Abstract
Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a rare and disabling neurological disorder caused by, a mutation of the transthyretin gene. One of the disease's characteristics that mostly affects patients' quality of life is its influence on locomotion, with a variable evolution timing. Quantitative motion analysis is useful for assessing motor function, including gait, in diseases affecting movement. However, it is still an evolving field, especially in TTR-FAP, with only a few available studies. A single markerless RGB-D camera pros ides 3-D body joint data in a less expensive, more portable and less intrusive way than reference multi-camera marker-based systems for motion capture. In this contribution, we investigate if a gait analysis system based on a RGB-D camera can be used to detect gait changes over time for a given TTR-FAP patient. 3-D data provided by that system and a reference system were acquired from six TTR-FAP patients, while performing a simple gait task, once and then a year and a half later. For each gait cycle and system, several gait parameters were computed. For each patient, we investigated if the RBG-D camera system is able to detect the existence or not of statistically significant differences between the two different acquisitions (separated by 1.5 years of disease evolution), in a similar way to the reference system. The obtained results show the potential of using a single RGB-D camera to detect relevant changes in spatiotemporal gait parameters (e.g., stride duration and stride length), during TTR-FAP patient follow-up.

Abstract
The concept of health indicates physical, mental and social well-being. Psychological stress is commonly present among freshmen due to social and environmental changes. An approach to study the impact of stress on students relied on biological data assessment. In this work, electrocardiogram signals from first year students, from the Biomedical Engineering course, were collected during an oral presentation, acquiring the RR time series. Linear and nonlinear methodologies are used to extract features that best characterize the RR time series in young subjects under stress conditions.

Abstract

Abstract
The purpose of this work is to address the imageCLEF 2019 coral challenge - to develop a system for the detection and identification of substrates in coral images. Initially a revision of the 13 classes was carried out by identifying a number of sub-classes for some substrates. Four features were considered - 3 related to greyscale intensity (1) and texture (2), and 1 related to the colour content. The Breiman's Random forest algorithm was used to classify the corals in one of 13 classes defined. A classification accuracy of about 49% was obtained.

Abstract
Convolutional Neural Networks (CNN) have become the gold standard in many visual recognition tasks including medical applications. Due to their high variance, however, these models are prone to over-fit the data they are trained on. To mitigate this problem, one of the most common strategies, is to perform data augmentation. Rotation, scaling and translation are common operations. In this work we propose an alternative method to rotation-based data augmentation where the rotation transformation is performed inside the CNN architecture. In each training batch the weights of all convolutional layers are rotated by the same random angle. We validate our proposed method empirically showing its usefulness under different scenarios.