radial basis function neural network

In this study, an artificial neural network (ANN) model is presented in order to predict the tenacity and hairiness of carded cotton yarns. Fiber measurement values generated by using a high-volume instrument (HVI) and an advanced fiber information system (AFIS) were used in the ANN model as input parameters. The radial basis function neural network (RBFNN) was used as ANN structure. The best RBFNN model was determined by analyzing the effect of epochs and the number of neurons on prediction performance. By using this ANN structure, the comparison between the performance of predicting yarn properties from HVIs and from AFISs was carried out. In the study, four different yarn counts (Ne20, Ne24, Ne30, and Ne40) for 10 different blends were applied. Each yarn count was spun at 4.34 alpha(e) twist factor. In this study, the model presented a good rate of accuracy for predicting yarn tenacity and hairiness by using HVI and AFIS fiber values. The study showed that there was no significant difference between the accuracy of predicting these yarn properties from HVI fiber measurement results and those from an AFIS by using the RBF. From the results, it was noted that the performance of predicting yarn hairiness was better than that of predicting yarn tenacity. Also, this study could provide researchers with exclusive information on how to select the most appropriate ANN architecture and how to evolve the model for testing

Age-associated changes in nitric oxide metabolites nitrite and nitrate

WOS: 000089506400004PubMed ID: 11043501Aging is an important determinant of vascular disease. Endothelial dysfunction accompanying vascular disease may be related to cardiovascular risk factors such as aging, hypertension, and atherosclerosis. Experimental models suggest that endothelium-derived nitric oxide is reduced with aging, and this reduction is implicated in atherogenesis. The aim of this study was to determine whether increased age resulted in altered serum nitrite and nitrate levels, end-products of nitric oxide, in healthy subjects. Sixty-nine healthy individuals were divided into five different age groups: group I (6-15 years), group II (16-30 years), group III (31-45 years), group IV (46-60 years), and group V (>61 years). In these subjects, serum nitrite was measured by the Griess reaction and nitrate by the nitrate reductase method. Statistical analysis showed that serum nitrite levels were not significantly different in any of the groups, while serum nitrate concentrations exhibited significant differences (P<0.001). These findings suggest that nitric oxide synthesis and/or secretion is reduced with age and consequently endothelium-dependent vasodilation is impaired

Performance and mechanism research of hierarchically structured Li-rich cathode materials for advanced lithium–ion batteries

The hierarchically structured cathode material Li1.165Mn0.501Ni0.167Co0.167O2 (LMNCO) is successfully synthesized via a facile ultrasonic-assisted co-precipitation method with a two-step heat treatment by adopting graphene and carbon nanotubes (CNTs) as functional framework and modified material. The structure and electrochemical performance degeneration mechanism were systematically investigated in this work. The obtained LMNCO microspheres possess a hierarchical nano-micropore structure assembled with nanosized building blocks, which originates from the oxidative decomposition of the transition metal carbonate precursor and carbonaceous materials accompanied with the release of CO2 (but still remain carbon residue). What’s more, the positive electrode exhibits enhanced specific capacities (276.6 mAh g−1 at 0.1 C), superior initial coulombic efficiency (80.3 %), remarkable rate capability (60.5 mAh g−1 at 10 C) and high Li+ diffusion coefficient (~10−9 cm2 s−1). The excellent performances can be attributed to the pore structure, small particle sizes, large specific surface area and enhanced electrical conductivity. (1 C = 250 mA g−1).Department of Electrical Engineerin