ECMWF short-term prediction accuracy improvement by deep learning.

This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.

Bupivacaine hydrochloride complexation with some alpha- and beta-cyclodextrins studied by potentiometry with membrane electrodes.

Membrane electrodes selective to bupivacaine cations were developed and those with PVC-dibutylphthalate membrane containing sparingly soluble bupivacaine phosphotungstate appeared to be the most suitable. Inclusion complexation of bupivacaine cations with cyclodextrins was studied by potentiometric measurements of the free bupivacaine cation concentration in aqueous solutions of bupivacaine hydrochloride with cyclodextrin using the prepared electrodes. Native alpha-cyclodextrin (alpha-CD) and beta-cyclodextrin (beta-CD), as well as their random-substituted derivatives hydroxypropyl-alpha-cyclodextrin (HP-alpha-CD), hydroxypropyl-beta-cyclodextrin (HP-beta-CD) and methyl-beta-cyclodextrin (M-beta-CD), were chosen for the study.