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In this study, pot culture experiments were carried out to investigate the most suitable level of fly ash for the amelioration of soil that can enhance the management of fly ash. The fly ash recovered from the Mong Duong 2 coal-fired thermal power plant and had the main chemical composition of oxygen (43%), Si (26%), Al (15%), K (9%), and Fe (6%). The addition of fly ash to light texture soil increased some of the basic properties of the soil, such as moisture, cation exchange capacity, mechanical composition, and organic matter content. The results of the performance evaluation of an experimental crop (soybeans) showed the effects of the amended soil on the growth of the experimental plants, namely the number of fruits, number of nodules, and their dry biomass. The incorporation of manure into the sandy soil was amended with fly ash in the pot experiment as a single application of fly ash may not provide enough nutrients for plant growth despite its ameliorant effect on soil structure. The appropriate fly ash addition rate from 10-30% w/w is recommended in soil amelioration applications based on the results of this study. This research opens up an application direction for fly ash, a by-product generated from the operation of coal-fired power plants in Vietnam.

Utilizing firm performance in the prediction of macroeconomic conditions is an interesting research trend with increasing momentum that supports to build nowcasting and early warning systems for macroeconomic management. Firmlevel data is normally high volume, with which the traditional statistics-based prediction models are inefficient. This study, therefore, attempts to assess achievements of Machine Learning on firm performance prediction and proposes an emerging idea of applying it for macroeconomic prediction. Inspired by "micromeso-macro" framework, this study compares different machine learning algorithms on each Vietnamese firm group categorized by the Vietnamese Industry Classification Standard. This approach figures out the most suitable classifier for each group that has specific characteristics itself. Then, selected classifiers are used to predict firms' performance in the short term, where data was collected in wide range enterprise surveys conducted by the General Statistics Office of Vietnam. Experiments showed that Random Forest and J48 outperfomed other ML algorithms. The prediction result presents the fluctuation of firms' performance across industries, and it supports to build a diffusion index that is a potential early warning indicator for macroeconomic management.

This study aims to investigate the factors that influence corporate social responsibility disclosure (CSRD) in the banking sector in an emerging country. The quantitative model is estimated for a sample of banks in Vietnam for the period from 2013 to 2019. To explain the determinants of CSRD in banking, regression analysis using panel data was employed while taking bank size, bank age, financial performance, state ownership, and regulation as independent variables, and CSRD as a dependent variable. The results revealed that bank size, bank age, and regulation have positive impacts on CSRD, whereas state ownership has a negative impact, and financial performance was found to be insignificant. This study enriches the knowledge of CSRD, and it contributes empirical evidence of the impact of bank characteristics on CSRD. Particularly, empirical evidence suggests that regulation is an effective instrument for promoting the CSRD of banks in Vietnam. Therefore, the study identified the need for government regulation to increase disclosure because voluntary disclosure does not seem to be sufficient to achieve the desired results.

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