• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2893-2900
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• 2014

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

• Journal of Nutrition and Health
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• v.40 no.1
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• pp.89-96
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• 2007

This study was performed to weigh the average meal portion sizes served for preschoolers by kindergarten teacher. The subjects were 53 teachers from 8 kindergartens, which are random sampled by meal service number. Using the weighing method assessed the meal portion sizes of food items at lunch. The data was complied by performing $\chi^2-test$ using SPSS WIN 11.0. The result was as followed: 98.0% of teacher agreed with the meal service because of 'better food habit and table etiquette'(68.0%), 'health promotion with balanced diet'(22.0%), 'owing to extending school time'(6.0%) and 'demand of parents'(2.0%). Preschooler eat lunch at class (84.9%) and meal serving size was decided by teacher (79.2%). Teachers thought that they know very well about portion size 3.8%, 96.2% of teacher thought that they don't know much about portion size. Portion size were not significantly different by food tray types but there was much different (almost 100%) compared with minimum and maximum within dishes. Most average portion size was not met dietary reference intake except cooked rice, soups and fish cutlet. Working experience effected on portion size. More served, more working experienced of teacher. For example Bulgogi was served 26.8 g by teacher who has over 6 years working experience compared with 2-6 years (20.4 g) and less than 2 years (17.1 g) (p < 0.01). Spinach portion size was significantly different by teacher's working experience (p < 0.01). Portion size were not significantly different by preschooler's age. The reference of dietary for preschooler was different by age, but teachers served meal by their experience. According to the results of this study, it is necessary to educate meal portion size for kindergarten teacher who take charge in meal serving. To provide guidance to teacher about reasonable portion sizes for preschoolers, teacher need to take nutrition education about meal service and child nutrition in college. This study would be useful to those who plan meals for preschoolers and to researchers studying dietary intakes of preschooler.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.1-7
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• 2020

The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.9
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• pp.1371-1377
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• 2002

During a 5-yr period, 1994-1998, pre-weaning and weaning data were collected on 591 calves produced by mating either straightbred Jersey, straightbred Limousin or $F_1$ (Limousin${\times}$Jersey) bulls to mature purebred Jersey or Limousin cows. Traits recorded included birth and weaning weight, height, length, girth, fat depth and a measure of muscle (ratio of stifle to hip width expressed as a percentage). All traits were analyzed assuming a model with sire and dam random effects that included effects of year and date of birth, sex, breed and year${\times}$sex interaction. Main effects were generally significant with few exceptions. Direct genetic effects were large for weight, height, girth and muscle with a breed trend from purebred Jersey (small) to purebred Limousin (large). At weaning, the maternal effect of the Jersey dam was positive for weight (10.9${\pm}$4.9 kg), girth (3.7${\pm}$1.0 cm) and muscle (6.0${\pm}$0.9%). Heterosis was highly significant and positive only for fat depth (1.5${\pm}$0.2 mm) with the $F_1$ progeny being the fattest, followed by the backcrosses, then purebred Jersey and purebred Limousin. Also, significant (p<0.001) but negative heterosis was observed for weight, girth and muscle. The change in ranking for fat depth relative to other traits is a reflection of the large heterotic effects relative to direct effects on fat depth. Epistatic effects were not significant on any trait at birth or weaning. This study has indicated the possibility of exploiting the positive heterotic and maternal effects for fat depth and muscularity to meet specific meat quality and quantity demand by consumers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.691-697
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• 2016

Remanufacturing refers to restoring a used product to an acceptable condition for resale in the market of remanufactured items. In this paper, we deal with the acquisition price and remanufacturing decision for remanufacturing systems in the case where the demand for the remanufactured product in a single period is known and the return quantity of the used product is determined by its acquisition price. The quality of the acquired used product is categorized into two classes, high and low, through inspection and different qualities incur different remanufacturing costs. The probability that the acquired used product is categorized as high class can be a constant or random variable. We derive the expected total cost functions, obtain the optimal solutions, and interpret the managerial meaning of the optimal solution for each case. The sensitivity of the optimal solution with respect to the variation of the inspection cost and uncertainty of the quality of the used product is investigated through numerical examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5400-5418
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• 2016

Femtocells are envisioned as a key solution to embrace the ever-increasing high data rate and thus are extensively deployed. However, the dense and random deployments of femtocell access points (FAPs) induce severe intercell inference that in turn may degrade the performance of spectral efficiency. Hence, unrestrained proliferation of FAPs may not acquire a net throughput gain. Besides, given that numerous FAPs deployed in ultra-dense networks (UDNs) lead to significant energy consumption, the amount of FAPs deployed is worthy of more considerations. Nevertheless, little existing works present an analytical result regarding the optimal FAP density for a given User Equipment (UE) density. This paper explores the realistic scenario of randomly distributed FAPs in UDN and derives the coverage probability via Stochastic Geometry. From the analytical results, coverage probability is strictly increasing as the FAP-to-UE ratio increases, yet the growing rate of coverage probability decreases as the ratio grows. Therefore, we can consider a specific FAP-to-UE ratio as the point where further increasing the ratio is not cost-effective with regards to the requirements of communication systems. To reach the optimal FAP density, we can deploy FAPs in line with peak traffic and randomly switch off FAPs to keep the optimal ratio during off-peak hours. Furthermore, considering the unbalanced nature of traffic demands in the temporal and spatial domain, dynamically and carefully choosing the locations of active FAPs would provide advantages over randomization. Besides, with a huge FAP density in UDN, we have more potential choices for the locations of active FAPs and this adds to the demand for a strategic sleeping policy.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.4
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• pp.607-621
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• 2009

In order to deal with consumer's demand changes in market condition, it is necessary to have a study on the consumer behavior to promote seafood consumption. The objective of this study is to examine the factors that have influences on consumer involvement of seafood. It is also aim to examine how the situation of seafood factors influences the consumer's behavior in purchasing and consuming the seafood. In this study, I have conducted a survey by using Busan housewives as random sampling. To examine this variable, situation factors that I used if the factor analysis. I also performed a multiple regression analysis to understand how the situation factors influenced the consumer's emotion and their satisfaction within the level of involvement. The result showed that consumer involvement seafood is created by not only interests and preferences, but also knowledge, perceived risk and profits. The seafood situation factors are divided into purchase situation, consumption situation and communication situation: purchase situation includes store factor, while consumption situation includes health factor and cooking-motive factor and communication situation includes information factor. Results of examining whether there are differences in the involvement explains that the involvement is different according to the level and it is divided into 2 groups. The first groups is the high-involvement group that shows preferences and interests, perceived risk and profits. Another group is the low-involvement group that shows preferences and interests, knowledge and profits. The result on examining whether situation sectors have influences on the involvement shows that high-involvement group is only affected by store factor in purchase situation and low-involvement group is only affected by information factor in communication situation.

• Journal of Ginseng Research
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• v.41 no.4
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• pp.444-449
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• 2017

The development of molecular markers is one of the most useful methods for molecular breeding and marker-based molecular associated selections. Even though there is less information on the reference genome, molecular markers are indispensable tools for determination of genetic variation and identification of species with high levels of accuracy and reproducibility. The demand for molecular approaches for marker-based breeding and genetic discriminations in Panax species has greatly increased in recent times and has been successfully applied for various purposes. However, owing to the existence of diverse molecular techniques and differences in their principles and applications, there should be careful consideration while selecting appropriate marker types. In this review, we outline the recent status of different molecular marker applications in ginseng research and industrial fields. In addition, we discuss the basic principles, requirements, and advantages and disadvantages of the most widely used molecular markers, including restriction fragment length polymorphism, random amplified polymorphic DNA, sequence tag sites, simple sequence repeats, and single nucleotide polymorphisms.