Electrodeposition, a fundamental technique in materials science, has been developed to produce nanostructured coatings with improved mechanical, chemical, and physical properties. This study encompasses a systematic review of approaches based on prediction and optimization models at electrodeposition processes applicable to various materials. It discusses the theoretical background, such as mechanisms of nucleation and growth, and the key factors influencing the characteristics of coatings. The paper reviews traditional thermodynamic models as well as advanced data-driven techniques, with a special focus on machine learning methods, such as artificial neural networks (ANNs), dynamic ANNs (DANNs), and support vector machines (SVMs). The models are validated by the prediction of properties such as hardness, adhesion, and corrosion resistance. We also compare optimization strategies, such as genetic algorithms, particle swarm optimization, and their hybrids, to analyze their capability to improve both coating quality and process efficiency. The development discussed in this research is representative of the increased usage of AI and computational approaches, which allow for process control in real time, decreasing experimental costs and designing performance coatings. At the same time, new trends like sustainable electrodeposition, electrochemical 3D printing, or intersection with additive manufacturing are highlighted as well. This study highlights that predictive and optimization models have the potential to significantly impact the development of electrodeposition technologies targeted for industrial uses.

This research was conducted for assessing the predictive ability of future cash flows from operating activities by using accounting earnings and cash flows information in the past. Data were collected from the firms listed on Ho Chi Minh Stock Exchange (HOSE) from 2009 to 2018, including the sample of 242 non-financial listed companies. Three statistical methods approaches were employed to address econometric issues and to improve the accuracy of the regression coefficients based on Ordinary Least Squares (OLS), Random Effects Model (REM), and Fixed Effects Model (FEM). The findings showed that earnings and cash flows and aggregated accruals had remarkable ability to forecast future cash flows and the model of operating cash flows combined with aggregated accruals had the most effective prediction ability for companies listed on Ho Chi Minh Stock Exchange.