This study explores the determinants of political participation among Thai youth, focusing on the roles of political interest, knowledge, and efficacy. Employing stratified random sampling, data were collected from 191 university students in Bangkok. Structural Equation Modeling (SEM) via Smart PLS was utilized to test hypotheses regarding the direct and mediating effects of political interest and knowledge on participation, highlighting the mediating role of political efficacy. The findings indicate that political efficacy significantly enhances participation, while political interest boosts knowledge significantly but does not directly influence efficacy. Furthermore, political knowledge positively affects efficacy but not participation directly. Notably, the indirect effects of political interest on participation through efficacy alone are insignificant, but the pathways from interest to participation through both knowledge and efficacy, and from knowledge to participation through efficacy, are significant. These results elucidate the complex interactions between political interest, knowledge, and efficacy in shaping the political engagement of Thai youth.

Cassava’s adaptability to different agroecological conditions, high yield, as well as its ability to thrive under harsh climatic conditions, makes it an essential food security crop. In South Africa, the cassava value chain is currently uncoordinated and underdeveloped, with a couple of smallholder farmers growing the crop for household consumption and as a source of income. Other farmers regard it as a secondary crop and hardly any producers grow it for industrial purposes. Hence, this study sought to analyze the determinants of household participation in the cassava value chain in South Africa. The study employed the multivariate probit model to analyze the determinants of household participation in the cassava value chain in South Africa, using a primary dataset collected through a simple sample method from smallholder farmers in KwaZulu-Natal, Mpumalanga, and Limpopo provinces. Results show that livestock ownership has a positive and significant effect on the likelihood of farmers participating in the value chain by growing cassava for household food consumption. Also, findings reveal that hiring labour in cassava production and an increase in the yield during the previous season increases the probability of farmers’ interest in selling cassava tubers along the value chain. Hence, the positive and statistically significant influence of hiring labour during cassava production in driving the farmers’ interest in selling cassava tubers and cuttings implies that the development of the cassava value chain presents great opportunities for creating jobs (employment) in the country. Also, policy interventions that ensure land tenure security and empower farmers to increase their cassava yields are bound to encourage further participation in the value chain with an interest in selling fresh tubers, among other derived products to generate income. Lastly, programmes that empower and encourage youth participation in the cassava value chain can increase the number of farmers interested in selling cassava products.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

This study investigates the utilization of artificial intelligence (AI) technology to enhance practical content development within the media specialization program at Palestine Technical University, Kadoorie. The primary objective is to examine the extent to which media specialty lecturers employ AI technology in developing practical content. A mixed-methods approach is employed, qualitative data are gathered through in-depth interviews with faculty members to elucidate their perceptions and experiences regarding the integration of AI technology in practical content development. The study aims to provide valuable insights into the benefits and challenges of AI integration in practical content development for media specialization programs The study reveals diverse views on AI integration in media education at Palestine Technical University, Kadoorie. Faculty recognize AI’s benefits like personalized learning and productivity but also express concerns about over-reliance and ethics. Consensus exists on cautious AI implementation to maximize benefits and address drawbacks. Obstacles to AI adoption include cost, skills gaps, and ethical considerations, highlighting the complexity of integration. The study emphasizes a balanced approach, offering insights for enhancing practical content development in media specialization programs at Palestine Technical University, Kadoorie.

With the declaration of the Sustainable Development Goals (SDGs), the importance of localisation principles and, consequently, the local-level institutions in implementing development policies came to the forefront. India adopted a thematic approach by condensing the seventeen goals into nine themes, to be worked upon by the local administrative units, furthering that each Village Panchayat (constitutionally known as Grama Panchayats) should select a theme in a plan year and strive towards attaining it. For the South Indian state of Kerala, with its good trajectory of decentralised governance, this localisation process of SDGs was rather smooth. In this article, we discuss the case of the best-performing Grama Panchayat (GP) in Kerala, which has identified ‘Village with Self-Sufficient Infrastructure’ as the development theme. Through qualitative research methodology, we examine how the Panchayat included projects specific to this theme in the development plans and how the implementation helped produce effects on multidimensional aspects of SDGs using the SDG Impact Assessment Tool. The case studies of different infrastructure-based projects endorse that with proper planning and implementation of such projects, the lowest tier of administration can significantly contribute to the improvement of development goals. We have delineated full fund utilisation through convergence schemes, community participation, and strong monitoring mechanisms as the factors leading the selected Panchayat to be the champion of the cause. The accomplishment exhibited by the Panchayat by integrating SDGs into the Village Development Plan through the projects on the theme of self-sufficient infrastructure can be well emulated by other local bodies across the world.