Quantum-dot Cellular Automata (QCA) is a new and efficient technology to implement logic Gates and digital circuits at the nanoscale range. In comparison with the conventional CMOS technology, QCA has many attractive features such as: low-power, extremely dense and high speed structures. Adders are the most important part of an arithmetic logic unit (ALU). In this paper, four optimized designs of QCA serial adders are presented. One of the proposed designs is optimized in terms of the number of cells, area and delay without any wire crossing methods. Also, two new designs of QCA serial adders and a QCA layout equivalent to the internal circuit of TM4006 IC are presented. QCADesigner software is used to simulate the proposed designs. Finally, the proposed QCA designs are compared with the previous QCA, CNTFET-based and CMOS technologies.

The computational intelligence such as artificial neural network (ANN) and fuzzy inference system (FIS) is a strong tool for prediction and simulation in engineering applications. In this paper, radial basis function (RBF) network and adaptive neuro-fuzzy inference system (ANFIS) are used for prediction of IC50 (the 50% inhibitory concentration) values evaluated by the MTT assay in human cancer cell lines. For developing of the proposed models, the input parameters are the concentration of the drug and the types of cell lines and the output is IC50 values in the A549, H157, H460 and H1975 cell lines. The predicted IC50 values using the proposed RBF and ANFIS models are compared with the experimental data. The obtained results show that both RBF and ANFIS models have achieved good agreement with the experimental data. Therefore, the proposed RBF and ANFIS models are useful, reliable, fast and cheap tools to predict the IC50 values determined by the MTT assay in human cancer cell lines.