This study aims to examine the evolution of the system of support sources in Hungary, focusing on the specific goals supporting higher education in the development programs Széchenyi 2020 (2014–2020) and Széchenyi Plan Plus (2021–2027). The study provides insights into development program evolution and changes, aiming to inform EU funding opportunities for Hungarian higher education institutions over a nearly 10-year period. By focusing on the operational programs that are the basis for the upcoming tenders, the study will display the target system of EU funds that can be utilized to bolster higher education institutions in Hungary. The study is based on document analysis, examining the Hungarian policy tools of the development programs and the operational program strategies of the ten-year time period from 2014 to 2024. By analyzing the support landscape for higher education institutions in Hungary, this study contributes to a better understanding of how the key objectives and criteria of strategic programs have evolved. It also examines the aspects and elements defined in two different development programs over the last ten years. The result of the study can contribute to anticipate the types of funding opportunities that may be available in the future and inform future decision-making processes.

Innovation has always been a key driver of economic development, particularly in the context of small and medium-sized enterprises (SMEs). Despite their significant contributions, many of these enterprises currently lack strong research and development capabilities, face challenges in innovation investment, and struggle to produce high-quality innovative results. To address these issues and overcome funding obstacles, many SMEs are turning to supply chain finance (SCF) as a supplementary financing method. This study utilizes stata16 and fixed effects models to analyze the impact and mechanism of SCF on enterprise innovation performance (EIP), focusing on companies listed on the SME Board and GEM in Shenzhen, China from 2011 to 2020. The findings reveal that SCF can effectively enhance enterprise innovation output, facilitating the conversion of resources into high-quality innovation results. Additionally, the study demonstrates that supply chain concentration acts as a mediator between SCF and EIP. Moreover, SCF is found to significantly boost EIP with low supplier concentrations and high customer concentrations. This suggests that SMEs encounter obstacles to innovation from suppliers and customers, and SCF may not fully address the challenges posed by these relationships. Overall, this research offers new empirical insights into the economic implications of companies adopting SCF, providing valuable guidance for enterprises in optimizing innovation decisions and for the government in enhancing supplier and customer information disclosure systems.

The effectiveness of frailty intervention programs for older adults in Korean communities has been inconsistent, posing challenges for public health nurses (PHNs). This study aims to develop an evidence-based intervention using the Intervention Mapping (IM) Protocol. The program followed the IM Protocol’s six steps, which provide a systematic method for developing and implementing theory-based health promotion programs. In Step 1, the needs of the subjects were identified through systematic review and interviews. In Step 3, the theme of the program was established as ‘health promotion for frail older adults’, and the components and scope were confirmed. The contents of the program included concepts of social support and social networks. In Step 4, after conducting a pilot test, the results were reflected and modifications were made. In Step 6, the evaluation tool was revised, and an effective evaluation plan was established. The final program was designed based on the program and interview results. The pilot test in Step 4 involved a one-group pretest-posttest and focus group interview with 15 pre-frail older adults. The IM Protocol-based health promotion program effectively addressed the needs of the subjects and improved frailty issues.

This study aims to scrutinize specific long-term sustainability industrial indicators in Thailand as a representative of an emerging economy. The study uses a Bloomberg database comprising all Thai listed companies on the Stock Exchange of Thailand from 2013 to 2023. The research employs a two-step Generalized Method of Moments (GMM) statistics to assess the enduring impact on industrial sustainability. These results provide consistent, significant and positive relationships between asset turnover and sales with all industrial sustainability. The results additionally reveal that some other factors may moderate industrial sustainability but reveal the GDP growth rate and institutional shareholders are less likely to be corporate sustainability to all indicators. The results provide insight into valuable guidance to management teams, financial statements’ users, investors and other stakeholders on designing effective operations and investment strategies to improve sustainability.

This study investigates the impact of corporate carbon performance on financing costs, focusing on S&P 500 companies from 2015 to 2022. Utilizing a fixed-effects regression model, the research reveals a complex U-shaped nonlinear relationship between carbon intensity (CI) and cost of debt (COD). The sample comprises 2896 firm-year observations, with CI measured by the ratio of Scope 1 and 2 greenhouse gas (GHG) emissions to annual sales. The findings indicate that companies with higher CI initially face increased COD due to heightened regulatory and operational risks. However, as CI falls below a certain threshold, further reductions in emissions can paradoxically lead to increased COD, likely due to the substantial investments required for advanced technologies. Additionally, a positive relationship between CI and cost of equity (COE) is observed, suggesting that shareholders demand higher returns from companies with greater environmental risks. These results underscore the importance of balancing short-term and long-term environmental strategies. The study highlights the need for corporate managers to communicate the long-term benefits of environmental efforts effectively to creditors and investors. Policymakers should consider these dynamics when designing regulations that incentivize lower carbon emissions.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.