Spatio-temporal Kriging for spatial irradiance estimation with short-term forecasting in a thermosolar power plant.

This article proposes a method to improve the efficiency of solar power plants by estimating and forecasting the spatial distribution of direct normal irradiance (DNI) using a sensor network and anemometer data. For this purpose, the proposed approach employs spatio-temporal kriging with an anisotropic spatio-temporal variogram that depends on wind speed to accurately estimate the distribution of DNI in real-time, making it useful for short-term forecast and nowcast of DNI. Finally, the method is validated using synthetic data from varying sky conditions, outperforming another state-of-the-art technique.

Observation of the decay.

Using proton-proton collision data corresponding to an integrated luminosity of collected by the CMS experiment at , the decay is observed for the first time, with a statistical significance exceeding 5 standard deviations. The relative branching fraction, with respect to the decay, is measured to be , where the first uncertainty is statistical, the second is systematic, and the third is related to the uncertainties in and .

Constrained numerical deconvolution using orthogonal polynomials.

In this article, we present an enhanced version of Cutler's deconvolution method to address the limitations of the original algorithm in estimating realistic input and output parameters. Cutler's method, based on orthogonal polynomials, suffers from unconstrained solutions, leading to the lack of realism in the deconvolved signals in some applications. Our proposed approach incorporates constraints using a ridge factor and Lagrangian multipliers in an iterative fashion, maintaining Cutler's iterative projection-based nature.