Since rice is the main food in Korea, there are no regulations on corn milling yet. Corn is known as one of the world's top three food crops along with wheat and rice, and it is known that 3.5 billion people worldwide use corn for food. In addition, corn mills are not developed or sold in Korea, but the use of corn mills is increasing significantly in many countries in Southeast Asia. In the Philippines, as Korea's rice mill import increases, Korea's KAMICO (Korea Agricultural Machinery Industry Cooperative) and domestic company A agreed to develop a corn mill jointly with PHilMech, an organization affiliated with the Philippine Ministry of Agriculture. However, research on corn milling was very insignificant, so the development was carried out based on the technology of Korea's rice mill. Rice milling is performed by peeling off the skin of rice and producing brown or white rice, so it is carried out by removing the skin and cutting the skin. On the other hand, in the corn mill, the skin of the corn is peeled, pulverized and selected to produce main products suitable for edible use. Therefore, in order to develop a corn mill, processes such as peeling, transfer, grinding, sorting, and by-product separation are required, and suitable parts must be developed. In addition, the performance must be gradually improved through experiments in which corn is repeatedly milled. The Philippines produces 7.98 million tons/year of corn, which is about 100 times that of Korea, and is mostly consumed as a staple food. This is about 10% of the total crop production in the Philippines. In addition, the main cultivation complexes of corn are the mountainous regions of Tarlac or Pangasinan, and the produced corn is 72.4% of the so-called yellow corn called Arabel and Sarangani, and the remaining 27.6% are known as white corn. In this study, it was intended to produce grains of 2.5 mm or less suitable for food for yellow corn and to develop a corn mill for 200 kg per hour. Detailed conditions for development are stipulated as more than 55% of the main product recovery rate, more than 31% of the by-product recovery rate, less than 5% of the raw material loss rate, and more than 80% of the embryo dislocation rate. In this study, to achieve this, the overall process of the corn mill was developed, and the optimal conditions for the corn mill were obtained through the development of parts and empirical tests to improve performance. In addition, it was intended to achieve the development goal by evaluating and analyzing the performance of each part so that it did not conflict.

The initial development plans for the six reactor designs, soon after the release of Generation IV International Forum (GIF) TRM in 2002, were characterized by high ambition [1]. Specifically, the sodium-cooled fast reactor (SFR) and very-high temperature reactor (VHTR) gained significant attention and were expected to reach the validation stage by the 2020s, with commercial viability projected for the 2030s. However, these projections have been unrealized because of various factors. The development of reactor designs by the GIF was supposed to be influenced by events such as the 2008 global financial crisis, 2011 Fukushima accident [2, 3], discovery of extensive shale oil reserves in the United States, and overly ambitious technological targets. Consequently, the momentum for VHTR development reduced significantly. In this context, the aims of this study were to compare and analyze the development progress of the six Gen IV reactor designs over the past 20 years, based on the GIF roadmaps published in 2002 and 2014. The primary focus was to examine the prospects for the reactor designs in relation to spent nuclear fuel burning in conjunction with small modular reactor (SMR), including molten salt reactor (MSR), which is expected to have spent nuclear fuel management potential.

This research examines the impact of visualizing virtual luxury products in the metaverse on consumers' perceptions of luxury products in the real world. We explore the metaverse as a marketing platform and investigate the relationship between the quality of visualization of virtual luxury products and consumers’ evaluations of real luxury products. The study hypothesizes that poor visualization quality of virtual luxury products will decrease the evaluation of authentic luxury goods, and this effect will be mediated by decreased perceived authenticity. Additionally, we predict that the negative effect will be mitigated by high-quality visualization.

Blockchain is an immutable ledger that records transactions and tracks assets using a common communication protocol. It stores a copy of the blockchain and implements a consensus function to verify transactions. Blockchain is applied to industries beyond finance, such as retail, to maintain security and transparency. Consumers with knowledge of blockchain technology are likely to be affected when evaluating products with blockchain embedded, impacting their product evaluation. The study investigates the impact of blockchain technology on consumers' product evaluation and how knowledge of blockchain and product quality moderate its effects.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

The effect of oxidation time on the characteristics and mechanical properties of spent nuclear fuel cladding was investigated using Raman spectroscopy, tube rupture test, and tensile test. As oxidation time increased, the Raman peak associated with the tetragonal zirconium oxide phase diminished and merged with the Raman peak associated with the monoclinic zirconium oxide phase near 333 cm−1. Additionally, the other tetragonal zirconium oxide phase peak at 380 cm−1 decreased after 100 d of oxidation, whereas the zirconium monoclinic oxide peak became the dominant peak. The oxidation time had no effect on the tube rupture pressure of the oxidized zirconium alloy tube. However, the yield and tensile stresses of the oxidized nuclear fuel cladding tube decreased after 100 d of oxidation. The results of the scanning electron microscopy and transmission electron microscopy were represented with the in-situ Raman analysis result for the oxide characteristics generated on the cladding of spent nuclear fuel.

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars (‘Arihyang’, ‘Jukhyang’, ‘Keumsil’, ‘Maehyang’, and ‘Seollhyang’). The coefficient of determination (R2) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R2 value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars ‘Jukhyang’, ‘Seolhyang’, and ‘Maehyang’ could be non-destructively predicted using the model developed in this study, as they had R2 values over 0.96. The cultivars ‘Arihyang’ and ‘Geumsil’ had slightly low R2 values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.