Abstract
The main focus of an Intelligent environment, as with other applications of Artificial Intelligence, is generally on the provision of good decisions towards the management of the environment or the support of human decision-making processes. The quality of the system is often measured in terms of accuracy or other performance metrics, calculated on labeled data. Other equally important aspects are usually disregarded, such as the ability to produce an intelligible explanation for the user of the environment. That is, asides from proposing an action, prediction, or decision, the system should also propose an explanation that would allow the user to understand the rationale behind the output. This is becoming increasingly important in a time in which algorithms gain increasing importance in our lives and start to take decisions that significantly impact them. So much so that the EU recently regulated on the issue of a “right to explanation”. In this paper we propose a Human-centric intelligent environment that takes into consideration the domain of the problem and the mental model of the Human expert, to provide intelligible explanations that can improve the efficiency and quality of the decision-making processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the "intelligent" Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells.

Abstract

Abstract
The major challenge in coordinating between fast-acting energy storage systems (FA-ESSs) and renewable energy sources (RESs) in the existing transmission grid is to determine the location and capacity of the FA-ESS in the power systems. The optimal allocation of FA-ESS with conventional hourly discrete time method (DTM) can result in the increased operation cost, non-optimal placements and larger storage capacity and therefore, having an opposite effect on the operation. Accordingly, in this paper, a continuous-time method (CTM) is proposed to coordinate FA-ESS and RESs to cover fast fluctuations of renewable generations (RGs). Besides, based on the CTM, an adaptive interval-based robust optimization framework, to deal with uncertainty of the RGs, has been proposed. The proposed optimal allocation of FA-ESS with CTM provides the best sitting and sizing for the installation of the FA-ESSs and the best possible continuous-time scheduling plan for FA-ESSs. Also, in other to have better implementations of their ramping capability to track the continuous-time changes and deviations of the RGs rather than hourly DTM. The proposed model has been implemented and evaluated on the IEEE Reliability Test System (IEEE-RTS).

Abstract
As transformações digitais têm provocado alterações na Educação, mas ainda não disseminadas. A compreensão da natureza, limites e potencialidades dessas transformações, exige um repensar das epistemologias e das teorias de aprendizagem.  Propomos um quadro de interpretação dessas alterações, que encara o ensinar e o aprender enquanto percursos que se coengendram num habitar e co-habitar cada vez mais atópico, em contextos híbridos e multimodais. Por meio desse quadro, é possível compreender a transformação digital na Educação enquanto deslocamento disruptivo num espaço-tempo de interações ecossistêmicas de inovação. Este quadro de interpretação nasce do cruzamento das contribuições de Di Felice sobre os movimentos epistemológico do processo de digitalização que nos leva a uma nova condição habitativa, pós-urbana e atópica, originando  epistemologias reticulares; e da perspectiva dos ecossistemas educacionais como sistemas vivos e cognitivos de Capra, Latour e Di Felice, onde se conectam diferentes ecologias, para além das humanas.

Abstract
This article describes tsmp, an R package that implements the MP concept for TS. The tsmp package is a toolkit that allows all-pairs similarity joins, motif, discords and chains discovery, semantic segmentation, etc. Here we describe how the tsmp package may be used by showing some of the use-cases from the original articles and evaluate the algorithm speed in the R environment. This package can be downloaded at https://CRAN.R-project.org/package=tsmp.