Abstract
This paper describes the journey of a multinational and multidisciplinary team enrolled in the European Project Semester (EPS) at the Instituto Superior de Engenharia do Porto (ISEP) during the spring semester of 2019. The team embraced the idea of repurposing coffee leftovers to cultivate oyster mushrooms and benefited from the background diversity of the team members as well as from newly acquired marketing, sustainability and design ethics skills to consolidate and strengthen the overall feasibility of the project. The project was set to design, develop and test grey oyster mushroom growth kits with an automated monitoring system, using coffee grounds as growing substrate and complying with the applicable regulations and pre-defined requirements. The ulterior aims of the project were to reconnect people with the food they eat and to disseminate sustainable food production processes, which are not only healthy but environmentally friendly. To achieve these goals, the team developed a circular economy business model where grey oyster mushroom growth kits reuse coffee grounds as growing beds and food buckets as containers. The designed growth kits include a controlled fruiting chamber with an integrated monitoring system. This allows easy domestic cultivation, monitoring through a smart phone. Moreover, the proposed solution contemplates information sharing on the mushroom cultivation process, monitoring system and recipes as well as the maintenance of a dedicated discussion forum. Tests have been conducted to test the concept, cultivation process, monitoring system and fruiting chamber from the incubation of mycelium all the way to the harvesting. Results show the feasibility of creating a business based on the devised concept. © 2019 ACM.

Abstract
Together with the significant improvement in health and longevity came a number of health and economic concerns related to the demand for healthcare services and resources: changes in the patterns of health and illness, increasing amount and complexity of healthcare services demanded, rising health expenditures and uncertainty about whether there will be enough human, physical and financial resources to deliver the healthcare services needed. This paper aims to draw attention to the importance of planning the demand for healthcare in the aforementioned context, to create awareness of the need for a comprehensive study on the demand for healthcare services and resources and to propose an integrated approach for planning them, to inform managers and policy-makers on what can be the main challenges on assuring healthcare delivery in the future.

Abstract
Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases. However, these procedures typically only take into account the characteristics of each individual class. This segmented view of the data can have a negative impact. We propose a new method that uses an integrated view of the data classes to generate new examples and remove cases. ClUstered REsampling (CURE) is a method based on a holistic view of the data that uses hierarchical clustering and a new distance measure to guide the sampling procedure. Clusters generated in this way take into account the structure of the data. This enables CURE to avoid common mistakes made by other resampling methods. In particular, CURE prevents the generation of synthetic examples in dangerous regions and undersamples safe, non-borderline, regions of the majority class. We show the effectiveness of CURE in an extensive set of experiments with benchmark domains. We also show that CURE is a user-friendly method that does not require extensive fine-tuning of hyper-parameters. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network.

Abstract
Regional contribution to left ventricular (LV) ejection is of much clinical importance but its assessment is notably challenging. While deformation imaging is often used, this does not take into account loading conditions. Recently, a method for intraventricular pressure estimation was proposed, thus allowing for loading conditions to be taken into account in a non-invasive way. In this work, a method for 3D automatic myocardial performance mapping in echocardiography is proposed by performing 3D myocardial segmentation and tracking, thus giving access to local geometry and strain. This is then used to assess local LV stress-strain relationships which can be seen as a measure of local myocardial work. The proposed method was validated against F-18-fluorodeoxyglucose positron emission tomography, the reference method to clinically assess local metabolism. Averaged over all patients, the mean correlation between FDG-PET and the proposed method was 0.67 +/- 0.18. In conclusion, stress-strain loops were, for the first time, estimated from 3D echocardiography and correlated to the clinical gold standard for local metabolism, showing the future potential of real-time 3D echocardiography ( RT3DE) for the assessment of local metabolic activity of the heart.