Surface diffusion of hydrocarbons in activated carbon: Comparison between constant molar flow, differential permeation and differential adsorption bed methods

This paper presents the comparison of surface diffusivities of hydrocarbons in activated carbon. The surface diffusivities are obtained from the analysis of kinetic data collected using three different kinetics methods- the constant molar flow, the differential adsorption bed and the differential permeation methods. In general the values of surface diffusivity obtained by these methods agree with each other, and it is found that the surface diffusivity increases very fast with loading. Such a fast increase can not be accounted for by a thermodynamic Darken factor, and the surface heterogeneity only partially accounts for the fast rise of surface diffusivity versus loading. Surface diffusivities of methane, ethane, propane, n-butane, n-hexane, benzene and ethanol on activated carbon are reported in this paper.

A new diffusion and flow theory for activated carbon from low pressure to capillary condensation range

In this paper, we develop a theory for diffusion and flow of pure sub-critical adsorbates in microporous activated carbon over a wide range of pressure, ranging from very low to high pressure, where capillary condensation is occurring. This theory does not require any fitting parameter. The only information needed for the prediction is the complete pore size distribution of activated carbon. The various interesting behaviors of permeability versus loading are observed such as the maximum permeability at high loading (occurred at about 0.8-0.9 relative pressure). The theory is tested with diffusion and flow of benzene through a commercial activated carbon, and the agreement is found to be very good in the light that there is no fitting parameter in the model. (C) 2001 Elsevier Science B.V. All rights reserved.

Mass balance characterisation of Al-7Si-Mg alloy microstructures as a function of solution treatment time

Mass balance calculations were performed to model the effect of solution treatment time on A356 and A357 alloy microstructures. Image analysis and electron probe microanalysis were used to characterise microstructures and confirm model predictions. In as-cast microstructures, up to 8 times more Mg is tied up in the pi-phase than in Mg2Si. The dissolution of pi is accompanied by a corresponding increase in the amount of beta-phase. This causes the rate of pi dissolution to be limited by the rate of beta formation. It is predicted that solution treatments of the order of tens of minutes at 540degreesC produce near-maximum T6 yield strengths, and that Mg contents in excess of 0.52 wt% have no advantage.

Materialized view selection under the maintenance time constraint

A data warehouse is a data repository which collects and maintains a large amount of data from multiple distributed, autonomous and possibly heterogeneous data sources. Often the data is stored in the form of materialized views in order to provide fast access to the integrated data. One of the most important decisions in designing a data warehouse is the selection of views for materialization. The objective is to select an appropriate set of views that minimizes the total query response time with the constraint that the total maintenance time for these materialized views is within a given bound. This view selection problem is totally different from the view selection problem under the disk space constraint. In this paper the view selection problem under the maintenance time constraint is investigated. Two efficient, heuristic algorithms for the problem are proposed. The key to devising the proposed algorithms is to define good heuristic functions and to reduce the problem to some well-solved optimization problems. As a result, an approximate solution of the known optimization problem will give a feasible solution of the original problem. (C) 2001 Elsevier Science B.V. All rights reserved.

Estimating economic relationships subject to firm- and time-varying equality and inequality constraints

Applied econometricians often fail to impose economic regularity constraints in the exact form economic theory prescribes. We show how the Singular Value Decomposition (SVD) Theorem and Markov Chain Monte Carlo (MCMC) methods can be used to rigorously impose time- and firm-varying equality and inequality constraints. To illustrate the technique we estimate a system of translog input demand functions subject to all the constraints implied by economic theory, including observation-varying symmetry and concavity constraints. Results are presented in the form of characteristics of the estimated posterior distributions of functions of the parameters. Copyright (C) 2001 John Wiley Sons, Ltd.

Time of treatment influences the appearance of drug-resistant parasites in Plasmodium falciparum infections

A deterministic mathematical model which predicts the probability of developing a new drug-resistant parasite population within the human host is reported, The model incorporates the host's specific antibody response to PfEMP1, and also investigates the influence of chemotherapy on the probability of developing a viable drug-resistant parasite population within the host. Results indicate that early, treatment, and a high antibody threshold coupled with a long lag time between antibody stimulation and activity, are risk factors which increase the likelihood of developing a viable drug-resistant parasite population. High parasite mutation rates and fast PfEMP1 var gene switching are also identified as risk factors. The model output allows the relative importance of the various risk factors as well as the relationships between them to be established, thereby increasing the understanding of the conditions which favour the development of a new drug-resistant parasite population.

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Path integrals for continuous-time Markov chains

This note presents a method of evaluating the distribution of a path integral for Markov chains on a countable state space.