The cosmetics industry expects low irritation and skin improvement effects by utilizing natural ingredients and botanical extracts. In particular, Poncirus trifoliata fruit extract is a traditionally recognized ingredient with proven pharmacological efficacy and contains various bioactive compounds. According to previous studies, Poncirus trifoliata fruit has been reported to possess antioxidant, whitening, and skin-improving effects. In this study, the antioxidant and whitening efficacy of Poncirus trifoliata fruit extract was evaluated according to different extraction solvents, and the formulation stability of a cleanser containing this extract was analyzed. The antioxidant activity of Poncirus trifoliata fruit extract was confirmed by measuring DPPH and ABTS⁺ radical scavenging activity, while its whitening efficacy was assessed through tyrosinase inhibition activity. Additionally, the total polyphenol and flavonoid content was measured, confirming its strong antioxidant effects. These findings suggest that Poncirus trifoliata fruit extract may contribute to anti-aging and skin-whitening effects. To evaluate the stability of a cleansing formulation containing Poncirus trifoliata fruit extract, pH and viscosity changes were measured over 28 days under temperature conditions of 5°C, 25°C, and 45°C. The results indicated a decline in stability over long-term storage. Therefore, further studies are required to optimize the extract concentration and improve the formulation for enhanced long-term stability. This study suggests that Poncirus trifoliata fruit extract has potential as a functional cosmetic ingredient for skin whitening and anti-aging and can serve as fundamental data for future research on stability enhancement.

This study develops a model to determine the input rate of the chemical for coagulation and flocculation process (i.e. coagulant) at industrial water treatment plant, based on real-world data. To detect outliers among the collected data, a two-phase algorithm with standardization transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied. In addition, both of the missing data and outliers are revised with linear interpolation. To determine the coagulant rate, various kinds of machine learning models are tested as well as linear regression. Among them, the random forest model with min-max scaled data provides the best performance, whose MSE, MAPE, R2 and CVRMSE are 1.136, 0.111, 0.912, and 18.704, respectively. This study demonstrates the practical applicability of machine learning based chemical input decision model, which can lead to a smart management and response systems for clean and safe water treatment plant.

Many manufacturers applying third party logistics (3PLs) have some challenges to increase their logistics efficiency. This study introduces an effort to estimate the weight of the delivery trucks provided by 3PL providers, which allows the manufacturer to package and load products in trailers in advance to reduce delivery time. The accuracy of the weigh estimation is more important due to the total weight regulation. This study uses not only the data from the company but also many general prediction variables such as weather, oil prices and population of destinations. In addition, operational statistics variables are developed to indicate the availabilities of the trucks in a specific weight category for each 3PL provider. The prediction model using XGBoost regressor and permutation feature importance method provides highly acceptable performance with MAPE of 2.785% and shows the effectiveness of the developed operational statistics variables.