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.

In this study, graphene was rapidly grown by chemical vapor deposition using a liquid cell for supplying methanol as a carbon source of graphene. To realize the rapid growth, methanol which is carbon-contained organic solvent was used instead of methane gas, a widely used carbon source for graphene growth. The graphene grown with the growth time as a variable was transferred to a SiO2/ Si substrate with an oxide thickness of 300 nm to confirm whether it was grown with full coverage with an optical microscope. The results confirmed a full coverage in 0.5 min of growth. The Raman spectra also confirmed the G-peak position at 1585.0 cm−1 and an intensity ratio of 2D/G at 2.3 or higher. Concerning electrical transport characteristics, at an induced carrier density of 1 × 1012 cm−2, the hole (μh) and electron (μe) mobilities were 1524 cm2 V−1 s−1 and 1528 cm2 V−1 s−1, respectively. Thus, our study confirmed that high-quality, large-area graphene can be grown within 0.5 min.

The important thing in the field of deep learning is to find out the appropriate hyper-parameter for image classification. In this study, the main objective is to investigate the performance of various hyper-parameters in a convolutional neural network model based on the image classification problem. The dataset was obtained from the Kaggle dataset. The experiment was conducted through different hyper-parameters. For this proposal, Stochastic Gradient Descent without momentum (SGD), Adaptive Moment Estimation (Adam), Adagrad, Adamax optimizer, and the number of batch sizes (16, 32, 64, 120), and the number of epochs (50, 100, 150) were considered as hyper-parameters to determine the losses and accuracy of a model. In addition, Binary Cross-entropy Loss Function (BCLF) was used for evaluating the performance of a model. In this study, the VGG16 convolutional neural network was used for image classification. Empirical results demonstrated that a model had minimum losses obtain by Adagrad optimizer in the case of 16 batch sizes and 50 epochs. In addition, the SGD with a 32 batch sizes and 150 epochs and the Adam with a 64 batch sizes and 50 epochs had the best performance based on the loss value during the training process. Interestingly, the accuracy was higher while performing the Adagrad and Adamax optimizer with a 120 batch sizes and 150 epochs. In this study, the Adagrad optimizer with a 120 batch sizes and 150 epochs performed slightly better among those optimizers. In addition, an increasing number of epochs can improve the performance of accuracy. It can help to create a broader scope for further experiments on several datasets to perceive the suitable hyper-parameters for the convolutional neural network. Dataset: https://www.kaggle.com/c/dogs-vs-cats/data

Rare earth magnets with excellent magnetic properties are indispensable in the electric device, wind turbine, and e-mobility industries. The demand for the development of eco-friendly recycling techniques has increased to realize sustainable green technology, and the supply of rare earth resources, which are critical for the production of permanent magnets, are limited. Liquid metal extraction (LME), which is a type of pyrometallurgical recycling, is known to selectively extract the metal forms of rare earth elements. Although several studies have been carried out on the formation of intermetallic compounds and oxides, the effect of oxide formation on the extraction efficiency in the LME process remains unknown. In this study, microstructural and phase analyses are conducted to confirm the oxidation behavior of magnets pulverized by a jaw crusher. The LME process is performed with pulverized scrap, and extraction percentages are calculated to confirm the effect of the oxide phases on the extraction of Dy during the reaction. During the LME p rocess, Nd i s completely e xtracted a fter 6 h, w hile D y remains as D y2Fe17 and Dy-oxide. Because the decomposition rate of Dy2Fe17 is faster than the reduction rate of Dy-oxide, the importance of controlling Dy-oxide on Dy extraction is confirmed.

We investigate 20 post-coronal mass ejection (CME) blobs formed in the post-CME current sheet (CS) that were observed by K-Cor on 2017 September 10. By visual inspection of the trajectories and projected speed variations of each blob, we nd that all blobs except one show irregular \zigzag" trajectories resembling transverse oscillatory motions along the CS, and have at least one oscillatory pattern in their instantaneous radial speeds. Their oscillation periods are ranging from 30 to 91 s and their speed amplitudes from 128 to 902 kms􀀀1. Among 19 blobs, 10 blobs have experienced at least two cycles of radial speed oscillations with dierent speed amplitudes and periods, while 9 blobs undergo one oscillation cycle. To examine whether or not the apparent speed oscillations can be explained by vortex shedding, we estimate the quantitative parameter of vortex shedding, the Strouhal number, by using the observed lateral widths, linear speeds, and oscillation periods of the blobs. We then compare our estimates with theoretical and experimental results from MHD simulations and uid dynamic experiments. We nd that the observed Strouhal numbers range from 0.2 to 2.1, consistent with those (0.15{3.0) from uid dynamic experiments of blu spheres, while they are higher than those (0.15{0.25) from MHD simulations of cylindrical shapes. We thus nd that blobs formed in a post-CME CS undergo kinematic oscillations caused by uid dynamic vortex shedding. The vortex shedding is driven by the interaction of the outward-moving blob having a blu spherical shape with the background plasma in the post-CME CS.

본 보고는 2020년 산림 지역에서 대발생하여 큰 피해를 준 매미나방과 대벌레에 의한 피해면적과 피해수종에 대해 기술하였다. 매미나방에 의한 식엽 피해는 강원(1,638 ha), 경기(1,134 ha), 충북(726 ha), 서울(476 ha) 등 중부지방을 중심으로 심각하였다. 대벌레는 서울시 은평구와 고양시 덕양구 사이에 위치한 봉산(약 19 ha)에서 대발생하였다.

Several factors, including genetic and environmental insults, impede protein folding and secretion in the endoplasmic reticulum (ER). Accumulation of unfolded or mis-folded protein in the ER manifests as ER stress. To cope with this morbid condition of the ER, recent data has suggested that the intracellular event of an unfolded protein response plays a critical role in managing the secretory load and maintaining proteostasis in the ER. Tauroursodeoxycholic acid (TUDCA) is a chemical chaperone and hydrophilic bile acid that is known to inhibit apoptosis by attenuating ER stress. Numerous studies have revealed that TUDCA affects hepatic diseases, obesity, and inflammatory illnesses. Recently, molecular regulation of ER stress in tooth development, especially during the secretory stage, has been studied. Therefore, in this study, we examined the developmental role of ER stress regulation in tooth morphogenesis using in vitro organ cultivation methods with a chemical chaperone treatment, TUDCA. Altered cellular events including proliferation, apoptosis, and dentinogenesis were examined using immunostaining and terminal deoxynucleotidyl transferase dUTP nick end labeling assay. In addition, altered localization patterns of the formation of hard tissue matrices related to molecules, including amelogenin and nestin, were examined to assess their morphological changes. Based on our findings, modulating the role of the chemical chaperone TUDCA in tooth morphogenesis, especially through the modulation of cellular proliferation and apoptosis, could be applied as a supporting data for tooth regeneration for future studies.

We investigated the cytotoxic potential of three different commercially available absorbent feminine hygiene products and one transdermal patch using direct contact and extract exposure methods. Two different cell lines were used – mouse fibroblast L929 and normal human skin fibroblast CCD-986sk cell lines. The test samples were extracted using three different methods in accordance to International Organization for Standardization (ISO). Viability of cells was analyzed using MTT assay and morphology of the cells were also observed using phase contrast microscopy. Overall, the direct contact method using L929 cells showed that all the test samples had no toxic effect when exposed to extracts for 1 h. For the exposure method, no toxic effect was observed in both L929 and CCD986sk cells incubated with all the test samples regardless of the extraction methods used.