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Publications

2021

Evaluating the Quality of an Online Course in Information Literacy Applied to Engineering Students

Authors
Oliveira Ramos, T; Morais, C; Ribeiro, C;

Publication
Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning - Advances in Mobile and Distance Learning

Abstract
An academic library created an online course in information literacy skills in 2007 for engineering students. This chapter reports the evaluation of the course's effectiveness in developing those skills. In the academic year 2015/2016, a case study with a mixed-methods approach was applied to 5th-year students (N=91) enrolled in a course unit for Master Dissertation's preparation in the informatics and computing engineering programme. Students showed high confidence in their information literacy skills. Online assignments' performance was good, but activities revealed quality issues. Performance in the course unit's assignments reveals a poor application of acquired skills. But satisfaction is high: students value independent learning and online access to resources and content. Despite evidence of some positive impact, the course lacks effectiveness due to issues in the course unit's assignments. Needed improvements include a better realignment with students' needs and a redesign with an instructional model to assure the promotion of students' success.

2021

A green lateral collaborative problem under different transportation strategies and profit allocation methods

Authors
Joa, M; Martins, S; Amorim, P; Almada Lobo, B;

Publication
JOURNAL OF CLEANER PRODUCTION

Abstract
Collaboration between companies in transportation problems seeks to reduce empty running of vehicles and to increase the use of vehicles' capacity. Motivated by a case study in the food supply chain, this paper examines a lateral collaboration between a leading retailer (LR), a third party logistics provider (3 PL) and different producers. Three collaborative strategies may be implemented simultaneously, namely pickup-delivery, collection and cross-docking. The collaborative pickup-delivery allows an entity to serve customers of another in the backhaul trips of the vehicles. The collaborative collection allows loads to be picked up at the producers in the backhauling routes of the LR and the 3 PL, instead of the traditional outsourcing. The collaborative cross-docking allows the producers to cross-dock their cargo at the depot of another entity, which is then consolidated and shipped with other loads, either in linehaul or backhaul routes. The collaborative problem is formulated with three different objective functions: minimizing total operational costs, minimizing total fuel consumption and minimizing operational and CO2 emissions costs. The synergy value of collaborative solutions is assessed in terms of costs and environmental impact. Three proportional allocation methods from the literature are used to distribute the collaborative gains among the entities, and their limitations and capabilities to attend fairness criteria are analyzed. Collaboration is able to reduce the global fuel consumption in 26% and the global operational costs in 28%, independently of the objective function used to model the problem. The collaborative pickup-delivery strategy outperforms the other two in the majority of instances under different objectives and parameter settings. The collaborative collection is favoured when the ordering loads from producers increase. The collaborative cross-docking tends to be implemented when the producers are located close to the depot of the 3 PL.

2021

Crowdsourced Data Stream Mining for Tourism Recommendation

Authors
Leal, F; Veloso, B; Malheiro, B; Burguillo, JC;

Publication
Advances in Intelligent Systems and Computing - Trends and Applications in Information Systems and Technologies

Abstract

2021

Crowdsourcing Urban Narratives for a Post-Pandemic World

Authors
Chaves, R; Schneider, DS; Motta, C; Correia, A; Paredes, H; Caetano, BP; de Souza, JM;

Publication
24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021, Dalian, China, May 5-7, 2021

Abstract

2021

Deep learning for drug response prediction in cancer

Authors
Baptista, D; Ferreira, PG; Rocha, M;

Publication
Briefings in Bioinformatics

Abstract
Abstract Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:mrocha@di.uminho.pt

2021

Experimental investigation of a strain gauge sensor based on Fiber Bragg Grating for diameter measurement

Authors
Cardoso, VHR; Caldas, P; Giraldi, MTR; Frazao, O; de Carvalho, CJR; Costa, JCWA; Santos, JL;

Publication
Optical Fiber Technology

Abstract
A strain gauge sensor based on Fiber Bragg Grating (FBG) for diameter measurement is proposed and experimentally demonstrated. The sensor is easily fabricated inserting the FBG on the strain gauge—it was fabricated using a 3D printer—and fixing the FBG in two points of this structure. The idea is to vary the diameter of the structure. We developed two experimental setups, the first one is used to evaluate the response of the FBG to strain and the second one to assess the possibility of using the structure developed to monitor the desired parameter. The results demonstrated that the structure can be used as a way to monitor the diameter variation in some applications. The sensor presented a sensitivity of 0.5361 nm/mm and a good linear response of 0.9976 using the Strain Gauge with FBG and fused taper. © 2020 Elsevier Inc.

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