Effect of silica doping on the densification and grain growth in zinc oxide

The ability of silica (SiO₂) in controlling the densification and grain growth behavior of nano crystalline zinc oxide (ZnO) has been systematically studied. It has been observed that SiO₂ acts as a sintering inhibitor in the ZnO–SiO₂ system up to 4 wt.% limiting value beyond which densification behavior of the system remains almost unchanged, especially above 1100 °C. The addition of SiO₂ to ZnO retards grain growth which in turn results a finer ultimate grain size as compared to the undoped ZnO. However, stabilization in grain size occurs at ≥4 wt.% SiO₂ addition. It has been observed that SiO₂ incorporation changes the grain growth mechanism up to 4 wt.% addition, beyond which no remarkable changes was noticed. The grain growth (n) shows distinctly different slopes as a function of sintering time for the SiO₂ doped ZnO systems than undoped ZnO. The different slopes tend to indicate that different diffusion mechanisms and probably the formation of a secondary phase (Zn–Si–O) at the grain boundary control the densification and grain growth. The thermal expansion coefficient of the system has been found to decrease substantially beyond 4 wt.% SiO₂ addition to ZnO.     

Light Scattering Study of Aggregation Kinetics in Dense,Gelling Aerosols

We report the results of a study of the kinetics of a dense aggregating aerosol system that show that the predictions of the Diffusion Limited Cluster Aggregation (DLCA) model no longer hold when the system is dense. We studied a soot aerosol using the small angle light scattering technique created by exploding a mixture of a hydrocarbon gas and oxygen in a closed chamber. The soot particles started as individual monomers, ca. 38 nm radius, grew to bigger fractal clusters with time and finally stopped evolving after spanning a network across the whole system volume. This spanning is aerosol gelation. The study of the kinetics of the aggregating system showed that as the system evolved from a cluster dilute to cluster dense system, the aggregation kernel homogeneity λ evolved from the dilute limit, DLCA value of zero to a value 0.42 +/? 0.05 at the gel point. This evolution is consistent with previous simulation and theory. The experimental value at the gel point is nearly equal to the value of 0.5 predicted by simulation and theory. In addition, the magnitude of the aggregation kernel showed an increase with increasing volume fraction.     

Nonlinear vibration and stability of a shallow unsymmetrical orthotropic sandwich shell of double curvature with orthotropic core

In this paper, nonlinear behaviours for a shallow unsymmetrical, orthotropic sandwich shell of double curvature with orthotropic core having different elastic characteristics have been studied by a new set of uncoupled differential equations. The face sheet may be of unequal thickness of different materials. However, a restriction that the elements radii of curvature be large compared to the overall thickness of the sandwich has been imposed.

A simple approach used in the present analysis can be applied for stability as well as vibration. For the symmetrical case, where the face sheets are of equal thickness and of same materials, these equations can be shown to reduce to those given by Grigolyuk in 1957. Numerical results of a square rectangular simply supported curved plate, and of a rectangular sandwich cylindrical shell under mechanical and dynamic loading, have been computed and compared with other known results.     

Synthesis and biological activity of novel thiazolidinediones

Novel compounds having a dual pharmacophore were synthesised and evaluated for their insulin sensitiser and anti-inflammatory properties in different animal models.     

A kinetic study of nickel complexation in model systems by adsorptive cathodic stripping voltammetry

Adsorptive cathodic stripping voltammetry (AdCSV) in conjunction with the competing ligand-exchange method (CLEM) was investigated as a tool for measuring dissociation rate coefficients of nickel complexes in model systems. Dimethylglyoxime (DMG) was used as the competing ligand. Citric acid (CA) and a well-characterized fulvic acid (FA) were used as model ligands. The rate coefficients were calculated, and the consistency of equilibrium and kinetic data was discussed. The contributions of the disjunctive pathway (proceeding by the dissociation of the initial complex) and the adjunctive pathway (proceeding by the formation of an intermediate complex as a result of direct attack of the competing ligand on the initial complex) on the overall reactions were investigated. The reactions of Ni-CA or Ni-FA complexes with DMG were demonstrated to proceed by both disjunctive and adjunctive pathways. The predominant pathway for the overall reaction depends on the nickel-to-initial ligand and the DMG-to-initial ligand ratios. The reactions follow predominantly the disjunctive pathway for [DMG] > or = 3 mM and Ni-to-dissolved organic carbon (DOC) ratios greater than 10 nM Ni 2+/g of DOC. Since free nickel ion in freshwaters is reported to be toxic, its rate and pathway of formation are of environmental concern.     

Offline/realtime traffic classification using semi-supervised learning

Identifying and categorizing network traffic by application type is challenging because of the continued evolution of applications, especially of those with a desire to be undetectable. The diminished effectiveness of port-based identification and the overheads of deep packet inspection approaches motivate us to classify traffic by exploiting distinctive flow characteristics of applications when they communicate on a network. In this paper, we explore this latter approach and propose a semi-supervised classification method that can accommodate both known and unknown applications. To the best of our knowledge, this is the first work to use semi-supervised learning techniques for the traffic classification problem. Our approach allows classifiers to be designed from training data that consists of only a few labeled and many unlabeled flows. We consider pragmatic classification issues such as longevity of classifiers and the need for retraining of classifiers. Our performance evaluation using empirical Internet traffic traces that span a 6-month period shows that: (1) high flow and byte classification accuracy (i.e., greater than 90%) can be achieved using training data that consists of a small number of labeled and a large number of unlabeled flows; (2) presence of “mice” and “elephant” flows in the Internet complicates the design of classifiers, especially of those with high byte accuracy, and necessitates the use of weighted sampling techniques to obtain training flows; and (3) retraining of classifiers is necessary only when there are non-transient changes in the network usage characteristics. As a proof of concept, we implement prototype offline and realtime classification systems to demonstrate the feasibility of our approach.     

Evaluation of high‐resolution and mobile display systems for digital radiology in dark and bright environments using human and computational observers

Abstract— As digital display systems replace film traditionally used for reading radiographic images, resource‐intensive acceptance testing must be performed to ensure that quality meets and maintains desired specifications. If machine observers can replace human readers, whose performances are highly variable, the results will be more consistent and less costly. To be effective, however, the automated observers must track human performance. An approach for a model observer, validated with human readers, for the evaluation of the visibility of low‐contrast small targets in high‐resolution and mobile displays under different ambient illumination, will be described. The displays were tested using CDMAM‐like digital phantoms containing disks of varying diameters and contrasts on a flat background. For this task, we find the best indicator of display performance to be the display's ability to represent small luminance contrast, not resolution or pixel size. The results confirm that high‐resolution systems perform better under low illumination while illuminance has a minor impact on the mobile‐display performance. Finally, the results show that the machine observer tracks the performance of human readers. Machine observers with proper validation can replace humans in the acceptance testing procedures, saving the testers both time and money.     

An enhanced Radial Basis Function Network for voltage stability monitoring considering multiple contingencies

This paper proposes a scheme for online voltage stability monitoring using an enhanced Radial Basis Function Network (RBFN). A single RBFN is used to predict MW margins for different contingencies. A sequential learning strategy is used along with a regularization technique to design the RBFN and the weights in the output layer are determined by using linear optimization. The proposed network can be adapted with changing operating scenario of the power system. A network pruning strategy is used to limit the growth of the network size due to adaptive training. The proposed scheme is applied on the New England 39-bus power system model.     

Incremental Beam search

Beam search is a heuristic search algorithm that explores a state-space graph by expanding w most promising nodes at each level (depth) of the graph, where w is called the beam-width which is taken as input from the user. The quality of the solution produced by beam search does not always monotonically improve with the increase in beam-width making it difficult to choose an appropriate beam-width for effective use. We present an algorithm called Incremental Beam Search (IncB) which guarantees monotonicity, and is also anytime in nature. Experimental results on the sliding-tile puzzle, the traveling salesman, and the single-machine scheduling problems show that IncB significantly outperforms basic monotonic methods such as iterative widening beam search as well as some of the state-of-the-art anytime heuristic search algorithms in terms of the quality of the solution produced at the end as well as the anytime performance.     

An Efficient and Scalable Quasi-Aggregate Signature Scheme Based on LFSR Sequences

Aggregate signatures can be a crucial building block for providing scalable authentication of a large number of users in several applications like building efficient certificate chains, authenticating distributed content management systems, and securing path vector routing protocols. Aggregate signatures aim to prevent resources (signature and storage elements, and computation) from growing linearly in the number of signers participating in a network protocol. In this paper, we present an efficient and scalable quasi-aggregate signature scheme, {rm CLFSR}- {rm QA}, based on third-order linear feedback shift register (cubic LFSR) sequences that can be instantiated using both XTR and GH public key cryptosystems. In the proposed {rm CLFSR}-{rm QA} construction, signers sign messages sequentially; however, the verfier need not know the order in which messages were signed. The proposed scheme offers constant length signatures, fast signing, aggregation, and verification operations at each node, and requires the least storage elements (public keys needed to verify the signature), compared to any other aggregate signature scheme. To the best of our knowledge, {rm CLFSR}- {rm QA} is the first aggregate signature scheme to be constructed using LFSR sequences. We believe that the {rm CLFSR}- {rm QA} signature scheme can be catalytic in improving the processing latency as well as reducing space requirements in building secure, large-scale distributed network protocols. We perform extensive theoretical analysis including correctness and security of {rm CLFSR}- {rm QA} and also present a performance (computation and communication costs, storage overhead) comparison of the proposed scheme with well-known traditional constructions.