0000000000225087

AUTHOR

Maria G. Douvika

showing 4 related works from this author

Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks

2019

The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use…

Artificial neural networkComputer science0211 other engineering and technologiesMechanical engineering02 engineering and technologyengineering.materiallcsh:Technologylcsh:ChemistrySoft computing technique0202 electrical engineering electronic engineering information engineeringGeneral Materials Sciencesoft computing techniquesInstrumentationlcsh:QH301-705.5021101 geological & geomatics engineeringFluid Flow and Transfer ProcessesArtificial neural networklcsh:TProcess Chemistry and Technologyartificial intelligence techniquesGeneral EngineeringArtificial intelligence techniqueTribologyTribological performancelcsh:QC1-999Computer Science Applicationslcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Tool steelengineering020201 artificial intelligence & image processinglcsh:Engineering (General). Civil engineering (General)artificial neural networkslcsh:PhysicsTribometerHardening (computing)
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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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A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon

2020

This article belongs to the special issue: Soft computing techniques in materials science and engineering Summarization: The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of dai…

medicine.medical_specialtyOperations researchComputer scienceHeuristic (computer science)media_common.quotation_subjectPopulationPhenomenonEpidemiologyPandemicmedicineQuality (business)educationmedia_commoneducation.field_of_studyGaussian-functionSARS-CoV-2Public healthCOVID-19Pandemic trendComputer Science ApplicationsHeuristic modelAlgorithmModeling and SimulationRisk assessmentPredictionSoftware
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A Novel Heuristic Global Algorithm to Predict the COVID-19 Pandemic Trend

2020

SummaryMathematical models are useful tools to predict the course of an epidemic. A heuristic global Gaussian-function-based algorithm for predicting the COVID-19 pandemic trend is proposed for estimating how the temporal evolution of the pandemic develops by predicting daily COVID-19 deaths, for up to 10 days, starting with the day the prediction is made. The validity of the proposed heuristic global algorithm was tested in the case of China (at different temporal stages of the pandemic). The algorithm was used to obtain predictions in six different locations: California, New York, Iran, Sweden, the United Kingdom, and the entire United States, and in all cases the prediction was confirmed…

Coronavirus disease 2019 (COVID-19)Mathematical modelComputer scienceHeuristicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicAlgorithm
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