In the modern economy, non-financial reporting has become an essential tool for evaluating the social performance of companies. This article explores the importance of non-financial reporting as a central element in assessing sustainable performance, focusing on analyzing sustainability reports published by 20 companies listed on the Bucharest Stock Exchange (BVB). The study examines how these companies approach environmental, social, and governance (ESG) aspects in their reports and what is the relationship between these aspects and financial reporting indicators. Through the statistical analysis of the non-financial reports published by companies participating in the study with the help of the Pearson coefficient and the regression equations, the correlation between the financial and non-financial indicators is determined in order to validate the research hypotheses. The results indicate increased attention to transparency and social responsibility, highlighting the correlation between sound reporting practices and cooperative performance by combining social and environmental aspects with financial information. The research also highlights the challenges encountered in the reporting process and the level of compliance with international sustainability standards.

Private banking institutions serve the financial sector’s wealthiest clientele via a dedicated value proposition. Based on the relevant tendencies and statistics, a remarkable expansion can be outlined since the mid-1990s. The aim of this study is to elaborate the Hungarian private banking market’s development as a case study. The paper also intends to add to the literature on this unique segment of the financial market. Based on the available statistics, the analysis primarily focuses on the Hungarian private banking market’s rapid development process. This can be underpinned by the clientele’s savings, number of accounts and respective segmentation limits of the institutions. Referring to the amount of savings, a correlation analysis indicates significant co-movements with specific social and economic variables. The growth rate of the Hungarian clientele’s savings outperformed the respective indicator in Western Europe during the review time period (2007–2020). The current paper also includes a section that summarises general challenges that private banking managers need to address during the development process. Generally, the literature on private banking can still be considered scarce, whereas there is a lack of studies on the Central-Eastern European region. The analysis of the Hungarian sector’s development path can serve with relevant information to any financial expert in the field.

An exhaustive analysis and evaluation of fertility indicators in a society including many ethnic groups might provide valuable insights into any discrepancies. This study aims to systematically analyse the fertility rates over specific periods and investigate the differences in levels and patterns between local and expatriate women in Saudi Arabia using the existing data. This analysis used data from credible sources published by the General Authority for Statistics in the Saudi census 2022. The calculation of period fertility indicators started with the most straightforward rates and advanced to more complex ones, followed by a comprehensive description of the advantages and disadvantages of each. The aim was to ascertain fluctuations in fertility rates and analyse temporal patterns. Multiple studies consistently show that the fertility rate among expats in Saudi Arabia is lower than that among Saudi native women. However, the reason for this discrepancy still needs to be discovered since the definitive effect of contraceptive techniques has yet to be confirmed. Moreover, the reproductive trends that have occurred since the early 1980s will persist, although with additional precautions in place.

This research focused on the design and implementation of the flipped classroom approach for higher mathematics courses in medical colleges. Out of 120 students, 60 were assigned to the experimental group and 60 to the control group. In the continuous assessment, which included homework and quizzes, the average score of the experimental group was 85.5 ± 5.5, while that of the control group was 75.2 ± 8.1 (P < 0.05). For the final examination, the average score in the experimental group was 88.3 ± 6.2, compared to 78.1 ± 7.3 in the control group (P < 0.01). The participation rate of students in the experimental group was 80.5%, significantly higher than the 50.3% in the control group (P < 0.001). Regarding autonomous learning ability, the experimental group spent an average of 3.2 hours per week on self-study, compared to 1.5 hours in the control group (P < 0.005). Other potential evaluation indicators could involve the percentage of students achieving high scores (90% or above) in problem-solving tasks (25.8% in the experimental group vs. 10.3% in the control group, P < 0.05), and the improvement in retention of key concepts after one month (70.2% in the experimental group vs. 40.5% in the control group, P < 0.01). In conclusion, the flipped classroom approach holds substantial promise in elevating the learning efficacy of higher mathematics courses within medical colleges, offering valuable insights for educational innovation and improvement.

This research aims to delineate the ecocity indicators from the local perspectives in urban communities in the Northeast of Thailand. The research was quantitative survey research. Data was collected from a sample of 400 people who live in Khon Kaen Municipality and Udon Thani Municipality using a questionnaire. Data was analyzed by descriptive statistics and factor analysis. We found that the eco-city indicators from the perspective of people in the urban communities in the Northeast of Thailand were divided into three main criteria: a) economic perspectives; b) social perspectives; and c) environmental perspectives. When considering each aspect, it was found that the economic perspective had a total of 9 issues with an average of 3.06 out of 5.00, the social perspective had a total of 16 issues with an average of 3.76 out of 5.00, and the environmental perspective had a total of 14 issues with an average at 3.00 out of 5.00.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.