• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.4
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• pp.383-387
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• 2013

This study was carried out to examine whether the leaf size is likely to be used as a selection criterion for large seed genotype in soybean (Glycine. max (L.) Merr.) breeding program. Two hundred twenty nine soybean germplasms which had collected in Korea, United States, China and Japan were used in this experiment. The area of unifoliate leaf, middle leaflet of first trifoliate and third trifoliate leaf ranged from $3.2cm^2$ to $33.8cm^2$, 9.2 to $29.5cm^2$, and 7.2 to $58.9cm^2$, respectively. One hundred seed weight also showed great variation from 2.7 to 39.0 gram. The average leaf area of unifoliate, middle leaflet of first trifoliate and third trifoliate leaf were $15.7cm^2$, $18.1cm^2$ and $32.7cm^2$, respectively, and that of seed average weight was 17.2 gram per one hundred seed. Significantly positive correlations were observed between seed weight and leaf area of unifoliate (r=$0.80^{**}$), first trifoliate (r=$0.75^{**}$) and third trifoliate (r=$0.67^{**}$), respectively. Both the leaf length and leaf width of unifoliate, middle leaflet of first trifoliate and third trifoliate leaf were significantly positively correlated with seed weight and both the correlations of unifoliate were higher than the other leaves. The correlations of leaf width in soybean leaflet were higher than those of leaf length. Leaf length/width (L/W) ratio of upper leaf was higher than that of lower leaf in the leaf size. Both the leaf area and leaf width of unifoliate leaf are the most suitable predictive characteristics of early selection in related to seed weight for soybean breeding program.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.73-86
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• 2007

Risk-based remediation strategy (RBRS) is a consistent decision-making process for the assessment and response to chemical release based on protecting human health and the environment. The decision-making process described integrates exposure and risk assessment practices with site assessment activities and remedial action selection to ensure that the chosen actions are protective of human health and the environment. The general sequences of events in Tier 1 is as follows: initial site assessment, development of conceptual site model with all exposure pathways, data collection on pollutants and receptors, and identification of risk-based screening level (RBSL). If site conditions do not meet RBSL, it needs further site-specific tier evaluation, Tier 2. In most cases, only limited number of exposure pathways, exposure scenarios, and chemicals of concern are considered the Tier 2 evaluation since many are eliminated from consideration during the Tier 1 evaluation. In spite of uncertainties due to the conservatism applied to risk calculations, limitation in site-specific data collections, and variables affecting the selection of target risk levels and exposure factors, RBRS provides us time- and cost-effectiveness of the remedial action. To ensure reliance of the results, the development team should consider land and resource use, cumulative risks, and additive effects. In addition, it is necessary to develop appropriate site assessment guideline and reliable toxicity assessment method, and to study on site-specific parameters and exposure parameters in Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.120-129
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• 2021

Securing a range of wheat resources is of particular importance with respect to wheat breeding, as it provides a broad genetic foundation. Although wheat breeders have used different wheat germplasms as material resources in current breeding systems, the traits of most germplasms collected from foreign countries differ from the unique traits that have evolved in the Korean environment. In this study, conducted over a 2-year period (2018 and 2019), we therefore evaluated the agricultural traits 1,967 wheat germplasms collected not only in Korea but also worldwide. During the period from sowing to February, the average temperature in 2019 was greater than 1℃, whereas from March to June, the average temperature was approximately 0.9℃ higher in 2018. Compared with the growth recorded in 2018, the stem length in 2019 increased by approximately 20 cm in 2019, and there were notable differences heading date and maturation between 2018 and 2019. In 2019, the heading dates of 973 and 713 wheat resources were earlier and later than those in 2018, respectively. Moreover, stem length was found to be highly correlated with the heading date and maturation. In Korea, where the rainy season and tine of rice transplantation overlap with the time of wheat harvest, early flowering time with high grain yield has been the most important selection target with respect to wheat breeding. We anticipate that the findings of this study will provide would use a foundation for the selection of elite materials and the development of resource core-sets for Korean wheat breeding programs.

• Journal of Mushroom
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• v.18 no.4
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• pp.331-338
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• 2020

This study was conducted to cultivate new Lepista nuda varieties with shorter cultivation period and better fruiting body compared to that of wild strains, for mass production and commercial application. Eighteen genetic resources of L. nuda were collected and grown in boxes using rice straw-fermented growth medium. Four lines with fruiting bodies were formed and selected as cross-breeding lines. Although 657 combinations were crossed through monospore crossing, only 17 combinations were bred between the 'CBMLN-19' line and the 'CBMLN-30' line. Among them, 8 lines with fast mycelial growth and high density were selected. After inoculating the rice straw-fermented growth medium with 14 genetic resources and 8 cross-breeding lines, their incubation period was investigated. Six of the cross-breeding lines completed their incubation in 20 days, while 7 of the 14 genetic resources took more than 40 days to complete their incubation, reducing the incubation period by more than 20 days in most cross-breeding lines. After the incubations were completed, the clay loam soil was covered with for post-cultivation, and when the mycelial cultivation was complete, the formation of fruiting bodies was induced after scraping the mycelial bodies under these environmental conditions: 14℃, 95% relative humidity or higher, and 1,500 to 2,000 ppm CO2 concentration. The temperature was reduced to 6℃ at night, resulting in a low temperature shock. Thus, 4 lines of fruiting bodies occurred from two genetic resources 'CBMLN-31' and 'CBMLN-44' and two cross-bred lines 'CBMLN-96' and 'CBMLN-103'. After inoculation, the longest period for fruiting bodies to occur was 100 days for the control:, the genetic resource 'CBMLN-31', and the shortest period (45 days) was observed for the cross-breeding line 'CBMLN-103'. The result of the investigation of the fruiting body characteristics shows that the cross-bred line 'CBMLN-103' showed a small form with 1.9 g of individual weight and 123validstipes per box, which was the highest incidence among the four lines. Another cross-bred line, 'CBMLN-96', had an individual weight of 5.5 g, which is larger than that of 'CBMLN-103'; however, the number of valid stipes per box was 30 less than that of 'CBMLN-103'. Quantity analysis showed that the control, 'CBMLN-31', had the highest quantity of 783 g per box, followed by the cross-bred line, 'CBMLN-96' with 165 g per box, and then the 'CBMLN-103' with 232 g. The quantity of the two crossbred lines was lower than that of the control 'CBMLN-31'; however, the amount of fruiting bodies was higher, and the cultivation period was shortened by 32 to 33 days. Therefore, these two lines would be selected as superior lines.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.35-55
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• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.53-74
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• 2012

A challenge in fostering virtual communities is the continuous supply of knowledge, namely members' willingness to contribute knowledge to their communities. Previous research argues that giving away knowledge eventually causes the possessors of that knowledge to lose their unique value to others, benefiting all except the contributor. Furthermore, communication within virtual communities involves a large number of participants with different social backgrounds and perspectives. The establishment of mutual understanding to comprehend conversations and foster knowledge contribution in virtual communities is inevitably more difficult than face-to-face communication in a small group. In spite of these arguments, evidence suggests that individuals in virtual communities do engage in social behaviors such as knowledge contribution. It is important to understand why individuals provide their valuable knowledge to other community members without a guarantee of returns. In virtual communities, knowledge is inherently rooted in individual members' experiences and expertise. This personal nature of knowledge requires social interactions between virtual community members for knowledge transfer. This study employs the social capital theory in order to account for interpersonal relationship factors and identity theory for individual and group factors that may affect knowledge contribution. First, social capital is the relationship capital which is embedded within the relationships among the participants in a network and available for use when it is needed. Social capital is a productive resource, facilitating individuals' actions for attainment. Nahapiet and Ghoshal (1997) identify three dimensions of social capital and explain theoretically how these dimensions affect the exchange of knowledge. Thus, social capital would be relevant to knowledge contribution in virtual communities. Second, existing research has addressed the importance of identity in facilitating knowledge contribution in a virtual context. Identity in virtual communities has been described as playing a vital role in the establishment of personal reputations and in the recognition of others. For instance, reputation systems that rate participants in terms of the quality of their contributions provide a readily available inventory of experts to knowledge seekers. Despite the growing interest in identities, however, there is little empirical research about how identities in the communities influence knowledge contribution. Therefore, the goal of this study is to better understand knowledge contribution by examining the roles of social capital and identity in virtual communities. Based on a theoretical framework of social capital and identity theory, we develop and test a theoretical model and evaluate our hypotheses. Specifically, we propose three variables such as cohesiveness, reciprocity, and commitment, referring to the social capital theory, as antecedents of knowledge contribution in virtual communities. We further posit that members with a strong identity (self-presentation and group identification) contribute more knowledge to virtual communities. We conducted a field study in order to validate our research model. We collected data from 192 members of virtual communities and used the PLS method to analyse the data. The tests of the measurement model confirm that our data set has appropriate discriminant and convergent validity. The results of testing the structural model show that cohesion, reciprocity, and self-presentation significantly influence knowledge contribution, while commitment and group identification do not significantly influence knowledge contribution. Our findings on cohesion and reciprocity are consistent with the previous literature. Contrary to our expectations, commitment did not significantly affect knowledge contribution in virtual communities. This result may be due to the fact that knowledge contribution was voluntary in the virtual communities in our sample. Another plausible explanation for this result may be the self-selection bias for the survey respondents, who are more likely to contribute their knowledge to virtual communities. The relationship between self-presentation and knowledge contribution was found to be significant in virtual communities, supporting the results of prior literature. Group identification did not significantly affect knowledge contribution in this study, inconsistent with the wealth of research that identifies group identification as an important factor for knowledge sharing. This conflicting result calls for future research that examines the role of group identification in knowledge contribution in virtual communities. This study makes a contribution to theory development in the area of knowledge management in general and virtual communities in particular. For practice, the results of this study identify the circumstances under which individual factors would be effective for motivating knowledge contribution to virtual communities.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.105-114
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• 2014

We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

• Korean Journal of Environmental Agriculture
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• v.37 no.2
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• pp.73-78
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• 2018

BACKGROUND: Soil salinity of reclaimed tidal land in Korea is highly important factor. High salinity is harmful to crop productivity. Jerusalem artichoke (Helianthus tuberosus L.) is known to be salt-tolerant and has high adaptability to diverse pedo-climatic conditions. The objective of this study was to assess the changes of soil properties and crop productivity according to salt concentration in the reclaimed tidal lands. METHODS AND RESULTS: Experimental sites were selected at Saemangeum ($35^{\circ}46^{\prime}N$, $126^{\circ}37^{\prime}E$) reclaimed tidal land, and their dominant soil series were Munpo (coarse loamy, mixed, non-acid, mesic, typic Fluvaquents). H. tuberosus L were collected from 12 locations across Korea. Tubers were planted at $75{\times}25cm$ with EC 2 to $7dS\;m^{-1}$. Soil samples were periodically collected from both 0~20 cm and 20~40 cm depths of each site. Soil salinity and soil moisture contents were varied depending on weather conditions. Soil electrical conductivity varied from 1.0 to $5.9dS\;m^{-1}$, and soil moisture contents varied from 9.2 to 28.7%. The white-colored tubers of H. tuberosus L. collected from 'Yeongwol-gun' exhibited the highest height (207 cm), followed by the white-colored tubers of H. tuberosus L. collected from 'Iksan-si'(202 cm). The white-colored tubers of H. tuberosus L. collected from 'GyeongJu-si' showed the highest yield (549 kg/10a). The purple-colored tubers of H. tuberosus L. collected from 'Yeongwol-gun' showed the highest yield (615 kg/10a). CONCLUSION: Our results indicate that the plant height and tuber yield did not appear to be correlated. Considering yield and inulin content, the GyeongJu-si seemed to be suitable as the white-colored tubers of H. tuberosus L. and the Yeongwol-gun seemed to be suitable as the purple-colored tubers of H. tuberosus L. in the reclaimed tidal lands. However, it is necessary to consider the relationship between the inulin content and the yield.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.