Autism is often referred to as autism spectrum disorder that constitutes a diverse group of conditions related to brain development (which is a neurodevelopmental disorder). Autism spectrum disorder patients often have difficulty communicating and interacting socially, and are characterized by restricted and repetitive patterns of behavior and interests that have been shown to be the same in cultures of countries around the world. However, the interpretation of symptoms and recognition in terms of policies and laws in countries are not the same. Accordingly, some countries recognize autism spectrum disorder as one of the types of disability and some countries do not, including Vietnam. Currently, Vietnam’s Law on Persons with Disabilities 2010 does not recognize the term “autism” in the Law. At the same time, there is a lack of legal issues related to the “autism spectrum” from the time of diagnosis such as policies on practical support appropriate to each individual’s needs and interests so that they can develop and be integrated in the medical field, education and enjoyment of other benefits such as persons with disabilities. This is an overlooked term that leads to the community having a misperception of “autism” when they are not aware that autism is a disease or a disability, what causes autism and why, etc. The article points out the current situation of adjustment by policies and laws on autistic people in Vietnam. On that basis, the article focuses on analyzing the contents that need to reform those policies and laws to ensure human rights of autistic people and their families.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers’ capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.

The proportion of elderly people is growing steadily in many countries, and this trend is expected to continue. As a result, ageism—negative discrimination often tied to perceptions of the elderly—becomes especially harmful. Ageism prevents older generations from being fully accepted by society and, in turn, hinders their ability to adapt to today’s technological changes. In this article, we present the results of our survey mapping the extent of ageism among youth in Uzbekistan, known for its cultural tolerance in Central Asia, and in Hungary, a more individualistic society in Central Europe. To interpret the survey results accurately, we included specific questions to measure social desirability bias, enabling a realistic comparison of ageism levels between the two countries. Data was collected through a survey translated into multiple languages, with a final sample of nearly 400 respondents, each either currently pursuing or already holding a college-level diploma. Our methodological approach was twofold. First, we conducted simple chi-square tests to compare levels of negative and positive ageism between the two countries under study. Upon finding significant differences, we used multivariable OLS regression to explain the variance in types of ageism in Uzbekistan and Hungary, accounting for the possible effects of social desirability bias. Uzbek youth demonstrated higher levels of positive ageism and lower levels of negative ageism compared to Hungarian youth. This finding confirms that the cultural tolerance in Uzbek society remains strong and, in many ways, could serve as a model for Hungary. Additionally, our literature review highlights that adequate infrastructure is essential for a society to treat older adults equitably alongside other citizens.

This study addresses the rising concerns of technostress experienced by teachers due to the increased reliance on educational technology in both classroom and online settings. Technostress, defined as the adverse psychological effects arising from the use of information communication technologies, has been documented to impact teacher performance and overall well-being. Despite the importance of educational technology in enhancing teaching and learning experiences, many educators report elevated levels of anxiety, stress, and pressures associated with their use of these tools. This study presents practical strategies to help teachers alleviate or prevent technostress while using educational technology. This study used a quantitative approach with a survey conducted among 113 university and schoolteachers. The data analysis included frequency and percentage distribution of categorical variables, Cronbach’s alpha for reliability, chi-square test, and exploratory factor analysis to identify strategies for symptom prevention. The results indicated that while many teachers experienced symptoms of technostress due to several factors, some did not. The study concluded with specific strategies, and many teachers agreed highly. The implications of this study are profound for educational institutions, policymakers, and teacher training programs as they underscore the necessity of providing comprehensive training, support, and resources to help educators manage technostress effectively. By integrating these strategies into professional developmental programs and fostering a supportive teaching environment, schools and universities can promote better mental health for teachers, improving students’ educational outcomes.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

This study investigates the role of Chat-GPT with augmented reality applications in enhancing tourism experiences in Thailand, focusing on behavioral intentions and innovation adoption to reduce stress in the tourism industry. The research addresses two key objectives: identifying factors driving consumers’ behavioral intentions to adopt AR apps and evaluating the robustness of a modified innovation framework for analyzing these intentions. A conceptual model integrating innovativeness, attitudes, perceived enjoyment, and revisit intentions was developed and tested using Structural Equation Modeling with data from 430 Thai tourists who have one to three years of mobile application experience. The findings highlight that service and technology innovation significantly influence perceived enjoyment and attitude, which in turn mediate the impact on behavioral intention to adopt augmented reality applications. At a significance level of p < 0.001, perceived enjoyment and attitude were identified as critical determinants of BI, underscoring the importance of intrinsic user experiences. Tourists are more likely to adopt augmented reality technologies based on personal perceptions and enjoyment rather than external recommendations. This research provides actionable insights for stakeholders in the tourism technology ecosystem, including technology providers, marketers, and policymakers. By emphasizing the interplay of social, emotional, and hedonic factors in shaping user attitudes, the study introduces a robust framework for advancing augmented reality applications in tourism. The findings underscore the importance of user-centric design to drive technology adoption and offer strategic guidance for developers and entrepreneurs aiming to enhance tourism experiences through innovative augmented reality solutions.