• Korean Journal of Clinical Laboratory Science
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• v.56 no.2
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• pp.147-155
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• 2024

Gallium-68-prostate-specific membrane antigen-11 (⁶⁸Ga-PSMA-11) is a positron emission tomography radiopharmaceutical that labels a Glu-urea-Lys-based ligand with ⁶⁸Ga, binding specifically to the PSMA. It is used widely for imaging recurrent prostate cancer and metastases. On the other hand, the preparation and quality control testing of ⁶⁸Ga-PSMA-11 in medical institutions takes over 60 minutes, limiting the daily capacity of ⁶⁸Ge/⁶⁸Ga generators. While the generator provides 1,110 MBq (30 mCi) nominally, its activity decreases over time, and the labeling yield declines irregularly. Consequently, additional preparations are needed, increasing radiation exposure for medical technicians, prolonging patient wait times, and necessitating production schedule adjustments. This study aimed to reduce the ⁶⁸Ga-PSMA-11 preparation time and optimize the automated synthesis system. By shortening the reaction time between ⁶⁸Ga and the PSMA-11 precursor and adjusting the number of purification steps, a faster and more cost-effective method was tested while maintaining quality. The final synthesis time was reduced from 30 to 20 minutes, meeting the standards for the HEPES content, residual solvent EtOH content, and radiochemical purity. This optimized procedure minimizes radiation exposure for medical technicians, reduces patient wait times, and maintains consistent production schedules, making it suitable for clinical application.

• Journal of Nutrition and Health
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• v.57 no.3
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• pp.292-306
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• 2024

Purpose: This study investigated fruit and vegetable intake and associated socio-demographic and dietary factors, and compared the nutritional intake according to the fruit and vegetable intake level among Korean adolescents. Methods: This study was conducted on 1,676 adolescents who participated in the 2016-2019 Korea National Health and Nutrition Examination Survey. The subjects were classified into four groups based on the fruit and vegetable intake recommendations in 2020 Dietary Reference Intakes for Koreans: Application (KDRIs Application): sufficient fruit intake (SF) group, sufficient vegetables intake (SV) group, sufficient fruit and vegetables intake (SFV) group, and not sufficient fruit and vegetable intake (NS) group The nutrient intake per day in each group was compared.. Logistic regression analysis was performed to examine the factors influencing fruit and vegetables intake. Results: In the sample of adolescents surveyed, only 1.40% met the recommended daily intake of fruits and vegetables, while 79.54% fell below the established threshold for adequate consumption. Female adolescents, those with fathers holding university degrees or above, and those who ate breakfast at least three times a week were likelier to have adequate fruit intake. Male adolescents and those from higher-income households were likelier to consume vegetables. Females, those who ate out daily, those from lower-income households, and those who understood food labels were likelier to have adequate fruit and vegetable intake. The daily nutrient intake and intake-to-requirement ratio significantly differed according to the fruit and vegetable intake groups. The NS and SF group had lower ratios for calcium and iron, while the NS group had the lowest vitamin A and C intake. By contrast, the SFV group met almost all daily nutrient requirements, except for calcium and vitamin A. Conclusion: This study highlights the need for nutrition education programs to encourage adolescents to consume adequate amounts of fruits and vegetables.

• Journal of Nutrition and Health
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• v.57 no.3
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• pp.336-348
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• 2024

Purpose: The use of food delivery services is increasing continuously in Korea, which may lead to nutritional problems and obesity. Despite this, the research on the association between delivered food consumption and obesity has been insufficient. This study examined the relationship between delivered food consumption and dietary behaviors and obesity among young adults in Jeju. Methods: An online survey was conducted from March 15 to April 5, 2023; 312 participants aged 19-39 years were included in the final analysis. The frequency, types, and time of delivered food consumption were measured using a questionnaire. The dietary behaviors included the following: eating out, breakfast consumption, recognition of nutrition labels, and eating salty foods, vegetables, and fruit. Obesity was defined using the body mass index based on self-reported body weight and height. Results: Approximately 59.3% of the participants ordered delivery foods more than one time/week. The frequency of delivered food consumption was higher in people who consumed breakfast < 5 times/week than those who consumed ≥ 5 times/week (p = 0.0088). People who usually eat salty foods tended to consume delivered food more frequently than those who did not (p = 0.0377). On the other hand, people who consumed fruits ≥ 1 time/day had a higher frequency of delivered food consumption than those who consumed fruits < 1 time/day (p = 0.0110). After adjusting for the confounding variables, the group who consumed delivered foods more than three times/week had an increased odds ratio (OR) of obesity compared to those who consumed less one time/week (OR, 2.38; 95% confidence intervals, 1.12-5.06). Conclusion: Young adults in Jeju who frequently consume delivered foods tended to have poor dietary habits including skipping breakfast and eating salty, and they had an increased odds of obesity. The overall dietary patterns can be improved by providing nutrition education and developing policies to promote or support healthy food choices when ordering delivered foods or eating out.

• Journal of Distribution Research
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• v.16 no.4
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• pp.1-27
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• 2011

Introduction: Sales of private labels(PU have been growing m recent years. Globally, PLs have already achieved 20% share, although between 25 and 50% share in most of the European markets(AC. Nielson, 2005). These products are aimed to have comparable quality and prices as national brand(NB) products and have been continuously eroding manufacturer's national brand market share. Stores have also started introducing premium PLs that are of higher-quality and more reasonably priced compared to NBs. Worldwide, many retailers already have a multiple-tier private label architecture. Consumers as a consequence are now able to have a more diverse brand choice in store than ever before. Since premium PLs are priced higher than regular PLs and even, in some cases, above NBs, stores can expect to generate higher profits. Brand extensions and private label have been extensively studied in the marketing field. However, less attention has been paid to the private label extension. Therefore, this research focuses on private label extension using the Multi-Attribute Attitude Model(Fishbein and Ajzen, 1975). Especially there are few studies that consider the hierarchical effect of the PL's two parent brands: store brand and the original PL. We assume that the attitude toward each of the two parent brands affects the attitude towards the extended PL. The influence from each parent brand toward extended PL will vary according to the perceived fit between each parent brand and the extended PL. This research focuses on how these two parent brands act as reference points to one another in the consumers' choice consideration. Specifically we seek to understand how store image and attitude towards original PL affect consumer perceptions of extended premium PL. How consumers perceive extended premium PLs could provide strategic suggestions for retailer managers with specific suggestions on whether it is more effective: to position extended premium PL similarly or dissimilarly to original PL especially on the quality dimension and congruency with store image. There is an extensive body of research on branding and brand extensions (e.g. Aaker and Keller, 1990) and more recently on PLs(e.g. Kumar and Steenkamp, 2007). However there are no studies to date that look at the upgrading and influence of original PLs and attitude towards store on the premium PL extension. This research wishes to make a contribution to this gap using the perceived fit difference between parent brands and extended premium PL as the context. In order to meet the above objectives, we investigate which factors heighten consumers' positive attitude toward premium PL extension. Research Model and Hypotheses: When considering the attitude towards the premium PL extension, we expect four factors to have an influence: attitude towards store; attitude towards original PL; perceived congruity between the store image and the premium PL; perceived similarity between the original PL and the premium PL. We expect that all these factors have an influence on consumer attitude towards premium PL extension. Figure 1 gives the research model and hypotheses. Method: Data were collected by an intercept survey conducted on consumers at discount stores. 403 survey responses were attained (total 59.8% female, across all age ranges). Respondents were asked to respond to a series of Questions measured on 7 point likert-type scales. The survey consisted of Questions that measured: the trust towards store and the original PL; the satisfaction towards store and the original PL; the attitudes towards store, the original PL, and the extended premium PL; the perceived similarity of the original PL and the extended premium PL; the perceived congruity between the store image and the extended premium PL. Product images with specific explanations of the features of premium PL, regular PL and NB we reused as the stimuli for the Question response. We developed scales to measure the research constructs. Cronbach's alphaw as measured each construct with the reliability for all constructs exceeding the .70 standard(Nunnally, 1978). Results: To test the hypotheses, path analysis was conducted using LISREL 8.30. The path analysis for verification of the model produced satisfactory results. The validity index shows acceptable results(${\chi}^2=427.00$(P=0.00), GFI= .90, AGFI= .87, NFI= .91, RMSEA= .062, RMR= .047). With the increasing retailer use of premium PLBs, the intention of this research was to examine how consumers use original PL and store image as reference points as to the attitude towards premium PL extension. Results(see table 1 & 2) show that the attitude of each parent brand (attitudes toward store and original pL) influences the attitude towards extended PL and their perceived fit moderates these influences. Attitude toward the extended PL was influenced by the relative level of perceived fit. Discussion of results and future direction: These results suggest that the future strategy for the PL extension needs to consider that positive parent brand attitude is more strongly associated with the attitude toward PL extensions. Specifically, to improve attitude towards PL extension, building and maintaining positive attitude towards original PL is necessary. Positioning premium PL congruently to store image is also important for positive attitude. In order to improve this research, the following alternatives should also be considered. To improve the research model's predictive power, more diverse products should be included in study. Other attributes of product should also be included such as design, brand name since we only considered trust and satisfaction as factors to build consumer attitudes.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.23 no.1
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• pp.5-20
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• 1997

War and poverty depress the consumption of cosmetics, while peace and prosperity encourage their proliferation. With the end of World War II, the US, Europe and Japan witnessed rapid growth of their cosmetic industries. The ending of the Cold War has stimulated the growth of the industry in Eastern Europe. Improved economies, and mass communication are also responsible for the fast growth of the cosmetic industries in many Asian nations. The rapid development of the cosmetic industry in mainland China over the past decade proves that changing economies and political climates can deeply affect the health of our business. In addition to war, economy, political climate and mass communication, factors such as lifestyle, religion, morality and value concepts, can also affect the growth of our industry. Cosmetics are the product of the society. As society and the needs of its people change, cosmetics also evolve with respect to their contents, packaging, distribution, marketing concepts, and emphasis. In many ways, cosmetics mirror our society, reflecting social changes. Until the early 70's, cosmetics in the US were primarily developed for white women. The civil rights movement of the 60's gave birth to ethnic cosmetics, and products designed for African-Americans became popular in the 70's and 80's. The consumerism of the 70's led the FDA to tighten cosmetic regulations, forcing manufacturers to disclose ingredients on their labels. The result was the spread of safety-oriented, "hypoallergenic" cosmetics and more selective use of ingredients. The new ingredient labeling law in Europe is also likely to affect the manner in which development chemists choose ingredients for new products. Environmental pollution, too, can affect cosmetics trends. For example, the concern over ozone depletion in the stratosphere has promoted the consumption of suncare products. Similarly, the popularity of natural cosmetic ingredients, the search of non-animal testing methods, and ecology-conscious cosmetic packaging seen in recent years all reflect the profound influences of our changing world. In the 1980's, a class of efficacy-oriented skin-care products, which the New York Times dubbed "serious" cosmetics, emerged in the US. "Cosmeceuticals" refer to hybrids of cosmetics and pharmaceuticals which have gained importance in the US in the 90's and are quickly spreading world-wide. In spite of regulatory problems, consumer demand and new technologies continue to encourage their development. New classes of cosmeceuticals are emerging to meet the demands of increasingly affluent Asian consumers as we enter the 21st century. as we enter the 21st century.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Nutrition and Health
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• v.55 no.4
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• pp.476-491
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• 2022

Purpose: The purpose of this study was to evaluate the energy and nutrient contents of home meal replacement (HMR) rice products per single serving package based on nutrition labels. Methods: The market research was conducted from February to July 2021 on products sold on the internet, at convenience stores, etc. A total of 406 products were investigated. The products were divided into the following 6 classifications: instant rice (n = 45), cup rice (n = 64), frozen rice (n = 188), rice bowls with toppings (n = 32), gimbap (n = 38), and triangular gimbap (n = 39). Results: The mean packaging weight per serving was the highest in the rice bowl with toppings at 297.1 g, followed by cup rice (264.0 g), frozen rice (239.5 g), gimbap (230.2 g), instant rice (193.4 g), and triangular gimbap (121.6 g) (p < 0.001). The energy per serving package for the rice bowl with toppings was significantly the highest at 496.0 kcal (p < 0.001). The sodium content per serving package of gimbap was the highest at 1,021.8 mg and that of the instant rice was lowest at 37.4 mg (p < 0.001). The price per serving package of the rice bowl with toppings at 4,333.8 won was the highest. The contribution to the daily nutritional value per serving package of all types of HMR rice products surveyed showed an average range of 10-25% for energy, 11-22% for carbohydrates, and 2-51% for sodium. Conclusion: These results indicate the energy and nutrient contents of HMR rice products, vary by type. Therefore, consumers should review the nutrition labeling to select an appropriate HMR rice product based on their intended consumption.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.