The attitudes of preservice teachers toward problem-solving in mathematics education play a significant role in shaping how they approach teaching and facilitate student learning in the future. This study explores the factors that influence preservice teachers' attitudes towards problem-solving, particularly focusing on their mathematical background, pedagogical training, teaching experience, and personal beliefs about mathematics. The research involved a mixed-methods approach, utilizing surveys and semi-structured interviews to gather data from preservice teachers at a teacher education institution. The findings reveal that preservice teachers' attitudes are strongly influenced by their mathematical experiences, their exposure to problem-solving strategies during teacher preparation programs, and their personal beliefs regarding the importance of mathematics. Additionally, challenges such as anxiety about mathematics and lack of problem-solving skills were identified as barriers to developing positive attitudes. The study emphasizes the importance of addressing these factors within teacher education programs to improve preservice teachers’ readiness to incorporate problem-solving in their future classrooms.

Pathophysiology is a cornerstone of medical and health sciences education, bridging basic science and clinical practice. However, students often struggle with its complexity and abstract concepts. This article explores innovative teaching strategies, including case-based learning, virtual simulations, and adaptive learning platforms, to improve pathophysiology education. We assess the effectiveness of these methods in fostering deeper comprehension, retention, and clinical reasoning skills. Additionally, we discuss challenges in implementation and propose best practices for educators.

This article provides detailed information about the components that constitute the image of TV anchors within the television system. It analyzes the perspectives of TV presenters working on national, local, and international channels, as well as the ethical guidelines and examples related to the image of TV anchors. The article outlines the principles of image-making and the specific requirements for TV presenters on screen. It includes graphic examples and opinions from international researchers. Additionally, the article presents important statistics on the influence of a TV anchor's image on a show's ratings.

The rapid integration of artificial intelligence into automated and connected vehicles is reshaping the foundations of road transport, introducing unprecedented opportunities for safety enhancement while simultaneously exposing critical socio-technical risks. Contemporary vehicles are no longer isolated mechanical systems; they are complex cyber-physical entities embedded within extended digital ecosystems, regulated by evolving international standards and public policies. This research article develops a comprehensive, theoretically grounded analysis of how functional safety, cybersecurity, data governance, and regulatory oversight intersect in AI-enabled automated driving systems. Drawing strictly on established international standards, regulatory instruments, accident investigation reports, and peer-reviewed academic literature, the study explores how safety assurance practices are transitioning from traditional quality management approaches toward risk-based, system-of-systems governance models capable of addressing machine learning uncertainty, human–machine interaction complexity, and extended vehicle architectures. The methodology adopts an integrative qualitative research approach, synthesizing normative frameworks such as ISO 26262, ISO 20077, UN Regulation No. 155, and emerging European Union artificial intelligence legislation with empirical insights derived from safety incidents, regulatory assessments, and software engineering research. The findings demonstrate that safety in AI-driven mobility cannot be achieved through isolated compliance with individual standards; instead, it requires a harmonized governance architecture that aligns technical design, organizational safety culture, regulatory accountability, and transparent data practices. The discussion critically examines limitations of current frameworks, including residual ambiguity in responsibility allocation, challenges in validating adaptive AI behavior, and tensions between innovation and precaution. The article concludes by outlining future research and policy directions necessary to sustain public trust and ensure ethically aligned, resilient, and socially acceptable deployment of automated vehicle technologies.

This article examines the issue of choosing between state courts and arbitration institutions for resolving commercial disputes arising in business activities, with a particular focus on the Republic of Kazakhstan. Based on a comparative analysis, four key criteria are investigated: the amount in dispute, the nature and essence of the conflict, the time and quality of proceedings, and the impartiality of the decision rendered. It is concluded that state courts offer lower direct costs (such as court fees), but are characterized by a relatively lengthy process due to workload and multiple layers of jurisdiction. In contrast, arbitration provides the parties with procedural flexibility, the ability to select arbitrators, and often resolves disputes more quickly, although it requires payment of arbitration fees and may be vulnerable to the risk of "pocket" adjudications. In conclusion, it is emphasized that under both Kazakhstani and international practice, entrepreneurs should take into account the specific features of their dispute and carefully assess the reliability of arbitration in advance. A combination of analysis of national legislation, international legal norms (the New York Convention, the UNCITRAL Model Law), and examples from practical cases allows for the formulation of practical recommendations on selecting the optimal mechanism for resolving commercial conflicts.