Results 161 to 170 of about 165,948 (320)
Numerical Values of Some Integrals involving Bessel Functions [PDF]
D. L. George
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A Gaussian Process Regression IV Model for PV Outdoor Data
Each electrical performance measure of PV modules needs to be considered in the context of dynamically changing outdoor conditions. In this paper, we introduce a statistical model based on Gaussian process regressions designed to analyze PV outdoor data aiming to utilize all available information.
Timon S. Vaas+4 more
wiley +1 more source
Unbiased calculation, evaluation, and calibration of ensemble forecast anomalies
Standard methods for calculating ensemble forecast anomalies result in statistical inconsistencies between forecast and verification anomalies, even if the underlying forecasts are perfectly reliable. An unbiased evaluation of anomaly‐based ensemble forecasts must account for differences in climatological sampling uncertainty between forecasts and ...
Christopher D. Roberts+1 more
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On constructing distribution functions: With applications to Lommel polynomials and Bessel functions [PDF]
Daniel P. Mäki
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High Ionic Conduction in Rb‐ and Cs‐Mixed Cation Amide for Energy Storage
A mixed cation‐amide solid solution, Rb0.5Cs0.5NH2, based on the interaction between RbNH2‐ and CsNH2, has been developed. The Rb⁺/Cs⁺ cationic substitution leads to structural stabilization, resulting in high ionic conductivity in the Rb0.5Cs0.5NH2 compared to the single compounds.
Thi Thu Le+11 more
wiley +1 more source
Radiation of piezoelectric rings into the air through cylindrical waveguides
In the paper the results of the investigations of the radiation of piezoelectric rings into the air through solid (e.g. glass, plexiglass, aluminium) cylinders are presented. The obtained radiation patterns approximate Bessel distribution. In the case of
W. PAJEWSKI+2 more
doaj
Calculation of the modified Bessel functions of the second kind with complex argument [PDF]
Fr. Mechel
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On the developments in Bessel’s functions [PDF]
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Self‐Driving Microscopes: AI Meets Super‐Resolution Microscopy
This review examines the use of machine learning to automate super‐resolution optical microscopy, enabling the microscope to autonomously make decisions on what, when, and how to image. By eliminating the need for human intervention, this approach has the potential to enhance the versatility and accessibility of super‐resolution microscopy.
Edward N. Ward+3 more
wiley +1 more source
Bessel functions of integral order and complex argument [PDF]
Marion C. Gray
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