There is a large literature on public-private-partnership, covering many different areas and aspects. This article deals with a specific but important aspect: the decision-making mechanisms to choose the management of PPP enterprises. In this sector, a suitable choice of managers is of particular importance because the persons chosen must balance the public and private interests. This is often difficult to achieve. Two new procedures are discussed, “Directed Random Choice” and “Rotating CEOs”. In each case, the advantages and disadvantages of the procedure of choosing the managers of PPP enterprises are discussed and evaluated. It is concluded that the two novel mechanisms should be seriously considered when choosing the managers of PPP enterprises.

A topic of current interest in forestry science concerns the regeneration of degraded forests and areas. Within this topic, an important aspect refers to the time that different forests take to recover their original levels of diversity and other characteristics that are key to resume their functioning as ecosystems. The present work focuses on the premontane rainforests of the central Peruvian rainforest, in the Chanchamayo valley, Junín, between 1,000 and 1,500 masl. A total of 19 Gentry Transects of 2 × 500 m, including all woody plants ≥2.5 cm diameter at breast height were established in areas of mature forests, and forests of different ages after clear-cutting without burning. Five forest ages were considered, 5-10, 20, 30, 40 and ≥50 years. The alpha-diversity and composition of the tree flora under each of these conditions was compared and analyzed. It was observed that, from 40 years of age, Fisher’s alpha-diversity index becomes quite similar to that characterizing mature forests; from 30 years of age, the taxonomic composition by species reached a similarity of 69–73%, like those occurring in mature forests. The characteristic botanical families, genera and species at each of the ages were compared, specifying that as the age of the forest increases, there are fewer shared species with a high number of individuals. Early forests, up to 20 years of age, are characterized by the presence of Piperaceae; after 30 years of age, they are characterized by the Moraceae family.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.