The promulgation of the Curriculum Standards for ordinary high School (2017 edition, 2020 revision) has effectively promoted the reform of high school mathematics classroom. In order to cope with the change of textbook content in the new curriculum reform, it has become one of the important tasks for high school mathematics teachers to implement teaching activities better and sort out and analyze the differences between the old and new textbooks. This paper analyzes the differences between old and new textbooks from the three dimensions of system structure, course content and example exercises, and gives some reasonable teaching suggestions. Among them, the new textbook uses 2019 "Ordinary High School Textbook" person-taught A version of Compulsory Mathematics 1, and the old textbook uses 2004 "Ordinary High School Mathematics Curriculum Standard Experimental Textbook" person-taught A version of compulsory Mathematics 4. In general, the adjustment of the new teaching materials is more in line with the cognitive characteristics of students, pay attention to the penetration of mathematical culture and pay attention to the development of students' mathematical core literacy.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

Thailand and the EU started negotiating a free trade agreement (FTA) in 2005, but negotiations were subsequently suspended in 2014 after the country’s military coup. The significance of these negotiations are important because of the mutual benefit of achieving higher levels of trade and investment between the world’s largest single market and the second largest ASEAN economy. The Specific Factors (SF) model of production and trade is applied to identify potential winner and loser industries and factors of production in Thailand. The model identifies short-run loses for some labor inputs, return to capital, and output in agriculture and services. In the manufacturing and energy sectors, higher output will benefit some labor inputs and capital owners. Understanding the short-run impact of an FTA could allow policymakers in Thailand to reinforce the institutional infrastructure such as implementing trade adjustment assistance programs (TAA), to help re-train workers who may become unemployed due to free trade.

Rural sub-Saharan Africa faces limited medical access, healthcare worker shortages, and inadequate health information systems. Mobile health (mHealth) technologies offer potential solutions but remain underdeveloped in these settings. This review aims to explore the sociocultural context of mHealth adoption in rural sub-Saharan Africa to support sustainable implementation. A comprehensive Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) search was conducted in databases like PubMed, MEDLINE, and African Journals Online, covering peer-reviewed literature from 2010 to 2024. Qualitative studies of mHealth interventions were included, with quality assessed via the Critical Appraisal Skills Program (CASP) checklist and data synthesized using a meta-ethnographic approach. Out of 892 studies, 38 met the inclusion criteria. Key findings include sociocultural factors like community trust influencing technology acceptance, local implementation strategies, user empowerment in health decisions, and innovative solutions for infrastructure issues. Challenges include privacy concerns, increased healthcare worker workload, and intervention sustainability. While mHealth can reduce healthcare barriers, success depends on sociocultural alignment and adaptability. Future interventions should prioritize community co-design, privacy protection, and sustainable, infrastructure-aware models.

This paper critically reviews the prevailing generalizations in current research on Generation Z (Gen-Z) travel behavior. While various studies have characterized Gen-Z’s transportation preferences as leaning towards sustainable and technology-integrated modes of transport, this paper argues that the findings are largely based on observations from developed countries and may not accurately reflect behavior in developing countries. This paper is written using a narrative literature study approach. Through a comprehensive literature review, the paper highlights the differences in Gen-Z travel patterns across different geographical regions, emphasizing the need for context-specific analysis. The paper addresses often overlooked factors such as economic limitations, infrastructure challenges, and cultural nuances that shape mobility choices. The aim is to dissect the cohort effect and look at its validity across different socio-economic landscapes through existing literature. As such, the paper provides nuanced insights into the heterogeneity of Gen-Z travel behavior and suggests cautioning against over-generalization, as well as advocating for a more localized approach in transportation policy and planning. The paper also encourages similar research in developing countries to gain a more comprehensive understanding of Gen-Z travel behavior globally.