Anti-CR1 monoclonal antibody, clone E11 [R-PE]

Featured Application GBCNet will soon operate in-field in the CAVIT s.c. vineyards, also integrated with additional technological supports such as low-cost spectrometers, for yield estimation and grape ripening prediction. We introduce here the Grape Berries Counting Net (GBCNet), a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting. We test GBCNet using cross-validation procedure on two original datasets CR1 and CR2 of grape pictures taken in-field before veraison. A total of 35,668 berries have been manually annotated for the task. GBCNet achieves good performances on both the seven grape varieties dataset CR1, although with a different accuracy level depending on the variety, and on the single variety dataset CR2: in particular Mean Average Error (MAE) ranges from 0.85% for Pinot Gris to 11.73% for Marzemino on CR1 and reaches 7.24% on the Teroldego CR2 dataset.

We study the temperature and field dependence of the magnetization M(T,H) for the order-disorder transition in DMMn and the chromium-doped DMMn close to T-c. By analyzing the experimental data for M(T,H) from the literature according to the power-law formula, values of the critical exponent (beta) and the critical isotherm (delta) are deduced and also, the temperature dependence of magnetization M(T) is calculated from the molecular field theory for those compounds. Our calculated M(T) and the values of beta and delta from the analysis of M(T,H) indicate that DMMn and chromium-doped DMMn undergo nearly second order transition. (C) 2018 Elsevier Ltd. All rights reserved.