Abstract
The main purpose of this work is to study the biomechanical behaviour of a peripheral nerve tissue when submitted to mechanical stretching forces. Nerve injury reduces life quality and has a strong impact on the national productivity, so it is essential to study the biomechanical properties of peripheral nerves to improve the repair and regeneration procedure. The study was conducted on a 2D and a 3D model of a sciatic nerve bifurcation in the lower thigh. These models were inspired on a real nerve bifurcation and have been developed using several computational tools. It was used the finite element method (FEM), Radial Point Interpolation Method (RPIM) and Neighbour Radial Point Interpolation Method (NNRPIM) to perform the analyses in FEMAS (R). The results of the analysis with the three different methods are very similar, the main stress is observed always in the same region in both 2D and 3D models and the displacement results for the selected points in the two models are concordant. The results obtained by the three different analysis methods are very similar which not only allow to conclude that these methods are appropriate numerical tools to analyse the biomechanical behaviour of peripheral nerve tissue but also confirms the robustness of the used methods.

Abstract
Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Despite the outburst of recent scientific advances, there is no totally effective treatment, especially when diagnosed in an advanced stage. Screening tests, such as cytology or colposcopy, have been responsible for a substantial decrease in cervical cancer deaths. Cervical cancer automatic screening via Pap smear is a highly valuable cell imaging-based detection tool, where cells must be classified as being within one of a multitude of ordinal classes, ranging from abnormal to normal. Current approaches to ordinal inference for neural networks are found to not sufficiently take advantage of the ordinal problem or to be too uncompromising. A non-parametric ordinal loss for neuronal networks is proposed that promotes the output probabilities to follow a unimodal distribution. This is done by imposing a set of different constraints over all pairs of consecutive labels which allows for a more flexible decision boundary relative to approaches from the literature. Our proposed loss is contrasted against other methods from the literature by using a plethora of deep architectures. A first conclusion is the benefit of using non-parametric ordinal losses against parametric losses in cervical cancer risk prediction. Additionally, the proposed loss is found to be the top-performer in several cases. The best performing model scores an accuracy of 75.6% for seven classes and 81.3% for four classes.

Abstract
Cervical cancer ranks as the fourth most common cancer among females worldwide with roughly 528,000 new cases yearly. Significant progress in the realm of artificial intel-ligence particularly in neural networks and deep learning help physicians to diagnose cervical cancer more accurately. In this paper, we address a classification problem with the widely used VGG16 architecture. In addition to classification error, our model considers a regularization part during tuning of the weights, acting as prior knowledge of the colposcopic image. This embedded regularization approach. using a 2D Gaussian kernel, has enabled the model to learn which sections of the medical images are more crucial for the classification task. The experimental results show an improvement compared with standard transfer learning and multimodal approaches of cervical cancer classification in literature.

Abstract
Medical image quality assessment plays an important role not only in the design and manufacturing processes of image acquisition but also in the optimization of decision support systems. This work introduces a new deep ordinal learning approach for focus assessment in whole slide images. From the blurred image to the focused image there is an ordinal progression that contains relevant knowledge for more robust learning of the models. With this new method, it is possible to infer quality without losing ordinal information about focus since instead of using the nominal cross-entropy loss for training, ordinal losses were used. Our proposed model is contrasted against other state-of-the-art methods present in the literature. A first conclusion is a benefit of using data-driven methods instead of knowledge-based methods. Additionally, the proposed model is found to be the top-performer in several metrics. The best performing model scores an accuracy of 94.4% for a 12 classes classification problem in the FocusPath database.

Abstract
Ordinal classification tasks are present in a large number of different domains. However, common losses for deep neural networks, such as cross-entropy, do not properly weight the relative ordering between classes. For that reason, many losses have been proposed in the literature, which model the output probabilities as following a unimodal distribution. This manuscript reviews many of these losses on three different datasets and suggests a potential improvement that focuses the unimodal constraint on the neighborhood around the true class, allowing for a more flexible distribution, aptly called quasi-unimodal loss. For this purpose, two constraints are proposed: A first constraint concerns the relative order of the top-three probabilities, and a second constraint ensures that the remaining output probabilities are not higher than the top three. Therefore, gradient descent focuses on improving the decision boundary around the true class in detriment to the more distant classes. The proposed loss is found to be competitive in several cases.

Abstract
Pollutant emissions from passenger cars give rise to harmful effects on human health and the environment. Predicting traffic flow is a challenging problem, but essential to understand what factors influence car traffic and what measures should be taken to reduce carbon dioxide emissions. In this work, we developed a predictive model to forecast traffic flow in several locations in the city of Porto for 24 h later, i.e., the next day at the same time. We trained a XGBoost Regressor with multi-modal data from 2018 and 2019 obtained from traffic and weather sensors of the city of Porto and the geographic location of several points of interest. The proposed model achieved a mean absolute error, mean square error, Spearman's rank correlation coefficient, and Pearson correlation coefficient equal to 80.59, 65395, 0.9162, and 0.7816, respectively, when tested on the test set. The developed model makes it possible to analyse which areas of the city of Porto will have more traffic the next day and take measures to optimise this increasing flow of cars. One of the ideas present in the literature is to develop intelligent traffic lights that change their timers according to the expected traffic in the area. This system could help decrease the levels of carbon dioxide emitted and therefore decrease its harmful effects on the health of the population and the environment.