Abstract
In this paper, Lagrangian dual formulation is used to solve the Environmental/Economic Dispatch problem. The proposed method, that results quite different from the metaheuristic methods employed in literature, was tested on a six generating units system. The results obtained improve others reported in previous investigations, by simultaneously diminishing the total fuel cost and pollutants emissions.

Abstract
<p>Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Wind Task on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users.<br>Collaboration is open to IEA Wind member states, 12 countries are already therein.</p><p>The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks for NWP models. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.</p><p>The main result is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, and Designing and executing forecasting benchmarks and trials, and their Evaluation) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls which we have seen over the years, are avoided. <br><br>Other results include a paper on possible uses of uncertainty forecasts, an assessment of the uncertainty chain within the forecasts, and meteorological data on an information portal for wind power forecasting. This meteorological data is used for a benchmark exercise, to be announced at the conference. The poster will present the latest developments from the Task, and announce the next activities.</p>

Abstract
This paper presents a method capable of detecting and segmenting pulmonary nodules in clinical computed tomography images, using UNet convolutional neural network powered by The Lung Image Database Consortium image collection - LIDC-IDRI, that in the training process was submitted to different training tests, where for each of them, their hyper-parameters were modified so that the results could be collected from different media, getting quite satisfactory results in the segmentation task, highlighting the areas of interest almost perfectly, resulting in 91.61% on the IoU (Intersection over Union) metric.

Abstract
The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

Abstract
The last few decades have witnessed a significant evolution of technology in different domains, changing the way the world operates, which leads to an overwhelming amount of data generated in an open-ended way as streams. Over the past years, we observed the development of several machine learning algorithms to process big data streams. However, the accuracy of these algorithms is very sensitive to their hyper-parameters, which requires expertise and extensive trials to tune. Another relevant aspect is the high-dimensionality of data, which can causes degradation to computational performance. To cope with these issues, this paper proposes a stream k-nearest neighbors (kNN) algorithm that applies an internal dimension reduction to the stream in order to reduce the resource usage and uses an automatic monitoring system that tunes dynamically the configuration of the kNN algorithm and the output dimension size with big data streams. Experiments over a wide range of datasets show that the predictive and computational performances of the kNN algorithm are improved.

Abstract
This paper reports on recent activities by the Portugal Chapter of IEEE Education Society. It begins with a reflection on the work of a Chapter and on several aspects that may impact its performance. The major activities are also presented, as well as the policies developed by the Portugal Chapter in the last year, concluding with the presentation of the two awards received at the last FIE, namely the Chapter Achievement Award and the Distinguished Chapter Leadership Award.