Results 181 to 190 of about 14,298 (230)

Investigation of Lacosamide solubility in supercritical carbon dioxide with machine learning models. [PDF]

Sci Rep
Esfandiari N.
europepmc   +1 more source

Predicting the Lifespan of Twisted String Actuators Using Empirical and Hybrid Machine Learning Approaches. [PDF]

Sensors (Basel)
Nguyen H, Eang C, Lee S.
europepmc   +1 more source

Ecosystem carbon use efficiency at global scale from upscaling eddy-covariance data with machine learning and MODIS products. [PDF]

Sci Rep
Campos-Taberner M, Gilabert MA, Sánchez-Ruiz S, Martínez B, Jiménez-Guisado A, García-Haro FJ.   +5 more
europepmc   +1 more source

Leveraging current steering and the biophysics of spike generation for cellular-resolution electrical stimulation of neurons. [PDF]

Cell Rep
Vasireddy PK, Vilkhu RS, Lotlikar A, Brown JB, Phillips AJ, Gogliettino AR, Hays MR, Baum C, Kato EJ, Sharon A, Hottowy P, Sher A, Litke AM, Mitra S, Shah NP, Chichilnisky EJ.   +15 more
europepmc   +1 more source

Optimal Sensor Placement in Buildings: Stationary Excitation. [PDF]

Sensors (Basel)
Ghahari F, Swensen D, Haddadi H.
europepmc   +1 more source

PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.

Ecol Inform
Kovács DD, De Clerck E, Verrelst J.
europepmc   +1 more source

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Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey

Astrophysics and Space Science, 2020
This study aims to predict daily ionospheric Total Electron Content (TEC) using Gaussian Process Regression (GPR) model and Multiple Linear Regression (MLR). In this case, daily TEC values from 2015 to 2017 of two Global Navigation Satellite System (GNSS) stations were collected in Turkey.
Samed Inyurt, AliIhsan Sekertekin
exaly   +2 more sources

On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach

Journal of Energy Storage, 2021
Abstract To characterize the performance of nanofluids for heat transfer applications in solar systems, an accurate estimation of their specific heat capacity (SHC) is of paramount importance. To this end, having such properties of nanofluids via computational approaches has gained attention as an effective method to eliminate the time-consuming ...
Mehdi Jamei, Iman Ahmadianfar, Ismail Adewale Olumegbon   +2 more
exaly   +2 more sources