Domain structure and finite-size effects in Sr2-La FeMoO6 nanoparticles: A study by magnetic measurements

Duong Nguyen Phuc,Nguyet Dao Thi Thuy,Loan To Thanh,Man Nguyen Khac,Anh Luong Ngoc 한국물리학회 2023 Current Applied Physics Vol.49 No.-

In this work, a model for the microstructure of Sr2-xLaxFeMoO6 nanoparticles samples (0 ≤ x ≤ 0.4) was established based on scanning electron microscopy and magnetic studies. The analysis reveals antiphase crystalline domains in the particles with the size distribution centered around mean values below ~12 nm for all the samples. Such small-size domains are responsible for the superparamagnetic properties observed for temperatures below room temperature, which are dependent on their dipole interactions. According to the fraction of Fe atoms at the antiphase boundaries calculated under the spherical domain assumption, these domains have lower degrees of atomic disorder than those estimated from the saturation magnetization values at 5 K, which are consistent with the high spin polarization values previously reported. The Curie temperature values, and the broadening of the magnetic phase transition can be satisfactorily explained based on the domain size distribution.

Optimal control of vibration by multiple tuned liquid dampers using Taguchi method

Nguyen Van Khang,Do The Duong,Nguyen Thi Van Huong,Nguyen Duc Thi Thu Dinh,Vu Duc Phuc 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.4

This paper investigates the multiple tuned liquid dampers (MTLD), which consist of a number of MTLD whose first natural frequencies of sloshing are distributed over a certain range around the natural frequency of a structure. The liquid motion in the MTLD as well as the MTLD-structure interaction are numerically simulated using a shallow water-wave theory. An optimal procedure based on Taguchi method is proposed to determine the optimum parameters of MTLD to suppress the vibration amplitude of the considered structure. A number of numerical tests are included to demonstrate and verify the effectiveness of the proposed procedure.

Purchase Intention Towards Japanese Convenience Goods: A Cross-Sectional Study in Vietnam

Nga Thi Quynh NGUYEN,Duong Tuan NGUYEN,Quynh Thi Phuc NGUYEN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.10

The primary purpose of this study is to identify determinants influencing Vietnamese consumers’ purchase intention towards Japanese convenience goods. The research model consisting of six factors affecting the dependent variable is proposed based on the Theory of Consumption Values and Theory of Perceived Value of purchase. This study employs a survey method in a convenient sampling method to collect data of target respondents. Data consisting of 180 samples was collected and analyzed using the SMARPLS3 software. The measurement model is assessed to confirm the validity and reliability of the construct, then hypotheses testing is performed with Bootstrapping analysis. The results demonstrated that five factors affect Vietnamese consumers’ purchase intention towards Japanese convenience goods, including the price of the product, quality of the product, functional value, emotional value, and conditional value. Major findings of this study suggest that the functional value that consumer perceives about Japanese convenience goods has the most significant relationship with their purchase intention, followed by the quality of the product and the price of the product. Besides, emotional value and conditional value have a moderate influence on consumers’ purchase intention. Whereas the influence of the epistemic value of the product on consumers’ purchase intention is insignificant in this presented study.

Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition

Nguyen, Nhan Duc,Bui, Duong Trong,Truong, Phuc Huu,Jeong, Gu-Min Hindawi Limited 2018 Journal of sensors Vol.2018 No.-

<P>This paper proposes a novel approach to recognize activities based on sensor-placement feature selection. The method is designed to address a problem of multisensor fusion information of wearable sensors which are located in different positions of a human body. Precisely, the approach can extract the best feature set that characterizes each activity regarding a body-sensor location to recognize daily living activities. We firstly preprocess the raw data by utilizing a low-pass filter. After extracting various features, feature selection algorithms are applied separately on feature sets of each sensor to obtain the best feature set for each body position. Then, we investigate the correlation of the features in each set to optimize the feature set. Finally, a classifier is applied to an optimized feature set, which contains features from four body positions to classify thirteen activities. In experimental results, we obtain an overall accuracy of 99.13% by applying the proposed method to the benchmark dataset. The results show that we can reduce the computation time for the feature selection step and achieve a high accuracy rate by performing feature selection for the placement of each sensor. In addition, our proposed method can be used for a multiple-sensor configuration to classify activities of daily living. The method is also expected to deploy to an activity classification system-based big data platform since each sensor node only sends essential information characterizing itself to a cloud server.</P>

A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models

Duong Minh Bui,Phuc Duy Le,Tien Minh Cao,Hung Nguyen,Trang Thi Pham,Duy Anh Pham 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5

Reliability assessment of the SCADA-system based load data is necessary for improving accuracy of short-term load forecasting (STLF) methods in a distribution network (DN). Specifi cally, the reliability evaluation of the load data is to properly eliminate noise/outliers caused by random power consumption behaviors or the sudden change in load demand from industrial and residential customers in the DN. Thus, this paper proposes a novel statistical data-fi ltering method, working at an input data pre-processing stage, which will evaluate the reliability of input load data by analyzing all possible data confi dence levels in order to fi lter-out the noise/outliers for accuracy improvement of diff erent short-term load forecasting models. The proposed statistical data-fi ltering method is also compared to other existing data-fi ltering methods (such as Kalman Filter, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA)). Moreover, several case studies of short-term load forecasting for a typical 22 kV distribution network in Vietnam are conducted with an Artifi cial Neural Network (ANN) model, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model, a combined model of Long Short-Term Memory Network and Convolutional Neural Network (LSTM-CNN), and a conventional Autoregressive Integrated Moving Average (ARIMA) model to validate the statistical data-fi ltering method proposed. The achieved results demonstrate which the STLF using ANN, LSTM-RNN, LSTMCNN, and ARIMA models with the statistical data-fi ltering method can all outperform those with the existing data-fi ltering methods. Additionally, the numerical results also indicate that in case the SCADA-based load data is normally distributed, time-series forecasting models should be more preferred than neural network models; otherwise, when the SCADA-based load data contains multiple normally distributed sub-datasets, neural network-based prediction models are highly recommended.

Size-Dependent Magnetic Properties of MnFe₂O₄ Nanoparticles Synthesized by Coprecipitation

Nguyen Thi Lan,Than Due Hien,Nguyen Phuc Duong,Tran Duc Hoang 한국자기학회 2007 한국자기학회 학술연구발표회 논문개요집 Vol.- No.-

Classification of Five Ambulatory Activities Regarding Stair and Incline Walking Using Smart Shoes

Nguyen, Nhan Duc,Bui, Duong Trong,Truong, Phuc Huu,Jeong, Gu-Min IEEE 2018 IEEE SENSORS JOURNAL Vol.18 No.13

<P>In this paper, we propose a novel method to classify five ambulatory activities, i.e., level ground, incline descent, incline ascent, stair descent, and stair ascent walking using smart shoes which contain eight plantar-pressure sensors on each shoe. Pressure data are collected using an insole-based monitoring system regarding the walking activities conducted by participants at their self-imposed “normal” speed. We present three new features based on an analysis of step patterns to characterize the ambulatory activities and utilize a k-nearest neighbor algorithm to classify the activities from the created features. In experimental results, we obtain walking activity-recognition error rates of 2.16% at the sixth walking step. Furthermore, a proposed method outperforms two reference methods in terms of F1-score and overall accuracy rate.</P>

A Novel Chitosan-Gossypol Based Nanocarrier for Anticancer Curcumin Drug Delivery

( Ngoan Thi Nguyen ),( Lam Dai Tran ),( Duong Quang Le ),( Dien Gia Pham ),( Phuc Xuan Nguyen ),( Jun Seo Park ),( Jae Kweon Park ) 한국키틴키토산학회 2012 한국키틴키토산학회지 Vol.17 No.2

Gossypol chitosan (GPCS) nanoparticles have been successfully synthesized at the size of 20-40 nm through amidation process to forming base shift of polymer chains of chitosan using gossypol as a crosslinking agent. Their structure was confirmed by Fourier Transformed Infra Red (FT-IR) spectra. Morphology, stability, loading and releasing efficiency of GPCS and Cur/GPCS (Cur stands for curcumin) were thoroughly investigated by Field Emission Scanning Electron Microscopy (FESEM), FT-IR, Ultra Violet-Visible spectrophotometry (UV-Vis) and High Resolve Photoluminescence System (HRPLS). The obtained nanocarrier exhibited an excellent solubility for Cur anticancer drug as well as high stability for in vitro studies. An intensive fluorescence, allowing drug monitor/delivery, was achieved. Drug release profile was also studied. GPCS, therefore, could be considered to be a potential drug carrier for therapeutic treatment.

How Destination Image Factors Affect Domestic Tourists Revisit Intention to Ba Ria-Vung Tau Province, Vietnam

GIAO, Ha Nam Khanh,NGAN, Nguyen Thi Kim,PHUC, Nguyen Pham Hanh,TUAN, Huynh Quoc,HONG, Ha Kim,ANH, Huynh Diep Tram,NHU, Duong Thi Huynh,LAN, NgoThi Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.6

This research examines the affect of destination image factors on revisit intention of domestic tourists at Ba Ria Vung-Tau (BRVT), by questioning directly 510 domestic tourists. The convenient sampling method is used in dividing the crowds into four groups; the four main surveyed areas in BRVT are: Vung Tau, Long Hai-Phuoc Hai, Xuyen Moc and Con Dao. A combination of qualitative and quantitative methodologies were utilized.A focus group of 10 domestic tourists wasset up to review and explore the variousfactors as well as the conceptual model. An in-depth interview with 12 participants was developed to fine-tune measurement items. The questionnaire applied a 5-point Likert scale. Checking the reliability by Cronbach's Alpha, exploratory factor analyzing and linear multiple regression were used with the SPSS program. The results show that there are eight main destination image factors affecting domestic tourists revisit intention to BRVT using linear regression and arranged by decreasing importance: Infrastructure, Variety Seeking, Accessibility, Local food, Atmosphere, Environment, Price Value, Leisure and Entertainment. From that, the research offers some suggestions and policy recommendations for the tourism business and provincial policy-makers to identify the main issues and develop better customer services to increase domestic tourists'revisit intention.

Risk Factors Affecting Equipment Management in Construction Firms

Cuong Phu PHAM,Phong Thanh NGUYEN,Phuong Thanh PHAN,Quyen Le Hoang Thuy To NGUYEN,Loan Phuc LE,My Tien Ha DUONG 한국유통과학회 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.11

Mechanization and automation constitute an essential stage in the production and operation of any company, as one of the determinants of increase in labor productivity and decrease in product price, while significantly contributing to shortening of the lead time. Businesses are, therefore, able to quickly put projects into operation, improving economic efficiency, quality, and aesthetics, which speeds up the national economic growth. For the construction industry to be the most effective, modern construction equipment is a necessity. It is one of the five main resources of a construction project. Thus, effective construction equipment management contributes to the success of a project and benefits the relevant construction companies economically. This paper presents the critical risk factors affecting equipment management and proposes suitable solutions. The questionnaire-based survey with experienced experts in the construction sector on the management of the likelihood and consequence of risk factors revealed thirty-two risks for equipment management in construction companies. These factors fell into six groups: (i) site organization-related risks; (ii) management-related risks; (iii) owner-related risks; (iv) supplier-related risks; (v) legal risks, and (vi) site condition-related and external risks. The results showed that management-related factors contributed to the most significant risks and problems for equipment management in construction companies.