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.

Abstract
The fields of artificial intelligence, knowledge inference, data science, etc. have been deeply studied over time and many theoretical approaches have been developed, including its application to health and diseases inference. The creation of prototype and consumer systems has been restrained by the technology limitations on data acquisition and processing, which has been greatly overcome with the new sensors and mobile devices technologies. So, in this work we go through a literature review of the current state of the art on record to the usage of mobile technologies for diseases inference. The review methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The criteria were based on journal articles, prior to 2008, and using the defined keywords. A total of 14 selected articles were analyzed. A general conclusion was attained regarding the current state of maturity of the field, leading to fully functional consumer and professional market products.

Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs' capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants' health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

Abstract
The continuous spreading of the Internet-of-Things across application domains, aided by the continuous growth on the number of devices and systems that are Internet-connected, created both a rise in the complexity of these systems and made noticeable a lack of human resources with the expertise to design, develop and maintain them. Recent works try to mitigate these issues by creating solutions that abstract the complexity of the systems, such as using visual programming languages. Node-RED, as one of the most common solutions for the visual development IoT systems, stills has several limitations, such as the lack of observability and inadequate debugging mechanisms. In this work, we address some of these limitations by enhancing Node-RED with new features that improve the user's system development, debugging, and understanding tasks. We proceed to empirically evaluate the impact of these enhancements, concluding that, overall, such enhancements reduce the development time and the number of failed attempts to deploy the system.