This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.

Corporate finance courses are increasingly adopting data-driven teaching methods. Modern corporate finance courses are focusing more on students' career development. Through simulation practice and career planning guidance, students are better prepared to face challenges in the workplace after graduation. Students need to learn how to utilize data analysis tools and techniques to extract useful information from large datasets and make more accurate decisions. Data-driven teaching is a significant innovation in current curriculum reforms. In recent years, with the development of technology and the emergence of financial innovation, corporate finance courses have been undergoing continuous changes and innovations. These courses have started to emphasize emerging areas such as digital finance, blockchain technology, and sustainable development. Taking the example of corporate finance, this paper integrates the demands of skill development in the era of digital finance, focusing on aspects like teaching methods, reform methodologies, practical experiments, feedback mechanisms, and data analysis.

This paper analyzes the relevance of social accounting information for managing financial institutions, using Banca Transilvania Financial Group (BTFG) as a case study. It explores how social accounting data can enhance decision-making processes within these institutions. Social information from BTFG’s annual integrated reports was used to construct a social balance sheet, and financial data was collected to calculate economic value added (EVA) and social value added (SVA). Research question include: Does social accounting represent a lever for substantiating the managerial decision in financial institutions? Results show that SVA is a valuable indicator for financial institution managers, reflecting the institution’s contributions to social well-being, environmental impact, and community support. Policy implications suggest regulatory bodies should mandate the inclusion of social accounting metrics in financial reporting standards to encourage socially responsible practices, enhance transparency, and incentivize institutions achieving high SVA. This paper contributes to the literature by demonstrating the practical application of social accounting in financial institutions and highlighting the importance of SVA as a managerial tool. It aligns with existing research on integrating corporate social responsibility (CSR) metrics into financial decision-making, enhancing the understanding of combining social and economic indicators for comprehensive performance assessment The abstract covers motivation, methodology, results, policy implications, and contributions to the literature.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

The Primary and secondary shadow education refers to a kind of unofficial education that exists outside the traditional mainstream primary and secondary education system in China, with both commercial and educational attributes. As the primary and secondary school stage is an important key stage for further education, existing research mainly focuses on the spatial distribution of primary and secondary school basic education facilities and non-subject training, with fewer studies targeting primary and secondary school subject tutoring shadow education. With the changes in China’s education industry and the introduction of the Double Reduction Policy, there is an urgent need to conduct in-depth research on the spatial aggregation characteristics and influencing factors of Shadow Education Enterprises for primary and secondary school students. This paper takes the main urban area of Zhengzhou City as the study area, and takes primary and secondary school Shadow Education Enterprises as the research object, and applies spatial analysis methods such as kernel density, nearest-neighbor index, and geographic detector to quantitatively analyze the spatial distribution characteristics of primary and secondary school shadow education tutoring enterprises in Zhengzhou City and the factors affecting them The results show that: 1) The overall spatial pattern of primary and secondary school tutoring Shadow Education Enterprises in the main urban area of Zhengzhou City has largely formed a core-edge structural feature that spreads from the urban center to the periphery, and presents the spatial agglomeration feature of “double nuclei many times” distributed along both sides of the Beijing-Guangzhou Line. 2) The distribution of mentoring Shadow Education Enterprises in the main urban area of Zhengzhou City in relation to provincial model primary and secondary schools is significant and there is a significant difference between the distribution around secondary schools and primary schools. 3) The spatial distribution of Shadow Education Enterprises in the main urban area of Zhengzhou City is mainly influenced by factors such as the size of the school-age population, the level of commercial development, the location of school buildings and the accessibility of transport.