Abstract
The Regulation (EU) 2019/6 establishes that the veterinary prescriptions should follow a cascade, according to their availability of the market. In sum, the veterinarian is authorized to use a medicine for human use only if there is no product available for the same or other therapeutic indication, in the same or another animal species. This study aims to analyse the application of Regulation (EU) 2019/6 in the pharmacological prescription at the Veterinary Hospital of the University of León. A total of 121 clinical cases, 89 dogs (73.55%) and 32 cats (26.45%) were included. Results revealed that 95 medicines were prescribed, 51 (53.68 %) as veterinary medicines and 44 (46.32 %) as human medicines. From the human medicines, 22 (50.00%) did not have a veterinary alternative in the market; four (9.00%) presented a veterinary medicine in the appropriate formulation for the species; 10 (23.00%) had no alternative in the desired formulation; and 8 (18.00%) had no alternatives for the target species. This study suggested that the cascade was not strictly followed, and several reasons may justify it, such as the lack of veterinary products, different formulations, and differences in costs. An effective, safe and sustainable use of the therapeutic option available can only be accomplished with a rational use of the prescription cascade and a correct use of the Regulation (EU) 2019/6. © 2024 State University of Santa Catarina. All rights reserved.

Abstract
Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition. Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.

Abstract
In the modern world, making healthy food choices is increasingly important due to the rise in food-related illnesses. Existing tools, such as Nutri-Score and comprehensive food labels, often pose challenges for many consumers. This paper proposes an application that uses Optical Character Recognition (OCR) technologies to read and interpret food labels, thus upgrading current solutions that rely mainly on reading product barcodes. By using advanced optical character recognition and machine learning techniques, the system aims to accurately extract and analyze nutritional information directly from food packaging without relying on a database of pre-registered products. This innovative approach not only increases consumer awareness, but also supports personalized diet management for diseases such as diabetes and hypertension, while promoting healthier eating habits and better health outcomes. Two minimalist functional prototypes were developed as a result of this work: a desktop application and a mobile application.