Abstract: The dengue virus has become an increasingly critical problem for humanity due to its extensive spread. This is transmitted through a vector that sprouts in certain climatic conditions (tropical and subtropical climates). The transmission of the disease can be associated with certain climatic variables that reinforce the outbreak. Data were collected on dengue cases by epidemiological week registered in Loreto-Peru from January 1, 2016, to January 31, 2022. Likewise, data on meteorological variables (maximum and minimum temperature; dry and humid bulb temperature; wind speed and total precipitation in the area). In this study, four Machine learning modeling techniques were considered: Support Vector Machine (SVM), Decision Tree, Random Forest and AdaBoost; and the parameters defined to evaluate the models are: Accuracy, Precision, Recall and F-1. As a result, optimal AUC values were obtained in a range from 0.818 to 0.996 for the SVM, Random Forest and AdaBoost algorithms, likewise, in all weather stations the ROC curve showed good performance for all models, except for the Decision Tree algorithm. As a conclusion for this study, we propose the optimal model to associate dengue cases with climatic conditions is SVM.

Abstract: Bayesian approach was applied to the management of the supply chain in a dynamic food product portfolio for a company in the retail sector. We propose a quasi-experimental method considering pre and posttest and a control group. The sample size of 93 products, out of a population of 120 products from two categories: classic sauces and gourmet sauces. R and Python programming languages were used and libraries for random sampling of the a priori distribution of the products to obtain posterior values área presented on the research. Forecast accuracy increased with the Bayesian approach by 10%. Likewise, it was possible to reduce the coverage inventory from 2 to 1.2 months and the discrepancy between the values of the Bayesian estimate with the traditional method was possible to reach a 5% error in the variation.

Abstract: Laser imaging detection and ranging (LiDAR) sensors can be used in numerous applications mainly due to their ability to measure distances or perform 3D surface mappings. However, to perform these functions efficiently in certain situations, such as in a moving vehicle, LiDAR sensors require a stabilizing platform to prevent terrain irregularities from affecting their measurements. Moreover, accurate platform control is one of the main challenges of modern control engineering. Although several approaches concerning this challenge have been proposed, the high computational cost of control algorithms continues to be their largest drawback. Within this context, the present work seeks to implement a control system to achieve the horizontal stabilization of a two-degree-of-freedom platform for a LiDAR sensor using inertial systems and embedded software platforms, thereby guaranteeing not only portability but also low computational costs.