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Trong Luan NGUYEN,Minh Khang HUYNH,Nguyet Nuong HO,Tran Gia Bao LE,Nguyen Duy Hau DOAN 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.4
The UN’s 17 Sustainable Development Goals (2018) were created to address environmental pollution and climate change (SDGs). The goal of this study is to find out how well-informed Vietnamese students are about the SDGs. Knowledge, attitudes, and practice (KAP) questionnaires were used to survey 1,010 students across Vietnam’s universities, and the data was analyzed using SPSS software version 20. The findings suggest that both knowledge and attitude have a positive impact on the practice level. However, when comparing the correlation between the variables and the level of practice, advantage belongs to the relationship between the attitude and the level of practice (r = 0.982**, n = 1010, p = 0.00), the correlation between knowledge and practice level is weaker (r = 0.616**, n = 1010, p = 0.00). Statistical data also show that many Vietnamese students do not have access to information about the SDGs. The majority of the target population who have been contacted and have a basic understanding of the SDGs have done so through their academic degree. From there, it is clear that education is the most effective strategy for Vietnamese students to modify their environmental understanding and actions.
Trong Luan NGUYEN,Minh Khang HUYNH,Nguyet Nuong HO,Tran Gia Bao LE,Nguyen Duy Hau DOAN 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.1
Humans are facing many environmental challenges. Climate change, water pollution, global warming, and hazardous waste disposal are all issues that many countries throughout the world are dealing with. People’s psychology and consumer behavior are significantly affected by these challenges, particularly generation Z, which is immediately affected by environmental changes. Young people have a strong sense of curiosity and have access to readily updated knowledge. Today’s youth, in particular, live a civilized and responsible lifestyle. As a result, people recognize the significance of their own consumption behavior in affecting environmental change and are increasingly replacing them with green, ecologically friendly products as a fantastic method to mitigate their harmful consequences. In this research, there are four factors related to the young generation and environmental awareness that affect green consumption intention: perceived environmental responsibility, green knowledge, green attitude, and green product value. The goal of this study is to look into how detrimental environmental changes affect Generation Z’s green consumption habits. This study used primary data from over 1000 people in the age group, which was processed using the AMOS 20 software. All the characteristics described above had an impact on Generation Z’s green consumption intentions, according to the findings.
Trong-Ha Nguyen,Ngoc-Long Tran,Duy-Duan Nguyen 한국강구조학회 2022 International Journal of Steel Structures Vol.22 No.1
The steel oval hollow section (OHS) provides an aesthetic architecture and a greater local buckling strength. However, the existing design codes do not specify the eff ective width in calculating the load-bearing capacity of OHS members. This study aims to predict the axial compression capacity (ACC) of cold-formed steel OHS columns using artifi cial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) models. A total of 128 data sets collected from the literature were utilized to develop the ANN and ANFIS models. The performance of the two machine learning models was compared with three existing design codes. The results demonstrated that the developed ANN and ANFIS models predicted the ACC of steel OHS columns more accurately compared to the existing formulas. Specifi cally, the ANN model revealed a superior performance with the highest coeffi cient of determination and the smallest root means square errors. Moreover, the formulas based on ANN and ANFIS models, which accommodates all input parameters, were proposed to predict the ACC of coldformed steel OHS columns. The thickness of the cross-section was the most infl uential parameter on the ACC of the OHS column. By contrast, the column length negatively aff ected the ACC value of the steel column. Finally, a graphical user interface tool was developed to readily calculate the ACC of the steel OHS columns.
Trong-Ha Nguyen,Ngoc-Long Tran,Duy-Duan Nguyen 한국강구조학회 2021 International Journal of Steel Structures Vol.21 No.4
The web tapered I-section steel (WTIS) columns have been widely used in civil and industrial steel structures. However, the existing theoretical and empirical equations demonstrate a signifi cant discrepancy in estimating the critical axial load of the WTIS columns. This study aims to develop eff ective artifi cial neural networks (ANNs) for predicting the critical buckling load of the WTIS columns. A database of 269 fi nite element models of WTIS columns was generated, after verifying with experimental results, to develop the ANN model. The results of the proposed ANN model were also compared with those of existing formulas, highlighting that the ANN model in this study predicts the critical buckling load of the WTIS columns more accurately than the existing formulas. Moreover, the infl uences of input parameters on the critical buckling load of the WTIS columns were thoroughly investigated. An ANN-based formula, which considers input variables, was thereafter proposed to estimate the critical buckling load of the WTIS columns. Additionally, a graphical user interface tool has been developed for simplifying the design practice of the WTIS columns.
Trong Duy Nguyen,Gilbert Foo Hock Beng,King-Jet Tseng,Don Mahinda Vilathgamuwa,Xinan Zhang 전력전자학회 2012 JOURNAL OF POWER ELECTRONICS Vol.12 No.5
This paper presents the modeling and position-sensorless vector control of a dual-airgap axial flux permanent magnet (AFPM) machine optimized for use in flywheel energy storage system (FESS) applications. The proposed AFPM machine has two sets of three-phase stator windings but requires only a single power converter to control both the electromagnetic torque and the axial levitation force. The proper controllability of the latter is crucial as it can be utilized to minimize the vertical bearing stress to improve the efficiency of the FESS. The method for controlling both the speed and axial displacement of the machine is discussed. An inherent speed sensorless observer is also proposed for speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a prototype machine.
Nguyen, Trong Duy,Beng, Gilbert Foo Hock,Tseng, King-Jet,Vilathgamuwa, Don Mahinda,Zhang, Xinan The Korean Institute of Power Electronics 2012 JOURNAL OF POWER ELECTRONICS Vol.12 No.5
This paper presents the modeling and position-sensorless vector control of a dual-airgap axial flux permanent magnet (AFPM) machine optimized for use in flywheel energy storage system (FESS) applications. The proposed AFPM machine has two sets of three-phase stator windings but requires only a single power converter to control both the electromagnetic torque and the axial levitation force. The proper controllability of the latter is crucial as it can be utilized to minimize the vertical bearing stress to improve the efficiency of the FESS. The method for controlling both the speed and axial displacement of the machine is discussed. An inherent speed sensorless observer is also proposed for speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a prototype machine.
Trong-Ha Nguyen,Van-Tien Phan,Duy-Duan Nguyen 한국강구조학회 2023 International Journal of Steel Structures Vol.23 No.6
This study develops an artificial neural network (ANN) to estimate the critical buckling load (CBL) of corroded web-tapered steel I-section (WTSI) columns in pre-engineered steel buildings. A total of 387 datasets are employed to develop the ANN model. The datasets are generated from the proposed analytical model and Newton–Raphson method. The input parameters of the developed ANN model contain the cross-sectional dimensions of the steel column (i.e., the top and bottom flange width, top and bottom flange thickness, maximum section height, minimum section height, and web thickness), elastic modulus of material, and the column height. Meanwhile, the CBL is the output parameter of the ANN model. A predictive process for the CBL of the corroded WTSI columns has been proposed based on the ANN model and previous corrosion model. Results reveal that the ANN model showed an excellent performance in predicting the CBL of the corroded steel columns. The R2 values of the training, testing, and validation data are 0.99975, 0.99916, and 0.99951, respectively. The root-mean-squared errors of the training, testing, and validation data are 96.705 (kN), 103.402 (kN), and 103.200 (kN), respectively. Additionally, the a20-index is very close to 1.0. Moreover, a graphical user interface tool is constructed to facilitate the CBL calculation of the corroded WTSI columns.