• Research in Community and Public Health Nursing
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• v.7 no.2
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• pp.384-399
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• 1996

The concept of parish nursing began in the late 1960s in the United States when increasing numbers of churches employed registered nurses (RNs) to provide holistic, preventive health care to the members of their congregations. Parish nursing role was developed in 1983 by Lutheran chaplain Granger Westberg, and provides care to a variety of church congregation of various denominations. The parish nurse functions as health educator, counselor, group facilitator, client advocate, and liaison to community resources. Since these activities are complementary to the population-focused practice of community health' CNSs, parish nurses either have a strong public health background or work directly with both baccalaureate-prepared public health nurses and CNSs. In a Midwest community in U.S.A., the Healthy People 2000(1991) objectives are being addressed in health ministries through a coalition between public health nurses and parish nurses. Parish nursing is in the beginning state in Korea and up untill now, there has been no research was conducted on concrete role of korean parish nurses. The main purpose of this study was to identify, classify and analyze activities of parish nurses. The other important objective of this study was to establish an effective approach and direction for parish nursing and provide a database for korean parish nursing model through analysis and' classification of the content of the nursing record which included nursing activities. This study was a descriptive survey research. The parish nurses were working in churches where the demonstration project developed on parish nursing. The study was done on all nursing records which were working in churches where the demonstration project developed on parish nursing. The study was done on all nursing records which were documented by parish nurses in three churches from March, 1995 to February, 1996. Namsan, Taegu Jeei and Nedang presbyterian churches in Taegu and Keimyung nursing college incooperated together for the parish nursing demonstration project. The data analysis procedure was as follows: First, a record analysis tool was developed and second, the data was collected, coded and analyzed, the classification for nursing activities was developed through a literature review, from which the basic analysis tool was produced and cotent validity review was also done. The classification of the activities of parish nurses showed 7 activitity categories. 7 activity categories consisted of visitation nursing, health check-ups, health education, referring, attending staff meetings, attending inservices and seminar, volunteers coordinating. The percentage of activities were as follows: Visitation nursing(A: 51.6%, B: 55%, C: 42.6%) Health check-ups(A: 13.5%, B: 12.1%, C: 22.3%) Health education(A: 13.5%, B: 13.2%, C: 18.2%) Referring(A: 1.4%, B: 4.2%, C: 2.4%) Attending staff meeting(A: 18.8%, B: 13.0%, C: 12.2%) Attending inservices and seminar(A: 1.5%, B: 2.2%, C: 2.1%) Volunteers coordinating(A: 0.3%, B: 0.4%, C: 0.0%) To establish and develope parish nursing delivery network in Korea, parish nurses role, activities and boundaries of practice should be continuously monitored and refined every 2 years. Also, It is needed to develope effective nursing recording system based on the need assessment research data of various congregation members. role, activities and boundaries of practice and arrangement of the working structure, continuing education, cooperation with community resources and structuring and organizing parish nursing delivery network. Also, It is needed to develope effective nursing recording system based on the need assessment research data of various congregation members.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.2
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• pp.27-36
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• 2018

This study suggests the necessity of behavior analysis as changes to a park environment to reflect user desires can be implemented only by grasping the needs of park users. Online data (blog) were defined as the basic data of the study. After collecting data by 5 - year units, data mining was used to derive the characteristics of the time series behavior while the significance of the online data was verified through social network analysis. The results of the text mining analysis are as follows. First, primary results included 'walking', 'photography', 'riding bicycles'(inline, kickboard, etc.), and 'eating'. Second, in the early days of the collected data, active physical activity such as exercise was the main factor, but recent passive behavior such as eating, using a mobile phone, games, food and drinking coffee also appeared as a new behavior characteristic in parks. Third, the factors affecting the behavior of park users are the changes of various conditions of society such as internet development and a culture of expressing unique personalities and styles. Fourth, the special behaviors appearing at Olympic Park were derived from educational activities such as cultural activities including watching performances and history lessons. In conclusion, it has been shown that people's lifestyle changes and the behavior of a park are influenced by the changes of the various times rather than the original purpose that was intended during park planning and design. Therefore, it is necessary to create an environment tailored to users by considering the main behaviors and influencing factors of Olympic Park. Text mining used as an analytical method has the merit that past data can be collected. Therefore, it is possible to form analysis from a long-term viewpoint of behavior analysis as well as to measure new behavior and value with derived keywords. In addition, the validity of online data was verified through social network analysis to increase the legitimacy of research results. Research on more comprehensive behavior analysis should be carried out by diversifying the types of data collected later, and various methods for verifying the accuracy and reliability of large-volume data will be needed.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.197-208
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• 2020

This study was carried out to analyze spatial and temporal incidence characteristics of scrub typhus and predict the future incidence of scrub typhus since the incidences of scrub typhus have been rapidly increased among vector-borne diseases. A maximum entropy (MaxEnt) ecological model was implemented to predict spatial distribution and incidence rate of scrub typhus using spatial data sets on environmental and social variables. Additionally, relationships between the incidence of scrub typhus and critical spatial data were analyzed. Elevation and temperature were analyzed as dominant spatial factors which influenced the growth environment of Leptotrombidium scutellare (L. scutellare) which is the primary vector of scrub typhus. A temporal number of diseases by scrub typhus was predicted by a deep neural network (DNN). The model considered the time-lagged effect of scrub typhus. The DNN-based prediction model showed that temperature, precipitation, and humidity in summer had significant influence factors on the activity of L. scutellare and the number of diseases at fall. Moreover, the DNN-based prediction model had superior performance compared to a conventional statistical prediction model. Finally, the spatial and temporal models were used under climate change scenario. The future characteristics of scrub typhus showed that the maximum incidence rate would increase by 8%, areas of the high potential of incidence rate would increase by 9%, and disease occurrence duration would expand by 2 months. The results would contribute to the disease management and prediction for the health of residents in terms of public health.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.331-350
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• 2022

This study aims to understand topics of incivility related to COVID-19 from analyzing Twitter posts including COVID-19-related hate speech. To achieve the goal, a total of 63,802 tweets that were created between December 1st, 2019, and August 31st, 2021, covering three targets of hate speech including region and public facilities, groups of people, and religion were analyzed. Frequency analysis, dynamic topic modeling, and keyword co-occurrence network analysis were used to explore topics and keywords. 1) Results of frequency analysis revealed that hate against regions and public facilities showed a relatively increasing trend while hate against specific groups of people and religion showed a relatively decreasing trend. 2) Results of dynamic topic modeling analysis showed keywords of each of the three targets of hate speech. Keywords of the region and public facilities included "Daegu, Gyeongbuk local hate", "interregional hate", and "public facility hate"; groups of people included "China hate", "virus spreaders", and "outdoor activity sanctions"; and religion included "Shincheonji", "Christianity", "religious infection", "refusal of quarantine", and "places visited by confirmed cases". 3) Similarly, results of keyword co-occurrence network analysis revealed keywords of three targets: region and public facilities (Corona, Daegu, confirmed cases, Shincheonji, Gyeongbuk, region); specific groups of people (Coronavirus, Wuhan pneumonia, Wuhan, China, Chinese, People, Entry, Banned); and religion (Corona, Church, Daegu, confirmed cases, infection). This study attempted to grasp the public's anti-citizenship public opinion related to COVID-19 by identifying domestic COVID-19 hate targets and keywords using social media. In particular, it is meaningful to grasp public opinion on incivility topics and hate emotions expressed on social media using data mining techniques for hate-related to COVID-19, which has not been attempted in previous studies. In addition, the results of this study suggest practical implications in that they can be based on basic data for contributing to the establishment of systems and policies for cultural communication measures in preparation for the post-COVID-19 era.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.158-170
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• 2013

With the recent 'Creative economy' and 'Cultural prosperity' coming to the fore as a new code to build up a city or a region, it is necessary to focus on strengthening the regional creative capacity as well as developing spontaneous regional culture. In such trend this research aims to explore the Kyunglidan-gil, Seoul, Korea in which creative-class are appearing autogenously in clusters and forming new cultural landscape, to identify the factors of their accumulation and changing aspect of cultural landscape. This study has the following purposes: First, Investigating the historical context of the Kyunglidan-gil's landscape. Second, considering the process of the creative-class being flowed into the Kyunglidan-gil as the subject leading to the modification of the region. Third, their activity was analyzed to consider the unique aspect of forming the cultural landscape at the Kyunglidan-gil. Regarding why the creative-class should flow in, results of the study drew five factors including region in issue compared to inexpensive rents, coexistence with nature, quiet atmosphere seeming isolated from the urban confusion, location possible to test and share individual materials one likes, and a site with synergy effect of activity through the network with acquaintances. Also, five characteristics of cultural landscape forming by the people's activity were drawn - space of communication for increasing creativity, temporary and flexible spatial use, expression of one's identity and taste, distinguishing, and positive use of the existing facilities. Like this, by exposing the 'creative-class', a subject of the leader in changing process of the Kyunglidan-gil, this research identified the aspect of forming cultural landscape.

• Economic and Environmental Geology
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• v.39 no.4 s.179
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• pp.369-384
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• 2006

Korean Peninsula, located on the southeastern part of Eurasian plate, belongs to the intraplate region. The characteristics of intraplate earthquake show the low and rare seismicity and the sparse and irregular distribution of epicenters comparing to interplate earthquake. To evaluate the exact seismic activity in intraplate region, long-term seismic data including historical earthquake data should be archived. Fortunately the long-term historical earthquake records about 2,000 years are available in Korea Peninsula. By the analysis of this historical and instrumental earthquake data, seismic activity was very high in 16-18 centuries and is more active at the Yellow sea area than East sea area. Comparing to the high seismic activity of the north-eastern China in 16-18 centuries, it is inferred that seismic activity in two regions shows close relationship. Also general trend of epicenter distribution shows the SE-NW direction. In Korea Peninsula, the first seismic station was installed at Incheon in 1905 and 5 additional seismic stations were installed till 1943. There was no seismic station from 1945 to 1962, but a World Wide Standardized Seismograph was installed at Seoul in 1963. In 1990, Korean Meteorological Adminstration(KMA) had established centralized modem seismic network in real-time, consisted of 12 stations. After that time, many institutes tried to expand their own seismic networks in Korea Peninsula. Now KMA operates 35 velocity-type seismic stations and 75 accelerometers and Korea Institute of Geoscience and Mineral Resources operates 32 and 16 stations, respectively. Korea Institute of Nuclear Safety and Korea Electric Power Research Institute operate 4 and 13 stations, consisted of velocity-type and accelerometer. In and around the Korean Peninsula, 27 intraplate earthquake mechanisms since 1936 were analyzed to understand the regional stress orientation and tectonics. These earthquakes are largest ones in this century and may represent the characteristics of earthquake in this region. Focal mechanism of these earthquakes show predominant strike-slip faulting with small amount of thrust components. The average P-axis is almost horizontal ENE-WSW. In north-eastern China, strike-slip faulting is dominant and nearly horizontal average P-axis in ENE-WSW is very similar with the Korean Peninsula. On the other hand, in the eastern part of East Sea, thrust faulting is dominant and average P-axis is horizontal with ESE-WNW. This indicate that not only the subducting Pacific Plate in east but also the indenting Indian Plate controls earthquake mechanism in the far east of the Eurasian Plate. Crustal velocity model is very important to determine the hypocenters of the local earthquakes. But the crust model in and around Korean Peninsula is not clear till now, because the sufficient seismic data could not accumulated. To solve this problem, reflection and refraction seismic survey and seismic wave analysis method were simultaneously applied to two long cross-section traversing the southern Korean Peninsula since 2002. This survey should be continuously conducted.

• Korean Journal of Social Welfare
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• v.58 no.1
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• pp.115-141
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• 2006

The purpose of this study is to understand the changes from adopting daily two-shift roster in child care institutions. To accomplish this purpose, we collected data mainly from depth interview with managers, child care workers, and children in child care institutions adopting daily two-shift roster, and analysed these data through qualitative case study approach. The results of this study are as follows. First, child care workers take the chance of recreation, their working conditions improved, they were participated in self-development activity, and they could make relationship with persons in social network. But, some participants worried about decrease of responsibility of workers. Second, one hand, possibility of high-quality care for child is increased, on the other hand, possibility of improving attachment relationship between workers and children is decreased. some children is confused by shift. But, most important strength is that partners have complementary parenting roles through discussion about parenting skills. They have developed communication skills by trial and error, and growed with children through sharing. Third, many qualified persons have applied for institution because of improvement of working conditions, thus institutions had the chance of adopting qualified workers. These workers have various abilities and resources, could mobilize resources from community, and could progress various programs and intervene for children. But, institutions had many difficulties in process adopting daily two-shift roster because of unstable financial support and previous structure. Most of participants worried about that local government may cut down a subsidy.

• Bulletin of the Korean Chemical Society
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• v.31 no.3
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• pp.606-610
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• 2010

Aminoacyl-tRNA synthetases (aaRSs) play vital roles in protein biosynthesis of living organisms and are interesting antibacterial drug targets. In order to find out new inhibitor candidate molecules as antibacterial agent, the binding modes of the candidate molecules were investigated at the active sites of aaRSs by molecular docking study. The docking simulations were performed with 48 compounds from four different scaffolds into the eight different aaRSs. The results show that scaffolds 3 and 4 compounds have consistently better binding capabilities, specifically for HisRS (E. coli) and IleRS (S. aureus). The binding modes of the best compounds with the proteins were well compatible with those of two ligands in crystal structures. Therefore, we expect that the final compounds we present may have reasonable aaRS inhibitory activity.