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| 1 | Xanthine oxidase inhibitors from Archidendron clypearia(Jack.) I.C. Nielsen: Results from systematic screening of Vietnamese medicinal plants显示文摘Objective: To screen Vietnamese medicinal plants for xanthine oxidase(XO) inhibitory activity and to isolate XO inhibitor(s) from the most active plant. Methods: The plants materials were extracted by methanol. The active plant materials were fractionated using different organic solvents, including n-hexane, ethyl acetate, and n-butanol. Bioassay-guided fractionation and column chromatography were used to isolate compounds. The compounds structures were elucidated by analysis of spectroscopic data, including IR, MS, and NMR. Results: Three hundreds and eleven methanol extracts(CME) belonging to 301 Vietnamese herbs were screened for XO inhibitory activity. Among these plants, 57 extracts displayed XO inhibitory activity at 100 μg/m L with inhibition rates of over 50%. The extracts of Archidendron clypearia, Smilax poilanei, Linociera ramiflora and Passiflora foetida exhibited the greatest potency with IC_(50) values below 30 μg/m L. Chemical study performed on the extract of Archidendron clypearia resulted in the isolation of six compounds, including 1-octacosanol, docosenoic acid, daucosterol, methyl gallate, quercitrin and(-)-7-O-galloyltricetiflavan. The compound(-)-7-O-galloyltricetiflavan showed the most potent XO inhibitory activity with an IC_(50) value of 25.5 μmol/L. Conclusions: From this investigation, four Vietnamese medicinal plants were identified to have XO inhibitory effects with IC_(50) values of the methanol extracts below 30 μg/m L. Compound(-)-7-O-galloyltricetiflavan was identified as an XO inhibitor from Archidendron clypearia with IC_(50) value of 25.5 μmol/L. | Nguyen Thuy Duong Pham Duc Vinh Phuong Thien Thuong Nguyen Thi Hoai Le Nguyen Thanh Tran The Bach Nguyen Hai Nam Nguyen Hoang Anh | 2017 | Asian Pacific Journal of Tropical Medicine2017，10，6： | 5 |
| 2 | Ecoregional variations of aboveground biomass and stand structure in evergreen broadleaved forests显示文摘Biotic and abiotic factors control aboveground biomass(AGB)and the structure of forest ecosystems.This study analyses the variation of AGB and stand structure of evergreen broadleaved forests among six ecoregions of Vietnam.A data set of 1731-ha plots from 52 locations in undisturbed old-growth forests was developed.The results indicate that basal area and AGB are closely correlated with annual precipitation,but not with annual temperature,evaporation or hours of sunshine.Basal area and AGB are positively correlated with trees>30 cm DBH.Most areas surveyed(52.6%)in these old-growth forests had AGB of 100–200 Mg ha^-1;5.2%had AGB of 400–500 Mg ha^-1,and 0.6%had AGB of>800 Mg ha^-1.Seventy percent of the areas surveyed had stand densities of 300–600 ind.ha^-1,and 64%had basal areas of 20–40 m^2 ha^-1.Precipitation is an important factor influencing the AGB of old-growth,evergreen broadleaved forests in Vietnam.Disturbances causing the loss of large-diameter trees(e.g.,>100 cm DBH)affects AGB but may not seriously affect stand density. | Tran Van Do Mamoru Yamamoto Osamu Kozan Vo Dai Hai Phung Dinh Trung Nguyen Toan Thang Lai Thanh Hai Vu Thanh Nam Trieu Thai Hung Hoang Van Thang Tran Duc Manh Cao Chi Khiem Vu Tien Lam Nguyen Quang Hung Tran Hoang Quy Pham Quang Tuyen Trinh Ngoc Bon Nguyen Thi Thu Phuong Ninh Viet Khuong Nguyen Van Tuan Dang Thi Hai Ha Tran Hai Long Dang Van Thuyet Dang Thinh Trieu Nguyen Van Thinh Tran Anh Hai Duong Quang Trung Nguyen Van Bich Dinh Hai Dang Pham Tien Dung Nguyen Huy Hoang Le Thi Hanh Phan Minh Quang Nguyen Thi Thuy Huong Hoang Thanh Son Nguyen Thanh Son Nguyen Thi Van Anh Nguyen Thi Hoai Anh Pham Dinh Sam Hoang Thi Nhung Hoang Van Thanh Nguyen Huu Thinh Tran Hong Van Ho Trung Luong Bui Kieu Hung | 2020 | Journal of Forestry Research2020，31，5： | 1 |
| 3 | 高校教师胜任力评价方法研究综述显示文摘伴随着我国教育事业和高校的不断发展、教育事业和高校的不断改革以及高校从业人员晋升机制的不断发展和完善,不同类型高校以及高校内部存在着多种不同的人员晋升和测评机制。高校教师作为高校的核心力量,其胜任力关系到高校的发展和前景,因此对高校教师的胜任力进行评价非常必要。本文对目前关于高校教师胜任力的研究进行了综述,并对其中存在的问题进行了说明。 | NGUYEN Thai Hoc PHAM Duc Hoang NGYTEN Tho Bon | 2016 | 河北企业2016，0，10： | 1 |
| 4 | Intravenous Drug Use Among Street-Based Sex Workers: A High-Risk Behavior for HIV Transmission显示文摘 | Nguyen Anh Tuan Nguyen Tran Hien Pham Kim Chi Le Truong Giang Bui Duc Thang Hoang Thuy Long Tobi Saidel Roger Detels | 2004 | Sexually Transmitted Diseases2004，，： | 1 |
| 5 | Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system显示文摘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 pavements.In 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 pavements.The 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)algorithms.These 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 models.Therefore,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. | 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 | 2023 | Frontiers of Structural and Civil Engineering2023，17，5： | 0 |
| 6 | Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data显示文摘Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time. | Pham Huy Thong Florentin Smarandache Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 7 | Viral co-infections among children with confirmed measles at hospitals in Hanoi,Vietnam,2014显示文摘Objective:To characterize viral co-infections among representative hospitalized measles cases during the 2014 Hanoi outbreak.Methods:Throat swabs were collected from 54 pediatric patients with confirmed measles,and molecular diagnostics performed for 10 additional viral respiratory pathogens(Influenza A/H1N1pdm09;A/H3N2 and influenza B;Parainfluenza 1,2,3;Respiratory Synctial Virus,RSV;human Metapneumovirus,hM PV;Adenovirus and Picornavirus).Results:Twenty-one cases(38.9%) showed evidence of infection with other respiratory viruses:15 samples contained measles plus one additional virus,and 6 samples contained measles plus 2 additional viruses.Adenovirus was detected as a predominant cause of co-infections(13 cases;24.1%),followed by RSV(6 cases;11.1%),A/H1N1pdm09(3 cases;5.6%),PIV3(3 cases;3.7%),Rhinovirus(3 cases;3.7%) and hM PV(1 case;1.96%).Conclusions:Viral co-infections identified from pediatric measles cases may have contributed to increased disease severity and high rate of fatal outcomes.Optimal treatment of measles cases may require control of multiple viral respiratory pathogens. | Hang Le Khanh Nguyen Loan Phuong Do Van Thanh Thi Trieu Son Vu Nguyen Phuong Vu Mai Hoang Hien Thi Pham Thanh Thi Le Huong Thi Thu Tran Cuong Duc Vuong Mai Thi Quynh Le | 2017 | Asian Pacific Journal of Tropical Medicine2017，10，2： | 0 |
| 8 | Applying Wide & Deep Learning Model for Android Malware Classification显示文摘Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile market.With the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android malware.Deep learning technology is designed to handle a large amount of raw and continuous data,such as image content data.However,it is incompatible with discrete features,i.e.,features gathered from multiple sources.Furthermore,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine learning.On the other hand,a wide learning model can expand the feature set to enhance the classification accuracy.To maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning method.The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component.The proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw image.The accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model. | Le Duc Thuan Pham Van Huong Hoang Van Hiep Nguyen Kim Khanh | 2023 | Computer Systems Science & Engineering2023，45，6： | 0 |
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