A comprehensive study on mechanical properties of Bi1.8Pb0.4Sr2MnxCa2.2Cu3.0Oy superconductors

This study manifests the crucial change in the mechanical performances of Bi_1.8Pb_0.4Sr₂Mn_xCa_2.2Cu_3.0O_y superconductor samples (x = 0, 0.03, 0.06, 0.15, 0.3 and 0.6) prepared by conventional solid-state reaction method by use of Vickers microhardness (H_v) measurements carried out at different applied loads, (0.245 N ≤ F ≤ 2.940 N). Load dependent microhardness, load independent microhardness, Young’s (elastic) modulus and yield strength values being account for the potential technological and industrial applications are evaluated from the hardness curves and compared with each other. It is found that the H_v, elastic modulus and yield strength obtained decrease (increase) with the enhancement of the applied load for the undoped (doped) samples. Surprisingly, the results of the H_v values illustrate that the samples doped with x = 0.03, 0.06, 0.15, 0.3 and 0.6 exhibit reverse indentation size effect (RISE) feature whereas the pure sample obeys indentation size effect (ISE) behavior. Furthermore, the experimental results are examined with the aid of the available methods such as Meyer’s law, proportional sample resistance model (PSR), elastic/plastic deformation (EPD), Hays–Kendall (HK) approach and indentation-induced cracking (IIC) model. The results inferred show that the hardness values calculated by PSR and EPD models are far from the values of the plateau region, meaning that these models are not adequate approaches to determine the real microhardness value of the Mn doped Bi-2223 materials. On the other hand, the HK approach is completely successful for the explanation of the ISE nature for the pure sample while the IIC model is obtained to be the best model to describe the hardness values of the doped materials exhibiting the RISE behavior. Additionally, the bulk porosity analysis for the samples reveals that the porosity increases monotonously with the increment in the Mn inclusions inserted in the Bi-2223 system, presenting the degradation of the grain connectivity.     

Degradation mechanisms in Li‐ion batteries: a state‐of‐the‐art review

One of the most prominent energy storage technologies which are under continuous development, especially for mobile applications, is the Li‐ion batteries due to their superior gravimetric and volumetric energy density. However, limited cycle life of Li‐ion batteries inhibits their extended use in stationary energy storage applications. To enable wider market penetration of Li‐ion batteries, detailed understanding of the degradation mechanisms is required. A typical Li‐ion battery comprised of an active material, binder, separator, current collector, and electrolyte, and the interaction between these components plays a critical role in successful operation of such batteries. Degradation of Li‐ion batteries can have both chemical and mechanical origins and manifests itself by capacity loss, power fading or both. Mechanical degradation mechanisms are associated with the volume changes and stress generated during repetitive intercalation of Li ions into the active material, whereas chemical degradation mechanisms are associated with the parasitic side reactions such as solid electrolyte interphase formation, electrolyte decomposition/reduction and active material dissolution. In this study, the main degradation mechanisms in Li‐ion batteries are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.     

The organization and control of an evolving interdependent population

Starting with Darwin, biologists have asked how populations evolve from a low fitness state that is evolutionarily stable to a high fitness state that is not. Specifically of interest is the emergence of cooperation and multicellularity where the fitness of individuals often appears in conflict with that of the population. Theories of social evolution and evolutionary game theory have produced a number of fruitful results employing two-state two-body frameworks. In this study, we depart from this tradition and instead consider a multi-player, multi-state evolutionary game, in which the fitness of an agent is determined by its relationship to an arbitrary number of other agents. We show that populations organize themselves in one of four distinct phases of interdependence depending on one parameter, selection strength. Some of these phases involve the formation of specialized large-scale structures. We then describe how the evolution of independence can be manipulated through various external perturbations.     

The Artificial Bee Colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates

In this study the layer optimization was carried out for maximizing the lowest (first) fundamental frequency of symmetrical laminated composite plates subjected to any combination of the three classical boundary conditions, and the applicability of the Artificial Bee Colony (ABC) algorithm to the layer optimization was investigated. The finite element method was used for calculating the first natural frequencies of the laminated composite plates with various stacking sequences. The ABC algorithm maximizes the first natural frequency of the laminated composite plate defined as an objective function. The optimal stacking sequences were determined for two layer numbers, twenty boundary conditions and two plate length/width ratios. The outer layers of the composite plate had a stiffness increasing effect, and as the number of clamped plate edges was increased both he stiffness and natural frequency of the plate increased. The optimal stacking sequences were in good agreement with those determined by the Ritz-based layerwise optimization method (Narita 2003: J. Sound Vibration 263 (5), 1005–1016) as well as by the genetic algorithm method combined with the finite element method.     

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.     

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Engineering design problems are generally large scale or nonlinear or constrained optimization problems. The Artificial Bee Colony (ABC) algorithm is a successful tool for optimizing unconstrained problems. In this work, the ABC algorithm is used to solve large scale optimization problems, and it is applied to engineering design problems by extending the basic ABC algorithm simply by adding a constraint handling technique into the selection step of the ABC algorithm in order to prefer the feasible regions of entire search space. Nine well-known large scale unconstrained test problems and five well-known constrained engineering problems are solved by using the ABC algorithm and the performance of ABC algorithm is compared against those of state-of-the-art algorithms.     

Efficient fast Hartley transform algorithms for hypercube-connectedmulticomputers

Although fast Hartley transform (FHT) provides efficient spectral analysis of real discrete signals, the literature that addresses the parallelization of FHT is extremely rare. FHT is a real transformation and does not necessitate any complex arithmetics. On the other hand, FHT algorithm has an irregular computational structure which makes efficient parallelization harder. In this paper, we propose an efficient restructuring for the sequential FHT algorithm which brings regularity and symmetry to the computational structure of the FHT. Then, we propose an efficient parallel FHT algorithm for medium-to-coarse grain hypercube multicomputers by introducing a dynamic mapping scheme for the restructured FHT. The proposed parallel algorithm achieves perfect load-balance, minimizes both the number and volume of concurrent communications, allows only nearest-neighbor communications and achieves in-place computation and communication. The proposed algorithm is implemented on a 32 node iPSC/2 hypercube multicomputer, high-efficiency values are obtained even for small size FHT problems     

Artificial immune algorithm for IIR filter design

Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse response (IIR) digital filters since these algorithms have the ability of finding global optimal solution in a nonlinear search space. One of the modern heuristic algorithms is the artificial immune algorithm which implements a learning technique inspired by human immune system. However, the immune system has not attracted the same kind of interest from researchers as other heuristic algorithms. In this work, an artificial immune algorithm is described and applied to the design of IIR filters, and its performance is compared to that of genetic and touring ant colony optimisation algorithms.     

A comprehensive survey on optimizing deep learning models by metaheuristics

Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn higher levels of feature hierarchy established by lower level features by transforming the raw feature space to another complex feature space. Although deep networks are successful in a wide range of problems in different fields, there are some issues affecting their overall performance such as selecting appropriate values for model parameters, deciding the optimal architecture and feature representation and determining optimal weight and bias values. Recently, metaheuristic algorithms have been proposed to automate these tasks. This survey gives brief information about common basic DNN architectures including convolutional neural networks, unsupervised pre-trained models, recurrent neural networks and recursive neural networks. We formulate the optimization problems in DNN design such as architecture optimization, hyper-parameter optimization, training and feature representation level optimization. The encoding schemes used in metaheuristics to represent the network architectures are categorized. The evolutionary and selection operators, and also speed-up methods are summarized, and the main approaches to validate the results of networks designed by metaheuristics are provided. Moreover, we group the studies on the metaheuristics for deep neural networks based on the problem type considered and present the datasets mostly used in the studies for the readers. We discuss about the pros and cons of utilizing metaheuristics in deep learning field and give some future directions for connecting the metaheuristics and deep learning. To the best of our knowledge, this is the most comprehensive survey about metaheuristics used in deep learning field.