Sustainability has become a generalized concern for society, specifically businesses, governments, and academia. In the specific case of universities, sustainability has been approached from different perspectives, some viewing it from environmental practices, management initiatives, operational criteria, green buildings, and even education for sustainable development. This research focuses on sustainability as a managerial practice and investigates how it affects the performance of five private universities in Medellin, Colombia. For this purpose, a literature review using a mixed sequential approach, including bibliometric and content analysis, was initially conducted. In the s second phase, more than 5000 responses from students, professors, and employees of the five mentioned private universities were collected. A previously validated instrument for both sustainability and performance was applied in the quantitative phase, and a novel dimensionality of the constructs was proposed by conducting an exploratory factor analysis using the SPSS software. Results were then processed through a structural equation analysis with the Smart PLS software. The impact of sustainability on university performance is verified, making some managerial recommendations.

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

Richard’s equation was approximated by finite-difference numerical scheme to model water infiltration profile in variably unsaturated soil^[1]. The published data of Philip’s semi-analytical solution was used to validate the simulated results from the numerical scheme. A discrepancy was found between the simulated and the published semi-analytical results. Morris method as a global sensitivity tool was used as an alternative to local sensitivity analysis to assess the results discrepancy. Morris method with different sampling strategies were tested, of which Manhattan distance method has resulted a better sensitivity measures and also a better scan of input space than Euclidean method. Moreover, Morris method at p = 2 , r = 2 and Manhattan distance sampling strategy, with only 2 extra simulation runs than local sensitivity analysis, was able to produce reliable sensitivity measures (μ^*, σ). The sensitivity analysis results were cross-validated by Sobol’ variance-based method with 150,000 simulation runs. The global sensitivity tool has identified three important parameters, of which spatial discretization size was the sole reason of the discrepancy observed. In addition, a high proportion of total output variance contributed by parameters β and θ_s is suggesting a greater significant digits to reduce its input uncertainty range.

[Objective]In order to explore the sustainable food security level in the Yangtze River Economic Belt, ensure food security and sustainable development of agricultural modernization, it is necessary to establish a scientific food security evaluation system to safeguard local food security.[Methods]This paper takes the food system of the Yangtze River Economic Belt in China as the research object, based on the food security research results at home and abroad, based on sustainable development thinking, combined with a new perspective of dynamic equilibrium research: Beginning with food normalcy, a comprehensive analysis of food production, food economy, social development, ecological security, and technical support for sustainable development is presented using the entropy-weighted TOPSIS model to build a food security evaluation system for sustainable development. [Conclusion]After systematic analysis, it is concluded that (1) the average value of food security score of the Yangtze River Economic Belt from 2008 to 2021 is 0.429, and the overall food in the Yangtze River Economic Belt is in general security level (0.400 ≤ Q1 ≤ 0.600), and the overall situation of food security is not optimistic, (2) from the segmentation of the Yangtze River Economic Belt, the high and low level of food security are divided into sections: midstream > downstream > upstream, and each province and city is slowly rising to different degrees. In this way, we propose general countermeasures to ensure local food security from the perspective of sustainable development.

This study examines the effectiveness of Kazakhstan’s grant funding system in supporting research institutions and universities, focusing on the relationship between funding levels, expert evaluations, and research outputs. We analyzed 317 projects awarded grants in 2021, using parametric methods to assess publication outcomes in Scopus and Web of Science databases. Descriptive statistics for 1606 grants awarded between 2021 and 2023 provide additional insights into the broader funding landscape. The results highlight key correlations between funding, evaluation scores, and journal publication percentiles, with a notable negative correlation observed between international and national expert evaluations in specific scientific fields. A productivity analysis at the organizational level was conducted using non-parametric methods to evaluate institutional efficiency in converting funding into research output. Data were manually collected from the National Center of Science and Technology Evaluation and supplemented with publication data from Scopus and Web of Science, using unique grant numbers and principal investigators’ profiles. This comprehensive analysis contributes to the development of an analytical framework for improving research funding policies in Kazakhstan.