Buckling of laminated sandwich plates subjected to partial edge compression

The buckling characteristics of sandwich plates having laminated stiff layers are studied for different types of partial edge loadings using a refined plate theory. With this plate theory, the through thickness variation of transverse shear stresses is represented by piecewise parabolic functions where the continuity of these stresses is satisfied at the layer interfaces by taking jumps in the transverse shear strains at the interfaces. The transverse shear stresses free condition at the plate top and bottom surfaces is also satisfied. It is quite interesting to note that this plate model having all these refined features requires unknown parameters only at the reference plane. To have a generality in the present analysis, finite element technique is adopted and it is carried out with newly developed triangular element, as existing finite elements cannot accommodate this plate model. So far, no solution exists in the literature for the problem of sandwich plate subjected to partial edge loading. The present analysis is first validated for the case of an isotropic plate subjected to partial edge compression and then it is extended to analyze sandwich plates. Few results are presented.     

An expert system for predicting shear stress distribution in circular open channels using gene expression programming

The shear stress distribution in circular channels was modeled in this study using gene expression programming (GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the shear stress distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number (Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network (ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.     

Exact Analysis of Non-Linear Fractionalized Jeffrey Fluid. A Novel Approach of Atangana-Baleanu Fractional Model

It is a very difficult task for the researchers to find the exact solutions to mathematical problems that contain non-linear terms in the equation. Therefore, this article aims to investigate the viscous dissipation (VD) effect on the fractional model of Jeffrey fluid over a heated vertical flat plate that suddenly moves in its own plane. Based on the Atangana-Baleanu operator, the fractional model is developed from the fractional constitutive equations. VD is responsible for the non-linear behavior in the problem. Upon taking the Laplace and Fourier sine transforms, exact expressions have been obtained for momentum and energy equations. The influence of relative parameters on fluid flow and temperature distribution is shown graphically. As special cases, and for the sake of correctness, the corresponding results for second-grade fluid and Newtonian viscous fluid are also obtained. It is interesting to note that fractional parameter α provides more than one line as compared to the classical model. This effect represents the memory effect in the fluid which is not possible to elaborate by the classical model. It is also worth noting that the temperature profile of the generalized Jeffrey fluid rises for higher values of Eckert number which is due to the enthalpy difference of the boundary layer.     

Engineering Cell Surface Function with DNA Origami

A specific and reversible method is reported to engineer cell‐membrane function by embedding DNA‐origami nanodevices onto the cell surface. Robust membrane functionalization across epithelial, mesenchymal, and nonadherent immune cells is achieved with DNA nanoplatforms that enable functions including the construction of higher‐order DNA assemblies at the cell surface and programed cell–cell adhesion between homotypic and heterotypic cells via sequence‐specific DNA hybridization. It is anticipated that integration of DNA‐origami nanodevices can transform the cell membrane into an engineered material that can mimic, manipulate, and measure biophysical and biochemical function within the plasma membrane of living cells.     

Analysis of Silver Nanoparticles in Engine Oil: Atangana–Baleanu Fractional Model

The present article aims to examine the heat and mass distribution in a free convection flow of electrically conducted, generalized Jeffrey nanofluid in a heated rotatory system. The flow analysis is considered in the presence of thermal radiation and the transverse magnetic field of strength B₀. The medium is porous accepting generalized Darcy’s law. The motion of the fluid is due to the cosine oscillations of the plate. Nanofluid has been formed by the uniform dispersing of the Silver nanoparticles in regular engine oil. The problem has been modeled in the form of classical partial differential equations and then generalized by replacing time derivative with Atangana–Baleanu (AB) time-fractional derivative. Upon taking the Laplace transform technique (LTT) and using physical boundary conditions, exact expressions have been obtained for momentum, energy, and concentration distributions. The impact of a number of parameters on fluid flow is shown graphically. The numerical tables have been computed for variation in the rate of heat and mass transfer with respect to rooted parameters. Finally, the classical solution is recovered by taking the fractional parameter approaching unity. It is worth noting that by adding silver nanoparticles in regular engine oil, its heat transfer rate increased by 14.59%, which will improve the life and workability of the engine.     

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

The main goal of this research is to develop and apply a robust Artificial Neural Networks (ANNs) model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer. Concentrated salt and sugar solutions were used as the osmotic solutions at 27C. Series of experiments were performed at various temperatures of 35C, 40C, and 55C for conduction heat input under vacuum ( −760 mm Hg) condition. Some experiments were also performed in a pure vacuum without heat addition. Dimensionless moisture content (DMC), effective moisture diffusivity, and mass flux were considered as the performance parameters in this study. Results revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87% compared to 5.3% in the salt solution. Furthermore, a significant enhancement of drying performance of about 27% in DMC was observed for both samples at vacuum and 40C compared to pure vacuum drying conditions. Using the experimental data, a robust artificial neural network (ANN) was proposed to describe the osmotic dehydration’s behavior on the drying process. The ANN model outputs are the dimensionless moisture contents (DMC), the diffusivity, and the mass flux. Whereas the ANN inputs were the drying time, the percent of sugar solution, and the percent of salt solution. For the ANN apple’s model, the minimum root mean square error (RMSE) values were 0.0261, 0.0349 and 0.0406, for DMC, diffusivity, and mass flux, respectively. Whereas the best correlation coefficients of the above three parameters’ determination values were 0.9909, 0.9867 and 0.9744, respectively. For the ANN potato’s model, the minimum RMSE values were 0.0124, 0.0140 and 0.0333, for DMC, diffusivity, and mass flux, respectively. And the best correlation coefficients of the parameters’ values were found 0.9969, 0.9968 and 0.9736, respectively. Accordingly, the ANN model’s prediction has a perfect agreement with the experimental dataset, which confirmed the ANN model’s accuracy.     

Pruritus: Is there a grain of salty truth?

Hemodialysis patients characteristically suffer from a range of unpleasant symptoms. Uremic pruritus effects close to half of the chronic kidney disease population, reducing quality of life and associated with increased mortality. Its pathophysiology though is poorly understood; currently deployed therapeutic approaches are ineffective. Excessive levels of skin and soft tissue sodium accumulate in dialysis patients, producing a range of biological consequences, including inflammation. We report an index case of a hemodialysis patient with debilitating pruritus and extreme levels of tissue sodium, measured with Sodium-23 magnetic resonance imaging. Both the tissue sodium loading and pruritus responded fully to initiation of expanded hemodialysis therapy with a recently introduced medium cutoff dialysis membrane-based dialyzer.     

Emotion Recognition from Occluded Facial Images Using Deep EnsembleModel

Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.     

Erratum to: Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear

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