Age-related macular degeneration (AMD) is one of the leading causes of vision loss in elderly adults around the world and is among the main visual impairments in Mexico. The difficulty of diagnosing AMD in its early stages motivates the use of advanced deep-learning methods that offer significant potential to improve diagnostic accuracy in retinal image analysis. In recent years, Transformer architectures for computer vision, such as Vision Transformer (ViT), Swin Transformer and BERT Pre-training of Image Transformers (BEiT) have provided a novel perspective for image analysis. This study presents a comparative analysis of these architectures, applied to AMD detection, focusing on each model's capability to classify the early stages of the disease. Although the small size of medical image datasets represented a challenge, our results suggest that ViT-based architectures and their derivatives achieve significant performance in AMD detection. BEiT is particularly notable for its consistently superior performance.

En ésta investigación, se empleó la metodología de dinámica de sistemas (Forrester, 1961), la cual fue idónea para determinar las relaciones causales que presentan los clústeres industriales como estructuras complejas. Estableciendo los factores que intervienen en la competitividad de una región para el desarrollo de éstos; factores basados en el dinamismo que presenta el diamante de Porter (1990) y los contextos: macroeconómico, político-legal y social. Por lo tanto, el diseño del estudio es de corte cualitativo, debido a la naturaleza de la metodología. En consecuencia, el resultado obtenido es un modelo causal, que presenta las relaciones formales de las variables que intervienen en la competitividad de una región para el desarrollo de un clúster industrial.

The pandemic caused by the COVID-19 virus has had negative effects on different industrial sectors, the world economy and especially on Supply Chains. The pandemic revealed the vulnerability of key logistics processes of companies and their Supply Chains as operational risks increased, causing breaks in inventories, production lines and the distribution process caused by increased customer demand. However, this pandemic makes it necessary to reevaluate the models and strategies that companies had established as the standard, and more was needed to face the shortage of components, raw materials and inputs needed to keep the production processes active. Therefore, this article proposes developing a conceptual model under the System Dynamics methodology, which allows identifying the main impacts that the COVID-19 pandemic had on the performance of the Supply Chain through the design of Causal Loop Diagrams.