• Journal of Crop Science and Biotechnology
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• v.10 no.1
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• pp.33-38
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• 2007

Nitrogen management at the panicle initiation stage(PI) should be fine-tuned for securing a concurrent high yield and high quality rice production. For calibration and testing of the recommendation models of N topdressing rates at PI for target grain yield and protein content of rice, three split-split-plot design experiments including five rice cultivars and various N rates were conducted at the experimental farm of Seoul National University, Korea from 2003 to 2005. Data from the first two years of experiments were used to calibrate models to predict grain yield and milled-rice protein content using shoot fresh weight(FW), chlorophyll meter value(SPAD), and the N topdressing rate(Npi) at PI by stepwise multiple regression. The calibrated models explained 85 and 87% of the variation in grain yield and protein content, respectively. The calibrated models were used to recommend Npi for the target protein content of 6.8%, with FW and SPAD measured for each plot in 2005. The recommended N rate treatment was characterized by an average protein content of 6.74%(similar to the target protein content), reduced the coefficient of variation in protein content to 2.5%(compared to 4.6% of the fixed rate treatment), and increased grain yield. In the recommended N rate treatments for the target protein content of 6.8%, grain yield was highly dependent on FW and SPAD at PI. In conclusion, the models for N topdressing rate recommendation at PI were successful under present experimental conditions. However, additional testing under more variable environmental conditions should be performed before universal application of such models.

• Fibers and Polymers
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• v.3 no.4
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• pp.129-133
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• 2002

It is well known residual gum exists in degummed or rotted hemp fibers. Gum removal results in improvement in fiber fineness and the properties of the resultant hemp yams. However, it is not known what correlation if any exists between the residual gum content in retted hemp fibers and the fiber fineness, described in terms of fiber width in this paper. This study examined the mean width and coefficient of variation (CV) of fiber width of seventeen chemically rotted hemp samples with reference to residual gum content. The mean and CV of fiber width were obtained from an Optical fiber diameter analyser (OFDA 100). The linear regression analysis results show that the mean fiber width is directly proportional to the residual gum content. A slightly weaker linear correlation also exists between the coefficient of variation of fiber width and the residual gum content. The strong linear co-relation between the mean of fiber width and the residual gum content is a significant outcome, since testing for fiber width using the OFDA is a much simpler and quicker process than testing the residual gum content. Scanning Electron Microscopy (SEM) reinforces the OFDA findings. SEM micrographs show a flat ribbon like fiber cross-section hence the term \"fiber width\" is used instead of fiber diameter. Spectral differences in the untreated dry decorticated skin samples and chemically treated and subsequently carded samples indicate delignification. The peaks at $1370cm^{-1}$, $1325cm^{-1}$, $1733cm^{-1}$, and $1600cm^{-1}$ attributed to lignin in the untreated samples are missing from the spectra of the treated samples. The spectra of the treated samples are more amine-dominated with some of the OH character lost.cter lost.

• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.57-68
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• 2013

Recently, cloud computing has been viewed as a technology of great influence. Despite the importance of personal cloud services, the majority of cloud computing studies has been focused on an enterprise level. Therefore, in this research, we list variables of personal cloud computing characteristics (accessibility, storage, trust, perceived ease of use, availability) from preceding research done on an enterprise level. So, through pilot testing, we analyze whether examined characteristic variables affect the intention to use personal cloud computing. Next, we investigate the relevancy between user characteristics, including gender, age, and occupation, and the usage rate of personal cloud computing. Lastly, we implement an analysis of variance in order to speculate whether there is any difference between the five characteristic variables in using personal cloud computing between cloud service enterprises. For empirical testing people with experience in personal cloud computing are surveyed. Frequency analysis, exploratory factor analysis, internal consistency analysis, T-test, ANOVA, and multiple regression analysis are implemented with 325 valid samples. The analysis results are as follows: First, among user characteristic variables, 'occupation' shows a significant relationship with cloud service usage, Second, between cloud service enterprises, all five characteristic variables of 'Naver N Drive' are thoroughly evaluated and 'Google Drive' receives a high evaluation in availability and accessibility. Third and finally, all of the selected characteristic variables affect the intention to use personal cloud computing. However, availability is identified to be the most important factor. This research will serve as a reference to enterprises for the practical implementation of personal cloud computing service.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Safety and Health at Work
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• v.2 no.2
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• pp.169-175
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• 2011

Objectives: The questionnaire of occupational stress index (OSI) has been popular in the workplace, and it has been tailored for bus drivers in Taiwan. Nevertheless, its outcomes for participants are based on self-evaluations, thus validation by their physiological stress biomarker is warranted and this is the main goal of this study. Methods: A cross-sectional study of sixty-three city bus drivers and fifty-four supporting staffs for comparison was conducted. Questionnaire surveys, 24-hour urine cortisol testing, and blood draws for dehydroepiandrosterone-sulfate (DHEA-S) testing were performed. The measured concentrations of these biological measures were logarithmically transformed before the statistical analysis where various scores of stressor factors, moderators, and stress effects of each OSI domain were analyzed by applying multiple linear regression models. Results: For drivers, the elevated 24-hour urine cortisol level was associated with a worker's relationship with their supervisor and any life change events in the most recent 3 months. The DHEA-S level was higher in drivers of younger age as well as drivers with more concerns relating to their salary and bonuses. Non-drivers showed no association between any stressor or satisfaction and urine cortisol and blood DHEA-S levels. Conclusion: Measurements of biomarkers may offer additional stress evaluations with OSI questionnaires for bus drivers. Increased DHEA-S and cortisol levels may result from stressors like income security. Prevention efforts towards occupational stress and life events and health promotional efforts for aged driver were important anti-stress remedies.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.127-134
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• 2021

This study aims to determine the role of innovative capabilities as a mediator in analyzing entrepreneurial marketing's effect on sustainable competitive advantage in food and beverage micro-, small-, and medium- enterprises (MSMEs). Data was obtained from a food and beverage store manager in Tangerang City, comprising 119 samples. Furthermore, the G⁎Power, a tool used to calculate statistical power analysis for various t-tests, F tests, χ2 tests, z tests, and several exact tests, was used to determine the number of research samples, the α error probability of 5%, and 3 variables. The data collection method used questionnaires with Likert Scale 1-5 to indicate strongly disagree to strongly agree. To analyze data, we used Path Analysis supported by SmartPLS statistics software. Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It aims to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. The data processing process took place in two stages, namely the estimation model testing with validity and reliability, and the structural model testing to decide the impact or correlation between variables utilizing the t-test. The result showed a positive and significant effect of entrepreneurial marketing to innovative capability and competitive advantage through the innovative capability of MSMEs.

• Tuberculosis and Respiratory Diseases
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• v.82 no.1
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• pp.53-61
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• 2019

Background: Antiphospholipid antibody syndrome (APS), an important cause of acquired thrombophilia, is diagnosed when vascular thrombosis or pregnancy morbidity occurs with persistently positive antiphospholipid antibodies (aPL). APS is a risk factor for unprovoked recurrence of pulmonary embolism (PE). Performing laboratory testing for aPL after a first unprovoked acute PE is controversial. We investigated if a specific phenotype existed in patients with unprovoked with acute PE, suggesting the need to evaluate them for APS. Methods: We retrospectively reviewed patients with PE and APS (n=24) and those with unprovoked PE with aPL negative (n=44), evaluated 2006-2016 at the Asan Medical Center. We compared patient demographics, clinical manifestations, laboratory findings, and radiological findings between the groups. Results: On multivariate logistic regression analysis, two models of independent risk factors for APS-PE were suggested. Model I included hemoptysis (odds ratio [OR], 12.897; 95% confidence interval [CI], 1.025-162.343), low PE severity index (OR, 0.948; 95% CI, 0.917-0.979), and activated partial thromboplastin time (aPTT; OR, 1.166; 95% CI, 1.040-1.307). Model II included age (OR, 0.930; 95% CI, 0.893-0.969) and aPTT (OR, 1.104; 95% CI, 1.000-1.217). Conclusion: We conclude that patients with first unprovoked PE with hemoptysis and are age <40; have a low pulmonary embolism severity index, especially in risk class I-II; and/or prolonged aPTT (above 75th percentile of the reference interval), should be suspected of having APS, and undergo laboratory testing for aPL.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.726-741
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• 2022

The purpose of this study is to provide instructive theoretical models for art (music) education institutions especially when unpredictable risks, such as pandemics, occur again. Based on the customer behavior theory of the business-to-business-to-customer (B2B2C) platform, and in combination with the technology acceptance model (TAM) and expectation confirmation model (ECM), this study proposes an online education model from the perspective of art education. The framework is based on the three decision-making processes of the customer, and includes the product owner, content owner, and customer area. This paper highlights the factors that influence customers in making decisions when art education institutions are product owners. Regression analysis was introduced to study the factors influencing the expectation confirmation, and the overall fitting testing and six hypotheses testing of 385 effective samples were performed using the structural equation modeling (SEM). The results show that the course-design and after-service positively influenced the expectation confirmation, and the domain image positively influenced the continuance behavior. Negative emotions skipped the mediator (expectation confirmation) and directly exerted a significant negative impact on customers' willingness to continue system usage (continuance behavior). In addition, expectation confirmation positively influenced continuance behavior. The paths of detailed items comprising course-design, after-service, and negative emotion were also analyzed and discussed. In this path analysis, ordinary art learners did not believe that AI partners can play a very good auxiliary role. The findings contribute to the scope of information systems acting as an art education platform academically, and provide effective and theoretical support for the actual operation of art education institutions.

• Women's Health Nursing
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• v.28 no.3
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• pp.235-249
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• 2022

Purpose: This study explored the degree to which pregnant women in Korea made informed choices regarding noninvasive prenatal testing (NIPT) and investigated factors influencing whether they made informed choices. Methods: In total, 129 pregnant women in Korea participated in a web-based survey. Multidimensional measures of informed choice regarding NIPT and decisional conflict were used to measure participants' levels of knowledge, attitudes, deliberation, uptake, and decisional conflict related to NIPT. Additional questions were asked about participants' NIPT experiences and opinions. Results: All 129 pregnant women were recruited from an online community. Excluding those who expressed neutral attitudes toward NIPT, according to the definition of informed choice used in this study, only 91 made an informed choice (n=63, 69.2%) or an uninformed choice (n=28, 30.8%). Of the latter, 75.0% had insufficient knowledge, 39.3% made a value-inconsistent decision, and 14.3% did not deliberate sufficiently. No difference in decisional conflict was found between the two groups. A significant difference was found between the two groups in the reasons why NIPT was introduced or recommended (p=.021). Multiple logistic regression analysis showed that pregnant women who were knowledgeable (odds ratio [OR], 4.77; 95% confidence interval [CI], 2.17-10.47) and deliberated (OR, 0.74; 95% CI, 0.57-0.98) were significantly more likely to make an informed choice. Conclusion: The results of this study help healthcare providers, including nurses in maternity units, understand pregnant women's experiences of NIPT. Counseling strategies are needed to improve pregnant women's knowledge of NIPT and create an environment that promotes deliberation regarding this decision.

• Structural Engineering and Mechanics
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• v.82 no.1
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• pp.121-131
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• 2022

The main goal of this study is to prepare a program for analyzing High Strength Steel Fibrous Reinforced Concrete (HSSFRC) slabs and predict the response and strength of the slab instead of preparing a prototype and testing it in the laboratory. For this purpose, new equations are proposed to represent the material properties of High Strength Steel Fibrous Reinforced Concrete. The proposed equations obtained from performing regression analysis on many experimental results using statistical programs. The finite element method is adopted for non-linear analysis of the slabs. The eight-node "Serendipity element" (3 DoF) is chosen to represent the concrete. The layered approach is adopted for concrete elements and the steel reinforcement is represented by a smeared layer. The compression properties of the concrete are modeled by a work hardening plasticity approach and the yield condition is determined depending on the first two stress invariants. A tensile strength criterion is adopted in order to estimate the cracks propagation. many experimental results for testing slabs are compared with the numerical results of the present study and a good agreement is achieved regarding load-deflection curves and crack pattern. The response of the load deflection curve is slightly stiff at the beginning because the creep effect is not considered in this study and for assuming perfect bond between the steel reinforcement and the concrete, however, a great agreement is achieved between the ultimate load from the present study and experimental results. For the models of the tension stiffening and cracked shear modulus, the value of Bg and Bt (Where Bg and Bt are the curvature factor for the cracked shear modulus and tension stiffening models respectively) equal to 0.005 give good results compared with experimental result.