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Artificial neural network (ANN) models were applied for simulating and predicting the ultimate capacities of cellular steel beams. To do this, at the first, different neural networks by various learning algorithms and number of neurons in the hidden layer were simulated. The required data for networks in training, validating, and testing state were obtained from a reliable database. Next, the best network according to its predictive performance was chosen, and a new formula was derived to predict the failure loads of cellular steel beams subjected to LTB. The attempt was done to evaluate the most exact practical formula using different algorithm and method for LTB strength assessment of cellular beams. Next, a comparison was made between the ANN-based formula and a formula based on the stepwise regression (SR) to show the predictive power of the ANN model. The results provided some evidence that ANN model obtained more accurate predictions than SR model. At the end, a sensitivity analysis was developed using Garson’s algorithm to determine the importance of each input parameter which was used in the proposed ANN formulation.

A new model based on Artificial Neural Network (ANN) was established as a trustworthy technique for predicting ultimate lateral torsional buckling (LTB) capacity of castellated steel beams. The required information for training, validating and testing of the developed model obtained from a reliable database. Consequently, a new formulation based on the ANN has been offered for predicting the failure load of castellated steel beams exposed to LTB. All parameters which may affect the LTB capacity of castellated beams were considered for presentation of this formula. Then, outcomes of the proposed formula were compared with predictions of Australian Standard (AS4100) for LTB capacity of castellated beams. This comparison indicated that proposed formula has a good performance for prediction of ultimate strength in castellated beams subjected to LTB. At the end, Garson’s algorithm has been established as a sensitivity analysis to determinate importance of each input in the proposed formula.

ABSTRACTA new approach for designing a quaternary arithmetic logic unit (ALU) based on carbon nanotube field-effect transistors (CNTFETs) is presented in this paper. The proposed design performs basic arithmetic and logic functions and is extendable to perform additional functions (both arithmetic and logic functions). Efficient circuit-level optimisation techniques such as data gating are used to reduce the power consumption and enhance the performance of the proposed design. In addition, the unique properties of CNTFETs such as adjusting the threshold voltages of CNTFETs by tuning the CNTs diameters are utilised for designing a quaternary ALU. The proposed design is simulated and examined in different conditions and in the presence of major process variations using the Synopsys HSPICE simulator with Stanford 32nm CNTFET technology.

AbstractA stepwise regression (SR) model is developed as a reliable modeling method for simulating and predicting the compressive strength of mortars containing metakaolin at the age of 3, 7, 28, 60, and 90 days. The required data in training and testing state obtained from a reliable data base. A new formulation based on the model have been proposed to predict the compressive strength of mortars containing metakaolin. Besides, to model verification a nonlinear least squares regression (NLSR) has been also developed. Then, a comparison has been made between the proposed SR formulae and NLSR to have an idea about the predictive power of the SR method.

ABSTRACTThis paper investigates the use of carbon nanotube field effect transistors (CNFETs) for the design of ternary full adder cells. The proposed circuits have been designed based on the unique properties of CNFETs such as having desired threshold voltages by adjusting diameter of the CNFETs gate nanotubes. The proposed circuits are examined using HSPICE simulator with the standard 32 nm CNFET technology. The proposed methods are simulated at different conditions such as different supply voltages, different temperature, and operational frequencies. Simulation results show that the proposed designs are faster than the state of the art CNFET-based ternary full adders.

Several analytical models of interconnection networks of multi-cluster systems under uniform traffic pattern have been proposed in the literature. However, there has been hardly any work reported yet that deals with other important non-uniform traffic patterns found in many parallel applications. In this paper we propose a new analytical model of adaptive routing in fat-tree-based interconnection networks in the presence of traffic pattern generated by bit-reversal permutation, which is an important communication operation in parallel applications such as FFT computations. The model is validated through comprehensive simulation, through which the proposed model exhibited a good degree of accuracy for various system organisations and under different working conditions.