• Management & Information Systems Review
• /
• v.36 no.5
• /
• pp.121-149
• /
• 2017

This study looks into individual entrepreneurship engaged in an enterprise; the effect of creative efficacy and internal and external networks on organizational commitment based on the previous studies. The hypothesis, the internal and external networks constructed in social context by individuals in the relationship between individual entrepreneurship and organizational commitment; and mediating effect through creative efficacy obtained by the previous experience can be in existence, is to be confirmed through an empirical study. The analysis data is collected from 244 of currently working employees via a survey. The determination of employee-oriented study is summarized as follows: first, the promotion of employee's individual entrepreneurship is significant as well as of the leader for the result of organizational commitment, because there are positive effects between the individual entrepreneurship and organizational commitment. Second, the internal and external networks owned by individuals affect one's own outcome as the internal and external networks of enterprise mediate the relationship between individual entrepreneurship and organizational commitment. Third, it is confirmed that the confidence in individual creativity is an essential factor as creative efficacy exhibits a mediating effect in relationship between individual entrepreneurship and organizational commitment. Particularly, it is verified that an enterprise is in need to expand education or programs not only for networks leading to an outcome but also for creativity improvement of affiliated individuals from the fact that creative efficacy, a hybridized concept of creativity and self-efficacy studied in the previous research, mediates the relationship between individual entrepreneurship and an outcome. In the conclusion, additional implications are offered; the thresholds and frameworks for the study are discussed.

• Tuberculosis and Respiratory Diseases
• /
• v.61 no.1
• /
• pp.34-40
• /
• 2006

Background: Extubation failure was associated with poor prognosis and high hospital mortality. Cuff leak test (CLT) has been proposed as a relatively simple method for detecting laryngeal obstruction that predispose toward postextubation stridor (PES) and reintubation. We examined the risk factors of extubation failure and evaluated the usefulness and limitation of CLT for predicting PES and reintubation. Methods: Thirty-four consecutive patients intubated more than 24 hours were examined. The subjects were evaluated daily for extubation readiness, and CLT was performed prior to extubation. Several parameters in the extubation success and failure group were compared. The accuracy and limitation of CLT were evaluated after choosing the thresholds values of the cuff leak volume (CLV) and percentage (CLP). Results: Of the 34 patients studied, 6 (17.6%) developed extubation failure and 3 (8.8%) were accompanied by PES. The patients who had extubation failure were more likely to have a longer duration of intubation and more severe illness. The patients who developed PES had a smaller cuff leak than the others: according to the CLV ($22.5{\pm}23.8$ vs $233.3{\pm}147.1ml$, p=0.020) or CLP ($6.2{\pm}7.3$ vs $44.3{\pm}24.7%$, p=0.013). The best cut off values for the CLV and CLP were 50ml and 14.7%, respectively. The sensitivity, negative predictive value, and specificity of CLT were relatively high, but the positive predictive value was low. Conclusion: The likelihood of developing extubation failure increases with increasing severity of illness and duration of intubation. A low CLV or CLP (<50ml or 14.7%) is useful in identifying patients at risk of PES, but the CLT is not an absolute predictor and should not be used an indicator for delaying extubation.

• Journal of Korea Water Resources Association
• /
• v.53 no.12
• /
• pp.1159-1172
• /
• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Pediatric Infection and Vaccine
• /
• v.30 no.2
• /
• pp.73-83
• /
• 2023

Purpose: This study aimed to investigate the viral load dynamics in children with underlying malignancies diagnosed with symptomatic coronavirus disease 2019 (COVID-19). Methods: This was a retrospective longitudinal cohort study of patients <19 years old with underlying hemato-oncologic malignancies that were diagnosed with their first symptomatic severe acute respiratory syndrome coronavirus 2 polymerase chain reaction (PCR)-confirmed COVID-19 infection during March 1 to August 30, 2022. Review of electronic medical records and telephone surveys were undertaken to assess the clinical presentations and transmission route of the patients. Thresholds of negligible likelihood of infectious virus was defined as E gene reverse transcription (RT)-PCR cycle threshold (Ct) value ≥25. Results: During the 6-month study period, a total of 43 children with 44 episodes of COVID-19 were included. Of the 44 episodes, the median age of the patients included was 8 years old (interquartile range [IQR], 4.9-10.5), and the most common underlying disease was acute lymphoid leukemia (n=30, 68.2%), followed by patients post-hematopoietic stem cell transplantation (n=8, 18.2%). Majority of the patients had mild COVID-19 (n=32, 72.7%), and three patients (7.0%) had severe/critical COVID-19. Furthermore, 2.3% (n=1) died of COVID-19 associated acute respiratory distress syndrome. The largest percentage of the patients showed E gene RT-PCR Ct value ≥25 between 15-21 days (n=13, 39.4%), followed by 22-28 days (n=10, 30.3%). In 15.2% (n=5), E gene RT-PCR Ct value remained <25 beyond 28 days after initial positive PCR. Refractory malignancy status (β, 67.0; 95% confidence interval, 7.0-17.0; P=0.030) was significantly associated with prolonged duration of E gene RT-PCR <25. A patient with prolonged duration of E gene RT-PCR Ct value <25 was suspected to have infectivity shown by the transmission of the virus to his mother at day 86 after his initial positive test. Conclusions: Children that acquire symptomatic COVID-19 during refractory malignancy state are at a high risk for prolonged shedding warranting PCR-based transmission precautions in this cohort of patients.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.979-995
• /
• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of the Korean Society of Radiology
• /
• v.82 no.3
• /
• pp.670-681
• /
• 2021

Purpose This study aimed to investigate the optimal threshold value in Hounsfield units (HU) on CT to detect the solid components of pulmonary subsolid nodules using pathologic invasive foci as reference. Materials and Methods Thin-section non-enhanced chest CT scans of 25 patients with pathologically confirmed minimally invasive adenocarcinoma were retrospectively reviewed. On CT images, the solid portion was defined as the area with higher attenuation than various HU thresholds ranging from -600 to -100 HU in 50-HU intervals. The solid portion was measured as the largest diameter on axial images and as the maximum diameter on multiplanar reconstruction images. A linear mixed model was used to evaluate bias in each threshold by using the pathological size of invasive foci as reference. Results At a threshold of -400 HU, the biases were lowest between the largest/maximum diameter of the solid portion of subsolid nodule and the size of invasive foci of the pathological specimen, with 0.388 and -0.0176, respectively. They showed insignificant difference (p = 0.2682, p = 0.963, respectively) at a threshold of -400 HU. Conclusion For quantitative analysis, -400 HU may be the optimal threshold to define the solid portion of subsolid nodules as a surrogate marker of invasive foci.

• Progress in Medical Physics
• /
• v.22 no.1
• /
• pp.42-51
• /
• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.3
• /
• pp.262-268
• /
• 2010

Virtual communities (VCs) have developed rapidly, with more and more people participating in them to exchange information and opinions. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. A business-to-consumer virtual community (B2CVC) is a commercial group that creates a trustworthy environment intended to motivate consumers to be more willing to buy from an online store. B2CVCs create a social atmosphere through information contribution such as recommendations, reviews, and ratings of buyers and sellers. Although the importance of B2CVCs has been recognized, few studies have been conducted to examine members' word-of-mouth behavior within these communities. This study proposes a model of involvement, statistics, trust, "stickiness," and word-of-mouth in a B2CVC and explores the relationships among these elements based on empirical data. The objectives are threefold: (i) to empirically test a B2CVC model that integrates measures of beliefs, attitudes, and behaviors; (ii) to better understand the nature of these relationships, specifically through word-of-mouth as a measure of revenue generation; and (iii) to better understand the role of stickiness of B2CVC in CRM marketing. The model incorporates three key elements concerning community members: (i) their beliefs, measured in terms of their involvement assessment; (ii) their attitudes, measured in terms of their satisfaction and trust; and, (iii) their behavior, measured in terms of site stickiness and their word-of-mouth. Involvement is considered the motivation for consumers to participate in a virtual community. For B2CVC members, information searching and posting have been proposed as the main purpose for their involvement. Satisfaction has been reviewed as an important indicator of a member's overall community evaluation, and conceptualized by different levels of member interactions with their VC. The formation and expansion of a VC depends on the willingness of members to share information and services. Researchers have found that trust is a core component facilitating the anonymous interaction in VCs and e-commerce, and therefore trust-building in VCs has been a common research topic. It is clear that the success of a B2CVC depends on the stickiness of its members to enhance purchasing potential. Opinions communicated and information exchanged between members may represent a type of written word-of-mouth. Therefore, word-of-mouth is one of the primary factors driving the diffusion of B2CVCs across the Internet. Figure 1 presents the research model and hypotheses. The model was tested through the implementation of an online survey of CTrip Travel VC members. A total of 243 collected questionnaires was reduced to 204 usable questionnaires through an empirical process of data cleaning. The study's hypotheses examined the extent to which involvement, satisfaction, and trust influence B2CVC stickiness and members' word-of-mouth. Structural Equation Modeling tested the hypotheses in the analysis, and the structural model fit indices were within accepted thresholds: ${\chi}^2^$/df was 2.76, NFI was .904, IFI was .931, CFI was .930, and RMSEA was .017. Results indicated that involvement has a significant influence on satisfaction (p<0.001, ${\beta}$=0.809). The proportion of variance in satisfaction explained by members' involvement was over half (adjusted $R^2$=0.654), reflecting a strong association. The effect of involvement on trust was also statistically significant (p<0.001, ${\beta}$=0.751), with 57 percent of the variance in trust explained by involvement (adjusted $R^2$=0.563). When the construct "stickiness" was treated as a dependent variable, the proportion of variance explained by the variables of trust and satisfaction was relatively low (adjusted $R^2$=0.331). Satisfaction did have a significant influence on stickiness, with ${\beta}$=0.514. However, unexpectedly, the influence of trust was not even significant (p=0.231, t=1.197), rejecting that proposed hypothesis. The importance of stickiness in the model was more significant because of its effect on e-WOM with ${\beta}$=0.920 (p<0.001). Here, the measures of Stickiness explain over eighty of the variance in e-WOM (Adjusted $R^2$=0.846). Overall, the results of the study supported the hypothesized relationships between members' involvement in a B2CVC and their satisfaction with and trust of it. However, trust, as a traditional measure in behavioral models, has no significant influence on stickiness in the B2CVC environment. This study contributes to the growing body of literature on B2CVCs, specifically addressing gaps in the academic research by integrating measures of beliefs, attitudes, and behaviors in one model. The results provide additional insights to behavioral factors in a B2CVC environment, helping to sort out relationships between traditional measures and relatively new measures. For practitioners, the identification of factors, such as member involvement, that strongly influence B2CVC member satisfaction can help focus technological resources in key areas. Global e-marketers can develop marketing strategies directly targeting B2CVC members. In the global tourism business, they can target Chinese members of a B2CVC by providing special discounts for active community members or developing early adopter programs to encourage stickiness in the community. Future studies are called for, and more sophisticated modeling, to expand the measurement of B2CVC member behavior and to conduct experiments across industries, communities, and cultures.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.77-97
• /
• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• The Journal of Korean Society for Radiation Therapy
• /
• v.31 no.1
• /
• pp.33-41
• /
• 2019

Purpose : To evaluate the effectiveness of Jaw-tracking(JT) technique in Intensity-modulated radiation therapy(IMRT) and Volumetric-modulated arc therapy(VMAT) for radiation therapy of esophageal cancer by analyzing volume dose of perimetrical normal organs along with the low-dose volume regions. Materials and Method: A total of 27 patients were selected who received radiation therapy for esophageal cancer with using $VitalBeam^{TM}$(Varian Medical System, U.S.A) in our hospital. Using Eclipse system(Ver. 13.6 Varian, U.S.A), radiation treatment planning was set up with Jaw-tracking technique(JT) and Non-Jaw-tracking technique(NJT), and was conducted for the patients with T-shaped Planning target volume(PTV), including Supraclavicular lymph nodes(SCL). PTV was classified into whether celiac area was included or not to identify the influence on the radiation field. To compare the treatment plans, Organ at risk(OAR) was defined to bilateral lung, heart, and spinal cord and evaluated for Conformity index(CI) and Homogeneity index(HI). Portal dosimetry was performed to verify a clinical application using Electronic portal imaging device(EPID) and Gamma analysis was performed with establishing thresholds of radiation field as a parameter, with various range of 0 %, 5 %, and 10 %. Results: All treatment plans were established on gamma pass rates of 95 % with 3 mm/3 % criteria. For a threshold of 10 %, both JT and NJT passed with rate of more than 95 % and both gamma passing rate decreased more than 1 % in IMRT as the low dose threshold decreased to 5 % and 0 %. For the case of JT in IMRT on PTV without celiac area, $V_5$ and $V_{10}$ of both lung showed a decrease by respectively 8.5 % and 5.3 % in average and up to 14.7 %. A $D_{mean}$ decreased by $72.3{\pm}51cGy$, while there was an increase in radiation dose reduction in PTV including celiac area. A $D_{mean}$ of heart decreased by $68.9{\pm}38.5cGy$ and that of spinal cord decreased by $39.7{\pm}30cGy$. For the case of JT in VMAT, $V_5$ decreased by 2.5 % in average in lungs, and also a little amount in heart and spinal cord. Radiation dose reduction of JT showed an increase when PTV includes celiac area in VMAT. Conclusion: In the radiation treatment planning for esophageal cancer, IMRT showed a significant decrease in $V_5$, and $V_{10}$ of both lungs when applying JT, and dose reduction was greater when the irradiated area in low-dose field is larger. Therefore, IMRT is more advantageous in applying JT than VMAT for radiation therapy of esophageal cancer and can protect the normal organs from MLC leakage and transmitted doses in low-dose field.