Superior Thermoelectric Performance of Textured Ca3Co4−xO9+δ Ceramic Nanoribbons

Calcium cobaltite Ca₃Co_4−xO_9+δ (CCO) is a promising p-type thermoelectric (TE) material for high-temperature applications in air. The grains of the material exhibit strong anisotropic properties, making texturing and nanostructuring mostly favored to improve thermoelectric performance. On the one hand multitude of interfaces are needed within the bulk material to create reflecting surfaces that can lower the thermal conductivity. On the other hand, low residual porosity is needed to improve the contact between grains and raise the electrical conductivity. In this study, CCO fibers with 100% flat cross sections in a stacked, compact form are electrospun. Then the grains within the nanoribbons in the plane of the fibers are grown. Finally, the nanoribbons are electrospun into a textured ceramic that features simultaneously a high electrical conductivity of 177 S cm⁻¹ and an immensely enhanced Seebeck coefficient of 200 µV K⁻¹ at 1073 K are assembled. The power factor of 4.68 µW cm⁻¹ K⁻² at 1073 K in air surpasses all previous CCO TE performances of nanofiber ceramics by a factor of two. Given the relatively high power factor combined with low thermal conductivity, a relatively large figure-of-merit of 0.3 at 873 K in the air for the textured nanoribbon ceramic is obtained.     

An efficient numerical algorithm for the study of time fractional Tricomi and Keldysh type equations

Engineering with Computers - This work addresses a hybrid scheme for the numerical solutions of time fractional Tricomi and Keldysh type equations. In proposed methodology, Haar wavelets are used...     

GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure

Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art.

     

A quantum mutation-based backtracking search algorithm

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.

     

Scattering of elastic P- and SV-waves by a circular coated nanofiber based on Gurtin–Murdoch model of surface elasticity: Scattering cross section results

In this article, an analytical study of elastic P- and SV-wave scattering by a circular nanofiber is presented. The nanofiber is assumed to be surrounded by an inhomogeneous interphase layer, and Gurtin–Murdoch's model of surface elasticity is utilized to study the surface/interface effects in the regions between the fiber and interphase and also interphase and matrix. The simultaneous effects of surface elasticity and interphase inhomogeneity are considered here; by taking the inhomogeneous interphase to be composed of several sublayers, a transfer matrix approach is used to find the unknown field variables and, consequently, the scattering cross sections. The results indicate that considering the effects of surface elasticity and interphase inhomogeneity has a considerable impact on the calculated scattering cross sections.     

Electrical and Dielectric Properties of Y<Superscript>3+</Superscript>-Substituted Barium Hexaferrites

In this study, Y³⁺ ion-substituted M-type barium hexaferrites (BaM; BaFe₁₂O₁₉) were fabricated via facile ceramic route. As-prepared powders were characterized by X-ray powder diffractometry (XRD), Fourier transform infrared (FT-IR) spectroscopy, and impedance spectroscopy. XRD (Rietveld) analyses confirmed the presence of a single characterization of all samples (except x = 0.0 and 0.1 samples). The crystallite sizes of products are found in the range of 47.2–63.2 nm. Spectral analysis (FT-IR) also presented the formation of spinel structure for all products. The ac conductivity of the substituted samples was found to initially decrease slightly with increase in Y³⁺ compared with unsubstituted, and then variation tendency changes at the medium substitution ranges are observed with a different attitude against temperature. In the end, the lower conductivity for high substitutions is recorded and increases as functions of frequency while it also increases with the elevation of temperature. It was observed that ac conductivities of products increased by increasing frequency which indicate that observed ac conductivity is due to both electronic and polaron hopping mechanism.     

Lateral Magnetically Modulated Multilayers by Combining Ion Implantation and Lithography

The combination of lithography and ion implantation is demonstrated to be a suitable method to prepare lateral multilayers. A laterally, compositionally, and magnetically modulated microscale pattern consisting of alternating Co (1.6 µm wide) and Co‐CoO (2.4 µm wide) lines has been obtained by oxygen ion implantation into a lithographically masked Au‐sandwiched Co thin film. Magnetoresistance along the lines (i.e., current and applied magnetic field are parallel to the lines) reveals an effective positive giant magnetoresistance (GMR) behavior at room temperature. Conversely, anisotropic magnetoresistance and GMR contributions are distinguished at low temperature (i.e., 10 K) since the O‐implanted areas become exchange coupled. This planar GMR is principally ascribed to the spatial modulation of coercivity in a spring‐magnet‐type configuration, which results in 180° Néel extrinsic domain walls at the Co/Co‐CoO interfaces. The versatility, in terms of pattern size, morphology, and composition adjustment, of this method offers a unique route to fabricate planar systems for, among others, spintronic research and applications.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.