• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.280-289
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• 2022

Purpose: In this study, the characteristics of fire occurrence according to ignition heat sources such as operating equipment, cigarette/lighter fire, and flame/fire were analyzed. Method: One-way ANOVA and cross-analysis were used to analyze the characteristics of fire occurrence by verifying the difference between the ignition environment, fire damage status and scale, and cause of ignition according to the ignition heat source. Result: The fire occurrence characteristics were analyzed through As a result of the analysis, it was found that fires caused by operating devices occurred more frequently on weekdays than other ignition heat sources, and the number of victims and the number of victims were the highest, so mobilization of firefighting power and property damage were the greatest. The initial ignition was generated by electric and electronic devices, and the combustion was expanded by the synthetic resin. For fires caused by cigarette and lighter fires, the most fires occurred on Saturdays and Sundays, and the mobilization of the police force was more characteristic than the mobilization of the firefighting force. In particular, it was found that the initial ignition and combustion expansion were caused by paper, wood, and hay. Fires caused by sparks and sparks occurred most frequently on Saturdays and Sundays, and initial ignition and combustion expansion were found to be caused by paper, wood, and hay. In particular, it showed the characteristic that it occurred in the place farthest from the fire station. The common characteristic of all ignition heat sources was that the fire occurred most frequently in the afternoon time, and the fire type was predominantly the building structure fire, and only the ignition point was burned the most. Conclusion: In order to prevent fire and minimize damage, it is necessary to analyze the tendency of fire occurrence and to prepare appropriate preparations according to the fire occurrence factors. In order to analyze the characteristics of fire occurrence using public data in the future, it is necessary to standardize disaster data and to open and activate data.

• Journal of Food Hygiene and Safety
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• v.37 no.3
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• pp.136-142
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• 2022

The research aims to develop a rapid and easy analytical method for methoprene using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A simple, highly sensitive, and specific analytical method for the determination of methoprene in livestock products (beef, pork, chicken, milk, eggs, and fat) was developed. Methoprene was effectively extracted with 1% acetic acid in acetonitrile and acetone (1:1), followed by the addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium acetate. Subsequently, the lipids in the livestock sample were extracted by freezing them at -20℃. The extracts were cleaned using MgSO₄, primary secondary amine (PSA), and octadecyl (C₁₈), which were then centrifuged to separate the supernatant. Nitrogen gas was used to evaporate the supernatant, which was then dissolved in methanol. The matrix-matched calibration curves were constructed using 8 levels (1, 2.5, 5, 10, 25, 50, 100, 150 ng/mL) and the coefficient of determination (R²) was above 0.9964. Average recoveries spiked at three levels (0.01, 0.1, and 0.5 mg/kg), and ranged from 79.5-105.1%, with relative standard deviations (RSDs) smaller than 14.2%, as required by the Codex guideline (CODEX CAC/GL 40). This study could be useful for residue safety management in livestock products.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.107-123
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• 2018

The global impact of climate change on agriculture is now increasing. The purpose of this study was to investigate the effect of climate change on livestock productivity. The variables that have the greatest influence on climate change factors were examined through previous studies and expert surveys. We also used the actual productivity data of livestock farmers to investigate the relationship with climate change. In order to evaluate the climate for changes in livestock productivity, national representative data (such as bullocks, dairy, pigs, laying hens, and broilers) were surveyed in Korea. Also, to select and classify evaluation indexes, we selected climate change factor variables as prior studies and studied the weighting factor of climate variable factors. In this study, the researchers of industry, academia, and farmers in the livestock sector conducted questionnaires on the indicators of vulnerability to climate change using experts, and then weighed the selected indicators using the hierarchical analysis process (AHP). In order to verify the validity of the evaluation index, was examined using domestic climate data (temperature, precipitation, humidity, etc.). Correlation and regression analysis were performed. The empirical relationship between climate change and livestock productivity was examined through this study. As a result, we used data with high reliability of statistical analysis and found that there are significant variables.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Journal of the Korean earth science society
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• v.29 no.2
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• pp.163-174
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• 2008

In this study, we compared textbook knowledge organization with students' mental models to contribute to a more well-designed instruction scheme. The selected science content was the cause of moon phases. We investigated 9 textbooks and 25 third-year middle school students. Patterns and features in participants' mental models were identified through cross inter-rater data analysis by 9 researchers, including in service teachers and experts in science education. According to the results, observing and modeling are the main activities engaged in when dealing with moon phases. The activities consisted of such concepts as: lunar revolution, the sun's parallel rays, the illuminated half of moon, and the relative positions of the sun, moon, and earth. Each concept involved inquiry skills such as: creating and manipulating models, utilizing the relationship between time and space, and communicating. However, the most important skills which are required for authentic scientific inquiry, namely controlling variables and formulating hypotheses, were missing. We categorized students' mental models into three types: scientific models, mixed models, and alternative models. The knowledge structure of each of the models was also discussed in this paper. Consequently, it was found that, typically, students were not given enough opportunities to strengthen the connection among ideas.

• Management & Information Systems Review
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• v.30 no.2
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• pp.185-210
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• 2011

For many firms, customers are their most valuable assets. This is a shift in the interest of managers and researchers from a traditional focus on product management to a more recent focus on customer management. And they manage their customers with relationship marketing. Despite the recent academic interest in the study of customer equity, prior research has focused on brand equity and limited relationship mediated variate such as satisfaction, commitment and trust. This paper suggests customer equity drives with mediated variate and examines how relationship marketing can generate drives of customer equity and influence on customer behaviors. The results are like this. First, customer equity drives include value, brand and relationship equity and they mediate between relationship marketing activities and customer behaviors. Second, financial, social and structural activities have significant impact on repurchase, positive word of mouth, and cross purchase through customer equity drives. Third, this study tried to organize literature on customer equity systematically. It will become the foundation of follow-up studies.

• Culinary science and hospitality research
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• v.21 no.6
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• pp.49-62
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• 2015

Give that statistical analysis is an essential component of foodservice-related research, the purpose of this review is to analyse research trends of statistical methods applied to foodservice-related research. To achieve these objective, this study carried out a content analysis on a total of 251 out of 415 research articles published in The Korean Journal of Culinary Research(TKJCR) from January 2010 to December 2013. Of the total 164 research articles focussing on natural science research, qualitative research, articles written in English were excluded from the scope of this study. The results of this study are as follows. First, it turned out that 269 research articles applied quantitative research methods, and only 10 articles applied qualitative research methods among the 279 research articles based on social science research methods. Second, 20 article (8.0%) among the 251 did not specify the statistical methods or computer programs that were used for statistical analysis. Third, it was found that 228 articles (90.8%) used the SPSS program for data analysis. Fourth, in terms of frequency of use, it was revealed frequency analysis was most used, followed in order by reliability analysis, exploratory factor analysis, correlation analysis, regression analysis, structural equation modeling, confirmatory factor analysis, t-test, variance analysis, and cross tabs analysis, However, 3 out of 56 research articles that used a t-test did not suggest a t-value. 10 out of 64 articles that used ANOVA and demonstrated a significant difference in between-group mean did not conducted post-hoc test. Therefore, the researchers with interest in foodservice fields need to keep in mind that choosing and applying the correct statistical technique both determine the value and the success or failure of a study. To enhance the value and success of a study, it is necessary to use the proper statistical technique in an efficient way in order to prevent statistical errors.

• Environmental and Resource Economics Review
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• v.13 no.4
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• pp.593-619
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• 2004

This study analyzes the interfuel substitution of energy demand in Korean manufacturing sector using static and dynamic linear logit models. For the period of 1981~2002, this study uses petroleum, electricity, natural gas and coal as energy sources. According to the empirical results, firstly, the own-price elasticity of coal has been increased steadily even though its elasticity is smallest compared with those of other energy sources. On the other hand, price elasticity of natural gas is largest, but its value has been decreased after 1997. Price elasticities of petroleum and electricity are very stable over the sample period. One of the main features in trends of interfuel substitution is as follows. Substitution effect of a change in price of natural gas on both petroleum and coal has been increased especially after 1997. The implication of the empirical results is summarized as follows: First, the fact of inelastic own-price elasticity of petroleum implies that the dependency of Korean manufacturing sector on petroleum and coal will be persistent even in a sharp fluctuation of petroleum price. Second, the effects of price increase in natural gas on demand for petroleum and coal are very significant. Thus, price decline of natural gas rather than price declines of coal and petroleum could be more effective as an energy price policy for the reduction of $CO_2$ emission. The assessment on this implication will remain for future researches.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.340-348
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• 2020

The purpose of this study was to investigate the mutual relationship between parenting stress and children's problem behaviors among those participating in dream start services. In order to verify the effect of parental and child effects on child's problem behavior and parenting stress based on repeated measures from the first grade of elementary school to the third grade of elementary school among children participating in dream start services, autoregressive cross-lagged modeling was applied. As a result, the stability coefficient showed that the two variables were significantly stable for 3 years. In other words, the measurement was maintained at a similar level with time. The causal relationship between parents 'parenting stress and child' s problem behavior was generally explained by the parental effect. In other words, parental stress is related to the child's problem behavior. These results show that the economic stress of poverty increases parents' parenting stress, which can negatively affect the adaptive development of children in the process of raising children.