McDaniel Murphy (ferrythrill77)

[This corrects the article doi 10.1590/0001-3765202020180773].The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ETo). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature. Two scenarios were considered (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated. When comparing the ANN models with the HS equation, mean RMSE reduced from 0.96 to 0.87 and from 0.84 to 0.73, in regional and local scenarios, respectively. ANN and MARS performed similarly, with a slight advantage for ANN.This project presents the results obtained from a new strategy based on Virtual Reality techniques, which intends to minimize the operational issues caused in electric power substations due to the lack of spatial and functional information on the traditional operation interfaces. For this purpose, a three-dimensional interactive virtual reality environment was built in a realistic and accurate way regarding an energy supplier substation in Minas Gerais - Brazil and subsequently implanted it in its operation center for tasks related to its functioning. Lastly, tests were applied to operators to obtain results aiming at the contextualized problems.The immobilization of the enzyme tyrosinase (Tyr) in lipid matrices can be explored to produce biosensors for detecting polyphenols, which is relevant for the food industry. Herein, we shall demonstrate the importance of the lipid composition to immobilize the enzyme tyrosinase in Langmuir-Blodgett (LB) films. Tyr could be incorporated into Langmuir monolayers of arachidic acid (AA), 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dipalmitoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (sodium salt) (DPPG), having as the main effect an expansion in the monolayers. Results from polarization-modulated infrared reflection-absorption spectroscopy (PM-IRRAS) pointed to electrostatic interactions between the charged residues of Try and the lipid headgroups, in addition to changes in the order of lipid chains. The interaction between Tyr and DPPC in Langmuir monolayers can be correlated with the superior performance of DPPC/Tyr LB films used as biosensors to detect catechol by cyclic voltammetry. The molecular-level interactions assessed via PM-IRRAS are therefore believed to drive an immobilization process for Tyr in the lipid LB matrix and may serve as a general criterion to identify matrices that preserve enzyme activity.Microbial β-glucosidases can be used in several industrial processes, including production of biofuels, functional foods, juices, and beverages. In the present work, production of β-glucosidase by solid state cultivation of the fungus Thermoascus crustaceus in a low-cost cultivation medium (comprising agroindustrial residues) was evaluated. The highest production of β-glucosidase, about 415.1 U/g substrate (or 41.51 U/mL), was obtained by cultivating the fungus in wheat bran with 70% humidity, during 96 h at 40°C. The enzymatic activity was optimum at pH 4.5 and 65°C.