DEA efficiency analysis: A DEA approach with double frontiers

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.     

Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

In this study, diagnosis of hepatitis disease, which is a very common and important disease, is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA). Simulated annealing is a stochastic method currently in wide use for difficult optimization problems. Intensively explored support vector machine due to its several unique advantages is successfully verified as a predicting method in recent years. We take the dataset used in our study from the UCI machine learning database. The classification accuracy is obtained via 10-fold cross validation. The obtained classification accuracy of our method is 96.25% and it is very promising with regard to the other classification methods in the literature for this problem.     

Design patterns selection: An automatic two-phase method

Over many years of research and practices in software development, hundreds of software design patterns have been invented and published. Now, a question which naturally arises is how software developers select the right design patterns from all relevant patterns to solve design problems in the software design phase. To address this issue, in this paper, we propose a two-phase method to select a right design pattern. The proposed method is based on a text classification approach that aims to show an appropriate way to suggest the right design pattern(s) to developers for solving each given design problem. There are two advantages of the proposed method in comparison to previous works. First, there is no need for semi-formal specifications of design patterns and second, the suitable design patterns are suggested with their degree of similarity to the design problem. To evaluate the proposed method, we apply it on real problems and several case studies. The experimental results show that the proposed method is promising and effective.     

Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail.     

Target optimization for a 10-MeV E-beam neutron converter

Accelerator-based target design and optimization is an approach for neutron generation. The target plays an important role for a neutron source on an electron accelerator. For optimizing a neutron source using 10 MeV electron beams of Rhodotron-TT200, Pb, Ta, or W alloys with Be were calculated as photo-neutron converter. The neutron yield, flux and energy were simulated using the MCNPX code. The results indicate that a 10 MeV electron beam is capable of producing high-intensity neutron flux of 1013n·cm–2·s–1 with average energy of 0.8 MeV.     

A regional suspended load yield estimation model for ungauged watersheds

Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield (SLY) in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques (DRTs), e.g., principal component analysis (PCA), Gamma test (GT), and stepwise regression (SR), have been used to select the most effective variables. The artificial neural network (ANN) and multiple linear regression (MLR) are also common tools for SLY modeling. We conducted this study (1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and (2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region (in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient, relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.     

Connectivity analysis for dynamic movement of vehicular ad hoc networks

Speed variation is one of the main challenges in deriving the connectivity related predictions in mobile ad-hoc networks, especially in vehicular ad hoc networks (VANETs). In such a dynamic network, a piece of information can be rapidly propagated through dedicated short-range communication, or can be carried by vehicles when multihop connectivity is unavailable. This paper proposes a novel analytical model that carefully computes the connectivity distance for a single direction of a free-flow highway. The proposed model adopts a time-varying vehicular speed assumption and mathematically models the mobility of vehicles inside connectivity. According to the dynamic movability scenario, a novel and accurate closed form formula is proposed for probability density function of connectivity. Moreover, using vehicular spatial distribution, joint Poisson distribution of vehicles in a multilane highway and tail probability of the expected number of vehicles inside single lane in a multilane highway are mathematically investigated. The accuracy of analytical results is verified by simulation. The concluded results provide helpful insights towards designing new applications and improving performance of existing applications on VANETs.     

A CMOS Quadrature LC Oscillator using Automatic Phase/Amplitude Calibration

In this paper, a novel LC quadrature oscillator (QO) is presented that can simply and automatically cancel the phase and amplitude errors raised by mismatches in LC tanks; the major source of phase and amplitude errors. The design is based on method of using unequal coupling factors in a parallel quadrature oscillator (P-QO). This method shows how we can cancel phase and amplitude errors simultaneously by choosing appropriate inversely proportional coupling factors. To cancel the errors, the proposed circuits first sense the phase error and increase or decrease the difference between the coupling factors accordingly. When tuning tail currents, the coupling factors can be simply adjusted. The entire system has a block diagram like a PLL. The dynamics of the proposed circuit is analyzed, and it is shown how the phase and amplitude error is canceled in response to an imposed LC tank mismatch. To evaluate the circuit, a QO has been designed to oscillate at 5 GHz with 1.8 V supply and 6.2 mA current consumption. The circuit has been simulated using TSMC 0.18 CMOS practical model where all the results confirm the analytical results.     

Ultra-low-power carbon nanotube FET-based quaternary logic gates

This paper presents low-power carbon nanotube field-effect transistor (CNTFET)-based quaternary logic circuits. The proposed quaternary circuits are designed based on the CNTFET unique properties, such as the same carrier mobility for N- and P-type devices and also providing desirable threshold voltages by adopting proper diameters for the nanotubes. In addition, no paths exist between supply and ground rails in the steady states of the proposed designs, which eliminates the ON state static current and also the stacking technique is utilised in order to significantly reduce the leakage currents. The results of the simulations, conducted using Synopsys HSPICE with the standard 32 nm CNTFET technology, confirm the significantly lower power consumption, higher energy efficiency and lower sensitivity to process variation of the proposed designs compared to the state-of-the-art quaternary logic circuits. The proposed quaternary logic circuits have on average 92, 99 and 91% less total power, static power and PDP, respectively, compared with the most low-power and energy-efficient CNTFET-based quaternary logic circuits, recently presented in the literature.