Abstract
Glaucoma is a condition that affects the optic nerve, with loss of retinal nerve fibers, increased excavation of the optic nerve, and a progressive decrease in the visual field. It is the leading cause of irreversible blindness in the world. Manual classification of glaucoma is a complex and time-consuming process that requires assessing a variety of ocular features by experienced clinicians. Automated detection can assist the specialist in early diagnosis and effective treatment of glaucoma and prevent vision loss. This study developed a deep learning model based on vision transformers, called ViT-BRSET, to detect patients with increased excavation of the optic nerve automatically. ViT-BRSET is a neural network architecture that is particularly effective for computer vision tasks. The results of this study were promising, with an accuracy of 0.94, an F1-score of 0.91, and a recall of 0.94. The model was trained on a new dataset called BRSET, which consists of 16,112 fundus images of patients with increased excavation of the optic nerve. The results of this study suggest that ViT-BRSET has the potential to improve early diagnosis through early detection of optic nerve excavation, one of the main signs of glaucomatous disease. ViT-BRSET can be used to mass-screen patients, identifying those who need further examination by a doctor.

Abstract
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones.

Abstract
Mammography imaging is the gold standard for breast cancer detection and involves capturing two projections: mediolateral oblique and craniocaudal projections. The implementation of an approach that allows the acquisition of only one projection and reconstructs the other could mitigate patient burden, minimize radiation exposure, and reduce costs. Image-to-image translation has showcased the ability to generate realistic synthetic images in different medical imaging modalities which make these techniques a great candidate for the novel application in mammography. This study aims to compare five image-to-image translation approaches to assess the feasibility of reconstructing a mammography projection from its counterpart. The results indicate that ResViT shows the best overall performance in translating between both projections.

Abstract
In the last few decades, the area of robotics has evolved immensely, creating new and improved robot mobility solutions for industrial, scientific, medical, and several other purposes. Among these solutions is the car-like robot, using wheels to move. However, there are many different options within this solution, different types of wheels and configurations on the robot that each offer key advantages for a variety of objectives. Choosing a wheel configuration for robot vehicles is extremely important for the robot’s mobility, and its purpose must be considered while studying all the options. Starting on an existing prototype with a differential configuration, other configurations were implemented to study their differences, their strong and weak points, and the trajectories they allow the robot to make. This analysis will make the choice of configuration for each scenario clearer. This paper presents three types of robot configurations and compares them according to requirements using real prototype robots that are shared with the community for many purposes, such as education, among others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
We model the financial markets as a game and make predictions using Markov chain estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/her risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then, we estimate the market's mixed probabilities by defining Markov chains and utilizing its transition matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. From this, we obtained a model for the financial markets that has a good performance in terms of accuracy and profitability.

Abstract
Faced with the current unsustainability and recognizing the importance of engineering (and technology) in the Capitalocene, it is important to develop educational approaches that facilitate the awareness and training of engineering students to the sustainable future's construction. The main objective of the study is the evaluation of the educational approach developed (educational board game). It was used an action-research methodology and a quasi-experimental method. These results show that the developed game can be an important contribution in the engineers training to change the role of engineering to an ethical and responsible construction of a sustainable and fair future.