0000000000641582

AUTHOR

Paulo B. Lourenço

showing 4 related works from this author

Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks

2021

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, …

Male0304 Medicinal and Biomolecular Chemistry 0601 Biochemistry and Cell Biology 1103 Clinical SciencesBiochemistry & Molecular BiologyGreeceModels GeneticThrombomodulinCOVID-19Complement System ProteinsCell BiologyMiddle AgedPolymorphism Single NucleotideHospitalizationSettore ICAR/09 - Tecnica Delle CostruzioniIntensive Care UnitsComplement Factor HHumansMolecular MedicineFemaleNeural Networks ComputerMorbidityartificial intelligence complement complement inhibition COVID-19 genetic susceptibility SARS-CoV2Complement ActivationJournal of Cellular and Molecular Medicine
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Mapping and holistic design of natural hydraulic lime mortars

2020

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cemconres.2020.106167.

Design0211 other engineering and technologies02 engineering and technologyengineering.materialCompatibilityFlexural strengthEngenharia e Tecnologia::Engenharia CivilConsistency (statistics)021105 building & constructionGeneral Materials ScienceGeotechnical engineeringMathematicsScience & TechnologyAggregate (composite)Artificial neural networksMonument protectionHydraulic limeExperimental dataBuilding and Construction021001 nanoscience & nanotechnologyCompressive strengthCompatibility (mechanics):Engenharia Civil [Engenharia e Tecnologia]engineeringNatural hydraulic limeMortar0210 nano-technologyMortar characteristicsCement and Concrete Research
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Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

2022

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has …

Settore ICAR/09 - Tecnica Delle CostruzioniGeneral Materials ScienceBuilding and ConstructionArtificial neural networks Compressive strength Concrete Machine learning Metakaolin Mix designCivil and Structural Engineering
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Masonry Compressive Strength Prediction Using Artificial Neural Networks

2019

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of m…

Computer science0211 other engineering and technologiesSocial SciencesCompressive strength020101 civil engineering02 engineering and technology0201 civil engineeringEngenharia e Tecnologia::Engenharia CivilBack-Propagation Neural Networks (BPNNs)11. Sustainability021105 building & constructionMasonryArtificial Neural Networks (ANNs)Science & TechnologyArtificial neural networkbusiness.industryMasonry unitArts & HumanitiesStructural engineeringMasonryMortarSettore ICAR/09 - Tecnica Delle CostruzioniNonlinear systemSoft-computing techniquesCompressive strengthBuilding materialsBuilding materialMortarbusiness
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