Joy of Ugly Feelings: Korean “Bad Taste” Webtoons as a Case Study

Cognitive narratology has contributed significantly to our understanding of reading fiction, namely, what happens when we read and why we read at all. According to scholars such as Lisa Zunshine, Alan Palmer, and George Butte, we have an evolved craving to read the minds of others, and reading fiction ultimately is a busy act of reading and misreading minds of characters in the storyworld. My paper questions this cognitivist belief by using Korean “bad taste” webtoons (online comics using violent verbal and visual for amusement) as a case study. I discuss ways in which the absence of readable minds and empathy as well as technological properties of these webtoons debunk the myths of mind reading and readable minds and their impact on immersive reading experience. Instead they allow for what neurologists call “detachment manipulation condition” and the concurrence of mixed feelings (negative and positive), leading to a mode of reading that works the best when readers remain irrelevant to and detached from the fictional world that they witness, but do not experience. Overall, this paper reconsiders what constitutes immersion in a fictional world, the role of mind reading and empathy in fiction, and the intersection between narrative and technology among other things

Characteristics of Twitter Influencers, Electronic Word of Mouth, and Film Viewership: Focused on the Korean Film Industry

503-508Despite the successive increase sales in Korean film industry, film revenues have been concentrated more in commercial films, not in diversity films. In general, diversity films relatively made with low budgets have trouble marketing with a limited budget. As one of the low-cost marketing strategies, it has been studied that using influencers who spread strong messages to other people for maximizing electronic word of mouth (eWOM) effects. Therefore, it is worth that identifying and characterizing each influencer of successful movies to use influencers as a cost-effective and powerful marketing tool in the film industry. This study intends to identify film influencers on the SNS, Twitter. And comparative analysis of influencers between 4 types of high-ranked films is conducted to characterize of each influencer and their influential power. Four films released in June 2013, each representing a Korean or foreign, commercial or diversity film, are chosen and 753 Twitter data are collected. To identify each influencer, centrality indices from social network analysis are measured using Condor 2.6.6. The findings reveal that influencers which have high centrality indices are classified into five types and these have different characteristics by film types. The results will attribute to select potential influencers for targeting and benchmarking strategies of diversity films

Prevalence, Awareness, Treatment, and Control of Type 2 Diabetes in South Korea (1998 to 2022):Nationwide Cross-Sectional Study

BACKGROUND: Type 2 diabetes poses an increasing disease burden in South Korea. The development and management of type 2 diabetes are closely related to lifestyle and socioeconomic factors, which have undergone substantial changes over the past few decades, including during the COVID-19 pandemic.OBJECTIVE: This study aimed to investigate long-term trends in type 2 diabetes prevalence, awareness, treatment, and control. It also aimed to determine whether there were substantial alterations in the trends during the pandemic and whether these changes were more pronounced within specific demographic groups.METHODS: This study examined the prevalence, awareness, treatment, and control of type 2 diabetes in a representative sample of 139,786 South Koreans aged &gt;30 years, using data from the National Health and Nutrition Examination Survey and covering the period from 1998 to 2022. Weighted linear regression and binary logistic regression were performed to calculate weighted β coefficients or odds ratios. Stratified analyses were performed based on sex, age, region of residence, obesity status, educational background, household income, and smoking status. β (difference) was calculated to analyze the trend difference between the prepandemic period and the COVID-19 pandemic. To identify groups more susceptible to type 2 diabetes, we estimated interaction terms for each factor and calculated weighted odds ratios.RESULTS: From 1998 to 2022, a consistent increase in the prevalence of type 2 diabetes was observed among South Koreans, with a notable rise to 15.61% (95% CI 14.83-16.38) during the pandemic. Awareness followed a U-shaped curve, bottoming out at 64.37% (95% CI 61.79-66.96) from 2013 to 2015 before increasing to 72.56% (95% CI 70.39-74.72) during the pandemic. Treatment also increased over time, peaking at 68.33% (95% CI 65.95-70.71) during the pandemic. Control among participants with diabetes showed no substantial change, maintaining a rate of 29.14% (95% CI 26.82-31.47) from 2020 to 2022, while control among treated participants improved to 30.68% (95% CI 27.88-33.48). During the pandemic, there was a steepening of the curves for awareness and treatment. However, while the slope of control among participants being treated increased, the slope of control among participants with diabetes showed no substantial change during the pandemic. Older populations and individuals with lower educational level exhibited less improvement in awareness and control trends than younger populations and more educated individuals. People with lower income experienced a deceleration in prevalence during the pandemic.CONCLUSIONS: Over the recent decade, there has been an increase in type 2 diabetes prevalence, awareness, treatment, and control. During the pandemic, a steeper increase in awareness, treatment, and control among participants being treated was observed. However, there were heterogeneous changes across different population groups, underscoring the need for targeted interventions to address disparities and improve diabetes management for susceptible populations.</p

A Meta-Analysis Comparing Factors Affecting the Growth of SMEs: The Case of Germany and South Korea

852-857This study analyzed the effect size of predictors that affects the growth of SMEs in Germany and Korea using meta-analysis. A total of 34,154 studies from six databases in English and Korean were collected, and finally 38 studies were selected by sorting related empirical studies. A total of 288 effect sizes was used by classifying the predictors from these studies. As a result, the effect size and ranking of factor of predictors that lead SME growth in Germany and Korea were different. However, the key factorsin both countries for firm growth was entrepreneurship and innovation. In Germany, investment in human capital and physical capital for R&D was the important factor that led a firm to grow with global competitiveness. In Korea, various characteristics of innovation were found to be simultaneously necessary factors for actual results of innovation success

Effect of VIRP1 Protein on Nuclear Import of Citrus Exocortis Viroid (CEVd)

Before replicating, Pospiviroidae viroids must move into the plant nucleus. However, the mechanisms of viroid nuclear import are not entirely understood. To study the nuclear import of viroids, we established a nuclear import assay system using onion cell strips and observed the import of Alexa Fluor-594-labeled citrus exocortis viroid (CEVd). To identify the plant factors involved in the nuclear import of viroids, we cloned the Viroid RNA-binding Protein 1 (VIRP1) gene from a tomato cultivar, Seokwang, and heterologously expressed and purified the VIRP1 protein. The newly prepared VIRP1 protein had alterations of amino acid residues at two points (H52R, A277G) compared with a reference VIRP1 protein (AJ249595). VIRP1 specifically bound to CEVd and promoted its nuclear import. However, it is still uncertain whether VIRP1 is the only factor required for the nuclear import of CEVd because CEVd entered the plant nuclei without VIRP1 in our assay system. The cause of the observed nuclear accumulation of CEVd in the absence of VIRP1 needs to be further clarified

Key Drivers and Performances of Smart Manufacturing Adoption: A Meta-Analysis

This study focused on the smart factory, one of the critical paradigms in the digital transformation in manufacturing, and attempted a meta-analysis to systematically integrate statistical results from existing empirical analysis studies. An integration model, key factors—smart manufacturing adoption—performances, was established from collecting 42 Korean examples of literature. To compare effect sizes between domestic and foreign empirical study results, 11 foreign articles were added, and the moderating effect verification was conducted. As a result of the analysis, (1) the key factors of the adoption and continuous use of smart manufacturing were the network effect, social influences, finances, performance expectancy, facilitating condition, technological capabilities, and entrepreneurship. (2) The adoption and continuous use of smart manufacturing had a significant impact on business performances, especially the financial performance. (3) The impacts of entrepreneurship and the network effect as factors influencing the decision making of smart manufacturing adoption in Korea can be seen to be significantly higher than those of foreign countries. (4) The impact of smart manufacturing adoption on performances in Korea was higher than other countries. The findings of this study will provide practical implications for practitioners optimizing digital transformation manufacturing policies and supporting the adoption of smart manufacturing systems

Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments

This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping

Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments

This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping

Data augmentation using image translation for underwater sonar image segmentation

Copyright: © 2022 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In underwater environment, the study of object recognition is an important basis for implementing an underwater unmanned vessel. For this purpose, abundant experimental data to train deep learning model is required. However, it is very difficult to obtain these data because the underwater experiment itself is very limited in terms of preparation time and resources. In this study, the image transformation model, Pix2Pix is utilized to generate data similar to experimental one obtained by our ROV named SPARUS between the pool and reservoir. These generated data are applied to train the other deep learning model, FCN for a pixel segmentation of images. The original sonar image and its mask image have to be prepared for all training data to train the image segmentation model and it takes a lot of effort to do it what if all training data are supposed to be real sonar images. Fortunately, this burden can be released here, for the pairs of mask image and synthesized sonar image are already consisted in the image transformation step. The validity of the proposed procedures is verified from the performance of the image segmentation result. In this study, when only real sonar images are used for training, the mean accuracy is 0.7525 and the mean IoU is 0.7275. When the both synthetic and real data is used for training, the mean accuracy is 0.81 and the mean IoU is 0.7225. Comparing the results, the performance of mean accuracy increase to 6%, performance of the mean IoU is similar value.N