This study seeks to explore the uses, behaviors and perceptions of university students regarding mobile phones to help elucidate whether there is a relationship between the use of mobiles and the academic performance of university students. A quantitative approach based on an ad hoc questionnaire, applied before the pandemic, was used to gather evidence in this regard, which revealed the uses and educational visions of mobile phones in a convenience sample of 314 university students from nine different degree courses in two Spanish universities. Three major conclusions are formulated as part of future lines of development. First, although there is frequent use of mobile phones, the image of the mobile as a learning resource in the university classroom does not reach one-third of students. Second, although this study does not determine the causal relationship, there is a statistically significant negative relationship between average grades achieved and hours of dedication to the mobile phone. Finally, students who are unable to spend more than one hour without checking their phone obtain a significantly lower average mark than those who can stay more than one hour without checking their phone.

This study aims to investigate the alignment of emerging skills and competencies with Continuous Professional Development (CPD) programs in the accounting and auditing professions. The research focuses on enhancing the intellectual capital within these sectors, as dictated by the demands of the modern knowledge economy. Employing the World Economic Forum’s (WEF) framework of emerging skills for professional services, a comprehensive content analysis is conducted. This involves reviewing 1009 learning outcomes across 248 CPD courses offered by the global professional accounting body. The analysis reveals that while the existing courses cover all WEF-identified skills, there is an unaddressed requirement for a specialized focus on specific competencies. The study also notes gaps in clearly articulated learning outcomes, highlighting the need for more explicit statements to facilitate effective skills development and knowledge transfer. This research contributes to the ongoing discourse on intellectual capital management strategies, providing actionable recommendations for professional organizations. It fills a critical gap in understanding how CPD offerings can be optimized to better prepare accounting and auditing professionals for the evolving knowledge economy.

The aim of this study is to investigate the effect of tourist resources, conditions and opportunities of sacral tourism in Kazakhstan using panel data (time series and cross-sectional) regression analysis for a sample of 14 regions of Kazakhstan observed over the period from 2004 to 2022. The article presents an overview of modern methods of assessment of the tourist and recreational potential of sacral tourism, as used by national and foreign scientific works. The main focus is on the method of estimating the size and effectiveness of the tourist potential, which reflects the realization and volume of tourist resources and their potential. The overall results show a significant positive effect in that the strongest impact on the increase in the number of tourist residents is the proposed infrastructure and the readiness of regions to receive tourists qualitatively. This study is expected to be of value to firm managers, investors, researchers, and regulators in decision- making at different levels of government.

The quality of preschool education is related to the stability of the early childhood teaching force. With the help of qualitative research methods, the study analyzed the data of eight teachers who left the profession and explored the process of teachers leaving the profession, and found that the encounter between "settling down" and "professional feelings", the struggle for transformation between "professional feelings" and "the situation", and the struggle for transformation between "settling down" and "the situation" are all related to the stability of the early childhood education workforce. It was found that the encounter and tug-of-war between "settling down" and "professional feelings", the struggle for transformation between "professional feelings" and "the situation", and the rational weighing between "settling down" and "the situation" are the important factors affecting the departure from the profession. The essence is the tension between "teachers as human beings" and "human beings as teachers". Therefore, it is necessary to pay attention to the unity of "person" and "teacher", and to alleviate the problem of teachers leaving the organization by creating a fair, democratic and professional working atmosphere and strengthening the awareness of professional education.

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.