This article presents the methodology of creation of an innovative used by intelligent chatbots which support the admission process in universities. The lifecycle of the ontology, unlike the classical lifecycles, has six stages: conceptualization, formalization, development, testing, production and maintenance. This leads to sustainability of the chatbot, called Ana, which has been implemented at the “Iuliu Hatieganu” University of Medicine and Pharmacy from Cluj-Napoca during the admission session throughout July–September 2022, for international candidates. The sustainability of the chatbot comes from the continuous maintenance and updates of the ontology, based on candidates’ interraction with the system and updates of the admission procedures. Over time, the chatbot is able to answer the questions according to the present situation of the admission and the real needs of the candidates. Ana had a huge impact, succeeding to resolve a number of 5173 applicants requests, and only 809 messages was transferred to the human operators, statistics which show a high cost-benefit improvement in terms of reducing the travel expenses for the candidates and also for the university. The article also summarizes the good practices in developing and use of such an intelligent chatbot.

The demands and preferences of men are often prioritized in societal challenges, despite women comprising half of the world’s population. Gender-Responsive Research and Innovation (GRRI) offers an approach to address this bias, promoting comprehensive and equitable solutions to better-understood real-world problems. The successful implementation of GRRI depends on recognizing obstacles, staying informed about relevant initiatives, and using this knowledge to develop effective strategies, ultimately contributing to a fairer and more responsive society. This research, using a mixed-methods approach, identified 10 critical GRRI concerns, measures, and strategies to address them, as well as 18 noteworthy initiatives across research, industrial, and regulatory contextual fields. It also enhances the understanding of GRRI, laying the groundwork for an ontology of GRRI that can benefit policymakers, organizations, and researchers seeking more inclusive strategies and practices.

Land bonitation, or land rating, is a core instrument in agricultural policy used to evaluate land productivity based on environmental and climatic indicators. However, conventional Bonitation Coefficient (BC) methods are often rigid, require complete indicator sets, and lack mechanisms for handling missing or forecasted data—limiting their applicability under data scarcity and climate variability. This paper proposes a GIS-integrated, modular framework that couples classical BC computation with machine learning-based temporal forecasting and spatial generalization. Specifically, we apply deep learning models (LSTM, GRU, and CNN) to predict monthly precipitation—one of the 17 indicators in the Romanian BC formula—using over 61 years of data. The forecasts are spatially interpolated using Voronoi tessellation and then incorporated into the bonitation process via an adaptive logic that accommodates both complete and incomplete datasets. Results show that the ensemble forecast model outperforms individual predictors, achieving an $R^2$ of up to 0.648 and an RMSE of 18.8 mm, compared to LSTM ($R^2=0.59$), GRU ($R^2=0.61$), and CNN ($R^2=0.57$). While the case study focuses on precipitation, the framework is generalizable to other BC indicators and regions. This integration of forecasting, spatial generalization, and classical land evaluation addresses key limitations of existing bonitation methods and lays the groundwork for scalable, AI-enhanced land assessment systems. The forecasting module supports BC computation by supplying missing climate indicators, reinforcing that the primary aim remains adaptive land bonitation.