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Plastic and Aesthetic Research 2014年第1期1卷 85-88页

作者： Trung-Hau LêThua Duc-Phu Bui dang-nhat pham Vu-Bao Lê Albert De Mey Willy Boeckx Department of Plastic Reconstructive and Hand SurgeryHue Central HospitalHue City 47000Thừa Thiên-HuếVietnam Departments of Surgery Hue University of Medicine and PharmacyHue City 47000Thừa Thiên-HuếVietnam Department of Surgery An Sinh HospitalHo Chi Minh City 70000Vietnam Department of Plastic Surgery Brugmann University HospitalFree University of BrusselsBrussels 1020Belgium

Aim:The anatomical study and clinical application for the vascularized corticoperiosteal fl ap from the medial femoral condyle have been performed and described *** prior studies have described the composite osteomyocutaneous fl ap from the medial femoral condyle,a detailed analysis of the vascularity of this region has not yet been fully ***:This anatomical study described the variability of the arteries from the medial femoral condyle in 40 cadaveric ***:The descending genicular artery(DGA)was found in 33 of 40 cases(82.5%).The superomedial genicular artery(SGA)was present in 10 cases(25%).All 33 cases(100%)of the DGA had articular branches to the periosteum of the medial femoral *** branches and saphenous branches of the DGA were present in 25 cases(62.5%)and 26 cases(70.3%),***:The current study demonstrates that the size and length of the vessels to the medial femoral condyle are suffi cient for a vascularized bone fl ap.A careful preoperative vascular assessment is essential prior to use of the vascularized composite osteomyocutaneous fl ap from the medial femoral condyle,because of the considerable anatomical variations in different branches of the DGA.

关键词： Descending genicular artery medial femoral condyle osteomyocutaneousﬂap superomedial genicular artery

Tissue engineering using mesenchymal stem cells with periosteal wrap for bone defect repair in rabbits

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Plastic and Aesthetic Research 2015年第1期2卷 340-345页

作者： Trung-Hau LêThua dang-nhat pham Khanh-Linh Lê Minh-Tuan Lê Quang-Ton-Quyen Nguyen Phan-Huy Nguyen Ngoc-Vu Tran Ngoc-Luong Nguyen Willy Boeckx Albert Demey Department of Plastic Reconstructive and Hand SurgeryCenter of Orthopaedic and Plastic SurgeryHue Central HospitalHue City 531120Vietnam Department of Hematology Hue Central HospitalHue City 531120Vietnam Department of Biology College of SciencesHue UniversityHue City 521120Vietnam Department of Plastic Surgery Brugmann University HospitalUniversitélibre de Bruxelles1020 BrusselsBelgium

Aim:Mesenchymal stem cells(MSCs)are an excellent potential source of cells for bone tissue engineering due to their excellent renewal ability and osteogenic differentiation *** study was designed to evaluate the bone formation properties of a demineralized cancellous bone scaffold seeded with MSCs,with or without periosteum,in a critical size bone defect model in ***:Rabbit culture-expanded bone marrow(BM)-MSCs were seeded onto a human demineralized cancellous bone(HDCB)*** defects measuring 15 mm in length were created in each radius.A total of 56 bone defects in 28 rabbits were randomly assigned to one of the 4 groups for scaffold implantation:Group 1:HDCB graft only;Group 2:periosteum-wrapped HDCB graft;Group 3:HDCB graft seeded with BM-MSCs and Group 4:periosteum-wrapped HDCB graft seeded with *** rabbits were sacrificed 12 weeks after surgery for gross observation,radiological assessment,histological analyses and biomechanical ***:New bone(NB)formation and bone healing were successfully achieved,both radiologically and histologically,on demineralized cancellous bone graft seeded with *** were improved when BM-MSCs were associated with ***:This study demonstrates that repair of bone defects in a rabbit model can be achieved through bone grafting using BM-MSCs,implanted on a demineralized cancellous bone *** formation of NB was optimized when combined with the preservation of periosteum at the site of injury.

关键词： Bone defects bone marrow bone tissue engineering mesenchymal stem cells periosteum

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

An improved CRNN for Vietnamese Identity Card Information Recognition

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Computer Systems Science & Engineering 2022年第2期40卷 539-555页

作者： Trinh Tan Dat Le Tran Anh dang Nguyen nhat Truong pham Cung Le Thien Vu Vu Ngoc Thanh Sang pham Thi Vuong pham The Bao Information Science Faculty Sai Gon UniversityHCM City700000Vietnam Faculty of Electrical&Electronics Engineering University of TechnologyHCM City700000Vietnam

This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)***,we apply Mask-RCNN to segment and align the CID card from the ***,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST ***,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions *** length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label *** process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long *** may directly learn the proposed model from a sequence of words without detailed *** evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques.

关键词： Vietnamese text recognition OCR CRNN BLSTM attention mechanism joint CTC-Attention

Thermal Performance Prediction in the Air Gap of a Rotor-Stator Configuration: Effects of Numerical Models

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Journal of Thermal Science 2020年第1期29卷 206-218页

作者： dang Dinh-Dong pham Xuan-Tan NGUYEN Chi-Cong Department of Mechanical Engineering École de technologie supérieureMontrealQCCanada Hydro-Québec’s research institute VarennesQCCanada

This study is dedicated to a numerical investigation of convective heat transfer on the rotor surfaces of a rotor-stator configuration that is typically found in large *** computational fluid dynamics calculations with two turbulence modelling approaches are used to predict the flow structure and heat transfer in the air gap of the rotor-stator *** steady state mixing plane approach is employed at the interface to couple the rotor and stator *** show that the location of mixing plane interface in the air gap plays an important role in the prediction of heat transfer on the pole ***,it is indicated that the prediction of temperature distribution on the pole face is greatly affected by the turbulence models ***,through a comparison between the pure convective and conjugate heat transfer methodologies,it is shown that the inclusion of solid domain into the numerical model significantly improves the thermal prediction of the solid components of the machine.

关键词： hydro-generators electrical cooling mixing plane windage loss conjugate heat transfer

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： Nguyen, Truong-Thang dang, Viet-Hung pham, Thanh-Tung Hanoi Univ Civil Engn Fac Bldg & Ind Construct Hanoi 11600 Vietnam Hanoi Univ Civil Engn Res Grp Dev & applicat Adv Mat & Modern Technol Co Hanoi 11600 Vietnam

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 operations. This 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 structures. The 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 detection. The 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： Huynh, Thanh-Canh Hoang, nhat-Duc pham, Quang-Quang Truong, Gia Toai Nguyen, Thanh-Truong Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn Da Nang 550000 Vietnam Danang Architecture Univ Bridge & Rd Dept Da Nang 550000 Vietnam Dong A Univ Fac Civil Engn & Technol Da Nang 550000 Vietnam Ho Chi Minh City Univ Technol HCMUT Ind Maintenance Training Ctr Ho Chi Minh City 700000 Vietnam

The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location, especially when damage introduces nonlinearities in admittance features. This 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 structure. Raw admittance data set is augmented with white noise to simulate realistic measurement conditions. Stratified K-fold cross-validation technique is employed for training and testing the network. 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 error. The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features, overcoming limitations associated with traditional piezoelectric admittance approaches. By 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

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

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International Journal of Digital Earth 2016年第11期9卷 1077-1097页

作者： Dieu Tien Bui Binh Thai pham Quoc Phi Nguyen nhat-Duc Hoang Geographic Information System Group Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBøi TelemarkNorway Faculty of Geomatics and Land Administration Hanoi University of Mining and GeologyHanoiVietnam Department of Civil Engineering Gujarat Technological UniversityAhmedabadGujaratIndia Department of Geotechnical Engineering University of Transport TechnologyHa NoiVietNam Department of Environmental Sciences Hanoi University of Mining and GeologyHanoiVietnam Faculty of Civil Engineering Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as *** LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM *** this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid *** receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed *** results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating *** model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision *** result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

关键词： Shallow landslide Least-Squares Support Vector Machines differential evolution GIS Vietnam

Forecasting measured responses of structures using temporal deep learning and dual attention

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 832-850页

作者： Viet-Hung dang Trong-Phu NGUYEN Thi-Lien pham Huan X.NGUYEN Department of Structural Mechanics Hanoi University of Civil EngineeringHanoi 11600Vietnam Faculty of Applied Science University of Economics-Technology for IndustriesHanoi 11600Vietnam London Digital Twin Research Centre Faculty of Science and TechnologyMiddlesex UniversityLondon NW44BTUK International School Vietnam National UniversityHanoi 11600Vietnam

The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random *** key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series *** proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future *** viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel ***,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

关键词： structural dynamic time-varying excitation deep learning signal processing response forecasting

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)