• Journal of Life Science
• /
• v.19 no.8
• /
• pp.1170-1176
• /
• 2009

Porcine reproductive and respiratory syndrome virus (PRRSV) has plagued pig populations worldwide causing severe economical impacts. In order to establish effective strategies for prevention of PRRS, infection patterns on the herd level are primarily evaluated. In the present study, therefore, serological and virological analyses were conducted in 20 pig farms suffering from PRRS. Seroprevalence levels in each farm were grouped into 3 patterns: SN (Stable sow groups/Not infected piglet groups, SI (Stable sow groups and Infected piglet groups), and UI (Unstable sow groups and Infected piglet groups). The rates of each serological pattern were 15% (n=3), 10% (n=2), and 75% (n=15), respectively. In addition, the pattern analysis was extended to virological monitoring on the same farms that further included suckling pig groups. As a result, the infection pattern was classified into 4 categories: SNI (Stable sow groups/Not infected suckler groups/Infected piglet groups), SII (Stable sow groups/Infected suckler groups/Infected piglet groups), UNI (Unstable sow groups/Not infected suckler groups/Infected piglet groups), and UII (Unstable sow groups/Infected suckler groups/Infected piglet groups). The rates of each viroprevalence were estimated at 50% (n=10), 30% (n=6), 10% (n=2), and 10% (n=2), respectively. PRRSV viroprevalence results of suckling pig groups revealed that 8 farms were considered virus positive. In 2 farms among these farms, PRRSV appeared to be transmitted vertically to suckling piglets from their sows. In contrast, piglet-to-piglet horizontal transmission of PRRSV seemed to occur in sucking herds of the remaining farms. Thus, this virological analysis on suckling piglets will provide useful information to understand PRRSV transmission routes during the suckling period and to improve a PRRS control programs. Our seroprevalence and viroprevalence data found that infection patterns between sow and piglet groups are not always coincident in the same farm. Remarkably, 15 farms belonging to the UI seroprevalence pattern showed four distinct viroprevalence patterns (SNI; 7, SII; 4, UNI; 2 and UII; 2). Among these farms, 11 farms with unstable seroprevalence sow groups were further identified as the stable viroprevalence pattern. These results indicated that despite the absence of typical seroconversion, PRRSV infection was detected in several farms, implying the limitation of serological analysis. Taken together, our data strongly suggests that both seroprevalence and viroprevalence should be determined in parallel so that a PRRS control strategies can be efficiently developed on a farm level.

• Geophysics and Geophysical Exploration
• /
• v.5 no.4
• /
• pp.257-261
• /
• 2002

We have carried out integrated research including field survey and numerical modeling to appraise the applicability of SP method to the leakage problems of irrigation facilities. The leakage pattern of the dike studied here can be classified into the three categories: leakage through the abutment, leakage by piping through dike, and leakage due to the composite effects of landslide and distortion of the dike structure. for the numerical modeling to interpret quantitatively SP survey results acquired at dike, we have modified the computer code proposed by Sill (1983) to apply to the leakage problems. The numerical studies match the characteristic patterns of SP anomalies according to the leakage types and appear to be very useful to interpret the leakage zone and path. The SP monitoring results were also well coincided with tidal variations observed at every embankment so we found the SP method is quite effective not only to detect the leakage zone but also to determine the leakage trend. The numerical modeling results also reproduced the SP anomalies due to seawater leakage in the embankment.

• Journal of Environmental Health Sciences
• /
• v.47 no.5
• /
• pp.432-445
• /
• 2021

Background: The European Centre for Ecotoxicology and Toxicology of Chemicals' Targeted Risk Assessment (ECETOC TRA) tool has been recognized by EU REACH as a preferred approach for calculating worker health risks from chemicals. Objectives: The applicability of the ECETOC TRA to occupational exposure estimation from industrial uses of methanol was studied by inputting surveyed and varied parameters for TRA estimation as well as through comparison with measured data. Methods: Information on uses of methanol was collected from seven working environment monitoring reports along with the measured exposure data. Input parameters for TRA estimation such as operating conditions (OCs), risk management measures (RMMs) and process categories (PROCs) were surveyed. To compare with measured exposures, parameters from the surveyed conditions of ventilation but no use of respiratory protection were applied. Results: PROCs 4, 5, 8a, 10, and 15 were assigned to ten uses of methanol. The uses include as a solvent for manufacturing sun cream, surfactants, dyestuffs, films and adhesives. Methanol was also used as a component in a release agent, hardening media and mold wash for cast products as well as a component of hard-coating solution and a viscosity-controlling agent for manufacturing glass lenses. PROC 8a and PROC 10 of a cast product manufacturer without LEV (local exhaust ventilation) and general ventilation as well as no respiratory protection resulted in the highest exposure to methanol. Assuming the identical worst OCs and RMMs for all uses, exposures from PROC 5, 8a, and 10 were the same and the highest followed by PROC 4 and 15. The estimation resulted in higher exposures in nine uses except one use where measured exposure approximated exposures without RMMs. Conclusions: The role of ECETOC TRA as a conservative exposure assessment tool was confirmed by comparison with measured data. Moreover, it can guide which RMMs should be applied for the safe use of methanol.

• Nuclear Engineering and Technology
• /
• v.53 no.8
• /
• pp.2534-2546
• /
• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• Journal of Veterinary Clinics
• /
• v.36 no.1
• /
• pp.62-67
• /
• 2019

This study was performed to apply the measurement of heart rate and velocity in training horses for assessing race performance. Additionally, we aimed to identify parameters that can be used to evaluate the training level and exercise capacity. Eleven healthy 2- to 6-year-old Thoroughbreds were trained by the standard training program and heart rate and velocity were measured by using heart monitoring system and GPS. Regression analysis in heart rate and velocity data was performed to calculate velocity parameters. The mean maximal heart rate in gallop was $214{\pm}11bpm$. The mean $V_{140}$, $V_{180}$, $V_{200}$ and $VHR_{max}$ were $13.8{\pm}4.3km/h$, $37.5{\pm}3.8km/h$, $49.3{\pm}4.3km/h$ and $57.4{\pm}7.1km/h$ respectively. The mean $V_{140}$ of high performance racehorses was significantly higher than that of low performance racehorses (P < 0.05). Moreover, analyzing the correlation between velocity parameters and racing ability-related categories showed that $V_{140}$ was positively correlated with rating (P < 0.05), $V_{180}$ and $VHR_{max}$ were positively correlated with prize money per race (P < 0.05). Also, $V_{140}$ was significantly correlated with G1F (P < 0.05). The results of this study have shown that the measurement of heart rate and velocity during training could be useful methods to assess fitness for races or performance potential. Especially, $V_{140}$ is a good parameter to evaluate a performance of racehorses in Korea.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.50 no.6
• /
• pp.42-57
• /
• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.

• Journal of The Korean Association For Science Education
• /
• v.28 no.4
• /
• pp.316-330
• /
• 2008

During the past two decades or so, science (or scientific or scientific & technological) culture has become one of the main themes not only of policy makers but also of science educators. Although, the idea of science culture has been taken as a desirable goal, there is little agreement about what it means and how to measure it. Particularly in Korea, there has been a rapid growth of science culture projects and programs, either by governmental or non-governmental, but with little systemic monitoring and evaluation for its practice. The purpose of this study is, thus, to explore a model of measuring science culture and develop a comprehensive indicator system for it. We reviewed many literatures on definitions of science culture and the surveys for related terms, particularly, of recent national and international surveys (e.g. US Science and Engineering Indicators, Eurobarometer, Japanese Science and Technology Indicators). Based on this review, a model for science culture is proposed and then used to define the Science Culture Indicators (SCI). This model encompasses two dimensions(i.e. individual and social), which are further divided into two aspects (i.e. potential and practice). Each dimension is expected to represent citizen literacy of and national infrastructure of science culture respectively. Each category in this $2{\times}2$ matrix is further divided into several sub-categories. The discussion concerning how the model and the indicators can be used to check the states of science culture at social as well as individual levels will be given with some concrete examples, such as indicators particularly related to science education.

• Korean Journal of Ecology and Environment
• /
• v.53 no.1
• /
• pp.31-45
• /
• 2020

The biological indices based on the community structure with species richness and/or abundance are commonly used to assess aquatic ecosystem health. Meanwhile, recently functional traits-based approach is considered in ecosystem health assessment to reflect ecosystem functioning. In this study, we developed a database of biological traits for 136 taxa consisting of major stream insects (Ephemeroptera, Plecoptera, Trichoptera, Coleoptera, and Odonata) collected at Korean streams on the nationwide scale. In addition, we obtained environmental variables in five categories (geography, climate, land use, hydrology and physicochemistry) measured at each sampling site. We evaluated the relationships between community indices based on taxonomic diversity and functional diversity estimated from biological traits. We classified sampling sites based on similarities of their environmental variables and evaluated relations between clusters of sampling sites and diversity indices and biological traits. Our results showed that functional diversity was highly correlated with Shannon diversity index and species richness. The six clusters of sampling sites defined by a hierarchical cluster analysis reflected differences of their environmental variables. Samples in cluster 1 were mostly from high altitude areas, whereas samples in cluster 6 were from lowland areas. Non-metric multidimensional scaling (NMDS) displayed similar patterns with cluster analysis and presented variation of taxonomic diversity and functional diversity. Based on NMDS and community-weighted mean trait value matrix, species in clusters 1-3 displayed the resistance strategy in the life history strategy to the environmental variables whereas species in clusters 4-6 presented the resilience strategy. These results suggest that functional diversity can complement the biological monitoring assessment based on taxonomic diversity and can be used as biological monitoring assessment tool reflecting changes of ecosystem functioning responding to environmental changes.

• Journal of Food Hygiene and Safety
• /
• v.32 no.5
• /
• pp.348-354
• /
• 2017

Internationally different food safety regulation and standards could cause troubles in trade unless those are built on scientific knowledge. In this research, we monitored the microbial population and analyzed the results to determine the level of microbial contamination in foodstuffs using relatively new statistical analysis (microbiological sampling plan, International Commission on Microbiological Specification for Foods). The goal of this research falls on establishing entirely new standards for various food categories addressed in the Korean "Food Code". Targets for monitoring were indicator organisms (i.e. total aerobic count, coliform and Escherichia coli) and foodborne pathogens (i.e. Bacillus cereus, Staphylococcus aureus and Clostridium perfringens) in ready-to-eat (RTE) products. As the result of the monitoring, total aerobic count, coliform, E. coli, and B. cereus in RTE products were found at the mean values of 2.10 log CFU/g, -0.60 log CFU/g, -1.33 log CFU/g and -1.23 log CFU/g, respectively. S. aureus was detected with the level of -1.35 log CFU/g only in fresh-cut food, while C. perfringens was -1.37 log CFU/g only in ready-to-cook food. Other samples did not have any food borne pathogens. Total aerobic count, B. cereus, S. aureus and C. perfringens satisfied the Food Code (the MFDS). On the basis of the analysis, we proposed a draft of microbial criteria for RTE products.

• Journal of The Korean Association For Science Education
• /
• v.41 no.6
• /
• pp.471-481
• /
• 2021

The importance of science education for cultivating the competencies required by an intelligent information society is gradually being strengthened. The government's roles and responsibilities for science education are stipulated by laws and policies in Korea. In order to systematically support science education, continuous monitoring of related policies is essential. This study aims to develop indicators that can be used to systematically and continuously monitor the national policies on science education in Korea. To achieve this goal, we first derive the framework for the indicators that has two dimensions (learner and science education context) and three categories (input, process, and outcome) from literature reviews. In order to derive the components and subcomponents of the indicators, the contents of science education-related indicators developed in Korea or abroad were reviewed. In order to verify the suitability and validity of the framework and components of the initial indicators, a two-round Delphi method was conducted with 25 expert participants with five different professions in science education. Finally, three components of the 'input' category (student characteristics, teacher characteristics, and educational infrastructure), three components of the 'process' category (science curriculum implementation, science educational contents and programs implementation, and teacher professional development program implementation), and five components of the 'outcome' category (science competency, participation and action, affective achievement, cognitive achievement, and satisfaction) were derived. An instrument to collect data from students, teachers, and institutions was developed based on the components and subcomponents, and content validity and internal consistency of the instrument were analyzed. Korea's Science Education Indicators developed in this study can comprehensively measure the current status of science education and is expected to contribute to a more efficient and effective science education policy planning and implementation.