This research aims to validate the effectiveness of the "Specialized Entrepreneurship University Program," which was conducted as part of government entrepreneurship support initiatives from 2018 to 2022. Based on previous studies, a research model was derived consisting of three laboratory entrepreneurship support factors that influence program satisfaction and entrepreneurial outcomes (infrastructure support, educational mentoring support, and business linkage support). Surveys were collected and analyzed from 126 laboratory entrepreneurship firms participating in the program, and empirical analysis of the research model was conducted using SPSS 23.0 statistical software. The analysis results indicated that the three variables, namely infrastructure support, educational mentoring support, and business linkage support, were significant factors affecting program satisfaction, and program satisfaction was confirmed to influence entrepreneurial outcomes. Furthermore, it was found that the three business operation factors indirectly influenced entrepreneurial outcomes by partially mediating program satisfaction. This study is considered significant as an empirical study for the initial stage of the second-phase program enhancement, verifying the effectiveness of laboratory entrepreneurship support factors. The findings can be applied to similar government entrepreneurship support initiatives and contribute to the effective strategy and planning of stakeholders involved. The limitations of this study include the need for further research on the perception of the extent to which it contributes to entrepreneurial outcomes, emphasizing caution in interpreting the research model, and the necessity for expanding the survey population and improving survey items in future research.

To investigate the national science and technology policy direction in the field of construction machinery, an analysis was conducted on projects selected as national research and development (R&D) initiatives by the government. Assuming that the project titles contain key keywords, text mining was employed to substantiate this assumption. Project information data spanning nine years from 2014 to 2022 was collected through the National Science & Technology Information Service (NTIS). To observe changes over time, the years were divided into three-year sections. To analyze research trends efficiently, keywords were categorized into groups: ‘equipment,’ ‘smart,’ and ‘eco-friendly.’ Based on the collected data, keyword frequency analysis, N-gram analysis, and topic modeling were performed. The research findings indicate that domestic government R&D in the construction machinery field primarily focuses on smart-related research and development. Specifically, investments in monitoring systems and autonomous operation technologies are increasing. This study holds significance in analyzing objective research trends through the utilization of big data analysis techniques and is expected to contribute to future research and development planning, strategic formulation, and project management.

The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

BNKT Ceramics, one of the representative Pb free based piezoelectric ceramics, constitutes a perovskite(ABO3) structure. At this time, the perovskite structure (ABO3) is in the form where the corners of the octahedrons are connected, and in the unit cell, two ions, A and B, are cations, A ion is located at the body center, B ion is located at each corner, and an anion O is located at the center of each side. Since Bi, Na, and K sources constituting the A site are highly volatile at a sintering temperature of 1100℃ or higher, it is difficult to maintain uniformity of the composition. In order to solve this problem, there should be suppression of volatilization of the A site material or additional compensation of the volatilized. In this study, the basic composition of BNKT Ceramics was set to Bi0.5(Na0.78K0.22)0.5TiO3 (= BNKT), and volatile site (Bi, Na, and K sources) were coated in the form of a shell to compensate additionally for the A site ions. In addition, the physical and electrical properties of BNKT and its coated with shell additives(= @BNK) were compared and analyzed, respectively. As a result of analyzing the crystal structure through XRD, both BNKT(Core) and @BNK(Shell) had perovskite phases, and the crystallinity was almost similar. Although the Curie temperature of the two sintered bodies was almost the same (TC = 290 ~ 300 ℃), it was confirmed that the d33 (piezoelectric coefficient) and Pr (residual polarization) values were different. The experimental results indicated that the additional compensation for a shell additive causes the coarsening, resulting in a decrease in sintering density and Pr(remanent polarization). However, coating shell additives to compensate for A site ion is an effective way to suppress volatilization. Based on these experimental results, it would be the biggest advantage to develop an eco-friendly material (Lead-free) that replaced lead (Pb), which is harmful to the human body. This lead-free piezoelectric material can be applied to a biomedical device or products(ex. earphones (hearing aids), heart rate monitors, ultrasonic vibrators, etc.) and skin beauty improvement products (mask packs for whitening and wrinkle improvement).

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.