215 research outputs found
Mental Imagery in an In-store Apparel Shopping Context: Do Women and Men Differ?
This study examined how mental imagery experienced during in-store shopping influences consumersā affective (anticipatory emotion), cognitive (perceived ownership and decision satisfaction) responses and conative response (behavioral intentions) and further investigated how men and women differ in the way mental imagery influences consumer responses
Impacts of Innovation School System in Korea: A Latent Space Item Response Model with Neyman-Scott Point Process
South Korea's educational system has faced criticism for its lack of focus on
critical thinking and creativity, resulting in high levels of stress and
anxiety among students. As part of the government's effort to improve the
educational system, the innovation school system was introduced in 2009, which
aims to develop students' creativity as well as their non-cognitive skills. To
better understand the differences between innovation and regular school systems
in South Korea, we propose a novel method that combines the latent space item
response model (LSIRM) with the Neyman-Scott (NS) point process model. Our
method accounts for the heterogeneity of items and students, captures
relationships between respondents and items, and identifies item and student
clusters that can provide a comprehensive understanding of students'
behaviors/perceptions on non-cognitive outcomes. Our analysis reveals that
students in the innovation school system show a higher sense of citizenship,
while those in the regular school system tend to associate confidence in
appearance with social ability. We compare our model with exploratory item
factor analysis in terms of item clustering and find that our approach provides
a more detailed and automated analysis
A Qualitative Study on the Difference in Organizational Fit of IT Supporting Organizations
For many years, factors that increase the competitive advantage of organizations have been studied in organizational research. For an Information Technology (IT) organization, the main issues are the fitness of the IT and organization strategies and methods for revitalizing IT knowledge management (Earl, 2001; Rathnam, Johnsen, & Wen, 2005; Zack, 2002). However, there are few studies that have evaluated the competitiveness of an organization with reference to the correspondence of these constituent factors with organizational objectives. In this research a multi-contingency view was applied to Korean agencies, and the regional agencies with good performance and those with poor performance were compared with regard to this measure of fitness. The results of this study confirmed that the regions that received a favorable evaluation from experts exhibited good fit overall, and the constituent parts of the organization were consistent with the firmās objectives
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
Recently, deep learning models have shown the potential to predict breast
cancer risk and enable targeted screening strategies, but current models do not
consider the change in the breast over time. In this paper, we present a new
method, PRIME+, for breast cancer risk prediction that leverages prior
mammograms using a transformer decoder, outperforming a state-of-the-art risk
prediction method that only uses mammograms from a single time point. We
validate our approach on a dataset with 16,113 exams and further demonstrate
that it effectively captures patterns of changes from prior mammograms, such as
changes in breast density, resulting in improved short-term and long-term
breast cancer risk prediction. Experimental results show that our model
achieves a statistically significant improvement in performance over the
state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p <
0.05) on held-out test sets
Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation
Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.open
Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea
Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned
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