Nowaday, expanding the application of deep learning technology is attracting attention of many researchers in the field of remote sensing. This paper presents methodology of using deep convolutional neural network model to determine the position of shoreline on Sentinel 2 satellite image. The methodology also provides techniques to reduce model retraining while ensuring the accuracy of the results. Methodological evaluation and analysis were conducted in the Mekong Delta region. The results from the study showed that interpolating the input images and calibrating the result thresholds improve accuracy and allow the trained deep learning model to externally test different images. The paper also evaluates the impact of the training dataset on the quality of the results obtained. Suggestions are also given for the number of files in the training dataset, as well as the information used for model training to solve the shoreline detection problem.
State Capital and Investment Corporation (SCIC) is a sovereign wealth fund (SWF) operating as a governmental specialized economic entity. SCIC serves two main functions: first, manage and represent the equity share of state at public companies, limited companies, and second, invest state capital into strategic industries and fields of the economy. This study compares the impacts of the ownership of SCIC and the state on the performance of target firms in Vietnam. In addition, the current study aims to extend the extant literature by examining the impact of sovereign wealth fund (SWF, in this case, SCIC in Vietnam) on the performance of target firms in Vietnam. Previous studies only consider the influence of SWF on target firms’ operating performance without considering the country where the target firms reside. Finally, the research investigates the influence of the ownership of SCIC and the state not only on financial but also on the non-financial performance of target firms, whereas previous research chiefly discusses the financial impact of SWFs. The author uses the Generalized System Method of Moments for a sample of listed firms collected from Thomson Reuters for the period of 2008-2017 to deal with the potential endogeneity issue as well as other defects such as heteroskedasticity and autocorrelation. The results show that SCIC ownership has a positive impact on target firms’ financial performance, compared to state ownership. However, SCIC ownership exerts a more negative impact on target firms’ non-financial performance, compared to state one. This finding implies that SCIC may prioritize financial indicators over non-financial (or social) ones. This could justify the reason why SCIC ownership has a better financial but lower non-financial performance compared with state ownership.
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