The distributions of common minke whales observed in the East Sea in ten surveys in May of 2003, 2005, 2006, 2007, 2009, 2010, 2012, 2015, 2016 and 2020 were investigated using satellite sea surface temperature (SST) derived from the Moderate Resolution Imaging Spectrometer (MODIS). Most of the minke whales were observed in the waters off the Korean Peninsula at 36-38.5° N, which is expected as the highly productive coastal upwelling area. Yet, no minke whale was observed in 2006 when a relatively larger scale coastal upwelling occurred with SST at 11°C. In 2016 and 2020, the warm water higher than 17°C extended widely in the area, and the minke whales were observed in the offshore waters, deeper than 1,000 m. 87.5% of minke whales observed in May appeared in the SST from 13 to 16°C, and they seemed to avoid relatively high temperatures. This suggests that optimum habitat water temperature of minke whales in May is 13-16°C. The SST in the area had risen 1.67°C from 2003 to 2021, and it was remarkably higher than in other parts of the surrounding areas. The future temperature rising may change the route and timing of the migration of minke whales in the study area.

We present the analysis of a planetary microlensing event OGLE-2019-BLG-0362 with a shortduration anomaly (∼0.4 days) near the peak of the light curve, which is caused by the resonant caustic. The event has a severe degeneracy with Δχ2 = 0.9 between the close and the wide binary lens models both with planet-host mass ratio q ≃ 0.007. We measure the angular Einstein radius but not the microlens parallax, and thus we perform a Bayesian analysis to estimate the physical parameters of the lens. We find that the OGLE-2019-BLG-0362L system is a super-Jovian-mass planet Mp = 3.26+0.83 −0.58 MJ orbiting an M dwarf Mh = 0.42+0.34 −0.23 M⊙ at a distance DL = 5.83+1.04 −1.55 kpc. The projected star-planet separation is a⊥ = 2.18+0.58 −0.72 AU, which indicates that the planet lies beyond the snow line of the host star.

This paper aims to examine the progressive development process of the ASEAN under the UN 2030 Agenda for sustainable development. As of 2022, the ASEAN Member States have a total population of 622 million people and a combined GDP of USD 3.2 trillion. The ASEAN’s main focus is integration by connectivity which has been facilitated by “the ASEAN Way.” The ASEAN connectivity was upgraded into a single community through the ASEAN Vision 2020 comprehensively formalized by the Bali Concord II in 2003. The ASEAN has been geographically expanding towards Northeast Asia (ASEN+3) and then Oceania with India (ASEAN+6). It was also connected to the Regional Comprehensive Economic Partnership (RCEP) which is the biggest mega FTA in the contemporary world. With the Vision 2025, furthermore, the ASEAN Community reset its direction to sustainable development goals which are the main objective to attain for the Association under the Master Plan 2025.

PURPOSES : Abroad, road pavement materials vary depending on the speed and traffic volume of vehicles, but owing to the negative perception of block pavements, sidewalks, parking lots, and parks are primarily used in Korea. In addition, since speed restriction policies such as safety speed 5030 have been implemented recently, it is necessary to use block pavements for roadways, which are considered to have the effect of reducing speed. Therefore, it is necessary not only to actively discuss the introduction of block pavements for roadways but to continue research on the effectiveness of the performance evaluation and to change the perception of roads in Korea. METHODS : In this study, five indicators (surface damage, surface temperature, driving speed of vehicle, noise, and suspended dust) were selected for a sustainable road environment. The performance evaluation index of block pavement for roadways was decided according to the domestic and international literature, and the data were collected based on the evaluation index in the section with block pavement for roadways in Korea. RESULTS : The damage rate was calculated 0.35% according to the breakage of block and Maintenance Control Index(MCI) was ranked A~B even though the pavement was used for more than 4~5 years. The surface temperature of block pavement has a temperature reduction effect of 7 ℃ compared with ordinary asphalt pavement and a speed reduction of approximately 4 km/h on average; therefore, the traffic calming effect of block pavements can be expected. The noise of block pavement and asphalt pavement exhibited a similar level, and the noise level experienced by pedestrians did not change significantly as a result of frequency analysis. The measurement of road suspended dust around the road confirmed the possibility of reducing the concentration of road dust in the air owing to the smooth surface drainage and the results indicated the possibility. CONCLUSIONS : The results of this study are expected to contribute to the recognition and functional improvement of domestic block pavements by continuous monitoring to ensure the reliability of blocks. In addition, it is necessary to ensure the reliability of the quality and functional evaluation of paving materials through continuous on-site monitoring.

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.