Electronic,Magnetic, Electric and Magnetocaloric Properties of the Multiferroic Orthorhombic YMnO3

Journal of Superconductivity and Novel Magnetism - Multiferroic oxide materials have attracted much intention in recent years due to their application in different fields such as magnetic...     

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     

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.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Nanoparticle–Plant Interactions: Two‐Way Traffic

In this Review, an effort is made to discuss the most recent progress and future trend in the two‐way traffic of the interactions between plants and nanoparticles (NPs). One way is the use of plants to synthesize NPs in an environmentally benign manner with a focus on the mechanism and optimization of the synthesis. Another way is the effects of synthetic NPs on plant fate with a focus on the transport mechanisms of NPs within plants as well as NP‐mediated seed germination and plant development. When NPs are in soil, they can be adsorbed at the root surface, followed by their uptake and inter/intracellular movement in the plant tissues. NPs may also be taken up by foliage under aerial deposition, largely through stomata, trichomes, and cuticles, but the exact mode of NP entry into plants is not well documented. The NP–plant interactions may lead to inhibitory or stimulatory effects on seed germination and plant development, depending on NP compositions, concentrations, and plant species. In numerous cases, radiation‐absorbing efficiency, CO₂ assimilation capacity, and delay of chloroplast aging have been reported in the plant response to NP treatments, although the mechanisms involved in these processes remain to be studied.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.