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.

Encouraging the social empowerment of persons with disabilities—also known as “people of determination” is a crucial step toward advancing equality and inclusion in our communities. Consequently, the current study aimed to identify the mechanisms for activating social empowerment for people of determination from the deaf category. Identify the most prominent mechanisms and proposals from the point of view of the deaf. The study used a social survey approach based on a questionnaire on a sample of (30) deaf males in the Kuwaiti Sports Club for the Deaf, and it is the full sample size. The study reached several results, the most important of which are: integrating deaf people with disabilities into jobs integrated into society, raising the level of cultural awareness of sign language, in addition to spreading awareness of how to deal with deaf people. The study presented some recommendations and proposals, including media focus on the deaf group, and working to hold conferences and workshops targeting the community to spread awareness about the deaf group.

The issue of policy changes to support teacher professional development is an important factor shaping the career trajectory, efficacy, and ultimately the success of Junior Reserve Officer Training Corps (JROTC) instructors and the performance of the secondary students they serve and whose lives they affect. Although a rich body of research associated with policies regarding teacher preparation and professional development exists, a more closely related area of research focused specifically on the policies regarding preparation and professional development of JROTC instructors is limited. This lack of research presents a unique opportunity to explore the experiences of JROTC instructors and their perspectives on policies affecting teacher preparation and professional development. This qualitative exploratory single-case study can help to advance understanding of the complexities and nuances of teacher preparation and professional development policies supporting the JROTC instructors serving in high schools across the United States and overseas. One-on-one interviews with 14 JROTC personnel who had completed required teacher preparation requirements and professional development initiatives were conducted. Data analysis revealed 11 themes. Recommendations for improving policies concerning JROTC instructor preparation and professional development, including placing greater emphasis on the unique requirements, as well as suggestions for future research, are provided.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This research systematically reviews the relationship between populism and economic policies, analyzing their impact on state development and growth. It is the first study to comprehensively examine the interaction between these two concepts through a systematic literature review. The review process adhered to the PRISMA protocol, utilizing the Scopus, EBSCO, and Web of Science databases, covering the period from 2012 to 2024. The findings reveal a deep interconnection between populism and economic policies, with significant implications for governance and socioeconomic well-being. The review identifies that neoliberal populism combines pro-corporate elements with populist rhetoric, favoring economic elites while presenting itself as beneficial for the “people.” Additionally, it underscores that neoliberal globalization has facilitated market liberalization but also increased inequality and undermined national sovereignty. The review concludes that while populism may offer quick fixes to immediate economic issues, its simplistic and polarizing approaches can be counterproductive in the long term. Thus, there is a critical need to reevaluate and reformulate economic and governance policies to balance global economic integration with the protection of citizens’ rights and well-being.

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.