Search results for "0913 Mechanical Engineering"

showing 4 items of 4 documents

Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension

2016

We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term “crowd-averse.” Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For th…

0209 industrial biotechnologyStochastic stabilityMathematical optimizationCollective behaviorTechnologyComputer sciencePopulationcontrol designcrowd-averse robust mean-field games state space extension dynamic agents linear stochastic differential equation Brownian motion adversarial disturbance cost functional cross-coupling mean-field term collective behavior stock market application production engineering example dynamic demand management problem robust mean-field game approximation error stochastic stability microscopic dynamics macroscopic dynamicscontrol engineering02 engineering and technology01 natural sciencesStochastic differential equationoptimal control020901 industrial engineering & automationQuadratic equationAutomation & Control SystemsEngineeringClosed loop systemsSettore ING-INF/04 - AutomaticaApproximation errorRobustness (computer science)Control theory0102 Applied MathematicsState space0101 mathematicsElectrical and Electronic EngineeringeducationBrownian motioneducation.field_of_studyScience & TechnologyStochastic process010102 general mathematicsRelaxation (iterative method)Engineering Electrical & ElectronicOptimal controlComputer Science Applications0906 Electrical and Electronic EngineeringIndustrial Engineering & AutomationMean field theoryControl and Systems EngineeringSettore MAT/09 - Ricerca Operativa0913 Mechanical Engineering
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Alpha-amylase serum levels in professional soccer players are not related with physical fitness.

2017

Backgorund Recent evidence has showed that serum or salivary values of α-amylase predict endurance running performance. In this study we investigate whether serum α-amylase concentration may be associated with training status during a competitive season and after a detraining period in professional soccer players. Methods The study population consisted in 15 male professional soccer players from an Italian major league team (age [mean±SD] 27±5 years, weight 76.9±4.1 kg, height 1.82±0.05 m). Serum α-amylase levels were measured 3 times during the last part of a competitive season (January, March and May) and just before preseason training (July). Results Metabolic and cardiovascular fitness …

AdultMaleeducationPhysical fitnessPhysical Therapy Sports Therapy and RehabilitationAthletic PerformanceBody weightRunningYoung Adult0913 Mechanical Engineering 1106 Human Movement and Sports SciencesEndurance trainingSoccerMedicineHumansOrthopedics and Sports MedicineYoung adultCardiovascular fitnessbusiness.industryBody WeightAlpha-amylasefitnessItalyPhysical FitnessPhysical EndurancePopulation studyAlpha-amylase soccer fitnessalpha-Amylasesbusinesshuman activitiesSport SciencesBiomarkersDemographyThe Journal of sports medicine and physical fitness
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Training During the COVID-19 Lockdown: Knowledge, Beliefs, and Practices of 12,526 Athletes from 142 Countries and Six Continents

2021

Abstract Objective Our objective was to explore the training-related knowledge, beliefs, and practices of athletes and the influence of lockdowns in response to the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods Athletes (n = 12,526, comprising 13% world class, 21% international, 36% national, 24% state, and 6% recreational) completed an online survey that was available from 17 May to 5 July 2020 and explored their training behaviors (training knowledge, beliefs/attitudes, and practices), including specific questions on their training intensity, frequency, and session duration before and during lockdown (March–Jun…

PANDEMIASmedicine.medical_specialtySports medicine[SHS.EDU]Humanities and Social Sciences/EducationeducationPhysical Therapy Sports Therapy and RehabilitationCoachingInterval trainingIntensity Frequency Session durationAthletic training[SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO]medicineHumansPlyometricsharjoitteluOrthopedics and Sports MedicineOriginal Research ArticlevalmennusPandemicsGeneral fitness trainingbiologySARS-CoV-2business.industryAthleteskuntoliikuntaCOVID-19biology.organism_classificationMental healthC600AthletespoikkeusolotCommunicable Disease Control0913 Mechanical Engineering 1106 Human Movement and Sports Sciences 1302 Curriculum and PedagogyPhysical therapy[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologieAthletes/psychology; COVID-19; Communicable Disease Control; Humans; Pandemics; SARS-CoV-2businessSport Sciencesurheilijathuippu-urheilijat
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Benchmarking of strength models for unidirectional composites under longitudinal tension

2018

© 2018 Elsevier Ltd Several modelling approaches are available in the literature to predict longitudinal tensile failure of fibre-reinforced polymers. However, a systematic, blind and unbiased comparison between the predictions from the different models and against experimental data has never been performed. This paper presents a benchmarking exercise performed for three different models from the literature: (i) an analytical hierarchical scaling law for composite fibre bundles, (ii) direct numerical simulations of composite fibre bundles, and (iii) a multiscale finite-element simulation method. The results show that there are significant discrepancies between the predictions of the differe…

TechnologyMaterials scienceComposite numberMaterials Science02 engineering and technologyFiber-reinforced composite0901 Aerospace EngineeringEngineering0203 mechanical engineeringFragmentationUltimate tensile strengthMicro-mechanicsCOMPUTED-TOMOGRAPHYLOAD-TRANSFERComposite material0912 Materials EngineeringMaterialsStress concentrationEPOXY COMPOSITESTRESS-CONCENTRATIONSScience & TechnologyDAMAGE ACCUMULATIONTension (physics)FIBER-REINFORCED COMPOSITESPolymer-matrix compositesExperimental dataMicromechanics021001 nanoscience & nanotechnologyFinite element methodEngineering Manufacturing020303 mechanical engineering & transportsWIDE FAILURE EXERCISEMechanics of MaterialsMaterials Science CompositesHYBRID COMPOSITES[PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci]Ceramics and CompositesStrength0210 nano-technologyFINITE-ELEMENT0913 Mechanical Engineering
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