Reckien, D, Heidrich O, Church J, Pietrapertos F, De Gregorio-Hurtado S, D'Alonzo V, Foley A, Simoes SG, Lorencová EK, Orruk H, Orrum K, Wejs A, Flacke J, Olazabal M, Geneletti D, Feliu E, Vasilier S, Nador C, Krook-Riekkola A, Matosović M, A. Fokaides P, I. Ioannou B, Flamos A, Spyridaki N.
2018.
How are cities planning to respond to climate change? Assessment of local climate plans from 885 cities in the EU-28 Journal of Cleaner Production. doi: 10.1016/j.jclepro.2018.03.220. 191:207-219.
Cidade, MT, Ramos DJ, Santos J, Calero N, Muñoz J, Borges JP.
2018.
Injectable hydrogels based on pluronic/water systems filled with alginate microparticles: Rheological characterization. Publisher Logo Conference Proceedings. 1981:020091.
AbstractIn this paper the rheological characterization of Pluronic/water systems filled with alginate microparticles is presented. The rheological characterization of the Pluronic/water systems allowed for the choice of the best Pluronic concentration taking into account its applications as injectable hydrogels for tissue repair. The effect on the rheological behavior of the addition of alginate microparticles, to be loaded with the drug, was analyzed and the maximum concentration of microparticles determined.
Palma, S, Traguedo AP, Porteira AR, Frias MJ, Gamboa H, Roque ACA.
2018.
Machine learning for the meta-analyses of microbial pathogens’ volatile signatures. Scientific Reports. 8:3360.
AbstractNon-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62–100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86–90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.
Gouveia, JP, Seixas J, Long G.
2018.
Mining households' energy data to disclose fuel poverty: Lessons for Southern Europe. Journal of Cleaner Production. 178:534-550.
AbstractFuel poverty is a recognized and increasing problem in several European countries. A growing body of literature covers this topic, but dedicated analysis for Portugal are scarce despite the high perception of this condition. This paper contributes to fill this knowledge gap focusing on a European southern city while bringing new datasets and analysis to the assessment of this topic; consumer groups identification and to policy discussion. Daily electricity smart meters' registries were combined with socio-economic data, collected from door-to-door surveys, to understand the extent and the determinants of energy consumption for two contrasting consumer groups (herein called fuel poverty and fuel obesity groups). The analysis is based on the amount and annual profile of electricity consumption and was complemented with building energy simulations for relevant building typologies in those groups, to identify heating and cooling thermal performance gaps. The existence of these gaps allowed confirming and/or discarding the initial hypothesis of the poverty or obesity conditions. Results disclose socio-economic variables, as income, and consumers' behavior as key determinants of electricity consumption. It was identified a severe lack of thermal comfort levels inside households of both groups, either in cooling (98% for fuel poverty and 87% for fuel obesity) and heating seasons (98% for fuel poverty and 94% for fuel obesity). Major conclusion refers that electricity consumption cannot be used alone to segment consumer groups. This assessment may serve to support energy policy measures and instruments targeted to different consumers' groups. For example, distinct campaigns and differentiated incentives may apply to achieve energy efficiency and reduction while keep or improve indoor comfort levels.
Beira, JM, Silva MP, Condesso M, Cosme P, Almeida PL, Corvo M, Sebastião PJ, Figueirinhas JL, de Pinho MN.
2018.
Molecular order and dynamics of water in hybrid cellulose acetate–silica asymmetric membranes. Molecular Physics. :1–8.: Taylor & Francis
AbstractIn this work 2H NMR spectroscopy and 1H NMR relaxometry and diffusometry were used to characterise water order and dynamics in cellulose acetate/silica asymmetric membranes. Two hydrated membranes were characterised allowing the identification of extra ordering of the water molecules and the presence in each membrane of up to two spectral components with different degrees of order and different T1 values. The mechanism behind this order increase was ascribed to the rapid exchange of the water molecules between the pore walls and its interior. T1 relaxometry dispersions allowed for the identification of the relevant mechanisms of pore-confined water motion, with rotations mediated by translational displacements (RMTD) as the dominant mechanism in the low frequency region. Using the RMTD low cut off frequency along with the in situ directly measured diffusion constant it was possible do determine characteristic lengths of correlated water motion in both membranes studied, which fall in ranges compatible with typical pore dimensions in similar membranes.
Lopez, A, Bacelar R, Pires I, G.Santos T, PedroSousa J, Quintino L.
2018.
Non-destructive testing application of radiography and ultrasound for wire and arc additive manufacturing. Additive Manufacturing. 21:298-306.
AbstractThe present work addressed the challenges of identifying applicable Non-Destructive Testing (NDT) techniques suitable for inspection and materials characterization techniques for Wire and Arc Additive Manufacturing (WAAM) parts. With the view of transferring WAAM to the industry and qualifying the manufacturing process for applications such as structural components, the quality of the produced parts needs to be assured. Thus, the main objective of this paper is to review the main NDT techniques and assess the capability of detecting WAAM defects, for inspection either in a monitoring, in-process or post-process scenario. Radiography and ultrasonic testing were experimentally tested on reference specimens in order to compare the techniques capabilities. Metallographic, hardness and electrical conductivity analysis were also applied to the same specimens for material characterization. Experimental outcomes prove that typical WAAM defects can be detected by the referred techniques. The electrical conductivity measurement may complement or substitute some destructive methods used in AM processing.
Almeida, T.
2018.
O vidro enquanto material e o vidro enquanto arte. Specularis - looking through. (
Teresa Almeida, Machado, Graciela, Eds.)., Porto: i2ADS - Instituto de Investigação em Arte, Design e Sociedade Faculdade de Belas Artes da Universidade do Porto
Seixas, J, Simoes SG, Fortes P, Gouveia J.
2018.
The pivotal role of electricity in the deep decarbonization of energy system: cost-effective options for Portugal. Limiting Global Warming to Well Below 2°C: Energy System Modelling and Policy Development. (
Giannakidis G., K. Karlsson, M. Labriet, B. Ó Gallachóir, Eds.).: Springer, Lecture Notes in Energy 64. Springer International publishing, Doi: 10.1007/978-3-319-74424-7
Barbosa, AJM, Oliveira AR, Roque ACA.
2018.
Protein- and Peptide-Based Biosensors in Artificial Olfaction. Trends in Biotechnology. 36(12):1244-1258.
AbstractAnimals’ olfactory systems rely on proteins, olfactory receptors (ORs) and
odorant-binding proteins (OBPs), as their native sensing units to detect odours.
Recent advances demonstrate that these proteins can also be employed as
molecular recognition units in gas-phase biosensors. In addition, the interactions
between odorant molecules and ORs or OBPs are a source of inspiration
for designing peptides with tunable odorant selectivity. We review recent
progress in gas biosensors employing biological units (ORs, OBPs, and peptides)
in light of future developments in artificial olfaction, emphasizing examples
where biological components have been employed to detect gas-phase
analytes.