Abstract

Abstract
The purpose of this communication is to contribute to the reflection around Archive Systems in the 21st century: professionals and Institutions in liquid times that, targeting a professional, cultural and social approach and a concern about the role of archives and archivists, stress the importance of not dissociating the theoretical and methodological dimension from the practical one. This poses starting challenges similar to the essay it adopts as a motto (Archives in liquid times), and ends up introducing the discussion around the need to clarify epistemological positions essential for an operationalization in increasingly complex and digital-dominated contexts. Having stated some of the questions raised by the theme focused, we make a brief retrospective around the records management/archives, the records-lifecycle and records-continuum models and the growing closeness to information concept. We also approach the valorisation of the dynamic and continuous info-communicational flow and the continuous interaction with the information producer/consumer, taking into account, from the outset, the rapid changes and flexibility required by the organizational challenges. A case study is presented with the description of the general features of a research focused on the Portuguese public university. It concludes with the presented proposals of an multidimensional analysis matrix and an intervention model - the MGSI-AP - within the framework of the trans and interdisciplinary paradigmatic change that has long been taking place in the scientific field of Information Science and, more specifically, in the transversal and applied studies area of Information Management, which impacts at theoretical, methodological and applied levels.

Abstract
In this paper, we explore the use of reference values (predictors) for the optimal objective function value of hard combinatorial optimization problems, instead of bounds, obtained by data mining techniques, and that may be used to assess the quality of heuristic solutions for the problem. With this purpose, we resort to the rectangular two-dimensional strip-packing problem (2D-SPP), which can be found in many industrial contexts. Mostly this problem is solved by heuristic methods, which provide good solutions. However, heuristic approaches do not guarantee optimality, and lower bounds are generally used to give information on the solution quality, in particular, the area lower bound. But this bound has a severe accuracy problem. Therefore, we propose a data mining-based framework capable of assessing the quality of heuristic solutions for the 2D-SPP. A regression model was fitted by comparing the strip height solutions obtained with the bottom-left-fill heuristic and 19 predictors provided by problem characteristics. Random forest was selected as the data mining technique with the best level of generalisation for the problem, and 30,000 problem instances were generated to represent different 2D-SPP variations found in real-world applications. Height predictions for new problem instances can be found in the regression model fitted. In the computational experimentation, we demonstrate that the data mining-based framework proposed is consistent, opening the doors for its application to finding predictions for other combinatorial optimisation problems, in particular, other cutting and packing problems. However, how to use a reference value instead of a bound, has still a large room for discussion and innovative ideas. Some directions for the use of reference values as a stopping criterion in search algorithms are also provided.

Abstract

Abstract
Endoscopic capsules are vitamin-sized devices that leverage from a small wireless camera to create 8 to 10 hour videos of the patients' entire digestive tract, still being the leading tool to diagnose small bowel diseases. The revision of the produced videos is a very time-consuming task, currently conducted manually and frame-by-frame by an expert. Since endoscopic videos usually contain a considerable amount of frames where the mucosa is not clearly visible, the segmentation of the informative regions is a vital component to reduce the necessary time to review each exam. In this work, a CNN encoder-decoder architecture is applied to segment informative regions in small bowel frames of videos of endoscopic capsules. The network was trained and tested with a dataset of 2,929 manually annotated images, achieving a 91.2% Dice coefficient and 83.9% IoU. Furthermore, a video-wise analysis based on the amount of informative pixels in each frame is done.

Abstract
Cancer is a serious disease that causes significant disability and suffering, so naturally Health Related Quality of Life (HRQoL) is a major concern of patients, families and clinicians. This paper intends to relate biometric indices, in terms of HRV metrics, with self-perceived HRQoL from patients with lymphoma. Patients (N = 12) answered FACT questionnaire and used a smartband that collected biometrical data in real-time along the chemotherapy treatment. Our results revealed that Physical Well-Being, Total, Lymphoma subscale and FACT-Lym Trial Outcome domains seem to have a similar pattern that HRV metrics across the treatment cycles. In specific, the FACT domains and the HRV metrics have the lowest average levels on the first cycle and seem to increase along the following cycles (3 rd and 6 th cycles). This approach of continuous assessment of HRQoL will enable a better accuracy and more supported clinical decision. © Springer Nature Switzerland AG 2019.