The effect of silica characterization on the microstructure of BaFe12O19 ferrites

The anisotropically formed BaFe₁₂O₁₉ ferrites were prepared from the hot-rolled mill scale and silica was added to the ferrite during fine milling in the range 0.15 to 0.50 wt%. These ferrites were sintered at 1220° C for 2 h. The grain growth of the ferrites is dominantly influenced by the sizes of the silica added. Coarse-grain ( 1m) silica tends to promote discontinuous grain growth, which increases drastically with slightly increasing amounts of silica added, while fine-grain ( 0.013m) silica tends to retain fine grain microstructures with the same increasing amount of silica. The average grain size of the ferrite without silica addition was 8 to 10m. The size was increased to as large as 30m on addition of 0.15% coarse-grain silica and the microstructure was full of extremely large grains on the addition of 0.50% coarse-grain silica.     

Observing Inequality: Can Ergonomic Observations Help Interventions Transform the Role of Gender in Work Activity?

Computer Supported Cooperative Work (CSCW) - Work activity ergonomics (sometimes called francophone ergonomics) draws heavily on observation in order to support transformation of work to arrive at...     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

Neural Computing and Applications - Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient...     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Exploring brain connectivity with two-dimensional neural maps

We introduce two-dimensional neural maps for exploring connectivity in the brain. For this, we create standard streamtube models from diffusion-weighted brain imaging data sets along with neural paths hierarchically projected into the plane. These planar neural maps combine desirable properties of low-dimensional representations, such as visual clarity and ease of tract-of-interest selection, with the anatomical familiarity of 3D brain models and planar sectional views. We distribute this type of visualization both in a traditional stand-alone interactive application and as a novel, lightweight web-accessible system. The web interface integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. Anecdotal and quantitative comparisons of the present method with a recently proposed 2D point representation suggest that our representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the 2D path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.     

Intelligent copyright protection system using a matching video retrieval algorithm

Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm. This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes access to illegal content by many video service sites.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

Laser glazing of an Fe-C-Sn alloy

Morphology and geometry of melted zones, cooling rates, microstructure and microhardness in the laser-glazed Fe-4%C-10%Sn alloy have been investigated. The computer simulation on the basis of the moving gaussian source model was used successfully to predict the maximum width and depth of the melted zone and the cooling rate. The microstructure from the surface to the bottom of the laser-melted zone is a non-crystalline phase, dendritic grains and a microcrystalline zone successively. Values of the averaged-spacing of the non-crystalline phase are 0.205⁶ and 0.121⁹nm, respectively; twinned martensites, having an axial ratioc/a of 1.128, existed in dendritic grains, and carbides of Fe₃ C at the interdendritic regions; the microcrystalline zone was composed of -Fe and a new bet (a=0.415 nm,c=0.955 nm) phase. The different microstructure in the melted zone can be explained by the results of the heat flow calculation. A fine eutectic structure (-Fe + Fe₃C) was observed in heat-affected zones. Microhardness of the eutectic structure can be predicted by the empirical relation of fracture stress to the interlamellar spacing of pearlite.     

Electrical properties of monoazacrown ether-substituted phthalocyanines

D.c. conductivities of polycrystalline monoazacrown ether-substituted phthalocyanines (M=2H, Ni, Zn, Pb, Cu) and diphthalocyanine (M=Lu) are measured as Au-MPc-Au sandwiches to be of the order 10^–10–10^–12 S m^–1. Chemical doping with oxidants (e.g. NOBF₄) and enhancing the stacking of planar phthalocyanine moieties through the formation of alkali metal adducts with sodium and potassium ions leads to increase in conductivity of the order 10¹–10². The low conductivity and the diamagneticity of the bis(phthalocyaninato)-lutetium can be ascribed to the lack of radical nature in LuH(Pc)₂. For the a.c. conductivities, lead and lutetium complexes form a group with higher conductivities and the rest show lower conductivity. The conduction activation energies calculated from Arrhenius plots exhibit the lowest value (0.40 eV) for the lutetium compound.Part of this work was presented at NATO-ASI on Semiconductor Materials and Processing Technologies, Erice, Sicily, 1–13 July 1991.