An appraisal of the groundwater potential of Alex Ekwueme Federal University Ndufu Alike was carried out by integrating datasets from geology, geographic information system and electrical resistivity survey of the area. The study area is underlain by the Asu River group of Albian age. The Asu River Group in the Southern Benue Trough comprises of Shales, Limestones and Sandstone lenses of the Abakaliki Formation in Abakaliki and Ikwo areas. The shales are generally weathered, fissile, thinly laminated and highly fractured and varies between greyish brown to pinkish red in colour. Twenty (20) Vertical Electrical Sounding data were acquired using SAS 1000 ABEM Terrameter and processed to obtain layer parameters for the study area. A maximum current electrode spacing (AB) of 300 meters was used for data acquisition. Computer aided iterative modelling using IPI2 Win was used to determine layer parameters. In-situ Hydraulic Conductivity measurements at seven parametric locations within the study area were conducted and integrated with Electrical Resistivity measurements to determine aquifer parameters (e.g., Hydraulic conductivity and Transmissivity) in real time. This technique reduces the attendant huge costs associated with pumping tests and timelines required to carry out the technique. Accurate delineation of aquifer parameters and geometries will aid water resource planners and developers on favourable areas to site boreholes in the area. Several correlative cross-sections were generated from the interpreted results and used to assess the groundwater potential of the study area. Results show that the resistivity of the the aquifer ranges from 7.3 Wm–530 Wm while depth to water ranges from 11.4 m to 55.3 m. Aquifer thicknesses range from 8.7 m at VES 5 to 36.3 m at VES 6 locations. Hydraulic conductivity ranges from 1.55 m/day at VES 15.18, and 19 locations to 9.8 m/day at VES 3 and 4 locations respectively. Transmissivity varies from 17.48 m²/day at VES 19 to 98 m²/day at VES 3 locations respectively. Areas with relatively high transmissivities coupled with good aquifer thicknesses should be the target of water resource planners and developers when proposing sites for drilling productive boreholes within Alex Ekwueme federal University Ndufu Alike.

Women's financial literacy and financial inclusion have gained prominence in recent years. Despite progress, knowledge and access to finance remain common barriers for women, especially in emerging economies. Globally, domestic and economic violence has been recognized as a relevant social concern from a gender perspective. In this context, financial literacy and financial inclusion are considered to play a key role in reducing violence against women by empowering them with the necessary knowledge to manage their financial resources and make informed decisions. This study aims to evaluate the determinants that influence Peruvian female university students' financial literacy and financial inclusion. To this end, a theoretical behavioral model is proposed, and a survey is applied to 427 female university students. The results are analyzed using a Partial Least Squares Structural Equation Model (PLS-SEM). The results validate all the proposed hypotheses and highlight significant relationships between financial literacy and women's financial inclusion. A relevant relationship between financial attitude and financial behavior is also observed, as well as the influence of financial behavior and financial self-efficacy on financial literacy. The results also reveal that women feel capable of making important financial decisions for themselves and consider that financial literacy could help reduce gender-based violence. Based on these findings, theoretical and practical implications are raised. It highlights the proposal of a theoretical model based on antecedents, statistically validated in a sample of women in Peru, which lays the foundation for understanding financial literacy and financial inclusion in the Latin American region.

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 paper aims to explore the issue of university dropouts in Greece, which has become a growing concern in recent years due to its impact on individuals, educational institutions, and society as a whole. One of the main contributing factors to students discontinuing their higher education in Greece is the choice of the wrong faculty. Financial challenges, unrelated to tuition fees, also significantly affect students’ ability to pursue their studies. Family background plays a crucial role, with students from families with higher educational attainment exhibiting greater persistence. The study found that gender, age, and academic performance can influence students’ perceptions of dropout factors, which can change as they progress through their educational journey. To address these challenges, the research proposes a multifaceted approach, including early intervention programs, expanded financial support, improved mental health and counseling services, flexible learning options, and strong academic advising.

In regard to national development (ND), this review article (which is basically a perspective approach) presents retroactive and forward-looking perspectives on university education in Nigeria. In the past, particularly during the 1970s, the Nigerian university (NU) sector was among the most outstanding in Africa as well as globally. The best institutions drew students from around Africa, who flocked to Nigeria to study. The NU structure evidently contained four essential components for an international and effective university system, viz., world-class instructors, world-class students, a conducive learning environment, and global competitiveness. The NU structure, nevertheless, has undergone some neglect over the past thirty years and lost its distinctive identity, which raises questions about its function and applicability at the current stage of ND. Hence, some retrospective and forward-looking observations on university education in Nigeria in connection to ND are conveyed in this perspective article uses basically published articles and other relevant literature, as well as other sources and data from available literature. Hitherto, there is an urgent need for reinforcement of the university system in order to give it the desired and comparable international quality and functionality needed to meet the demands of current issues and the near future. However, this article conveys an intense belief and conviction that the NU system is still important for both the political and socioeconomic development (growth) of the nation. The article concludes by recommending the way forward in this regard.

The presented article focusses on the analysis of perception of the university social responsibility through the eyes of Slovak university students. The aim is to compare how the values, efficiency of the organisation (university), and the educational process influence the perception of social responsibility among university students themselves. The research is based on the application of quantitative methodology towards the evaluation of differences and similarities in perceptions using two types of tests for statistical analysis, comparative (Mann-Whitney U test) and correlational (bivariate correlation matrix of Spearman’s rho).The results of the research provide a deeper understanding of how universities can shape students’ approach to social responsibility through their values and educational processes, which has important implications for the development of university policies and practices.