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Sliding wear behaviour of AZ31b magnesium alloy and nano-composite

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Transactions of Nonferrous Metals Society of China 2012年第1期22卷 60-65页

作者： M.SRINIVASAN C.LOGANATHAN M.KAMARAJ q.b.nguyen M.GUPTA R.NARAYANASAMY Department of Mechanical Engineering Saranathan College of EngineeringTiruchirappalliTamilnadu 620012India St.Jospeh's College of Engineering and Technology ThanjavurTamilnadu 613403India Department of Metallurgical and Materials Engineering Indian Institute of Technology MadrasChennaiTamilnadu 600036India Department of Mechanical Engineering National University of Singapore9 Engineering Drive 1Singapore 117576Singapore Department of Production Engineering National Institute of TechnologyTiruchirappalliTamilnadu 620015India

AZ31b magnesium alloy and nano-composite were manufactured by hybrid casting process and hot extruded at 350 °C. The sliding wear behaviour of alloy and nano-composite was estimated at room temperature using the standard pin-on-disc wear test equipment. The tests were conducted under a normal load of 10 N at different sliding speeds ranging from 0.60 to 1.2 m/s for distance up to 2000 m. The wear mechanisms of the worn out surface were studied using SEM analysis. The influence of test parameters on wear rate of the pins was established using a linear regression model statistically. Compared with the AZ31b magnesium alloy, the nano-composite shows lower wear rates due to higher hardness improvement caused by the reinforcement. The wear mechanism appears to be a mix-up of ploughing, rows of furrows, delamination and oxidation.

关键词： AZ31b magnesium alloy nano composite wear mechanism

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)

Overexpression of Arabidopsis Sorting Nexin AtSNX2b Inhibits Endocytic Trafficking to the Vacuole

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Molecular Plant 2008年第6期1卷 961-976页

作者： nguyen q. Phan Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 USA Interdepartmental Genetics Program Iowa State University Ames IA 50011 USA Plant Sciences Institute Iowa State University Ames IA 50011 USA

Sorting nexins are conserved proteins that function in vesicular trafficking and contain a characteristic phox homology （PX） domain. Here, we characterize the ubiquitously expressed Arabidopsis thaliana sorting nexin AtSNX2b. Sub-cellular fractionation studies indicate that AtSNX2b is peripherally associated with membranes. The AtSNX2b PX domain binds to phosphatidylinositol 3-phosphate in vitro and this association is required for the localization of GFPAtSNX2b to punctate structures in vivo, identified as the trans-Golgi network, prevacuolar compartment and endosomes. Overexpression of GFP-tagged AtSNX2b produces enlarged GFP-labeled compartments that can also be labeled by the endocytic tracer FM4-64. Endocytic trafficking of FM4-64 to the vacuole is arrested in these GFP-AtSNX2b compartments, and similar FM4-64-accumulating compartments are seen upon overexpression of untagged AtSNX2b. This suggests that exit of membrane components from these enlarged or aggregated endosomes is inhibited. Vacuolar proteins containing an N-terminal propeptide, but not those with a C-terminal propeptide, are also present in these enlarged compartments. We hypothesize that AtSNX2b is involved in vesicular trafficking from endosomes to the vacuole.

关键词： phox homology Overexpression Vacuole

A Correlation Coefficient Approach for Evaluation of Stiffness Degradation of beams Under Moving Load

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Computers, Materials & Continua 2019年第7期61卷 27-53页

作者： Thanh q.nguyen Thao T.D.nguyen H.nguyen-Xuan Nhi K.Ngo Laboratory of Applied Mechanics(LAM) Ho Chi Minh City University of TechnologyHo Chi Minh City740030Vietnam Saigon Hi-Tech Park Training Center Ho Chi Minh City715650Vietnam Ho Chi Minh City Department of Transportation and Communication Ho Chi Minh City710100Vietnam CIRTECH Institute Ho Chi Minh City University of Technology(HUTECH)Ho Chi Minh City717349Vietnam Department of Physical Therapy Graduate Institute of Rehabilitation ScienceChina Medical UniversityTaichung40402Taiwan

This paper presents a new approach using correlation and cross-correlation coefficients to evaluate the stiffness degradation of beams under moving *** theoretical study of identifying defects by vibration methods showed that the traditional methods derived from the vibration measurement data have not met the needs of the actual *** show that the correlation coefficients allow us to evaluate the degree and the effectiveness of the defects on *** the same time,the cross-correlation model is the basis for determining the relative position of *** results of this study are experimentally conducted to confirm the relationship between the correlation coefficients and the existence of the *** particular,the manuscript shows that the sensitivity of the correlation coefficients and cross-correlation is much higher than the parameters such as changes in stiffness(EJ)and natural frequency values(Δf).This study suggests using the above parameters to evaluate the stiffness degradation of beams by vibration measurement data in practice.

关键词： Correlation coefficient cross-correlation vibration signal vibration amplitude frequency

A Note on Skew Cyclic Codes over a Class of Rings

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Algebra Colloquium 2020年第4期27卷 703-712页

作者： Hai q.Dinh bac T.nguyen Songsak Sriboonchitta Division of Computational Mathematics and Engineering Institute for Computational Science Ton Due Thang University Ho Chi Minh CityVietnam Faculty of Mathematics and Statistics Ton Due Thang University Ho Chi Minh CityVietnam Department of basic Sciences University of Economics and Business Administration Thai Nguyen UniversityThai Nguyen ProvinceVietnam Centre of Excellence in Econometrics Faculty of Economics Chiang Mai UniversityChiang Mai 52000Thailand

We study skew cyclic codes over a class of rings R=F0■F1■⋯■Ft−1,where each Fi(i=0,…,t−1)is a finite *** prove that a skew cyclic code of arbitrary length over R is equivalent to either a usual cyclic code or a quasi-cyclic code over ***,we discuss possible extension of our results in the more general setting ofδR-dual skew constacyclic codes over R,whereδR is an automorphism of R.

关键词： skew constacyclic codes dual codes repeated-root codes quasi-cyclic codes local rings

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

On a Class of Constacyclic Codes of Length 4p^8 over Fpm[u]/〈 u^a 〉

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Algebra Colloquium 2019年第2期26卷 181-194页

作者： Hai q. Dinh bac T. nguyen Songsak Sriboonchitta Division of Computational Mathematics and Engineering Institute for Computational Science Ton Due Thang University Ho Chi Minh City Vietnam Faculty of Mathematics and Statistics Ton Due Thang University Ho Chi Minh City Vietnam nguyen Tat Thanh University Ho Chi Minh City Vietnam Department of basic Sciences University of Economics and Business Administration Thai Nguyen University Thai Nguyen Province Vietnam Faculty of Economics Chiang Mai University Chiang Mai 52000 Thailand

For any odd prime p such that p^m ≡ 3 (mod 4), consider all units Λ of the finite commutative chain ring Ra=Fpm[u]/〈u^a〉=Fp^m+uFp^m+…+u^a1Fp^m that have the form Λ=Λ0 + uΛ1 ++ u^a1 Λa1, where Λ0,Λ1,…,Λa1 p^m,Λ0 ≠ 0,Λ1 ≠ 0. The class of Λ-constacyclic codes of length 4p^s over a is investigated. If the unit Λ is a square, each Λ-constacyclic code of length 4p^s is expressed as a direct sum of a λ-constacyclic code and a λ-constacyclic code of length 2p^s. In the main case that the unit Λ is not a square, we prove that the polynomial x^4 λ0 can be decomposed as a product of two quadratic irreducible and monic coprime factors, where λ^p^s0=Λ0. From this, the ambient ring Ra[x]/is proven to be a principal ideal ring, whose maximal ideals are and , where λ0 =4η^4. We also give the unique self-dual Type 1 Λ-constacyclic codes of length 4p^s over a. Furthermore, conditions for a Type 1 Λ-constacyclic code to be self-orthogonal and dual-containing are provided.

关键词： constacyclic codes dual codes chain rings

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