• Analytical Science and Technology
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• v.5 no.4
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• pp.389-399
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• 1992

Liquid Chromatographic behavior of Pd(II) in Isonitrosoethylacetoacetate lmine, $Pd(IEAA-NR)_2$ (R=H, $CH_3$, $C_2H_5$, $n-C_3H_7$, $C_6H_5-CH_2$, $n-C_4H_9$) chelates were investigated by reversed-phase HPLC on Micropak MCH-5 column using methanol/water as mobile phase. The optimum conditions for the separation of $Pd(IEAA-NR)_2$ chelates were examined with respect to the effect of the flow rate, sample solvent, mobile phase strength and column temperature. It wass found that metal chelates were properly eluted in an acceptable range of capacity factor value($0{\leq}log\;k^{\prime}{\leq}1$). The dependence of the logarithm of capacity factor(k') on the volume fraction of water in the binary mobile phase was examined. Also, the dependence of k' on the liquid-liquid extration distribution ratio($D_c$) in methanol-water/n-alkane extration system was investigated. Both kinds of dependence are linear, which susggests that the retention of the electroneutral metal chelate is largely due to the solvophobic effect. Standard adsorption enthalpy changes (${\Delta}H^{\circ}$) and standard adsorption entropy changes (${\Delta}S^{\circ}$) of Pd(II) Isonitrosoethylacetoacetate imine chelates on Micropak MCH-5 column were calculated by measuring capacity factor with changing temperature of the column.

• The Journal of the Korean life insurance medical association
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• v.24
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• pp.79-96
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• 2005

1. 연구배경과 문제제기 - 보험시장의 환경변화 : 보험업법 개정, 방카슈랑스 도입, 고(高)보장성 생존급부(CI, LTC)상품의 등장, 통신판매 전문보험회사의 설립 허용 - 현행 언더라이팅 시스템의 문제점 : 위험난이도와 판매 채널별 특성이 고려되지 않고 언더라이터에 전건 배정 되어 업무의 효율성이 낮음 - 보험시장의 환경변화에 맞는 EUS(Expert Underwriting System) 도입으로 언더라이팅의 효율성을 증대하고자함 2. 국내/외 생보사 언더라이팅 시스템 현황 비교 및 개선방안 - 국내 언더라이팅 시스템 현황 : 청약서 입력/스캔 후 진단 및 적부 유무(有無)에 따라 자동으로 언더라이터에게 심사가 배정됨 - 미국 언더라이팅 시스템 현황 : EUS에 의한 1차 전산승낙여부 결정 후(後)언더라이터에게 심사가 배정됨 - 위험난이도의 고저(高低)와 관계없이 언더라이터에 배정되는 심사시스템의 문제점을 극복하고 체계적인 위험평가를 위해 EUS도입이 필요함 3. EUS 선행요건 - 고객정보의 확보 - 국내 생보사의 고객정보 수집원 : 청약서, 모집인 보고서, 건강진단서,적부조사, 보험사고정보조회시스템 (ICPS), 고액보험 및 상해보험 중복가입자에 대한 정보 교환제도 - 북미 생보사의 고객정보 수집원 : 청약서, 모집인 보고서, 의사소견서 및 진료기록서, 건강검진, 적부조사, 정보교환제도( 북미보험사간 의료정보 공유-MIB) - 정확한 고객정보의 확보방안 : 법률/제도의 정비, 청약서 질문 내용의 세분화, 의료정보교환제도의 구축 4. EUS 개요 및 현황 - EUS의 정의: 고객의 정보를 입력하여 청약부터 보험증권 발행 단계까지 One-Stop 서비스를 제공하는 것으로 언더라이터가 청약서를 가지고 언더라이팅 하는 것과 동일한 업무를 할 수 있는 전문가 시스템 - EUS의 장점: (1) 비용절감 및 인력의 효율적 활용 (2) 업무별 시스템화 되는 조직속성에 적합함. (3) 언더라이팅 정책이 경영 환경 변화에 대처하는데 신속함 - 국외 EUS 현황 (예: Cologne Re) 및 사례연구 5. 위험분류 및 EUS 개요현황 (언더라이팅 시스템 도입) - 위험관리 선행요건으로 위험요소별 분류가 체계적으로 수립되어야 함. - 데이터웨어하우스 (의사결정을 목적으로 설계된 조회와 분석이 가능한 통합된 정보저장소) 시스템 사용 - EUS 도입을 통한 언더라이팅 프로세스: 데이터마이닝 과정을 통해 "자동승낙, 언더라이터에게 심사배정, 적부의뢰, 진단의뢰, 텔레 언더라이터, 보완지시"등이 결정됨. 6. 판매채널별 EUS 활용방안 - 대면채널: 효용성 높은 정보제공과 정확한 위험분석이 가능한 시스템으로 고(高)보장, 고(高)위험 상품에 대해 언더라이터가 집중 심사 할 수 있게 함. - 방카슈랑스: 3S(간결, 신속, 서비스)의 특성에 맞는 전과정 무인자동심사시스템 - 비대면채널: 판매상품과 타겟시장을 명확히 한 후 도덕적 위험과 재무적 위험에 대한 평가시스템 및 의사결정 시스템을 도입 7. 결론 - EUS 도입의 기대효과 (1) 심사기일의 단축으로 고객만족 실현 (2) 체계적 과학적 리스크 관리로 위험률차익 증대에 기여 (3) 업무효율의 증대와 언더라이터의 역량강화 (4) CRM 활용증대와 모바일 청약시스템 구축의 근간 - EUS 도입시 경제적 법률적 제도적 문제 극복과 생보 업계 공동의 관심과 노력이 필요함 - EUS를 활용하여 종합적.체계적 리스크 관리가 가능한 금융회사로의 경쟁력 향상에 기여함.

• Journal of radiological science and technology
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• v.45 no.6
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• pp.523-529
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• 2022

Gamma spectroscopy analysis is widely used for radioactivity analysis, and various factors are required for radioactivity calculations. Among the factors, K3 for each sample significantly influences the results. The previous methods of correcting the self-absorption effect include a computational simulation method and a method that requires making a CRM(certified reference material) identical to the sample medium. However, the above methods have limitations when used in small institutions because they require specialized program utilization skills or high manufacturing costs and large facilities. The aim of this study is to develop a method that can be easily and rapidly applied to radioactivity analysis. After filling the beaker with water, we placed the radiation source in a uniform position and used the measured value as the benchmark. Next, a correction factor was derived based on the difference in the radiation source count of the benchmark and the identically measured sample. For the radiation source, Eu-152, which emits a broad range of energy within the measurement range of gamma rays, and Cs-134 and Cs-137, which are indicator nuclides in environmental radiation analysis, were used. The sample was selected within the density range of 0.26-2.11 g/cm³, and the correction factor was derived by calculating the count difference of each sample compared to the reference value of water. This study presents a faster and more convenient method than the existing research methods for determining the self-absorption effect correction, which has become increasingly necessary.

• Analytical Science and Technology
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• v.36 no.3
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• pp.105-112
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• 2023

Lycopene, a carotenoid hydrocarbon is known to have effects on reducing cardiovascular risk factors, blood lipids, and blood pressure. Thus, a lot of supplementary foods with lycopene in several dosage forms like soft capsule filled with liquid and hard capsule filled with powder are available in a market. Recently, however, our research group found that the lycopene assay in Supplementary Food Code of South Korea is only valid for oily lycopene preparation. Thus, here, we developed a simple method to determine lycopene in solid preparations for Supplementary Food Code of South Korea using saponification and liquid chromatography with an absorbance detector. The method was validated following Ministry of Food and Drug Safety guidelines. All validation parameters observed in this study were within acceptable criteria of the guidelines (selectivity, linearity of r² ≥ 0.991, lower limit of quantification of 0.0149 mg/mL, accuracy as recovery (R) between 92.70 and 97.18 %, repeatability as relative standard deviation (RSD) values of R between 0.85 and 1.59 %, and reproducibility as the RSD value of interlaboratory R of 3.70 %). Additionally, the practical sample applicability of the validated method was confirmed by accuracy between 98.81 and 101.59 % observed from its lycopene certified reference material (CRM) analyses. Therefore, the present method could contribute to fortify the supplementary food safety management system in South Korea.

• Analytical Science and Technology
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• v.21 no.4
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• pp.316-325
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• 2008

An inductively coupled plasma mass spectrometry (ICP-MS) instrument equipped with flow injection-hydride generation system was used for the determination of trace arsenic in seawater sample. The accuracy in this method was verified by the analysis of certified reference materials (CRM) of seawater (CASS-4, NASS-5). The analytical results agreed with certified value within the range of uncertainty. The expanded uncertainties for CASS-4 and NASS-5 in this experiment were ranged from 6.2% to 6.8% obtained from repeated analyses of the CRMs (n=5). The detection limit of $As^+$ (m/z=74.9216) in this method was confirmed about 0.01 ug/kg. Linearity obtained from calibration curve of arsenic was excellent ($R^2=1$). The detection at $As^+$ (m/z=74.9216) and $AsO^+$ (m/z=90.9165) by using oxygen reaction gas in DRC mode was compared. Sensitivity at $AsO^+$ (m/z=90.9165) was decreased about 25-fold, but the analytical results are the same that at $As^+$ (m/z=74.9216).

• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future 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