Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In ‘offset printing’ mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called ‘spot color’ ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through ‘Delta E’ provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

The printing process can have to print various colors with a limited capacity of printing facility such as ink containers that are needed cleaning to change color. In each container, cleaning time exists to assign corresponding inks, and it is considered as the setup cost required to reduce the increasing productivity. The existing manual method, which is based on the worker’s experience or intuition, is difficult to respond to the diversification of color requirements, mathematical modeling and algorithms are suggested for efficient scheduling. In this study, we propose a new type of scheduling problem for the printing process. First, we suggest a mathematical model that optimizes the color assignment and scheduling. Although the suggested model guarantees global optimality, it needs a lot of computational time to solve. Thus, we decompose the original problem into sequencing orders and allocating ink problems. An approximate function is used to compute the job scheduling, and local search heuristic based on 2-opt algorithm is suggested for reducing computational time. In order to verify the effectiveness of our method, we compared the algorithms' performance. The results show that the suggested decomposition structure can find acceptable solutions within a reasonable time. Also, we present schematized results for field application.

The purpose of this study was to identify the effect of chiropractic treatment on the alignment of the lower extremities and plantar pressure in subjects with pelvic misalignment. The study included 20 subjects with pelvic misalignment. They were divided into two groups: the chiropractic treatment group (n=10) and stretching group (n=10). They performed the intervention for 10 minutes, twice a week, for weeks, and pelvic displacement, functional leg length, and plantar pressure were measured In chiropractic group, both pelvic displacement and plantar pressure distribution were significantly decreased and leg length difference was significantly increased (p<.05). However, in the stretching group, only pelvic displacement was significantly reduced (p<.05), and leg length difference and plantar pressure distribution decreased slightly. There was no significant difference in all variables between the groups. This study suggests that chiropractic and stretching are effective in changing pelvic displacement, leg length difference, and plantar pressure distribution.

Antioxidants partially ameliorated the detrimental effects of reactive oxygen species (ROS) on sperm characteristics during in vitro storage. The objective of the present study was to investigate the single or synergetic antioxidative effect of curcumin and Vit. E on the characteristics of fresh boar sperm during in vitro storage. The sperm viability in curcumin, Vit. E supplementation and curcumin+Vit. E+H2O2 groups remained over 85.0% in 3 hr incubation period, but in 6 hr incubation period, curcumin+Vit. E+H2O2 groups was sharply dropped than those of curcumin and Vit. E group. The membrane intergrity in all evaluated groups except for H2O2 group did not significantly difference in 3 hr incubation period. The viability in curcumin or Vit. E supplementation were significantly increased than in curcumin+H2O2 and Vit. E+H2O2 group in 6 hr incubation period. The percentage of mitochondrial activity and acrosome intergrity obtained similar trends within same incubation periods irrespective of treatment. The lipid peroxidation of spermatozoal plasma membrane ranged from 11.6∼17.5 nM/l×106 and 14.0∼ 19.0 nM/l×106 in 3 hr and 6 hr incubation periods. In conclusion, curcumin or Vit. E rpplementation alone or cooperatively improved sperm viability index (motility, membrane intergrity, viability and survival rates) and fertility index (mitochondria activity, acrosome intergrity and lipid peroxidation) of fresh boar sperm, indicating that curcumin and Vit. E have a antioxidative properties through its scavenging activity against hydrogen peroxide.

Suwon295 at the Honam Agricultural Research Institute (HARI) in 2005. An elite line, 953017-BG-BN-BN-53N, was selected in2000 and designated as Milyang126. It showed good agronomic performance in the regional adaptation yield trials (RYT) from2003 to 2005