• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Korean Journal of Environmental Biology
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• v.17 no.3
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• pp.321-330
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• 1999

Nematodes were isolated using silkwom trap through the investigation of 100 soil samples from various biotopes in Korea. The 30 nematode strains from soil and dead insects by the pathogenicity aganinst silkworms (Bombyx mori mori) and insect pests of Calliphora vomitoria, Pseufazetia separata, Palomena angulosa, and Melolontha incana. Mortailty of the silkworm larvae and pupae were as high as 100% by nematode infection, those of insect of pests were varied from 20 to 100%. The 30 strains of entemopathogenic nematodes were classified into five groups of Rhabditidae, Diplogatroidae, Heterorhabitidae, Steinernematidae, and Tylenchida by morphological criteria. The genetic relationships among the 30 nematode strains were analyzed by various RAPD bands with twenty primers. The 30 nematode strains were classified into six major subgroups on the basis of the genetic similarity coefficient of 0.853. The grouping by RAPD was agree with those of morphological taxa in discrimination of the higher group, however, was not completely agree in the subgroup. The family Steinernematidae belong to Rhabditida was clarified as closer to the Tylenchida, rather than the other Rhabditida of Heterorhabitidae, Rhabditidae, and Diplogatroidae in genetic distance valule. From the result of the morphological classification and RAPD of the genomic DNA showed that genetic relationship analysis furnish infurmation on phylogenetic classification and relationships of entomopathogenic nematodes. The application of genetic similarity will overcome the limitation of taxonomy and classification of morphologically simple nematode. Several primers were confirmed those utility of identification for individual nematode strains, the methods of molecular genetics secured the simplicity, rapidity and accuracy on the selection of entomopathogenic nematodes.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

• Korean Journal of Community Nutrition
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• v.15 no.1
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• pp.137-148
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• 2010

This study was conducted on 175 child-care teachers, who participated in in-service education, to research the methods to improve child-care teacher's nutrition management capability for infants and children. Investigated results of child-care teachers' nutrition knowledge, dietary attitude status, and needs on nutrition education in child-care centers are as follows: The score of child-care teachers' nutrition knowledge was 10.83 points out of 15, which is about 72%. Total score increased as teachers' age but not significantly different from their career duration, since teachers who have a child-care career less than 5 years acquired 10.91 points, which is higher than 10.64 points of teachers having more than 5 years of child-care career. Teachers' average recognition to the nutrition knowledge was 90.6%, increased significantly by the older they are, and decreased according to the accumulation of their career. The average accuracy of the nutrition knowledge was 79.7%, increased in proportion to the teachers' age. The marks of child-care teachers' dietary attitude were 41.3 points (possible score range 5-50) and 83%, older teachers tended to have more desirable dietary attitude. As indicated by the increment of child-care career, the score of emotional attitude tended to be increased but which of cognitive and behavioral attitude showed a declining tendency. Nutrition information which child-care teachers were mainly interested in were correct selection of food (58.1%), obesity and weight management (52.7%), and nutrient content of food (44.9%). Nutrition education contents which child-care teachers needed were 'nutritious food and menu for child' (72.2%), 'health management of child' (69.2%) and meal management of child (40.2%). Nutrition education methods, which child-care teachers considered as of desirable ones, were cooking class of small scale (31.8%), visiting class at child-care center (26.5%). In consequence, the nutrition knowledge and dietary attitude of child-care teachers were not good and showed different issues by age and career duration. Therefore, it is requisite to intensify nutrition management courses in child-care teachers' qualification and in-service education courses which has actual necessity and suitability based on teachers' age, career, and the type of child-care center, and to disseminate these through public health centers and child-care & education information centers to pursue the efficient balance of nutrition education programs.

• The korean journal of orthodontics
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• v.33 no.6 s.101
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• pp.407-418
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• 2003

The purpose of this study is to analyze dentoalveolar compensation in normal occlusion samples previously classified into 9 skeletal types, and to provide clinically applicable diagnostic criteria for individual malocclusion patients. Cephalometric measurements of the 294 normal occlusion samples previously divided into 9 types were analyzed. The descriptive features of dentoalveolar variables were compared for the 9 types using analysis of variance, followed by post hoc multiple comparisons. In addition, the correlation between skeletal and dentoalveolar variables were analyzed. Discriminant analysis with a stepwise entry of variables was designed to find out several potential variables for use in skeletal typing. The dentoalveolar compensation pattern of the skeletal types varied, especially with regards to the variables that indicated the inclination of incisors and the occlusal plane. Stepwise variable selection identified four variables: AB-MP, SN-AB, PMA and ANB. Discriminant analysis assigned a classification accuracy of $87.8\%$ to the predictive model. On the basis of these results, this study could provide rudimentary information for the development of diagnostic criteria and treatment guidelines for individual skeletal types.

• The Korea Journal of Herbology
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• v.28 no.6
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• pp.47-51
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• 2013

Objectives : The Illicii Veri Fructus was not only traditional medicine but also food in Asia. The aim of this study was selection of optimum solvent in the fruit of Illicii Veri Fructus because an appropriate solvent affect a medicinal effect. Methods : Illicii Veri Fructus was carried out ultrasonic-assisted extraction as various solvents. Two main compounds, p-anisaldehyde and anethole, were successfully analyzed by high performance liquid chromatography-photodiode array detector (HPLC-PDA) and carried out method validation according to ICH guideline. The optimum solvent selected by comparing with yields of two main ingredients. Results : The p-anisaldehyde and anethole were detected at approximately 8.0 min and 19.8 min, respectively. It was all below 5.0% that RSD of retention time and peak area for two main peaks. Calibration curves of two compounds were good linearity as $R^2$ >0.9999. All of the precisions and accuracy were good intra-day and inter-day as below 5.0% RSD. Limited of detection (LOD) of p-anisaldehyde and anethole were analyzed as $0.134{\mu}g/mL$ and $4.286{\mu}g$, respectively. Limited of quantification (LOQ) of two compounds were $0.407{\mu}g$ and $12.989{\mu}g$, respectively. As a result of this study, p-anisladehyde was detected as 0.209 ~ 0.467%, however anethole was not detected in the distilled water. Conclusions : Anethole was main component as 5.329 ~ 6.815% except for water extraction. Methanol extraction among various solvents was detected the highest contents of p-anisaldehyde and anethole as 0.467(${\pm}0.008$)% and 6.815(${\pm}0.220$)%, respectively.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.5
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• pp.19-32
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• 2023

The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.599-610
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• 2020

The AED (Automated External Defibrillator) is not evaluated for spatial accuracy and temporal availability even if it is located within a building or a specific area that needed necessary to partition by spatial analysis and location allocation analysis. As a result of the analysis, the spatial analysis was performed using the existing public data of AED with applied the GIS location analysis method. A public institution (119 safety center, police box) was selected as a candidate for a public AED base that can operate 24 hours a day, 365 days a year according to the characteristics of each residential area. In addition, Thiessen Polygons were created for each candidate site and divided by regions. In the analysis of the service was analyzed regional in terms of accessibility to emergency medical services in consideration of the characteristics of AED, that emergency vehicles could arrive within 4 minutes of the time required for emergency medical treatment in most areas of the study area, but it did not areas outside of the city center. As a result, It was found that the operation of the AED base service center centered on vehicles of public institutions is effective for responding to AED patients at night and weekend hours. 19 Safety Center under and police box the jurisdiction of Daegu City to establish an AED service center for public institutions, location-based distance, attribute analysis, and minimization of overlapping areas that the method of using a vehicle appeared more efficient than using the existing walking type AED.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.1
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• pp.77-85
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• 2004

Regulations and guidelines for radioactive waste disposal require detailed information about the characteristics of radioactive waste drums prior to transport to the disposal sites. However, estimation of radionuclide concentrations in the drummed radioactive waste is difficult and unreliable. In order to overcome this difficulty, scaling factor (SF) method has been used to assess the activities of radionuclides, which could not be directly analyzed. A radioactive waste assay system has been operated at Korean nuclear power plant (KORI site) since 1996 and consolidated SF concept has played a dominant role in the determination of radionuclide concentrations. However, SFs are somewhat dispersive and limited in KORI site. Therefore establishment of the assay system using more improved SFs is planned and progressed. In this paper, the scope of research is briefly introduced. For the selection of more reliable activity determination method, the accuracy of predicted SF values for each activity determination method is compared. From the comparison of each activity determination method, it is recommended that SF determination method should be changed from the arithmetic mean to the geometrical mean for more reliable estimation of radionuclide activity. Arithmetic mean method and geometric mean method are compared based on the data set in KORI system. And, this change of SF determination method will prevent an inordinate over-estimation of radionuclide inventory in radwaste drum.