Industrial heritage is a legacy from the past that we live with today and pass on to future generations. The economic value of this heritage can be defined as the amount of welfare that it generates for society, and this value should not be ignored. However, current research based on economic analysis has mostly focused on qualitative statements instead of quantitative assessment. This study proposes an innovative methodology combining qualitative (field research) and quantitative (willingness to pay and contingent valuation) methods to assess the economic value of industrial heritage. The industrial heritage of Tangshan, China, was chosen as a case study, and the research found that museums and cultural creative parks are effective ways to conserve industrial heritage. The entrance fee can be used to represent the economic value of the heritage site. There was a positive correlation between the influence of economic value and the entrance fees residents would prefer to pay. The results indicate the locals would prefer lower entrance fees for the transformed heritage museums (The average current cost: $2.23). Locals were most concerned about the entrance fees for the Kailuan Coal Mine and Qixin Cement Plant Museums, which have both been renewed as urban landmarks for city tourism. Renewal methods have been applied to six industrial heritage sites in Tangshan; these sites have their own conservation and renewal practices based on city-level development or industrial attributes. Thus, when residents recognize the economic value of a heritage site, they are willing to pay a higher entrance fee. This research demonstrates the economic value of industrial heritage using a mixed methods approach and provides a basis for assessing the value of cultural heritage for urban tourism analysis.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

Using company size as a moderator, this article examines the MENA region’s gender balance on boards and how it influences capital structure. The study uses the Generalized Method of Moments (GMM) estimate technique to analyze data from a sample of 556 non-financial organizations across 10 MENA countries from 2010 to 2023. The results show that a lower debt ratio is connected with a higher percentage of female board members. Further steps towards debt reduction include increasing the number of independent female board members and decreasing the board’s overall size. The opposite is true for larger enterprises, more profitability, more expansion opportunities, and macroeconomic variables like inflation and GDP growth, which tend to raise the debt ratio. Capital structure decisions in the MENA area are influenced by gender diversity on boards and business characteristics. Therefore, Companies in the MENA area would do well to support initiatives that increase the representation of women on corporate boards. One way to achieve this goal is to establish gender diversity targets or launch programs to increase the number of women serving on boards of directors, particularly in positions of power.

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

The article presents the experience of formation and development of economic competences of non-economic specialty students. The modern world is quite complex, diverse, and multidimensional, in order to adapt to it, work effectively, it is necessary to have information about market relations, relations in the sphere of production, consumption, exchange, distribution, and also to be able to connect these areas, navigate the laws operating in these areas. It should be noted that the formation and development of a specialist’s economic competence occurs throughout his or her entire professional life. In our study, the process of forming economic competence is considered as its formation at the stage of mastering economic disciplines, relevant special courses and methodical support. Training in higher education should lead to the acquired knowledge being transferred into the activity of combining elements into an interconnected structure, into the skillful distribution of resources, into the activity that brings profit and has the form of capital investment, in other words, the individual, acquiring knowledge for himself, should be able to transform it into a socially significant value. This requires the search for and implementation of new approaches in the content and organization of the educational process at all levels of education. Research devoted to the role of education in the preparation of future non-economists for economic competence focuses on the preparation of an individual for the economic literacy of an entrepreneur. One of the main tasks of the education system should be preparation for successful socialization in the context of involvement in entrepreneurial relations. It is students and young specialists who have advantages in entrepreneurship in the current conditions: they have the opportunity to obtain specialized knowledge and skills in the field of economics; they can start their own business, relying on economic knowledge. Therefore, the role of higher education is increasing, since it helps to meet the needs of society and implement its socially significant goals. This poses new challenges for universities to transfer the necessary economic knowledge, skills and abilities to students, and to develop their economic competence. The development of basic economic competences in a student is a guarantee of his competitiveness in the labor market and the basis for making reasonable economic decisions in the daily life of every person.