Search results for "76"

showing 10 items of 2584 documents

"Table 15" of "Measurement of the inclusive jet cross-section in p anti-p collisions at s**(1/2) =1.96-TeV"

1970

Detailed systematic errors for the absolute value of the jet rapidity in the range 0.4 to 0.8: (1st) Non-Gaussian tails (2nd) Zero-suppression (3rd) Resolution (4th) eta-intercalibration fit (5th) JES MPF bias (6th) JES MPF bias.

DSYSInclusive20477612047761PBAR P --> JET XJet Production
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"Table 10" of "Measurement of the inclusive jet cross-section in p anti-p collisions at s**(1/2) =1.96-TeV"

1970

Detailed systematic errors for the absolute value of the jet rapidity in the range 0.0 to 0.4: (1st) Non-Gaussian tails (2nd) Zero-suppression (3rd) Resolution (4th) eta-intercalibration fit (5th) JES MPF bias (6th) JES MPF bias.

DSYSInclusive20477612047761PBAR P --> JET XJet Production
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"Table 36" of "Measurement of the inclusive jet cross-section in p anti-p collisions at s**(1/2) =1.96-TeV"

1970

Detailed systematic errors for the absolute value of the jet rapidity in the range 2.0 to 2.4: (1st) Rapidity unfolding (2nd) Trigger matching (3rd) Dijet response fit (4th) Dijet response fit (5th) Trigger matching (6th) CC response fit.

DSYSInclusive20477612047761PBAR P --> JET XJet Production
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"Table 31" of "Measurement of the inclusive jet cross-section in p anti-p collisions at s**(1/2) =1.96-TeV"

1970

Detailed systematic errors for the absolute value of the jet rapidity in the range 1.6 to 2.0: (1st) Rapidity unfolding (2nd) Trigger matching (3rd) Dijet response fit (4th) Dijet response fit (5th) Trigger matching (6th) CC response fit.

DSYSInclusive20477612047761PBAR P --> JET XJet Production
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"Table 30" of "Measurement of the inclusive jet cross-section in p anti-p collisions at s**(1/2) =1.96-TeV"

1970

Detailed systematic errors for the absolute value of the jet rapidity in the range 1.6 to 2.0: (1st) Non-Gaussian tails (2nd) Zero-suppression (3rd) Resolution (4th) eta-intercalibration fit (5th) JES MPF bias (6th) JES MPF bias.

DSYSInclusive20477612047761PBAR P --> JET XJet Production
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Juliana Colomer, Desirée: Fiesta y urbanismo: Valencia en los siglos XVI y XVII

2020

Daniel 353 355UNESCO::HISTORIADesirée: Fiesta y urbanismo: Valencia en los siglos XVI y XVII Muñoz Navarro0210-9093 553 Estudis: Revista de historia moderna 561078 2020 46 7649205 Juliana ColomerFiesta y urbanismo: Valencia en los siglos XVI y XVII Muñoz Navarro [Desirée]:HISTORIA [UNESCO]Revista de historia moderna 561078 2020 46 7649205 Juliana Colomer [0210-9093 553 Estudis]
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Proclama de nuestro amado monarca Fernando VII á los españoles de ambos hemisferios, con motivo de su entrada en esta Capital, y con relación a los g…

Amb tít. epígrafe

Daoiz y Torres Luis 1767-1808 lemacVelarde Pedro 1779-1808 lemac
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"Table 4" of "Measurement of dielectron production in central Pb-Pb collisions at $\sqrt{{\textit{s}}_{\mathrm{NN}}}$ = 2.76 TeV"

2018

Data-to-cocktail ratio as a function of the invariant-mass. In the hadronic cocktail, random correlations of dielectrons from charm decays are assumed to simulate the effects of the interaction between charm quarks and the medium. The statistical and systematic uncertainties of data are represented by vertical bars and boxes.

Data-to-cocktail ratio (random charm correlations)2760.0High Energy Physics::PhenomenologyHigh Energy Physics::ExperimentPB PB --> E+ E- XNuclear ExperimentDATA/COCKTAIL
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El management (de sí y del cuerpo) en dispositivos de la gubernamentalidad neoliberal

2020

DavidNemesiaMaría inésUNESCO::SOCIOLOGÍAHijós1137-7038 8537 Arxius de sociologia 562372 2020 42 7674028 El management (de sí y del cuerpo) en dispositivos de la gubernamentalidad neoliberal LandaMuñoz RodríguezAna Lúcia 7 17:SOCIOLOGÍA [UNESCO]de Castro
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Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

2018

Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how “Big Data enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrh…

Decision support systemProcess (engineering)Computer scienceBig datacomputer.software_genre01 natural sciencesClinical decision support systemlcsh:QA75.5-76.9503 medical and health sciences0302 clinical medicinebig datalcsh:AZ20-999Health care030212 general & internal medicine0101 mathematicsdata analyticsdata integrationImplementationbusiness.industry010102 general mathematicslearning health care cyclelcsh:History of scholarship and learning. The humanitiesData scienceData warehousedata warehouseslcsh:Electronic computers. Computer sciencebusinesscomputerData integrationFrontiers in Digital Humanities
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