• Journal of Nutrition and Health
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• v.55 no.2
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• pp.278-295
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• 2022

Purpose: This study was undertaken to revise and update the Nutrition Quotient (NQ) for Korean adults, a tool used to evaluate dietary quality and behavior. Methods: The first 31 items of the measurable food behavior checklist were adopted based on considerations of the previous NQ checklist, recent literature reviews, national nutrition policies, and recommendations. A pilot survey was conducted on 100 adults aged 19 to 64 residing in Seoul and Gyeonggi Province from March to April 2021 using a provisional 26- item checklist. Pilot survey data were analyzed using factor analysis and frequency analysis to determine whether checklist items were well organized and responses to questions were well distributed, respectively. As a result, the number of items on the food behavior checklist was reduced to 23 for the nationwide survey, which was administered to 1,000 adults (470 men and 530 women) aged 19 to 64 from May to August 2021. The construct validity of the developed NQ (NQ-2021) was assessed using confirmatory factor analysis, linear structural relations. Results: Eighteen items in 3 categories, that is, balance (8 items), moderation (6 items), and practice (4 items), were finally included in NQ-2021 food behavior checklist. 'Balance' items addressed the intake frequencies of essential foods, 'moderation' items the frequencies of unhealthy food intakes or behaviors, and 'practice' items addressed eating behaviors. Items and categories were weighted using standardized path coefficients to calculate NQ-2021 scores. Conclusion: The updated NQ-2021 appears to be suitable for easily and quickly assessing the diet qualities and behaviors of Korean adults.

• Journal of Preventive Medicine and Public Health
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• v.32 no.3
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• pp.361-373
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• 1999

Objectives: Receive Operating Characteristic(ROC) curve with the area under the ROC curve(AUC) is one of the most popular indicator to evaluate the criterion validity of the measurement tool. This study was conducted to develop a standardized questionnaire to discriminate workers at high-risk of work-related musculoskeletal disorders using ROC analysis. Methods: The diagnostic results determined by rehabilitation medicine specialists in 370 persons(89 shipyard CAD workers, 113 telephone directory assistant operators, 79 women with occupation, and 89 housewives) were compared with participant's own replies to 'the questionnair on the worker's subjective physical symptoms'(Kwon, 1996). The AUC's from four models with different methods in item selection and weighting were compared with each other. These 4 models were applied to 225 persons, working in an assembly line of motor vehicle, for the purpose of AUC reliability test. Results: In a weighted model with 11 items, the AUC was 0.8155 in the primary study population, and 0.8026 in the secondary study population(p=0.3780). It was superior in the aspects of discriminability, reliability and convenience. A new questionnaire of musculoskeletal disorder could be constructed by this model. Conclusion: A more valid questionnaire with a small number of items and the quantitative weight scores useful for the relative comparisons are the main results of this study. While the absolute reference value applicable to the wide range of populations was not estimated, the basic intent of this study, developing a surveillance fool through quantitative validation of the measures, would serve for the systematic disease prevention activities.

• Korean Journal of Ecology and Environment
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• v.53 no.1
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• pp.31-45
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• 2020

The biological indices based on the community structure with species richness and/or abundance are commonly used to assess aquatic ecosystem health. Meanwhile, recently functional traits-based approach is considered in ecosystem health assessment to reflect ecosystem functioning. In this study, we developed a database of biological traits for 136 taxa consisting of major stream insects (Ephemeroptera, Plecoptera, Trichoptera, Coleoptera, and Odonata) collected at Korean streams on the nationwide scale. In addition, we obtained environmental variables in five categories (geography, climate, land use, hydrology and physicochemistry) measured at each sampling site. We evaluated the relationships between community indices based on taxonomic diversity and functional diversity estimated from biological traits. We classified sampling sites based on similarities of their environmental variables and evaluated relations between clusters of sampling sites and diversity indices and biological traits. Our results showed that functional diversity was highly correlated with Shannon diversity index and species richness. The six clusters of sampling sites defined by a hierarchical cluster analysis reflected differences of their environmental variables. Samples in cluster 1 were mostly from high altitude areas, whereas samples in cluster 6 were from lowland areas. Non-metric multidimensional scaling (NMDS) displayed similar patterns with cluster analysis and presented variation of taxonomic diversity and functional diversity. Based on NMDS and community-weighted mean trait value matrix, species in clusters 1-3 displayed the resistance strategy in the life history strategy to the environmental variables whereas species in clusters 4-6 presented the resilience strategy. These results suggest that functional diversity can complement the biological monitoring assessment based on taxonomic diversity and can be used as biological monitoring assessment tool reflecting changes of ecosystem functioning responding to environmental changes.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.11 no.3
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• pp.190-197
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• 2001

Noise-induced hearing loss(NIHL) was the highest rate (43.5%~58.5% from 1996 to 1998) of positive findings through specific medical program in Korea. There were much more NIHL at workers of automobile manufacturing factories than other manufacturing factories. The specific aim of the present study was to determine the noise exposure of automobile press lines, according to their job titles, press line types(auto, semiauto), dosimeter parameters setting. There were a total 11 press lines sampled at a automobile manufacturing company. Among those press lines, 10 press lines were autolines with acoustic enclosure, one semiauto press line was no aucostic enclosure Noise exposure data were sampled for an work shift using noise dosimeter, which recorded both time-weighted average(TWA) and 1-min average. The mean OSHA TWA(Korea TWA with threshold 90) was $80.7dB(A){\pm}4.7dB(A)$ for leader, $82.8dB(A{\pm}4.5dB(A)$ for pallette man, $76.7dB(A){\pm}4.3dB(A)$ for press operators, $76.6dB(A){\pm}5.6dB(A)$ for crane operators, $77.1dB(A){\pm}2.8dB(A)$ for forklift drivers, whereas the mean NIOSH TWA was $88.9dB(A){\pm}1.7dB(A)$ for leader, $89.6dB(A){\pm}2.1dB(A)$ for pallette man, $86.7dB(A){\pm}1.8dB(A)$ for press operators, $88.5dB(A){\pm}2.0dB(A)$ for crane operators, $87.7dB(A){\pm}1.0dB(A)$ for forklift drivers. While L10 for NIOSH TWA samples was 84.8 dB(A) ~ 87.3 dB(A), L10 for OSHA TWA samples was 69.5 dB(A) ~ 77.4 dB(A). L10 means that the TWA for 90% of the samples exceeded L10. Among OSHA TWA(Korea TWA with threshold 90) samples for pallette man, 7.7 % exceeded 90 dB(A), the OSHA permissible exposure level, but OSHA TWA samples for the other job titles didn't. Among NIOSH TWA samples, the samples over 85 dB(A), the NIOSH recommended exposure limit, was 100% (leaders), 83.3 %(operators), 97.4%(palletteman), 100%(forklift drivers), 91.7 %(crane operator). The results of One-way random effects analysis of variance models shows that the difference between job titles was significant by OSHA TWA(p<0.05), but not significant by NIOSH TWA(p>0.05). NIOSH TWA samples were significantly higher than OSHA TWA samples(P<0.05). Regression analysis was used to obtain relationships between OSHA TWA samples and NIOSH TWA samples. In this case the coefficient of determination = 0.90, which shows the high degree association between two methods. Regression equation, NIOSH TWA = 0.552 * OSHA TWA + 42.13 dB(A), shows that if OSHA TWA is known, NIOSH TWA can be predicted by the equation. The mean TWA difference between threshold 80 dBA and 90 dBA was significant(p<0.01). While the TWA noise exposures were 7.7% above the Korea(OSHA) PEL, they were more than 83.3% over NIOSH REL. Automobile workers were exposed to noise level that could be potentially damaging to their hearing. It found that there is approximately 25% excess risk of hearing loss even if a worker is protected to the PEL in according to NIOSH study.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used