• Journal of the Korean Society for Library and Information Science
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• v.14
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• pp.93-130
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• 1987

Scientific communication is an information exchange activity between scientists. Scientific communication is carried out in a variety of informal and formal ways. Basically, informal communication takes place by word of mouth, whereas formal communication occurs via the written word. Science is a highly interdependent activity in which each scientist builds upon the work of colleagues past and present. Consequently, science depends heavily on scientific communication. In this study, three mathematical models, namly Brillouin measure, logistic equation, and Markov chain are examined. These models provide one with a means of describing and predicting the behavior of scientific communication process. These mathematical models can be applied to construct quality filtering algorithms for subject literature which identify synthesized elements (authors, papers, and journals). Each suggests a different type of application. Quality filtering for authors can be useful to funding agencies in terms of identifying individuals doing the best work in a given area or subarea. Quality filtering with respect to papers can be useful in constructing information retrieval and dissemination systems for the community of scientists interested m the field. The quality filtering of journals can be a basis for the establishment of small quality libraries based on local interests in a variety of situations, ranging from the collection of an individual scientist or physician to research centers to developing countries. The objective of this study is to establish the theoretical framework for informetrics which is defined as the quantitative analysis of scientific communication, by investigating mathematical models of scientific communication.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.386-390
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• 2007

At recent times, an essential issue in the replacement of the old analogue I&C to computer-based digital systems in nuclear power plants becomes the quantitative software reliability assessment. Software reliability models have been successfully applied to many industrial applications, but have the unfortunate drawback of requiring data from which one can formulate a model. Software that is developed for safety critical applications is frequently unable to produce such data for at least two reasons. First, the software is frequently one-of-a-kind, and second, it rarely fails. Safety critical software is normally expected to pass every unit test producing precious little failure data. The basic premise of the rare events approach is that well-tested software does not fail under normal routine and input signals, which means that failures must be triggered by unusual input data and computer states. The failure data found under the reasonable testing cases and testing time for these conditions should be considered for the quantitative reliability assessment. We presented the quantitative reliability assessment methodology of safety critical software for rare failure cases in this paper.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• Journal of Educational Research in Mathematics
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• v.27 no.1
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• pp.23-49
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• 2017

This study examined two current learning models for covariational reasoning(Carlson et al.(2002), Thompson, & Carlson(2017)), applied the models to teaching two $9^{th}$ grade students, and analyzed the results according to the types of graphs(a quantitative graph or qualitative graph). Results showed that the model of Thompson and Carlson(2017) was more useful than that of Carlson et al.(2002) in figuring out the students' levels in their quantitative graphing activities. Applying Carlson et al.(2002)'s model made it possible to classify levels of the students in their qualitative graphs. The results of this study suggest that not only quantitative understanding but also qualitative understanding is important in investigating students' covariational reasoning levels. The model of Thompson and Carlson(2017) reveals more various aspects in exploring students' levels of quantitative understanding, and the model of Carlson et al.(2002) revealing more of qualitative understanding.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.701-714
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• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• Spatial Information Research
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• v.19 no.2
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• pp.29-36
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• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• Safety and Health at Work
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• v.10 no.3
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• pp.370-376
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• 2019

Background: Respirator fit testing is a method to assess if the respirator provides an adequate face seal for the worker. Methods: Workers from four Norwegian smelters were invited to participate in the study, and 701 respirator fit tests were performed on 127 workers. Fourteen respirator models were included: one FFABE1P3 and 11 FFP3 respirator models produced in one size and two silicone half masks with P3 filters available in three sizes. The workers performed a quantitative fit test according to Health and Safety Executive 282/28 with 5-6 different respirator models, and they rated the respirators based on comfort. Predictors of overall fit factors were explored. Results: The pass rate for all fit tests was 62%, 56% for women, and 63% for men. The silicone respirators had the highest percentage of passed tests (92-100%). The pass rate for the FFP3 models varied from 19-89%, whereas the FFABE1P3 respirator had a pass rate of 36%. Five workers did not pass with any respirators, and 14 passed with all the respirators tested. Only 63% passed the test with the respirator they normally used. The mean comfort score on the scale from 1 to 5 was 3.2. The respirator model was the strongest predictor of the overall fit factor. The other predictors (age, sex, and comfort score) did not improve the fit of the model. Conclusion: There were large differences in how well the different respirator models fitted the Norwegian smelter workers. The results can be useful when choosing which respirators to include in respirator fit testing programs in similar populations.

• Korean Journal of Computational Design and Engineering
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• v.15 no.4
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• pp.289-296
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• 2010

Energy consumption by buildings accounts for a large part of the world‘s energy consumption. Methods to analyze building energy consumption before construction have been studied for decades. With BIM (Building Information Modeling) technology, architects can easily export building information to data models in order to analyze the design‘s effect on building energy efficiency. Although several BIM-based energy simulation applications are currently available, utilizing these applications for energy efficiency simulation is difficult. In this paper, by comparing existing BIM-based energy applications, the authors test the building energy efficiencies estimated by some BIM models, offer ideas and solutions to problems that appeared during the test process and propose new methods for BIM-based energy evaluation based on quantitative factors.

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.46-51
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• 2005

Mutagen X (MX) exists in our drinking water as the bi-products of chlorine disinfection. Being one of the most potent mutagen, it attracted much attention from many researchers. MX and its analogs are tested and modeled by quantitative structure activity relationship (QSAR) methods. As a result, factors affecting this class of compounds have been found to be steric and electrostatic effects. We tried to collect all the data available from the literature. The quantitative structure-activity relationship of a set of 29 MX was analyzed using Molecular Field Analysis (MFA) and Receptor Surface Analysis (RSA). The best models gave $q^{2}=0.918,\;r^{2}=0.949$ for MFA and $q^{2}=0.893,\;r^{2}=0.954$ for RSA. The models indicate that an electronegative group at C6 position of the furanone ring increases mutagenicity.