Tomato (Solanum lycopersicon L.) is a highly valued crop in the world, particularly in Nigeria with high nutritional and economic benefits. However, its production in Iwollo, Southeast Nigeria, is constrained by unfavorable weather conditions. To address this, a study was conducted at the Teaching and Research Farm, Department of Horticultural Technology, Enugu State Polytechnic, Iwollo, Southeast Nigeria to evaluate and select the best cultivar for high tunnel production using the Rank Summation Index. Completely Randomized Design with three replications was used, and six high-yielding cultivars, namely Roma VF, BHN-1021, Supremo, Pomodro, Money maker, and Iwollo local, were evaluated. Data were collected on key agronomic characters and analyzed with Analysis of Variance (ANOVA) at a 0.05 level of probability. There were significant differences in the number of leaves per plant, plant height, number of branches per plant, days to fruit maturity, fresh fruit weight, number of harvested fresh fruits per plant, and fresh fruit yield per plant among the cultivars. These characters that showed significant differences were ranked and summed up to obtain the Rank Summation Index (RSI) score. The results revealed that the Supremo cultivar had the lowest and best score (18). This suggests Supremo as the best cultivar for high tunnel tomato production in the study area, based on its superior performance across key agronomic traits.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

This paper provides a disaster resilience-based approach. For the definition of the approach, a three-step method (definition of components, analysis of the resilience pillars and definitions of resilience-based actions) has been followed. To validate the approach, an application scenario for mitigating the COVID-19 pandemic is provided in the paper. The proposed approach contributes to stimulating the co-responsibility quadruple helix of actors in the implementation of actions for disaster management. Moreover, the approach is adaptable and flexible, as it can be used to manage different kinds of disasters, adjusting or changing itself to meet specific needs.

This study uses the opening of the new Mass Rapid Transit (MRT) in stages between 2010 and 2012 in Singapore as the exogenous event to empirically test the impact of the new Circle Line (CL) on housing wealth. Applying a "differences-in-differences" approach to the non-landed private housing transaction data covering the period from 2009 to 2013, we find that the average housing prices increase by 1.6% in the post-opening of the CL. We find significant capitalization of the new CL into housing prices, especially households living within a 400-meter radius (the treatment zone) from the closest MRT stations on the CL. The treatment effects that are measured by the "marginal willingness to pay" for houses located within the treatment zone is 13.2% relative to houses located outside the treatment zone. The new CL opening creates an estimated S$1.23 billion housing wealth effects for households living in close proximity to the CL MRT stations. However, we do not find significant "anticipative" effects on house prices in the six-month window prior to the opening of CL. The strongest treatment effect is found after the opening of the phase 1 of CL, and the treatment intensity declines in phases 2 and 3 of the CL opening.