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Journal of Advanced Dielectrics 2020年第4期10卷 13-21页

作者： nguyen truong-tho Le Dai Vuong Department of Physics College of Sciences Hue UniversityHue CityVietnam Faculty of Natural Sciences Thu Dau Mot University Binh Duong Province Vietnam

Sintered lead-free Bi_(0.5)(Na_(0.8)K_(0.2)T_(0.5)(Ti0:96Sn0:04TO_(3)ceramics(BNKTS)have been fabricated via a solid-state *** effect of sintering temperature on the structural,morphological,dielectric,ferroelectric and energy storage properties of BNKTS ceramics was investigated,and it was found that the electrical properties of the synthesized ceramics increased with the increase in the sintering temperature,and the highest values were achieved at 1100℃.The ceramics sintered at the optimized temperature of 1100℃exhibited the best physical,dielectric,ferroelectric and energy storage properties,namely,high density(the relative density,ρ=5:88 ***^(-3),approximate to 96.7%of the theoretical value),high densification factor(DF=0:93),high dielectric constant(ε_(r)=1215),low dielectric loss(tanδ=0:051),highest dielectric constant(ε_(max)=4335),high remanent polarization(Pr=9:5μ***^(-2),high coercive field(E_(c)=14:3 kV/cm),high energy storage density(0.12 J/cm^(3),and high energy storage efficiency(41.7%at 46.3 kV/cm).

关键词： Lead-free ceramics BNKT sintering temperature dielectric constant

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

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

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)

Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

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Computers, Materials & Continua 2023年第2期74卷 2771-2787页

作者： Van-truong nguyen Department of Mechatronics Engineering Hanoi University of IndustryHanoi159999Vietnam

In this paper,a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external *** proposed approach is a hybrid scheme of the online non-negative adaptive mechanism,tracking differentiator,and nonsingular fast terminal sliding mode control(NFTSMC).Based on the online non-negative adaptive mechanism,the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC *** proposed controller has several advantages such as simple structure,easy implementation,rapid response,chattering-free,high precision,robustness,singularity avoidance,and finite-time *** all control parameters are online updated via tracking differentiator and non-negative adaptive law,the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ***,simulation results validate the effectiveness of the proposed method.

关键词： Parallel manipulator uncertainties and disturbances nonsingular fast terminal sliding mode control non-negative adaptive mechanism tracking differentiator

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

ON BOUNDEDNESS PROPERTY OF SINGULAR INTEGRAL OPERATORS ASSOCIATED TO A SCHRODINGER OPERATOR IN A GENERALIZED MORREY SPACE AND APPLICATIONS

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Acta Mathematica Scientia 2020年第5期40卷 1171-1184页

作者： Xuan truong LE Thanh Nhan nguyen Ngoc Trong nguyen Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Due Thang UniversityHo Chi Minh CityVietnam Faculty of Mathematics and Statistics Ton Due Thang UniversityHo Chi Minh CityVietnam Department of Mathematics Ho Chi Minh City University of EducationHo Chi Minh CityVietnam Department of Primary Education Ho Chi Minh City University of EducationHo Chi Minh CityVietnam

In this paper,we provide the boundedness property of the Riesz transforms associated to the Schrodinger operator■=-Δ+V in a new weighted Morrey space which is the generalized version of many previous Morrey type *** additional potential V considered in this paper is a non-negative function satisfying the suitable reverse Holder's *** results are new and general in many cases of *** an application of the boundedness property of these singular integral operators,we obtain some regularity results of solutions to Schrodinger equations in the new Morrey space.

关键词： weighted Morrey spaces Schodinger operator riesz transforms Regularity estima tes

A Complex Fuzzy LSTM Network for Temporal-Related Forecasting Problems

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Computers, Materials & Continua 2024年第9期80卷 4173-4196页

作者： nguyen tho thong nguyen Van Quyet Cu nguyen Giap nguyen Long Giang Luong Thi Hong Lan Institute of Information Technology Vietnam Academy of Science and TechnologyHoang Quoc VietCau GiayHanoi100000Vietnam Academic Affairs Department Thai Nguyen University of EducationThai Nguyen250000Vietnam Center of Science and Technology Research and Development Thuongmai UniversityHo Tung MauCau GiayHanoi100000Vietnam Faculty of Information Technology Hanoi University of IndustryBac Tu LiemHanoi100000Vietnam

Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development ofmanagement platforms and systems based on the Internet and cutting-edge information communication *** the time series data including time series prediction has many practical *** new techniques were developed for use with various types of time series data in the prediction *** those,this work suggests a unique strategy to enhance predicting quality on time-series datasets that the timecycle matters by fusing deep learning methods with fuzzy *** order to increase forecasting accuracy on such type of time-series data,this study proposes integrating deep learning approaches with fuzzy ***,it combines the long short-termmemory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model(CFLSTM).The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory(LSTM)technique to have greater power for processing time series *** on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposedmodel’s utility compared to other well-known forecasting *** results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.

关键词： Complex fuzzy set long short-term memory(LSTM) CFLSTM T-CFLSTM

Development of an automatic and knowledge-infused framework for structural health monitoring based on prompt engineering

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Frontiers of Structural and Civil Engineering 2024年第11期18卷 1752-1774页

作者： truong-Thang nguyen Viet-Hung DANG Thanh-Tung PHAM Faculty of Building and Industrial Construction Hanoi University of Civil EngineeringHanoi 11600Vietnam Research Group of Development and application of advanced materials and modern technologies in construction Hanoi University of Civil EngineeringHanoi 11600Vietnam

Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’safety without interrupting their normal *** paper develops a novel framework using prompt engineering for seamlessly integrating users’domain knowledge about vibration signals with the advanced inference ability of well-trained large language models(LLMs)to accurately identify the actual states of *** proposed framework involves formulating collected data into a standardized form,utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals,and implementing an in-house program with the help of LLMs to perform damage *** advantages,as well as limitations,of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature,demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.

关键词： structural health monitoring vibration large language model signal processing prompt engineering

Electromechanical admittance-based automatic damage assessment in plate structures via one-dimensional CNN-based deep learning models

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Frontiers of Structural and Civil Engineering 2024年第11期18卷 1730-1751页

作者： Thanh-Canh HUYNH Nhat-Duc HOANG Quang-Quang PHAM Gia Toai truong Thanh-truong nguyen Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Faculty of Civil Engineering Duy Tan UniversityDa Nang 550000Vietnam Bridge and Road Department Danang Architecture UniversityDa Nang 550000Vietnam Faculty of Civil Engineering and Technology Dong A UniversityDa Nang 550000Vietnam Industrial Maintenance Training Center Ho Chi Minh City University of Technology(HCMUT)Ho Chi Minh City 700000Vietnam

The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance *** study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored *** admittance data set is augmented with white noise to simulate realistic measurement *** K-fold cross-validation technique is employed for training and testing the *** experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing *** proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance *** eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.

关键词： convolutional neural network electromechanical admittance electromechanical impedance piezoelectric transducer damage localization plate structure deep learning structural health monitoring