Influence of oxygen vacancies on the structural,dielectric, and magnetic properties of (Mn,Co) co-doped ZnO nanostructures

We analysed the variation and effect of oxygen vacancies on the structural, dielectric and magnetic properties in case of Mn (4%) and Co (1, 2 and 4%) co-doped ZnO nanoparticles (NPs), synthesized by chemical precipitation route and annealed at 750 °C for 2 h. From the XRD, the calculated average crystallite size increased from15.30?±?0.73 nm to 16.71?±?012 nm, when Co content is increased from 1 to 4%. Enhancement of dopants (Mn, Co) introduced more and more oxygen vacancies to ZnO lattice confirmed from EDX and XPS. The high-temperature annealing leads to reduction of the dielectric properties due to enhancement in grain growth (large grain volume and lesser number of grain boundaries) with the incorporation of Co and Mn ions into the ZnO lattice. The electrical conductivity of the Mn doped and (Mn, Co) co-doped ZnO samples were enhanced due to increase in the volume of conducting grains and charge density (liberation of trapped charge carriers in oxygen vacancies and free charge carriers at higher frequencies). The Mn-doped and (Mn, Co) co-doped ZnO NPs show ferromagnetic (FM) behaviour. The saturation and remnant magnetizations (M_s and M_r) elevates from (0.235 to 1.489)?×?10^?2 and (0.12 to 0.27)?×?10^?2 emu/g while Coercivity (H_c) reduced from 97 to 36 Oe with enhancement in the concentration of dopants in ZnO matrix. Oxygen vacancies were found to be the main reason for room-temperature ferromagnetism (RTFM) in the doped and co-doped ZnO NPs. The results show that the enhanced dielectric and magnetic properties of Mn doped and (Mn, Co) co-doped ZnO is strongly correlated with the concentration of oxygen vacancies. The observed enhanced RTFM, dielectric properties and electrical conductivity makes TM doped ZnO nanoparticles suitable for spintronics, microelectronics and optoelectronics based applications.     

NAROAS: a neural network-based advanced operator support system for the assessment of systems reliability

We have developed and implemented a computerized reliability monitoring system for nuclear power plant applications, based on a neural network. The developed computer program is a new tool related to operator decision support systems, in case of component failures, for the determination of test and maintenance policies during normal operation or to follow an incident sequence in a nuclear power plant. The NAROAS (Neural Network Advanced Reliability Advisory System) computer system has been developed as a modularized integrated system in a C++ Builder environment, using a Hopfield neural network instead of fault trees, to follow and control the different system configurations, for interventions as quickly as possible at the plant. The observed results are comparable and similar to those of other computer system results. As shown, the application of this neural network contributes to the state of the art of risk monitoring systems by turning it easier to perform online reliability calculations in the context of probabilistic safety assessments of nuclear power plants.     

Edge Fracture Prediction Using Uncoupled Ductile Fracture Models for DP780 Sheet

This paper is a contribution to the prediction of edge fracture behavior using uncoupled ductile fracture models. A fully integrated simulation framework for the edge fracture prediction is proposed with the shear-induced pre-damage considered. User-defined material subroutines are coded with uncoupled ductile fracture models (Lou-Huh, Oh, Brozzo) incorporated, which are calibrated using the fracture strains of various loading paths. A series of 3D numerical simulations are performed and compared with the results of hole-expansion tests. The effects of pre-damage field and fracture models are analyzed and discussed.

     

Monitoring particle adsorption by use of laser reflectometry near the critical angle

We investigate the use of laser reflectometry near the critical angle to monitor particle adsorption onto a flat glass surface. Experimental results show that positive particles are adsorbed onto the glass surface and that their adsorption kinetics depend strongly on the volume fraction occupied by the particles in suspension but not appreciably on the particle size. The reflectance near the critical angle is dominated by the particles on the surface, with the contribution of the particles in suspension being very low. We compare the reflectance change near the critical angle with the change in reflectance near the Brewster angle when particles are adsorbed onto the glass surface. We find that reflectometry near the critical angle is 3000 times more sensitive than it is near the Brewster angle. Some optical images are presented to validate our results.     

A hybrid evolutionary approach for solving constrained optimization problems over finite domains

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.     

Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies

Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.     

Equipment selection and justification through total productivity model

Traditional methods of economic analysis to justify equipment selection and use are based on several methods such as the Net Present Value, Break-Even Analysis, Pay-Back Period, Return on Investment, among others. However, with the introduction of automation, Flexible Manufacturing Systems (FMS), and Computer Integrated Manufacturing Systems (CIMS), such economic analyses have often resulted in decisions that management did not like to support but did not have much choice to avoid on account of pressure from manufacturing engineers, design engineers and others in the operations areas. The real problem of equipment selection and justification lies in the methodologies used.

This paper presents a unique approach to equipment justification by applying Sumanth's TOTAL PRODUCTIVITY MODEL, whereby the anticipated impact of the proposed equipment on profitability is assessed from the point of total productivity and the five partial productivities. Then, only if the equipment increases the total productivity level beyond its break even point, the equipment is selected or justified.

This method offers a non-traditional approach to justifying and selecting equipment in all types of environment. A numerical example is presented by using LOTUS 1-2-3^TM on an IBM PC. Advantages and limitations to this approach are discussed to provide a balanced perspective on the important issue of equipment selection and justification.     

Quantum-inspired design of resilient substitution boxes: From coding to hardware implementation

S-boxes constitute a cornerstone component in symmetric-key cryptographic algorithms, such as DES and AES encryption systems. In block ciphers, they are typically used to obscure the relationship between the plaintext and the ciphertext. Non-linear and non-correlated S-boxes are the most secure against linear and differential cryptanalysis. In this paper, we focus on a twofold objective: first, we evolve regular S-boxes with high non-linearity and low auto-correlation properties; then automatically generate evolvable hardware for the obtained S-box. Targeting the former, we use a quantum-inspired evolutionary algorithm to optimize regularity, non-linearity and auto-correlation, which constitute the three main desired properties in resilient S-boxes. Pursuing the latter, we exploit the same algorithm to automatically generate the evolvable hardware designs of substitution boxes that minimize hardware space and encryption/decryption time, which form the two main hardware characteristics. We compare our results against existing and well-known designs, which were produced by using conventional methods as well as through genetic algorithm. We will show that our approach provides higher quality S-boxes coding as well as circuits.     

Robust image-based visual servoing using invariant visual information

This paper deals with the use of invariant visual features for visual servoing. New features are proposed to control the 6 degrees of freedom of a robotic system with better linearizing properties and robustness to noise than the state of the art in image-based visual servoing. We show in this paper that by using these features the behavior of image-based visual servoing in task space can be significantly improved. Several experimental results are provided and validate our proposal.