Rheological behavior of starch–poly(vinyl alcohol)–TiO2 nanofluids and their main and interactive effects

In this study, an investigation of the rheological behavior of starch/poly(vinyl alcohol) (PVA)/titanium oxide (TiO₂) nanofluids was performed. It revealed that the rheological behavior of starch suspensions displays a particular change due to the presence of PVA and TiO₂. All examined fluids demonstrated non‐Newtonian behavior and followed the Power law model. The main and interacting effects of starch, PVA, and TiO₂ nanoparticles concentrations were studied using the analysis of variance. The results indicated that the flow behavior index (n), as well as the consistency index (K) of suspensions, is influenced by the PVA and TiO₂ contents. The flow behavior index (n) decreased and consistency index (K) increased by an increase in PVA concentration. A reverse trend is observed by the addition of TiO₂ nanoparticles to starch and PVA blend suspensions. The difference in rheological behaviors was ascribed to the presence of binary and triplet interactions between starch, PVA, and TiO₂ nanoparticles. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44062.     

A Novel Design Approach for Ternary Compressor Cells Based on CNTFETs

Using multiple-valued logic provides more information transmission over a signal line. So it could solve the binary logic circuits problems such as interconnections requirement. In this paper, a universal method for designing ternary 3-2 and 4-2 compressor cells based on carbon nanotube field-effect transistors (CNTFETs) is presented. The proposed circuits use unique properties of CNTFETs, such as adjustable threshold voltage by changing CNT diameter and ballistic carrier transportation. In both designs transmission gates, ternary decoder and standard ternary buffers with different threshold voltages are used. The proposed compressors receive three (for 3-2 compressor) or four (for 4-2 compressor) ternary digits, produce the summation of these digits and show the results in two ternary digits (Sum, Carry). For evaluation and simulation the proposed circuits, Synopsys HSPICE simulator with 32 nm compact model is used in different simulation conditions.     

Wavelet-Clustering-Neural Network Model for Freeway Incident Detection

Abstract:   An improved freeway incident-detection model is presented based on speed, volume, and occupancy data from a single detector station using a combination of wavelet-based signal processing, statistical cluster analysis, and neural network pattern recognition. A comparative study of different wavelets (Haar, second-order Daubechies, and second- and fourth-order Coifman wavelets) and filtering schemes is conducted in terms of efficacy and accuracy of smoothing. It is concluded that the fourth-order Coifman wavelet is more effective than other types of wavelets for the traffic incident detection problem. A statistical multivariate analysis based on the Mahalanobis distance is employed to perform data clustering and parameter reduction to reduce the size of the input space for the subsequent step of classification by the Levenberg–Marquardt backpropagation (BP) neural network. For a straight two-lane freeway using real data, the model yields an incident detection rate of 100%, false alarm rate of 0.3%, and detection time of 35.6 seconds.     

Parallel-Vector Algorithm for Optimization of Large Steel Structures on a Shared-Memory Machine

An efficient parallel-vector algorithm is presented for optimization of large-space steel structures subjected to realistic code-specified constraints and multiple-loading conditions. The algorithm has been used for minimum-weight design of several high-rise building structures, including an 81-story building structure with 9245 members. It is shown that both parallel processing and vectorization performance improve with the increase in size of the structure. For the largest 81-story structure, speedups of 6.58 and 17.8 are achieved due to parallel processing (using eight processors) and vectorization, respectively. When vectorization is combined with parallel processing, a high speedup of 99.2 is achieved.     

Repetitive control of electrically driven robot manipulators

This article presents a novel robust discrete repetitive control of electrically driven robot manipulators for tracking of a periodic trajectory. We propose a novel model, which presents the highly non-linear dynamics of robot manipulator in the form of linear discrete-time time-varying system. Based on the proposed model, we develop a two-term control law. The first term is an ordinary time-optimal and minimum-norm (TOMN) control by employing parametric controllers to guarantee stability. The second term is a novel robust control to improve the control performance in the face of uncertainties. The robust control estimates and compensates uncertainties including the parametric uncertainty, unmodelled dynamics and external disturbances. Performance of the proposed method is compared with two discrete methods, namely the TOMN control and an adaptive iterative learning (AIL) control. Simulation results confirm superiority of the proposed method in terms of the convergence speed and precision.     

Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction

Case-based reasoning (CBR) solves many real-world problems under the assumption that similar observations have similar outputs. As an implementation of this assumption and inspired by the technique for order performance by the similarity to ideal solution (TOPSIS), this paper proposes a new type of multiple criteria CBR method for binary business failure prediction (BFP) with similarities to positive and negative ideal cases (SPNIC). Assuming that the binary prediction of business failure generates two results, i.e., failure and non-failure, we set the principle of this CBR forecasting method which is termed as SPNIC-based CBR as follows: new observations should have the same output as the positive or negative ideal case to which they are more similar. From the perspective of CBR, the SPNIC-based CBR forecasting method consists of R⁴ processes: retrieving positive and negative ideal cases, reusing solutions of ideal cases to forecast, retain cases, and reconstruct the case base. As a demonstration, we applied this method to forecast business failure in China with three data representations of a formerly collected dataset from normal economic environment and a representation of a recently collected dataset from financial crisis environment. The results indicate that this new CBR forecasting method can produce significantly better short-term discriminate capability than comparative methods, except for support vector machine, in normal economic environment; On the contrary, it cannot produce acceptable performance in financial crisis environment. Further topics about this method are discussed.     

A consistent formulation for imposing packaging constraints in shape optimization using Vertex Morphing parametrization

This paper aims at imposing no-penetration condition over arbitrary surfaces which act as bounding surfaces, also known as packaging constraints, on the design surface of shape optimization problem. We use Vertex Morphing technique for the shape parametrization. Vertex Morphing is a consistent surface control approach for node-based shape optimization. The suitability of this technique has been assessed and demonstrated for a wide range of engineering applications without geometric shape constraints. In this contribution, a consistent formulation is presented for the implementation of numerous point-wise geometric constraints in four main steps. First, a potential contact between optimization surface points and the bounding surface is identified via the so-called gap function. Second, the shape gradients of objective functions and active constraints are mapped onto the Vertex Morphing’s control space, where the optimization problem is formulated. Third, the linear least squares method is used to project the steepest-descent search direction onto the subspace tangent to the mapped active constraints. Finally, the feasible design update is mapped onto the geometry space. To verify the perfect consistency between the geometry space (where the constraints are formulated) and the control space (where the optimization problem is solved) two applications of CFD shape optimization in the automotive industry are presented.     

How crystal configuration affects the position detection accuracy in pixelated molecular SPECT imaging systems?

It is well known that inter-crystal scattering and penetration(ICS-P) are major spatial resolution limiting parameters in dedicated SPECT scanners with pixelated crystal.In this study,the effect of ICS-P on crystal identification in different crystal configurations was evaluated using GATE Monte Carlo simulation.A ~(99m)Tc pencil-beam toward central crystal element was utilized.Beam incident angle was assumed to vary from 0° to 45° in 5° steps.The effects of various crystal configurations such as pixel-size,pixel-gap,and crystal material were studied.The influence of photon energy on the crystal identification(CI) was also investigated.Position detection accuracy(PDA) was defined as a factor indicating performance of the crystal.Furthermore,a set of ~(99m)Tc point-source simulations was performed in order to calculate peak-to-valley(PVR) ratio for each configuration.The results show that the CsI(Na)manifests higher PDA than NaI(TI) and YAP(Ce).In addition,as the incident angle increases,the crystal becomes less accurate in positioning of the events.Beyond a crystal-dependent critical angle,the PDA monotonically reduces.The PDA reaches 0.44 for the CsI(Na) at 45° beam angle.The PDAs obtained by the point-source evaluation also behave the same as for the pencil-beam irradiations.In addition,the PVRs derived from flood images linearly correlate their corresponding PDAs.In conclusion,quantitative assessment of ICS-P is mandatory for scanner design and modeling the system matrix during iterative reconstruction algorithms for the purpose of resolution modeling in ultra-high-resolution SPECT.     

An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways.     

Optimization of photooxidative removal of p-nitrophenol in a spinning disc photoreactor using response surface methodology

In this article, response surface methodology (RSM) was used to obtain optimum conditions for removal of p-nitrophenol (PNP) by UV/H₂O₂ process using spinning disk photoreactor (SDP). For this purpose, the effect of five independent variables, the initial concentration of PNP, the initial concentration of H₂O₂, pH, solution volume, and irradiation time on the PNP removal percent, was investigated. Central composite design, one of the response surface techniques used for process optimization. The results showed a good agreement between the RSM predicted and experimental data with “R²” and “Adjusted R²” of 0.9692 and 0.9480, respectively. In addition, “Predicted R²” of 0.8909 is in reasonable agreement with “Adjusted R²” of 0.9488. At optimal conditions, that is, PNP concentration of 20.78?mg L^?1, H₂O₂ concentration of 1355.83?mg L^?1, solution volume of 566.08?mL, irradiation time of 12.30?min, and pH of 4.59 the removal percent predicted by RSM is 100% which has good correspondence with its experimental value (98.67%).