The Psychological Characteristics of Women in the Obesity Clinic (비만클리닉에 내원한 여성의 심리적 특성)
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- Korean Journal of Psychosomatic Medicine
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- v.11 no.2
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- pp.137-148
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- 2003
Introduction: This research was performed to contract the attitude of dietary restriction and the psychological problems such as depressive mood and perceived stress and to investigate the relationship of these and obesity in women who visited the obesity clinic. Methods: During May 2001, sociodemographic variables, physical characteristics, Three Factor Eating Questionnaire(TFEQ), Symptom Check List-90-R(SCL-90-R) and Perceived Stress Scale were assessed from 150 female who visited the obesity clinics which were located at downtown, Seoul and the Hospital of Ajou University, Medical College. Hamilton depression rating scale(HDRS) was estimated by author. And then 116 female cases who filled up the questionnaire faithfully were included. Results: Obese group more than Body Mass Index(BMI)
Recently strategic alliance between business firms has become prevalent to overcome increasing competitive threats and to supplement resource limitation of individual firms. As one of allianced sales promotion activities, a new type of discount program, so called "Alliance Card Discount", is introduced with the partnership of credit cards and loyalty cards. The program mainly pursues short-term sales growth by larger discount scheme while spends less through cost share among alliance partners. Thus this program can be regarded as cost efficient discount promotion. But because there is no solid evidence that it can really deliver profitable sales growth, an empirical study for its effects on sales and profit should be conducted. This study has two basic research questions concerning the effects of allianced discount program ; 1)the possibility of sales increase 2) the profitability of the discount driven sales. In F&B industry, sales increase mainly comes from increased guest count. Especially in family restaurants, to increase the number of guests we need to enlarge the size of visitor group (number of visitors for one group) because customers visit by group in a special occasion. And because they pay the bill by group(table), the increase of sales per table is a key measure for sales improvement. The past researches for price & discount sensitivity and reference discount rate explain that price sensitive consumers have narrow reference discount zone and make rational purchase decision. Differently from all time discount scheme of regular sales promotions, the alliance card discount program only provides the right to get discount like discount coupon. And because it is usually once a month opportunity given by the past month usage level, customers tend to perceive alliance card discount as a rare chance to get. So that we can expect customers try to maximize the discount effect when they use the limited discount opportunity. Considering group visiting practice and low visit frequency of family restaurants, the way to maximize discount effect should be the increase the size of visit group. And their sensitivity to discount and rational consumption behavior defer the additional spending for ordering high price menu, even though they get considerable amount of savings from the discount. From the analysis of sales data paid by alliance discount cards for four months, we found the below. 1) The relation between discount rate and number of guest per table is positive : 25% discount results one additional guest 2) The relation between discount rate and the spending per guest is negative. 3) However, total profit amount per table is increased when discount rate is increased. 4) Reward point accumulation & redemption did not show any significant relationship with the increase of number of guests. These results suggest that the allianced discount program substantially contributes to sales increase and profit improvement by increasing the number of guests per table. Though the spending per guest is decreased by discount rate increase, the total amount of profit per table is improved. It seems the incremental profit by increased guest count offsets the profit decrease. Additional intriguing finding is the point reward system does not have any significant impact on the increase of number of guest, even if the point accumulation & redemption of loyalty program are usually regarded as another saving offers by customers. In sum, because it is proved that allianced discount program with credit cards and loyalty cards is effective to both sales drive and profit increase, the alliance card program could be recommended as strategically buyable program.
Purpose: I-131 is a radioisotope widely used for thyroid gland treatments. The physical half life is 8.01 and characterized by emitting beta and gamma rays which is used in clinical practice for the purpose of acquiring treatment and images. In order to reduce the recurrence rate after surgery in high-risk thyroid cancer patients, the remaining thyroid tissue is either removed or the I-131 is used for treatment during relapse. In cases of using a high dosage of radioactive iodine requiring hospitalization, the patient is administered dosage in the hospital isolation ward over a certain period of time preventing I-131 exposure to others. By checking the radiation amount emitted from patients before discharge, the patients are discharged after checking whether they meet the legal standards (50 uSv/h). After patients are discharged from the hospital, the contamination level is checked in many parts of the ward before the next patients are hospitalized and when necessary, decontamination operations are performed. It is expected that there is exposure to radiation when measuring the ward contamination level and dose check emitted from patients at the time of discharge whereby the radiation exposure by health workers that come from the patients in this process is the main factor. This study analyzed the correlation between discharge dose of patients and ward contamination level through a variety of factors such as renal functions, gender, age, dosage, etc.). Materials and Method: The study was conducted on 151 patients who received high-dosage radioactive iodine treatment at Soon Chun Hyang University Hospital during the period between 8/1/2011~5/31/2012 (Male: Female: 31:120,
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