This study presents a comprehensive two-dimensional numerical analysis of natural laminar convection within a square cavity containing two circular heat sources, which simulate electric cables generating heat due to Joule heating. This scenario is particularly relevant in aeronautics, where excessive heating of electrical installations can lead to significant material and human safety risks. The primary objective of this research is to identify the optimal spacing between the two heat sources to mitigate the risk of overheating and ensure the safe operation of the electrical installation. To achieve this, various configurations were analyzed by adjusting the distance between the heat sources while also varying the Rayleigh number across a range from 10³ to 10⁶. The governing equations for the fluid flow and heat transfer were solved using a FORTRAN-based numerical code employing the finite volume method. The results indicate that the heat transfer characteristics within the cavity are significantly influenced by both the distance between the heat sources and the Rayleigh number. The analysis revealed that the average Nusselt number (Nu_avg) peaked at a value of 14.69 when the distance between the heat sources was set at 0.7 units and the Rayleigh number was at 10⁶. This finding suggests that maintaining this specific spacing between the electrical cables can optimize heat dissipation and enhance the safety of the installation. In conclusion, the study recommends adopting a spacing of 0.7 units between the electrical cables to ensure optimal thermal performance and minimize the risk of overheating, thereby safeguarding both the materials and personnel involved in aeronautical operations.

It is important for society to know the actions implemented by companies in the construction sector to reduce the environmental pollution generated by this industry and to contribute to the solution of economic and social problems in their environment; however, the variables that allow identifying their contributions and impacts are not known. Based on this problem, the study focuses on identifying the factors that influence sustainability management within the construction sector in Colombia. The research presents a predictive approach and uses a quantitative methodology, applying statistical modeling techniques. The sample corresponds to 84 Colombian companies. As a result, a system of equations of the form y=mx+b is presented to describe the deviation of the environmental, economic, social, compensation measures, management, indicators and sustainability reports. The analysis of the intersections constitutes a projective tool to evaluate the relationships and balance points between the dimensions analyzed, helping to identify strengths and opportunities for improvement.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.