Alternating active-phase algorithm for multimaterial topology optimization problems: a 115-line MATLAB implementation

Structural and Multidisciplinary Optimization - A new algorithm for the solution of multimaterial topology optimization problems is introduced in the present study. The presented method is based on...     

Buoyancy effects on gaseous slip flow in a vertical rectangular microchannel

Consideration is given to the buoyancy effects on the fully developed gaseous slip flow in a vertical rectangular microduct. Two different cases of the thermal boundary conditions are considered, namely uniform temperature at two facing duct walls with different temperatures and adiabatic other walls (case A) and uniform heat flux at two walls and uniform temperature at other walls (case B). The rarefaction effects are treated using the first-order slip boundary conditions. By means of finite Fourier transform method, analytical solutions are obtained for the velocity and temperature distributions as well as the Poiseuille number. Furthermore, the threshold value of the mixed convection parameter to start the flow reversal is evaluated. The results show that the Poiseuille number of case A is an increasing function of the mixed convection parameter and a decreasing function of the channel aspect ratio, whereas its functionality on the Knudsen number is not monotonic. For case B, the Poiseuille number is decreased by increasing each of the mixed convection parameter, the Knudsen number, and the channel aspect ratio.     

Study of the High‐Pressure Vapour‐Liquid Equilibria for Strongly Non‐ideal Binary and Ternary Mixtures

High‐pressure vapour‐liquid equilibria for binary and ternary high polar and asymmetric systems are calculated using the Peng‐Robinson‐Stryjek‐Vera equation of state coupled with the Twu‐Coon (TWMR), the Orbey‐Sandler (OSMR) and the Wong‐Sandler (WSMR) mixing rules. Modified UNIFAC model is used for determining the activity coefficient and excess Gibbs free energy. The calculated results are compared with the experimental data and good agreement is observed. The average absolute deviation percents (AAD%)s indicated that the WSMR has less AAD% than other mixing rules in most of cases.     

Extraction of 2-hydroxyphenol by surfactant coated nanosized magnetic particles

A colloidal suspension of magnetic particles coated with double layer surfactant molecules was prepared, characterized and used for separation of some chemicals. The aqueous magnetic fluid was prepared by chemical precipitation method and characterized by total organic carbon analyzer (TOC), thermogravimetric analysis (TGA) and transmission electron microscopy (TEM). The TOC and TGA measurements indicate that the prepared magnetic particles are coated with surfactant molecules in double layers. The outer surfactant layer has free polar heads. TEM measurement results show that magnetic particles are nanosized. A mixture of 2-hydroxyphenol (strong polar) and 2-nitrophenol (weak polar) was chosen as the target system. Results show that the magnetic fluid can selectively adsorb 2-hydroxyphenol from the mixture, whereas the adsorption of 2-nitrophenol is insignificant. For 2-hydroxyphenol, the adsorption equilibrium can be described well by Langmuir isotherm, and the adsorption kinetic can be fitted well by a linear driving force mass transfer model.     

Determination of residual organic matter in extracted oil sands using a low temperature ashing method

The application of a low temperature ashing method for estimating total residual organic matter (toluene insolubles) in oil sands is described. A linear correlation exists between organic carbon content and loss on ignition at 400 ± 10 °C of solvent extracted oil sands. The ratio between total organic carbon and the weight loss on ignition (CT/LOl) owing to the removal of residual organic matter is much lower than that obtained for toluene soluble bitumen fractions, indicating very different chemical composition for the residual organic matter. The measured carbon content of the residual organic matter in oil sands suggests that this material could be a mixture of various fractions contained in resins, asphaltenes, asphaltic acids and humic acids.     

Eco-friendly electronics,based on nanocomposites of biopolyester reinforced with carbon nanotubes: a review

ABSTRACT

Fabrication of electronic materials from nanocomposite of biopolyesters reinforced with carbon nanotubes can be regarded as the effective alternative for conventional nanocomposites consisting of non-biodegradable polymers. Commercial availability of biopolyester-based nanocomposites is limited because of their high cost compared to other polymers, but the factor of their compostable nature is worthless for environmental protection. Such nanocomposites have potential applications in biodegradable sensors, EMI materials, etc. In this review, the current progress of biopolyester/CNTs nanocomposites in the field of biodegradable electronics is reviewed and also the impact of CNTs dispersion on electrical, thermal and mechanical properties of eco composites is stipulated.     

A new technique for the construction of confusion component based on inverse LA-semigroups and its application in steganography

The present article introduced a novel idea for information hiding namely steganography. We have used new notions for the construction of the nonlinear component for block cipher based on inverse LA-semigroups. This nonlinear component fundamentally provides confidentiality in the proposed steganographic algorithm. The construction of the algorithm is fundamentally twofold. Firstly, we have constructed a novel scheme to design confusion component namely substitution box (S-box). Secondly, we have utilized the anticipated nonlinear component in digital steganography. The suggested algorithm is tested for different standard digital images. The authentication of the proposed algorithm is confirmed through statistical analysis.

     

Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

Neural Processing Letters - Deep learning is an important subcategory of machine learning approaches in which there is a hope of replacing man-made features with fully automatic extracted features....     

An H∞non‐fragile observer‐based adaptive sliding mode controller design for uncertain fractional‐order nonlinear systems with time delay and input nonlinearity

In this paper the problem of non‐fragile adaptive sliding mode observer design is addressed for a class of nonlinear fractional‐order time‐delay systems with uncertainties, external disturbance, exogenous noise, and input nonlinearity. An H_∞ observer‐based adaptive sliding mode control considering the non‐fragility of the observer is proposed for this system. The sufficient asymptotic stability conditions are derived in the form of linear matrix inequalities. It is proven that the sliding surface is reachable in finite time. An illustrative example is provided which corroborates the effectiveness of the theoretical results.     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.