct *** Boosting(GB)technique was developed and evaluated in combination with three different optimization algorithms,namely Particle Swarm Optimization(PSO),Random Annealing Optimization(RA),and Simulated Annealing Optimization(SA).The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output *** values and two-dimensional PDP analysis were then carried *** may use the findings in this work to define beam’s geometrical components and material used to achieve the desired shear strength of SB without reinforcement.

关键词： slender beam shear strength gradient boosting optimization algorithms

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 928-945页

作者： Van Quan TRAN hai-Van Thi MAI Thuy-Anh NGUYEN hai-bang LY Faculty of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam

The compressive strength of self-compacting concrete(SCC)needs to be determined during the construction design *** paper shows that the compressive strength of SCC(CS of SCC)can be successfully predicted from mix design and curing age by a machine learning(ML)technique named the Extreme Gradient Boosting(XGB)algorithm,including non-hybrid and hybrid *** ML techniques,such as Linear regression(LR),K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Decision Trees(DTR),Random Forest(RF),Gradient Boosting(GB),and Artificial Neural Network using two training algorithms LBFGS and SGD(denoted as ANN_LBFGS and ANN_SGD),are also compared with the XGB ***,the hybrid models of eight ML techniques and Particle Swarm Optimization(PSO)are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid *** highest number of SCC samples available in the literature is collected for building the ML *** with previously published works’performance,the proposed XGB method,both hybrid and non-hybrid models,is the most reliable and robust of the examined techniques,and is more accurate than existing ML methods(R2=0.9644,RMSE=4.7801,and MAE=3.4832).Therefore,the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.

关键词： compressive strength self-compacting concrete machine learning techniques particle swarm optimization extreme gradient boosting

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

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Frontiers of Structural and Civil Engineering 2023年第2期17卷 284-305页

作者： hai-Van Thi MAI May Huu NGUYEN Son Hoang TRINH hai-bang LY Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately *** the machine learning(ML)approach to estimating the CS of FRSCC,the current research gaps include the limitations of samples in databases,the applicability constraints of models owing to limited mixture components,and the possibility of applying recently proposed *** study developed different ML models for predicting the CS of FRSCC to address these *** neural network,random forest,and categorical gradient boosting(CatBoost)models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique.A database of 381 samples was created,representing the most significant FRSCC dataset compared with previous studies,and it was used for model *** findings indicated that CatBoost outperformed the other two models with excellent predictive abilities(root mean square error of 2.639 MPa,mean absolute error of 1.669 MPa,and coefficient of determination of 0.986 for the test dataset).Finally,a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of *** results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 899-917页

作者： Huong-Giang Thi HOANG hai-Van Thi MAI Hoang Long NGUYEN hai-bang LY University of Transport Technology Hanoi 100000Vietnam

Complex modulus(G^(*))is one of the important criteria for asphalt classification according to AASHTO M320-10,and is often used to predict the linear viscoelastic behavior of asphalt *** addition,phase angle(φ)characterizes the deformation resilience of asphalt and is used to assess the ratio between the viscous and elastic *** is thus important to quickly and accurately estimate these two *** purpose of this investigation is to construct an extreme gradient boosting(XGB)model to predict G^(*)andφof graphene oxide(GO)modified asphaltat medium and high *** data sets are gathered from previously published experiments,consisting of 357 samples for G^(*)and 339 samples forφ,and the se are used to develop the XGB model using nine inputs representing theasphalt binder *** findings show that XGB is an excellent predictor of G^(*)andφof GO-modified asphalt,evaluated by the coefficient of determination R^(2)(R^(2)=0.990 and 0.9903 for G^(*)andφ,respectively)and root mean square error(RMSE=31.499 and 1.08 for G^(*)andφ,respectively).In addition,the model’s performance is compared with experimental results and five other machine learning(ML)models to highlight its *** the final step,the Shapley additive explanations(SHAP)value analysis is conducted to assess the impact of each input and the correlation between pairs of important features on asphalt’s two physical properties.

关键词： complex modulus phase angle graphene oxide asphalt extreme gradient boosting machine learning

Detecting entanglement of quantum channels

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Communications in Theoretical Physics 2021年第11期73卷 77-84页

作者： Chaojian Li bang-hai Wang Bujiao Wu Xiao Yuan School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Advanced Institute of Information Technology Peking UniversityChina Center on Frontiers of Computing Studies Department of Computer SciencePeking UniversityBeijing 100871China

Entanglement is the crucial resource for different quantum information processing *** conventional studies focus on the entanglement of bipartite or multipartite quantum states,recent works have extended the scenario to the entanglement of quantum channels,an operational quantification of the channel entanglement manipulation *** on the recently proposed channel entanglement resource framework,here we study a further task of resource detection—witnessing entanglement of quantum *** first introduce the general framework and show how channel entanglement detection is related to the Choi state of the channel,enabling channel entanglement detection via conventional state entanglement detection *** also consider entanglement of multipartite quantum channels and use the stabilizer formalism to construct entanglement witnesses for circuits consisting of controlled-Z *** study the effectiveness of the proposed detection methods and compare their performance for several typical *** work paves the way for systematic theoretical studies of channel entanglement and practical benchmarking of noisy intermediate scaled quantum devices.

关键词： quantum entanglement quantum channel entanglement detection entanglement witness

A hybrid machine learning model to estimate self-compacting concrete compressive strength

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Frontiers of Structural and Civil Engineering 2022年第8期16卷 990-1002页

作者： hai-bang LY Thuy-Anh NGUYEN Binh Thai PHAM May Huu NGUYEN Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 115 samples from different sources,taking into account nine key SCC *** validation of the proposed model was evaluated via six indices,including correlation coefficient(R),mean squared error,mean absolute error(MAE),IA,Slope,and mean absolute percentage *** addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence *** results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s *** that,an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

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Frontiers of Structural and Civil Engineering 2022年第2期16卷 224-238页

作者： hai-bang LY Huong-Lan Thi VU Lanh Si HO Binh Thai PHAM Department of Civil Engineering University of Transport TechnologyHanoi100000Vietnam Civil and Environmental Engineering Program Hiroshima UniversityHiroshima739-8527Japan Department of Science Technology and International CooperationUniversity of Transport TechnologyHanoi100000Vietnam

The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft *** general,the C_(v) is determined experimentally in the ***,the experimental tests are time-consuming as well as ***,researchers tried several ways to determine C_(v) via other simple soil *** this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of *** conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for *** performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 *** results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters.

关键词： soil consolidation coefficient machine learning random forest Relief

Carbon quantum dots-based semiconductor preparation methods,applications and mechanisms in environmental contamination

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Chinese Chemical Letters 2020年第10期31卷 2556-2566页

作者： Qi-Shi Si Wan-Qian Guo Hua-Zhe Wang bang-hai Liu Nan-Qi Ren State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090China

Photocatalyst is the most widespread method in advanced oxidation technologies,but due to the photoinduced electron combine easily with hole and the wavele ngth of adsorption is limited which will affect some practical *** quantum dots(CQDs)is non-toxic and harmless green materials,it has the ability to improve the photocatalytic effect which is attributed to its good electrical and optical *** up-conversion effect,photosensitization and electrical conductivity are assistants which help promote the photocatalytic effect in environmental *** key mechanisms of CQDs to improve photocatalysis can be roughly divided into three categories:1)Up-conversion effect conve rts the incident light into the emitted light with high ene rgy to solve the problem which is the light absorption range;2)CQDs act as a photosensitizer instead of valence band to provide electrons to the conduction band of semiconductor;3)CQD s can be used as the internal or external electronic conductor in materials to alleviate the trend of electron and hole ***,CQDs and CQDs-based photocatalysts have different views to solve environmental problems,so it is necessary to integrate different ***,this review is mainly aimed at the recent researches about the preparation processes of CQD,CQD s-based photocatalysts,and their ability to remove environmental pollutants,with a special emphasis on the mechanism for depredating ***,this paper analyzes and discusses the prospects and challenges of CQDs in the environmental field.

关键词： CQDs CQDs modified semiconductor Photodegradation Environmental treatment Photoactive mechanism

Hybridization of Differential Evolution and Adaptive-Network-Based Fuzzy Inference Systemin Estimation of Compression Coefficient of Plastic Clay Soil

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 149-166页

作者： Manh Duc Nguyen Ha Nguyenhai Nadhir Al-Ansari MahdisAmiri hai-bang Ly Indra Prakash Binh Thai Pham University of Transport and Communications Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran University of Transport Technology Hanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the *** this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil *** of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for *** showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.

关键词： Compression coefficient differential evolution adaptive-network-based fuzzy inference system machine learning vietnam