• Journal of the Korean Dietetic Association
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• v.12 no.2
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• pp.157-171
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• 2006

This study was intended to suggest HACCP-based standard recipe for bellflower roots, classified as no thermal cooking process, served in a university foodservice operation. The time-temperature and microbial contamination level in each cooking step were analyzed. The temperatures of bellflower root, peeled garlic and green onion at receiving were at 13.8$\pm$2.8, 12.6$\pm$2.9 and 13.7$\pm$$0.8^\circC$ respectively, which were above the temperature limit. The time consumed for pre-preparation was up to 90 min at room temperature having high microbial growth potential. The levels of total plate counts (TPC) of bellflower root and garlic were over the limit of $10^6$ CFU/g as were the numbers of coliforms in bellflower roots. There were no microbial reductions in pre-preparation and cooking, which resulted in over $10^5$- $10^6$ CFU/g of TPC at service step. Two CCPs identified were washing/sanitation at pre-preparation and service steps. The control measures were washing/sanitation and temperature control. It was verified that CCPs for no cooking process developed in preceding studies were applicable for the microbiological food safety of this menu item. The HACCP-based standard recipe was developed to produce a quantity for 100 servings by observing the critical limits established for CCPs. These results suggest that the selection of proper provider is imperative to control the microbial contamination of raw materials at purchasing step. Also, the sanitary education program should be developed for the employees to understand and comply the HACCP plan and standard recipe.

• Earthquakes and Structures
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• v.3 no.3_4
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• pp.341-363
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• 2012

The spectral representation method is a quick and versatile tool for the generation of spatially variable, response-spectrum-compatible simulations to be used in the nonlinear seismic response evaluation of extended structures, such as bridges. However, just as recorded data, these simulated accelerations require processing, but, unlike recorded data, the reasons for their processing are purely numerical. Hence, the criteria for the processing of acceleration simulations need to be tied to the effect of processing on the structural response. This paper presents a framework for processing acceleration simulations that is based on seismological approaches for processing recorded data, but establishes the corner frequency of the high-pass filter by minimizing the effect of processing on the response of the structural system, for the response evaluation of which the ground motions were generated. The proposed two-step criterion selects the filter corner frequency by considering both the dynamic and the pseudo-static response of the systems. First, it ensures that the linear/nonlinear dynamic structural response induced by the processed simulations captures the characteristics of the system's dynamic response caused by the unprocessed simulations, the frequency content of which is fully compatible with the target response spectrum. Second, it examines the adequacy of the selected estimate for the filter corner frequency by evaluating the pseudo-static response of the system subjected to spatially variable excitations. It is noted that the first step of this two-fold criterion suffices for the establishment of the corner frequency for the processing of acceleration time series generated at a single ground-surface location to be used in the seismic response evaluation of, e.g. a building structure. Furthermore, the concept also applies for the processing of acceleration time series generated by means of any approach that does not provide physical considerations for the selection of the corner frequency of the high-pass filter.

• Convergence Security Journal
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• v.18 no.2
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• pp.123-131
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• 2018

The goal of this study is to look deeper into a reading education method for improving cognitive abilities of petty officer majoring students in community college level. Lack of the cognitive ability through the passing status of reading information processing highly can cause a problem for understanding information of context. Therefore, this study redefines the reading step to improve the cognitive ability. also, it sets up progress steps; material selection - learning - inspection - practice based on the cognitive abilities. To achieve those goals, there are two major ways. The first, setting up a proper reading assignment that is suitable for petty officer major students in community college level is a key step for this study. Second, the instructor leads the students to judge their own cognitive ability objectively by using a portfolio curriculum which contains a checking list of the portfolio, structuring a curriculum based on weekly achievements, self-checking, and setting up a direction of practice. The two presented ways are the most effective ways to develop students' cognitive ability based on continuous reading and checking. For the last, the study mentions a proposal for further tasks in this field of the study.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.229-239
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• 2016

The aim of this paper is to extend the TOPSIS(Technique for Order Performance by Similarity to Ideal Solution) to the fuzzy environment for solving the new professor selection problem in a university. In order to achieve the goal, the rating of each candidate and the weight of each criterion are described by linguistic terms which can be expressed in trapezoidal fuzzy numbers. In this paper, a vertex method is proposed to calculate the distance between two trapezoidal fuzzy numbers. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all candidates. This research derived; 1) 4 evaluation criteria(research results, education and research competency, personality, major suitability) for new professor selection, 2) the 5 step procedure of the proposed fuzzy TOPSIS method for the group decision, 3) priorities of 4 candidates in the new professor selection case. The results of this paper will be useful to practical expert who is interested in analyzing fuzzy data and its multi-criteria decision-making tool for personal selection problem in personal management. Finally, the theoretical and practical implications of the findings were discussed and the directions for future research were suggested.

• Korean Journal of Environmental Biology
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• v.26 no.2
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• pp.121-127
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• 2008

As a step toward developing conservation and restoration plans for an endangered species of the Oriental White Stork (Ciconia boyciana), we studied daily foraging behavior and ecology. We released two individuals, male and female, after enclosing study site with a fence and cutting out a part of two or three feathers of primaries. Research was conducted from 16 June to 11 July, 2007 for 25 days at Whawonri, Mewonmyung, Chungbuk province. We investigated diural and daily variation of foraging time budget, diet composition, and habitat selection of the feeding individuals. The storks quadratically invested for foraging time and resting time according to time of day, but no significant variation in the foraging time and resting time appeared daily over 25 days. As a result, the storks mainly used wetland as a foraging site in the center of the study area to that in the edge as well as to other types of habitat. The high usage for wetland in the center did not change over the 25 days while Rice field decreased and Wetland in the edge and Pathway increased. Female invested more time for foraging than male, but the foraging efficiency did not differ between them. We discussed the significance of foraging behavior and habitat selection for management plan.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1544-1557
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• 2020

Objective: Genomic selection (GS) is becoming popular in animals' genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson's correlation of genomic estimated breeding value (GEBV) with EBV of all animals (r_M1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (r_M2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results: The r_M1 estimates in the study were between 0.90 and 0.96 among five traits. The r_M1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average r_M2 estimates were much smaller than r_M1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, r_M2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of r_M2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the r_M2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based r_M2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.

• Research in Plant Disease
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• v.17 no.1
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• pp.52-58
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• 2011

The aim of this research was to develop specific and sensitive PCR-based procedures for simultaneous detection of economically important plant pathogenic bacteria and seed borne virus in commercial Brassicaceae crop seeds, Xanthomonns campestris pv. campestris (Xcc) and Lettuce Mosaic Virus (LMV). Bacterial and virus diseases of Brassicaceae leaves are responsible for heavy losses. PCR with arbitral primers: selection of specific primers, performance of PCR with specific primers and determination of the threshold level for pathogens detection. To detect simultaneously the Xcc and LMV in commercial Brassicaceae crop seeds (lettuce, kohlrabi, radish, chinese cabbage and cabbage), two pairs of specific primer (LMV-F/R, Xcc-F/R) were synthesized by using primer-blast program (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). The multiplex PCR for the two pathogens in Brassicaceae crop seeds could detect specifically without interference among primers and/or cDNA of other plant pathogens. The pathogen detection limit was determined at 1 ng of RNA extracted from pathogens. In the total PCR results for pathogen detection using commercial kohlrabi (10 varieties), lettuce (50 varieties), radish (20 varieties), chinese cabbage (20 varieties) and cabbage (20 varieties), LMV and Xcc were detected from 39 and 2 varieties, respectively. In the PCR result of lettuce, LMV and Xcc were simultaneously detected in 8 varieties.

• Computers and Concrete
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• v.28 no.6
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• pp.567-583
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• 2021

The use of waste materials as a binder or aggregate in the concrete mixture is a great step towards sustainability in the construction industry. Waste rubber (WR) can be used as coarse and fine aggregates in concrete and improves the crack resistance, impact resistance, and fatigue life of the produced concrete. However, the mechanical properties of rubberized concrete degrade significantly by replacing the natural aggregate with WR. To have accurate estimations of the mechanical properties of rubberized concrete, two machine learning methods consisting of artificial neural network (ANN) and neuro-fuzzy system (NFS) were served in this study. To do this, a comprehensive dataset was collected from reliable literature, and two scenarios were addressed for the selection of input variables. In the first scenario, the critical ratios of the rubberized concrete and the concrete age were considered as the input variables. In contrast, the mechanical properties of concrete without WR and the percentage of aggregate volume replaced by WR were assumed as the input variables in the second scenario. The results show that the first scenario models outperform the models proposed by the second scenario. Moreover, the developed ANN models are more reliable than the proposed NFS models in most cases.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.255-277
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• 2011

Product Service System(PSS), which is an integrated combination of product and service, provides new value to customer and makes companies sustainable as well. The objective of this paper draws Critical Successful Factors(CSF) of PSS through multiple case study. First, we review various concepts and types in PSS and Platform business literature currently available on this topic. Second, after investigating various cases with the characteristics of PSS and platform business, we select four cases of 'iPod of Apple', 'Kindle of Amazon', 'Zune of Microsoft', and 'e-book reader of Sony'. Then, the four cases are categorized as successful and failed cases according to criteria of case selection and PSS classification. We consider two methodologies for the case selection, i.e., 'Strategies for the Selection of Samples and Cases' proposed by Bent(2006) and the seven case selection procedures proposed by Jason and John(2008). For case selection, 'Stratified sample and Paradigmatic cases' is adopted as one of several options for sampling. Then, we use the seven case selection procedures such as 'typical', 'diverse', 'extreme', 'deviant', 'influential', 'most-similar', and 'mostdifferent' and among them only three procedures of 'diverse', 'most?similar', and 'most-different' are applied for the case selection. For PSS classification, the eight PSS types, suggested by Tukker(2004), of 'product related', 'advice and consulancy', 'product lease', 'product renting/sharing', 'product pooling', 'activity management', 'pay per service unit', 'functional result' are utilized. We categorize the four selected cases as a product oriented group because the cases not only sell a product, but also offer service needed during the use phase of the product. Then, we analyze the four cases by using cross-case pattern that Eisenhardt(1991) suggested. Eisenhardt(1991) argued that three processes are required for avoiding reaching premature or even false conclusion. The fist step includes selecting categories of dimensions and finding within-group similarities coupled with intergroup difference. In the second process, pairs of cases are selected and listed. The second step forces researchers to find the subtle similarities and differences between cases. The third process is to divide the data by data source. The result of cross-case pattern indicates that the similarities of iPod and Kindle as successful cases are convenient user interface, successful plarform strategy, and rich contents. The differences between the successful cases are that, wheares iPod has been recognized as the culture code, Kindle has implemented a low price as its main strategy. Meanwhile, the similarities of Zune and PRS series as failed cases are lack of sufficient applications and contents. The differences between the failed cases are that, wheares Zune adopted an undifferentiated strategy, PRS series conducted high-price strategy. From the analysis of the cases, we generate three hypotheses. The first hypothesis assumes that a successful PSS system requires convenient user interface. The second hypothesis assumes that a successful PSS system requires a reciprocal(win/win) business model. The third hypothesis assumes that a successful PSS system requires sufficient quantities of applications and contents. To verify the hypotheses, we uses the cross-matching (or pattern matching) methodology. The methodology matches three key words (user interface, reciprocal business model, contents) of the hypotheses to the previous papers related to PSS, digital contents, and Information System (IS). Finally, this paper suggests the three implications from analyzed results. A successful PSS system needs to provide differentiated value for customers such as convenient user interface, e.g., the simple design of iTunes (iPod) and the provision of connection to Kindle Store without any charge. A successful PSS system also requires a mutually benefitable business model as Apple and Amazon implement a policy that provides a reasonable proft sharing for third party. A successful PSS system requires sufficient quantities of applications and contents.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.95-101
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• 2008

Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.