• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.241-255
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• 2021

Space observation data includes research data such as stars, galaxies, Sun, space plasma, planets, and minor bodies observed through space missions, including processing and utilizing the observation data. Astronomy and space science observation systems are getting larger, and space mission opportunities and data size are increasing. Accordingly, the need for systematic and efficient management of space observation data is growing. Therefore, in Korea, a strategy and policy for space observation data should be established. As a stage of preparation, National Aeronautics and Space Administration (NASA)'s data strategy, which developed from extensive understanding and long-term experience for space observation data, was analyzed. Based on the analysis results, we propose a strategic direction and 10 recommendations for Korean space observation data strategies that will be the basis for establishing space observation data policies in the future.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.514-522
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• 2022

Non-destructive testing (NDT) technology is a widely used inspection method for agricultural products. Compared with the conventional inspection method, there is no extensive sample preparation for NDT technology, and the sample is not damaged. In particular, NDT technology is used to inspect the internal structure of agricultural products infested by pests. The introduction and spread of pests during the import and export process can cause significant damage to the agricultural environment. Until now, pest detection in agricultural products and quarantine processes have been challenging because they used external inspection methods. However, NDT technology is advantageous in these inspection situations. In this pilot study, we investigated the feasibility of X-ray computed tomography (X-ray CT) and magnetic resonance imaging (MRI) to identify pest infestation in agricultural products. Three kinds of artificially pest-infested fruits (mango, tangerine, and chestnut) were non-destructively inspected using X-ray CT and MRI. X-ray CT was able to identify all pest infestations in fruits, while MRI could not detect the pest-infested chestnut. In addition, X-ray CT was superior to the quarantine process than MRI based on the contrast-to-noise ratio (CNR), image acquisition time, and cost. Therefore, X-ray CT is more appropriate for the pest quarantine process of fruits than MRI.

• The Korean Journal of Pain
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• v.35 no.4
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• pp.468-474
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• 2022

Background: The new type of corona virus has a wide range of symptoms. Some people who have COVID-19 can experience long-term effects from their infection, known as post-COVID conditions. The authors aimed to investigate prolonged musculoskeletal pain as a symptom of the post-COVID-19 condition. Methods: This is a descriptive study on the patients who were diagnosed with COVID-19 in a university hospital, between March 2020 and March 2021. Patient records and an extensive questionnaire were used to obtain relevant demographic and clinical characteristics, including hospitalization history, comorbidities, smoking history, duration of the pain, the area of pain, and the presence of accompanying neuropathic symptoms. Results: Of the diagnosed patients, 501 agreed to participate in the study. Among the participants, 318 had musculoskeletal pain during COVID-19 infection, and 69 of them reported prolonged pain symptoms as part of their a post-COVID condition which could not be attributed to any other cause. The mean duration of pain was 4.38 ± 1.73 months, and the mean pain level was 7.2 ± 4.3. Neuropathic pain symptoms such as burning sensation (n = 16, 23.2%), numbness (n = 15, 21.7%), tingling (n = 10, 14.5%), stinging (n = 4, 5.8%), freezing (n = 1, 1.4%) were accompanied in patients with prolonged musculoskeletal pain. Conclusions: Patients with COVID-19 may develop prolonged musculoskeletal pain. In some patients, neuropathic pain accompanies it. Awareness of prolonged post-COVID-19 pain is crucial for its early detection and management.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.4
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• pp.337-344
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• 2021

Background: The primary indication for using long-acting anesthetics in dentistry is extensive dental procedures that require pulpal anesthesia beyond 90 min and management of postoperative pain. Ropivacaine is an amide local anesthetic that is available at various concentrations with inherent vasoconstrictive properties at low concentrations. Ropivacaine has a 75% greater margin of safety than bupivacaine. Ropivacaine can be a good alternative to bupivacaine as a local anesthetic in dental implant surgery as it provides a longer duration of both pulpal and soft tissue anesthesia after mandibular nerve block and lowers CNS and cardiovascular toxicity. This study aimed to evaluate and compare the clinical efficacy of ropivacaine and lignocaine for implant surgery anesthesia. Methods: Fifteen patients with bilateral edentulous sites indicated for implant placement were recruited for this study. Patients aged 20-60 years of both sexes were randomly recruited. Thirty implant placements were performed in the test and control groups using ropivacaine and lignocaine with adrenaline as local anesthetics, respectively. Results: The results were analyzed statistically. The duration of anesthesia was significantly higher in the test group than in the control group. Ropivacaine was found to be superior to lignocaine in terms of the quality of anesthesia. The comparison of mean visual analog scale scores showed ropivacaine to have better anesthetic and analgesic effects than the control group. Conclusion: Ropivacaine 0.75% provides a significantly longer duration of anesthesia than lignocaine 2% with adrenaline. Ropivacaine 0.75% decreased intraoperative and postoperative analgesia compared to lignocaine 2% with adrenaline. Hence, ropivacaine 0.75% can be used as an alternative to lignocaine in implant surgeries and other intraoral surgical procedures that require a longer duration of anesthesia and analgesia.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• The Plant Pathology Journal
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• v.38 no.3
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• pp.182-193
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• 2022

Turfgrass, the most widely grown ornamental crop, is severely affected by fungal pathogens including Sclerotinia homoeocarpa, Rhizoctonia solani, and Magnaporthe poae. At present, turfgrass fungal disease management predominantly relies on synthetic fungicide treatments. However, the extensive application of fungicides to the soil increases residual detection frequency, raising concerns for the environment and human health. The bacterial volatile compound, 2,3-butanediol (BDO), was found to induce plant resistance. In this study, we evaluated the disease control efficacy of a combination of stereoisomers of 2,3-BDO and commercial fungicides against turfgrass fungal diseases in both growth room and fields. In the growth room experiment, the combination of 0.9% 2R,3R-BDO (levo) soluble liquid (SL) formulation and 9% 2R,3S-BDO (meso) SL with half concentration of fungicides significantly increased the disease control efficacy against dollar spot and summer patch disease when compared to the half concentration of fungicide alone. In field experiments, the disease control efficiency of levo 0.9% and meso 9% SL, in combination with a fungicide, was confirmed against dollar spot and large patch disease. Additionally, the induction of defense-related genes involved in the salicylic acid and jasmonic acid/ethylene signaling pathways and reactive oxygen species detoxification-related genes under Clarireedia sp. infection was confirmed with levo 0.9% and meso 9% SL treatment in creeping bentgrass. Our findings suggest that 2,3-BDO isomer formulations can be combined with chemical fungicides as a new integrated tool to control Clarireedia sp. infection in turfgrass, thereby reducing the use of chemical fungicides.

• Korean Journal of Organic Agriculture
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• v.30 no.3
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• pp.351-374
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• 2022

In recent years, the development of rice industry has been highly valued by the state. In hubei area, due to the development of the modernization and the shortage of labor, traditional way of rice cultivation methods is facing serious challenges, and economic benefits have become the decisive factor for the effective promotion and application of rice cultivation methods. According to the research results, first, in the input-side analysis, in the CCR model, D5, D12, D26, D28, D32, D36 farmers with high efficiency appear. The analysis result shows that among the 60 farmers, the average efficiency is 89%, and there is an inefficiency of 11%. In the BCC model, 14 farmers were identified as high-efficiency farmers, with an average efficiency of 0.9453. Second, in direct seeding cultivation of rice, the average scale efficiency is 0.9227, while the average pure technical efficiency is 0.9644. This shows that the effect of scale efficiency is greater than that of purely technical factors, ignoring the reasons for the low operational efficiency of direct seeding cultivation farmers. It can be predicted that with the further deepening of farmers' understanding of this planting mode, the proportion of rice direct seeding may be further expanded in the future. Relevant agricultural departments should further promote this technology to farmers, study the direct seeding technology using scientific methods, and evaluate the changes of this cultivation mode. The agricultural departments of government should concern about the climate risk assessment of direct seeding rice, the environmental impact assessment caused by the extensive use of herbicides, the application of mechanical technology in the process of direct seeding, the lodging of direct seeding rice, and other related issues.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of the Korean Orthopaedic Association
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• v.55 no.6
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• pp.545-549
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• 2020

Although rare, the treatment options for a medial collateral avulsion fracture during total knee arthroplasty (TKA) range from conservative management using a cast and orthosis to internal fixation using metal screws. Bioabsorbable suture anchors have been used to replace metal fixators with distinct advantages, such as biocompatibility, radiolucency, and unnecessary second removal surgery, but complications, such as osteolysis, have been reported. This paper reports a potential risk of an extensive osteolysis-related suture anchor fixation of a medial collateral ligament (MCL) avulsion fracture during TKA in conjunction with a literature review.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.305-316
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• 2022

This study attempted to analyze the jobs of early childhood special education teachers working in kindergarten's special classes using the DACUM technique. To this end, the DACUM Committee was formed with six early childhood special education teachers with extensive field experience. A total of six DACUM workshops were conducted to derive job definition, job descriptions, and job specifications. As a result, the job description of early childhood special education teachers consisted of 9 duties and 53 tasks, and the job specification consisted of 80 knowledge, 98 skills, and 22 attitudes. Early childhood special education teachers working in kindergarten's special classes were tasks differentiated from special education teachers working in elementary and secondary school including home-linked guidance, health and safety management, elementary school transfer guidance, environmental composition according to play topics, and indoor and outdoor play activities guidance. In the case of knowledge, the Special Education Act on the Disabled, etc. and knowledge on behavioral modification took precedence. Skill showed high frequency of observation-record, parental interviews and parental education skills. Attitudes as experts and administrators took priority. The results of this job analysis can help organize a curriculum to train early childhood special education teachers with high job competency in universities.