检索结果-南通市图书馆

Chinese Journal of Natural Medicines 2021年第11期19卷 844-855页

作者： HU Xiao-long SHEN Wei WANG Rong long Huan WANG Quan FENG Jia-Hao pham Thi-Anh XIONG Fei YE Wen-Cai WANG Hao State Key Laboratory of Natural Medicines School of Traditional Chinese PharmacyChina Pharmaceutical UniversityNanjing 210009China State Key Laboratory of Bioelectronics Jiangsu Laboratory for Biomaterials and DevicesSoutheast UniversityNanjing 210009China Institute of Traditional Chinese Medicine and Natural Products Jinan UniversityGuangzhou 510632China

The fruits of Eucalyptus globulus *** known to have a plenty of medicinal properties,such as anti-tumor,anti-inflammatory,and immunosuppressive *** previous study found that the phloroglucinol-sesquiterpene adducts in the fruits of *** were immunosuppressive active constituents,especially Eucalyptin C(EuC).Phosphoinositide 3-kinases-γ(PI3Kγ)plays a pivotal role in T cell mediated excessive immune *** this study,EuC was first discovered to be a novel selective PI3Kγinhibitor with an IC50 value of 0.9μmol·L^(−1) and selectivity over 40-fold towards the other PI3K *** docking,molecular dynamics simulation,and cellular thermal shift assay showed that EuC bound to PI3Kγ.Furthermore,EuC suppressed the downstream of PI3Kγto induce the apoptosis and inhibit the activation of primary spleen cells derived from allergic contact dermatitis *** work highlights the role of the fruits of *** as a source of bioactive plant with immunosuppressive activity.

关键词： Phosphoinositide 3-kinase-γ(PI3Kγ) Eucalyptin C Immunosuppressive effects Activated spleen cells Phloroglucinol-sesquiterpene adducts

Upconversion luminescence of Gd2O3:Er3+and Gd2O3:Er3+/silica nanophosphors fabricated by EDTA combustion method

在线全文

学校读者我要写书评

暂无评论

Journal of Rare Earths 2019年第11期37卷 1126-1131页

作者： Le Quoc Minh Tran Kim Anh Nguyen duy Hung pham Thi Minh Chau Nguyen Thi Quy Hai Ho Van Tuyen Vu Thi Thai Ha Vu Duc Tu Wieslaw Strek Institute of Theoretical and Applied Research Duy Tan UniversityDa Nang 55000Viet Nam Advanced Institute for Science and Technology Hanoi University of Science and Technology1 Dai Co VietHai Ba TrungHanoiViet Nam Institute of Materials Science Vietnam Academy of Science and Technology18 Hoang Quoc Viet RoadCau Giay DistrictHanoiViet Nam Institute of Low Temperature and Structure Research Polish Academy of Science 2 OkolnaWroclawPoland

Gd2O3:Er^3+nanophosphors were fabricated by the combustion method in presence of Na2 ethylene diamine tetra acetic acid(EDTA-Na2)as fuel at not high temperature(≤350℃)within a very short time of 5 *** added concentration of Er^3+ions in Gd2O3 matrix was changed from 0.5 mol%to 5.0 mol%.The X-ray diffraction pattern of samples indicates the monoclinic structure of Gd2O3:Er^3+.The morphology and chemical composition analysis of the Gd2O3:Er^3+samples are characterized by a field emission scanning electron microscope(FESEM)and a Fourier-transform infrared spectrometer(FTIR).The photoluminescence(PL),photo luminescence excitation(PLE)and upconversion(UC)at room temperature of the prepared materials with different concentrations of Er^3+were *** PL of Gd2O3:Er^3+nanomaterials are shown in visible at 545,594,623,648,688 nm under excitation at 275 *** emission bands from transitions of Er^3+from 2P3/2 to 4F9/2 are observed,UC luminescent spectra of the Gd2O3:Er^3+/silica nanocomposites under 976 nm excitation show the bands at 548 and 670 *** influence of excitation power at 980 nm for transitions were measured and *** results indicate that the upconversion process of Gd2O3:Er^3+/silica is two photons absorption *** low temperature dependence of UC luminescent intensities of the main bands of Gd2O3:Er^3+was investigated towards development of a nanotemperature sensor in the range of 10-300 K.

关键词： Upconversion luminescence Gd2O3:Er3+ Gd2O3:Er3+/silica Nanophosphors EDTA combustion Rare earths

Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

在线全文

学校读者我要写书评

暂无评论

Geoscience Frontiers 2021年第3期12卷 333-342页

作者： Binh Thai pham Abolfazl Jaafari Tran Van Phong Hoang Phan Hai Yen Tran Thi Tuyen Vu Van Luong Huu duy Nguyen Hiep Van Le Loke Kok Foong University of Transport Technology Ha Noi 100000Viet Nam Research Institute of Forests and Rangelands Agricultural ResearchEducation and Extension Organization(AREEO)TehranIran Institute of Geological Sciences Vietnam Academy of Sciences and Technology84 Chua Lang StreetDong daHa NoiViet Nam Department of Geography School of Social EducationVinh UniversityViet Nam Department of Resource and Environment Management School of Agriculture and ResourcesVinh UniversityViet Nam Faculty of Geography VNU University of Science334 Nguyen TraiHa NoiViet Nam Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood *** this study,we proposed and validated three ensemble models based on the Best First Decision Tree(BFT)and the Bagging(Bagging-BFT),Decorate(Bagging-BFT),and Random Subspace(RSS-BFT)ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner.A total number of 126 historical flood events from the Nghe An Province(Vietnam)were connected to a set of 10 flood influencing factors(slope,elevation,aspect,curvature,river density,distance from rivers,flow direction,geology,soil,and land use)for generating the training and validation *** models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood *** on the Area Under the receiver operating characteristic Curve(AUC),the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT(AUC=0.982)and Bagging-BFT(AUC=0.967)models.A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.

关键词： Machine learning Ensemble learners Hybrid modeling

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

在线全文

学校读者我要写书评

暂无评论

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

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

在线全文

学校读者我要写书评

暂无评论

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

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2023年第6期135卷 2219-2241页

作者： Huong Thi Thanh Ngo Nguyen Duc Dam Quynh-Anh Thi Bui Nadhir Al-Ansari Romulus Costache Hang Ha Quynh duy Bui Sy Hung Mai Indra Prakash Binh Thai pham University of Transport Technology Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Departement of Geodesy and Geomatics National University of Civil EngineeringHanoi100000Vietnam Faculty of Hydraulic Engineering National University of Civil EngineeringHanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of *** types of floods are mostly associated with landslides and erosion of roads within a short *** of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic *** this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,*** the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental *** performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

关键词： Flash flood deep learning neural network(DL) machine learning(ML) receiver operating characteristic curve(ROC) Vietnam

Stock-Price Forecasting Based on XGBoost and LSTM

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第1期40卷 237-246页

作者： pham Hoang Vuong Trinh Tan Dat Tieu Khoi Mai pham Hoang Uyen pham The Bao Information Science Faculty Sai Gon UniversityHo Chi Minh City700000Vietnam University of Economics and Law Ho Chi Minh City700000Vietnam

Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for *** learning and deep learning have been shown to obtain better forecasts of stock prices than traditional *** study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning ***,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant ***,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock *** deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual *** complex structure enables this network to capture more stochasticity within the stock *** method does not change when applied to stock data or Forex *** results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error.

关键词： stock-price forecasting ARIMA XGBoost LSTM deep learning

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

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

Study on Development of Formulated Feed for Improving Growth and Pigmentation of Koi Carp （Cyprinus carpio L,, 1758） Juveniles

在线全文

学校读者我要写书评

暂无评论

Journal of Life Sciences 2014年第5期8卷 433-441页

作者： Nguyen Van Nguyen Tran Van Khanh pham duy Hai Research Institute for Aquaculture No. 2 Ho Chi Minh City Vietnam

The aim of the present study was to develop a formulated feed for improving skin pigmentation and growth performance of koi carp juveniles. Two experiments were conducted for determination of an optimal dietary astaxanthin and FM （fish meal） ration for koi carp juveniles. In the first experiment, three isonitrogenous and isoenergetic dietary treatments were designed with three different levels of astaxanthin consisted of 60, 80 and 100 mg/kg and a commercial feed served as a control. For the second experiment, four isonitrogenous and isoenergetic dietary treatments were formulated with graded inclusions of dietary FM replaced from 0% to 60% at 20% increments by SM （soybean meal）, PBM （poultry by-product meal）, and a CD （control diet）. Obtained results demonstrated that skin pigmentation of koi carp juveniles fed the diet containing 80 mg/kg astaxanthin, 36.02% protein, 7.78% lipid, 4.20 Kcal/g GE （gross energy） were more better than those at the diet with lower estaxanthin content and commercial diet. Moreover, the highest growth and feed utilization of fish were observed at this diet with WG （weight gain）, SGR （specific growth rate） and FCR （feed conversion ratio） were 121.80%, 0.95 （%/day） and 1.6, respectively.

关键词： Formulated feed pigmentation fish meal replacement koi carp juveniles.

A New Method Based on Kinematics of Robots to Analyze the Kinematics of Persian Joint

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Environmental Science and Engineering(B) 2014年第1期3卷 55-59页

作者： pham Thanh long Tran The long Department of Mechatronics Engineering Thai Nguyen University of Technology Thai Nguyen 23000 Vietnam Department of Manufacturing Engineering Thai Nguyen University of Technology Thai Nguyen 23000 Vietnam

This paper has been focused on investigating kinematics of Persian joint. In this study, instead of using the 3D （three dimensional） rotation matrix method as in the previous research, Persian joint is considered as a robotic mechanism, and the methods and tools are applied to present the relationship of angular displacements of input shaft and output shaft. When the angle between intersecting shafts changes from 0 degree to 135 degrees, the results show that the velocities of input shaft and output shaft are almost the same with the actual values. This study provides a new method to investigate this mechanism in detail. The obtained results not only provide the sub-parameters （the displacement of guide arms and connecting arms）, but also help to have a deeper understanding about the operation of Persian joint in order to develop the design of Persian joint in the future.

关键词： lnverse kinematic problem Persian joint constant-velocity joint.