The purpose of the work is to study the transformation processes of constructing professional identity under the influence of new information technologies and to consider the evolution of views on the processes of scientific and practical understanding of new media resources in the context of the development of convergent journalism as a phenomenon of the modern information society. It was established based on the conducted research that the values and beliefs of journalists, reflecting the process of professional self-identification, are forming in the process of choosing certain options among a variety of alternatives and transforming further under the current conditions of the information and communication environment. In the process of the study, the article identifies the features, content, and main trends in the transformational processes of professional identity and professional culture of journalists in the context of technological changes in the media industry. The dynamics of the development of media convergence are shown from the point of view of the mutual influence of traditional and new media and the tendency of improving their technological and dialogue features and capabilities in content creation and broadcasting. An assessment is made of the degree of adaptation of regional media to modern conditions of the information and communication environment in the context of organizational, professional, and communicative convergence.

This study systemically examines the numerous impacts of climate change on agriculture in Tunisia. In this study, we establish an empirical and comprehensive methodology to assess the effects of climate changes on Tunisian agriculture by investigating current climatic patterns using crop yields and socioeconomic variables. The study also assesses the types of adaptation strategies agriculture uses in Tunisia and explores their effectiveness in coping with climate-related adversities. We also consider some resilience factors, namely the ecological aspect and economic and social camouflage pursued by the (very) men in Tunisian agriculture. We also extensively discuss the complex interconnected relationship between policy interventions and community-based adaptations, a crucial part of the ongoing debate on climate change adaptation and resilience in agriculture. The findings of this study contribute to this important conversation, particularly for areas facing similar challenges.

This article examines the legal challenges associated with the utilization of marine genetic resources (MGR) at both the national level and beyond national jurisdiction (BBNJ). The legal challenges addressed are as follows: 1) MGR are located across various jurisdictions, encompassing both national and international domains. The analysis starts with an overview of the international regulations that govern the utilization of genetic resources (GR) and their influence on national legislation. It emphasizes the principle of state sovereignty over natural resources while defining MGR and determining ownership; 2) It further highlights the intersection of national and international laws, particularly in transboundary contexts and within Indigenous and Afro-descendant peoples (IADP) territories, analyzing how these regulations are interpreted and applied in such scenarios; 3) The legal challenges related to the use of MGR in international waters are examined. Special emphasis is placed on the recent United Nations (UN) Agreement concerning this issue. This includes an analysis of its impact and specific provisions related to the utilization of MGR, such as the quantity to be collected, the methodology employed, collection sites, among others. The article concludes by asserting that the equitable distribution of benefits from the use of GR should begin at the earliest stages of access to these resources, including project planning and sample collection, rather than being delayed until the patenting and commercialization phases. Early benefit-sharing is essential for promoting fairness and equity in the use of MGR.

Latin America is increasingly contributing to scientific research on leadership, although less than other regions. What are the predominant paradigms on leadership within the scientific community in Mexico? The article reviews doctoral dissertations on leadership from the National Autonomous University of Mexico (UNAM) and the Anahuac University of Mexico (UA) defended before 2021. The findings highlight that 1) the number of doctoral dissertations has grown from 2016 onwards, especially in educational leadership. 2) In both universities a “functionalist” paradigm prevails, based on the transformational leadership model. 3) Two other leadership paradigms are present, referred to in this article as ‘political’ and ‘humanistic’. 4) These three paradigms have their characteristics and preferences in terms of research methodology, language, and reference authors. 5) The use of a paradigm is associated with the type of faculty rather than the type of university (public or private): in business faculties the functionalist paradigm predominates, in education faculties the humanist paradigm, and in political science or communication faculties the political paradigm. In conclusion, it is recommended to confirm the exploratory result obtained and to promote the dialogue between leadership paradigms.

Manyanda tradition, a tradition of taking over social roles after death, in addition to successfully maintaining social continuity in the family structure, is also a potential capital in strengthening social cohesion. However, this context has not been discussed comprehensively in previous studies so it is very important to explain. In addition to responding to the shortcomings of previous studies, this study also aims to explain the mechanisms, factors and implications of the practice of this tradition as a reflection of social cohesion based on customary and religious values. By using a qualitative descriptive case study approach, this study shows three important findings. First, the spontaneity of the community and traditional leaders when hearing the news of death and social activities forty days afterwards. Second, the dominance of spiritual and cultural factors in addition to social and structural factors that encourage the community to preserve this tradition. Third, the Manyanda tradition has implications for strengthening the community’s commitment and belief in the meaning of death, the importance of a replacement figure who takes over social roles and strengthens the tribal identity of the Nagari (local village) community. This study recommends the importance of this tradition to be preserved as the root of social cohesion.

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.