• Journal of Obesity & Metabolic Syndrome
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• v.27 no.4
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• pp.238-247
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• 2018

Background: We aimed to estimate the proportion of patients with diabetes who achieved target glycemic control, to estimate diabetes-related costs attributable to poor control, and to identify factors associated with them in the United Arab Emirates. Methods: This retrospective cohort study used administrative claims data handled by Abu Dhabi Health Authority (January 2010 to June 2012) to determine glycemic control and diabetes-related treatment costs. A total of 4,058 patients were matched using propensity scores to eliminate selection bias between patients with glycosylated hemoglobin (HbA1c) <7% and HbA1c ${\geq}7%$. Diabetes-related costs attributable to poor control were estimated using a recycled prediction method. Factors associated with glycemic control were investigated using logistic regression and factors associated with these costs were identified using a generalized linear model. Results: During the 1-year follow-up period, 46.6% of the patients achieved HbA1c <7%. Older age, female sex, better insurance coverage, non-use of insulin in the index diagnosis month, and non-use of antidiabetic medications during the follow-up period were significantly associated with improved glycemic control. The mean diabetes-related annual costs were $2,282 and $2,667 for patients with and without glycemic control, respectively, and the cost attributable to poor glycemic control was $172 (95% confidence interval [CI], $164-180). The diabetes-related costs were lower with mean HbA1c levels <7% (cost ratio, 0.94; 95% CI, 0.88-0.99). The costs were significantly higher in patients aged ${\geq}65$ years than those aged ${\leq}44$ years (cost ratio, 1.45; 95% CI, 1.25-1.70). Conclusion: More than 50% of patients with diabetes had poorly controlled HbA1c. Poor glycemic control may increase diabetes-related costs.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.535-543
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• 2019

Building on/with expansive soils with no treatment brings complications. Compacted expansive soils specifically fall short in satisfying the minimum requirements for transport embankment infrastructures, requiring the adoption of hauled virgin mineral aggregates or a sustainable alternative. Use of hauled aggregates comes at a high carbon and economical cost. On average, every 9m high embankment built with quarried/hauled soils cost $12600MJ.m^{-2}$ Embodied Energy (EE). A prospect of using mixed cutting-arising expansive soils with industrial/domestic wastes can reduce the carbon cost and ease the pressure on landfills. The widespread use of recycled materials has been extensively limited due to concerns over their long-term performance, generally low shear strength and stiffness. In this contribution, hydromechanical properties of a waste tyre sand-sized rubber (a mixture of polybutadiene, polyisoprene, elastomers, and styrene-butadiene) and expansive silt is studied, allowing the short- and long-term behaviour of optimum compacted composites to be better established. The inclusion of tyre shred substantially decreased the swelling potential/pressure and modestly lowered the compression index. Silt-Tyre powder replacement lowered the bulk density, allowing construction of lighter reinforced earth structures. The shear strength and stiffness decreased on addition of tyre powder, yet the contribution of matric suction to the shear strength remained constant for tyre shred contents up to 20%. Reinforced soils adopted a ductile post-peak plastic behaviour with enhanced failure strain, offering the opportunity to build more flexible subgrades as recommended for expansive soils. Residual water content and tyre shred content are directly correlated; tyre-reinforced silt showed a greater capacity of water storage (than natural silts) and hence a sustainable solution to waterlogging and surficial flooding particularly in urban settings. Crushed fine tyre shred mixed with expansive silts/sands at 15 to 20 wt% appear to offer the maximum reduction in swelling-shrinking properties at minimum cracking, strength loss and enhanced compressibility expenses.

• Korea Journal of Hospital Management
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• v.23 no.4
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• pp.15-25
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• 2018

Purposes: The objectives of this study present the direction of the criteria for the separately reimbursement of therapeutic medical device. Methodology: We summarized experts' opinion using Delphi survey and Analytic Hierarchy Process(AHP). 48 experts were gathered from Medical Insurance Review Nurses Association, medical device industry, academy and association, Medical Device Expert Evaluation Committee. Descriptive statistics, consistency index, content validity ratio were analyzed. Findings: Clinical utility, patient safety, infection control, cost-homogeneity, cost-effectiveness showed high feasibility and importance, but market contribution and functional utility showed low feasibility and importance in a relative sense. The results of functional utility differed between clinical and non-clinical experts. Measurability was low across the whole area. Among the criteria for the separately reimbursement of therapeutic medical device. Patient safety/infection control and clinical utility showed the highest relative importance values, analyzed using AHP. Practical Implications: Patient safety and infection control are needed to be considered as one of Value Assessment Criteria. It is important to find out how to improve the measurability of therapeutic medical device.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.60-67
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• 2019

The main purpose of this study is to identify the tasks performed at the construction stage of apartment houses and analyze the major construction tasks that may affect the performance indicators and analyze the impact on project performance. Based on expert opinions through preliminary research, pilot data, construction database, and other research trends, domestic and foreign research trends are divided into five major tasks, and each is divided into process management (P'1 ~ P'6), construction management (C'1 ~ C'5), environmental management (E'1 ~ E'4), quality control (Q'1 ~ Q'5), safety management (S'1 ~ S'5). Subsequently, each major task was subdivided into 24 detailed tasks. An independent t-test was conducted to analyze the impact on cost. We compared the importance of achieving the project goal and identified the construction work of concrete important construction which affects not only the cost but also the schedule performance index for the degree of significance of less than 0.05.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.801-809
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• 2023

Road construction projects have a long duration so cost overruns and schedule delays are occurred. However, performance assessment system that can manage and prepare for this in advance is insufficient. In addition, road construction are affected by many factors during under construction. Therefore it is necessary to conduct performance assessment considering the characteristics of roads and prepare for similar projects in the future. The purpose of this study is to provide a framework to evaluate construction phase performance and present a performance management plan using road construction information. Also, This study conducted time adjustment between the start and the finish of the project and developed performance metrics based on absolute and relative indicator. This study analyzed the cost, schedule, and changes of the road project construction process, showing the possibility of advancement of performance assessment and how to use it when planning new road construction projects.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of the Korean Dietetic Association
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• v.6 no.2
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• pp.136-147
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• 2000

The aim of this research was to examine the management practices related to foodservice.clinical nutrition service provided by hospital foodservice operations. A survey of 30 hospital food nutrition service departments was undertaken and detailed information was collected from each, including surveys of 176 dietitians and 30 foodservice managers. Statistical data analysis was completed using the SAS/win 6.11 package for descriptive analysis and t-test. The results of this study can be summarized as follows : Out of 30 hospitals, 73.3%(22) were directly operated and 26.7%(8) were under contract foodservice management. Licensed number of beds were 768.7, with an average length of 11.4 days. The general characteristics of the dietitians were that 49.4% were aged between 25-29, and 60.8% were ordinary dietitians. 76.7% had bachelor's degree, 15.9% with master's degree and 7.4% were college graduates. Most hospital dietitians had internship training and 35.2% took a training course of 6 to 12 months. The average space of kitchen was 452.52 $m^2$, with 133.63$m^2$ for modified diet space and 18.13$m^2$ for nutrition counselling room space. The total number of normal meals was 1,255.47, with 502.93 of modified meals. The average calorie of normal meals was 2,145.04kcal, with 91.9g of protein contained in normal meal. The total food waste was 351.40kg. An average monthly cost for disposing food waste was 745,171.67 won. 83.3% of the kitchens were on the 1st basement and only 66.7%(20) of 30 hospitals had its own nutrition counselling room. 80% used dishwashers and 66.7% had an exclusive elevator for delivering meals. 80.0% of hospitals used centralized delivery service. 90% had established an area for hygiene division, 70% used dry zone within the dishwashers for sterilization, 66.7% provided spoon and chopstick, and 100% used dish cover. As means of food waste treatment, most hospitals(56.7%) used animal feed by contracters, followed by means of collection by contracters(30%) and in-house high speed fermentation machines(13.3%). It was found that only 33.3% hospitals regularly checked temperatures of the meals given to patients. Total productivity index was 3.72(meals/hour) in average, with an average productivity index for normal diet of 5.41. Average productivity index for modified diet was 4.62. Productivity indices for patient meals and clinical nutrition were 5.01(meals/hour) and 1.12(cases/hour) respectively and hospitals under self-operated foodservice management received higher points on clinical nutrition productivity index(P<0.01) than hospitals under contract foodservice management.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.243-253
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• 2005

Dissemination of XML data on the internet could breach the privacy of data providers unless access to the disseminated XML data is carefully controlled. Recently, the methods using encryption have been proposed for such access control. However, in these methods, the performance of processing queries has not been addressed. A query processor cannot identify the contents of encrypted XML data unless the data are decrypted. This limitation incurs overhead of decrypting the parts of the XML data that would not contribute to the query result. In this paper, we propose the notion of query-aware decryption for efficient processing of queries against encrypted XML data. Query-aware decryption allows us to decrypt only those parts that would contribute to the query result. For this purpose, we disseminate an encrypted XML index along with the encrypted XML data. This index, when decrypted, informs us where the query results are located in the encrypted XML data, thus preventing unnecessary decryption for other parts of the data. Since the size of this index is much smaller than that of the encrypted XML data, the cost of decrypting this index is negligible compared with that for unnecessary decryption of the data itself. The experimental results show that our method improves the performance of query processing by up to 6 times compared with those of existing methods. Finally, we formally prove that dissemination of the encrypted XML index does not compromise security.

• Environmental and Resource Economics Review
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• v.19 no.2
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• pp.383-411
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• 2010

Climate change and related policies and regulations influence the performance of the firms in various ways. Climate change influences corporate competitiveness through physical impacts, GHG regulations, changes in asset values, demand shift, etc. Therefore, corporate competitiveness could be maintained by reducing vulnerability to climate change and adapting to new circumstances. Without effective responses to the challenges, the firms would have difficulties in maintaining their competitiveness in the market and the cost of national economy will significantly increase as well. Even though it seems fairly easy to understand the meaning of competitiveness, deriving the driving forces of and measuring changes in competitiveness are complicated and disputable processes. A common way to overcome it is to develop a 'competitiveness index'. The objective of this study is to derive the main factors influencing corporate competitiveness related to climate change and develop 'competitiveness index' reflecting those factors. The index will make contribution to enhance the response capacity of the firms to climate change and increase the effectiveness of climate change policies for the industry by providing a quantitative tool to measure the changes in corporate competitiveness related to climate change.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.