• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.455-472
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• 2001

The purpose of this study was to serve as a basis for mapping out successful strategies to build the professional self-image of nurses through uniform, by examining their satisfaction with uniform and professional self-image by general characteristics, and identifying the correlational relationship between the two factors. The subjects in this study were 205 nurses who served in general departments of two university hospitals in Seoul. The departments where special uniform was required, including ICU, RR, OR and CSR, were excluded. The data were collected from September 15 through 22, 2000. For measurement of uniform satisfaction level, a uniform satisfaction scale was prepared by myself, and Arthur(1990)'s PSCNI translated and modified by Song Kyong Ae and Rho Chun Hee(1996) was employed. The Cronbach a of the uniform satisfaction scale and PSCNI was 0.90 and 0.86 respectively. The collected data were analyzed by SAS, and real number, percentage, average and standard deviation were calculated. Besides, t-test, one-way ANOVA, Pearson's r procedures were utilized, and Scheffe test was conducted as a posttest. The findings of this study were as below: 1. The uniform satisfaction of the nurses investigated was scored 2.52 on the basis of 4 points, which was on the medium level. By subarea, symbolicity satisfaction was 2.48, and aesthetic satisfaction was 2.60. The functionability satisfaction was 2.44. So the esthetic satisfaction was greatest. 2. Among general characteristics of the subjects, two factors made a significant difference to their uniform satisfaction : age(F=4.05, P=.0189), and total career(F=4.25, P=.0061). 3. Their professional self-image got 2.75 on the basis of 4 points, which was on upper middle level. The subarea score was 2.79 for professional work, 2.52 for satisfaction and 2.97 for communication. The communication area was rated highest, and the satisfaction area was scored lowest. 4. Among the general characteristics of the subjects, professional self-image was different according to five factors : age(F=17.83, P=.001), marital status(T=5.18, P=.0000), educational background(F=8.72, P=.0002), position(T=-5.29, P=.0000) and total career(F=15.23, p=.0001). Better professional self-image was possessed by the older group than the younger one, by the married group than the singles, by the better-educated group than the less-educated, by the nurses in position equal to or higher than charge nurse, or by the higher-career group. 5. The correlational relationship of uniform satisfaction to professional self-image was statistically significant, yet very weak(r=.1978, p=.0045). The satisfaction area of professional self-image was correlated to every uniform satisfaction area, including symbolicity(4=.4393, p=.0001), aesthetics(r=.2471, p=.0004), functionability(r=.3094, p=.0001) and total satisfaction(r=.4050, p=.0001). Therefore, the uniform satisfaction gave an impact on the satisfaction area of professional self-image of the nurses, and there was a significant correlational relationship between uniform symbolicity area and total professional self-image(r=.2416, p=.0005).

• Korean Journal of Food Science and Technology
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• v.29 no.2
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• pp.379-382
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• 1997

The effects of different concentrations of ${\alpha}-tocopherol,\;{\delta}-tocopherol$, BHA, BHT and TBHQ on the oxidative stability of perilla oils undergoing autoxidation during storage at $50^{\circ}C$ were studied. ${\alpha}-\;and,\;{\delta}-tocopherols$ were added as concentrations of 100, 200, 300 and 400 ppm to the perilla oils from the unroasted seeds or the roasted seeds at $190^{\circ}C$ for 20 min. BHA, BHT and TBHQ were also added to the perilla oils described above as concentrations of 50, 100, 150 and 200 ppm, respectively. The oxidative stability of perilla oils was estimated by the antioxidative index (AI: the induction periods of oils with antioxidants/the induction periods of oils without antioxidants) on the basis of the peroxide values. The roasted perilla seed oil was more stable than the unroasted seed oil in autoxidation. The addition of ${\alpha}-\;and,\;{\delta}-tocopherols$ accelerated the autoxidation of perilla oils. BHA did not show antioxidant effects, but BHT showed very weak antioxidant effects. The autoxidation of perilla oils, however, was effectively prevented by the addition of TBHQ. TBHQ showed activity in preventing 5 times on the autoxidation of perilla oils. Therefore, the oxidation stability of perilla oils seemed to be depend both on the roasting process and the kind of antioxidants.

• Journal of Preventive Medicine and Public Health
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• v.29 no.3 s.54
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• pp.657-668
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• 1996

To investigate the factors which affecting the cognitive impairment of the 60 or more age group, the authors surveyed for the subjects in some area of Kyungnam Province. 201 studied subjects were tested for cognitive function with mini-mental state examination(MMSE). Information on demographic characteristics and life style has been collected through direct interview. The concentration of Ai and Ca of subject's drinking water, which might be related with cognition, was measured by Inductively Coupled Argon Plasma Spectrometer. The main results were summarized as follows. 1. The prevalence rate of cognitive impairment was 18.4% in male and 45.2% in female and this sexual difference was statistically significant(p=0.03). And the uneducated or illiterated showed significantly high prevalence rate of cognitive impairment(p=0.02). 2. In stratified analysis by sex md education year, we can not see significant trend indicating the neurotoxic effects of aluminum and protective effects of calcium to the cognitive function(p>0.05). 3. The correlation between the concentration of aluminum in drinking water and the MMSE score in whole subjects showed weak negative relationship(r=-0.066). But there was no statistical significance(p=0.434).

• Journal of Microbiology and Biotechnology
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• v.20 no.1
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• pp.71-77
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• 2010

The time, yield, and related genes expression of PHB accumulation of Acidiphilium cryptum DX1-1 were investigated under four different initial C/N ratios, 1.2, 2.4, 7.5, and 24. The results of time and yield of poly-$\beta$-hydroxybutyrate (PHB) accumulation show that the initial C/N ratio of 2.4 was optimum for strain DX1-1 to accumulate PHB, but both higher and lower initial C/N ratios did not favor that process. Based on the genome of Acidiphilium cryptum JF-5, 13 PHB accumulation related genes in strain JF-5 were chosen and successfully cloned from strain DX1-1. The differential expressions of the 13 functional genes, in different C/N ratios as cited above, were then studied by real-time PCR. The results show that all the 13 genes were most upregulated when the initial C/N ratio was 2.4, and among which the gene Acry_3030 encoding poly-$\beta$-hydroxybutyrate polymerase and Aery_0626 encoding acetyl-CoA synthetase were much more upregulated than the other genes, which proved that they play the most important role for PHB accumulation, and acetate is the main initial substance for PHB accumulation for strain DX1-1. Potential regulatory motifs analysis showed that the genes related to PHB accumulation are regulated by different promoters and that the motif had weak similarity to the model promoters, suggesting that PHB metabolism in Acidiphilium cryptum may be mediated by a different mechanism.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.215-226
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• 2020

Chatbot is emerging as an important interface window for business. This change is due to the continued development of chatbot-related research from NLP to NLU and NLG. However, the reality is that the methodological study of drawing domain knowledge and developing it into a user-friendly interactive interface is weak in the process of developing chatbot. In this paper, in order to present the process criteria of chatbot development, we applied it to the actual project based on the methodology presented in the previous paper and improved the development methodology. In conclusion, the productivity of the test phase, which is the most important step, was improved by 33.3%, and the number of iterations was reduced to 37.5%. Based on these results, the "3 Phase and 17 Tasks Development Methodology" was presented, which is expected to dramatically improve the trial and error of the chatbot development.

• Korean Journal of Microbiology
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• v.40 no.4
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• pp.320-327
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• 2004

Three hundreds thirty two bacterial colonies which were able to degrade crude oil were isolated from soil sam­ples that were contaminated with oil in Daejeon area. Among them, one bacterial strain was selected for this study based on its higher oil degrading ability, and this selected bacterial strain was identified as Acinetobactor sp. B2 through physiological-biochemical tests and analysis of its 16S rRNA sequence. Acinetobactor sp. B2 was able to utilize various carbohydrates but did not utilize trehalose and mannitol as a sole carbon source. Acinetobactor sp. B2 showed a weak resistance to antibiotics such as kanamycin, streptomycin, tetracycline and spectinomycin, but showed a high resistance up to mg/ml unit to heavy metals such as Ba, Li, Mn, AI, Cr and Pb. The optimal growth temperature of Acinetobactor sp. B2 was $30^{\circ}C.$ The lipase produced by Acinetobactor sp. B2 was purified by ammonium sulfate precipitation, DEAE-Toyopearl 650M ion exchange chromatography and Sephadex gel filtration chromatography. Its molecular mass was about 60 kDa and condition for the optimal activity was observed at $40^{\circ}C$ and pH 10, respectively. The activation energy of lipase for the hydrolysis of p­nitrophenyl palmitate was 2.7 kcal/mol in the temperature range of 4 to $37^{\circ}C,$ and the enzyme was unstable at the temperature higher than $60^{\circ}C.$ The Michaelis constant $(K_m)\;and\;V_{max}$ for p-nitrophenyl palmitate were 21.8 uM and $270.3\;{\mu}M\;min^{-1}mg^{-1},$ respectively. This enzyme was strongly inhibited by 10 mM $Cd^{2+},\;Co^{2+},\;Fe^{2+},\;Hg^{2+},$ EDTA and 2-Mercaptoethalol.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• 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.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.541-552
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• 2023

Objective: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. Materials and Methods: A model that can utilize "pneumoperitoneum" and "non-pneumoperitoneum" classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. Results: In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers' performances improved with the assistance of the proposed model. Conclusion: The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.