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Effectiveness on post-stroke hemiplegia in patients:electroacupuncture plus cycling vs electroacupuncture alone

Journal of Traditional Chinese Medicine 2023年第2期43卷 352-358页

作者： Minh Duc nguyen Thanh Van Tran Quoc Vinh nguyen Ninh Khac nguyen Son Truong Vu luu trong nguyen Linh Vu Phuong Dang Senior Specialist Clinic National Hospital of AcupunctureHanoi 1000Vietnam Department of Senior Specialist National Hospital of Acupuncture Hanoi 1000Vietnam Department of Senior Specialist Military Institute of Traditional MedicineHanoi 1000Vietnam Department of Training and Science National Hospital of AcupunctureHanoi 1000Vietnam Department of Senior Specialist National Hospital of AcupunctureHanoi 1000Vietnam Department of Rehabilitation 108 Military Central HospitalHanoi 1000Vietnam Department of Medicine Dai Nam UniversityHanoi 1000Vietnam

OBJECTIVE:To evaluate the effectiveness of cycling in combination with electroacupuncture in treatment of poststroke hemiplegia patients at National Hospital of Acupuncture,Vietnam.METHODS:The study was designed as a single-centre,outcome-assessor-blinded parallel randomised controlled trial with 120 post-stroke hemiplegia patients randomly assigned into two groups:electroacupuncture plus cycling(CT group) and electroacupuncture(AT group).Patients were assessed before and after the treatment(using muscle grading,modified Rankin,Barthel,Orgorozo scores and electromyography).Statistical Man–Whitney U test,and Fisher’s exact tests were used to compare between CT and AT groups.RESULTS:The results reported statistically significant improvement in motor function in patients suffering from hemiplegia following ischemic stroke in both CT and AT groups.Patients in CT group experienced a greater improvement compared to those in AT group including better muscle contraction(increased frequency and amplitude of electromyography and increased muscle grading scale);increased recovery(Orgogozo scale),increased independency(Barthel scale) and decreased disability(Modified Rankin scale)(P < 0.01).CONCLUSIONS:Combination with cycling training significantly improves the recovery of post-stroke patients treated with electroacupuncture.

关键词： electroacupuncture cycle training post-stroke hemiplegia

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An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2201-2237页

作者： Thi-Kien Dao trong-The nguyen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Multimedia Communication Lab University of Information TechnologyHo Chi Minh CityVietnam

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

关键词： Node localization whale optimization algorithm wireless sensor networks siege whale optimization algorithm optimization

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Influence of different land-use types on selected soil properties related to soil fertility in A Luoi District,Thua Thien Hue,Vietnam

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Soil Ecology Letters 2024年第1期6卷 131-140页

作者： Khoa Phuc nguyen Tan trong Tran Huy Dinh Le Phuong Thuy nguyen Hien Thao Thi Pham Dien Thanh nguyen Ngu Huu nguyen University of Agriculture and Forestry Hue UniversityHue CityThua Thien Hue 52000Vietnam School of Biotechnology Tan Tao UniversityTan Duc ECityDuc HoaLong An850000Vietnam

Land use type affects the physicochemical properties of soil.The value of OM and TN is high in acacia soils.Strong acidity of soil is considered a serious constraint for agriculture.Suitable cash crops are acacia,cassava,banana,rice and maize.Soil fertility is affected by land-use types and land management,which exacerbates soil erosion and various other forms of soil degradation in the mountainous regions of Viet-nam.This study was conducted in A Luoi District,Thua Thien Hue,Vietnam to identify the effects of land-use types on specific soil physicochemical characteristics related to soil fertility.Soil physicochemical properties,such as organic matter(OM),total nitrogen(TN),total phosphorous(TP),and K+were significantly affected by land-use type.The results showed that the soils were sandy in rice but clay loam for acacia and cassava.The mean bulk density value of acacia soil was significantly greater than that of other soils.TN were higher in the acacia soils than those in the rice,maize,and banana soils.The OM content was significantly higher in the acacia,cassava,and banana soils than those in the rice and maize soils.The mean of exchangeable K+in the rice soil was higher compared to those in other soils and was affected by land-use type.The high exchangeable acidity content in the soils was probably due to intensive precipitation.However,both land use type and management did not affect the CEC value.Overall,the inappropriate land use caused the disturbance of soil physicochemical properties,indicating that the conditions of rice and maize soils are becoming worse than acacia soils.Therefore,lowering the intensity of cultivation,adopting incorporated soil fertility management,and applying organic fertilizer should preserve the existing conditions and enhance soil properties.

关键词： Land-use types soil characteristics soil frility suitable crop Vietnam

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Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

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Intelligent Automation & Soft Computing 2024年第2期39卷 281-299页

作者： nguyen trong Vinh Lam Thanh Hien Ha Manh Toan Ngo Duc Vinh Do Nang Toan Faculty of Information Technology Lac Hong UniversityBien HoaDong Nai76120Vietnam Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi10072Vietnam Faculty of Information Technology Hanoi University of IndustryHanoi11915Vietnam

Tuberculosis is a dangerous disease to human life,and we need a lot of attempts to stop and reverse it.Significantly,in theCOVID-19 pandemic,access to medical services for tuberculosis has become very difficult.The late detection of tuberculosis could lead to danger to patient health,even death.Vietnamis one of the countries heavily affected by the COVID-19 pandemic,andmany residential areas as well as hospitals have to be isolated for a long time.Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessingmedical services,such as an automatic tuberculosis diagnosis system.In our study,aiming to build that system,we were interested in the tuberculosis diagnosis problem from the chest X-ray images of Vietnamese patients.The chest X-ray image is an important data type to diagnose tuberculosis,and it has also received a lot of attention from deep learning researchers.This paper proposed a novel method for tuberculosis diagnosis and visualization using the deeplearning approach with a large Vietnamese X-ray image dataset.In detail,we designed our custom convolutional neural network for the X-ray image classification task and then analyzed the predicted result to provide visualization as a heat-map.To prove the performance of our network model,we conducted several experiments to compare it to another study and also to evaluate it with the dataset of this research.To support the implementation,we built a specific annotation system for tuberculosis under the requirements of radiologists in the Vietnam National Lung Hospital.A large experiment dataset was also from this hospital,and most of this data was for training the convolutional neural network model.The experiment results were evaluated regarding sensitivity,specificity,and accuracy.We achieved high scores with a training accuracy score of 0.99,and the testing specificity and sensitivity scores were over 0.9.Based on the X-ray image classification result,we visualize prediction results as heat-maps and also analyze t

关键词： Tuberculosis classification Vietnamese chest X-ray deep learning

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Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

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Intelligent Automation & Soft Computing 2024年第2期39卷 319-335页

作者： Do Nang Toan Lam Thanh Hien Ha Manh Toan nguyen trong Vinh Pham Trung Hieu Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi10072Vietnam Faculty of Information Technology Lac Hong UniversityBien HoaDong Nai76120Vietnam

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

关键词： CT image 3D dose prediction data preprocessing augmentation

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Face Mask Recognition for Covid-19 Prevention

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Computers, Materials & Continua 2022年第11期73卷 3251-3262页

作者： trong Hieu luu Phan nguyen Ky Phuc Zhiqiu Yu Duy Dung Pham Huu trong Cao College of Engineering Can Tho UniversityCan Tho city910900Viet Nam International University-Vietnam National University Vietnam National UniversityHoChiMinh City70000Vietnam Department of Industrial Management National Taiwan University of Science and TechnologyTaipei10607Taiwan

In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak,sing,or breathe,the virus can quickly spread and create severe problems for people’s health.According to some research as well as World Health Organization(WHO)recommendation,one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space.A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection.This simple method can reduce up to 95%of the spread of the particles.However,this solution depends heavily on social consciousness,which is sometimes unstable.In order to improve the effectiveness of wearing face masks in public spaces,this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask.The approach uses the deep learning technique that relies on GoogleNet,AlexNet,and VGG16 models.The results are synthesized by an ensemble method,i.e.,the bagging technique.From the experimental results,the approach represents a more than 95%accuracy of face mask recognition.

关键词： Face mask detection deep learning AlexNet GoogLeNet VGG16 ensemble method

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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 methods.This 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 map.Computing 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, i.e.TP 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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Fresh and hardened properties of high-performance fiber-reinforced concrete containing fly ash and rice husk ash:Influence of fiber type and content

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Frontiers of Structural and Civil Engineering 2022年第12期16卷 1621-1632页

作者： nguyen-trong HO Viet Quoc DANG Minh-Hieu nguyen Chao-Lung HWANG trong-Phuoc HUYNH Faculty of Civil Engineering VSB-Technical University of OstravaOstrava-Poruba 70800Czech Republic Faculty of Bridge and Road Construction Mientrung University of Civil Engineering(MUCE)Tuy Hoa City 56100Vietnam Vinh Long Department of Construction Vinh Long City 85000Vietnam Taiwan Building Technology Center Taiwan University of Science and TechnologyTaipei 10607China Faculty of Civil Engineering College of EngineeringCan Tho UniversityCan Tho City 94000Vietnam

Although fibers are used only infrequently as an additive in concrete in the construction industry,fiberenhanced concrete is known to provide a wide range of advantages over conventional concrete.The main objective of this study was to investigate the influences of fiber type and content on the mechanical properties and durability of highperformance fiber-reinforced concrete(HPFRC)designed using a novel densified mixture design algorithm with fly ash and rice husk ash.Three types of fiber,including polypropylene(PP)fiber,steel fiber(SF),and hybrid fiber(HF),were considered.Based on the results,the inclusion of fibers decreased HPFRC flowability,regardless of fiber type.Although the compressive strength of HPFRC with 1.6%PP fiber content was 11.2%below that of the reference HPFRC specimen at 91 d of curing age,the 91-d compressive strengths of both SF and HF-enhanced HPFRC specimens were significantly better than that of the reference HPFRC specimen.Furthermore,the HPFRC specimens incorporating SF and HF both exhibited better splitting tensile and flexural strengths as well as less drying shrinkage than the HPFRC specimens incorporating PP fiber.However,the fiber-enhanced specimens,especially those with added SF,registered less surface electrical resistivity and greater vulnerability to chloride ion penetration than the reference HPFRC specimen.

关键词： high-performance fiber-reinforced concrete fly ash rice husk ash durability mechanical strength

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Shear strength model of the reinforced concrete beams with embedded through-section strengthening bars

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 843-857页

作者： Linh Van Hong BUI Phuoc trong nguyen Faculty of Civil Engineering Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

In this study,finite element(FE)analysis is utilized to investigate the shear capacity of reinforced concrete(RC)beams strengthened with embedded through-section(ETS)bars.Effects of critical variables on the beam shear strength,including the compressive strength of concrete,stiffness ratio between ETS bars and steel stirrups,and use of ETS strengthening system alone,are parametrically investigated.A promising method based on the bond mechanism between ETS strengthening and concrete is then proposed for predicting the shear resistance forces of the strengthened beams.An expression for the maximum bond stress of the ETS bars to concrete is developed.This new expression eliminates the difficulty in the search and selection of appropriate bond parameters from adhesion tests.The results obtained from the FE models and analytical models are validated by comparison with those measured from the experiments.Consequently,the model proposed in this study demonstrates better performance and more accuracy for prediction of the beam shear-carrying capacity than those of existing models.The results obtained from this study can also serve researchers and engineers in selection of the proper shear strength models for design of ETS-strengthened RC beams.

关键词： embedded through-section strengthening fiber-reinforced polymer finite element shear strength model bond mechanism

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An Ontology-Based Question Answering System for University Admissions Advising

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Intelligent Automation & Soft Computing 2023年第4期36卷 601-616页

作者： Thi Thanh Sang nguyen Dang Huu trong Ho Ngoc Tram Anh nguyen School of Computer Science and Engineering International UniversityVNU-HCMCVietnam National UniversityHo Chi Minh CityVietnam

Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in manyﬁelds.However,these systems depend on learning methods and training data.Therefore,it is necessary to prepare such a good dataset,but it is not an easy job.An ontol-ogy-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions.This study proposes a novel chatbot model involving ontology to generate efﬁcient responses automatically.A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot.A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé.The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans.It includes a search engine reasoning the ontology and generat-ing answers to users’questions.Two experiments are carried out to test how the system reacts to different questions.Theﬁrst experiment examines questions made from some templates,and the second one examines normal questions taken from frequent questions.Experimental results have shown that the ontology-based chatbot can release meaningful and long answers.The results are analysed to prove the proposed chatbot is usable and promising.

关键词： Ontology chatbots answer-question systems domain knowledge base admissions advising

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