Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation.

This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon's information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA.

Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays.

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest.

Simulation Model of the Effect of the COVID-19 Biosecurity Protocol in the Queues for the Return to Face-to-Face Classes.

This article presents a simulation model of the effect of the COVID-19 Biosafety protocol in the Queues for the return to face-to-face classes as a basis for the return of educational activities. The restrictive measures to face the COVID-19 pandemic, greatly affected the normality of the development of activities carried out in higher education, constituting a great challenge since the measures and conditions of return and access to institutions are different from what was previously carried out before the pandemic. The objective of this article is to carry out a simulation of queues through SIMIO (Logistics process simulation software), where the number of students expected per day and the average entry times are taken into account to adjust them to an exponential distribution, from this information loaded into the simulation it is They will determine if the times increase or decrease taking into account the theory of queues and conditions presented in the study such as the probability distribution of the times of each of the processes involved in the process of accessing the University facilities.

Automatically generated algorithms for the vertex coloring problem.

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics.