Track My Ride

Cycling has become more and more popular as a healthy activity and a transport option across many countries. This is not different in Ireland, a country where in April 2016, 56,837 people cycled to work; an increase of 43% since 2011 (Central statistics office, 2017). Irish Government “committed to developing cycling as one of the most desirable modes of travel by 2020” as it plays an indispensable role in people’s lives (Sustainable transport division - department of transport, tourism and sport, 2009). The “Balance” team managed to visualize that a strong cycling culture was becoming important in Ireland. Hence our team was seeking to develop a mobile application called “Track my Ride” to contribute to the cycling community whilst by answering a crucial question: How can a cyclist manage and store its bike details? Answering that question, we have intended provide tools where the cyclists could discard common concerns such as: ● Is this second-hand bike reported as missing? ● Is there any bike parking space near a specific location? ● How can I warn people if my bike goes missing? Track My Ride is a bike management tool for bike-users, previous and/or future bike owners. Our main objective is to facilitate the way people manage and use their bikes, enhancing the cyclists experience while building an active online and collaborative cycling community

Early detection of weed in sugarcane using convolutional neural network

Weed infestation is an essential factor in sugarcane productivity loss. The use of remote sensing data in conjunction with Artificial Intelligence (AI) techniques, can lead the cultivation of sugarcane to a new level in terms of weed control. For this purpose, an algorithm based on Convolutional Neural Networks (CNN) was developed to detect, quantify, and map weeds in sugarcane areas located in the state of Alagoas, Brazil. Images of the PlanetScope satellite were subdivided, separated, trained in different scenarios, classified and georeferenced, producing a map with weed information included. Scenario one of the CNN training and test presented overall accuracy (0,983), and it was used to produce the final mapping of forest areas, sugarcane, and weed infestation. The quantitative analysis of the area (ha) infested by weed indicated a high probability of a negative impact on sugarcane productivity. It is recommended that the adequacy of CNN’s algorithm for Remotely Piloted Aircraft (RPA) images be carried out, aiming at the differentiation between weed species, as well as its application in the detection in areas with different culture crop

Enrichment of trace elements in the clay size fraction of mining soils

Reactive waste dumps with sulfide minerals pro- 14 mote acid mine drainage (AMD), which results in water and 15 soil contamination by metals and metalloids. In these systems, 16 contamination is regulated by many factors, such as mineral- 17 ogical composition of soil and the presence of sorption sites 18 on specific mineral phases. So, the present study dedicates 19 itself to understanding the distribution of trace elements in 20 different size fractions (<2-mm and <2-μm fractions) of min- 21 ing soils and to evaluate the relationship between chemical 22 and mineralogical composition. Cerdeirinha and Penedono, 23 located in Portugal, were the waste dumps under study. The 24 results revealed that the two waste dumps have high degree of 25 contamination by metals and arsenic and that these elements 26 are concentrated in the clay size fraction. Hence, the higher 27 degree of contamination by toxic elements, especially arsenic 28 in Penedono as well as the role of clay minerals, jarosite, and 29 goethite in retaining trace elements has management implica- 30 tions. Such information must be carefully thought in the reha- 31 bilitation projects to be planned for both waste dumps

XIPE: the X-ray Imaging Polarimetry Explorer

X-ray polarimetry, sometimes alone, and sometimes coupled to spectral and temporal variability measurements and to imaging, allows a wealth of physical phenomena in astrophysics to be studied. X-ray polarimetry investigates the acceleration process, for example, including those typical of magnetic reconnection in solar flares, but also emission in the strong magnetic fields of neutron stars and white dwarfs. It detects scattering in asymmetric structures such as accretion disks and columns, and in the so-called molecular torus and ionization cones. In addition, it allows fundamental physics in regimes of gravity and of magnetic field intensity not accessible to experiments on the Earth to be probed. Finally, models that describe fundamental interactions (e.g. quantum gravity and the extension of the Standard Model) can be tested. We describe in this paper the X-ray Imaging Polarimetry Explorer (XIPE), proposed in June 2012 to the first ESA call for a small mission with a launch in 2017 but not selected. XIPE is composed of two out of the three existing JET-X telescopes with two Gas Pixel Detectors (GPD) filled with a He-DME mixture at their focus and two additional GPDs filled with pressurized Ar-DME facing the sun. The Minimum Detectable Polarization is 14 % at 1 mCrab in 10E5 s (2-10 keV) and 0.6 % for an X10 class flare. The Half Energy Width, measured at PANTER X-ray test facility (MPE, Germany) with JET-X optics is 24 arcsec. XIPE takes advantage of a low-earth equatorial orbit with Malindi as down-link station and of a Mission Operation Center (MOC) at INPE (Brazil).Comment: 49 pages, 14 figures, 6 tables. Paper published in Experimental Astronomy http://link.springer.com/journal/1068

Building a National IoT Plan: Policy Recommendations and the Case of Brazil

The Internet of Things (“IoT”) is an expression that refers to a whole set of new services and devices that includes at least three fundamental aspects: connectivity, use of sensors or actuators, and computational capacity for data processing and storage. The Internet of Things goes beyond connecting objects to each other; it also gives them the power to process data (thereby making them "smart"). For developing countries such as Brazil, the opportunities offered by the Internet of Things can compensate for shortcomings in infrastructure and services, and can improve innovation, quality of life, productivity, and even the economic complexity of our basket of export products. However, the way in which each country will seize this opportunity will depend on its specific aspirations and strategies. The broader economic, social, political, and legal context of the country should be considered, as well as the local development of information and communication technologies. For this reason, the National Bank for Economic and Social Development (BNDES), in partnership with the Ministry of Science, Technology, Innovation and Communications (MCTIC), has commissioned this study, "Internet of Things: An Action Plan for Brazil." This study, mapped by a consortium comprised by McKinsey & Company, the CPqD Foundation, and Pereira Neto | Macedo Law Firm, outlines the local technological and economic challenges related to the topic, as well as well as how to address legal issues inherent to the development of IoT in Brazil