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Van Loi TA,Anh Duc DO,To Uyen PHAN,Quang Huy NGUYEN,Thi Thuy Hong NGUYEN,Thuy Duong LE,Thanh Phong NGUYEN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.4
This study aimed to explore the factors affecting the foreign direct investment (FDI) intentions of investors into Quang Ninh province, located in the north-eastern of Viet Nam. Researchers used two main methods, namely, Exploratory Factors Analysis (EFA) and the Structural Equation Model (SEM) based on partial least squares structural equation modeling (PLS SEM) to explore and measure the impact of factors affecting the investors’ FDI intentions into Quang Ninh province. The empirical analysis used data from the survey of 206 domestic and foreign investors into Quang Ninh province, including representatives of the Board of Directors, members, and management representatives at the department level, with reliable tools (SPSS 26 and SmartPLS 3.0 software). The research results identified the following factors affecting investment into Quang Ninh: FDI attraction policies have the strongest impact on the investors’ FDI intentions; it is followed by infrastructure, public services and human capital with strong effects on intentions of investors’ FDI; and finally the standards of living that affects the investors’ FDI intentions. There is also a positive relationship between all the factors and the investors’ FDI intentions. Several recommendations are further suggested to enhance attraction of foreign direct investment into Quang Ninh province.
Anh Linh Dang,Tuyen Quang Nguyen,Tri Thien Cao,Vinh Quang Dinh,Vinh Dinh Nguyen 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Traffic detection is a topic of great interest in recent years due to a high demand for better traffic detection systems. Existing traffic detection algorithms work well under ideal driving conditions, however their performance decreases under difficult conditions such as insufficient lighting and illumination. Recently, local patterns have been successfully applied in order to handle complex texture conditions, such as stereo matching, and texture classification. We propose a method that applies Local Tetra Pattern for data preprocessing, so as to improve the performance of deep learning models under said conditions. Our approach achieved better performance than the original raw-models while the changes in inference time are maintained within a negligible interval. By fusing local patterns and raw images, the model gains an acquisition of discriminative information in regions that are highly similar. In challenging conditions, these kinds of information are essential for the model to recover its consciousness of concerned objects which cause many re-cognitional obstructions. Experimental results show a percentage as high as 35.847%, an increase of 12.575% in comparison with the original result on the SKKU data set.