• Journal of Food Hygiene and Safety
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• v.38 no.3
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• pp.131-139
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• 2023

In this study, we investigated pesticide residue levels in 535 domestically distributed agricultural products in South Korea using multi-residue analysis. Agricultural products from 13 regions, including Seoul, were pretreated using QuEChERS and d-SPE, and subsequently analyzed using LC-MS/MS and GC-MS/MS. Residual pesticides were detected in 288 (53.8%) out of the 535 samples, including 40 of apples, 40 of peppers, 33 of mandarins, 31 of peaches, and 144 other commodities. Furthermore, one sample of Korean cabbage exceeded the permitted maximum residue limit (MRL), diniconazole (0.18 mg/kg), detected at about twice the MRL. In total, 91 types of residual pesticides were detected, including fungicides (42), insecticides (48), and a nematicide. The most frequently detected pesticides were dinotefuran (91), carbendazim (75), tebuconazole (61), and pyraclostrobin (59). Our results showed that continuous monitoring of agricultural products is necessary.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.183-228
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• 2024

This study aimed to develop a competency evaluation scale tailored to the specific needs of key talent in the ICT startup sector. Existing competency assessment tools are mostly designed for environments in large corporations or traditional small and medium-sized enterprises, failing to adequately reflect the dynamic requirements of rapidly evolving startups. For startups, where a small number of individuals directly impact company success, key talent is a critical asset. Accordingly, this study sought to create a scale that measures the competencies suited to the challenges and opportunities faced by startups, helping domestic startups establish more effective talent management strategies. The research initially selected 71 items through a literature review and in-depth interviews. Based on expert feedback that emphasized the need for more precise and clear descriptions, the item descriptions were revised, and a total of 65 items were developed through four rounds of content validation. Following preliminary and main surveys, a final set of 58 items was developed. The main survey conducted further factor analysis based on the three broad competency factors?knowledge, skills, and attitude?identified in the preliminary survey. As a result, 10 latent factors emerged: 6 items for task comprehension, 6 items for practical experience (tacit knowledge), 6 items for collaboration, 9 items for management and problem-solving, 9 items for practical skills, 4 items for self-direction, 5 items for goal orientation, 5 items for adaptability, 5 items for relationship orientation, and 3 items for organizational loyalty. The developed scale comprehensively covers the multifaceted nature of competencies, allowing for a thorough evaluation of essential skills such as technical ability, teamwork, innovation, and leadership, which are critical for startups. Therefore, the scale provides a tool that helps startup managers objectively and accurately assess candidates' competencies. It also supports the growth of employees within startups, maximizing the overall organizational performance. By utilizing this tool, startups can build a strong internal talent pool and continuously enhance employees' competencies, thereby strengthening organizational competitiveness. In conclusion, the competency evaluation scale developed in this study is a customized tool that aligns with the characteristics of startups and plays a crucial role in securing sustainable competitiveness in rapidly changing market environments. Additionally, it offers practical guidance to support the successful growth of domestic startups and help them maintain their competitive edge in the market, contributing to the development of the startup ecosystem and the growth of the national economy.

• Archives of design research
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• v.18 no.4 s.62
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• pp.257-266
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• 2005

The purpose of this study is to investigate the psychological reaction to the image of the worship space in the church buildings and to quantify its contribution of the stimulation elements causing such reaction, and finally to suggest basic data for realizing emotional worship space of the church architecture. For this, 143 christians were surveyed to analyze the relationship between 23 emotional expressions extracted from the worship space and 32 images of the worship space. The combined data was described with the two dimensional dispersion using the quantification theory III. The analysis found out that 'simplicity-complexity' of the image consisted of the horizontal axis (the x-axis) and 'creativity' of the image the vertical axis(the y-axis). In addition, to extract the causal relationship between the value of emotional reaction and its stimulation elements quantitatively, the author indicated 4 emotional word groups such as simple, sublime for x-axis and typical creative for y-axis based on its similarity by the cluster analysis, The quantification theory I was also used with total value of equivalent emotional words as the standard variance and the emotional stimulation elements of the worship space as the independent variance. 9 specific examples of the emotional stimulation elements were selected including colors and shapes of the wall and the ceiling, shapes and finish of the floor materials, window shapes, and the use of the symbolic elements. Furthermore, 31 subcategories were also chosen to analyse their contribution on the emotional reaction. As a result, the color and finish of the wall found to be the most effective element on the subjects' emotional reaction, while the symbolic elements and the color of the wall found to be the least effective. It is estimated that the present study would be helpful to increase the emotional satisfaction of the users and to approach a spatial design through satisfying the types and purposes of the space.

• Journal of Food Hygiene and Safety
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• v.32 no.3
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• pp.187-192
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• 2017

The consumption of herbal medicines has been increasing with growing interest in health. However, due to recent climate change and the complex distribution process of herbal medicines with high import dependence the likelihood of contamination with mycotoxin has been increased. Mycotoxins are emerging as key indicators for ensuring safety of herbal medicines. A total of 498 herbal medicine samples were screened for mycotoxin contamination in this study. Aflatoxin in the herbal medicine samples was extracted by using immunoaffinity column, then the extracted aflatoxin was quantified via high performance liquid chromatography coupled with fluorescence detection (HPLC-FLD) method. The extraction method was verified by linearity, recovery, LOD and LOQ. Aflatoxins were detected in 39/498 samples in an average of $7.670{\mu}g/kg$ ($0.610-77.452{\mu}g/kg$ range). Although safety standards for Corydalis Tuber is not currently available in korea, five of the 39 samples had high concentration of aflatoxins (average of $14.9{\pm}4.1{\mu}g/kg$). In conclusion, it is urgent to establish safety criteria of aflatoxin in Corydalis Tuber. The results of the current study suggest that continuous monitoring is necessary for proactive management of herbal medicine safety.

• Journal of Food Hygiene and Safety
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• v.32 no.4
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• pp.298-305
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• 2017

Benzovindiflupyr is a new pyrazole carboxamide fungicide that inhibits succinate dehydrogenase of mitochondrial respiratory chain. This study was carried out to develop an analytical method for the determination of benzovindiflupyr residues in agricultural commodities using LC-MS/MS. The benzovindiflupyr residues in samples were extracted by using acetonitrile, partitioned with dichloromethane, and then purified with silica solid phase extraction (SPE) cartridge. Correlation coefficient ($r^2$) of benzovindiflupyr standard solution was 0.99 over the calibration ranges ($0.001{\sim}0.5{\mu}g/mL$). Recovery tests were conducted on 5 representative agricultural commodities (mandarin, green pepper, potato, soybean, and hulled rice) to validate the analytical method. The recoveries ranged from 79.3% to 110.0% and then relative standard deviation (RSD) was less than 9.1%. Also the limit of detection (LOD) and limit of quantification (LOQ) were 0.0005 and 0.005 mg/kg, respectively. The recoveries of interlaboratory validation ranged from 83.4% to 117.3% and the coefficient of variation (CV) was 9.0%. All results were followed with Codex guideline (CAC/GL 40) and Ministry of Food and Safety guideline (MFDS, 2016). The proposed new analytical method proved to be accurate, effective, and sensitive for benzovindiflupyr determination and would be used as an official analytical method.

• The Korean Journal of Nuclear Medicine
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• v.34 no.4
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• pp.322-335
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• 2000

Purpose: For quantitative estimation of cerebrovascular reserve (CVR), we estimated the cerebral blood flow (CBF) using Lassen's nonlinearity correction algorithm and Tc-99m HMPAO brain SPECT images acquired with consecutive acquisition protocol. Using the values of CBF in basal and acetaBolamide (ACZ) stress states, CBF increase was calculated. Materials and Methods: In 9 normal subjects (age; $72{\pm}4$ years), brain SPECT was performed at basal and ACZ stress states consecutively after injection of 555 MBq and 1,110 MBq of Tc-99m HMPAO, respectively. Cerebellum was automatically extracted as reference region on basal SPECT image using threshold method. Assuming basal CBF of cerebellum as 55 ml/100g/min, CBF was calculated lot every pixel at basal states using Lassen's algorithm. Cerebellar blood flow at stress was estimated comparing counts of cerebellum at rest and ACZ stress and Lassen's algorithm. CBF of every pixel at ACZ stress state was calculated using Lassen's algorithm and ACZ cerebellar count. CVR was calculated by subtracting basal CBF from ACZ stress CBF for every pixel. The percent CVR was calculated by dividing CVR by basal CBF. The CBF and percentage CVR parametric images were generated. Results: The CBF and percentage CVR parametric images were obtained successfully in all the subjects. Global mean CBF were $49.6{\pm}5.5ml/100g/min\;and\;64.4{\pm}10.2ml/100g/min$ at basal and ACZ stress states, respectively. The increase of CBF at ACZ stress state was $14.7{\pm}9.6ml/100g/min$. The global mean percent CVR was 30.7% and was higher than the 13.8% calculated using count images. Conclusion: The blood flow at basal and ACZ stress states and cerebrovascular reserve were estimated using basal/ACZ Tc-99m-HMPAO SPECT images and Lassen's algorithm. Using these values, parametric images for blood flow and cerebrovascular reserve were generated.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.4
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• pp.223-237
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• 2011

The intermittent freshwater discharge has an critical influence upon the biophysical environments and the ecosystems of the Yeongsan Estuary where the estuary dam altered the continuous mixing of saltwater and freshwater. Though freshwater discharge is controlled by human, the extreme events are mainly driven by the heavy rainfall in the river basin, and provide various impacts, depending on its magnitude and frequency. This research aims to evaluate the magnitude and frequency of extreme freshwater discharges, and to establish the magnitude-frequency relationships between basin-wide rainfall and freshwater inflow. Daily discharge and daily basin-averaged rainfall from Jan 1, 1997 to Aug 31, 2010 were used to determine the relations between discharge and rainfall. Consecutive daily discharges were grouped into independent events using well-defined event-separation algorithm. Partial duration series were extracted to obtain the proper probability distribution function for extreme discharges and corresponding rainfall events. Extreme discharge events over the threshold 133,656,000 $m^3$ count up to 46 for 13.7y years, following the Weibull distribution with k=1.4. The 3-day accumulated rain-falls which occurred one day before peak discharges (1day-before-3day -sum rainfall), are determined as a control variable for discharge, because their magnitude is best correlated with that of the extreme discharge events. The minimum value of the corresponding 1day-before-3day-sum rainfall, 50.98mm is initially set to a threshold for the selection of discharge-inducing rainfall cases. The number of 1day-before-3day-sum rainfall groups after selection, however, exceeds that of the extreme discharge events. The canonical discriminant analysis indicates that water level over target level (-1.35 m EL.) can be useful to divide the 1day-before-3day-sum rainfall groups into discharge-induced and non-discharge ones. It also shows that the newly-set threshold, 104mm, can just separate these two cases without errors. The magnitude-frequency relationships between rainfall and discharge are established with the newly-selected lday-before-3day-sum rainfalls: $D=1.111{\times}10^8+1.677{\times}10^6{\overline{r_{3day}}$, (${\overline{r_{3day}}{\geqq}104$, $R^2=0.459$), $T_d=1.326T^{0.683}_{r3}$, $T_d=0.117{\exp}[0.0155{\overline{r_{3day}}]$, where D is the quantity of discharge, ${\overline{r_{3day}}$ the 1day-before-3day-sum rainfall, $T_{r3}$ and $T_d$, are respectively return periods of 1day-before-3day-sum rainfall and freshwater discharge. These relations provide the framework to evaluate the effect of freshwater discharge on estuarine flow structure, water quality, responses of ecosystems from the perspective of magnitude and frequency.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.199-208
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• 2021

Environmental DNA (eDNA) metabarcoding is effective method with high detection sensitivity for evaluating fish biodiversity and detecting endangered fish from natural water samples. We compared the richness of operational taxonomic units(OTUs) and composition of freshwater fishes according to filters(cellulose nitrate filter vs. glass fiber filter), extraction kits(DNeasy2^® Blood & Tissue Kit vs. DNeasy2^® PowerWater Kit), primer sets (12S rDNA vs. 16S rDNA), and PCR methods (conventional PCR vs. touchdown PCR) to determine the optimal conditions for metabarcoding analysis of Korean freshwater fish. The glass fiber filter and DNeasy2^® Blood & Tissue Kit combination showed the highest number of freshwater fish OTUs in both 12S and 16S rDNA. Among the four types, the primer sets only showed statistically significant difference in the average number of OTUs in class Actinopterygii (non-parametric Wilcoxon signed ranks test, p=0.005). However, there was no difference in the average number of OTUs in freshwater fish. The species composition also showed significant difference according to primer sets (PERMANOVA, Pseudo-F=6.9489, p=0.006), but no differences were observed in the other three types. The non-metric multidimensional scaling (NMDS) results revealed that species composition clustered together according to primer sets based on similarity of 65%; 16S rDNA primer set was mainly attributed to endangered species such as Microphysogobio koreensis and Pseudogobio brevicorpus. In contrast, the 12S rDNA primer set was mainly attributed to common species such as Zacco platypus and Coreoperca herzi. This study provides essential information on species diversity analysis using metabarcoding for environmental water samples obtained from rivers in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.