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Publications

2020

Evaluation of Lightweight Convolutional Neural Networks for Real-Time Electrical Assets Detection

Authors
Barbosa, J; Dias, A; Almeida, J; Silva, E;

Publication
Advances in Intelligent Systems and Computing

Abstract
The big growth of electrical demand by the countries required larger and more complex power systems, which have led to a greater need for monitoring and maintenance of these systems. To overcome this problem, UAVs equipped with appropriated sensors have emerged, allowing the reduction of the costs and risks when compared with traditional methods. The development of UAVs together with the great advance of the deep learning technologies, more precisely in the detection of objects, allowed to increase the level of automation in the process of inspection. This work presents an electrical assets monitoring system for detection of insulators and structures (poles and pylons) from images captured through a UAV. The proposed detection system is based on lightweight Convolutional Neural Networks and it is able to run on a portable device, aiming for a low cost, accurate and modular system, capable of running in real time. © 2020, Springer Nature Switzerland AG.

2020

Usability and Sense of Presence in Virtual Worlds for Distance Education: A Case Study with Virtual Reality Experts

Authors
Krassmann, AL; Rocha Mazzuco, AEd; Melo, M; Bessa, M; Bercht, M;

Publication
Proceedings of the 12th International Conference on Computer Supported Education

Abstract

2020

AS METODOLOGIAS ATIVAS COMO ESTRATÉGIAS PARA O DESENVOLVIMENTO DE COMPETÊNCIAS DE INTELIGÊNCIA EMOCIONAL NOS ESTUDANTES DO ENSINO SUPERIOR

Authors
Sá, S; Morais, J; Almeida, F;

Publication
New Trends in Qualitative Research - Investigação Qualitativa em Educação: avanços e desafios

Abstract

2020

A Head Mouse Alternative Solution Proposal for People with Motor Impairments: Design and Usability Assessment Study

Authors
Zengin, HA; Reis, A; Barroso, J; Rocha, T;

Publication
HCI International 2020 – Late Breaking Papers: Universal Access and Inclusive Design - Lecture Notes in Computer Science

Abstract

2020

Photocatalytic degradation of an agro-industrial wastewater model compound using a UV LEDs system: kinetic study

Authors
Ferreira, LC; Fernandes, JR; Rodriguez Chueca, J; Peres, JA; Lucas, MS; Tavares, PB;

Publication
Journal of Environmental Management

Abstract
An ultraviolet light emitting diode (UV-A LED) system was built to test the capability of performing heterogeneous photocatalysis using TiO2 P25. The LEDs maximum wavelength is 365 nm with an irradiance power of 85 W m-2. The device was tested in batch and continuous (CSTR) mode in a laboratorial scale reactor. The degradation of an agro-industrial wastewater model compound (p-hydroxybenzoic acid, pHBA) was investigated, assessing the effect of different experimental conditions such as pH, pHBA and TiO2 concentration keeping constant the UV-A LEDs power and temperature. The photodegradation of different concentrations of pHBA with [TiO2] = 500 mg L-1, IUV = 85 W m-2 and a T = 21 °C were analysed by pseudo-first order kinetics. The results were applied to the Langmuir-Hinshelwood model yielding kc = 0.885 mg L-1 min-1 and kLH = 0.217 L mg-1. In a comparative experiment the UV-A LEDs system showed faster kinetics (k = 0.0134 min-1) than solar radiation (IUV = 23 W m-2; k = 0.0077 min-1), with [pHBA] = 75 mg L-1 and [TiO2] = 500 mg L-1. The values of the Electric Energy per Order (EEO) = 115 kWh m-3 order-1 and the Specific Applied Energy (ESAE) = 318 kWh mol-1 order-1 were obtained with [TiO2] = 1000 mg L-1 and [pHBA] = 50 mg L-1. Analogous results were obtained ([TiO2] = 500 mg L-1) in a CSTR with a slight decrease in the first order kinetic constant due to the “non-ideal” reactor: from 0.0284 to 0.0158 min-1 and from 0.0143 to 0.00825 min-1 with [pHBA] = 50 mg L-1 and 75 mg L-1, respectively. This work shows that photocatalytic reactors with UV-A LEDs can advantageously replace conventional UV mercury lamps based reactors in the photodegradation of phenolic compounds. © 2020 Elsevier Ltd

2020

Pan-cancer analysis of whole genomes

Authors
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium,;

Publication
Nature

Abstract

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