• Journal of Environmental Impact Assessment
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• v.33 no.3
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• pp.131-141
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• 2024

The environmental impact assessment have been played important role for sustainable development of the country. Meantime, various studies have been conducted for scientific and effective environmental impact assessment. Howeverthe quantitative data such as regional distribution status and development characteristics according to the type of development project, environmental changes due to development projects, and spatial-temporal changes are insufficient, currently. In this study, we investigated the eco-friendliness of major development projects (industrial complex, urban development, tourist complexes, and waste disposal equipments) by integrating the EA-INDEX of each development projects which were reported by prior researches. As a result, we found that the eco-friendliness of development projects tended to gradually increase overtime due to the increase in eco-friendliness in the protection of living environment sector. The protection of living environment sector was a major factor in the the eco-friendliness of industrial complexes and urban development projects. In the case ofroad construction, landfill and tourist complex development projects, the natural environment conservation sector was a major factor in the eco-friendliness of the project. As a result of analyzing the eco-friendliness of development projects by local government, the eco-friendliness of development projects promoted and implemented in Ulsan, Jeonnam, and Gwangju was higher compared to other regions, and it was relatively low in Daejeon, Gyeongnam, and Daegu. This study is considered to be significant in that it conducted a quantitative analysis of the eco-friendliness of development projects carried out over a relatively long period of time throughout South Korea by dividing them by year and local government.

• Journal of Life Science
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• v.34 no.8
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• pp.567-575
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• 2024

New breeding techniques (NBT) recognize specific DNA sequences and remove, modify, or insert DNA at a desired location, and can be used to treat genetic diseases in humans or to improve the traits of livestock or crops. In this study, we conducted a comparative analysis of various agricultural traits and assessed the safety of gene transferability in third-generation genome-editing rice (OsCKq1-G₃) with T and G nucleotide insertions developed using the CRISPR/Cas9 SDN-1 method, in comparison to its parental line (Oryza sativa L., cv Ilmi). The analyzed traits included heading date, culm length, panicle length, tiller number, yield, germination rate, viviparous germination rate, shattering, after wintering seed viability, the presence of toxins and allergens. The target trait, heading date, exhibited a high significant difference of approximately 5 days. Culm length, panicle length, tiller number, yield showed no significant differences compared to the parental line. No T-DNA bands indicating gene transfer were detected. In the third generation of genome-edited rice, the T-DNA was confirmed to be eliminated as successive generations advanced through self-pollination. Through the analysis of germination rate, viviparous germination rate, shattering, and after wintering viability, we confirmed that the genome-editing rice has no potential for weediness. The ORF and amino acid sequences of the genome-editing rice did not reveal any toxins and allergens. The results of this study can be utilized as important data for the environmental risk assessment of genome-editing rice.

• Journal of Bio-Environment Control
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• v.33 no.3
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• pp.163-171
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• 2024

This study was aimed to investigate the effects of layer-by-floor environmental conditions and lower shelf supplemental lighting on the productivity of fresh shoots when growing rosemary in multi-layer cultivation. The 10-cm cuttings from stock plants of common rosemary (Rosemarinus officinalis) were planted in a 128-hole tray, rooted, and then transplanted into pots of 750, 1,300, and 2,000 mL. Afterwards, they were placed on multi-layer shelves (width × length × height: 149 × 60 × 57 cm, 3-layer) in a two-linked greenhouse and cultivated using the sub-irrigation. The productivity of young shoots by layer of the multi-layer shelf was the highest on the third floor (top floor), but productivity decreased sharply after September due to stem lignification caused by excessive light during the summer. Conversely, the lower two layers exhibited faster growth rate of young shoots until the late cultivation period, but the quality decreased due to stem softening and leaf epinasty. To address the excessive light problem on the third floor during the summer, shading was implemented at 30% opacity in July and August, resulting in a 210% increase in rosemary young shoots count and a 162% increase in fresh weight per unit area compared to the unshaded control. To improve the lighting deficiency on the lower layer, supplemental lighting with LED at 30 W increased rosemary young shoot harvest by 168% from June to September compared to no supplemental lighting, but it decreased productivity after September. Therefore, when growing rosemary in multi-layer, it is judged that intensive production of young shoots is possible if the third floor (top layer) is shaded with 30% of light from July to August to prevent stem lignification, and the lower layer is temporarily supplemented with LED 30 W from June to September to increase young shoot growth.

• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.48-59
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• 1999

Objectives. This study was conducted to evaluate the association between upper extremity musculoskeletal symptoms and Rapid Upper Limb Assessment(RULA) in vehicle assembly line workers. The goal of this study is to show the feasibility of RULA as a checklist for work related musculoskeletal symptoms (WMSDs) in Korean workers. Methods. The total number of 199 people from the department of assembly and 115 people from the department of Quality Control(QC) in automotive plant were subjects for this cross sectional study. A standard symptom questionnaire survey has been used for the individual characteristics, work history, musculosketal symptoms and non-occupational covariates. The data were obtained by applying one-on-one interview for the all subjects. RULA has been applied for ergonomic work posture analysis and the primary ergonomic risk sure was computed by RULA method. Association between upper extremity musculoskeletal symptoms and RULA were assessed by multiple logistic regression analysis. Results. A total of 314 workers was examined. The prevalence of musculoskeletal symptoms by NIOSH case definition was 62.4%. The distribution of musculoskeletal symptoms by the part of the body turned out to be following; back:41.4%, neck: 32.8%, shoulder: 26.4%, arm: 10.5% and hand:29.3%. The relationship of the individual RULA scores were statistically significant for the prevalence of musculoskeletal symptoms. As the result of the multiple logistic regressioin analysis, grand final score (OR=2.250 95% CI: 1.402-3.612) was associated with musculoskeletal symptoms in any part of the body.; upper arm score(OR=1.786 95% CI: 1.036-3.079) and posture score A(OR=1.634 95% CI: 1.016-2.626) in neck; muscel use score(OR=3.076 95% CI:1.782-5.310) and posture score A(OR=1.798 95% CI: 1.072-3.017) in shoulder; upper arm score(OR=1.715 95% CI: 1.083-2.715) and muscel use score(OR=2.057 95% CI:1.303-3.248) in neck & shoulder; muscle use score(OR=10.662 95% CI: 3.180-35.742) in arm; writst/wist score(OR=2.068 95% CI: 1.130-3.786) and muscle use score(OR=2.215 95% CI: 1.284-3.819) in hand & wrist.; muscle use score of trunk (OR=2.601 95% CI: 1.147-5.901) in back. Conclusions. Musculoskeletal symptoms of the extremities were strongly associated with individual RULA body score. These results show that RULA can be used as a useful assessment tool for the evaluation of musculoskeletal loading which is known to contribute to work-related musculoskeletal disorders. RULA also can be used as a screening tool or incorporated into a wider ergonomic assessment of epidemiological, physical, mental, environmental and organizational factors. As shown in this study, complement of the analysis system for the other risk factors and characterizing between the upper limb and back part will be needed for future work.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Progress in Medical Physics
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• v.26 no.3
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• pp.168-177
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• 2015

For evaluating the treatment planning accurately, the quality assurance for treatment planning is recommended when patients were treated with IMRT which is complex and delicate. To realize this purpose, treatment plan quality assurance software can be used to verify the delivered dose accurately before and after of treatment. The purpose of this study is to evaluate the accuracy of treatment plan quality assurance software for each IMRT plan according to MLC DLG (dosimetric leaf gap). Novalis Tx with a built-in HD120 MLC was used in this study to acquire the MLC dynalog file be imported in MobiusFx. To establish IMRT plan, Eclipse RTP system was used and target and organ structures (multi-target, mock prostate, mock head/neck, C-shape case) were contoured in I'mRT phantom. To verify the difference of dose distribution according to DLG, MLC dynalog files were imported to MobiusFx software and changed the DLG (0.5, 0.7, 1.0, 1.3, 1.6 mm) values in MobiusFx. For evaluation dose, dose distribution was evaluated by using 3D gamma index for the gamma criteria 3% and distance to agreement 3 mm, and the point dose was acquired by using the CC13 ionization chamber in isocenter of I'mRT phantom. In the result for point dose, the mock head/neck and multi-target had difference about 4% and 3% in DLG 0.5 and 0.7 mm respectively, and the other DLGs had difference less than 3%. The gamma index passing-rate of mock head/neck were below 81% for PTV and cord, and multi-target were below 30% for center and superior target in DLGs 0.5, 0.7 mm, however, inferior target of multi-target case and parotid of mock head/neck case had 100.0% passing rate in all DLGs. The point dose of mock prostate showed difference below 3.0% in all DLGs, however, the passing rate of PTV were below 95% in 0.5, 0.7 mm DLGs, and the other DLGs were above 98%. The rectum and bladder had 100.0% passing rate in all DLGs. As the difference of point dose in C-shape were 3~9% except for 1.3 mm DLG, the passing rate of PTV in 1.0 1.3 mm were 96.7, 93.0% respectively. However, passing rate of the other DLGs were below 86% and core was 100.0% passing rate in all DLGs. In this study, we verified that the accuracy of treatment planning QA system can be affected by DLG values. For precise quality assurance for treatment technique using the MLC motion like IMRT and VMAT, we should use appropriate DLG value in linear accelerator and RTP system.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.1
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• pp.19-28
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• 2020

This study was conducted to compare the agronomic characteristics and productivity according to the species and varieties of winter forage crops in reclaimed land. Winter forage crops used in this study were developed in National Institute of Crop Science, RDA. Oats ('Samhan', 'Jopung', 'Taehan', 'Dakyung' and 'Hi-early'), forage barley ('Yeongyang', 'Yuyeon', 'Yujin', 'Dacheng' and 'Yeonho'), rye ('Gogu', 'Jogreen' and 'Daegokgreen') and triticale ('Shinyoung', 'Saeyoung', 'Choyoung', 'Sinseong', 'Minpung' and 'Gwangyoung') were planted in the reclaimed land of Sihwa district in Hwaseong, Gyeonggi-do in the autumn of 2018 and cultivated using each standard cultivation method, and harvested in May 2019(oat and rye: 8 May, barley and triticale: 20 May.) The emergency rate was the lowest in rye (84.4%), and forage barley, oat and triticale were in similar levels (92.8 to 98.8%). Triticale was the lowest (416 tiller/㎡) and oat was the highest (603 tiller/㎡) in tiller number. Rye was the earliest in the heading date (April 21), triticale was April 26, and oat and forage barley were in early May (May 2 and May 5). The plant height was the highest in rye (95.6 cm), and triticale and forage barley was similar (76.3 and 68.3cm) and oat was the lowest (54.2 cm). Dry matter(DM) content of rye was the highest in the average of 46.04% and the others were similar at 35.09~37.54%. Productivity was different among species and varieties, with the highest dry matter yield of forage barley (4,344 kg/ha), oat was similar to barley, and rye and triticale were lowest. 'Dakyoung' and 'Hi-early' were higher in DM yield (4,283 and 5,490 kg/ha), and forage barley were higher in 'Yeonho', 'Yujin' and 'Dacheng' varieties (4,888, 5,433 and 5,582 kg/ha). Crude protein content of oat (6.58%) tended to be the highest, and TDN(total digectible nutrient) content (63.61%) was higher than other varieties. In the RFV(relative feed value), oats averaged 119, while the other three species averaged 92~105. The weight of 1,000 grain was the highest in triticale (43.03 g) and the lowest in rye (31.61 g). In the evaluation of germination rate according to the salt concentration (salinity), the germination rate was maintained at about 80% from 0.2 to 0.4% salinity. The correlation coefficient between germination and salt concentration was high in oat and barley (-0.91 and -0.92) and lowest in rye (-0.66). In conclusion, forage barley and oats showed good productivity in reclaimed land. Adaptability is also different among varieties of forage crops. When growing forage crops in reclaimed land, the selection of highly adaptable species and varieties was recommended.

• Journal of dental hygiene science
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• v.17 no.6
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• pp.523-532
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• 2017

The purpose of this study was to provide basic data to standardize the clinical dental hygiene curriculum, based on analysis of current clinical dental hygiene curricula in Korea. We emailed questionnaires to 12 schools to investigate clinical dental hygiene curricula, from February to March, 2017. We analyzed the clinical dental hygiene curricula in 5 schools with a 3-year program and in 7 schools with a 4-year program. The questionnaire comprised nine items on topics relating to clinical dental hygiene, and four items relating to the dental hygiene process and oral prophylaxis. The questionnaire included details regarding the subject name, the grade/semester/credit system, course content and class hours, the number of senior professors, and the number of patients available for dental hygiene clinical training purposes. In total, there were 96 topics listed in the curricula relating to clinical dental hygiene training, and topics varied between the schools. There was an average of 20.4 topic credits, and more credits and hours were allocated to the 4-year program than to the 3-year program. On average, the ratio of students to professors was 21.4:1. Course content included infection control, concepts for dental hygiene processes, dental hygiene assessment, intervention and evaluation, case studies, and periodontal instrumentation. An average of 2 hours per patient was spent on dental hygiene practice, with an average of 1.9 visits. On average, student clinical training involved 19 patients and 26.6 patients in the 3-year and 4-year programs, respectively. The average participation time per student per topic was 38.0 hours and 53.1 hours, in the 3-year and 4-year programs, respectively. Standardizing the clinical dental hygiene curricula in Korea will require consensus guidelines on topics, the number of classes required to achieve core competencies as a dental hygienist, and theory and practice time.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• 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.