• Journal of Nutrition and Health
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• v.41 no.6
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• pp.539-549
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• 2008

This study was conducted to examine food and nutrient intakes of weekday and weekend with 329 high school boys residing in Seoul using the 3-day food record. Frequency for breakfast skipping was the highest as compared with lunch and dinner skipping. Frequencies for breakfast and lunch skipping were significantly higher in weekend than weekday (p<0.05). The daily average food, vegetable food and animal food intakes were 1,327.6 g, 800.5 g, and 425.8 g, respectively. Total food and vegetable food intakes of weekday were significantly higher than weekend(p<0.05). The dietary variety score(DVS) was 20.5 in daily average, 23.3 in weekday and 15.1 in weekend, which showed significantly higher in weekday than weekend(p<0.05). The daily averages of energy, protein, fat and carbohydrate intake were 2244.9 kcal, 89 g, 72.6 g, and 311.2 g, respectively. Fat intake was significantly lower and carbohydrate, fiber, phosphate, iron, sodium, potassium, vitamin A, niacin, folate, and vitamin C intakes were significantly higher in weekday than weekend (p<0.05). The percentages of energy intake from carbohydrate, protein, and fat were 55.4%, 15.8%, 28.8% in daily average, 56.8%, 15.8%, 27.4% in weekday and 53.6%, 15.8%, 30.7% in weekend, respectively. The percentages of energy intake from carbohydrate in weekday and weekend were below 60%, and that from fat was above 27% in weekday and weekend. Carbohydrate intake was significantly higher and fat intake was significantly lower in weekday than weekend(p<0.05). Energy intakes of daily average, weekday and weekend were above 83% as compared with estimated energy requirement(EER). Intakes of dietary fiber, calcium, potassium, vitamin C, riboflavin and folate were below 75% as compared with adequate intake(AI) or recommended intake(RI). Mean adequacy ratios(MAR), an index of overall dietary quality were 0.78 in daily average, 0.80 in weekday and 0.75 in weekend. MAR of weekend showed significantly lower than weekday(p<0.05). This study revealed that the overall nutrient intake status was worse in weekend than weekday among high school boys.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.147-157
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• 2017

Recently, ICT Retail Convergence(IRC) has been rapidly increasing to improve consumer satisfaction and consumer experience. In this paper, we aim to diagnose IRC from consumers' point of view by reviewing the present status and value of IRC according to consumer purchase decision making process. Based on the previous studies in retail industry, we classified IRC into 4 types: Experience-specific tech(Virtual Reality and Augmented Reality); Information-specific tech(Artificial Intelligence and Big Data); Location-based tech(Radio Frequency Identification and Beacon); Payment-related tech(Fin-tech and Biometrics). Next, we found that there is a difference in value provided to consumers according to the type of technology, analysing the value by consumer purchase decision making process. This study can be useful to introduce IRC for improving consumer satisfaction as well as ICT and Retail. Also, it can be basic data for future technology studies with a consumer perspective.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.319-326
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• 2023

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has increased the workload of healthcare workers (HCWs), impacting their health. This study aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and identify factors associated with poor sleep among HCWs in Vietnam during the COVID-19 pandemic. Methods: In this cross-sectional study, 1000 frontline HCWs were recruited from various healthcare facilities in Vietnam between October 2021 and November 2021. Data were collected using a 3-part self-administered questionnaire, which covered demographics, sleep quality, and factors related to poor sleep. Poor sleep quality was defined as a total PSQI score of 5 or higher. Results: Participants' mean age was 33.20±6.81 years (range, 20.0-61.0), and 63.0% were women. The median work experience was 8.54±6.30 years. Approximately 6.3% had chronic comorbidities, such as hypertension and diabetes mellitus. About 59.5% were directly responsible for patient care and treatment, while 7.1% worked in tracing and sampling. A total of 73.8% reported poor sleep quality. Multivariate logistic regression revealed significant associations between poor sleep quality and the presence of chronic comorbidities (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.17 to 5.24), being a frontline HCW directly involved in patient care and treatment (OR, 1.59; 95% CI, 1.16 to 2.16), increased working hours (OR, 1.84; 95% CI,1.37 to 2.48), and a higher frequency of encountering critically ill and dying patients (OR, 1.42; 95% CI, 1.03 to 1.95). Conclusions: The high prevalence of poor sleep among HCWs in Vietnam during the COVID-19 pandemic was similar to that in other countries. Working conditions should be adjusted to improve sleep quality among this population.

• Information Systems Review
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• v.22 no.2
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• pp.77-99
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• 2020

Smart speaker is a device that provides an interactive voice-based service that can search and use various information and contents such as music, calendar, weather, and merchandise using artificial intelligence. Since AI technology provides more sophisticated and optimized services to users by accumulating data, early smart speaker manufacturers tried to build a platform through aggressive marketing. However, the frequency of using smart speakers is less than once a month, accounting for more than one third of the total, and user satisfaction is only 49%. Accordingly, the necessity of strengthening the user experience of smart speakers has emerged in order to acquire a large number of users and to enable continuous use. Therefore, this study analyzes the user experience of the smart speaker and proposes a method for enhancing the user experience of the smart speaker. Based on the analysis results in two stages, we propose ways to enhance the user experience of smart speakers by model. The existing research on the user experience of the smart speaker was mainly conducted by survey and interview-based research, whereas this study collected the actual review data written by the user. Also, this study interpreted the analysis result based on the smart speaker user experience dimension. There is an academic significance in interpreting the text mining results by developing the smart speaker user experience dimension. Based on the results of this study, we can suggest strategies for enhancing the user experience to smart speaker manufacturers.

• Journal of Animal Science and Technology
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• v.46 no.6
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• pp.887-896
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• 2004

The present study was carried out to investigate the blood markers of Mongolian horses. The blood redcell types and blood protein types(biochemical polymorphisrns) were tested from 19 Mongolian horses by serological and electrophoretic procedure, and their phenotypes and gene frequencies were estimated. The blood group and biochemical polymorphism phenotypes observed with high frequency were $A^{af}$(42.1%), $C^a$(89.5%), $K^-$(84.2%), $U^a$(63.2%), $P^a$(42.1%) $P^-$42.1%), $Q^c$(31.6%) $Q^-$(31.6%), $AL^{AB}$((52.6%), AI$B^K$(89.5%), $ES^1$(63.2%), $GC^F$(78.9%), $HB^BI$1(68.4%), PG$D^F$(84.2%), $TF^{FIR}$(21.1%), $TF^{F2R}$(21.1%)(21.1%), and genotypes $D^{cgm/dghm}$(15.8%), $D^{dghm/dghm}$(15.8%), $D^{ad/dghm}$(10.5%), $D^{ade/dghm}$(10.5%), in Mongolian horses, respectively. Alleles observed with high frequency were $A^a$(0.4211), $C^a$(0.8947), $K^-$(0.8421), $U^a$(0.6316), $P^a$(0.4474), $Q^c$(0.4474), $D^{dghm}$(0.4211), $AL^B$(0.6579), $AIB^K$(0.9211), $ES^I$(0.7895), $GC^F$(0.8947), $HB^{BI}$(0.7895), $PGD^F$(0.8421) and $TF^R$(0.3421) in Mongolian horses. These results present basic information for estimating the genetic relationships between the Korean native horse, and developing a system for parentage verification and individuals identification in Mongolian horse.

• Journal of Nutrition and Health
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• v.43 no.5
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• pp.513-523
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• 2010

The food and nutrient intakes on weekdays and weekends was examined with 196 high school girls residing in Seoul using the 3-day food record. Frequency for skipping breakfast was higher than that for lunch and dinner. Frequencies for skipping breakfast and lunch were significantly higher on weekends than on weekdays (p < 0.05). The daily average food, vegetable food, and animal food intakes were 1,074.8 g, 680.0 g, and 317.3 g, respectively. The total food and vegetable food intakes on weekdays were significantly higher than on weekends (p < 0.05). The daily average dietary variety score (DVS) was 20.5, and was significantly higher on weekdays (23.3) than on weekends (15.1)(p < 0.05). The daily averages of energy, protein, fat and carbohydrate intake were 1,732.9 kcal, 68 g, 56.2 g, and 240.9 g, respectively. Energy, protein, carbohydrate, phosphate, iron, sodium, potassium, thiamin, niacin, folate and vitamin C intakes were significantly higher on weekdays than on weekends (p < 0.05). Fat intake was significantly lower on weekdays than on weekends (p < 0.05). The daily average percentages of energy intake from carbohydrate, protein, and fat were 55.4%, 15.6%, and 29.0%: 56.1%, 15.8%, and 28.2% on weekdays and 54.7%, 15.3%, and 30.1% on weekends, respectively. The percentages of energy intake from carbohydrate on weekdays and weekends were below 60%, and that from fat was above 28% on weekdays and weekends. The daily averages of energy, vitamin A, riboflavin, niacin and phosphate intake were above 80% as compared with the estimated energy requirement (EER) or the recommended intake (RI). The intakes of calcium, potassium and folate were below 50% as compared with the adequate intake (AI) or (RI). The daily average mean adequacy ratio (MAR), an index of overall dietary quality, was 0.77, and significantly high school girls was worse on weekends than on weekdays.

• Tuberculosis and Respiratory Diseases
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• v.48 no.6
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• pp.956-963
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• 2000

Backgrounds : Obstructive sleep apnea syndrome(OSA) can divided into two groups, positional(PP) and non-positional(NPP) obstructive sleep apnea syndrome, according to the body position while sleeping. In this study, we evaluated the differences of anthropometric data and polysomnographic recordings between the two types of sleep apnea syndrome. Materials : Fifty patients with OSA were divided two groups by Cartwright's criteria. The supine respiratory disturbance index (RDI) was at least two times higher than the lateral RDI in the PP group, and the supine RDI was less than twice the lateral RDI in the NPP group. This patients underwent standardized polysomnographic recordings. The anthropometric data and polysomnographic data were analyzed, statistically. Results : Of all 50 patients, 30% were found to be positional OSA. BMI was significantly higher in the PP group(p<0.05). Total sleep time was significantly longer in the PP group (350.6$\pm$28.2min, 333.3$\pm$46.0min, (p<0.05). Sleep efficiency was high in the PP group(89.6$\pm$6.4%, 85.6$\pm$9.9%, p<0.05). Deep sleep was significantly higher and light sleep was lower in the PP group than in the NPP group but no difference was observed in REM sleep between the two groups. Apnea index(AI) and RDI were significantly lower( 17.0$\pm$10.6, 28.5$\pm$13.3, p<0.05) and mean arterial oxygen saturation was higher in the PP group(92.7$\pm$1.8%. p<0.05) than in the NPP group. Conclusion : Body position during sleep has a profound effect on the frequency and severity of breathing abnormalities in OSA patients. A polysomnographic evaluation for suspected OSA patients must include monitoring of the body position. Breathing function in OSA patients can be improved by controlling their obesity and through postural therapy.

• Korean Journal of Food Science and Technology
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• v.37 no.4
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• pp.660-670
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• 2005

Standard procedure for development of food list was established based on food intake data of 2001 National Health and Nutrition Survey and 2002 Seasonal Nutrition Survey for Total Diet Study. Foods were sorted in descending order of mean intake, and 54 items within cumulative percentage of 80 were selected, followed by selection of 16 additional items with consumption frequency of 10% or higher. Based on higher consumption in certain seasons, regions, sexes, and age classes, 14 additional items were added. Additional 17 items with probable high contents of heavy metals or 23 items with probable high pesticide residues were added. Altogether, 101 and 107 individual food items were included for heavy metal and pesticide residue lists, accounting for 84.9 and 83.3% mean energy intakes of Korean population, respectively.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.