• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.25-37
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• 2022

The government and private companies are endeavoring to help the digital healthcare industry grow. This includes easing regulations on the big data industry such as the amendment of the Data 3 Act. Despite these efforts, however, there have been constant demands for the amendment of laws related to the medical field and for securing medical data transmissions. In this paper, the Data 3 Act of Korea and the legal system related to healthcare are examined. Then the legal, institutional, and technical aspects of the strategies are compared to understand the issues and implications. Based on this, a legal and institutional strategy suitable for the digital healthcare industry in Korea is suggested. Additionally, a direction to improve social perception along with technical measures such as safe de-identification processing and data transmission are also proposed. This study hopes to contribute to the spread of various convergent industries along with the digital healthcare industry.

• Journal of Microbiology and Biotechnology
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• v.32 no.6
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• pp.681-698
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• 2022

The Nile tilapia Oreochromis niloticus, Atlantic salmon Salmo salar, rainbow trout Oncorhynchus mykiss, olive flounder Paralichthys olivaceus, common carp Cyprinus carpio, grass carp Ctenopharyngodon idella and rohu carp Labeo rohita are farmed commercially worldwide. Production of these important finfishes is rapidly expanding, and intensive culture practices can lead to stress in fish, often reducing resistance to infectious diseases. Antibiotics and other drugs are routinely used for the treatment of diseases and sometimes applied preventatively to combat microbial pathogens. This strategy is responsible for the emergence and spread of antimicrobial resistance, mass killing of environmental/beneficial bacteria, and residual effects in humans. As an alternative, the administration of probiotics has gained acceptance for disease control in aquaculture. Probiotics have been found to improve growth, feed utilization, immunological status, disease resistance, and to promote transcriptomic profiles and internal microbial balance of host organisms. The present review discusses the effects of single and multi-strain probiotics on growth, immunity, heamato-biochemical parameters, and disease resistance of the above-mentioned finfishes. The application and outcome of probiotics in the field or open pond system, gaps in existing knowledge, and issues worthy of further research are also highlighted.

• Journal of Forest and Environmental Science
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• v.38 no.2
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• pp.128-132
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• 2022

Wild boars (Sus scrofa) not only cause crop damage and human casualties, but also facilitate the spread of many infectious diseases in domestic animals and humans. To determine the efficiency of a fencing system in blocking the movement of wild boars, long-term monitoring was performed in a fenced area in Bukhansan National Park using camera traps. Upon monitoring for a period of 46 months, there was a 72.6% reduction in the number of wild boar appearances in the fence-enclosed area, compared to that in the unenclosed area. For 20 months after the fence installation, the blocking effect of the fence was effective enough to reduce the appearance of wild boars by 92.6% in the fence-enclosed area, compared to that in the unenclosed area. The blocking effect of the fence remained effective for 20 months after its installation, after which its effectiveness decreased. Maintaining a fence for a long time is likely to lead to habitat fragmentation. It can also block the movement of other wild animals, including the endangered species - the long-tailed goral. This study suggests a 20-month retention period for the fences installed to inhibit the movement of wild boars in wide forests such as Gangwon-do in South Korea. To identify how long the blocking effect of the fences lasts, further studies are needed focusing on the length and height of the fence, and the conditions of the ground surface.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.31-36
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• 2022

In modern socio-cultural conditions, the requirements for the quality of professional training of graduates of higher educational institutions are increasing. This issue has become especially acute with the advent of pandemic conditions. The ability to apply the existing competencies in the situation of challenges of the 21st century to solve complex social and professional problems is an important criterion for the quality of higher education. Modern professional art education is undergoing many reforms and transformations. Particular attention is paid to innovative teaching methods, thanks to which future specialists experience the breath of innovative education. This issue is especially relevant during the spread of the COVID-19 pandemic. Since students of creative specialties must also have certain competencies, within the framework of the educational process, the competence-based approach to teaching plays a key role, is the methodological basis in the system of modernization of higher professional art education. Thus, the main task of the study is to analyze the process of formation of professional competence among students of art institutions of higher education in the context of COVID-19. As a result of the study, the main aspects of the process of formation of professional competence among students of art institutions of higher education in the context of COVID-19 were investigated.

• Journal of Veterinary Science
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• v.21 no.4
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• pp.54.1-54.11
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• 2020

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of severe infections in humans and animals worldwide. Studies elucidating the population structure, staphylococcal cassette chromosome mec types, resistance phenotypes, and virulence gene profiles of animal-associated MRSA are needed to understand spread and transmission. Objectives: The objective of this study was to determine 1) clonal complexes and spa types, 2) resistance phenotypes, and 3) virulence/resistance gene profiles of MRSA isolated from animals in Switzerland. Methods: We analyzed 31 presumptive MRSA isolates collected from clinical infections in horses, dogs, cattle, sheep, and pigs, which had tested positive in the Staphaurex Latex Agglutination Test. The isolates were characterized by spa typing and DNA microarray profiling. In addition, we performed antimicrobial susceptibility testing using the VITEK 2 Compact system. Results: Characterization of the 31 presumptive MRSA isolates revealed 3 methicillinresistant Staphylococcus pseudintermedius isolates, which were able to grow on MRSA2 Brilliance agar. Of the 28 MRSA isolates, the majority was assigned to CC398 (86%), but CC8 (11%) and CC1 (4%) were also detected. The predominant spa type was t011 (n = 23), followed by t009 (n = 2), t034 (n = 1), t008 (n = 1), and t127 (n = 1). Conclusions: The results of this study extend the current body of knowledge on the population structure, resistance phenotypes, and virulence and resistance gene profiles of MRSA from livestock and companion animals.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1039-1046
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• 2022

After the COVID-19 pandemic, the spread of the untact culture and the Fourth Industrial Revolution, which generates various types of data, generated so much data that it was not compared to before. This led to higher data throughput, revealing little by little the limitations of the existing network system centered on vendors and hardware. Recently, SDN technology centered on users and software that can overcome these limitations is attracting attention. In addition, SDN-based load balancing techniques are expected to increase efficiency in the load balancing area of the server cluster in the data center, which generates and processes vast and diverse data. Unlike existing SDN load distribution studies, this paper proposes a load distribution technique in which a controller checks the state of a server according to the occurrence of an event rather than periodic confirmation through a monitoring technique and allocates a user's request by weighting it according to a load ratio. As a result of the desired experiment, the proposed technique showed a better equal load balancing effect than the comparison technique, so it is expected to be more effective in a server cluster in a large and packet-flowing data center.

• Journal of Advanced Technology Convergence
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• v.1 no.2
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• pp.01-09
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• 2022

Recently, safety issues in companies and public institutions are no longer a task that can be postponed, and when a major safety accident occurs, not only direct financial loss, but also indirect loss of social trust in the company and public institution is greatly increased. In particular, in the case of a fatal accident, the damage is even more serious. Accordingly, as companies and public institutions expand their investments in industrial safety education and prevention, open AI learning model creation technology that enables safety management services without being affected by user behavior in industrial sites where high-risk situations exist, edge terminals System development using inter-AI collaboration technology, cloud-edge terminal linkage technology, multi-modal risk situation determination technology, and AI model learning support technology is underway. In particular, with the development and spread of artificial intelligence technology, research to apply the technology to safety issues is becoming active. Therefore, in this paper, an open cloud platform design method that can support AI model learning for high-risk site safety management is presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.17-22
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• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.117-123
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• 2022

Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.