• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.469-477
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• 2020

The main objective of this paper is to examine the applicability of Linan and Chen's entrepreneurial intention model (EIM) in predicting the entrepreneurial intention. EIM is an adaptation of the Theory of Planned Behavior that focuses on entrepreneurial intention and hypothesizing slightly different patterns of relationship with regards to subjective norms. The model also includes human capital and demographic factors. Snowball sampling method was used to collect data using the entrepreneurial intention questionnaire (EIQ) through several social media platforms. The survey indicates that the overall entrepreneurial intention of Saudi students is high (mean = 5.41). Eight out of the seventeen hypothesized relationships were found to be significant. Among the demographic variables, gender-personal attitude was significant whereas self employment experience and years of business education were found to be significantly related with perceived behavioral control. The statistical analysis using partial least square structural equation modelling validated the model. All the three antecedents of entrepreneurial intention were significantly related with entrepreneurial intention. The results of this study will help policy makers to get deep understanding into the phenomenon of entrepreneurship among Saudi university students and thereby develop a conducive environment. This study also validates the entrepreneurial intention model in a different cultural context.

• Journal of Ginseng Research
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• v.37 no.2
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• pp.227-247
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• 2013

MicroRNAs (miRNAs) are a class of recently discovered non-coding small RNA molecules, on average approximately 21 nucleotides in length, which underlie numerous important biological roles in gene regulation in various organisms. The miRNA database (release 18) has 18,226 miRNAs, which have been deposited from different species. Although miRNAs have been identified and validated in many plant species, no studies have been reported on discovering miRNAs in Panax ginseng Meyer, which is a traditionally known medicinal plant in oriental medicine, also known as Korean ginseng. It has triterpene ginseng saponins called ginsenosides, which are responsible for its various pharmacological activities. Predicting conserved miRNAs by homology-based analysis with available expressed sequence tag (EST) sequences can be powerful, if the species lacks whole genome sequence information. In this study by using the EST based computational approach, 69 conserved miRNAs belonging to 44 miRNA families were identified in Korean ginseng. The digital gene expression patterns of predicted conserved miRNAs were analyzed by deep sequencing using small RNA sequences of flower buds, leaves, and lateral roots. We have found that many of the identified miRNAs showed tissue specific expressions. Using the insilico method, 346 potential targets were identified for the predicted 69 conserved miRNAs by searching the ginseng EST database, and the predicted targets were mainly involved in secondary metabolic processes, responses to biotic and abiotic stress, and transcription regulator activities, as well as a variety of other metabolic processes.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.364-375
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• 2019

Background: Ecologists continue to investigate the factors that potentially affect the pattern and magnitude of tree damage during catastrophic windstorms in forests. However, there still is a paucity of research on which trees are more vulnerable to direct damage by winds rather than being knocked down by the fall of another tree. We evaluated this question in a mixed hardwood-softwood forest within the Big Thicket National Preserve (BTNP) of southeast Texas, USA, which was substantially impacted by Hurricane Rita in September 2005. Results: We showed that multiple factors, including tree height, shade-tolerance, height-to-diameter ratio, and neighborhood density (i.e., pre-Rita stem distribution) significantly explained the susceptibility of trees to direct storm damage. We also found that no single factor had pervasive importance over the others and, instead, that all factors were tightly intertwined in a complex way, such that they often complemented each other, and that they contributed simultaneously to the overall susceptibility to and patterns of windstorm damage in the BTNP. Conclusions: Directly damaged trees greatly influence the forest by causing secondary damage to other trees. We propose that directly and indirectly damaged (or susceptible) trees should be considered separately when assessing or predicting the impact of windstorms on a forest ecosystem; to better predict the pathways of community structure reorganization and guide forest management and conservation practices. Forest managers are recommended to adopt a holistic view that considers and combines various components of the forest ecosystem when establishing strategies for mitigating the impact of catastrophic winds.

• Korean journal of communication and information
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• v.22
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• pp.211-247
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• 2003

This study explored how Korean immigrants education, length of stay and English fluency affect their political socialization, mediated through traditional news media and the news Web use. Political socialization included political knowledge, interest, and participation. The media usage patterns included U.S. news media, U.S. news Web, Korean news Web, and Korean news Media use by Korean immigrants in the United State. This study found as follows. First, education, length of stay, and English fluency indirectly increased political socialization(political knowledge, interest, and participation) through their relationship with U.S. news media use. Second, U.S. news Web played a potentially important role in Korean immigrants' political socialization by increasing their political interest. Third, Korean news media partly contributed to Korean immigrants' political socialization by increasing their political interest. Fourth, Korean news Web use did not contribute to Korean immigrants' political socialization in terms of political knowledge, interest, and participation at all. In conclusion, this study found that traditional news media's role was more important than news Web's one in the process of immigrants' political socialization to the host society.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.123-132
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• 2002

The purpose of this study is to predict the shear behavior of the weathered mudstone soil using dynamic neural network which mimics the biological system of human brain. SNN and RNN, which are kinds of the dynamic neural network realizing continuously a pattern recognition as time goes by, are used to predict a nonlinear behavior of soil. After analysis, parameters which have an effect on learning and predicting of neural network, the teaming rate, momentum constant and the optimum neural network model are decided to be 0.5, 0.7, 8$\times$18$\times$2 in SU model and 0.3, 0.9, 8$\times$24$\times$2 in R model. The results of appling both networks showed that both networks predicted the shear behavior of soil in normally consolidated state well, but RNN model which is effective fir input data of irregular patterns predicted more efficiently than SNN model in case of the prediction in overconsolidated state.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.819-829
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• 2017

The aim of this study is to analyze intraday price momentum. When price trends are formed, price momentum is the phenomenon that future prices tend to follow the trend. When the market opened and closed, a U-shaped trading volume pattern in which the trading volume was concentrated was observed. In this paper, we defined price momentum as the 10 minute trend after market opening is maintained until the end of market. The strategy is to determine buying and selling in accordance with the price change in the initial 10 minutes and liquidating at closing price. In this study, the strategy was empirically analyzed by using minute data, and it showed effectiveness, indicating the presence of an intraday price momentum. A pattern in which returns are increasing at an early stage is called a J-shaped pattern. If the J-shaped pattern occurs, we have found that the price momentum phenomenon tends to be stronger than otherwise. The DTW algorithm, which is well known in the field of pattern recognition, was used for J-shaped pattern recognition and the algorithm was effective in predicting intraday price movements. This study showed that intraday price momentum exists in the KOSPI200 futures market.

• Journal of Navigation and Port Research
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• v.37 no.2
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• pp.155-163
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• 2013

This study explores the structural behavior of module joints in floating concrete structures subjected to shear. Crack patterns, shear behavior and shear capacity of shear keys in joints of concrete module were investigated. Test parameters included shear key shape, or inclination of shear keys, confining stress levels and compressive strength of concrete. Test results showed that shear strength of joints increased as shear key inclination increased. Test results also showed that shear strength of concrete module joints increased with the increase of confining stress levels. The equation for predicting shear strength of joints was suggested, which was based on the test results. Shear strength prediction by using the equation suggested in this study showed good agreement with test results.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of Bone Metabolism
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• v.25 no.4
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• pp.251-266
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• 2018

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.297-303
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• 2019

This study aims to investigate the characteristics, concepts, types, and strategies of unplugged coding for young children based on computing thinking. The key to unplugged coding for young children is computing thinking. Unplugged coding based on computing thinking for young children can be used to solve problems that can be encountered in everyday life through playing games based on logical thinking by positively utilizing algorithm boards, s-blocks, coding robots, and smart devices without using programs And find new ways to play. Types of unplugged coding for young children include direct input to smart devices, using coding robots with dedicated apps, practicing coding procedures using algorithms, and using hybrid methods. Strategies include understanding algorithms, drawing flowcharts, dividing into smaller parts, finding patterns, inserting, and predicting outcomes.