This study addresses the challenge of detecting crimes against individuals in public security applications, particularly where the availability of quality data is limited, and existing models exhibit a lack of generalization to real-world scenarios. To mitigate the challenges associated with collecting extensive and labeled datasets, this study proposes the development of a novel dataset focused specifically on crimes against individuals, including incidents such as robberies, assaults, and physical altercations. The dataset is constructed using data from publicly available sources and undergoes a rigorous labeling process to ensure both quality and representativeness of criminal activities. Furthermore, a 3D convolutional neural network (Conv 3D) is implemented for real-time video analysis to detect these crimes effectively. The proposed approach includes a comprehensive validation of both the dataset and the model through performance comparisons with existing datasets, utilizing key evaluation metrics such as the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC). Experimental results demonstrate that the proposed dataset and model achieve an accuracy rate between 94% and 95%, highlighting their effectiveness in accurately identifying criminal activities. This study contributes to the advancement of crime detection technologies, offering a practical solution for implementation in surveillance and public safety systems in urban environments.

Los sistemas de red en chip (NoC) fueron desarrollados originalmente para proporcionar un alto rendimiento, mediante la disponibilidad de varias unidades de procesamiento, conectadas a través de una red cableada dentro del circuito integrado. Wireless NoC (WiNoC o WNoC) son una evolución natural de los sistemas NoC, que integran una comunicación jerárquica dentro del chip para mejorar la escalabilidad. El mapeo de tareas en los sistemas WNoC representa un proceso desafiante, que a menudo implica varios objetivos de optimización, como potencia, rendimiento, productividad, uso de recursos y métricas de red. Este artículo describe un algoritmo genético basado en un enfoque para encontrar soluciones óptimas de asignación de tareas en tiempo de diseño, para sistemas embebidos que trabajan sobre un WiNoC. Los objetivos de optimización fueron: Aceleración, Consumo de Energía y Ancho de Banda. La red de destino utilizada para la simulación puede ser vista como un WiNoC jerárquica de dos niveles. El primer nivel corresponde a un conjunto de subredes que están conectadas por cables y son de tipo malla. El segundo nivel corresponde a una topología en estrella de enlaces inalámbricos, que conectan las subredes de primer nivel. El algoritmo propuesto muestra un buen desempeño en relación con los objetivos de optimización y la WiNoC heterogéneo simulada.

Modeling for design and simulation has gained significance in both academic and industrial domains, primarily due to the availability of specialized software. In the domain of modeling mineral beneficiation crushers, the focus is on controlling variables related to equipment mechanics and material characteristics. In this study, the comminution process of a conical crusher in a stone aggregates production plant was specifically modeled using artificial neural networks. A Feedforward artificial neural network topology was employed to simulate the comminution process. Different network configurations were tested through 50 laboratory experiments, where size data were collected at the crusher’s entrance and exit. The aim was to select the configuration that best matched the actual data by optimizing mean square error, error dispersion, and linear fit. After thorough evaluation, the network configuration with hidden layers of 70 and 40 neurons emerged as the most effective. This configuration yielded a mean square error of 6.24, an error dispersion of 35.05, and a robust linear fit of 0.99. Subsequently, this model was applied to new size distribution data from the crusher’s entrance and exit, resulting in an R parameter of 0.999. The study highlights the effectiveness of the trained artificial neural network model in accurately predicting particle size distribution at the exit of a conical crusher. This has significant implications for optimizing the beneficiation process in stone aggregate production.

Tomato cultivation is a key component of global agriculture, but is significantly threatened by pests and diseases that impact yield and trade. To address this, the present study investigates the application of YOLOv9, a state-of-the-art object detection model, for automated disease detection in tomato leaves. Using a dataset of 4,323 images with 15,135 annotations and a modified PlantVillage dataset, YOLOv9 models were trained and evaluated. Among the evaluated models, YOLOv9e yielded the highest mean average precision (mAP) at 0.964, establishing a benchmark for accuracy. In contrast, the YOLOv9t model provided an optimal balance for practical applications, achieving a competitive mAP of 0.95 with a rapid inference time of 8.8 ms. Furthermore, this work contributes a public version of the PlantVillage dataset with bounding box annotations, providing a valuable resource for object detection research and extending the use of the original classification-focused dataset. The results indicate that YOLOv9 models are effective for real-time and accurate detection of various diseases in complex agricultural settings.