Retesting Defective Circuits to Allow Acceptable Faults for Yield Enhancement

Journal of Electronic Testing - Applications like Recognition, Mining, and Synthesis (RMS) with error-resilience properties can use circuits that produce acceptable results. In other words, the...     

Energy-efficient,EDFA lifetime-aware network planning along with virtualized elastic regenerator placement for IP-over-EON

Photonic Network Communications - In this paper, we focus on energy-efficient network planning (including traffic provisioning) along with optimal placement of virtualized elastic regenerators...     

4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

Multicriteria decision-making using Archimedean aggregation operators in Pythagorean hesitant fuzzy environment

In multicriteria decision-making (MCDM), the existing aggregation operators are mostly based on algebraic t-conorm and t-norm. But, Archimedean t-conorms and t-norms are the generalized forms of t-conorms and t-norms which include algebraic, Einstein, Hamacher, Frank, and other types of t-conorms and t-norms. From that view point, in this paper the concepts of Archimedean t-conorm and t-norm are introduced to aggregate Pythagorean hesitant fuzzy information. Some new operational laws for Pythagorean hesitant fuzzy numbers based on Archimedean t-conorm and t-norm have been proposed. Using those operational laws, Archimedean t-conorm and t-norm-based Pythagorean hesitant fuzzy weighted averaging operator and weighted geometric operator are developed. Some of their desirable properties have also been investigated. Afterwards, these operators are applied to solve MCDM problems in Pythagorean hesitant fuzzy environment. The developed Archimedean aggregation operators are also applicable in Pythagorean fuzzy contexts also. To demonstrate the validity, practicality, and effectiveness of the proposed method, a practical problem is considered, solved, and compared with other existing method.     

Sonochemical synthesis of PANI-BiVO4-GO semiconductor nanocomposite highly efficient visible-light photocatalytic performance

Abstract

Polyaniline/Bismuth Vanadate/Graphene Oxide (PANI-BiVO₄-GO) or BGPA composite was prepared by sonochemical deposition of bismuth vanadate-graphene oxide (BiVO₄) nanoparticles on the surface of polyaniline (PANI). The best photocatalytic degradation performance was obtained by 5wt% BGPA composites for MB, RhB, and SO dyes, which is approximately 4 times higher than that of 1% BGPA. Meanwhile, the photocatalytic stability of BiVO₄ was significantly improved by introducing PANI into the PANI-BiVO₄-GO composite. The dramatic promotion of the photocatalytic degradation performance and the photocatalytic stability can be attributed to the formation of a heterojunction free electron between PANI and BiVO₄-GO. The existence of those extra free electrons can dramatically enhance the efficiency of the photogenerated electrons, which accelerate the transfer of photogenerated holes from BiVO₄-GO to PANI, and therefore inhibit the self-oxidation of BiVO₄.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Cubic–Quartic Optical Solitons with Differential Group Delay for Kudryashov’s Model by Extended Trial Function

Journal of Communications Technology and Electronics - This paper implements mathematically rigorous extended trial function algorithm to address cubic–quartic optical solitons in...     

Design and performance study of phase‐locked loop using fractional‐order loop filter

The present work reports the realization of an analog fractional‐order phase‐locked loop (FPLL) using a fractional capacitor. The expressions for bandwidth, capture range, and lock range of the FPLL have been derived analytically and then compared with the experimental observations using LM565 IC. It has been observed that bandwidth and capture range can be extended by using FPLL. It has also been found that FPLL can provide faster response and lower phase error at the time of switching compared to its integer‐order counterpart. Copyright © 2014 John Wiley & Sons, Ltd.