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This study examines the role of financial development (FD) in enhancing the growth effect of foreign direct investment (FDI) in emerging and developing Asia from 1996 to 2019.

The study exploits the new broad-based Financial Development Index of the International Monetary Fund (IMF) and adopts panel smooth transition regression (PSTR) to perform alternative empirical models for a multidimensional analysis of the FD threshold effect in the growth–FDI nexus.

The results show two thresholds of FD mediating the nonlinear effect of FDI on growth. FD beyond a certain level will enhance the growth effect of FDI, but very high levels of FD will not induce foreign investment to benefit economic growth in emerging and developing Asian economies. The impact of financial institutions on the FDI–growth link is stronger than that of financial markets. Besides, FDI’s effect on growth has an inverted-U shape conditional on financial depth, whereas it is positively associated with the accessibility and efficiency of the financial system.

These results suggest policy implications for emerging and developing Asian countries, emphasizing the other side of “too much finance” and the potential for improvement in the access to and efficiency of the financial system to boost the effects of FDI and FD in the growth of these economies.

The study is the first multifaceted investigation into the influence of FD on the growth effect of FDI. Beyond the previous empirical evidence showing only the impact of credit from banking sector, this study shows different mediating effects of different financial sectors and three dimensions of financing (depth, access and efficiency). The study suggests essential implications for the region in adjusting long-run policies to enhance the FDI–FD–growth link.

The purpose of this study is to examine the effect of foreign direct investment (FDI) on economic growth contingent on the development level of the local financial system in emerging and developing Asia during the period 1996–2017.

The study adopts the threshold approach, namely the panel smooth transition regression (PSTR) model, for the annual data collection of 18 emerging and developing Asian countries in 22 years. The authors analyze the alternative PSTR models on different proxies of financial development (FD).

The results show new findings of two distinct thresholds of FD in the FDI–growth nexus. The growth-enhancing effect of FDI is realized only when the FD lies between the two threshold values. Notably, at very high levels of FD, the beneficial effect of FDI on growth is vanishing.

The authors provide new insights into the growth effect of FDI and the role of FD. The estimated nonlinear effect of FDI on growth and the thresholds of FD can be benchmarks for emerging and developing Asia in assessment of their situations. The results suggest important implications to the region in setting the long-run policies to boost the effect of FDI on economic growth.

This study aims to examine the efforts of two universities in Taiwan, particularly their engagement in coastal rural communities, and provides insights into the substantial contributions of University Social Responsibility (USR) projects toward achieving the sustainable development goals (SDGs) in rural settings. The objective of this research is to analyze the outcomes of these case projects on the SDGs and, consequently, to propose a theoretical model aimed at enhancing student participation in achieving SDGs through USR programs.

This study adapted the methodology comprising SDGs contribution analysis and participant observation. The SDGs contribution analysis unfolds in three steps. First, the authors conducted evaluation of the 2030 Agenda for Sustainable Development. This step involved a thorough analysis of all 17 SDGs and their respective targets to establish a foundational understanding. Second, analyzed the outcomes of the case projects to examine the specific contributions of case projects toward achieving the SDGs. Third, the information from the preceding steps is analyzed to determine the extent of the case projects’ contributions to the SDGs.

The study proposes a theoretical model to enhance student engagement in achieving SDGs, emphasizing student empowerment, community partnership and robust evaluation methods aligned with SDGs and social impacts. This model could offer guidance for higher education institutions (HEIs) globally on utilizing USR programs to contribute to the SDGs, while simultaneously enriching student learning experiences through practical engagement and empowerment.

This model can be enhanced and validated by applying more rigorous scientific methods. For instance, conducting surveys on students and community participants of events and activities, utilizing a statistically rigorous approach such as pre-post testing, can analyze the effectiveness of these programs on various SDG-related variables (e.g. awareness of SDGs). Additionally, exploring the relationships between the tested variables can be a potential research direction. For instance, examining whether community engagement can positively increase the social impacts of USR projects, or whether student empowerment can enhance community capacity building, are important issues worthy of discovery.

This model emphasizes the pivotal role of student empowerment, advocating for an educational approach that not only enhances students’ proficiency in community development but also potentially shapes their career trajectories, as evidenced by the case projects examined in this study. In essence, this model offers HEIs a structured pathway to enrich student engagement in realizing SDGs through USR initiatives. It posits student empowerment as the foundational element, fostering a learning environment where students gain valuable skills and insights into community development, potentially guiding their future professional endeavors. This research provides practical direction for those HEIs implementing USR projects, which will increase the positive impacts brought by HEIs, especially for the students and local community.

To the best of the authors’ knowledge, no previous studies have proposed a theoretical model specifically designed to engage students in achieving SDGs through USR programs in a rural context. The significance of this study lies in its potential to serve as a guide for higher education institutions globally, enabling them to effectively leverage USR programs to contribute toward SDGs. This makes the study an invaluable resource for researchers, policymakers and educators who are committed to fostering sustainability.

The purpose of this paper is to propose an automatic pattern matching template generating method for the automatic optical inspection system in TFT LCD assembly and positioning process, to improve the conventional image technology. Besides, focusing on integrating the image system with the existing control system, the double aligner mark searching time is decreased to reduce the working time of the integrated system.

The improved pattern matching method of genetic algorithm was adopted, including setting for template image selecting, encoding, calculating fitness function, pattern matching, template generating and genetic algorithm steps. The predetermined pixels were selected from the target template based on the minimum difference to the block image to be tested by utilizing the genetic algorithm, and the other pixels which have not been selected were neglected.

The selected pixels were encoded for recording by sequence mode, and then the target template and the image to be tested were compared based on the calculated fitness function. This method has the advantages of using the fitness function to reduce the searching time, with the help of genetic algorithm to find the optimal target template, and saving memory space by recording target template based on the sequence mode.

The genetic algorithm used in this study is a kind of optimal tool free from gradient data. As long as the fitness function and after continuous iteration are determined, the optimal solution can be found out, and then the optimal target template can be generated.

This system uses fitness function to reduce the pattern matching time. Plural pixels are preset inside the target template, and its fitness function value is calculated. When the target template is compared with the image to be tested, only the fitness function value (also the difference of the plural pixels) is calculated and compared.

The remaining pixels are neglected, so that the searching time can be reduced greatly. The sequence mode is used to save the required memory space for recording target template. Since sequence mode is adopted to record the information of selected pixels, lots of required memory space for recording target template information will be saved.