Electrochemical performance of La0.8Sr0.2MnO3 oxygen electrode promoted by Ruddlesden-Popper structured La2NiO4

The effects of introducing La₂NiO₄ nanocatalyst on the electrochemical performance of La_0.8Sr_0.2MnO₃ are investigated under solid oxide electrolysis cell and fuel cell modes, as well as open circuit voltage. Extracted data from impedance spectroscopy are interpreted with the analysis of distribution of relaxation times. La₂NiO₄ infiltration effectively reduces the activation energy of the oxygen reactions from 1.35 to 0.99 eV. It also changes the rate controlling process of the overall reaction. Polarization behavior of La₂NiO₄-infiltrated La_0.8Sr_0.2MnO₃ electrode shows superior performance under electrolysis mode compared to the fuel cell mode. Drastic increase in the size of low frequency arc during anodic current passage in the non-infiltrated La_0.8Sr_0.2MnO₃ electrode is hampered by infiltration of La₂NiO₄ nanocatalyst. By applying anodic current on infiltrated La_0.8Sr_0.2MnO₃, no displacement is observed in the position of high frequency peaks in the distribution of relaxation time graphs and only a small increase in height occurs for the low frequency arc. Additionally, La₂NiO₄-infiltrated electrode impressively decreases overpotential by 74% compared to the non-infiltrated one under electrolysis mode at 800°C.     

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.     

Simultaneous Learning of Nonlinear Manifolds Based on the Bottleneck Neural Network

Manifold learning methods are important techniques for nonlinear extraction of high-dimensional data structures. These methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds. These manifolds can share information for data reconstruction. To simultaneously extract these data manifolds, this paper proposes a nonlinear method based on the deep neural network (NN) named nonlinear manifold separator NN (NMSNN). Unlike unsupervised learning of bottleneck NN, data labels were used for simultaneous manifold learning. This paper makes use of NMSNN for extracting both expression and identity manifolds for facial images of the CK+ database. These manifolds have been evaluated by different metrics. The identity manifold is used for changing image identity. The result of identity recognition by K-nearest neighbor classifier shows that virtual identities are exactly sanitized. The virtual images for different expressions of test subjects are generated by expression manifold. The facial expression recognition rate of 92.86 % is achieved for virtual expressions of test persons. It is shown that NMSNN can be used to enrich datasets by sanitizing virtual images. As a result, 8 and 19 % improvements are gained in the face recognition task by a single image of each person on CK+ and Bosphorus databases, respectively.     

An integrated model based on statistical process control and maintenance

The close relationship between quality and maintenance of manufacturing systems has contributed to the development of integrated models, which use the concept of statistical process control (SPC) and maintenance. Such models not only help to improve quality of products but also lead to lower maintenance cost. In this paper, an integrated model is presented which considers complete failure and planned maintenance simultaneously. This model leads to six different scenarios. A new procedure for calculating average cost per time unit is also presented. Finally, a numerical example is used to evaluate sensitivity of the model parameters and compare performance of the developed model to a planned maintenance model. Results indicate satisfactory performance for the developed model.     

Efficient numerical algorithms for the solution of “good” Boussinesq equation in water wave propagation

This paper proposes three fast and high accuracy numerical methods for solving a nonlinear partial differential equation (PDE) describing water waves and called the Boussinesq (Bq) equation. We numerically solve the Bq equation with fourth-order time-stepping schemes in combination with discrete Fourier transform. We discretize the original PDE with discrete Fourier transform in space and obtain a system of ordinary differential equations (ODEs) which will be solved with fourth-order time-stepping methods. After transforming the equation to a system of ODEs, the linear operator is not diagonal, but we can implement the methods such as diagonal case which reduces the CPU time. Comparing numerical solutions with analytical solutions demonstrates that those methods are accurate and readily implemented. Also we investigate the conservation of mass for Bq equation.     

Experimental Modeling of Gas–Solid Heat Transfer in a Pipe with Various Inclination Angles

Gas–solid flow in a pipe with different configurations (vertical, horizontal, and inclined positions) is studied experimentally. Air with temperature around 170°C and sand particles with mean diameter of 253 μm are used as gas and solid mediums, respectively. Effects of different parameters (pipe slope and solid particles feed rate) are studied on heat transfer rate between gas and solid particles. The Nusselt number decreases at lower solids feed rate in a dilute regime of the mixture; however, it increases at higher solids feed rates. Furthermore, results show that a higher Nusselt number takes place at the angles closer to 45 degrees.     

Estimating shear stress in a rectangular channel with rough boundaries using an optimized SVM method

Neural Computing and Applications - The accurate simulation of wall and bed shear stresses in rectangular channels is one of the most important topics in hydraulic engineering. In this study, the...