Sol–gel synthesis of Nd-doped BiFeO3 multiferroic and its characterization

Neodymium doped bismuth ferrite (BiFeO₃, BFO) nanoparticles were successfully synthesized by a facile sol–gel route. The influence of annealing temperature, time, Bi content and solvent on the crystal structure of BFO was studied. Results indicated that the optimum processing condition of BFO products was 550–600 °C/1.5 h with excess 3–6% Bi and ethylene glycol as solvent. On the other hand, Nd³⁺ ion was introduced into the BFO system and the effect of Nd³⁺ concentration on the structure, magnetic and dielectric properties of BFO were investigated. It was found that the magnetization of BFO was enhanced significantly with Nd³⁺ substitution, being attributed to the suppression of the spiral cycloidal magnetic structure led by the crystal structure transition. Furthermore, with increasing Nd³⁺ content, the dielectric constant was found to decrease while the dielectric loss was enhanced, which was mainly due to the hoping conduction mechanism with the reduction of oxygen vacancies.     

Glibenclamide for the Treatment of Ischemic and Hemorrhagic Stroke

Ischemic and hemorrhagic strokes are associated with severe functional disability and high mortality. Except for recombinant tissue plasminogen activator, therapies targeting the underlying pathophysiology of central nervous system (CNS) ischemia and hemorrhage are strikingly lacking. Sur1-regulated channels play essential roles in necrotic cell death and cerebral edema following ischemic insults, and in neuroinflammation after hemorrhagic injuries. Inhibiting endothelial, neuronal, astrocytic and oligodendroglial sulfonylurea receptor 1–transient receptor potential melastatin 4 (Sur1–Trpm4) channels and, in some cases, microglial K_ATP (Sur1–Kir6.2) channels, with glibenclamide is protective in a variety of contexts. Robust preclinical studies have shown that glibenclamide and other sulfonylurea agents reduce infarct volumes, edema and hemorrhagic conversion, and improve outcomes in rodent models of ischemic stroke. Retrospective studies suggest that diabetic patients on sulfonylurea drugs at stroke presentation fare better if they continue on drug. Additional laboratory investigations have implicated Sur1 in the pathophysiology of hemorrhagic CNS insults. In clinically relevant models of subarachnoid hemorrhage, glibenclamide reduces adverse neuroinflammatory and behavioral outcomes. Here, we provide an overview of the preclinical studies of glibenclamide therapy for CNS ischemia and hemorrhage, discuss the available data from clinical investigations, and conclude with promising preclinical results that suggest glibenclamide may be an effective therapeutic option for ischemic and hemorrhagic stroke.     

Mechanical damage characterization of glass fiber-reinforced polymer laminates by ultrasonic maps

A method for simultaneous measurement of the thickness and density for Glass Fiber-Reinforced Polymer (GFRP) laminate plates with ultrasonic waves in C-Scan mode is presented in the form of maps. The method uses three different signals in immersion pulse-echo C-Scan mode. The maps obtained based on the density show the heterogeneity of the material at high resolution at the pixel level (1 × 1 mm²) and therefore they represent an efficient tool to assess and evaluate the damage of the composite structures after manufacturing and after an applied mechanical loading.     

A methodology for the estimation of transverse failure strength of an oxidized lamina from isothermal aging studies

Surface oxidation and ensuing damage substantially decrease the service life of High Temperature Polymer Matrix Composite (HTPMC) structures. Oxidative degradation behavior of composites is strongly dependent on the coupling between chemical and mechanical responses of the material. In a composite lamina, the onset of damage and subsequent coupled acceleration of both damage and oxidation are controlled by the transverse failure strength of the oxidized regions. The direct measurement of this strength from experimentation is challenging and cumbersome. A model-based methodology for estimating the mean transverse failure strength of the oxidized regions of a unidirectional composite is described in this paper. As the strength of the oxidized region is expected to show a high-degree of spatial variability, the estimated mean is shown to be relatively insensitive to the effect of strength variance. The developed methodology is illustrated with isothermal aging data available for a typical high-temperature composite system.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Synergistic effect of co-alloying elements on site preferences and elastic properties of Ni3Al: A first-principles study

The site preferences of co-alloying elements (Mo–Ta, Mo–Re, Mo–Cr) in Ni₃Al are studied using first-principles calculations, and the effects of these alloying elements on the elastic properties of Ni₃Al are evaluated by elastic property calculations. The results show that the Mo–Ta, Mo–Re and Mo–Cr atom pairs all prefer Al–Al sites and the spatial neighbor relation of substitution sites almost has no influence on the site preference results. Furthermore, the Young's modulus of Ni₃Al increases much higher by substituting Al–Al sites with co-alloying atoms, among which Mo–Re has the best strengthening effect. The enhanced chemical bondings between alloying atoms and their neighbor host atoms are considered to be the main strengthening mechanism of the alloying elements in Ni₃Al.     

Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

In-situ TEM observation of structural changes in nano-crystalline CoCrCuFeNi multicomponent high-entropy alloy (HEA) under fast electron irradiation by high voltage electron microscopy (HVEM)

The structural changes induced in a CoCrCuFeNi multicomponent nano-crystalline high-entropy alloy (HEA) under fast electron irradiation were investigated by in-situ transmission electron microscopy (TEM) using a high voltage electron microscope (HVEM). A fine-grained face centered cubic (fcc) single phase was obtained in the sputtered specimens. The fcc solid solution showed high phase stability against irradiation over a wide temperature range from 298 to 773 K, and remained as the main constituent phase even when the samples were irradiated up to 40 displacement per atom (dpa). Moreover, the irradiation did not seem to induce grain coarsening. This is the first report on the irradiation damage in 5-component HEA under MeV electron irradiation.     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.