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A complete analysis of the vascular system is a complex task since a large number of parameters are involved. In the research herein reported we present a novel medical framework called web-based integration for vascular expert research networks (Wivern) to be used in a multi-clinical department environment for the analysis of micro and macrocirculation. This tool can manage clinical information of several specialties, such as Neurology or Ophthalmology, and provides computer-aided tools to automatically analyze retinographies, carotid ultrasounds and blood pressure monitor signals, and to automatically compute cardiovascular risk stratification. Wivern is a web-based application with a user friendly interface that provides cross-platform compatibility and device independence. Several automated procedures are integrated within the framework, as a service on the web, to extract relevant parameters from clinical data, physiological signals and medical images. The application is planned for collecting and analyzing data in several clinical studies in different hospital centers to test their behavior and practical use of the different tools of the platform. The usefulness and validation of the system was achieved after the inclusion, by the different medical units, of 800 patients to analyze their hypertensive profile. Moreover, 800 retinal images were processed as well as 400 carotid were analyzed. Wivern provides a unique opportunity for vascular research since it enables an interdisciplinary and integrated study of the vascular network, allowing a more comprehensive evaluation of the consequences of any abnormality. The application also includes automated methods to process patient data in order to simplify the physician tasks.

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This paper provides an overview of the development of a selforiented solar mirror (SOSM) project within the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). While the main objective of the EPS@ISEP project-based educational framework is to foster teamwork, communication, interpersonal and problem solving skills in an international, multidisciplinary engineering environment, the goal of the SOSM is to track and reflect the Sun radiation onto a pre-defined area. In the spring of 2017 a group of five students chose to develop a proof-of-concept domestic SOSM called SUNO. The students undertook project supportive modules in Ethics, Sustainability, Marketing and Project Management together with project coaching meetings to assist the development of SUNO. The paper details this process, describing the initial project definition, the research of current technologies, the designing, the manufacturing and testing of the SUNO prototype, and discusses what the students gained from this learning experience.

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A comparison between co-training and self-training method for single-target regression based on multiples learners is performed. Data streaming systems can create a significant amount of unlabeled data which is caused by label assignment impossibility, high cost of labeling or labeling long duration tasks. In supervised learning, this data is wasted. In order to take advantaged from unlabeled data, semi-supervised approaches such as Co-training and Self-training have been created to benefit from input information that is contained in unlabeled data. However, these approaches have been applied to classification and batch training scenarios. Due to these facts, this paper presents a comparison between Co-training and Self-learning methods for single-target regression in data streams. Rules learning is used in this context since this methodology enables to explore the input information. The experimental evaluation consisted of a comparison between the real standard scenario where all unlabeled data is rejected and scenarios where unlabeled data is used to improve the regression model. Results show evidences of better performance in terms of error reduction and in high level of unlabeled examples in the stream. Despite this fact, the improvements are not expressive.

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