Results 111 to 120 of about 40,698 (136)

Sleep Reactivity Amplifies the Impact of Pre-Sleep Cognitive Arousal on Sleep Disturbances. [PDF]

J Sleep Res
Shaif NAS, Lim J, Reffi AN, Chee MWL, Massar SAA, Ong JL.   +5 more
europepmc   +1 more source

Investigation and response to rift valley fever outbreak in ruminant livestock from Ngoma District, eastern province of Rwanda, 2024. [PDF]

One Health
Niyonzima E, Nkurunziza F, Ngaboyimana JD, Nyirandahiriwe V, Mvuyekure F, Ndayisenga F, Uwituze S, Mugwaneza D, Iradukunda E, Uwitugabiye V, Murekatete C, Shyaka A.   +11 more
europepmc   +1 more source

Nonlinear Monte Carlo Methods with Polynomial Runtime for Bellman Equations of Discrete Time High-Dimensional Stochastic Optimal Control Problems. [PDF]

Appl Math Optim
Beck C, Jentzen A, Kleinberg K, Kruse T.
europepmc   +1 more source

The Answer is "No": A Comment on Peugh and Feldon (2020). [PDF]

CBE Life Sci Educ
Peugh JL, Feldon DF.
europepmc   +1 more source

The Hidden Variable in Radiological Accuracy: The Impact of Monitor Quality Under Real-Life Emergency Department Conditions. [PDF]

Tomography
Caglar B, Serin S.
europepmc   +1 more source

A blood-based DNA damage signature in patients with Parkinson's disease is associated with disease progression. [PDF]

Nat Aging
Sproviero D, Payán-Gómez C, Milanese C, Barnhoorn S, Sun S, Gyenis A, Delia D, Lashley T, Hoeijmakers JHJ, Vijg J, Mastroberardino PG.   +10 more
europepmc   +1 more source

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Multilevel monte carlo for cortical circuit models

Journal of Computational Neuroscience, 2022
Multilevel Monte Carlo (MLMC) methods aim to speed up computation of statistics from dynamical simulations. MLMC is easy to implement and is sometimes very effective, but its efficacy may depend on the underlying dynamics. We apply MLMC to networks of spiking neurons and assess its effectiveness on prototypical models of cortical circuitry under ...
Zhuo-Cheng Xiao, Kevin K. Lin
openaire   +3 more sources

Multilevel Monte Carlo by using the Halton sequence

Monte Carlo Methods and Applications, 2020
AbstractMonte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form.
Shady Ahmed Nagy, Mohamed A. El-Beltagy, Mohamed I. Wafa   +2 more
openaire   +2 more sources