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A Study of the Optical Properties in ZnWO<sub>4</sub>Nanorods Synthesized by Hydrothermal Method

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Materials Sciences and Applications 2011年第8期2卷 988-992页

作者： nguyen van minh nguyen Manh Hung Center for Nano Science and Technology and Department of Physics Hanoi National University of Education 136 Xuan Thuy Road Hanoi Vietnam

We investigate the effect of synthesized time on the structure, as well as optical properties in ZnWO4 nano rod prepared by hydrothermal method. The prepared rods were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman scattering, absorption and photoluminescent (PL) spectra techniques. The size and morphology of ZnWO4 nano-rod can be controlled by adjusting the reaction time. The resultant sample is a pure phase of ZnWO4 without any impurities. The results showed that the optical property of ZnWO4 nanoparticles obviously relied on their rod sizes.

关键词： Znwo4 Nano Rod Raman Spectroscopy Absorption Photoluminescence

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

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam nguyen Thanh Trung HUYNH Zhao REN Thanh Toan nguyen Phi Le nguyen Hongzhi YIN Quoc Viet Hung nguyen School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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

Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 467-500页

作者： Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models Duc-Dam nguyen nguyen Viet Tiep Quynh-Anh Thi Bui Hiep van Le Indra Prakash Romulus Costache Manish Pandey Binh Thai Pham Faculty of Civil Engineering University of Transport TechnologyThanh XuanHa Noi100000Vietnam Faculty of Hydraulic Engineering Hanoi University of Civil EngineeringHa Noi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India Research Institute of the University of Bucharest Bucharest050663Romania National Institute of Hydrology and Water Management Bucharest013686Romania Department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Marwadi University Research Center(MURC) Marwadi UniversityRajkotGujaratBharat360003India Department of Civil Engineering Faculty of Engineering&TechnologyMarwadi UniversityRajkotGujaratBharat360003India

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and ***,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and *** models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to *** factors were instrumental in predicting the output variable,which was the probability of landslide *** analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other *** AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of *** results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

关键词： Landslide susceptibility map spatial analysis ensemble modelling information values(IV)

Static analysis of corrugated panels using homogenization models and a cell-based smoothed mindlin plate element (CS-MIN3)

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Frontiers of Structural and Civil Engineering 2019年第2期13卷 251-272页

作者： Nhan nguyen-minh Nha TRAN-van Thang BUI-XUAN Trung nguyen-THOI Faculty of Applied Science Bach Khoa University (BKU)Ho Chi Minh CityVietnam Faculty of Mathematics and Computer Science Ho Chi Minh City University of Science (HCMUS)Ho Chi Minh CityVietnam Department of Mathematics L ouisiana State UniversityBaton RougeLA 70803USA Division of Computational Mathematics and Engineering Institute of Computational ScienceTon Due Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Due Thang UniversityHo Chi Minh CityVietnam

Homogenization is a promising approach to capture the behavior of complex structures like corrugated panels. It enables us to replace high-cost shell models with stiffness-equivalent orthotropic plate alternatives. Many homogenization models for corrugated panels of different shapes have been proposed. However, there is a lack of investigations for verifying their accuracy and reliability. In addition, in the recent trend of development of smoothed finite element methods, the cell-based smoothed three-node Mindlin plate element (CS-MIN3) based on the first-order shear deformation theory (FSDT) has been proposed and successfully applied to many analyses of plate and shell structures. Thus, this paper further extends the CS-MIN3 by integrating itself with homogenization models to give homogenization methods. In these methods, the equivalent extensional, bending, and transverse shear stiftriess components which constitute the equivalent orthotropic plate models are represented in explicit analytical expressions. Using the results of ANSYS and ABAQUS shell simulations as references, some numerical examples are conducted to verify the accuracy and reliability of the homogenization methods for static analyses of trapezoidally and sinusoidally corrugated panels.

关键词： homogenization corrugated panel asymptotic analysis smoothed finite element method (S-FEM) cell-based smoothed three-node Mindlin plate element (CS-MIN3)

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

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Nonlocal isogeometric analysis for bidirectional functionally graded porous curved microbeams with arbitrary boundary conditions

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Acta Mechanica Sinica 2024年第8期40卷 194-218页

作者： Thu-Huong nguyen Thi van Ke Tran van-minh Phung van Hai Trinh Quoc Hoa Pham Faculty of Mechanical Engineering Hanoi University of IndustryHanoi100000Vietnam Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoi100000Vietnam Institute of Vehicle and Energy Engineering Le Quy Don Technical UniversityHanoi100000Vietnam Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam

This research aims to analyze the static bending and free vibration behaviors of bidirectional functionally graded porous(BFGP)curved microbeams with elastic boundary conditions resting on a Pasternak’s elastic foundation in a hygro-thermal *** analysis based on non-uniform rational B-splines,first-order shear deformation theory,nonlocal elasticity theory combined with the modified strain gradient theories,modified Timoshenko beam theory are formulated to depict bending and shear deformations of BFGP curved ***,because using the modified Timoshenko beam theory,this study removes the requirement for a shear correction factor and describes shear stress by zero at the upper and lower cross-sectional sites of the BFGP curved *** from traditional boundary conditions,where the beginning and end positions of a curved beam are connected by an elastic system of straight and torsion *** allows for greater flexibility in controlling the stiffness of the springs to obtain arbitrary *** assess the accuracy and convergence of the proposed approach,validation numerical examples were conducted in the various examples.

关键词： Bidirectional functionally graded porous material Elastic boundary condition Isogeometric analysis Curved microbeams Nonlocal elasticity theory Modified strain gradient theory

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

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Computers, Materials & Continua 2024年第3期78卷 3585-3602页

作者： Duy Quang Tran Huy Q.Tran minh van nguyen Faculty of Civil Engineering Nha Trang UniversityNha Trang650000Vietnam Robotics and Mechatronics Research Group Faculty of Engineering and TechnologyNguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam Department of Civil Engineering Ho Chi Minh City University of Technology and EducationHo Chi Minh City700000Vietnam

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service *** volume is an influential parameter for planning and operating traffic *** study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning *** fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal ***,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition *** second aspect involves predicting traffic volume using the long short-term memory ***,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent ***,the fusion of the obtained results leads to a final traffic volume *** experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and *** achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the *** findings highlight the accuracy of traffic pattern ***,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

关键词： Ensemble empirical mode decomposition traffic volume prediction long short-term memory optimal hyperparameters deep learning