Tailoring insoluble nanobelts into soluble anti-UV nanopotpourris

Soluble, transparent and anti-UV nanopotpourris have been prepared by tailoring long nanobelts. The strains and layered structures facilitate the breaking of the as-synthesized nanobelts under an applied mechanical action. The developed tailoring process of nanobelts is a general top-down secondary processing of layered nanostructures at the nanoscale level, which can be expended to the modifications of layered nanowires, nanotubes and hierarchical nanostructures. By tailoring, the size, morphology and solubility are modified, which may open up an area of advanced processing of nanomaterials and hint at some potential applications. Because of the excellent solubility of the tailored nanopotpourris, they are easily dispersed in cosmetics or polymer films, which are quite useful for some anti-UV protection applications, such as anti-UV sunscreen creams and anti-UV window films for vehicles and buildings.     

Electric field effects on CNTs/vinyl ester suspensions and the resulting electrical and thermal composite properties

In this study, electrical conductivity of a vinyl ester based composite containing low content (0.05, 0.1 and 0.3 wt.%) of double and multi-walled carbon nanotubes with and without amine functional groups (DWCNTs, MWCNTs, DWCNT-NH₂ and MWCNT-NH₂) was investigated. The composite with pristine MWCNTs was found to exhibit the highest electrical conductivity. Experiments aimed to induce an aligned conductive network with application of an alternating current (AC) electric field during cure were carried out on the resin suspensions with MWCNTs. Formation of electric anisotropy within the composite was verified. Light microscopy (LM), scanning electron (SEM) and transmission electron microscopy (TEM) were conducted to visualize dispersion state and the extent of alignment of MWCNTs within the polymer cured with and without application of the electric field. To gain a better understanding of electric field induced effects, glass transition temperature (T_g) of the composites was measured via Differential Scanning Calorimetry (DSC). It was determined that at 0.05 wt.% loading rate of MWCNTs, the composites, cured with application of the AC electric field, possessed a higher T_g than the composites cured without application of the AC electric field.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

An application of modified reductive perturbation method to long water waves

In this work, we extended the application of “the modified reductive perturbation method” to long water waves and obtained the governing equations as the KdV hierarchy. Seeking a localized travelling wave solutions to these evolution equations we determined the scale parameter c₁so as to remove the possible secularities that might occur. The present method is seen to be fairly simple as compared to the renormalization method [Kodama, Y., & Taniuti, T. (1977). Higher order approximation in reductive perturbation method 1. Weakly dispersive system. Journal of Physics Society of Japan, 45, 298–310] and the multiple scale expansion method [Kraenkel, R. A., Manna, M. A., & Pereira, J. G. (1995). The Korteweg–deVries hierarchy and long water waves. Journal of Mathematics Physics, 36, 307–320].     

Contributions of higher order terms to nonlinear waves in fluid-filled elastic tubes: strongly dispersive case

In the present work, employing the nonlinear equations of an incompressible, isotropic and elastic thin tube and the approximate equations of an incompressible inviscid fluid, and then utilizing the modified reductive perturbation technique presented by us [15] the amplitude modulation of weakly nonlinear waves is examined. It is shown that the first order term in the perturbation expansion is governed by a nonlinear Schrödinger equation and the second order term is governed by the linearized Schrödinger equation with a nonhomogeneous term. In the longwave limit a travelling wave type of solution to these equations are also given.     

Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players. A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately. Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration. The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment. Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people. This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words. Finally, these are converted into the respective words/sentences within a reasonable time. This provide a real time solution for the deaf people to communicate and interact with the society.     

Light‐Emitting Diodes with Cu‐Doped Colloidal Quantum Wells: From Ultrapure Green,Tunable Dual‐Emission to White Light

Copper‐doped colloidal quantum wells (Cu‐CQWs) are considered a new class of optoelectronic materials. To date, the electroluminescence (EL) property of Cu‐CQWs has not been revealed. Additionally, it is desirable to achieve ultrapure green, tunable dual‐emission and white light to satisfy the various requirement of display and lighting applications. Herein, light‐emitting diodes (LEDs) based on colloidal Cu‐CQWs are demonstrated. For the 0% Cu‐doped concentration, the LED exhibits Commission Internationale de L'Eclairage 1931 coordinates of (0.103, 0.797) with a narrow EL full‐wavelength at half‐maximum of 12 nm. For the 0.5% Cu‐doped concentration, a dual‐emission LED is realized. Remarkably, the dual emission can be tuned by manipulating the device engineering. Furthermore, at a high doping concentration of 2.4%, a white LED based on CQWs is developed. With the management of doping concentrations, the color tuning (green, dual‐emission to white) is shown. The findings not only show that LEDs with CQWs can exhibit polychromatic emission but also unlock a new direction to develop LEDs by exploiting 2D impurity‐doped CQWs that can be further extended to the application of other impurities (e.g., Mn, Ag).     

Role of Learning Algorithm in Neural Network-Based Backcalculation of Flexible Pavements

Nondestructive testing (NDT) methods are widely used for the performance evaluation of flexible pavements. Falling weight deflectometer (FWD), which measures time-domain deflections resulting from applied impulse loads, is the most popular technique among all NDT methods. The evaluation of the FWD data requires the inversion of mechanical pavement properties using a backcalculation tool that includes both a forward pavement response model and an optimization algorithm. Neural networks (NNs) have also emerged as alternative tools that can be employed for pavement backcalculation problems relative to their real-time processing abilities. However, there have been no comprehensive analyses in previous studies that focus on the learning algorithm and the architecture of a NN model, which considerably affect backcalculation results. In this study, 284 different NN models were developed using synthetic training and testing databases obtained by layered elastic theory. Results indicated that both the learning algorithm and network architecture play important roles in the performance of the NN based backcalculation process.