As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

In this article, a new type of mesoporous carbon nanoparticles (MCN) was fabricated as a potential oral delivery system of insulin to reduce the adverse reactions by hypodermic injection. The mesoporous carbon nanoparticles-carried insulin (MCNI) was studied using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Fourier transform infrared spectroscopy (FT-IR) compared with the blank MCNs. The Brunauer–Emmett–Teller (BET) method was utilized to calculate the specific surface area. The pore volume and pore size distribution (PSD) curves were calculated by Barrett–Joyner–Halenda (BJH) model. The entrapment efficiency (EE%) and loading content (LC%) of insulin onto the MCNs were determined by RP-HPLC. In vitro insulin release from MCNI was determined in simulated intestinal fluid. To evaluate the pharmacodynamics of MCNIs orally, the variation of glycemia of diabetic rats after oral administration of MCNIs was compared with the rats receiving hypodermic injection of insulin. Besides, the absorption of FITC-labeled MCNs in HCT-116 cells was tested. The results showed that there is significant difference between MCNs and MCNIs through SEM, TEM, and FT-IR. The entrapment efficiency, loading content and in vitro insulin release met the requirements of the pharmacodynamic study. The specific surface area, pore volume and pore size of MCNIs were significantly decreased compared to that of MCNs. The pharmacodynamics study showed that the blood sugar level was significantly decreased after the oral administration of MCNIs. The FITC-labeled MCNs showed significant absorption in HCT-116 cells. The MCNIs were successfully synthesized with commendable entrapment efficiency and loading content which preferably decreased the blood sugar in diabetes rats via oral administration.