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Green Energy & Environment 2022年第2期7卷 266-274页

作者： Haifeng Yu Shouliang Wang Yanjie Hu Guanjie He le quoc bao Ivan P.Parkin Hao Jiang Key Laboratory for Ultrafine Materials of Ministry of Education Shanghai Engineering Research Center of Hierarchical NanomaterialsSchool of Materials Science and EngineeringEast China University of Science and TechnologyShanghai 200237China Christopher Ingold Laboratory Department of ChemistryUniversity College London20 Gordon StreetLondonWC1H 0AJUK Faculty of Applied Sciences Ton Duc Thang University19 Nguyen Huu ThoDistrict 7Ho Chi Minh CityViet Nam

LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523) cathode materials can operate at extremely high voltages and have exceptional energy ***,their use is limited by inherent structure instability during charge/discharge and exceptionally oxidizing Ni^(4+)at the ***,we have developed a citrate-assisted deposition concept to achieve a uniform lithium-conductive LiNbO_(3) coating layer on the NCM523 surface that avoids self-nucleation of Nb-contained compounds in solution *** electrode-electrolyte interface is therefore stabilized by physically blocking the detrimental parasitic reactions and Ni^(4+)dissolution whilst still maintaining high Li+***,the modified NCM523 exhibits an encouraging Li-storage specific capacity of 207.4 m Ah g-1at 0.2 C and 128.9 m Ah g-1 at 10 C over the range 3.0-4.5 ***,a 92% capacity retention was obtained after 100 cycles at 1 C,much higher than that of the pristine NCM523(73%).This surface engineering strategy can be extended to modify other Ni-rich cathode materials with durable electrochemical performances.

关键词： coating exceptional Lithium

Local synthesis of carbon nanotubes for direct integration in Si microsystems——design considerations

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Advances in Manufacturing 2013年第3期1卷 218-225页

作者： Knut E.Aasmundtveit bao quoc Ta quoc-Huy Nguyen Tormod B.Haugen Nils Hoivik Einar Halvorsen IMST-Department of Micro and Nano System Technology HiVe-Vesffold University College

The integration of nanomaterials such as carbon nanotubes （CNTs） into microsystems is highly desirable, in order to make use of the unique nanomaterial properties in real devices. However, the CNTtomicrosystem integration is challenging to implement in a manufacturable, cost effective industrial process. This paper presents our work towards a process for making complete, integrated CMOS / MEMS systems with integrated CNT. We demonstrate the feasibility of the process, using roomtemperature process ing, lowcost equipment and consumables, and electrical control with automation possibilities. CNTs are directly integrated at the desired positions in the Si microsystem, forming closed Si / CNT / Si circuits. We explore different designs with the aim to obtain uniform and welldefined CNT synthesis conditions, and show that simplified designs can perform comparably to more complex ones. The Si / CNT / Si circuits obtained can show rectifying （Schottky like） or nearohmic behavior. Gas sensing possibilities are demonstrated, indicating the possibility of monitoring aging/ fermenting of food. Functionalization of CNTs is demon strated, using thermal evaporation of Sn and Pd, opening for selective and sensitive sensors for various gases and ana lytes. Detailed microscopic characterization of the obtained CNTs are presented.

关键词： Micro/nano system integration Carbonnanotubes (CNTs) Nanoscale assembly - Gassensors

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

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International Journal of Sediment Research 2023年第1期38卷 128-140页

作者： Duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha quoc bao Pham Van Hieu Bui HUTECH University Center for River Harbor and Landslide Research Chittagong University of Engineering & Technology Govind Ballabh Pant University of Agriculture and Technology Institute of Applied Technology Thu Dau Mot University Faculty of Mechanical Engineering Thuyloi University

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution(GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory(LSTM) combined with the Discrete Element Method(DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R~2), root mean squared error(RMSE), and mean absolute error(MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

关键词： Numerical modeling Discrete element method LSTM Bed porosity Gravel-bed river

Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model

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International Soil and Water Conservation Research 2024年第2期12卷 279-297页

作者： Sliman Hitouri Mohajane Meriame Ali Sk Ajim Quevedo Renata Pacheco Thong Nguyen-Huy Pham quoc bao Ismail ElKhrachy Antonietta Varasano Geosciences Laboratory Department of GeologyFaculty of SciencesUniversity Ibn TofailKenitra14000Morocco Construction Technologies Institute National Research Council of ItalyPolo Tecnologico di San Giovanni a Teduccio80146NapoliItaly Department of Geography Faculty of ScienceAligarh Muslim University(AMU)AligarhUP202002India Earth Observation and Geoinformatics Division National Institute for Space Research(INPE)Sao Jose dos CamposSao Paulo12227010Brazil Centre for Applied Climate Sciences University of Southern QueenslandToowoomba4350QLDAustralia Faculty of Natural Sciences Institute of Earth SciencesUniversity of Silesia in KatowiceBedzinska street 6041-200SosnowiecPoland College of Engineering Civil Engineering DepartmentNajran UniversityNajran66291Saudi Arabia ITC-CNR Construction Technologies InstituteNational Research Council70124BariItaly

Gully erosion is one of the main natural hazards,especially in arid and semi-arid regions,destroying ecosystem service and human ***,gully erosion susceptibility maps(GESM)are urgently needed for identifying priority areas on which appropriate measurements should be ***,we proposed four new hybrid Machine learning models,namely weight of evidence-Multilayer Perceptron(MLP-WoE),weight of evidence–K Nearest neighbours(KNN-WoE),weight of evidence-Logistic regression(LR-WoE),and weight of evidence-Random Forest(RF-WoE),for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in *** of the developed models are composed of the dependent(i.e.,gully erosion points)and a set of independent *** this study,a total of 314 gully erosion points were randomly split into 70%for the training stage(220 gullies)and 30%for the validation stage(94 gullies)sets were identified in the study area.12 conditioning variables including elevation,slope,plane curvature,rainfall,distance to road,distance to stream,distance to fault,TWI,lithology,NDVI,and LU/LC were used based on their importance for gully erosion susceptibility *** evaluate the performance of the above models based on the following statistical metrics:Accuracy,precision,and Area under curve(AUC)values of receiver operating characteristics(ROC).The results indicate the RF-WoE model showed good accuracy with(AUC=0.8),followed by KNN-WoE(AUC=0.796),then MLP-WoE(AUC=0.729)and LR-WoE(AUC=0.655),*** erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.

关键词： watershed susceptibility mapping

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

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Geoscience Frontiers 2021年第3期12卷 384-398页

作者： Abdul-Lateef Balogun Fatemeh Rezaie quoc bao Pham Ljubomir Gigović Siniša Drobnjak Yusuf AAina Mahdi Panahi Shamsudeen Temitope Yekeen Saro lee Geospatial Analysis and Modelling(GAM)Research Group Department of Civil&Environmental EngineeringUniversitit Teknologi PETRONS(UTP)Seri Iskandar 32610PerakMalaysia Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132Republic of Korea Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-ro Yuseong-guDaejeon 34113Republic of Korea Institute of Research and Development Duy Tan UniversityDanang 550000Vietnam Department of Geography University of Defence11000 BelgradeSerbia Military Geographical Institute 11000 BelgradeSerbia Department of Geomatics Engineering Technology Yanbu Industrial CollegeYanbuSaudi Arabia Division of Science Education College of Education#4–301Gangwondaehak-gil Chuncheon-siKangwon National UniversityGangwon-do 24341Republic of Korea Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Vietnam

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility *** created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)*** landslide susceptibility map was produced using 14 causative *** applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive *** resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility ***-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of *** three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

关键词： Landslide Machine learning Metaheuristic Spatial modeling Support vector regression

Flood susceptibility modelling using advanced ensemble machine learning models

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Geoscience Frontiers 2021年第3期12卷 60-77页

作者： Abu Reza Md Towfiqul Islam Swapan Talukdar Susanta Mahato Sonali Kundu Kutub Uddin Eibek quoc bao Pham Alban Kuriqi Nguyen Thi Thuy Linh Department of Disaster Management Begum Rokeya UniversityRangpurBangladesh Department of Geography University of Gour BangaMaldaWest BengalIndia Environmental Quality Atmospheric Science and Climate Change Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityVietnam CERIS Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal Institute of Research and Development Duy Tan UniversityDanang 550000Vietnam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Vietnam

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human *** is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash ***,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood *** this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of *** application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS *** information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential *** the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were *** value of the Area Under the Curve(AUC)of ROC was above 0.80 for all *** flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark *** approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

关键词： Flood hazard Flood vulnerability Flash floods Debris flow Teesta River basin Bangladesh

软木塞基碳量子点的水热合成及其生物相融荧光成像应用

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新型炭材料 2022年第3期37卷 595-602页

作者： Quang Ngo Khoa Hieu Nguyen Ngoc bao Vo Van quoc Phuoc Vo Thi Ngoc le Xuan Diem Doc Luong Quang Tri Nguyen Minh Son le Vu Truong Son le Van Thanh Ha Che Thi Cam University of Sciences Hue University77 Nguyen HueHueVietnam Faculty of Environmental and Natural Sciences Duy Tan UniversityDa Nang550000Vietnam Institute for Research and Training in Medicine Biology and PharmacyDuy Tan UniversityDa Nang550000Vietnam University of Agriculture and Forestry Hue University102 Phung HungHueVietnam University of Science and Education The University of Da Nang459 Ton Duc ThangLien ChieuDa NangVietnam

以废弃酒瓶软木塞为原料,采用低成本、简单的水热方法合成碳量子点。通过TEM、FTIR、Raman、UV-Vis、PL光谱对碳量子点的结构和光学性能进行分析表征。结果表明,碳量子点的平均直径为6.2±2.7 nm,PL激发谱和碳量子点表面的官能团有关。... 详细信息

以废弃酒瓶软木塞为原料,采用低成本、简单的水热方法合成碳量子点。通过TEM、FTIR、Raman、UV-Vis、PL光谱对碳量子点的结构和光学性能进行分析表征。结果表明,碳量子点的平均直径为6.2±2.7 nm,PL激发谱和碳量子点表面的官能团有关。用硫酸奎宁作为参考,碳量子点的量子效率为1.54%。将获得的碳量子点应用在骨髓间充质干细胞的细胞生物成像上,发现用碳量子点处理后,骨髓间充质干细胞分别在320~380 nm、450~490 nm和450~490 nm范围显示绿色、黄色和红色荧光,表明了碳量子点在荧光成像领域具有潜在的应用价值。

关键词：碳点水热合成荧光荧光图像废酒瓶塞

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

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Geoscience Frontiers 2021年第2期12卷 857-876页

作者： Sk Ajim Ali Farhana Parvin Jana Vojteková Romulus Costache Nguyen Thi Thuy Linh quoc bao Pham Matej Vojtek Ljubomir Gigović Ateeque Ahmad Mohammad Ali Ghorbani Department of Geography Faculty of ScienceAligarh Muslim University(AMU)AligarhUP 202002India Department of Geography and Regional Development Faculty of Natural SciencesConstantine the Philosopher University in NitraTrieda A.Hlinku 194901 NitraSlovakia Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharest 050663Romania National Institute of Hydrology and Water Management București-Ploiești Road97E1st DistrictBucharest 013686Romania Thuyloi University 175 Tay SonDong DaHanoiViet Nam Institute of Research and Development Duy Tan UniversityDanang 550000Viet Nam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Viet Nam Department of Geography University of Defence11000 BelgradeSerbia Sustainable Management of Natural Resources and Environment Research Group Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityViet Nam

Hazards and disasters have always negative impacts on the way of *** is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout *** present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,*** this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were ***,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,*** geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover *** ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model ***,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier ***,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and *** results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

关键词： Landslide susceptibility modeling Geographic information system Fuzzy DEMATEL Analytic network process Naïve Bayes classifier Random forest classifier

Predicting soil erosion hazard in Lattakia Governorate(W Syria)

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International Journal of Sediment Research 2021年第2期36卷 207-220页

作者： Mohammed Safwan Khallouf Alaa Alshiehabi Omran bao Pham quoc Thi Thuy Linh Nguyen Nam Thai Van Tran Anh Duong Harsányi Endre Institution of Land Utilization Technology and Regional Planning Faculty of Agricultural and Food Sciences and Environmental Management University of Debrecen Administration of Natural Resources Management General Commission for Scientific Agricultural Research (GCSAR) 3. Department of Soil Sciences and Land Reclamation Faculty of Agriculture Al-Baath University Department of Soil and Environment Swedish University of Agricultural Sciences (SLU) Institute of Research and Development Duy Tan University Faculty of Environmental and Chemical Engineering Duy Tan University Faculty of Water Resource Engineering Thuyloi University Ho Chi Minh City University of Technology (HUTECH) Sustainable Management of Natural Resources and Environment Research Group Faculty of Environment and Labour Safety Ton Duc Thang University

The main objective of this study is to predict soil erosion in the Lattakia Governorate(W Syria) using the Water Erosion Prediction Project model(WEPP) and to compare the result with that of the RUSle. Field survey and data collection were carried out, and 44 soil samples were analyzed. In addition, all the necessary input files were prepared for use in the WEPP model and RUSle. Results show that more than of 80% of the locations studied experience slight to moderate erosion(less than 5 t/ha/y), whereas the rest of the locations experience severe soil erosion hazard. Moreover, the volume of runoff estimated by the WEPP model is in the range of 51 e321 mm, and the R~2 between the simulated soil erosion and the predicted runoff reached 0.68. Interestingly, the R~2 between the WEPP model and RUSle is 0.56, which indicates a good correlation between the two models.

关键词： Soil erosion Run-off WEPP RUSle Syria

Utilization and Satisfaction with HIV Services at 7 Provinces in Vietnam: A Cross-Sectional Study

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World Journal of AIDS 2022年第2期12卷 65-82页

作者： le bao Chau Pham Thi Huyen Chang Duong Minh Duc Hoang Van Minh Luu quoc Toan Bui Thi Tu Quyen le Thi Vui Bui Thi Thu Ha Hanoi University of Public Health Hanoi Vietnam

The study was carried out to determine utilization and satisfaction with essential HIV services from HIV individuals and high-risk populations in 7 socioeconomic provinces nationwide. A cross-sectional approach was applied in the study. Data were collected through face-to-face interviews using self-report questionnaires. Findings from 2380 respondents indicated a high percentage of utilization as well as a high level of satisfaction, in particular among men who have sex with men (MSM) and female sex worker (FSW) sub-group. Needle, syringe, and condom programs had lower usage and satisfaction mean scores in comparison with other services. The proportion of people who used drugs (PWID) and participated in the needle and syringe program was 53.6%. HIV counseling and testing uptake proportion of overall respondents in the last 12 months was 82.2% while the rate of antiretroviral treatment among infected PWID was 74.9%. The level of satisfaction with treatment services was found to be higher than preventive interventions (p le logistic regression suggested an association between some socioeconomic factors such as income, employment, educational attainment, and marital status with subjects’ satisfaction with several HIV services. Preventive services, especially needle, syringe and condom programs should be prioritized to scale up the coverage and effectiveness. Innovative approaches targeting at PWID should be promoted to increase their access to prevention programs as well as HIV care when needed.

关键词： HIV Service Utilization Satisfaction