This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

Precise combinations of probiotics can be useful in dog nutrition, treatment and care. Also, host specificity must be considered in order to increase the effectiveness of probiotics. In this study, Lactobacillus acidophilus HY7032 and Lactobacillus reuteri HY7506 were used, which were isolated from feces of healthy dogs through the verification of pH, bile salt tolerance, and antibacterial activity. In addition, the selected strains were confirmed for activity in immune cells. Briefly, L. acidophilus HY7032 and L. reuteri HY7506 enhanced oxidative burst and phagocytosis of innate immune cell activities in peripheral blood. In addition, beagle were administered vancomycin 50 mg and polymyxin B 100 KU for 7 days, and then 107 CFU of L. acidophilus HY7032 and L. reuteri HY7506 were orally administered for 3 weeks to confirm the effect of improving hair quality. Also, compared with the placebo group, the health improvement effect including stool pattern were confirmed. These results imply that the microflora imbalance caused by antibiotics can be gradually improved through the intake of probiotics. Through this study, it was confirmed that L. acidophilus HY7032 and L. reuteri HY7506 are good probiotics that contribute to the welfare and health of companion animals and have the effect of improving hair quality.

This study investigates the status of ICT in Education in Turkmenistan for achieving the United Nations’(UN) Sustainable Development Goal 4 (SDG4) targets. The study uses two methods for data collection: a detailed review of the literature and a survey. For data collection through survey, the National Education Institute of Turkmenistan and United Nations Development Programme (UNDP) Turkmenistan Office representatives take in part to capture different dimensions of the same phenomenon; the integration of ICT into education in terms of achieving the SDG4 in the country. The results indicate the specific issues such as harnessing ICT as an access tool for education, using ICT for the equity and quality of education as well as the teachers’ ICT competency which need to be improved for achieving the Education 2030 in Turkmenistan. The study also finds priority areas for upcoming years: ICT for transforming and expanding TVET and higher education, improving teacher competency as well as building and upgrading learning environments in Turkmenistan.

In this study, a safety evaluation was conducted to confirm if the Enterococcus faecium CKDB003 strain obtained by selection from a mixed fermentation of fruit and milk is suitable for use as a probiotic. The MIC value for the 10 antibiotics specified in the EFSA guidance was below the acceptable cut-off value. The antibiotic resistance genes aac(6')-li, eatAv, and msr(C) exist by whole genome sequencing, but are in the chromosome and not in the plasmid, thus confirming that there is no possibility of transmission to other microorganisms. It was confirmed that cytolysin (cylA, cylB, cylI, cylL-l, cylL-s, cylM, cylR1, cylR2), aggregation substance (asa1, asp1), collagen adhesion (ace), enterococcal surface protein (esp), endocarditis antigen (efaA), hyaluronidase (hyl) and gelatinase (gelE) were not present in the genome by examining the genes of factors related to virulence. Also, the biochemical analysis showed no toxic enzyme activities, and no virulence genes were detected by the PCR method. Thus, the E. faecium CKDB003 strain can be safely used as a health functional food probiotic, based on the results of the safety assessment.