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Understanding bike trip patterns leveraging bike sharing system open data

Frontiers of Computer Science 2017年第1期11卷 38-48页

作者： Longbiao CHEN Xiaojuan MA thi-mai-trang nguyen Gang PAN Jeremie JAKUBOWICZ Institut Mines-Telecom Telecom SudParis UMR CNRS Samovar Evry 91000 France Laboratoire d'Informatique de Paris 6 (LIP6) University of Paris 6 Paris 75005 France College of Computer Science Zhejiang University Hangzhou 310027 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.

关键词： bike sharing system open data ill-posed inverse problems urban computing

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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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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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New Results on H_(∞) Control for Nonlinear Conformable Fractional Order Systems

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Journal of Systems Science & Complexity 2021年第1期34卷 140-156页

作者： mai Viet Thuan nguyen thi Huyen Thu nguyen Huu Sau nguyen thi Thanh Huyen Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of General Science Hanoi University of Natural Resources and EnvironmentHanoiVietnam Faculty of Fundamental Science Hanoi University of IndustryHa NoiVietnam Department of Mathematics and Informatics Thainguyen University of SciencesThainguyenVietnam

this paper deals with H_(∞) control problem for nonlinear conformable fractional order systems. The authors first derive new sufficient condition for exponential stability of nonlinear conformable fractional order systems based on Lyapunov-like function method for conformable fractional order systems and linear matrix inequalities(LMIs) approach. Then, by introducing a new concepts of H_(∞) control problem for nonlinear conformable fractional order systems, the authors study H_(∞) performance analysis and H_(∞) state feedback controller design problems for the considered systems. In terms of LMIs, a sufficient condition is proposed to ensure the nonlinear conformable fractional order systems are not only exponentially stable, but also satisfy H_(∞) performance γ. An explicit expression for state feedback controllers is also designed to make the closed-loop system is exponentially stable with H_∞performance γ. Finally, numerical examples are given to illustrate the validity and effectiveness of the proposed results.

关键词： Conformable fractional-order H_(∞)control problems H_(∞)performance analysis linear matrix inequalities nonlinear systems

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Properties of poly（1-naphthylamine）/Fe3O4 composites and arsenic adsorption capacity in wastewater

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Frontiers of Materials Science 2016年第1期10卷 56-65页

作者： Minh thi TRAN thi Huyen trang nguyen Quoc Trung VU Minh Vuong nguyen Faculty of Physics Hanoi National University of Education 136-Xuanthuy Street Caugiay District Hanoi Vietnam Faculty of Chemistry Hanoi National University of Education 136-Xuanthuy Street Caugiay District Hanoi Vietnam Department of Physics Quynhon University 170 An Duong Vuong Quynhon Vietnam

The research results of poly（1-naphthylamine）lFe3O4 （PNA/Fe3O4） nanocomposites synthesized by a chemical method for As（Ⅲ） wastewater treatment are presented in this paper. XRD patterns and TEM images showed that the Fe3O4 grain size varied from 13 to 20 nm. The results of Raman spectral analysis showed that PNA participated in part of the PNA/Fe3O4 composite samples. The grain size of PNA/Fe3O4 composite samples is about 25-30 nm measured by SEM. The results of vibrating sample magnetometer measurements at room temperature showed that the saturation magnetic moment of PNA/Fe3O4 samples decreased from 63.13 to 43.43 emu/g, while the PNA concentration increased from 5% to 15%. The nitrogen adsorption-desorption isotherm of samples at 77 K at a relative pressure PIPo of about 1 was studied in order to investigate the surface and porous structure of nanoparticles by the BET method. Although the saturation magnetic moments of samples decreased with the polymer concentration increase, the arsenic adsorption capacity of the PNA/Fe3O4 sample with the PNA concentration of 5% is better than that of Fe3O4 in a solution with pH = 7. In the solution with pH 〉 14, the arsenic adsorption of magnetic nanoparticles is insignificant.

关键词： poly(1-naphthylamin)/Fe3O4 nanocomposite magnetization arsenic adsorption

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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

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A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area

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Big Earth Data 2023年第4期7卷 1005-1034页

作者： Thanh Trinh Binh Thanh Luu trang Ha thi Le Duong Huy nguyen Trong Van Tran thi Hai Van nguyen Khanh Quoc nguyen Lien thi nguyen Faculty of Computer Science Phenikaa UniversityYen NghiaHa DongHanoi 12116Vietnam Phenikaa Research and Technology Institute(PRATI) A&A Green Phoenix Group JSCNo.167 Hoang NganTrung HoaCau GiayHanoi 11313Vietnam Northwestern Geological Division General Department of Geology and Minerals of VietnamHanoiVietnam Youth union office Thuyloi University175 Tay SonDong DaHanoiVietnam Center of Remote Sensing and Geohazards Vietnam Institute of Geosciences and Mineral ResourcesHanoiVietnam

Landslide susceptibility maps (LSMs) are very crucial for planningpolicies in hazardous areas. However, the accuracy and reliability ofLSMs depend on available data and the selection of suitable *** study is conducted to produce LSMs by combinations of machinelearning methods and weighting techniques for Ha Giang province,Vietnam, where has limited data. In study area, we gather 11 landslideconditioning factors and establish a landslide inventory *** the weights of classes (or factors) is very important toprepare data for machine learning methods to generate LSMs. Wefrst use frequency ratio (FR) and analytic hierarchy process (AHP)techniques to generate the weights. Then, random forest (RF), supportvector machine (SVM), logistic regression (LR), and AHP methods arecombined with FR and AHP weights to yield accurate and reliableLSMs. Finally, the performance of these methods is evaluated by fivestatistical metrics, ROC and R-index. The empirical results have shownthat RF is the best method in terms of R-index and the five metrics, *** rate (0.9661), FP rate (0.0), ACC (0.9835), MAE (0.0046), and RMSE(0.0350) for this study area. this study opens the perspective of weightbasedmachine learning methods for landslide susceptibility mapping.

关键词： Landslide logistic regression random forest SvM Vietnam

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Effect of heating time on structural,morphology,optical,and photocatalytic properties of g-C3N4 nanosheets

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Chinese Physics B 2020年第5期29卷 504-508页

作者： nguyen Manh Hung Le thi mai Oanh Lam thi Hang Pham Do Chung Pham thi Duyen Dao Viet Thang nguyen Van Minh Center for Nano Science and Technology Hanoi National University of Education136 Xuan Thuy RoadCau Giay DistrictHanoi100000Vietnam Department of Physics Hanoi National University of Education136 Xuan Thuy RoadCau Giay DistrictHanoi100000Vietnam Faculty of Basic Sciences Hanoi University of Natural Resources and Environment41A Phu Dien RoadNorth Tu LiemHanoi100000Vietnam Hanoi University of Mining and Geology Duc Thang wardNorth Tu Liem DistrictHanoi100000Vietnam Military science Academy 322 Le Trong Tan streetDinh CongHoang MaiHanoi100000Vietnam

Effect of heating time on the structural,morphology,optical,and photocatalytic properties of graphitic carbon nitride(g-C3N4)nanosheets prepared at 550℃in Ar atmosphere is *** investigations are carried out by using x-ray diffraction(XRD),scanning electron microscopy(SEM),Fourier-transform infrared spectroscopy(FTIR),UV-vis absorption,and photoluminescence(PL).At a heating temperature of 550℃,g-C3N4 nanocrystals are formed after 0.5 h and become more orderly as the heating time *** surface area of the g-C3N4 nanosheets significantly increases as the preparation time *** g-C3N4 prepared in 2.5 h shows the highest photocatalytic performance,decomposing completely 10 ppm RhB solution under xenon lamp irradiation for 2.0 h.

关键词： nanosheets photocatalytic heating time Ar atmosphere

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Nano selenium-alginate edible coating extends hydroponic strawberry shelf life and provides selenium fortification as a micro-nutrient

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Food Bioscience 2023年第3期53卷 1000-1012页

作者： Thu Hong Tran Xuan Cuong Le thi Ngoc mai Tran Ngoc Thuy trang nguyen Bao Ngoc Pham Duong Vu Center of Radiation Technology and Biotechnology Nuclear Research Institute01 Nguyen Tu LucWard 8Dalat CityViet Nam Institute of Physics Vietnam Academy of Science and Technology18 Hoang Quoc VietHanoiViet Nam

Although selenium nanoparticles(Nano Se)have gained attention for their anti-cancer and antioxidant activity,this nanomaterial’s antimicrobial activity also has great *** aim of this study was to synthesize Nano Se edible coating solution through gamma irradiation technique with a bio-stabilizer of sodium alginate(Na-alg.),which is an ingredient commonly used in food *** light scattering and transmission electron microscopy identified 94-nm-diameter Nano Se,and energy dispersive X-ray spectroscopy and Raman spectroscopy confirmed the formation of Nano *** studies revealed that Na-alg./Nano Se coating solution well inhibited both Neopestalotiopsis rosae and Fusarium oxysporum in single species culture *** effectiveness of edible coatings containing Nano Se and *** the physicochemical properties of strawberries at 24±2℃ during 5-day storage period was *** results showed coated strawberries enhanced firmness by as much as 26%and a significantly less weight loss by as much as three fold compared to the *** coating application was effective in maintaining a natural appearance and preserving half the amounts of vitamin C and B9 in the *** addition,the Na-alg./Nano Se coating was utilized as an additive to increase Se nutrient in *** Se content was supplemented from 0.28 to 0.56μg per 100 g FW after *** study may potentially be used in the bio-preservation and Se-enrichment of various fresh fruits and other foods.

关键词： Nano-coating Irradiation Antifungal activity Food preservation Dietary supplementation

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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

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