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Cellular models of stress resistance may pave ways to fight neurodegenerative diseases

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Neural Regeneration Research 2025年第9期20卷 2579-2580页

作者： Thu nguyen minh Pham Christian Behl Institute of Pathobiochemistry University Medical Center of the Johannes Gutenberg UniversityMainzGermany

Alzheimer's disease(AD),the most common form of neurodegeneration,is characterized by selective neuronal vulnerability and brain regionselective neuron *** entorhinal cortex and hippoc,ampal CA1 projection neurons are at greater risk in AD whereas other regions display resistance to ***,the cerebellum,a phylogenetically very old region,is affected only very late in the disease progression.

关键词： cortex resistance Alzheimer

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine learning Models and Grid Search Optimization Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 691-712页

作者： Quynh-Anh Thi Bui Dam Duc nguyen Hiep Van le Indra Prakash Binh Thai Pham Civil Engineering Department University of Transport Technology54 Trieu KhucThanh XuanHanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement *** study used three Machine learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input ***,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the *** models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat *** validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the *** show that these models accurately predict SBS,with LGBM providing outstanding ***(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on ***,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

关键词： Shear bond asphalt pavement grid search optimization machine learning

OPT-BAG Model for Predicting Student Employability

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Computers, Materials & Continua 2023年第8期76卷 1555-1568页

作者： minh-Thanh Vo Trang nguyen Tuong le Faculty of Information Technology HUTECH UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ho Chi Minh City Open UniversityHo Chi Minh CityVietnam Laboratory for Artificial Intelligence Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh CityVietnam

The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a *** on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the *** addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their *** effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of *** model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be ***,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability ***,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability *** training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation ***,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student *** empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student *** this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most *** indicates that educational institutions should focus on these factors during the learning process to improve student employability.

关键词： Ensemble classifier grid search cross-validation OPT-BAG student employability

Atomistic simulation of free transverse vibration of graphene,hexagonal SiC, and BN nanosheets

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Acta Mechanica Sinica 2017年第1期33卷 132-147页

作者： Danh-Truong nguyen minh-Quy le Thanh-Lam Bui Hai-le Bui School of Mechanical Engineering Hanoi University of Science and Technology Hanoi 10000 Vietnam International Institute for Computational Science and Engineering Hanoi University of Science and TechnologyHanoi 10000 Vietnam Faculty of Mechanical Engineering Hanoi University of Industry Hanoi 10000 Vietnam

Free transverse vibration of monolayer graphene, boron nitride (BN), and silicon carbide (SiC) sheets is investigated by using molecular dynamics finite element method. Eigenfrequencies and eigenmodes of these three sheets in rectangular shape are studied with different aspect ratios with respect to various boundary conditions. It is found that aspect ratios and boundary conditions affect in a similar way on natural frequencies of graphene, BN, and SiC sheets. Natural frequencies in all modes decrease with an increase of the sheet’s size. Graphene exhibits the highest natural frequencies, and SiC sheet possesses the lowest ones. Missing atoms have minor effects on natural frequencies in this study.

关键词： Atomistic simulation Hexagonal sheet Transverse vibration Molecular dynamics finite element method

Development of deep neural network model to predict the compressive strength of FRCM confined columns

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Frontiers of Structural and Civil Engineering 2022年第10期16卷 1213-1232页

作者： Khuong le-nguyen Quyen Cao minh Afaq AHMAD Lanh Si HO Department of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam Faculty of Arts and Design University of CanberraBruce ACT 2617Australia Department of Civil Engineering University of Engineering and TechnologyTaxila 47080Pakistan Graduate School of Advanced Science and Engineering Hiroshima UniversityHiroshima 739-8527Japan

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neural Network model(artificial neural network(ANN)with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters,i.e.,section type(circle or square),corner radius rc,unconfined concrete strength fco,thickness nt,the elastic modulus of fiber Ef,the elastic modulus of mortar *** results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction *** ANN model with double hidden layers(APDL-1)was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical ***,the results also reveal that the unconfined compressive strength of concrete,type of fiber mesh for FRCM,type of section,and the corner radius ratio,are the most significant input variables in the efficiency of FRCM confinement *** performance of the proposed ANN models(including double and triple hidden layers)had high precision with R higher than 0.93 and RMSE smaller than 0.13,as compared with other models from the literature available.

关键词： FRCM deep neural networks confinement effect strength model confined concrete

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

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International Journal of Automation and computing 2019年第5期16卷 614-627页

作者： Viet-Anh le Hai-Xuan le Linh nguyen minh-Xuan Phan Department of Automatic Control Hanoi University of Science and TechnologyHanoi 10000Vietnam Centre for Autonomous Systems University of Technology SydneyNew South Wales 2007Australia

In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and reallife systems, where the results obtained by our method are highly promising.

关键词： 3D overhead crane adaptive control hierarchical sliding mode control neural network radial basis function

Effects of low energy (160 keV) X-ray on microbial inactivation,sprouting inhibition and genetic variation in potato

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Food Bioscience 2022年第3期47卷 262-270页

作者： nguyen An Son nguyen Thi Nguyet Ha nguyen Thi minh Sang le Doan Dinh Duc le Ngoc Trieu Faculty of Physic and Nuclear Engineering Da Lat UniversityViet Nam Dalat Vocational Training College Viet Nam Faculty of Biology Da Lat UniversityViet Nam

The effects of low energy X-ray irradiation on microbial inactivation,sprouting inhibition and genetic variation were *** was an inverse correlation between the exposure dose in range of 0 Gy–5000 Gy and the survival rate of *** dose of 1000 Gy seemed to be suitable for inactivation of microorganisms because the respective survival rate is relatively low (0.59%).Dose rate of 13.87 Gy/min was the most appropriate to inactivate the microorganisms,with the survival rate of 0.37%.Applying the irradiation dose of 1000 Gy and dose rate of 13.87 Gy/min,there was a significant sprouting inhibition of irradiated potatoes compared to the control for six months *** effect of irradiation dose (100–1200 Gy) and dose rate (8.63–19.62 Gy/min) on genetic variation of the irradiated samples was assessed by DNA fingerprints induced from using three molecular techniques including Random Amplified Polymorphic DNA (RAPD),Start Codon Targeted Polymorphism (SCoT) and CAAT box – derived polymorphism (CBDP).At the irradiation dose of 1000 Gy and dose rate of 13.87 Gy/min,the level of genetic variation was relatively low (4.85%).Thus,this treatment is a suitable condition of irradiation for potato treatment with the purpose of preservation.

关键词： Dose Dose rate Low energy X-ray Microbial inactivation Potato

Negative Index Materials and Their Applications:Recent Mathematics Progress(Dedicated to Ham Brezis for his 70th birthday with esteem)

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Chinese Annals of Mathematics,Series B 2017年第2期38卷 601-628页

作者： Hoai-minh nguyen EPFL SB MATHAA CAMA Station 8CH-1015 LausanneSwitzerland

Negative index materials are artificial structures whose refractive index has negative value over some frequency range. These materials were first investigated theoretically by Veselago in 1946 and were confirmed experimentally by Shelby, Smith, and Schultz in2001. Mathematically, the study of negative index materials faces two difficulties. Firstly,the equations describing the phenomenon have sign changing coefficients, hence the ellipticity and the compactness are lost in general. Secondly, the localized resonance, i.e.,the field explodes in some regions and remains bounded in some others as the loss goes to 0, might appear. In this survey, the author discusses recent mathematics progress in understanding properties of negative index materials and their applications. The topics are reflecting complementary media, superlensing and cloaking by using complementary media, cloaking a source via anomalous localized resonance, the limiting absorption principle and the well-posedness of the Helmholtz equation with sign changing coefficients.

关键词： Negative index materials, Localized resonance, Cloaking, Superlensing

Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy le Duy-Hung nguyen Thanh SANG-TO Samir KHATIR Hoang le-minh Amir H.GANDOMI Thanh CUONG-le Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines