The data presented here focuses on the physicochemical characterization of perovskite CaTiO3 nanoparticles (orthorhombic) as photocatalyts and the kinetic study of their photodegradation performance toward organic pollutant, i.e. brilliant green (BG) which is azo derivatives dye. The CaTiO3 nanoparticles was synthesized using chicken eggshell-derived CaCO3 and anatase TiO2 with molar ratio of (1:1), (1:3), (2:5), and (2:7). The physical and microstructural properties of CaTiO3 were characterized by X-ray diffractometer (XRD), scanning electron microscope (SEM), Fourier Transform Infrared (FTIR) and UV/vis spectrometer. The effect of initial dye concentration, catalyst composition, and catalyst dosage on the adsorption mechanism of dye on CaTiO3 was investigated in jacketed photoreactor under UV irradiation. The analysis reveals that BG molecules are efficiently chemisorbed, as indicated by pseudo first order kinetic, and degraded within 120 min. Considering the low-cost preparation process and high photocatalytic performance, the resultant CaTiO3 can further be used as an efficient photocatalyst for organic pollutant removal from aqueous and industrial wastewater.

Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data.