The Distribution of Active ingredients in Supported Catalysts Prepared by Impregnation

Supported catalysts, one of the commonest forms of heterogeneous catalysts in practical use, consist of small crystallites of a catalytically active component dispersed in a porous support of high surface area. Impregnation of the support with an aqueous solution of a compound containing the appropriate catalytic component is an important and frequently used method of preparing this type of catalyst. A nonaqueous solution should be used if the sup port surface is hydrophobic or if hydrolysis of the support surface is to be avoided. In its simplest form, this impregnation method involves three steps: (1) contacting the support with impregnating solution for a certain period of time, (2) drying the support to remove the imbibed liquid, and (3) activating the catalyst by calcination, reduction, or other appropriate treatment.     

Rheological behaviour of processed avocado pulp emulsions

Shear rate vs. shear stress data were obtained on avocado pulp in water emulsions using a concentric cylinder rheometer and fitted to a power law model. Dilution, as volume fraction of water, had a pronounced effect on the apparent viscosity of the pulp emulsions and the Richardson equation, (η_R= exp.( a φ) for the emulsion viscosity fitted the data well. A mean slope coefficient, a , of 4.57 can be used as a first approximation. Enzymatic treatment (40°C, 1 h), is slightly more effective than thermal treatment (65°C, 1 h), in reducing the initial apparent viscosity of the pulp-water emulsions     

Securing DSR against wormhole attacks in multirate ad hoc networks

A wormhole attack is one of the hardest problems to detect whereas it can be easily implanted in any type of wireless ad hoc network. A wormhole attack can easily be launched by the attacker without having knowledge of the network or compromising any legitimate nodes. Most existing solutions either require special hardware devices or make strong assumptions in order to detect wormhole attacks which limit the usability of these solutions. In this paper, we present a security enhancement to dynamic source routing (DSR) protocol against wormhole attacks for ad hoc networks which relies on calculation of round trip time (RTT). Our protocol secures DSR against a wormhole attack in ad hoc networks for multirate transmissions. We also consider the processing and queuing delays of each participating node in the calculation of RTTs between neighbors which to date has not been addressed in the existing literature. This work provides two test cases that show that not taking multirate transmission into consideration results in miss identifying a wormhole attack.     

Novel method to transform ZnO nanorods into interlaced tattered nanosheets by changing the target inclination angle

ZnO nanoparticles were synthesized by liquid-phase pulse laser ablation of a Zn foil target immersed in deionized water. Nanosecond Q-switched Nd:YAG laser pulses of 532 nm were applied to the Zn foil target at a perpendicular and inclined (θ = 45°) angles. X-ray diffraction analysis revealed that both cases feature a ZnO nanostructure with a hexagonal wurtzite structure and that the particle size increases with the inclined target angle. Field emission scanning electron microscopy results of a colloidal drop cast on a glass substrate showed the ZnO has a nanorod structure in the case of a perpendicular target angle and an interlaced tattered nanosheet structure in the case of an inclined target angle. Photoluminescence spectra showed emission peaks in the UV, violet, blue, and green spectral regions, which correspond to excitonic and various defects resulting in an enhancement of emissions at inclined target angle.     

Sampling Search-Engine Results

We consider the problem of efficiently sampling Web search engine query results. In turn, using a small random sample instead of the full set of results leads to efficient approximate algorithms for several applications, such as:

Network visualization by semantic substrates

Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations     

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.     

Just-in-time learning for the prediction of oil sands ore characteristics using GPS data in mining applications

For the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real-time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real-time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just-in-time based data-driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non-core hole points demonstrate promising results.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.