The transportation sector in India, which is a vital engine for economic growth, is progressively facing challenges related to climate change. Increased temperature, extreme weather conditions, and rising seas threaten physical infrastructure, service delivery, and the economy. This research examines efforts towards improving the climate resilience of India’s transport sector through policy interventions. Strategies encompass broadening the focus to cover the integration of sustainability, innovative technology deployment, and adaptive infrastructure planning. Multi-sectoral measures are proposed to guarantee longevity, equity and environmental protection. National transport infrastructure will be secured, people will be enabled to move sustainably, and India will take its position in the world economy as a climate-resilient country. Long-term resource management and promoting inclusive governance are critical to agri-transportation systems that can withstand the changing climate.

Teachers are instrumental in advancing the cognitive and motor skills of children with autism. Despite their importance, the incorporation of both educators and robotic aids in the educational frameworks of specialized schools and centers is infrequent. Extensive research has been conducted to evaluate the impact of robotic assistance on the learning outcomes for children with autism. This study investigates the effects of the Furhat robot on the educational experiences of autistic children in schools, analyzing its utility both with and without the presence of teachers. Interviews with educators were carried out to gauge the effectiveness of implementing Furhat robots in these settings. Data collected from sessions with autistic children were analyzed using ANOVA tests, offering insights into the Furhat Social Robot’s potential as a significant tool for fostering engagement and interaction. The findings highlight the robot’s effectiveness in enhancing social interaction and engagement, thereby contributing to the ongoing discussion on how social robots can improve the developmental progress and well-being of children with autism. Moreover, this paper underlines the innovative aspects of our proposed model and its wider implications. By presenting specific quantitative outcomes, our aim is to extend the reach of our findings to a broader audience. Ultimately, this research delineates significant contributions to the understanding of social robots, such as Furhat, in improving the overall well-being and developmental trajectories of children with autism.

In learning, one of the fundamental motivating factors is self-efficacy. Therefore, it is crucial to understand the level of students’ self-efficacy in learning programming. This article presents a quantitative study on undergraduate students’ perceived programming self-efficacy. 110 undergraduate computing students took part in this survey to assess programming self-efficacy. Before being given to the respondents, the survey instrument, which included a 28-item self-efficacy assessment and 30 multiple-choice programming questions, was pilot-tested. The survey instrument had a reliability of 0.755. The study results show that the students’ self-efficacy was low when they solved complex programming tasks independently. However, they felt confident when there was an assistant to guide them through the tasks. From this study, it could be concluded that self-efficacy is an essential achievement component in programming courses and can avoid education dropouts.

E-learning has become an integral part of higher education, significantly influencing the teaching and learning landscape. This study investigates the impact of student characteristics such as gender, grade, and major on E-learning satisfaction. Utilizing Structural Equation Modeling (SEM) and collecting data through 527 valid questionnaires from Nanjing Normal University students, this research reveals the nuanced relationships between these variables and E-learning satisfaction. The findings indicate that gender, grade, and major significantly and positively impact student satisfaction with E-learning, highlighting the need for tailored E-learning resources to meet diverse student needs. The study underscores the importance of continuous improvement in E-learning resources and platforms to enhance student satisfaction. This research contributes to the understanding of effective E-learning strategies in higher education institutions.

Objective: This study aimed to examine the psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) in a sample of Moroccan students. Method: A total of 208 Moroccan students participated in this study. The dimensionality of the DASS-21 scale was assessed using exploratory factor analysis. Construct validity was assessed using the Stress Perception (PSS-10), State Anxiety (SAI), and Depression (CESD-10) scales. Results: Correlation analyses between Depression, Anxiety, and Stress subscales showed significant results. The exploratory factor analysis results confirmed the DASS’s three-dimensional structure. Furthermore, correlation analyses revealed positive correlations between the DASS-18 sub-dimensions and the three scales for Stress (PSS-10), Anxiety (SAI), and Depression (CESD-10). Conclusion: In line with previous work, the results of this study suggest that the DASS-18 reflect adequate psychometric properties, making it an appropriate tool for use in the university context.

This study investigated the impact of social media on purchasing decision-making using data from a questionnaire survey of 257 randomly sampled students from the College of Business at Imam Muhammad Ibn Saud Islamic University. The study items were selected from the study community through a random sample, where several (257) students were surveyed. To achieve its objectives, the study follows the descriptive analytical approach in addressing its topic. The questionnaire was adopted as a tool for collecting data. The questionnaire collected data on the independent variable social media—and the dimensions of the dependent variables representing the stages of purchasing decision-making: Feeling the need for the advertised goods, collecting information about alternatives, evaluating available options, buying decisions, and post-purchase evaluation of the purchase decision. Then, the data were analyzed based on regression analysis using SPSS and AMOS. The important findings are summarized below: Social media use is directly related to feeling the need for and searching for information on advertised goods. Social communication and the evaluation of alternatives to advertised goods, in addition to the existence of a moral effect and a direct correlation between social media use and making the purchasing decision for advertised goods. Providing honest, sufficient, and accurate information via social media to the buyer can help them make the purchasing decision.