This work presents the results of the continuity of the research process carried out in the Energy Studies Center belonging to the Faculty of Technical Sciences of the University of Matanzas, which involves the establishment of a dimensionless model to determine the average condensation heat transfer coefficient of Air Coleed Condenser (ACC) systems in straight and inclined tubes. The research consists in obtaining in an analytical way the solution of the differential equation of the velocity profile, considering that condensation is of pellicular type, finally the empirical condition of Roshenow is combined with the theoretical solution to generate a numerical expression that allows obtaining with a 15.2% of deviation in 2,192 tests, a value of the average coefficient of heat transfer by condensation very similar to the one obtained with the use of the most referenced model in the consulted literature, the empirical model of Chato.

This paper discusses the use of workforce ecosystems to manage human intellectual capital. The need for work ecosystems has emerged in the digital age because of the rapid growth in the number of engaged partners and freelancers in the digitalization of enterprises. It is shown that this growth is directly related to the use of agile management systems in design and development: agile, DevOps, microservice architecture, turquoise practices, etc. The information systems needed to manage workforce ecosystems should have competency-based metrics to link business needs, recruitment and training, and finding new partners. At the same time, training should be prioritized over recruitment and the search for new partners in the context of staff shortages. When automating workforce ecosystems, a platform approach should be used to integrate both corporate HR, time and business process management systems, and similar systems from partners.

Effective harvesting strategies are crucial for maximizing annual catch and ensuring the sustainability of lobster (Homarus americanus) farming. This paper presents a nonlinear objective programming model to optimize harvesting intensity based on lobster life cycle dynamics and harvesting characteristics. We model the population dynamics of 1-4 year-old lobsters using differential equations to account for natural mortality, spawning, and harvesting effects. Solving the model with LINGO 12.0, we determine that the optimal harvesting intensity coefficient is 17.36, which maximizes annual catch to 3.88 × 10¹⁰ grams. Results indicate that maintaining harvesting intensity around this optimal value balances economic benefits and population stability, ensuring sustainable farm operations.