The purpose of this research is to develop an efficient model for human pose estimation (HPE). The main limitations of the study include the small size of the dataset and confounds in the classification of certain poses, suggesting the need for more data to improve the robustness of the model in uncontrolled environments. The methodology used combines MediaPipe for the detection of key points in images with a CNN1D model that processes preprocessed feature sequences. The Yoga Poses dataset was used for the training and validation of the model, and resampling techniques, such as bootstrapping, were applied to improve accuracy and avoid overfitting in the training. The results show that the proposed model achieves 96% overall accuracy in the classification of five yoga poses, with accuracy metrics above 90% for all classes. The implementation of the CNN1D model instead of traditional 2D or 3D architectures accomplishes the goal of maintaining a low computational cost and efficient preprocessing of the images, allowing for its use on mobile devices and real-time environments.

Municipal solid waste (MSW) accumulation is a critical global challenge for society and governments, impacting environmental and social sustainability. Efficient separation of MSW is essential for resource recovery and advancing sustainable urban management practices. However, manual classification remains a slow and inefficient practice. In response, advances in artificial intelligence, particularly in machine learning, offer more precise and efficient alternative solutions to optimize this process. This research presents the development of a light deep neural network called R3sNet (three “Rs” for Reduce, Reuse, and Recycle) with residual modules trained end-to-end for the binary classification of MSW, with the capability for faster inference. The results indicate that the combination of processing techniques, optimized architecture, and training strategies contributes to an accuracy of 87% for organic waste and 94% for inorganic waste. R3sNet outperforms the pre-trained ResNet50 model by up to 6% in the classification of both organic and inorganic MSW, while also reducing the number of hyperparameters by 98.60% and GFLOPS by 65.17% compared to ResNet50. R3sNet contributes to sustainability by improving the waste separation processes, facilitating higher recycling rates, reducing landfill dependency, and promoting a circular economy. The model’s optimized computational requirements also translate into lower energy consumption during inference, making it well-suited for deployment in resource-constrained devices in smart urban environments. These advancements support the following Sustainable Development Goals (SDGs): SDG 11: Sustainable Cities and Communities, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Action.

Since 2019, the Mexican Government has implemented the Sembrando Vida Program (SVP) to address environmental degradation and social marginalization in rural areas. This research analyzed the SVP’s targeting mechanisms to assess the fulfillment of its objectives in the central region of the state of Guerrero, Mexico. The methodology included an analysis of the Operating Rules; a review of the beneficiary registry; the construction of environmental, social, and economic indicators; proportional stratified sampling with a 90% confidence level; and an SVP evaluation using indicators and confidence intervals. Targeting mechanisms identified agrarian subjects aged ≥ 18 years with 2.5 hectares living below the poverty line. Changes in the indicator matrix highlight a focus on food self-sufficiency and poverty levels, among others. Key results from the indicators include 100% social recovery, 62.3% of beneficiaries increased their agricultural income, and 100% application of learned environmental techniques. The intervals showed that between 26.49–42.19% of individuals joined the agricultural sector; 55.68–71.58% diversified their crops; and 86.15–95.65% made improvements in health, housing, or education. The evaluation of the SVP demonstrated its impact on environmental preservation and the improvement of the socioeconomic well-being of the rural population in the study area.

Los principales sectores económicos del estado de Guerrero son: el turismo, agricultura, minería, comercio y pesca; con una población total de 3’540,685 habitantes, en 2022 el 60.4% de los guerrerenses vivía en situación de pobreza y el 22.2% en pobreza extrema. La presente investigación tiene por objetivo identificar los conceptos relacionados con la Economía Social Solidaria (ESS) aplicados en el funcionamiento de los Nodos de Impulso a la Economía Social y Solidaria (NODESS), identificando los que operan en el estado de Guerrero, su alcance y los sectores atendidos en la región a la que pertenecen. En cuanto a la metodología se realizó un estudio exploratorio y transversal utilizando la técnica de entrevista vía Microsoft Teams, plataformas digitales, correos electrónicos a los responsables de los NODESS, y también se solicitó información pública en la Plataforma Nacional de Transparencia. El estado de Guerrero se encuentra dividido en 8 regiones, en 6 de ellas existen 12 NODESS vigentes, en los cuales se atienden las seis dimensiones de la ESS de manera satisfactoria. Al realizar esta investigación se identificó un área de oportunidad para la creación de NODESS en la región Centro del estado de Guerrero.

Los modelos de Redes Neuronales Convolucionales (RNC) requieren datos procesados que aprendan patrones de imágenes para evitar la memorización. Esta investigación presenta la conformación de un dataset de 3208 imágenes de clasificación de Residuos Sólidos Urbanos (RSU) en orgánicos e inorgánicos para modelos de RNC. El procesamiento se realizó con una metodología de tres fases: 1. Identificación y selección de dataset: Kaggle y Github; 2. Creación del dataset: uniformidad de imágenes y ajustes de color; y 3. Conformación del dataset de residuos orgánicos e inorgánicos. Los resultados obtenidos fueron los conjuntos de datos de RSU orgánicos conformado por 1574 imágenes y 1634 inorgánicos. Esto permitirá el entrenamiento en modelos de Deep Learning para la clasificación binaria de RSU.