Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by delimiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society.

To alleviate soil compaction, subsoiling practices using subsoiler implements are commonly implemented. However, subsoiler bodies are subjected to great draft forces because they work deep in the soil. Therefore, to contribute to draft force reduction, in this work, a bio-inspired attack edge for a subsoiler body based on the internal and external contour lines of the claws of the Mexican ground squirrel (Spermophilus mexicanus) is proposed. As a first step, computational fluid dynamic (CFD) modeling was used to select the best bionic subsoiler (BS) according to the draft force requirements. Then, the BS was fabricated and field-evaluated, and its real draft force during tillage was contrasted with those of a curve subsoiler (CS) and a straight subsoiler (SS). The field evaluation demonstrated that the BS demands, on average, $12.37\%$ and $22.25\%$ less draft force than the CS and SS, respectively. Additionally, the BS was better at entering the soil since its mean tillage depths were $24.86\%$ and $5.73\%$ higher than those of the SS and CS geometries, respectively. Therefore, it was found that modeling the attacking edge of a subsoiler body after the Mexican ground squirrel clearly reduced the draft force during tillage.

In Latin America, the energy crisis has worsened due to the dependence on energy services and fossil fuel imports from highly industrialized countries at prices established by the international market; this is particularly relevant to the construction industry, which presents a significant deficit in optimal energy consumption. Hence, some governments have established public policies to maximize the efficiency of these services and, at the same time, minimize the carbon footprint. In this research study, we reviewed the public policies, strategies, and incentives for energy efficiency (EE) implementation in the residential sector established by the Mexican government. A scoping review methodology was chosen and implemented in the following steps: 1. Research inquiry identification. 2. Determination of the relevant literature and studies. 3. The literature selection. 4. Data graphing. 5. Results collection, overview, and submission. In this systematic review, we identified five mandatory standards (NOM-008-ENER-2001, NOM-009-ENER-2014, NOM-018-ENER-2011, NOM-020-ENER-2011, and NOM-024-ENER-2012), six optional standards, four strategies (Green Mortgage, Integral Sustainable Improvement in Existing Housing, ECOCASA, and NAMA), and three kinds of incentives (green bonds, credit and interest rates (Green Mortgage, FIDE, and Ecocasa), and taxes (Income Tax Reduction)). As a result of the implementation of the above, as of December 2020, NAMA financed 5106 developers of 38 projects in 15 states; contributed to a reduction of 126,779 tons of CO₂; and aided 19,913 people. From 2013 to December 2023, EcoCasa subsidized 71,440 households for a total of 224 projects in 25 states; contributed to a reduction of 2.6 million tons of CO₂; aided 285,760 Mexicans; and issued EcoCasa certificates for 3,473,556 m². The results of the EE indicators in residential buildings showed an increase in the housing unit number as well as an increase in household appliances, with those based on power consumption prevailing. The residential sector ranks third in power consumption in Mexico, consuming an estimated 790 pj, of which 76% corresponds to thermal energy and 24% to electric power. Among countries in Latin America and the Caribbean, Mexico has achieved an Energy Transition Index of 62%.