Determination of flavan-3-ols and trans-resveratrol in grapes and wine using HPLC with fluorescence detection

Concentrations of trans-resveratrol, catechin and epicatechin were analyzed in musts and wines produced from seven red and four white grape cultivars from various wine growing regions of Turkey. Phenolics were quantified using an HPLC method optimized for the separation of wine phenolics. Wine samples contained higher phenolics levels than the corresponding musts. With the exception of Semillion, white wines and musts contained lower concentrations of phenolics than red wines and musts. However, the white cultivar Semillion had the highest concentrations of catechin and epicatechin among all wine and must samples. Semillion wine catechin and epicatechin were 13.7 and 11.8 mg/L, respectively. The highest level of trans-resveratrol among the white cultivars was found in Narince wine (1.93 mg/L). Within the red wine and must cultivars, Bo?azkere, Öküzgozü, and Cabernet contained the highest concentrations of flavan-3-ols and trans-resveratrol. Catechin was the major phenolic in all wines and most musts. Epicatechin was the major phenolic in 6 of the 11 must samples, but none of the wine samples. trans-Resveratrol was generally found in lowest concentrations in both wines and musts.     

Neutronic study on the effect of first wall material thickness on tritium production and material damage in a fusion reactor

In this study, the effects of changing first wall materials and their thicknesses on a reactor were investigated to determine the displacement per atom（DPA） and gas production（helium and hydrogen） in the first wall, as well as the tritium breeding ratio（TBR） in the coolant and tritium breeding zones. Therefore, the modeling of the magnetic fusion reactor was determined based on the blanket parameters of the International Thermonuclear Experimental Reactor（ITER）. Stainless steel（SS 316 LNIG）, Oxi...     

Neural network and fuzzy logic-based hybrid attitude controller designs of a fixed-wing UAV

Neural Computing and Applications - In this paper, a mini unmanned aerial vehicle (UAV) is planned to be used in applications such as spraying pesticide and weed control in agricultural areas....     

Investigation of surface roughness in the milling of Al7075 and open-cell SiC foam composite and optimization of machining parameters

In the present study, aluminum alloy 7075 (Al7075)-based open-cell silicon carbide (SiC) foam composite was fabricated and the machinability of both Al7075 and the open-cell SiC foam Al metal matrix composite was investigated during milling using an uncoated carbide tool. The machining trials were conducted using the Taguchi L₂₇ full-factorial orthogonal array, and the milling parameters were optimized for surface roughness. Analysis of variance was employed to determine the effect of the cutting variables on surface roughness. The experimental results were evaluated by signal-to-noise ratio, 3D surface graphs, artificial neural networks (ANNs) and main effect graphs. The analysis results show that the feed rate was the most significant milling parameter affecting surface roughness of both Al7075 and the open-cell SiC foam composite. Prediction models have been developed for the surface roughness through regression analysis and ANNs. Confirmation experiments were performed to identify the performance of mathematical models, and the surface roughness was predicted with a mean squared error equal to 1.6 and 0.24 % in the milling of Al7075 and open-cell SiC foam composite, respectively. The test result showed that the three-dimensional open-pore SiC foam network reinforcement was restricted the movement of the soft matrix and provided an acceptable surface quality in the milling of MMCs.

     

The investigation of effects of digital proofing systems used in colour management on print quality with neural networks

The aim of this study is to assist users and to determine how digital proofing systems used in colour management effect print qualities. Also the main theme of this study is to determine the effects of digital proofing systems used in colour management on print quality by artificial neural network (ANN). The R² values are obtained 0.99702 and 0.99688 for training data as matte and cuated papers, respectively. Similarly, these values for testing data are 0.994707 and 0.99629, respectively. The ANN approach shows greater accuracy for evaluating colour management. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in colour management on print quality with highly confidence.