Structure–thermomechanical property correlation of moisture cured poly(urethane-urea)/clay nanocomposite coatings

A series of poly(urethane-urea)/clay nanocomposite coatings were prepared by moisture curing of isophorone diisocyanate (IPDI) capped hydroxyl terminated polybutadiene (HTPB)/clay dispersions in a relative humidity (RH) of 50% at 25 °C. The curing progress was studied by periodic measurement of gel fraction of the coating samples. The studies revealed tortuosity effects of clay toward moisture diffusion, thus delaying the induction period of gelation, time for complete cure and rate of gel formation of the nanocomposite coatings. The clay platelets were found to be intercalated in the poly(urethane-urea) matrix, evidenced from wide angle X-ray diffraction (WAXD) and transmission electron microscopy (TEM). Effects of nanoclay on state of the hard and soft segments were investigated by WAXD, differential scanning calorimetry (DSC), temperature modulated DSC (MDSC) and solid-state nuclear magnetic resonance spectroscopy (NMR). WAXD studies revealed unusually ordered hard segment morphology of the moisture cured poly(urethane-urea) and its nanocomposites. Slower soft segment dynamics upon clay addition was evident from concentration dependant broadening of the line widths of the NMR peaks, and decreasing reversible heat capacity changes at soft segment glass transition. The volume fraction of immobilized soft segments of the nanocomposites was determined from MDSC and was found to increase linearly with clay loading. The mechanical property analysis showed simultaneous reinforcement and toughening effect of nanoclay on the MCPU matrix. The increment in mechanical property of the nanocomposites varied proportionately with the volume fraction of immobilized soft segments.     

Markov blanket-embedded genetic algorithm for gene selection

Microarray technologies enable quantitative simultaneous monitoring of expression levels for thousands of genes under various experimental conditions. This new technology has provided a new way of biological classification on a genome-wide scale. However, predictive accuracy is affected by the presence of thousands of genes many of which are unnecessary from the classification point of view. So, a key issue of microarray data classification is to identify the smallest possible set of genes that can achieve good predictive accuracy. In this study, we propose a novel Markov blanket-embedded genetic algorithm (MBEGA) for gene selection problem. In particular, the embedded Markov blanket-based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on synthetic and microarray benchmark datasets suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. A detailed comparative study with other methods from each of filter, wrapper, and standard GA shows that MBEGA gives a best compromise among all four evaluation criteria, i.e., classification accuracy, number of selected genes, computational cost, and robustness.     

An Improved Process for Selective Liquid-Phase Air Oxidation of Toluene

An improved process for the oxidation of toluene to obtain benzaldehyde and benzyl alcohol with high selectivities using a Co/Mn/Br^– composite catalytic system in liquid phase is described. A protocol for recovery and reuse of the composite catalyst is developed. The use of low concentrations of composite catalytic systems aimed at minimizing corrosion of the reaction, and higher concentrations of toluene affording higher productivity and recyclability of the catalyst giving high turnover number, are the remarkable achievements of the present methodology. Investigation into the recycle, aging and spectroscopic studies of the catalytic system improves the understanding of the process, chemistry and mechanism of the reaction. As the market demand for each product fluctuates, the dynamic system developed here to meet changing demands is very important to obtain one of the products in excess quantities with a change of the ratio of Br^–/Cl^–.     

High pressure synthesis of terephthalic acid from p-dichlorobenzene

Terephthalic acid was obtained from p-dichlorobenzene, carbon monoxide and water when the reaction was carried out in the presence of nickel iodide supported on silica gel (Ni:SiO₂ = 50:50) the catalyst which showed the best catalytic activity among those tested. The maximum conversion of p-dichlorobenzene to terephthalic acid at the optimum conditions was 14.9%.     

Effect of microstructure on electrical properties of Li and Cr substituted nickel oxide

LCNO (Li_0.35Cr_0.10Ni_0.55O) sample was prepared by modified sol–gel method and annealed at different temperatures (400, 800 and 1000 °C) in order to have variation in the size of grains and grain boundaries. The crystallinity and phase purity have been studied by employing the X-ray diffraction (XRD) technique. All the samples are crystallize to cubic symmetry with \(Fm\overline 3 m\) space group and, XRD patterns could be analysed by employing the Rietveld method. The microstructural and elemental analysis of the sample has been carried out by using the field emission scanning electron microscopy (FESEM). The grain size increases with the increase in annealing temperature which leads to increase the dielectric constant with the grain size. Interestingly, the enhancement of dielectric constant with the increase in grain size could be explained by the Barrier Layer Capacitances (BLCs) model. The frequency dispersion of dielectric constant could be explained by the Maxwell Wagner relaxation model. Furthermore, it is also observed that the activation energy obtained from dielectric relaxation analysis is comparable with the activation energy obtained by impedance analysis (Cole–Cole). In addition, the correlation between microstructure (grains and grain boundaries) with electrical transport properties of LCNO has been reported.     

A multi-agent-based framework for cloud service discovery and selection using ontology

As cloud computing is getting matured day by day, there has been overwhelming interest among the users to avail a plethora of cloud services. Often, these services appear identical in terms of their functionality though they differ in pricing models, computational power, storage policies and Quality-of-Service parameters making the process of service discovery and selection an intricate task. In the absence of any standard specifications, cloud service providers continue to use their own vocabulary and this further complicates the selection process. Even popular search engines like Google and MSN are not efficient enough to properly identify the most appropriate cloud service that can meet customer requirements. Thus, in the presence of multiple selection parameters and constraints, selecting a required cloud service is a daunting task. In order to address this issue, we work toward developing a reasoning mechanism to optimally resolve the similarities across cloud services by using cloud ontology. A multi-agent-based framework has been proposed for effective cloud service discovery and selection with the help of a standardized service registry and by employing semantically guided searching process.     

A breakable multi-item multi stage solid transportation problem under budget with Gaussian type-2 fuzzy parameters

This paper is based on two mathematical models for multi-item multi-stage solid transportation problem with budget on total transportation cost in Gaussian type-2 fuzzy environment considering the fixed opening charge and operating cost in distribution center. The first model is about transportation of breakable/damageable items, and the second one considers non breakable/damageable items. The main aspect here is to develop the mathematical formulation of multi stage related solid transportation problem where several items are available for transportation. In order to deal with the Gaussian type-2 fuzziness, two chance-constrained programming models are developed based on generalized credibility measures for the objective function as well as the constraints sets with the help of the CV-based reductions method. Finally the reduced model is turned into its equivalent parametric programming problem. The problem is of high complexity and is difficult to find the optimal solution by any classical method and hence a time and space based meta-heuristic Genetic Algorithm has been proposed. Also the equivalent crisp models are solved using GA and LINGO 13.0 and after comparison, GA results are better. The proposed models and techniques are finally illustrated by providing numerical examples. Some sensitivity analysis and particular cases are presented and discussed. Degrees of efficiency is also evaluated for both the techniques.     

Automated Framework for Water Looped Network Equilibrium

In this paper, a novel algorithm is proposed for balancing water looped network in steady state through a fully automated general framework of hydraulic networks regardless of their topological complexity. The model is developed by combining the following two steps, firstly a set of independent loops are identified based on a graph theoretical analysis in a looped network. Further the second step is devoted to the equilibrium process by determining the flow rate distribution within the network ducts and the pressure in the delivery nodes. The above such equilibrium process gives rise to a system of non linear algebraic equations which are solved numerically using both Hardy Cross (HC) and Newton Raphson (NR) methods. In HC method, the flow correction term is modified and a generalized expression is given to consider various possibilities of independent loops selection. Some real networks topologies that were commonly used as benchmarks, for testing various independent loops selection algorithms, are taken as case studies to apply the general automatic framework for hydraulic network analysis. Such network analysis enhances proving the applicability as well as the effectiveness of the proposed approach. Also, during the equilibrium procedure, it is proved that NR method is capable of producing accurate results and it converges more rapidly comparing to the widely used HC method. Moreover, it is demonstrated that NR’s iterative process, contrary to HC’s one, converges to reliable results even with a choice of random initial flow rates which makes a NR algorithm quite simple to implement without affecting the accuracy of the results.     

Tailoring the microstructural,magnetic and dielectric properties of vanadium ions substituted nickel ferrite nanocrystals

This paper describes the composition-dependent microstructural, magnetic and dielectric properties of vanadium ions doped nickel ferrite nanoparticles. All the nanoferrite samples (NiV_xFe_2-xO₄: x = 0.00, 0.05, 0.10 and 0.15) were prepared using conventional co-precipitation method. X-ray diffraction patterns verified the single cubic spinel phase formation and achievement of nanosized ferrite particles with a homogeneous distribution, which was also supported by HRTEM micrographs. Mean crystallite diameter was seen in the range of 05 nm to 12 nm as evaluated from Williamson-Hall (W-H) curves for the different doped samples. A compressive nature microstrain was noticed in all the samples and was also seen to reduce with the enhancement of vanadium ions content. A blue shift was detected in the indirect band gap for higher vanadium doped samples. Magnetization at the saturation point as well as coercive field both were observed to reduce at room temperature with increasing vanadium concentration in nanosized nickel ferrites. Room temperature dielectric characteristics of all the synthesized samples ensured that the elementary charge conduction process was governed by the hopping of electrons. Cole-Cole plots also showed that the grain boundaries play an important task in deciding the dielectric responses.