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The purpose of this study is to make an exploratory analysis of the impact of the entrepreneurial ecosystem (EE) as defined by Acs et al. (2014) on opportunity-driven senior entrepreneurial activity in Latin America.

The research uses data from the Global Entrepreneurship Monitor and the Global Entrepreneurship and Development Institute of five Latin America countries (Argentina, Brazil, Chile, Colombia and Mexico), providing a total of 15,019 observations of people that are 50+ years old, between the years 2013 and 2017. A multi-level logistic regression model was used to estimate the relation between the total entrepreneurial activity by opportunity of seniors and some EE indicators. A total of three equations were estimated on the data set described.

This research confirms the relevance of some elements of EE on senior entrepreneurship in Latin America. Entrepreneurial attitudes have a positive relationship with senior entrepreneurs, generating higher levels of entrepreneurial ventures. The combination of institutions that support these attitudes on the EE enhances senior entrepreneurial activity. It also demonstrates that a higher level of entrepreneurial education at postschool stages is relevant to increasing senior entrepreneurial activity.

This research makes some interesting contributions in the field of measuring the impact of EE on senior entrepreneurship by opportunity in developing countries, filling a literature gap. It allows us to glimpse some measures that policymakers could take to improve the entrepreneurial activity of this segment in the region, such as implementing programs that facilitate networking opportunities and mentorship, along with providing training in business and financial literacy.

The Metaverse, an emergent Web 3.0 platform, offers users immersive virtual reality experiences. This study employs a case study approach to explore the concept of sustainability within the Metaverse. It examines the environmental, social, and economic implications of virtual interactions and the role of sustainable technologies in shaping user behavior and virtual economies. Through selected case studies, the research provides insights into the potential and challenges of integrating sustainable practices in the Metaverse, with implications for stakeholders ranging from policymakers to end-users.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.