Thermal stability in bulk cryomilled ultrafine-grained 5083 Al alloy

Thermal stability in bulk ultrafine-grained (UFG) 5083 Al that was processed by gas atomization followed by cryomilling, consolidation, and extrusion, and that exhibited an average grain size of 305 nm, was investigated in the temperature range of 473 to 673 K (0.55 to 0.79 T _m , where T _m is the melting temperature of the material) for different annealing times. Appreciable grain growth was observed at temperatures > 573 K, whereas there was limited grain growth at temperatures < 573 K even after long annealing times. The values of the grain growth exponent, n, deduced from the grain growth data were higher than the value of 2 predicted from elementary grain growth theories. The discrepancy was attributed to the operation of strong pinning forces on boundaries during the annealing treatment. An examination of the microstructure of the alloy suggests that the origin of the pinning forces is most likely related to the presence of dispersion particles, which are mostly introduced during cryomilling. Two-grain growth regimes were identified: the low-temperature region (<573 K) and the high-temperature region (>573 K). For temperatures lower than 573 K, the activation energy of 25 ± 5 kJ/mol was determined. It is suggested that this low activation energy represents the energy for the reordering of grain boundaries in the UFG material. For temperatures higher than 573 K, an activation energy of 124 ± 5 kJ/mol was measured. This value of activation energy, 124 ± 5 kJ/mol, lies between that for grain boundary diffusion and lattice diffusion in analogous aluminum polycrystalline systems. The results show that the strength and ductility of bulk UFG 5083 Al, as obtained from tensile tests, correlate well with substructural changes introduced in the alloy by the annealing treatment.     

Some aspects of deformation behavior

The deformation behavior of several single- and two-phase coarse microstructures has been examined using microhardness measurements. It has been found that the strength response of a coarse phase in isolation is distinctly different from its response when it exists in a two-phase system. The second phase alters the mechanical state of the first one andvice versa even in the plastically undeformed condition. This phenomenon is explained in terms of the existence of an appreciable amount of residual stresses in two-phase coarse microstructures. These stresses primarily arise due to the difference in thermal expansion coefficients of the phases. The in- fluence of elastic stress field on microhardness response is shown with a new type of experiment to support the proposed explanation. The present results question the existing expressions for deformation modeling of multiphase materials because of the uncertainties in the estimation of the average strength of the phases in a two-phase system.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

Developing a Dynamic Cluster Quantization based Lossless Audio Compression (DCQLAC)

In this paper, an approach has been made to produce a compressed audio without losing any information. The proposed scheme is fabricated with the help of dynamic cluster quantization followed by Burrows Wheeler Transform (BWT) and Huffman coding. The encoding algorithm has been designed in two phases, i.e., dynamic cluster selection (of sampled audio) followed by dynamic bit selection for determining quantization level of individual cluster. Quantization level of each cluster is selected dynamically based on mean square quantization error (MSQE). Bit stream is further compressed by applying Burrows Wheeler Transform (BWT) and Huffman code respectively. Experimental results are supported with current state-of-the-art in audio quality analysis (like statistical parameters (compression ratio, space savings, SNR, PSNR) along with other parameters (encoding time, decoding time, Mean Opinion Score (MOS) and entropy) and compared with other existing techniques.

     

AVCOL: Availability-aware information aggregation in large distributed systems under uncollaborative behavior

Aggregation of system-wide information in large-scale distributed systems, such as p2p systems and Grids, can be unfairly influenced by nodes that are selfish, colluding with each other, or are offline most of the time. We present AVCOL, which uses probabilistic and gossip-style techniques to provide availability-aware aggregation. Concretely, AVCOL is the first aggregation system that: (1) implements any (arbitrary) global predicate that explicitly specifies any node’s probability of inclusion in the global aggregate, as a mathematical function of that node’s availability (i.e., percentage time online); (2) probabilistically tolerates large numbers of selfish nodes and large groups of colluders; and (3) scales well with hundreds to thousands of nodes. AVCOL uses several unique design decisions: per-aggregation tree construction where nodes are allowed a limited but flexible probabilistic choice of parents or children, probabilistic aggregation along trees, and auditing of nodes both during aggregation as well as in gossip-style (i.e., periodically). We have implemented AVCOL, and we experimentally evaluated it using real-life churn traces. Our evaluation and our mathematical analysis show that AVCOL satisfies arbitrary predicates, scales well, and withstands a variety of selfish and colluding attacks.     

Biosynthesis of AgNPs using aqueous leaf extract of Ipomoea eriocarpa and their anti‐inflammatory effect on carrageenan‐induced paw edema in male Wistar rats

Silver nanoparticles (AgNPs) were synthesised from aqueous Ag nitrate through a simple, competent and eco‐friendly method using the leaf extract of Ipomoea eriocarpa as reducing as well as capping agent. Ultraviolet–visible absorption spectroscopy was used to confirm the formation of AgNPs which displayed the substantiation of surface plasmon bands at 425 nm. The NPs were also characterised using Fourier transformer infrared spectroscopy, X‐ray diffraction method, transmission electron microscope and zeta potential. The characterisation study confirmed the formation of AgNPs, their spherical shape and average diameter of 12.85 ± 8.65 nm. Zeta potential value of −20.5 mV suggested that the AgNPs are stable in the suspension. The aqueous extract and the AgNPs were further screened for in vivo anti‐inflammatory activity using carrageenan‐induced paw edema in male Wistar rats. The study demonstrated that the AgNPs (1 ml kg⁻¹) had a significant (p  < 0.05) anti‐edemic effect and inhibition was observed from the first hour (21.31 ± 1.34) until the sixth hour (52.67 ± 1.41), when the inhibitory effect was greatest and superior to the aqueous extract and the standard, diclofenac.Inspec keywords: silver, nanoparticles, nanofabrication, ultraviolet spectra, visible spectra, absorption coefficients, surface plasmons, Fourier transform infrared spectra, X‐ray diffraction, transmission electron microscopy, suspensions, drugs, nanomedicineOther keywords: biosynthesis, aqueous leaf extract, ipomoea eriocarpa, antiinflammatory effect, carrageenan‐induced paw edema, male Wistar rats, silver nanoparticles, aqueous nitrate, capping agent, ultraviolet‐visible absorption spectroscopy, surface plasmon band, Fourier transformer infrared spectroscopy, X‐ray diffraction, transmission electron microscopy, zeta potential, spherical shape, suspension, aqueous extract, in vivo antiinflammatory activity, antiedemic effect, inhibitory effect, diclofenac, wavelength 425 nm, size 12.85 nm to 8.65 nm, Ag     

Parallel crypto-devices for GF(p) elliptic curve multiplication resistant against side channel attacks

All elliptic curve cryptographic schemes are based on scalar multiplication of points, and hence its faster computation signifies faster operation. This paper proposes two different parallelization techniques to speedup the GF(p) elliptic curve multiplication in affine coordinates and the corresponding architectures. The proposed implementations are capable of resisting different side channel attacks based on time and power analysis. The 160, 192, 224 and 256 bits implementations of both the architectures have been synthesized and simulated for both FPGA and 0.13μ CMOS ASIC. The final designs have been prototyped on a Xilinx Virtex-4 xc4vlx200-12ff1513 FPGA board and performance analyzes carried out. The experimental result and performance comparison show better throughput of the proposed implementations as compared to existing reported architectures.     

Dynamic Grover search: applications in recommendation systems and optimization problems

In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212–219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212–219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.