• Management & Information Systems Review
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• v.22
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• pp.1-23
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• 2007

This paper investigates the relationships between exports, firm size, and firm dynamics. It is based on a longitudinal data covering listed firms in the Korean manufacturing industry. We found the stylized fact that the probability that a firm is exporter increases with firm size. A regression model for the determinant of export/sales ratio including dynamic adjustment process is tested on a cross-section sample for the year 2001. Empirical findings suggest that there is a positive and inversely U-shaped relationship between firm size and export/sales ratio, just for basic material and capital good industry. Except for firm size, the hypotheses concerning human capital intensity, physical capital intensity, R&D intensity, and patent are rejected. Using Granger causality test, we found that the rate of growth of total sales influences the change of the export/sales ratio with time lag for medium-sized firms. Finally, some policy implications are presented.

• Journal of Ecology and Environment
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• v.32 no.4
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• pp.221-227
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• 2009

This study describes time lag responses between hydrological variables and phytoplankton biomass in a regulated river system, the lower Nakdong River in South Korea. The lower Nakdong is a typical flow-controlled lotic system, and its limnological characteristics are influenced by climatic variation such as monsoons and summer typhoons. Mean rainfall in the area during summer is about 1,200 mm, which comprises >60% of annual rainfall. Our results show that the regulation of flow in the Nakdong by multi-purpose dams from 1995 to 2004 affected phytoplankton dynamics. Diatom blooms occurred in winter, when the limited discharge allowed for proliferation of the phytoplankton community. Using multiple regression analysis, we detected significant time-delayed relationships between hydrological variables and phytoplankton biomass. These results may be useful for water resource managers, and suggest that 'smart flow' control would improve water quality in large regulated river systems of the Republic of Korea.

• Journal of Preventive Veterinary Medicine
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• v.42 no.4
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Horticultural Science & Technology
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• v.30 no.3
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• pp.270-277
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• 2012

Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.

• Journal of Korea Water Resources Association
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• v.49 no.8
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• pp.645-656
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• 2016

The study performed a regional flood frequency analysis and proposed a regression equation to estimate design floods corresponding to return periods for ungauged basins in Geum-river basin. Five preliminary tests were employed to investigate hydrological independence and homogeneity of streamflow data, i.e. the lag-one autocorrelation test, time homogeneity test, Grubbs-Beck outlier test, discordancy measure test ($D_i$), and regional homogeneity measure (H). The test results showed that streamflow data were time-independent, discordant and homogeneous within the basin. Using five probability distributions (generalized extreme value (GEV), three-parameter log-normal (LN-III), Pearson type 3 (P-III), generalized logistic (GLO), generalized Pareto (GPA)), comparative regional flood frequency analyses were carried out for the region. Based on the L-moment ratio diagram, average weighted distance (AWD) and goodness-of-fit statistics ($Z^{DIST}$), the GLO distribution was selected as the best fit model for Geum-river basin. Using the GLO, a regression equation was developed for estimating regional design floods, and validated by comparing the estimated and observed streamflows at the Ganggyeong station.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.15-25
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• 2005

As implied by the terms of IT productivity Paradox, measuring the Information technology contribution to economic performance has been one of the challenging issues to both policy makers and business professionals. As such, diverse attempts with sophisticate analyses have been reported in the literature to analyze the effect of IT contributions. In this paper, we follow Growth Accounting Method to measure the IT contribution effect to manufacturing firm's economic performance in Korea. Various regression methods and statistical analyses are applied with fourteen years of industry Panel data. Using the Cobb-Douglas function, time lag analysis is made to understand IT effect to economic growth. Instead of capturing data from individual firm, industry level data from the National Statistics Bureau is used for IT capital, non-IT capital, and so on. Statistical analysis following the panel unit test and Panel co-integration test was performed to reveal the exact effect of IT contribution to economic performance. Empirical testing results for non-stationary nature of IT investment effect are reported as well as IT contribution to manufacturing industry's economic performance.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.7
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• pp.990-996
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• 2005

The effect of diets varying in level and source of nitrogen (N) and fermentable organic matter on dynamic characteristics of microbial populations in rumen liquor and their impact on substrate fermentation in vitro was studied. The diets tested were straw alone, straw+concentrate mixture and straw+urea molasses mineral block (UMMB) lick. The same diets were taken as substrates and tested on each inoculum collected from the diets. Diet had no effect on the amino acid (AA) composition of either bacteria or protozoa. Differences among the diets in intake, source of N and OM affected bacterial and protozoal characteristics in the rumen. Upper asymptote of gas production (Y$\alpha$) had a higher correlation with bacterial pool size and production rate than with protozoal pool size and production rate. Among the parameters of the gas production model, Y$\alpha$ and lag time in total gas has showed significant (p<0.01) correlation with bacterial characteristics. Though the rate constant of gas production significantly differed (p<0.01) between diet and type of straw, it was least influenced by the microbial characteristics. The regression coefficient of diet and type of straw for Y$\alpha$ indicated that the effect of diet on Y$\alpha$ was threefold higher than that of the straw. As microbial characteristics showed higher correlation with Y$\alpha$, and diet had more influence on the microbial characteristics, gas production on a straw diet could be used effectively to understand the microbial characteristics.

• Asian Journal of Business Environment
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• v.12 no.4
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• pp.35-44
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• 2022

Purpose: The main purpose of this study is to find out the impact of ownership structure on firm performance in the pharmaceutical and chemical industry of Bangladesh. Research design, data and methodology: The study has been conducted on 28 listed pharmaceutical and chemical companies from 2012 to 2020. Return on Assets (ROA) and Tobin's Q are selected as indicators of internal and market performance of the firms respectively whereas institutional ownership, directors' ownership and foreign ownership are selected as proxies of ownership structure. Panel analysis using random effects, lag method and time dummy method is used to analyse the relationship. Results: The study has found the existence of highly concentrated directors' ownership, a low percentage of institutional ownership and a very insignificant proportion of foreign ownership in the industry. The regression results show that directors' ownership has a positive and significant impact on firm performance, supporting the concept of agency theory. The study has also found a positive and significant impact of foreign ownership on firm performance. Unfortunately, the impact of institutional ownership is found to be insignificant. Conclusions: Directors' ownership and foreign ownership decreases agency cost that ultimately increases firm performance. However, the role of institutional investors is not significant enough to improve firm performance. It is suggested that institutional investors should be more active and involved in monitoring the activities of the organisations to improve performance.