• Korean Journal of Comparative Education
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• v.27 no.4
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• pp.187-209
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• 2017

The purpose of this study was to highlight the necessity of a conceptual reestablishment of world university evaluations. The hitherto most well-known and validated world university evaluation systems such as Times Higher Education (THE), Quacquarelli Symonds (QS) or Academic Ranking of World Universities (ARWU) primarily assess big universities with quantitative evaluation indicators and performance results in the rankings. Those Systems have instigated a kind of elitism in higher education and neglect numerous small or local institutions of higher education, instead of providing stakeholders with comprehensive information about the real possibilities of tertiary education so that they can choose an institution that is individually tailored to their needs. Also, the management boards of universities and policymakers in higher education have partly been manipulated by and partly taken advantage of the elitist ranking systems with an economic emphasis, as indicated by research-centered evaluations and industry-university cooperation. To supplement such educational defects and to redress the lack of world university evaluation systems, a new system called 'U-Multirank' has been implemented with the financial support of the European Commission since 2012. U-Multirank was designed and is enforced by an international team of project experts led by CHE(Centre for Higher Education/Germany), CHEPS(Center for Higher Education Policy Studies/Netherlands) and CWTS(Centre for Science and Technology Studies at Leiden University/Netherlands). The significant features of U-Multirank, compared with e.g., THE and ARWU, are its qualitative, multidimensional, user-oriented and individualized assessment methods. Above all, its website and its assessment results, based on a mobile operating system and designed simply for international users, present a self-organized and evolutionary model of world university evaluation systems in the digital and global era. To estimate the universal validity of the redefinition of the world university evaluation system using U-Multirank, an epistemological approach will be used that relies on Edgar Morin's Complexity Theory and Karl Popper's Philosophy of Science.

• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.239-247
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• 2021

In order to confirm the impact of the Positive List System (PLS) being applied to all agricultural products from January 1, 2019, we analyzed 28,693 points of inspection-related data on agricultural products distributed in Gyeonggi-do from 2018 to 2020. The ratio of cases exceeding the standard for the total inspection performance was 1.0% in 2018, 1.2% in 2019, and 1.2% in 2020. Out of the 114 cases exceeding the standard in 2019, 55 were applied on a Maximum Residue Limit (MRL) of 0.01 mg/kg, and out of the 115 cases exceeding the standard in 2020, 66 were applied on a MRL of 0.01 mg/kg. To improve this, it seems necessary to manage unintentional pollution, conventional use for unregistered crops, and illegal pesticides. Fluquinconazole detection resulted from unintentional contamination, and diazinon, chlorothalonil, and methabenzthiazuron detection resulted from conventional use in unregistered crops. Chinomethionat is a pesticide component that was discarded in the past and its current use has been attributed to the smuggling of pesticides. This study and future monitoring data can be used as reference data for system supplementation and on-site management reinforcement.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.27-42
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• 2020

Startup accelerators have emerged as new investment entities that help early startups, which are not easy to survive continuously due to lack of funds, commercialization capabilities, and experiences. As their positive performance on early startups and the ecosystem has been proven, the number of early startups which want to receive their investment is also increasing. However, they are vaguely preparing to attract accelerators' investment because they do not have any information on what factors the accelerators consider important. In addition, researches on startup accelerators are also at an early level, so there are no remarkable prior studies on factors that decide on investment. Therefore, this study aims to help startups prepare for investment attraction by looking at what factors are important for accelerators to invest, and to provide meaningful implications to academia. In the preceding study, we derived five upper level categories, 26 lower level accelerators' investment determinants through the qualitative meta-synthesis method, secondary data analysis, observation on US accelerators and in-depth interviews. In this study, we want to derive important implications by deriving priorities of the accelerators' investment determinants. Therefore, we used AHP that are evaluated as the suitable methodology for deriving importance and priority. The analysis results show that accelerators value market-related factors most. This means that startups that are subject to investment by accelerators are early-stage startups, and many companies have not fully developed their products or services. Therefore, market-related factors that can be evaluated objectively seem to be more important than products (or services) that are still ambiguous. Next, it was found that the factors related to the internal workforce of startups are more important. Since accelerators want to develop their businesses together with start-ups and team members through mentoring, ease of collaboration with them is very important, which seems to be important. The overall priority analysis results of the 26 investment determinants show that 'customer needs' and 'founders and team members' understanding of customers and markets' (0.62) are important and high priority factors. The results also show that startup accelerators consider the customer-centered perspective very important. And among the factors related to startups, the most prominent factor was the founder's openness and execution ability. Therefore, it can be confirmed that accelerators consider the ease of collaboration with these startups very important.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.481-495
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• 2022

This study was designed to identify the actual state of naturalized plants and invasive alien species that cause disturbances to the ecosystem, plants which are introduced after forest restoration, and explore the implications resulting from the project. Onsite examination included 29 sites which have been subjected to forest restoration by the Korea Forest Service. Once these were chosen, activity took place twice a year in the spring (May-June) and in the summer (August-September) in 2020 and 2021. Areas not relevant to the project sites were excluded from this activity so that we could identify the plants that could be understood to have been introduced or brought into the site after the actual forest restoration. And the correlation was analyzed, between the naturalized flora within the project sites and the working types applied to the site through confirmation of completion of the restoration project. The naturalized plants appearing on the entire site cover a total of 109 taxa, which includes 29 families, 80 genera, 108 species and 1 subspecies, while invasive plants included 3 families, 7 genera and 8 species. The number of classifications and the naturalization rate gradually decreased over time, after the project. While there was no significant difference between the number of classification groups and the naturalization rate for naturalized plants between project sites, given the number of taxa of naturalized plants, organized by type of damage, there were relatively more naturalized plants that appeared in the severed section of the Baekdudaegan Mountain Range, as well as at quarry and facility sites. Seeding apparently results in naturalization rates as high as 15.545%, on average, based on comparisons of naturalization rates by sowing, seeding, planting, herb planting, and sod pitching channels, all of these being methods of vegetation for planting/greening of bareland and slopes within the project areas. With no seeding, it was 9.167%, higher than the average. As for other vegetation, there was no significant difference depending on application of the working type. This means that unlike the plants subjected to planting, the working type of seed planting which makes it difficult to identify whether a certain plant is a naturalized plant greatly affects the introduction of naturalized plants to the restoration sites, even when using herb planting and sod pitching to control plants and results. Therefore the study suggests that there be inspection by experts of seeds when sowing within restoration sites. The results of this study suggest good practices that will help to direct effective vegetation restoration and follow-up management.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.43-51
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• 2022

This study was conducted to investigate the effect of ventilation at high temperature on the control of powdery mildew, silverleaf whitefly two-spotted spider mite occurred at Korean melon cultivation greenhouse, and on leaf rolling and flowering of the plant in summer season. 'Alchanggul' grafted onto 'Hidden Power' rootstock was planted on soil bed with the distance of 40 cm. Three ventilation temperatures of 45℃, 40℃, and 35℃ as set points were compared. Ventilation treatment was done by control of side window operation from 18th June to 13th July when silverleaf whitefly, mite, and powdery mildew were occurred in all greenhouses. The temperature inside greenhouse was increased up to the set temperature point on sunny days and maintained for about 9 hours with high relative humidity at 45℃ condition. The differences of day maximum air temperature and day minimum RH were the highest at 45℃ treatment. After 11 days of treatments, the damage by powdery mildew and two-spotted spider mite was almost recovered at 45℃ treatment but not at 40 and 35℃. The population of silverleaf whitefly and two-spotted spider mite were significantly decreased at 45℃ treatment at 14 days after treatment, while powdery mildew symptom was not significantly decreased. Leaf rolling was observed at high temperature but not severe at 45℃ treatment. After 26 days of treatments, female flowers did not bloom at all at 45℃ treatment, and the number of male flowers was 1.2 among 15 nodes of newly grown shoots. As the result, it indicates that ventilation at the high temperature of 45℃ for about 2 to 3 weeks can be an applicable method to control above mentioned pests and disease, and to recover the vegetative growth of Korean melon by reducing flowering of the plant.

• Korean small business review
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• v.42 no.4
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• pp.69-102
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• 2020

Shedding new light on the research trend on entrepreneurial ecosystems in the 40-year history of the Asia Pacific Journal of Small Business, this study aims at exploring a potential measurement framework of ecological inputs and outputs in an entrepreneurial ecosystem that promotes entrepreneurship at geographical and spatial levels. As a result of the analysis of research on the entrepreneurial ecosystem in the journal, we found that prior studies emphasized the managerial importance of various ecological factors on the premise of possible causalities between the factors and entrepreneurship. However, empirical research to verify the premised causality has been underexplored yet. This literature gap may lead to unbalanced development of conceptual and case studies that identify requirements for successful entrepreneurial ecosystems based on experiential facts, thereby hindering the generalization of the research results for practical implications. In that there is a growing interest in creating and operating productive entrepreneurial ecosystems as an innovation engine that drives national and regional economic growth, it is necessary to explore and develop the measurement framework for ecological factors that can be used in future empirical research. Hereupon, we apply a conceptual model of 'input-output-outcome-impact' to categorize individual environmental factors identified in prior studies. Based on the model. We operationalize ecological input factors as the financial, intellectual, institutional, and social capitals, and ecological output factors as the establishment-based, innovation-based, and performance-based entrepreneurship. Also, we propose several longitudinal databases that future empirical research can use in analyzing the potential causality between the ecological input and output factors. The proposed framework of entrepreneurial ecosystems, which focuses on measuring ecological input and output factors, has a high application value for future research that analyzes the causality.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.201-223
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• 2022

Currently, species extinctions are increasing due to climate change and continued anthropogenic impact. We selected 300 species for conservation with emphasis on plants co-occurring in the Baekdudaegan area, which is a large ecological axis of Korea. We aimed to investigate the vegetation community and environmental characteristics of Zabelia tyaihyonii in the limestone habitat among the target plant species in the Baekdudaegan region to derive effective conservation strategies. In Danyang-gun, Yeongwol-gun, and Jecheon-si, we selected 36 investigation sites where Z. tyaihyonii was present. We investigated the vegetation, flora, soil and physical environment. We also found notable plants such as Thalictrum petaloideum, Sillaphyton podagraria, and Neillia uekii at the investigation sites. We classified forest vegetation community types into 4 vegetation units and 7 species group types. With canonical correspondence analysis (CCA) of the vegetation community and habitat factors, we determined the overall explanatory power to be 75.2%, and we classified the environmental characteristics of the habitat of Z. tyaihyonii into a grouping of three. Among these, we detected a relationship between the environmental factors elevation, slope, organic matter, rock ratio, pH, potassium, and sodium. We identified numerous rare and endemic plants, including Thalictrum petaloideum, in the investigation site, and determined that these groups needed to be preserved at the habitat level. In the classification of the vegetation units analyzed based on the emerging plants and the CCA, we reaffirmed the uniqueness and specificity of the vegetation community in the habitat of Z. tyaihyonii. We anticipate that our results will be used as scientific evidence for the empirical conservation of the native habitats of Z. tyaihyonii.