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۱Nanoporous Ag–CNTs foamed electrode for lithium intercalation
نویسنده(ها): ،
اطلاعات انتشار: Journal of Nano Structures، اول،شماره۳، ۲۰۱۲، سال
تعداد صفحات: ۶
Intercalation of lithium into Ag–CNTs sample is reported here. We have used a nano–porous silver foam as a frame for deposition of the CNTs inside the pores by electrophoresis deposition (EPD) technique. By using chronopotentiometry method, we have noticed that the Li storage capacity of the prepared Ag–CNTs electrode was improved noticeably in comparison with literature. In addition, a very good functional stability for the prepared electrode has been tested during subsequent cycles of charge \ discharge (C&D) procedures. By scanning the cycle's regulated current from 0.2 up to 1.0 mA , it was shown that in the range of 0.4–0.6 mA reversibility of the C&D capacity became optimum and the voltage profiles were converged, as well.

۲A Comparative Study between Artificial Neural Networks and Adaptive Neuro–fuzzy Inference Systems for Modeling Energy Consumption in Greenhouse Tomato Production– A Case Study in Isfahan Province
اطلاعات انتشار: Journal of Agricultural Science and Technology، هفدهم،شماره۱، ۲۰۱۵، سال
تعداد صفحات: ۱۴
In this study greenhouse tomato production was investigated from energy consumption and greenhouse gas (GHG) emission point of views. Moreover, artificial neural networks (ANNs) and adaptive neuro–fuzzy inference systems (ANFIS) were employed to model energy consumption for greenhouse tomato production. Total energy input and output were calculated as 1,316.14 and 281.1 GJ ha–1. Among the all energy inputs, natural gas and electricity had the most significant contribution to the total energy input. Evaluations of GHG emission illustrated that the total GHG emission was estimated at 34,758.11 kg CO2eq ha–1 and, among all the inputs, electricity played the most important role, followed by natural gas. Comparison between ANN and ANFIS models showed that, due to employing fuzzy rules, the ANFIS–based models could model output energy more accurately than ANN models. Accordingly, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for the best ANFIS architecture were calculated as 0.983, 0.025, and 0.149, respectively, while these performance parameters for the best ANN model were computed as 0.933, 0.05414, and 0.279, respectively.
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