Abstract
The introduction of eco-routing systems has been suggested as a promising strategy to reduce carbon dioxide emissions and criteria pollutants. The objective of this study is to scrutinize the impacts of an eco-routing guidance system on emissions through the use of a case study in a commuting corridor. This research aims at assessing the potential environmental benefits in terms of different pollutant emissions. Simultaneously, it addresses the extent of variations in system travel time (STT) that each eco-routing strategy implies. The methodology consists of three distinct phases. The first phase corresponds to the adjustment of a microsimulation platform of traffic and emissions with empirical data previously collected. Second, to volume-emission-functions (VEF), developed based on the integrated modeling structure. Final, to different scenarios of traffic flow optimization performed at the network level based on a simplified assignment procedure. The results show that if the traffic assignment is performed with the objective to minimize overall impacts, then the total system environmental damage costs can be reduced up to 9% with marginal oscillations in total STT. However, if drivers are advised based on their own emissions minimization, total system emissions may be higher than under the standard user equilibrium flow pattern. Specifically, environmentally friendly navigation algorithms focused on individual goals may tend to divert traffic to roads with less capacity affecting the performance of the remaining traffic. This case study brings new insights about the difficulties and potentials of implementing such systems.

Abstract
Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes. © 2018 The Author(s).

Abstract

Abstract
Data stream is a challenging research topic in which data can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, for example, a concept drift. A concept drift occurs when the concepts associated with a dataset change when new data arrive. This paper proposes a new method based on k-Nearest Neighbors that implements a sliding window requiring less instances stored for training than existing methods. For such, a clustering approach is used to summarize data by placing labeled instances considered similar in the same cluster. Besides, instances close to the uncertainty border of existing classes are also stored, in a sliding window, to adapt the model to concept drift. The proposed method is experimentally compared with state-of-the-art classifiers from the data stream literature, regarding accuracy and processing time. According to the experimental results, the proposed method has better accuracy and less time consumption when fewer information about the concepts are stored in a single sliding window.

Abstract
Maintaining the quality control of scientific literature is one of the main characteristics of the peer review process. However, it depends on the peers' effectiveness in minimizing the intrinsic subjectivity to the process. Publishers try to achieve this through training and guides for reviewers. However, there is no consensus as to what the main criteria for a good review are, which results in poorly reasoned or vague reports that do not assist the editor in his decision nor the author in improvement of research. This project proposes a quality model for reviewing articles and a framework for their automatic classification through machine learning techniques. This proposal will be useful for: i) reviewers as a guideline for the preparation of the review report, editors as an indicator of the quality of the received revisions, and the authors as a model for self-evaluation of their research.

Abstract
This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino - Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions.