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%.     

Implementation of convolutional neural network categorizers in coronary ischemia detection

The heart is one of the most important and sophisticated organ of the human body. Coronary ischemia is a condition in which the coronary muscles do not receive sufficient blood and oxygen because of blocked or tightened heart vessels. This syndrome is called cardiac vessel illness. There have been numerous attempts to detect the impact of cardiac vessel illness on the heart muscles using noninvasive experiments. Most of the effects of ischemia as well as severe cardiac conditions on the muscles of the ventricle parts can be detected using ultrasonic images. If treatment is provided to suspected cases in the early stage of cardiac vessel illness, the chance of survival is high; for this, many software‐based detection approaches have been used. In this study, we propose an approach that can automatically diagnose the cardiac artery disease by using the cardiac echo images of the four parts of the heart.     

A novel offline handwritten text recognition technique to convert ruled-line text into digital text through deep neural networks

Multimedia Tools and Applications - Offline Handwritten Text Recognition (HTR) has been an active area of research due to its wide range of applications and challenges. Recently, many offline HTR...     

Asymmetric Fission Transmission of Linear-to-Circular Polarization Converter Using Bi-layer Split Ring Structure

A circular polarizer based on bi-layer split ring structure is proposed that could achieve asymmetric fission transmission of linearly polarized wave at the dual band. Firstly, a new approach of “Fission Transmission of Electromagnetic (FTEM) waves” is introduced to understand the polarization transformation behavior for linear-to-circular polarization. The designed structure achieves broadband circularly polarized wave with an asymmetric transmission over resonance frequencies by the principle of FTEM wave. The electronics circuit of proposed structure demonstrates the transformation behavior of EM waves when the electric and magnetic coupling between the upper and lower patterned SRR is reached at the certain strength. The physics of the giant circular dichroism effect and optical activity is illustrated by the surface currents distribution on the structure. The proposed structure achieves a right-handed circularly polarized wave and left hand circularly polarized wave with high transmission at 13.94–15.70 GHz and at 16.0–17.03 GHz, respectively. The axial ratio bandwidth of 11.76 and 6.86% is obtained at the dual band. The simulated and measured results exhibit good correspondence.