Search results for "Prediction."

showing 10 items of 490 documents

Atmospheric and astrophysical neutrinos above 1 TeV interacting in IceCube

2015

The IceCube Neutrino Observatory was designed primarily to search for high-energy (TeV--PeV) neutrinos produced in distant astrophysical objects. A search for $\gtrsim 100$~TeV neutrinos interacting inside the instrumented volume has recently provided evidence for an isotropic flux of such neutrinos. At lower energies, IceCube collects large numbers of neutrinos from the weak decays of mesons in cosmic-ray air showers. Here we present the results of a search for neutrino interactions inside IceCube's instrumented volume between 1~TeV and 1~PeV in 641 days of data taken from 2010--2012, lowering the energy threshold for neutrinos from the southern sky below 10 TeV for the first time, far bel…

HIGH-ENERGY NEUTRINOSNuclear and High Energy PhysicsParticle physicsAMANDAMesonSolar neutrinoAstrophysics::High Energy Astrophysical PhenomenaINDUCED CASCADESFOS: Physical sciencesCosmic rayAstrophysicsFLUX PREDICTIONS01 natural sciencesIceCube Neutrino ObservatoryIceCubeObservatorySEARCH0103 physical sciencesddc:530Blazar010303 astronomy & astrophysicsHigh Energy Astrophysical Phenomena (astro-ph.HE)Physics010308 nuclear & particles physicsHigh Energy Physics::PhenomenologyAstrophysics::Instrumentation and Methods for AstrophysicsASTRONOMYPERFORMANCEBLAZARSPROMPT LEPTONSGAMMA-RAYPhysics and AstronomyHigh Energy Physics::ExperimentNeutrino astronomyNeutrinoAstrophysics - High Energy Astrophysical PhenomenaphysicsPhysical Review D
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Discussion of “Soil Water Retention Characteristics of Vertisols and Pedotransfer Functions Based on Nearest Neighbor and Neural Networks Approaches …

2013

HYDRAULIC PROPERTIESArtificial neural networkPREDICTIONSWRCSoil scienceSoil Water Retention Curve Soil Shrinkage Characteristic CurveVertisolHYDRAULIC PROPERTIES; SHRINKAGE; PREDICTION; SWRC; ANNAgricultural and Biological Sciences (miscellaneous)k-nearest neighbors algorithmPedotransfer functionSoil waterSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSHRINKAGEANNWater Science and TechnologyCivil and Structural EngineeringMathematics
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Nutrition Knowledge Is Associated with Energy Availability and Carbohydrate Intake in Young Female Cross-Country Skiers

2021

The aim of this study was to provide information on energy availability (EA), macronutrient intake, nutritional periodization practices, and nutrition knowledge in young female cross-country skiers. A total of 19 skiers filled in weighted food and training logs before and during a training camp. Nutrition knowledge was assessed via a validated questionnaire. EA was optimal in 11% of athletes at home (mean 33.7 ± 9.6 kcal·kgFFM−1·d−1) and in 42% at camp (mean 40.3 ± 17.3 kcal·kgFFM−1·d−1). Most athletes (74%) failed to meet recommendations for carbohydrate intake at home (mean 5.0 ± 1.2 g·kg−1·d−1) and 63% failed to do so at camp (mean 7.1 ± 1.6 g·kg−1·d−1). The lower threshold of the pre-ex…

Health Knowledge Attitudes PracticeAdolescentPREDICTIONPHYSIOLOGICAL CAPACITYmacronutrientCONSENSUS STATEMENTtalviurheiluravitsemussuosituksetArticlehiihtäjätravitsemuskäyttäytyminenEatingPERIODIZATIONSkiingkestävyyslajitSurveys and QuestionnairesDietary CarbohydratesHumansravintoaineetTX341-641ExerciseNutrition. Foods and food supplyWORLDSNutritional Requirementswinter sportMUSCLESports Nutritional Physiological Phenomenasports nutritionAthletesendurance athleteperiodized nutritionFemaleproteiinitDietary Proteins3143 NutritionhiilihydraatitEnergy IntakeproteinNutritive ValueEXPENDITUREurheilijatSports
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A machine learning approach to determine airport asphalt concrete layer moduli using heavy weight deflectometer data

2021

An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing p…

Heavy weight deflectometerComputer scienceMaintenanceRunwayGeography Planning and DevelopmentTJ807-830Management Monitoring Policy and LawStiffness modulusTD194-195Machine learningcomputer.software_genreRenewable energy sourcesMachine learningPerformance predictionGE1-350Layer (object-oriented design)Environmental effects of industries and plantsArtificial neural networkRenewable Energy Sustainability and the Environmentbusiness.industryFeed forwardPavement managementBuilding and ConstructionBackpropagationEnvironmental sciencesAsphalt concreteShallow neural networkHeavy weight deflectometer; Machine learning; Maintenance; Runway; Shallow neural network; Stiffness modulusRunwayArtificial intelligencebusinesscomputer
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Determination of the Chiral Couplings L10 and C87 from Semileptonic τ Decays

2008

Using recent precise hadronic tau-decay data on the V-A spectral function, and general properties of QCD such as analyticity, the operator product expansion and chiral perturbation theory, we get accurate values for the QCD chiral order parameters L_10^r(M_rho) and C_87^r(M_rho). These two low-energy constants appear at order p^4 and p^6, respectively, in the chiral perturbation theory expansion of the V-A correlator. At order p^4 we obtain L_10^r(M_rho) = -(5.22\pm 0.06)10^{-3}. Including in the analysis the two-loop (order p^6) contributions, we get L_10^r(M_rho) = -(4.06\pm 0.39)10^{-3} and C_87^r(M_rho) = (4.89\pm 0.19)10^{-3}GeV^{-2}. In the SU(2) chiral effective theory, the correspon…

High Energy Physics - Lattice (hep-lat)Spectral functionsFOS: Physical sciencesFísicaPerturbation theoryLow-energy constantsHigh Energy Physics - PhenomenologyHigh Energy Physics - Phenomenology (hep-ph)High Energy Physics - LatticeStrange quark massQCD predictionsHigh Energy Physics::ExperimentUs-vertical-barHadronic width
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Measurement of Event Shape and Inclusive Distributions at $\sqrt{s} =$ 130 and 136 GeV

1997

Inclusive charged particle and event shape distributions are measured using 321 hadronic events collected with the DELPHI experiment at LEP at effective centre of mass energies of 130 to 136 GeV. These distributions are presented and compared to data at lower energies, in particular to the precise Z data. Fragmentation models describe the observed changes of the distributions well. The energy dependence of the means of the event shape variables can also be described using second order QCD plus power terms. A method independent of fragmentation model corrections is used to determine $\alpha_s$ from the energy dependence of the mean thrust and heavy jet mass. It is measured to be: % %\alpha_s…

High energyParticle physicsZ(0) RESONANCEPhysics and Astronomy (miscellaneous)Electron–positron annihilationHADRONIC Z-DECAYS; E+E-ANNIHILATION; ALPHA-S; POWER CORRECTIONS; Z(0) RESONANCE; MONTE-CARLO; QCD MODELS; ENERGY; FRAGMENTATION; PREDICTIONSHadronPREDICTIONSThrust01 natural sciences7. Clean energyPartícules (Física nuclear)Nuclear physicsENERGYFragmentation (mass spectrometry)POWER CORRECTIONSMONTE-CARLO0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]ALPHA-S010306 general physicsDetectors de radiacióDELPHIPhysicsQuantum chromodynamics010308 nuclear & particles physicsHigh Energy Physics::PhenomenologyE+E-ANNIHILATIONLARGE ELECTRON POSITRON COLLIDERCharged particleHADRONIC Z-DECAYSLarge Electron–Positron ColliderPARTICLE PHYSICS; LARGE ELECTRON POSITRON COLLIDER; DELPHIQCD MODELSPARTICLE PHYSICSHigh Energy Physics::ExperimentFRAGMENTATIONParticle Physics - Experiment
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Centrality and rapidity dependence of inclusive pion and prompt photon production in p+Pb collisions at the LHC with EPS09s nPDFs

2014

The centrality dependencies of the inclusive neutral pion and prompt photon nuclear modification factors for p+Pb collisions at the LHC are studied using a spatially dependent set of nuclear PDFs, EPS09s. The calculations are performed at mid- and forward rapidities searching for an observable which would optimally probe the spatial dependence of the nuclear PDFs. In addition, we discuss to which $x$ values of the nucleus the different observables are sensitive.

HistoryParticle physicsPhotonNuclear TheoryNuclear TheorynPDFsFOS: Physical sciencesinclusive pion114 Physical sciences01 natural sciencesnuclear parton distribution fucntionsEducationNuclear Theory (nucl-th)High Energy Physics - Phenomenology (hep-ph)Pion0103 physical sciencesmedicineRapiditySpatial dependenceNuclear Experiment010306 general physicsPhysicsLarge Hadron Collider010308 nuclear & particles physicsQCD PREDICTIONSphoton productionObservableComputer Science ApplicationsHigh Energy Physics - Phenomenologymedicine.anatomical_structureCentralityTO-LEADING-ORDERNucleusJournal of Physics: Conference Series
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Predicting event soil loss from bare plots at two Italian sites

2013

Abstract Including runoff in USLE-type empirical models is expected to improve plot soil loss prediction at the event temporal scale and literature yields encouraging signs of the possibility to simply estimate runoff at these spatial and temporal scales. The objective of this paper was to develop an estimating procedure of event soil loss from bare plots (length = 11–44 m, slope steepness = 14.9–16.0%) at two Italian sites, i.e. Masse, in Umbria, and Sparacia, in Sicily, having a similar sand content (5–7%) but different silt (33% at Sparacia, 59% at Masse) and clay (62% and 34%, respectively) contents. A test of alternative erosivity indices for the Masse station showed that the best perf…

HydrologyEmpirical modellingSoil scienceSiltSoil water erosion Soil loss prediction Empirical models USLE-MUSLE-MMSoil lossEmpirical modelSoil loss predictionEmpirical modelsErosionSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliUSLE-MUSLE-MMEnvironmental scienceSoil water erosionTemporal scalesSurface runoffScale (map)Earth-Surface ProcessesEvent (probability theory)CATENA
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WEPP calibration for improved predictions of interrill erosion in semi-arid to arid environments

2013

Abstract Modeling soil erosion contributes to the understanding of the erosion processes and needs to predict accurately the erosion rates under different environmental conditions. Few studies have investigated the WEPP's applicability for arid and semi-arid conditions that differ from those where the model was developed. This research was carried out to evaluate and improve the WEPP model for arid and semiarid regions for interrill erosion using a rainfall simulator at plot scale. The results showed that measured interrill erosion rates ranged from 9.3 × 10− 6 to 89.6 × 10− 6 kg m− 2 s− 1. In comparison, the WEPP-interrill erosion prediction values were on average 14.5 times lower than the…

HydrologyErosion predictionScale (ratio)ErosionCalibrationSoil ScienceEnvironmental scienceWEPPAridNash–Sutcliffe model efficiency coefficientStream powerGeoderma
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Standard Vs Uniform Binary Search and Their Variants in Learned Static Indexing: The Case of the Searching on Sorted Data Benchmarking Software Platf…

2023

Learned Indexes are a novel approach to search in a sorted table. A model is used to predict an interval in which to search into and a Binary Search routine is used to finalize the search. They are quite effective. For the final stage, usually, the lower_bound routine of the Standard C++ library is used, although this is more of a natural choice rather than a requirement. However, recent studies, that do not use Machine Learning predictions, indicate that other implementations of Binary Search or variants, namely k-ary Search, are better suited to take advantage of the features offered by modern computer architectures. With the use of the Searching on Sorted Sets SOSD Learned Indexing bench…

I.2FOS: Computer and information sciencesComputer Science - Machine Learninglearned index structuresH.2Databases (cs.DB)search on sorted data platformComputer Science - Information RetrievalMachine Learning (cs.LG)E.1; I.2; H.2Computer Science - Databasesbinary search variantsComputer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)E.1algorithms with predictionSoftwareInformation Retrieval (cs.IR)
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