In this research, we studied the relation of research and development (R&D) investment to turnover and number of listed companies by using the financial information of publicly listed enterprises in all industrial fields of the world from 2007 to 2015. First of all, the present condition (as of 2017) of number and distribution of publicly listed enterprises was investigated. Secondly, the industrial areas having top 10 average turnovers and R&D expenses during 9 years (2007 ~ 2015) were analyzed by using their descriptive statistics and CAGR values. Finally, the analyses of correlation and linear regression were performed by using average R&D expense (independent variable) and average turnover or the number of listed enterprises (dependent variables). In other words, two models with different combination of independent and dependent variables (Model A: R&D expense and turnover, Model B: R&D expense and number of listed firms) were developed for the statistical analyses. As a result, it was confirmed that both the turnover and the number of listed companies were influenced by the R&D investment because the coefficients of determination for Model A and Model B were 0.686 and 0.612, respectively (both p-values < 2.2 × 10− 16). From the results of this study, it is expected that the unlisted firms (e.g., start-up companies) can build the basis of their growth and innovation when they invest in R&D higher inducing the increases in (1) turnover and (2) probability of becoming a listed firm. Thus, the financial information of enterprises can be utilized effectively as the quantitative evidence in order to develop the research model and methodology related to their growth and innovation.

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.

Global competition has increased the importance of patents as a means to protect and strengthen technology and competitiveness. The purposes of our study were to identify what industries in South Korea are strong or weak in terms of patent applications and to identify some strategies to enable weak industries to become strong. For this, we gathered statistics on seven variables as follows: number of businesses, number of employees, research and development investment, number of full-time equivalent researchers, number of research institutions, domestic market size, and number of patent applications. Especially, to compare the ratio of patent applications and the ratio of domestic market size across industries, the industries were classified into the following three categories: strong-, weak-, and no-patent. Furthermore, data envelopment analysis (DEA) suggested some strategies to strengthen patent applications for each industry. In the DEA analysis, the number of patent applications was used as the output variable and the other six variables were used as input variables. Our study will particularly assist industries where protection by patents is an important aspect of their businesses.

In this study, the investigation into basic methodology for selecting the industrial areas suitable to the small and medium-sized enterprises (SMEs) in Korea was performed by using the statistical data about the corporations (2010 ~ 2012) as the quantitative evidences containing the number of companies, the number of workers, the annual sales, and the indices of market concentration and growth potential. From the Statistics Korea and the KISTI Market Analysis and Prediction System (K-MAPS), the statistical data organized by the Korean Standard Industrial Classification (KSIC) were obtained to conduct this research through the following procedure. First of all, the numbers of enterprises and employees and the annual sales of all industries were investigated and the largest number of workers and the highest annual sales were found in the sector of manufacturing among all sectors of KSIC. Secondly, the top three divisions with the highest annual sales in all divisions of manufacturing sector were selected. Thirdly, the subclasses having high values of annual sales and SMEs proportions among all subclasses in the top three divisions of the previous step were chosen as the candidates of SMEs-recommendable fields. Fourthly, the degree of market concentration was analyzed by using three-firm concentration ratio (CR3) and Herfindahl-Hirschman index (HHI) of the selected subclasses. Finally, the study for growth potential of chosen subclasses was performed through the analysis of compound annual growth rate (CAGR). After the overall process of this study was carried out with the synthetic consideration of the above-mentioned factors, the three subclasses of KSIC as industrial areas suitable to the SMEs could be found: (1) Manufacture of printed circuit boards, (2) Manufacture of parts and accessories for motor engines, and (3) Manufacture of parts and accessories for motor vehicle body. From this result, it was found that the values of annual sales, CR3, HHI, and CAGR can be very useful factors to discover the recommendable industry fields to the SMEs.

This research investigates the efficacy of integrating traditional gravity models with deep learning methodologies to predict trade flows of environmental goods. By merging machine learning with the gravity model, this study aims to enhance the accuracy of import and export predictions across various sectors. Analysing a comprehensive dataset spanning a 20-year period and employing advanced machine learning techniques, the paper identifies key variables (GDP, distance, common language, landlocked, etc.) influencing trade predictions and assesses their contributions within the models. The findings reveal that machine learning-enhanced models significantly improve predictive accuracy over the standalone use of traditional gravity models. This highlights the potential of combining advanced computational techniques with conventional economic models to better understand and forecast environmental goods trade dynamics. This study not only contributes to theoretical advancements in trade prediction but also offers practical insights for policymakers aiming to facilitate trade in environmental goods, ultimately supporting global sustainability efforts.