Strain‐induced deformation mechanisms of polylactide plasticized with acrylated poly(ethylene glycol) obtained by reactive extrusion

This work aimed at identifying the tensile deformation mechanisms of an original grade of plasticized polylactide (pPLA) obtained by reactive extrusion. This material had a glass transition temperature of 32.6 °C and consisted of a polylactide (PLA) matrix grafted with poly(acryl‐poly(ethylene glycol)) (poly(Acryl‐PEG)) inclusions. pPLA behaved like a rubber‐toughened amorphous polymer at 20 °C, and its tensile behavior evolved toward a rubbery semicrystalline polymer with increasing temperature. The drawing of pPLA involved orientation of amorphous and crystalline chains, crystallization, and destruction of crystals. It was found that crystal formation and crystal destruction were in competition below 50 °C, resulting in a constant or slightly decreasing crystallinity with strain. Increasing temperature enhanced crystal formation and limited crystal destruction, resulting in an increased crystallinity with the strain level. Drawing yielded a transformation of the initial spherical poly(Acryl‐PEG) inclusions into ellipsoids oriented in the tensile direction. This mechanism may engender the formation of nanovoids within the inclusions due to a decreased density, assumed to be responsible for the whitening of the specimen. © 2015 Society of Chemical Industry     

Heat transfer analysis for particle–fluid suspension thermomagnetohydrodynamic peristaltic flow with Darcy–Forchheimer medium

This theoretical analysis explores the effect of heat and mass transfer on particle–fluid suspension for the Rabinowitsch fluid model with the stiffness and dynamic damping effects through Darcy–Brinkman–Forchheimer porous medium. In this study, we also incorporate slip and transverse magnetic field effects. Using low Reynolds number, to neglect inertial forces and to keep the pressure constant during the flow, channel height is used largely as compared with the ratio of length of the wave. A numerical technique is used to solve flow governing system of differential equations. Particular attention is paid to viscous damping force parameter, stiffness parameter, and rigidity parameter; also, the numerical data for thermal profile, momentum, and concentration distribution are presented graphically. Outcomes are deliberated in detail for different fluid models (thinning, thickening, and viscous models). It is found that velocity profile increases for greater values of viscous damping effect and stiffness and rigidity parameter for shear thinning, but conflicting comportment is showed for thickening nature model. Viscous dissipation effects increases the thermal profile for all cases of fluid models. The scope of the present article is valuable in explaining the blood transport dynamics in small vessels while considering the important wall features with chemical reaction characteristics. The current analysis has extensive applications in biomedical engineering field, that is, peristaltic pumps.     

Simplification of genetic programs: a literature survey

Data Mining and Knowledge Discovery - Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the problem of excessive growth in individuals’ sizes....     

Influence of moisture content on the contact angle and surface tension measured on birch wood surfaces

European Journal of Wood and Wood Products - Surface tension of solid wood surfaces affects the wettability and thus the adhesion of various adhesives and wood coatings. By measuring the contact...     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

Boundary stabilisation of the wave equation in the presence of singularities

We study the boundary stabilisation of the wave equation by a nonlinear feedback active on a part of the boundary in geometric situations for which the solutions have singularities. These singularities appear at the interfaces at which the mixed Neumann–Dirichlet boundary conditions meet. Under a simple geometrical condition concerning the orientation of the boundary, we obtain sharp energy decay rates under a general growth assumption on the feedback. We show that the singularities do not affect the energy decay rates and give examples.