• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.107-120
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• 2022

The purpose of this study is to analyze the net effect and the combined effect of the determinants of the likelihood of start-up success of researchers at public research institutes. Based on the existing literature, the determinants of the researcher's likelihood of start-up success were reviewed, and a conceptual relationship between the determinants of the likelihood of start-up success was established. Data collection was conducted through a survey targeting researchers at public research institutes, and a total of 114 data were collected. The partial least squares (PLS) analysis method was used to analyze the net effect of the likelihood of start-up success determinant, and the fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze the combined effect of the likelihood of start-up success determinant. In the PLS analysis results, it was found that technology commercialization probability and creative self-efficacy had a significant positive effect independently on the likelihood of start-up success. In the fsQCA results, we found a combined effect of increasing the likelihood of start-up success when the technology commercialization probability, technology commercialization capability, and creative self-efficacy were high. These research results provide academic implications for understanding the determinants of the likelihood of start-up success of researchers in public research institutes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.89-100
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• 2024

Purpose : The objective of this study was to determine the major factors influencing the onset of diabetes and prediabetes and for collection of the basic data required to reduce the prevalence of diabetes and plan for administration of an effective health care system. By classifying the level of blood sugar management according to three categories: normal, prediabetes, and diabetes diagnosis, and determining the causes of diabetes in consideration of various variables, we will conduct an analysis of the main factors to be addressed for effective management of blood sugar and for preparation of basic data for use in early management. Methods : In this study, an analysis of raw data from the 8th National Health and Nutrition Examination Survey collected over a period of three years from 2019 to 2021, including 8,110 subjects in 2019, 7,359 subjects in 2020, and 7,090 subjects in 2021 was performed. A total of 22,559 subjects were aged 19 years or older, and 15,821 subjects were classified as subjects for inclusion in the final analysis. In the analysis, categorical variables were tested for difference, analysis of continuous variables using regression was performed, and analysis of influencing factors was performed using multinomial logistic analysis. Result : Significant factors related to the onset of diabetes and prediabetes included age (p<.001), marital status (p<.001), occupation (p<.001), hypertension (p<.001), dyslipidemia (p<.001), cardiovascular disease (p=.008), alcohol (p=.030) smoking (p=.005), systolic blood pressure (p<.001), diastolic blood pressure (p<.001), body mass index (p<.001) and waist circumference (p=.037), blood triglycerides (p<.001), and blood cholesterol (p<.001). Conclusion : Diabetes, a complex disease affected by a variety of diseases, requires active management from the prediabetes stage, and providing an appropriate level of medical information and services to elderly individuals without family support is considered a long-term health care system requirement in Korean society where the demographic structure is changing. In particular, determining the causes of prediabetes and development of a preventive approach to administering the health care system will be important for efficient management of diabetic patients.

• The Journal of Korean Medicine
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• v.45 no.1
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• pp.17-43
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• 2024

Objectives: Underpinned by the context of a Korean traditional medicine cohort study on healthy aging, this research primarily aims to guide the selection of Clinical Outcome Assessments (COAs) for elderly healthy aging patient registry research, offering insights into the selection process; and secondly, to streamline the resource-intensive process of obtaining permissions for validated COAs, benefiting future traditional Korean medicine clinical researchers. Methods : In this study, we identified outcomes through a review of previous studies, followed by a process involving expert consultations to select the final outcomes. Subsequently, for the selected outcomes that were Clinical Outcome Assessments (COAs) developed tools, we searched in commercial databases to confirm the availability of Korean versions and the necessity of obtaining permissions. Finally, we obtained permissions for their utilization and, when needed, acquired the original instrument questionnaire through payment. Results: Through a literature review of existing observational studies, a total of 57 outcomes were selected, with 19 of them identified as COA instruments. Upon verifying usage permissions for these 19 instruments, it was found that 17 required author-specific permissions, and among these, 2 needed a purchase as they were commercially available. Conclusion: This study provides a detailed overview of outcome selection and permission acquisition for elderly patient registry research. It underscores the importance of Clinical Outcome Assessment (COA) tools and the rigorous approval process, aiming to enhance research reliability. Continuous verification of COA information is essential, and future research should explore Core Outcome Set (COS) development through consensus-building approaches like Delphi studies.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.85-100
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• 2024

ESG management refers to corporate management that takes into account environmental, social, and governance factors, while digital transformation goes beyond the mere automation or digitization of existing tasks to drive an innovative change in the essence of work and the way value is created. Therefore, digital transformation can help companies achieve ESG goals and implement sustainable business practices, establishing a complementary relationship between digital transformation and ESG management for corporate sustainability and growth. This relationship maximizes the synergy of integrating digital transformation with ESG management, enabling companies to utilize resources efficiently and prevent redundant investments, ultimately enhancing sustainable management performance. In this study, we propose the simultaneous promotion of business process reengineering (BPR), in which both digital transformation and ESG management are integrated. This is because the collection, analysis, and decision-making processes related to various data for promoting ESG management must be organically integrated with digital transformation technologies. Therefore, we analyzed each ESG management objective presented in the K-ESG guidelines and identified the corresponding digital transformation technologies through expert interviews and a review of prior research. The K-ESG guidelines serve as a useful ESG diagnostic system that enables companies to identify improvement tasks and manage performance based on goals through self-assessment of ESG levels. By developing a model based on the K-ESG guidelines for the integrated promotion of digital transformation and ESG management, companies can simultaneously improve ESG performance and drive digital innovation, reducing redundant investments and trial-and-error while utilizing diverse resources efficiently. This study provides practical and academic implications by developing a concrete and actionable new research model for researchers and businesses.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.111-118
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• 2024

The concept of social stratification and hierarchy among human dates back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In other words, an actor can designate others as friends or foes. Trust networks are typically modeled as signed networks. A signed network is a directed graph in which the edges carry an edge weight of +1 (indicating trust) or -1 (indicating distrust). Examples of signed networks include the Slashdot Zoo network, the Epinions network and the Wikipedia adminship election network. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such "trust hierarchies" is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks In order to build hierarchies in signed networks, we look at two interpretations of trust namely presence of trust (or "good") and lack of distrust (or "not bad"). In order to develop a hierarchy signifying both trust and distrust effectively, the above interpretations are combined together for calculating the overall trustworthiness (termed as deserve) of actors. The actors are then arranged in a hierarchical fashion based on these aggregate deserve values, according to the following hypothesis: actor v is assigned as a child of actor u if: (i) v trusts u, and (ii) u has a higher deserve value than v. We describe this hypothesis with additional qualifiers in this thesis.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.127-145
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• 2023

As the importance of strengthening port security is increasing, it is necessary to conduct a perceptual study on port facility users who pay for port security services first. This study aims to identify improvements in the port security function of Busan Port and contribute to the future development of port security in Korea. A total of 125 questionnaires were collected from port facility security fee collectors at Busan Port. Based on the collected data, exploratory factor analysis, traditional IPA, and modified IPA were conducted. In conclusion, first, the physical function of port security is the most important and should be continuously maintained and strengthened. Second, improving the professionalism of port security personnel is most urgent, and the port security education system needs to be improved. Finally, it is necessary to gradually develop the port security information service function in consideration of future development possibilities.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.449-457
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• 2024

AIoT, the intelligent Internet of Things, refers to a technology that collects data measured by IoT devices and applies machine learning technology to create and utilize predictive models. Existing research on AIoT technology education focused on building an educational AIoT platform and teaching how to use it. However, there was a lack of case studies that taught the process of automatically creating and utilizing machine learning models from data measured by IoT devices. In this paper, we developed a case study using a machine learning model for AIoT technology education. The case developed in this paper consists of the following steps: data collection from AIoT devices, data preprocessing, automatic creation of machine learning models, calculation of accuracy for each model, determination of valid models, and data prediction using the valid models. In this paper, we considered that sensors in AIoT devices measure different ranges of values, and presented an example of data preprocessing accordingly. In addition, we developed a case where AIoT devices automatically determine what information they can predict by automatically generating several machine learning models and determining effective models with high accuracy among these models. By applying the developed cases, a variety of educational contents using AIoT, such as prediction-based object control using AIoT, can be developed.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.389-396
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• 2024

This is a descriptive study to investigate the association between types of menstrual sanitary products used and menstrual discomfort. The participants included 1,484 women who used either disposable sanitary pads or tampons, out of a total of 1,571 women aged 19-40 years and data collection was conducted from September 2020 to August 2021. The survey was conducted through an online and mobile survey platform, with participants proceeding to take part after clicking the 'agree' button. Data analysis involved 1:4 propensity score matching, descriptive statistics, chi-square tests, t-tests, and hierarchical regression analysis. The results indicated that among the participants, 94.1% used disposable sanitary pads, while 5.9% used tampons. In the final model, significant influencing factors identified were age 30 or older (β=-.157, p=.043), standing for 1-4 hours at work (β=-.131, p=.040), experiencing sleep disorders (β=.337, p<.001), and tampon use (β=.130, p=.005). Therefore, it is essential for nurses to incorporate information about various menstrual sanitary products' characteristics into their menstrual education for women of reproductive age.