It is not an overstatement that nowadays electronic mails are communicated more frequently as well as conveniently than phones and facsimiles, not only in routine life hot also in business transactions. Also, it is evident that emails will be used more and more as a communication method between internal and external organizations. If the information transferred and received via emails takes a role of business records, it is no wonder that emails should be uniformly managed as public records. Currently, however, specific policies or guidelines for the management of email records are not available, nor do most of public employees realize that emails are the actual records of the organization. In fact, the three research methods have been used for this study in the purpose of the establishment of email records management scheme. First of all, bibliographic research has been conducted in an effort to describes the definition and types of email records indicated in the guidelines of each nation, as well as the differences from the transitory email messages. Secondly, email management guidelines and policies of public institutions of England, The United States, Australia, and Canada, so-called the advanced countries of the records management, have been analyzed to examine the advanced examples of email management. In order to manage email records effectively, the functional requirements - capture, classification, storage, access, tracking, disposition, and role and responsibility were categorized in this thesis, based on the ISO 15489. As the designs of these foreign guidelines vary one another, common factors of them were extracted to be included in the realm of the seven stages. Lastly, this thesis has analyzed characteristics of the email system within the Electronic Document Management System of existing administrative institutions. Also, it has examined the overall environment of the email records management of public institutions and sought out its improvement. In essence, focused on the crucial factors on email management drawn out from the email management guidelines of foreign nations and the analysis of the policies, this thesis proposes an email records management scheme for Korean public intuitions, as well as an email management model suitable for forthcoming e-government era.
Lee, Jeongyoung;Ko, Sang Min;Kim, Meenjong;Ji, Yong Gu;Kim, Hoontae
The Journal of Society for e-Business Studies
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v.23
no.4
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pp.109-126
/
2018
With massive and dense production, the livestock industry is rapidly moving into a large-scale, capital-intensive industry especially in swine, poultry, and ducks. However, livestock epidemics can pose a serious threat to the livestock industry and the lives of the people. The government has established and operates the National Animal Protection and Prevention System (KAHIS) since 2013 in order to control the threat, in accordance with the five stages. The digitalized data and information are excellent in ease of management, but it is also pointed out that it is difficult to take countermeasures through linkage with the data in an emergency situation. Recently, the technology of the fourth industrial revolution such as Internet of Things (IoT), Big Data, Artificial intelligence (AI) has been rapidly implemented to the livestock industry, which makes smart livestock disease control system possible. Therefore, this study investigated the domestic and overseas cases which apply 4th Industrial Revolution technology in the industry, and derived 13 possible candidate tasks in the near future. In order to ascertain the priority of policy formulation, we surveyed the expert groups and examined the priority of each of the five stages of the prevention and the priority of each stage. The results of this study are expected to contribute to the establishment of policies for the advancement of smart livestock disease control research and livestock protection.
The public SW market is 3 trillion won, which is less than 10% of the total SW market. However, due to the nature of the domestic market, it is an important market with a relatively large impact on small and medium-sized software companies. In this market, government is operating the Public SW Project Demand Forecasting System in order to support the marketing activities of small and medium sized SW companies and establish a fair market order. The current system has limitations such as lack of user convenience, insufficient analysis capability and less business connection. This study was conducted to identify the problems of these systems and to propose a new system for improving the convenience of users and expanding the information utilization of SMEs. To this end, we analyzed the requirements of each stakeholder. We proposed the 2-phased forecasting cycle, the management cycle, and the system life cycle of public SW projects and created a unified identifier (UID) so that the information of those projects can be identified and linked among them. As a result, an integrated reference model of project information management based on system life cycle was developed, which can explain the demand forecasting and project information, and the improved processes was also designed to implement them. Through the result of this study, it is expected that integrated management of public SW projects will be possible.
The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient's basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.
Due to the spread of COVID-19, it is rapidly changing from face-to-face to non-face-to-face work environments and is changing to a digital work environment that can be accessed anytime, anywhere, providing convenience to all lives. However, the number of breaches, personal information leakage, and technology leakage targeting SMEs that are vulnerable to security continues to increase. Accordingly, the government has been continuously promoting the information security consulting support project for SMEs every year since 2014. Therefore, this study intends to develop a performance model and measurement methodology for continuous and more systematic support and efficient management of information protection support projects in consideration of the importance of information security for SMEs. It is intended to be used as basic data when setting future operational directions and goals. The main method of this study is to derive performance models and indicators for SME information security support projects based on domestic literature, case studies, and survey results, utilize expert advice to verify the developed performance measurement indicators, and use pilot-test questionnaires. Conduct evaluation through surveys. Based on the verified indicators, we would like to present a performance model and measurement index for the information security support project for SMEs.
Journal of Korea Entertainment Industry Association
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v.14
no.3
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pp.139-148
/
2020
Through this study, the policy proposals were intended to be presented to the head of the farming policy. Therefore, the meaning of this study is as follows. First, rather than judging the returning farmer on his own, he should provide sufficient information to consult with his family and increase access to agriculture. Second, by opening up SNS and YouTube to returning farmers, they should instill confidence in rural areas by providing them with information about the joy of rural life and the success of farming so that they can easily access information about rural life through villages, youth associations and women's associations. Efforts should be made to create vibrant rural areas by providing infrastructure around farmland, providing various amenities for settling young people and forming a family farmers' association. Third, the head of the return farming policy should come up with customized support policies for the return farming (e.g., one-year farm support, free farm support, institutional sales guarantee, tourism in advanced countries, modernization of farmland facilities). Public relations strategies should also be strengthened so that such policies can lead to agriculture. Finally, the government should provide subsidized economic activities (e.g. Airbnb, donation of expertise and work-related personnel before returning home, rural experience, etc.).
The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.
With the boom of platform businesses, digital transformation has become the most important topic for businesses. Digital transformation has now become the most urgent strategy for survival, from a strategy considered as an option to choose in the past. Many companies are desperately seeking the ways to be digitally transformed. Even though there have been many studies on digital transformation, most of them are on strategic and conceptual model levels based on simple case analyses. In this study, we analyze the benefits of data integration and network effects from it, based on platform business model at the core of digital transformation. The change based on platform can be categorized into the internal one for the integration of data and better decision making, and the external one for the expansion of the businesses and better prediction of consumer behaviors through the integration of external data sets by the platform business model based enterprises. While the progress for digital transformation is not mature enough yet, financial industry is one of the most promising industries for the change and realization of the aim of it with its relatively much more advanced IT infrastructure. Many companies are making various efforts for the integration of external data, and if the good results can be accomplished, financial industry will contribute to the advancement of digital transformation in other industries as well. For "My Data" project by Korean government, we suggest the data structure and transaction of data (of Korea) should be advanced and established more quickly.
Proceedings of the Korea Water Resources Association Conference
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2023.05a
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pp.31-31
/
2023
During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.
Hayde Flandez-Galvez;Darlon V. Lantican;Anand Noel C. Manohar;Maria Luz J. Sison;Roanne R. Gardoce;Barbara L. Caoili;Alma O. Canama-Salinas;Melvin P. Dancel;Romnick A. Latina;Cris Q. Cortaga;Don Serville R. Reynoso;Michelle S. Guerrero;Susan M. Rivera;Ernesto E. Emmanuel;Cristeta Cueto;Consorcia E. Reano;Ramon L. Rivera;Don Emanuel M. Cardona;Edward Cedrick J. Fernandez ;Robert Patrick M. Cabangbang;Maria Salve C. Vasquez;Jomari C. Domingo;Reina Esther S. Caro;Alissa Carol M. Ibarra;Frenzee Kroeizha L. Pammit;Jen Daine L. Nocum;Angelica Kate G. Gumpal;Jesmar Cagayan;Ronilo M. Bajaro;Joseph P. Lagman;Cynthia R. Gulay;Noe Fernandez-Pozo;Susan R. Strickler;Lukas A. Mueller
Proceedings of the Korean Society of Crop Science Conference
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2022.10a
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pp.30-30
/
2022
Philippines is the second world supplier of coconut by-products. As its first major genomics project, the Philippine Genome Center program for Agriculture (PGC-Agriculture) took the challenge to sequence and assemble the whole coconut genome. The project aims to provide advance genetics tools for our collaborating coconut researchers while taking the opportunity to initiate local capacity. Combination of different NGS platforms was explored and the Philippine 'Catigan Green Dwarf' (CATD) variety was selected with the breeders to be the crop's reference genome. A high quality genome assembly of CATD was generated and used to characterize important genes of coconut towards the development of resilient and outstanding varieties especially for added high-value traits. The talk will present the significant results of the project as published in various papers including the first report of whole genome sequence of a dwarf coconut variety. Updates will include the challenges hurdled and specific applications such as gene mining for host insect resistance and screening for least damaged coconuts (thus potentially insect resistant varieties). Genome-wide DNA markers as published and genes related to coconut oil qualitative/quantitative traits will also be presented, including initial molecular/biochemical studies that support nutritional and medicinal claims. A web-based genome database is currently built for ease access and wider utility of these genomics tools. Indeed, a major milestone accomplished by the coconut genomics research team, which was facilitated with the all-out government support and strong collaboration among multidisciplinary experts and partnership with advance research institutes.
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