The objective of this paper is to analyze the impact of infrastructure financing on economic growth in emerging markets through the application of both quantitative and qualitative research methodologies. In this study, the research will employ both primary and secondary data to investigate the impact of different structures of infrastructure financing on the performance of the economy through interviews with the stakeholders and policy documents alongside quantitative data from the World Bank and the IMF. The quantitative analysis employs the econometric models to establish the effect of infrastructure investment on the GDP growth of the selected countries, India, China, Brazil, and Nigeria. Additional secondary qualitative data obtained from interviews with policymakers and financial specialists from Brazil, India, and South Africa offer more practical information regarding the efficiency of the discussed financing approaches. This paper is therefore able to conclude that appropriate management of infrastructure investments, particularly those that involve the PPP, are central to the development of the economy. However, certain drawbacks such as the lack of regularity of data and the disparity in the effectiveness of financing instruments by the regions are pointed out. The research provides policy implications to policymakers and investors who wish to finance infrastructure in the emerging economy to enhance economic growth in the long run.

Educational quality policies are a basic principle that every Peruvian university educational institution pursues in accordance with Law No. 30220, with the objective of training highly competent professionals who contribute to the development of the country. This study to analyzes educational quality policies with the student’s satisfaction of public and private universities in Peru, according to social variables. The study was descriptive-comparative, quantitative, non-experimental, and cross-sectional. One thousand (1000) students from two Peruvian universities, one public (n = 500) and one private (n = 500), were purposively selected by quota using the SERVQUALing instrument. The findings indicate a moderate level of satisfaction reported by 49.2% of participants, with a notable tendency towards high satisfaction observed in 40.9% of respondents. These results suggest that most students perceive that the actual state of service quality policies are in a developmental stage. The results, therefore, indicate that regulatory measures, including university laws, licensing, and accreditation, significantly influence outcomes. These measures are essential for the effective functioning of universities. In addition, the analysis revealed that female and male students at private universities showed higher levels of satisfaction with the educational services offered. It is concluded that educational quality policies in Peru are still being executed, because the implementation of the University Law is in process, according to the satisfaction of the student, this must be improved in central aspects such as optimizing human resources, infrastructure, equipment, curricular plans that differ from the public to the private university, In addition, this should lead to improving and redefining current policies on educational quality and the economic policies that finance the educational service.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This study investigates the viability and sustainability of proposed landfill sites based on the uncapacitated facility location problem framework utilising the SmartPLS4 Structural Equation Modelling. Investigating the Cape Coast Metropolis, a stratified sampling method selected 400 samples out of which 320 valid respondents were used as the basis for the analysis. Through statistical analysis, significant correlations were identified among community acceptance, environmental impact, facility accessibility, site sustainability, and operational efficiency. However, no significant correlation was found between economic viability and site sustainability. Furthermore, the proposed indirect mediation pathway from operational efficiency to site sustainability via facility accessibility was also statistically insignificant. Employing the use of SmartPLS4 approach in studying the application of uncapacitated facility location problem framework, deepens the understanding of landfill viability and sustainability dynamics. This research contributes to the environmental sciences and sustainability by providing insights into landfill management strategies and emphasising the importance of community engagement and environmental performance in achieving sustainable outcomes. Future research could refine the model by including additional variables like technological advancements and regulatory frameworks, conducting longitudinal studies to track landfill dynamics over time, and undertaking comparative studies across different geographical regions. This could provide insights into management approaches’ applicability. Interdisciplinary collaborations are recommended to address the multifaceted challenges of landfill sustainability.

The purpose of the current study is to raise the question about making a comparison between international legislation in the United States, European Union, and legislation of Saudi Arabia derived from Islamic law regarding the poultry slaughtering process and the relationship of that to achieving safe and healthy food for humans. In addition, the study utilized the Holy Qur’an and the texts of the Prophet’s hadith as primary sources. Additionally, various national and international laws, reports, and legislations were referenced as secondary sources for the review. Moreover, this study addresses a research gap by providing a comparative analysis that links Islamic and international legislation regarding poultry slaughter and examines its impact on food quality and safety. The study’s findings indicate that Islamic Sharia provisions are in harmony with the regulations of the Kingdom of Saudi Arabia related to poultry slaughtering process. This alignment ensures the primary goal of the slaughter process, which is to quickly get rid of the blood and achieve the well-being of the poultry. Consequently, this results in high quality meat with low microbial content that can be preserved for a longer period compared to regulations in other global markets such as the USA and the European Union.

This study investigates the intricate relationship between a nation’s GDP growth rate and three key variables: the number of granted patents, research and development (R&D) expenditure, and education expenditure. The purpose of the research is to discern the impact of these factors on GDP growth rates. Drawing on theoretical frameworks, including Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Canonical Correlation Regression (CCR) techniques, the paper employs a robust methodological approach to unveil insights into the dynamics of economic growth. Contrary to conventional assumptions, the results reveal a negative correlation between R&D expenditure and GDP growth rate. In contrast, the number of patents granted and education expenditure shows a positively significant effect on the GDP growth rate, underscoring the pivotal roles of intellectual property creation and education investment in fostering economic growth. The conclusion emphasizes the importance of a nuanced understanding of these relationships for policymakers. The research’s implications highlight the need for balanced investments in innovation and education. The originality and value of this study lie in its unique findings challenging established beliefs about the impact of R&D expenditure on economic growth.