• Journal of Radiation Industry
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• v.7 no.2_3
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• pp.221-224
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• 2013

RI-Biomics is a new compound word of radiation technology and Biomics related to the study of life. RI-Biomics is high radiation fusion technology by combining evaluation of pharmacokinetics in vivo (RI-ADME) of new drugs and medical materials using radioisotope and molecular imaging technology using nuclear medicine equipments. RI-Biomics fields are emerging with the increasing usage of radioisotopes (RI). In this paper, we investigated the latest trends of radioisotope using in RI-Biomics fields. The representative radioisotopes are $^{14}C$, $^3H$ and $^{32}P$ for the optimization and the selection of candidates in the development process of new drugs among the RI-Biomics fields. As shown in the status of accumulated income of radioisotopes, using amounts of radioisotopes are showing a tendency to increase every year. $^{14}C$ is 61.6% increase of accumulated income growth rate and $^3H$ increased by 58.8% and $^{32}P$ increased by 33.9% in 2012 compared to 2007. These isotopes are used in a variety of fields as using of $^{14}C$ for microdosing test, development of [$^3H$]cholesterol absorption inhibitors, study of [$^{131}I$]pyronaridine tetraphosphate for malaria therapy. These are going on in vivo test sucessfully. So, clinical research step is expected to begin soon. Therefore, usages of radioisotopes are necessary and need for the evaluation of pharmacokinetics, optimization and the selection of new drug candidates in the development process of new drugs among the RI-Biomics fields. So, using of radioisotopes is predict to increase continuously except for primarily used $^{14}C$, $^3H$.

• FLOWER RESEARCH JOURNAL
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• v.19 no.3
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• pp.177-180
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• 2011

A cultivar 'Yellow Cream' of Phalaenopsis(P. Blume) was developed by cross breeding and line selection at the Flower Research Institute, Gyeongnam ARES, from 2000 to 2008. Characteristic trials for this cultivar were carried out three times from 2006 to 2008. 'Yellow Cream' was developed from a crossing between P. 'Sogo Firework' and P. 'Sogo Gold'. 'Yellow Cream' exhibits light yellow flower color with pink stripe and favorable flower shape. Individual flowers are formed with parallel arrangement and have long life with more than 55 days. 'Yellow Cream'(Grant No.3232) was registered to the Korea seed and variety Service(KSVS) for commercialization in 2010 and suitable for the cultivation under greenhouse conditions in Korea.

• Journal of IKEEE
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• v.24 no.4
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• pp.1050-1057
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• 2020

Immersive 360-degree media has a problem of slowing down the video recognition speed when the video is processed by the conventional method using a variety of rendering methods, and the video size becomes larger with higher quality and extra-large volume than the existing video. In addition, in most cases, only one scene is captured by fixing the camera in a specific place due to the characteristics of the immersive 360-degree media, it is not necessary to extract feature information from all scenes. In this paper, we propose a reference frame selection method for immersive 360-degree media and describe its application process to copyright protection technology. In the proposed method, three pre-processing processes such as frame extraction of immersive 360 media, frame downsizing, and spherical form rendering are performed. In the rendering process, the video is divided into 16 frames and captured. In the central part where there is much object information, an object is extracted using an RGB vector per pixel and deep learning, and a reference frame is selected using object feature information.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.1
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• pp.1-10
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• 2022

This study is a marketing tool for securing loyal customers and increasing sales by developing a customizing manual according to the change of segmented nail art consumption pattern due to the development of the nail industry and performing art to meet the needs of various customers and increase satisfaction. This was done to demonstrate the possibility of use. In order to develop a manual for the study, we conducted a survey that combined an online survey and in-person survey for ordinary citizens in their 20s and 50s living in Busan and Gyeongnam. Taste (50.0%) was the highest, and personal preference (62.9%) was also the highest for items related to nail art color selection, suggesting that the consumption pattern is changing to require a variety of personal art preferences rather than recommendations or recommendations from practitioners. Could know. As a result of performing nail art by applying the customizing manual developed based on customer selection, opinions were shown in the order of reliability (39.1%), attachment (39.1%), and rarity (26.1%). Utilization (73.9%) was also high in the question of 'If customizing manual was developed as an app', and overall satisfaction with the art selected by the customer was high, indicating that the customer had a high degree of attachment to the nail art design decided by the customer. As for the improvement points of the manual, it was possible to confirm the necessity of developing the app with the majority opinion that handwriting was inconvenient. Based on the nail art customizing manual of this study, the follow-up research proceeds with the app production and utilization process, and it is hoped that it will be used as a basic data for sales promotion by increasing customer satisfaction according to the rapidly changing consumption patterns of nail customers.

• Journal of Preventive Medicine and Public Health
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• v.55 no.1
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• pp.10-18
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• 2022

Disability-adjusted life-year (DALY) estimates may vary according to factors such as the standard life expectancy, age weighting, time preference and discount rate, calculation of disability weights, and selection of the estimation method. DALY estimation methods are divided into the following 3 approaches: the incidence-based approach, the pure prevalence-based approach, and the hybrid approach. These 3 DALY estimation approaches each reflect different perspectives on the burden of disease using unique characteristics, based on which the selection of a suitable approach may vary by the purpose of the study. The Global Burden of Disease studies, which previously estimated DALYs using the incidence-based approach, switched to using the hybrid approach in 2010, while the National Burden of Disease studies in Korea still mainly apply the incidence-based approach. In order to increase comparability with other international burden of disease studies, more DALY studies using the prevalence-based approach need to be conducted in Korea. However, with the limitations of the hybrid approach in mind, it is necessary to conduct more research using a disease classification system suitable for Korea. Furthermore, more detailed and valid data sources should be established before conducting studies using a broader variety of DALY estimation approaches. This review study will help researchers on burden of disease use an appropriate DALY estimation approach and will contribute to enhancing researchers' ability to critically interpret burden of disease studies.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.175-185
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• 2018

This study was conducted to evaluate the physiological and ecological characters of rice cultivars suitable for low fertilized condition. 5 rice cultivars(Jinmibyeo, Sobibyeo, Hwayeongbyeo, Nagdongbyeo and Junambyeo) were cultivated for selection under 3 different nitrogen application levels, and 1 cultivars were selected. The results obtained are summarized as follows ; High yielded rice cultivars under low N application level were Junambyeo, Jinheng and Sobibyeo. Also these cultivars were yielded highly under conventional level(11kg/10a). Milled rice yield under conventional level(11kg/10a) was positively correlated with them under low N levels. Milled rice yield was most affected by no. of grain/m². Rice cultivars that were high crop growth rate(CGR) before heading stage were Junambyeo, Sobibyeo and Nagdongbyeo. Grain filling rate was increased mostly until 20 days after heading, and decreased after this stage. Nitrogen use efficiency was higher under low N level(5.5kg/10a) than conventional level(11kg/10a). Especially, Junambyeo was most low in Apparent recovery of applied N(AR) under low N application level, but most high in Agronomic N use efficiency(ANUE). This characteristics of Junambyeo will to be useful for selection of variety suitable for growing under low fertilized condition.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.277-277
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• 2022

Lignin modification has been a breeding target for the improvements of forage digestibility and fermentation in whole crop silage(WCS) rice. In rice, gold hull and internode 2 (gh2) was identified as a lignin-deficient mutant. gh2 exhibits a reddish-brown pigmentation in the hull and the internode is located on the short arm of chromosome 2 and codes for cinnamyl-alcohol dehydrogenase (CAD). To develop WCS rice variety improved digestibility and fermentation, we measured acid detergent fiber (ADF), lignin and total digestible nutrient (TDN) calculated from ADF (TDN=88.9-(0.79% × ADF) and performed marker-assisted selection using CAD(Os2g0187800) gene first intron region specific marker with 55 Jungmo1038/J.collection lines. Those lines had lignin content range from 0.82 to 6.61%, ADF from 15.8 to 45.8%, TDN from 52.7 to 78.8 compared to 'Jungmo1038'(1.53,20.7,72.6), 'J.collection'(0.98,12.8,78.8%) and gh2 were introgressed into 44 lines. Considering on these genotype and low-lignin phenotype, we finally selected 2 elite lines(Suweon668, Suweon669). Suweon668 and Suweon669 line are high biomass-low lignin lines that the ADF content is relatively low, even though the dry matter weight is high. Also they have lodging and shattering resistance and glabrous leaf and hull important to improve cattle palatability. Our results will provide that rice can be improved for forage digestibility and fermentation with low lignin concentration.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.217-227
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• 2023

This study aims to stratify consumer market evaluation items from the Consumer Decision Journey(CDJ) perspective and understand the relationship between laws/systems and consumer orientation through the Korea Consumer Agency's '19 Korea Consumer Markets Evaluation Indicators. This study divided consumer market evaluation items into the selection comparison stage, selection decision stage, and post-purchase experience stage. And present a model that stratified the relationship with consumer orientation of laws/systems and verified using the CDJ model's experience as a control variable. Studies have shown that the relationship between the consumer market evaluation index that evaluates consumer orientation can be stratified according to the consumer decision-making stage and positively affects the relationship with consumer orientation of laws/systems. In addition, the impact of consumer market evaluation variables (reliability, and price) on the consumer orientation of laws/systems was different depending on the presence or absence of consumer damage experience.