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Predicting soil erosion hazard in lattakia Governorate(W Syria)

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

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One-step hydrothermal synthesis of Sn-doped α-Fe_(2)O_(3) nanoparticles for enhanced photocatalytic degradation of Congo red

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Journal of Semiconductors 2022年第12期43卷 22-31页

作者： van Nang lam Thi Bich Vu Quang Dat Do Thi Thanh Xuan le Tien Dai Nguyen T-Thanh-bao Nguyen Hoang Tung Do Thi Tu Oanh Nguyen Department of Natural Sciences Hoa Lu UniversityNinh NhatNinh Binh CityViet Nam Institute of Theoretical and Applied Research Duy Tan UniversityHanoi 100000Viet Nam Faculty of Natural Sciences Duy Tan UniversityDa Nang 550000Vietnam Institute of Physics Vietnam Academy of Science and Technology10 Dao TanBa DinhHanoiVietnam Institute of Materials Science Vietnam Academy of Science and Technology18 Hoang Quoc VietCaugiayHanoiVietnam

We report on the synthesis of Sn-doped hematite nanoparticles(Sn-α-Fe_(2)O_(3) NPs)by the hydrothermal *** prepared Sn-α-Fe_(2)O_(3) NPs had a highly pure and well crystalline rhombohedral phase with an average particle size of 41.4 *** optical properties of as-synthesizedα-Fe_(2)O_(3) NPs show a higher bandgap energy(2.40-2.57 eV)than that of pure bulkα-Fe_(2)O_(3)(2.1 eV).By doping Sn intoα-Fe_(2)O_(3) NPs,the Sn-doped hematite was observed a redshift toward a long wavelength with in-creasing Sn concentration from 0%to 4.0%.The photocatalytic activity of Sn-dopedα-Fe_(2)O_(3) NPs was evaluated by Congo red(CR)dye *** degradation efficiency of CR dye using Sn-α-Fe_(2)O_(3) NPs catalyst is higher than that of pureα-Fe_(2)O_(3) *** highest degradation efficiency of CR dye was 97.8%using 2.5%Sn-dopedα-Fe_(2)O_(3) NPs catalyst under visible-light *** results suggest that the synthesized Sn-dopedα-Fe_(2)O_(3) nanoparticles might be a suitable approach to develop a photocatalytic degradation of toxic inorganic dye in wastewater.

关键词： α-Fe_(2)O_(3)nanoparticles Sn Congo red photocatalytic properties photodegradation

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越南Orgarnicare展示商铺

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现代装饰 2019年第6期 36-41页

作者：无 Nguyēn Hai long Tran Thi Ngu Ngon Nguyēn Tuan Dang Pham Khánh duy lêvan.duy bao Saurav Shah Quang Dam(摄影) 范嘉苑(编译) Tropical Space 越南Tropical Space

Orgarnicare项目是一个为越南传统鱼露和有机产品而打造的展示空间。该项目由一座建于1975年之前的旧房子改造而成,其本身已历经过数次加固及扩建。对于此次的展示商铺改造,设计师旨在用具有越南特色的传统建材构建空间,让独具热带风情... 详细信息

Orgarnicare项目是一个为越南传统鱼露和有机产品而打造的展示空间。该项目由一座建于1975年之前的旧房子改造而成,其本身已历经过数次加固及扩建。对于此次的展示商铺改造,设计师旨在用具有越南特色的传统建材构建空间,让独具热带风情的空间元素与传统越南鱼露相互映衬,产生有趣的对话。

关键词：框架系统 Orgarnicare 热带风情

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菠萝蜜热泵干燥机理及工艺研究

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食品研究与开发 2015年第22期36卷 102-105页

作者：石维善李保国上海理工大学食品科学与工程研究所上海200093

进行热泵干燥菠萝蜜的的实验研究,通过单因素试验,分析了干燥温度和风速对菠萝蜜品质及营养成分的影响,并通过正交试验,确定菠萝蜜热泵干燥的最佳工艺参数。结果表明:在相对湿度为20%,物料尺寸为25(mm)×15(mm)×2(mm),干燥温度为45℃,... 详细信息

进行热泵干燥菠萝蜜的的实验研究,通过单因素试验,分析了干燥温度和风速对菠萝蜜品质及营养成分的影响,并通过正交试验,确定菠萝蜜热泵干燥的最佳工艺参数。结果表明:在相对湿度为20%,物料尺寸为25(mm)×15(mm)×2(mm),干燥温度为45℃,风速为3.5 m/s时,V_C损失量最小及感官品质最佳。试验表明物料大小,干燥温度与风速对菠萝蜜的干燥速率和干后品质有显著性的影响。

关键词：菠萝蜜热泵干燥相对湿度风速

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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

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Optimizing Bidders Selection of Multi-Round Procurement Problem in Software Project Management Using Parallel Max-Min Ant System Algorithm

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Computers, Materials & Continua 2021年第1期66卷 993-1010页

作者： Dac-Nhuong le Gia Nhu Nguyen arish Garg Quyet-Thang Huynh Trinh Ngoc bao Nguyen Ngoc Tuan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam Graduate School Duy Tan UniversityDa Nang550000Vietnam School of Mathematics Thapar Institute of Engineering and TechnologyPatiala147004India Hanoi University of Science and Technology Hanoi100000Vietnam Hanoi University Hanoi100000Vietnam Department of ICT Ministry of Education and TrainingHanoi100000Vietnam

This paper presents a Game-theoretic optimization via Parallel Min-Max Ant System(PMMAS)algorithm is used in practice to determine the Nash equilibrium value to resolve the confusion in choosing appropriate bidders of multi-round procurement problem in software project *** this end,we introduce an approach that proposes:(i)A Game-theoretic model of multiround procurement problem(ii)A Nash equilibrium strategy corresponds to multi-round strategy bid(iii)An application of PSO for the determination of global Nash *** balance point in Nash Equilibrium can help to maintain a sustainable structure not only in terms of project management but also in terms of future *** an alternative of procuring entities subjectively,a methodology to support decision making has been studied using Nash equilibrium to create a balance point on benefit in procurement where buyers and suppliers need multiple rounds of *** goal focus on the balance point in Nash Equilibrium to optimizing bidder selection in multi-round procurement which is the most beneficial for both investors and selected *** PMMAS algorithm is implemented based on MPI(message passing interface)to find the approximate optimal solution for the question of how to choose bidders and ensure a path for a win-win relationship of all participants in the procurement *** also evaluate the speedup ratio and parallel efficiency between our algorithm and other proposed *** the experiment results,the high feasibility and effectiveness of the PMMAS algorithm are verified.

关键词： Parallel min-max ant system multi-objective multi-round procurement software project management project conflicts Nash equilibrium game theory MPI

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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

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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

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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

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Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

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Computers, Materials & Continua 2020年第10期65卷 87-107页

作者： bao le Nguyen E.laxmi lydia Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin Mahmoud Mohamed Selim Gia Nhu Nguyen K.Shankar Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Department of Entrepreneurship and logistics Plekhanov Russian University of EconomicsMoscow115093Russia Department of logistics State University of ManagementMoscow109542Russia Department of Mathematics College of Science&Humanities in AlaflajPrince Sattam bin Abdulaziz UniversityAlaflaj16278Saudi Arabia Graduate School Duy Tan UniversityDa Nang550000Vietnam Department of Computer Applications Alagappa UniversityKaraikudi630003India

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and *** studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in *** this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data *** program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data ***,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning *** this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed *** security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data *** examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI *** simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

关键词： Blockchain optimization elliptical curve cryptosystem security ant colony optimization multi kernel support vector machine

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