Abstract
A patient's health literacy has a direct impact on their health, but more than a third of the USA population has "basic" or "below basic" levels of health literacy. An individual's wellbeing is also affected by the communication with their physician, as the use of technical terminology may hinder the patient's understanding. A patient's ability to, later on, recall or retrieve helpful information could reduce these comprehension problems and this can be improved by a good management of personal health information. To help overcome some of these problems, we created HealthTalks, a mobile app that empowers the patients, easing their daily health tasks and self-care ability. It does so by recording the audio of a medical appointment, transcribing its dialogue, giving more information about medical concepts employed, and allowing information associated with medical appointments to be easily managed by the patient. Usability tests were conducted with elderly people, ranging from the icons used to the general user experience. Results were very positive, with users accomplishing most tasks successfully and often with the least amount of clicks. We also evaluated the speech recognition software used, Google Cloud Speech API, reaching an error rate of 12 percent in medical texts.

Abstract
The importance of research data management is widely recognized. Dendro is an ontology-based platform that allows researchers to describe datasets using generic and domain-specific descriptors from ontologies. Selecting or building the right ontologies for each research domain or group requires meetings between curators and researchers in order to capture the main concepts of their research. Envisioning a tool to assist curators through the automatic extraction of key concepts from research documents, we propose 2 concept extraction methods and compare them with a term extraction method. To compare the three approaches, we use as ground truth an ontology previously created by human curators.

Abstract
This paper reports the participation of the InfoLab at the TREC Precision Medicine Track 2018. InfoLab is an informal group that brings together researchers with interest in the information area and is located at Faculty of Engineering of University of Porto. The experiments made in this participation include query expansion approaches for the disease and gene concepts. The expansion of the disease terms was done using Unified Medical Language System (UMLS). UMLS is a repository that provides the mapping between a large number of vocabularies. The gene terms were expanded using Ensembl. Ensembl provides a genome browser that maps genes to their synonyms. An additional layer was developed on top of Terrier to provide the execution of a large batch of experiments. Multiple runs were evaluated in order to measure the influence of each expansion approach. © 2018 Copyright held by the owner/author(s).

Abstract
The University of Trás-os-Montes e Alto Douro (UTAD), in an effort to streamline processes and reduce bureaucracy, decided to develop and use an in-house document management system to handle processes. However, this practice created additional needs such as the actual digital signing of documents associated with the institution business and administrative processes. This paper explores a solution proposal to this problem, documenting what are its functionalities and how it works. An initial application of the developed solution is also described and analyzed in order to demonstrate the overall adequacy of the proposed artefact and its overall impact to the institution administrative operations. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
This paper reports on results for the well-known resource-constrained project scheduling problem. A branch-and-bound procedure is developed that takes into account all best performing components from literature, varying branching schemes and search strategies, using the best performing dominance rules and assembling these components into a unified search algorithm. A composite lower bound strategy that statically and dynamically selects the best performing bounds from literature is used to find optimal solutions within reasonable times. An extensive computational experiment is set up to determine the best combination of the various components used in the procedure, in order to benchmark the current existing knowledge on four different datasets from the literature. By varying the network topology, resource scarceness and the size of the projects, the computational experiments are carried out on a diverse set of projects. The procedure was able to find some new lower bounds and optimal solutions for the PSPLIB instances. Moreover, new best known results are reported for other, more diverse datasets that can be used in future research studies. The experiments revealed that even project instances with 30 activities cannot be solved to optimality when the topological structure is varied.

Abstract
In a paper written by by Vanhoucke et al. (2016), an overview of artificial and empirical project databases has been given for integrated project management and control. These databases are collections of the most wellknown and widespread data instances available in literature for the construction of a baseline schedule, the analysis of schedule risk or the use for project control. The current paper serves as a follow-up study to further elaborate on the use of these data instances, and to give researchers an incentive to use these datasets for their research on the development and validation of new algorithms for project scheduling. Therefore, unlike the general focus of the previous paper on baseline scheduling, schedule risk analysis and project control, the focus on the current paper is restricted to resource-constrained project scheduling. The intention of this follow-up overview is fourfold. First and foremost, a procedure is proposed to facilitate the reporting of best known solutions for the well-known single- and multi-mode resource-constrained project scheduling problem to minimize the project makespan. Secondly, the paper reports our best known solutions we obtained so far, and reflects on the network and resource parameters that increase the project complexity. In doing so, areas to focus on for future research are detected, and an attempt to define hard problem instances is given. Thirdly, a new dataset is presented for the resource-constrained project scheduling problem that is much more diverse in both the network topology and resource scarceness and will enable the future researcher to develop algorithms to solve a wider range of project problems. Finally, the paper also adds some links to tutorials and other relevant information to stimulate researchers to download the data and update best known solutions once available.