Ultra-compact with improved data rate optical encoder based on 2D linear photonic crystal ring resonator

Photonic Network Communications - In the present work, a high-speed optical encoder is proposed based on two-dimensional photonic crystal ring resonator using coupled mode theory and resonance...     

Resistive organic memory devices based on nitrogen-doped CNTs/PSS composites

Journal of Materials Science: Materials in Electronics - Non-volatile organic memory devices were fabricated using polystyrene sulfonate (PSS)?+?nitrogen-doped multi-walled carbon...     

MoSe2 nanoflowers as a counter electrode for quantum dots sensitized solar cells

Journal of Materials Science: Materials in Electronics - Layered transition metal dichalcogenides hold tunable and promising photoelectrochemical properties. MoSe2 is a potential candidate of the...     

Highly enhanced energy storage properties of H2O2-hydroxylated rare earth ferrites (LaFeO3 and GdFeO3) nanofillers in poly(vinylidene fluoride)-based nanocomposite films

Journal of Materials Science: Materials in Electronics - Pure PVDF has higher breakdown strength but low dielectric and ferroelectric properties. Thus, we synthesized the LaFeO3 and GdFeO3...     

A numerical study on nonlinear DPL model for analyzing heat transfer in tissue during thermal therapy

In the present paper, therapeutic treatment of infected tumorous cells has been studied through mathematical modeling and simulation of heat transfer in tissues by using a nonlinear dual-phase lag bioheat transfer model with Dirichlet boundary condition. The components of volumetric heat source in this model such as blood perfusion and metabolism are assumed experimentally validated temperature-dependent function, which gives more accurate temperature distribution in tissues through this model. We have used the finite difference and RK (4, 5) techniques of numerical methods to solve the proposed problem and obtained the exact solution in a particular case. After comparison, we got a good agreement between them. We have used dimensionless quantities throughout this paper. The effect of relaxation and thermalization time with respect to dimensionless temperature distribution has been analyzed in the treatment process.     

Influence of radiation and viscous dissipation on MHD heat transfer Casson nanofluid flow along a nonlinear stretching surface with chemical reaction

The present article investigates the influence of Joule heating and chemical reaction on magneto Casson nanofluid phenomena in the occurrence of thermal radiation through a porous inclined stretching sheet. Consideration is extended to heat absorption/generation and viscous dissipation. The governing partial differential equations were transformed into nonlinear ordinary differential equations and numerically solved using the Implicit Finite Difference technique. The article analyses the effect of various physical flow parameters on velocity, heat, and mass transfer distributions. For the various involved parameters, the graphical and numerical outcomes are established. The analysis reveals that the enhancement of the radiation parameter increases the temperature and the chemical reaction parameter decreases the concentration profile. The empirical data presented were compared with previously published findings.     

Dufour and Soret influence on MHD boundary layer flow of a Maxwell fluid over a stretching sheet with nanoparticles

An analysis has been carried out to examine the heat and mass transfer properties of a two-dimensional incompressible electrically conducting Maxwell fluid over a stretching sheet in the existence of Soret, Dufour, and nanoparticles. In many practical scenarios, such as the polymer extrusion process, the problem presented here is crucial. The flow is examined in terms of the impacts of magnetohydrodynamics and elasticity. Brownian motion and thermophoresis are incorporated into the transport equations. Using adequate similarity variables, the governing partial differential equations and related boundary conditions are non-dimensionalized. The fourth–fifth-order Runge–Kutta–Fehlberg procedure is utilized to solve the consequent transformed ordinary differential equations. The effects of various embedded thermo-physical parameters on the fluid velocity, temperature, concentration, Nusselt number, and Sherwood number have been determined and discussed quantitatively. A comparison of a special case of our results with the one previously reported in the literature shows a very good agreement. An increase in the values of Du and Sr leads to an increase in the temperature and concentration distribution. Nusselt number estimates decrease as Nb estimations increase. Furthermore, this study leads to the study of different flows of electrically conducting fluid over a stretching sheet problem that includes the two-dimensional nonlinear boundary equations.     

Annealing of Mineral-Like Host Matrices for High-Level Waste Immobilization

Inorganic Materials - We have studied the effect of annealing on the chemical and physical properties of mineral-like host matrices for immobilization of the rare-earth–actinide fraction from...     

Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

A Hybrid Fractal Metamaterial Inspired Multiband Antenna for Wireless Applications

Wireless Personal Communications - In this paper a multiband (hepta-band) antenna loaded with hybrid fractal structures and metamaterial cell (SRR/CSRR) is proposed to cover the wireless...