• Korean journal of applied entomology
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• v.49 no.4
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• pp.313-324
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• 2010

With growing interest in biodiversity in rice paddies, efficient sampling methods for quantitative evaluation of aquatic macroinvertebrates are needed because of their important role in rice paddies' food webs. For this reason, we sought a proper sampling method through comparing data collected by using aquatic net, quadrat, and core in rice paddies located in Chuncheon-si, Kangwon-do, Hongseong-gun, and Chungcheongnam-do. Because the frame of the net was rectangular and had a flat bottom, the area, sampledwith the aquatic net, was calculated by multiplying the net width by the sweeping length, All samples were taken from the space between the rows of rice plants. Twenty four taxa of macro invertebrates were collected in Chuncheon and 28 taxa in Hongseong. In Chuncheon, the number of taxa was similar among three sampling methods (17-18 taxa), but the number of individuals was different (aquatic net, $1,317/m^2$; quadrat, $1,368/m^2$; core, $1,810/m^2$. In Hongseong, the number of taxa sampled by aquatic net was the highest (aquatic net, 25 taxa; quadrat, 21 taxa; core, 16 taxa), but the core was the highest in the number of individuals (aquatic net, $1,586/m^2$; quadrat, $2,595/m^2$; core, $3,704/m^2$. The efficiency of samplers differed among taxa. Most of aquatic insect taxa were more abundant in the aquatic net, while those living on or in the paddy substratum such as Oligochaeta and Chironomidae were collected more in the quadrat sampler. To collect quantitative data for aquatic insects as well as to produce inventory of rare taxa, we suggest, based on samplers used in this study, to take quantitative samples of 6 replications from each of the edge and inner zones of a rice paddy by using an aquatic net, and to take qualitative samples both from sides of levees and the inner zone of rice paddy by using an aquatic net.

• Journal of muscle and joint health
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• v.3 no.1
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• pp.90-103
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• 1996

The purpose of this paper is to compare two approach to assessment of coping patterns. The sampling method was a purposive sampling technique. The study participants were out patients 113 of rheumatoid arthritis center of one University hospitals in Seoul. Datas were collected from Nov. 13 to Nov. 24, 1995. The instruments used for this study were Graphic Rating Scales of pain, The Beck Depression Inventory and Coping Patterns tool. The collected data were analyzed for frequency, means, SD, factor analysis., Pearsons' correlations and ANOVA. The results were summerized as follows ; 1. When raw scores were used : there were not correlation in all three coping patterns. 2. When relative scores were used : there were significantly correlated in all three coping patterns. 1) Active coping and Positive-cognitive coping (r=-0.352, p< 0.0001) 2) Positive-cognitive coping and Negative-cognitive coping (r=-0.594, p< 0.0001) 3) Active coping and Negative-cognitive coping(r=-0.544, p< 0.0001) The results of this research with relative scales provided more insight into the correlation in all three coping patterns. 3. Pearsons' Correlations were computed for each coping pattern, age, pain level, duration of pain and BDI. 1) Using raw score : (1) Active coping was significantly related to pain level(sensory score ; r=0.268, p<0.05, affective score ; r=0.266, p< 0.05) (2) Positive-cognitive coping was significantly related to age (r=-0.252, p< 0.05), pain level (sensory score ; r= -0.244, p< 0.05) (3) Negative-cognitive coping was significantly related to depression level (r=0.312 p< 0.0001). 2) Using relative score (1) Active coping was significantly related to pain level(sensory score ; r=0.299, p<0.05, affective score ; r=0.246, p< 0.05) (2) Positive-cognitive coping was significantly related to age (r= -0.187, P< 0.05), pain level (sensory score ; r=-0.317, p<0.0001, affective score : r=-0.305, p<0.0001) and depression level(-0.339, p<0.0001)) (3) Negative-cognitive coping was significantly related to depression. level(r=0.313, p<0.0001). 4. When raw and realtive coping scores were compared to those of age groups, religious groups and BDI level(high, middle, low) ; 1) Using raw score : (1) Active coping : there were not significantly difference (2) Positive-cognitive coping ; 20-39 age group and 50-59age group had significantly higher scores than over 60age group. BDI-low level group had significantly higher scores than other groups. (3) Negative-cognitive coping : 20-39age group and over 60age group had significantly higher scores than 40-49age group. Non-religious group had significantly higher scores than christian group. BDI-high level group had significantly higher scores than other groups. 2) Using relative score : (1) Active coping : over 60 age group had significantly higher scores than 20-39 age group and 40-49age group had significantly higher scores than 20-39 age group (2) Positive-cognitive coping ; 40-49age group, 20-39age group and 50-59age group had significantly higher scores than over 60age group. Christian group had significantly higher scores than non-religious group. BDI-low level group had significantly higher scores than other groups. (3) Negative-cognitive coping ; Non-religious group had significantly higher scores than christian group and buddhistic group. BDI-high level group had significantly higher scores than other groups. The current data suggest that relative scores may yield a different perspective on coping patters than raw scores. The use of relative scores reveals the relation clearly, without its being blurred statistically by the effect of other coping strategies or being relegated to a partial correlation. The use of relative scores holds promise for delineating the relations between ways of coping and health-related behavior.

• Journal of Hospice and Palliative Care
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• v.6 no.1
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• pp.34-44
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• 2003

Purpose : The purpose of this study was to elucidate the effects of hospice care on pain management of the terminal cancer patients. Method : The subjects of the study were 37 terminal cancer patients hospitalized in the general hospital in JeonJu with the hospice care nit. The data were collected using the questionaire with interviews from July to Nov. in 2000. The severity and interference of pain were examined with the self reported survey based on the Korean version of Brief Pain Inventory (BPI-K). The data were analyzed with the mean, SDs, paired t-test. The hospice care to provide for three weeks. Results : 1) The mean scores of the worst pain for the last 24-hours measured with the pain severity of BPI-K were pre-intervention (6.35) and post (4.76). The pain interference of BPI-K in pre-intervention was enjoy (8.22), work (7.46), walk (7.08) and activity (7.08), while post was of enjoy (6.62), work (6.43), walk (6.11) and activity (5.78), respectively. 2) In pain severity, significant difference was found between the pre-intervention and post in all of followings. - worst pain for last 24 hours (t=4.085, P=.000) - least pain for last 24 hours (t=4.020, P=.000) - average pain for last 24 hours (t=4.254, P=.000) - pain right now (t=4.017, P=.000) 3) In pain interference, significant difference was found between the pre- intervention and the post in all of followings. - activity (t=3.137, P=.003) - mood (t=6.713, P=.000) - walk (t=2.027, P=.050) - work (t=2.132, P=.040) - relate (t=4.143, P=.000) - sleep (t=4.071, P=.000) - enjoy (t=3.881, P=.000) Conclusion : The terminal cancer patients who were offered hospice care had significantly lower hospice care pain and pain interference than those without hospice care. According to these results, hospice care can be regarded as an effective modality in relief of pain in the terminal ill-patients.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.48-61
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• 2022

South Korea is quite vegetation rich country which has 63% forests and 16% cropland area. Massive NOx emissions from megacities, therefore, are easily combined with BVOCs emitted from the forest and cropland area, then produce high ozone concentration. BVOCs emissions have been estimated using well-known emission models, such as BEIS (Biogenic Emission Inventory System) or MEGAN (Model of Emission of Gases and Aerosol from Nature) which were developed using non-Korean emission factors. In this study, we ran MEGAN v2.1 model to estimate BVO Cs emissions in Korea. The MO DIS Land Cover and LAI (Leaf Area Index) products over Korea were used to run the MEGAN model for June 2012. Isoprene and Monoterpenes emissions from the model were inter-compared against the enclosure chamber measurements from Taehwa research forest in Korea, during June 11 and 12, 2012. For estimating emission from the enclosed chamber measurement data. The initial results show that isoprene emissions from the MEGAN model were up to 6.4 times higher than those from the enclosure chamber measurement. Monoterpenes from enclosure chamber measurement were up to 5.6 times higher than MEGAN emission. The differences between two datasets, however, were much smaller during the time of high emissions. More inter-comparison results and the possibilities of improving the MEGAN modeling performance using local measurement data over Korea will be presented and discussed.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.1
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• pp.134-145
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• 2011

The purpose of this study was to examine the work values of undergraduate-level foodservice major students. In order to assess work values of the participating students, this study adopted 'Maryland Work Values Inventory'. Data from a total of 290 surveys were collected. The participants consisted of 146 commercial foodservice major students and 144 institutional foodservice major students. Among the seven work values, both groups ranked 'job advancement' as the most important work value. Commercial foodservice major students rated 'altruism' as the least important work value, whereas it was 'stimulation' for institutional foodservice major students. 'Need for work' was evaluated statistically higher by junior and senior students compared to sophomore students of commercial foodservice major (p<0.05). In both foodservice majors, a positive relationship was found between work value scores and grade levels. All work value scores tended to be higher for students in higher grades. This tendency was especially statistically clear for the value 'need for work' for commercial foodservice major students and 'money and prestige' for institutional foodservice major students (p<0.05). There were some gaps in the work values between students with different career field choices. For the commercial foodservice majors, those interested in fine dining perceived 'satisfaction and accomplishment' and 'altruism' as more important [4.33 (p<0.05) and 4.05 (p<0.01), respectively] compared to students who had interests in fast food restaurants (4.06 and 3.67 respectively). Scores for 'satisfaction & accomplishment' (p<0.05), 'money & prestige' (p<0.001), and 'stimulation' (p<0.001) were significantly different among institutional foodservice major students. In addition, students more interested in industrial foodservice field regarded 'money & prestige' and 'stimulation' as less important as indicated by noticeably lower scores (3.74, 3.55 respectively) in comparison to the other group (p<0.001). The results of this study, which explored foodservice major students' work values, suggest that there is an increased need for the students to build up their work values as well as for the foodservice industry to offer appropriate work values to future foodservice employees.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.16 no.1
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• pp.79-89
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• 2005

Objectives : To acquire an improved understanding of oppositional defiant disorder, we evaluated the characteristics of children who have the symptoms of ODD in community sample. Methods : 1200 children from an elementary school in Bucheon (an urban community near Seoul) were recruited by randomized sampling method. By Disruptive Behavior Disorder Scale according to DSM-III-R & DSM-IV, we evaluated the symptoms of ODD and selected subjects with ODD. Psychiatric comorbidity, character trait were compared in subjects with ODD and comparison group. Also we examined the association between prenatal/perinatal risk factors, family functions and the symptoms of ODD. Data were analyzed by appropriate statistical method using SPSS 11.5 window version. Result : Children with oppositional defiant disorder were revealed to have significantly higher rates of psychiatric comorbidity and significantly greater family dysfunction compared to comparison group. Among the prenatal/perinatal risk factors, severe emotional stress during pregnancy, postpartum depression, medication during pregnancy were revealed as risk factors of ODD. In character inventory, ODD group were evaluated to have high score in novelty seeking, harm avoidance, but low in reward dependency. Conclusion : These results support that 1) prenatal/perinatal and psycho-social risk factors could be a important role in the progression of ODD, and 2) children with ODD have diverse comorbid psychiatric symptoms.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.16 no.2
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• pp.251-260
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• 2005

Objectives : This study was performed to evaluate the effects of a mental health program within a mental health model middle school. Methods : Subjects of the study consisted of 748 students from the second grade and third grade students at the middle school chosen for a school mental health program by the Ministry of Education in Jeju. The subjects participated in 12 consecutive sessions of group discussion developed to prevent mental health problems. The authors investigated the effects of the program by evaluating the students with Young's Internet Addiction Scale (IAS), Conners & Wells' Adolescent Self Report Scale(CASS) and Minnesota Multiphasic Personality Inventory (MMPI) before the initial session and after the final session. The data was analyzed by t-test in SPSS PC+ 10.0. The range of significance was p<.05. Results : In MMPI, the percentage of students above clinical range reduced from $12.9\%\;to\;11.0\%$. It reduced in the second grade students, but increased in the third grade students. The scores of paranoia and mania subscales showed a statistically significant reduction. In IAS, the percentage of students above the range of Internee overuse reduced from $16.0\%\;to\;6.8\%$. The percentage of students who showed risk of attention problems in CASS reduced from $22.7\%\;to\;18.3\%$. Also, both IAS and CASS scores showed a statistically significant reduction. The clinical significance of the reduction of IAS scores was within moderate range. Conclusion : The mental health program reduced the percentage of students' risk of mental health problems, internet addiction and attention problems and it was clinically effective on preventing Internet addiction. These results support the effects of a school mental health program to promote students' mental health. The authors suggest to expand this program to other schools, to reconfirm the effect of the program by using proper & specified instruments and to evaluate long-term effect of the program.