• Journal of The Korean Society of Integrative Medicine
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• v.9 no.2
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• pp.131-140
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• 2021

Purpose : The purpose of this study was to systematically review the studies on the effects of social story intervention on the individuals with autism spectrum disorders(ASD) to provide the basis for evidence-based practice. Methods : In order to find out the studies on the effect of social story intervention on individuals with ASD, studies published from 2011 to December 2020 were searched on Google Academic Search. The keywords used were "autism spectrum disorder AND social story". A total of 16,900 studies were searched, and from these, 12 studies were selected based on the application of the selection and exclusion criteria. The included studies comprised of 10 single subject design studies and 2 randomized controlled trials design studies. The included studies were analyzed in accordance to population, intervention, outcome measures and results. Results : As a result of analysing the subjects of the included studies, we found out that pre-school children and adolescents were the most common groups of individuals diagnozsed with ASD. In addition to ASD, the diagnosis of the subjects included intellectual disability, Asperger, and Prader-willi. We found out that there were more interventions that were mixed with other interventions, than just the pure social story interventions. In particular, video modeling was found to be the most frequent intervention. This was followed by photo-based social stories. For outcome measure, autism social skills profile was found to be the most often used standardized assessment. For a non-standardized assessment, social desired behavior was found to be the most frequently evaluated behavior, followed by personal problem behavior. Of all the dependent variables, the dependent variable reported as "effective" was the most, followed by "some effective". Conclusion : This study is organized to help the individuals with ASD, families, researchers, and therapists understand the effects of social story intervention on the individuals with ASD in an easy fashion. Further, therapists can use this study as the basic data for evidence-based practice.

• Journal of Korea Trade
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• v.25 no.6
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• pp.78-104
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• 2021

Purpose - Semiconductors are a significant export item for Korea that is expected to continue to contribute significantly to the Korean economy in the future. Thus, the semiconductor industry is a critical component in the 4th Industrial Revolution and is expected to continue growing as the non-face-to-face economy expands as a result of the COVID-19 pandemic. In this context, this paper aims to empirically investigate how semiconductors are imported and exported in Korea from a global supply chain perspective by analysing import and export data at the micro-level. Design/methodology - This study conducts a multifaceted analysis of the global supply chain for semiconductors and related equipment in Korea by examining semiconductor imports and exports by semiconductor type, year, target country, mode of transportation, airport/port, and domestic region, using import/export micro-data. The visualisation, flow analysis, and Bayesian Network methodologies were used to compensate for the limitations of each method. Findings - Korea is a major exporter of semiconductor memory and has the world's highest competitiveness but is relatively weak in the field of system semiconductors. The trade deficit in 'semiconductor equipment and parts' is clearly growing. As a result, continued investment in 'system semiconductors' and 'semiconductor equipment and parts' technology development is necessary to boost exports and ensure a stable supply chain. Originality/value - Few papers on semiconductor trade in Korea have been published from the perspective of the global supply chain or value chain. This study contributes to the literature in this area by focusing on import and export data for the global supply chain of the Korean semiconductor industry using a variety of approaches. It is our hope that the insights gained from this study will aid in the advancement of SCM research.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.245-252
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• 2021

A turbidity current in a river and a lake occurs due to diverse nutrient loading including suspended sediment in sediment runoff, which affects water withdrawal and river environments. We developed one dimensional time-variant numerical model based on Python for the Nagdonggang mainstream. We examined the numerical stability and the applicability of the model by performing the simulation of quasi-steady flow in non-flooding for three cases, which are different according to the point and the amount of turbidity inflows in the Nagdonggang upstream and a tributary. The result was reasonable in the respect of the conservation of matter. The model will facilitate to simulate a large river if we can secure the data of turbidity variations in a target river reach or measured points in a field.

• Genomics & Informatics
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• v.19 no.2
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• pp.17.1-17.13
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• 2021

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.186-191
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• 2019

In this paper, we propose a implementation approach of the high-speed feature extraction algorithm. The proposed method is based on the block type classification algorithm which reduces the computation time when target macro block is divided to smooth block type that has no image features. It is quantitatively identified that occurs at 29.5% of the total image using 200 standard test images with $64{\times}64$ macro block size. This means that within a standard test image containing various image information, 29.5% can reduce the complexity of the operation. When the proposed approach is applied to the Canny edge detection, the required latency of the edge detection can be completely eliminated, such as 2D derivative filter, gradient magnitude/direction computation, non-maximal suppression, adaptive threshold calculation, hysteresis thresholding. Also, it is expected that operation time of the feature detection can be reduced by applying block type classification algorithm to various feature extraction algorithms in this way.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.453-460
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• 2018

The purpose of this study is to analyze the potential quantity of Korean Offset Credits (KOC) resulting from Certified Emission Reductions (CER) in 98 domestic Clean Development Mechanism (CDM) projects that were registered with the United Nations Framework Convention on Climate Change (UNFCCC) as of the end of 2016. Our results show that the total amount of potential KOC is 62,774 kt CO2eq. The potential KOC is only 23.4% of the total CER Issuance. During the first phase, this will be 3.2% of the allocated volume. This is because many projects are related to Renewable Portfolio Standard (RPS), HFC-23, and adipic acid N2O. There is a strong bias in some sectors and projects which could act as market distortion factors. Therefore, it is necessary to expand the target CDM project and activate non CDM offset projects. RPS projects bring fundamental changes to the energy sector, and it is worth reconsidering their acceptability. A wide variety of policy incentives are needed to address strong biases toward certain sectors and projects. The offset scheme has the advantage of allowing entities to reduce their GHG emissions cost effectively through a market mechanism as well as enabling more entities to participate in GHG reduction efforts both directly and indirectly. In contrast, having an inadequate offset scheme range and size might decrease the effort on GHG reduction or concentrate available resources on specific projects. As such, it is of paramount importance to design and operate the offset scheme in such a way that it reflects the situation of the country.

• Entomological Research
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• v.48 no.6
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• pp.533-539
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• 2018

The RNA interference (RNAi) has been considered as an important genetic tool and applied to develop a new living modified (LM) crop trait which is an improvement of nutrient quality or pest management. The RNAi of DvSnf7 has been used for resistance to LM maize and the Western Corn Rootworm which is a major agricultural pest for the US Corn Belt. Most of the environmental risk assessments (ERA) of double strand RNA (dsRNA) have been performed using in vitro transcript products, and not in vivo expressed product. A large amount of dsRNA was required for the acute toxicity assay of water fleas. Therefore development of massive dsRNA purification techniques is critical. Daphnia, a freshwater microcrustacean, is a model organism for studying cellular and molecular mechanism involved in life history traits and ecotoxicology. In this study, we established the massive dsRNA purification method using Escherichia coli and implemented acute toxicity assays to Daphnia magna. As a result, the present RNase A and DNase I, dsRNA was efficiently purified without any special techniques or equipment. Even though purified dsRNA existed during the acute toxicity test, lethality or abnormal behavior were not observed in D. magna. These results indicated that GFP and DvSnf7 dsRNA were not significantly affected to D. magna due to their lack of sequence matching in its genome. The purification method of dsRNA and the acute toxicity assay of water fleas using purified dsRNA would be suitable for the toxicological studies of LMOs to aquatic non-target organisms.

• Journal of The Korean Society of Integrative Medicine
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• v.6 no.4
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• pp.149-169
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• 2018

Purpose : This study was to develop a strategy for modeling future workforce projections to serve as a basis for analyzing annual supply of and demand for physical therapists across the South Korea into 2030. Methods : In-and-out movement model was used to project the supply of physical therapists. The demand was projected according to the demand-based method which consists of four-stages such as estimation of the utilization rate of the base year, forecasting of health care utilization of the target years, forecasting of the requirements of clinical physical therapists and non-clinical physical therapists based on the projected physical therapists. Results : Based on the current productivity standards, there will be oversupply of 39,007 to 40,875 physical therapists under the demand scenario of average rate in 2030, undersupply of 44,663 to 49,885 under the demand scenario of logistic model, oversupply of 16,378 to 19,100 under the demand scenario of logarithm, and oversupply of 18,185 to 20,839 under the demand scenario of auto-regressive moving average (ARIMA) model in 2030. Conclusion : The result of this projection suggests that the direction and degree of supply of and demand for physical therapists varied depending on physical therapists productivity and utilization growth scenarios. However, the need for introduction of a professional physical therapist system and the need to provide long-term care rehabilitation services are actively being discussed in entering the aging society. If community rehabilitation programs for rehabilitation of disabled people and the elderly are activated, the demand of physical therapists will increase, especially for elderly people. Therefore, healthcare policy should focus on establishing rehabilitation service infrastructure suitable for an aging society, providing high-quality physical therapy services, and effective utilization of physical therapists.

• Transactions of Materials Processing
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• v.28 no.1
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• pp.43-48
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• 2019

Aluminum nitride (AlN) is used by the semiconductor industry, and is a compound that is required when manufacturing high thermal conductivity. The AlN films with c-axis orientation and thermal conductivity characteristic were deposited by using the Pulsed Laser Deposition (PLD). The AlN thin films were characterized by changing the deposition conditions. In particular, we have researched the AlN thin film deposited under optimal conditions for growth atmosphere. The epitaxial AlN films were grown on sapphire ($c-Al_2O_3$) single crystals by PLD with AlN target. The AlN films were deposited at a fixed temperature of $650^{\circ}C$, while conditions of nitrogen ($N_2$) pressure were varied between 0.1 mTorr and 10 mTorr. The quality of the AlN films was found to depend strongly on the $N_2$ partial pressure that was exerted during deposition. The X-ray diffraction studies revealed that the integrated intensity of the AlN (002) peak increases as a function the corresponding Full width at half maximum (FWHM) values decreases with lowering of the nitrogen partial pressure. We found that highly c-axis orientated AlN films can be deposited at a substrate temperature of $650^{\circ}C$ and a base pressure of $2{\times}10^{-7}Torr$ in the $N_2$ partial pressure of 0.1 mTorr. Also, it is noted that as the $N_2$ partial pressure decreased, the thermal conductivity increased.