Application Of Parameters Research Articles

Theory of Fast Capillary Gas Chromatography - Part 3: Column Performancevs. Gas Flow Rate

At the high pressure drop required for the fast analysis of complex mixtures, the equations for the column plate height, H, and plate duration, Q, as functions of the carrier gas velocity, u, differ substantially from the equations for the same quantities expressed via the carrier gas flow rate, F. While u as an independent pneumatic variable is more convenient for the theoretical studies, F is a more convenient as a control parameter in practical applications. Equations for Hvs.u and for Q vs.u from Parts 1 and 2 are transformed here into expressions for H vs. F and Q vs. F. An efficiency-optimized flow rate (EOF) and a speed-optimized flow rate (SOF) are found. Expressions for these two quantities are considerably simpler than their velocity-based counterparts. In particular, SOF does not depend on column length, film thickness, and pressure drop.

Local-field correction for the ferromagnetic electron gas

A simple model for the local-field correction of a ferromagnetic electron gas is proposed and parametrized by using known constraints of this system. From these constraints, it is shown that the ferromagnetic local-field correction has a local maximum where it exceeds unity, which leads to the possibility of important effects. Useful parameters for practical applications and results for the ground-state properties are obtained and discussed in the full range of densities.

Hydrocarbon composition of sapropelite hydrogenates

Platinum filament pyrolyzers achieve very high temperature and heating rate and can provide useful parameters for practical applications in combustion, pyrolysis and gasification processes. The critical use of an experimental instrument is necessary to provide reliable data. In this work, a commercial pyrolyzer (CDS Pyroprobe 2000) is characterized to obtain a correspondence between the nominal and the effective operating conditions. This is the basis for the modeling estimation of the effective thermal history of the sample during each experimental run. The experimental results obtained performing the devolatilization of coals, biomass and waste fuels using the pyrolyzer are compared with those obtained in a conventional thermogravimetric balance, to evaluate the effects of extremely different operating conditions. The amount of volatile released programming the most severe thermal conditions using the pyrolyzer (thus in conditions more similar to large-scale plants) differs significantly from that of thermogravimetric runs. Global kinetics are obtained fitting the experimental results and using the thermal history of the sample from the model results. They depend strongly on the conditions used for the devolatilization. Global kinetics obtained in the thermogravimetric balance runs (low heating rate) overestimate the rate of devolatilization in the pyrolyzer (high heating rate).

Application of Systematic Error Bounds to Detection Limits for Practical Counting

Overly optimistic estimates of detection limits can result in the use of unrealistic conservatism for decisions about the presence of activity. In some practical counting situations, overly conservative detection limits can result in economically impractical actions. To help preclude such actions, systematic error bounds, uncertainties, and confidence levels can be used when determining critical levels (Lc), detection limits (Ld), and minimum detectable concentrations. This note discusses the selection of such error bounds and the development of detection limit parameters for practical applications. These parameters are shown to be successfully employed in sample activity and measurement process capability decisions for typical counting instruments.

A control panel interface for graphics and image processing applications

This paper describes a graphical interface for application programs. The interface is based on the notion of a control panel . A control panel contains a browsable list of an application's parameters and a set of functions to control the application's execution. A variety of graphical knobs and gauges may be associated with any or all of the parameters to permit fine-grain execution control, including animation of an application's output. The control panel interface is integrated into the framework of an interactive programming environment for graphics and image processing applications. This integration is an important feature of the overall interface design.

A control panel interface for graphics and image processing applications

This paper describes a graphical interface for application programs. The interface is based on the notion of a control panel . A control panel contains a browsable list of an application's parameters and a set of functions to control the application's execution. A variety of graphical knobs and gauges may be associated with any or all of the parameters to permit fine-grain execution control, including animation of an application's output. The control panel interface is integrated into the framework of an interactive programming environment for graphics and image processing applications. This integration is an important feature of the overall interface design.

The Bias in Certain Estimates of the Parameters of the Extreme-Value Distribution

Summary. This paper is mostly concerned with a modification of the maximumlikelihood estimate of the scale parameter of the extreme-value distribution for which the bias can be explicitly obtained. A formula for computing this bias is derived, and bias factors are tabulated for sample sizes from n = 2 to n = 112. A brief comparison is made between this estimator and the optimum linear estimator for a sample of size n = 6. Attention is called to a bias which results from the maximum-likelihood estimate of the second parameter, and formulas for the bias and the variance of this estimate are obtained. In the concluding section, the significance of certain aspects of the maximum-likelihood estimate of the scale parameter in practical applications is briefly discussed. 1. The equations for the maximum-likelihood estimate of the parameters. Given the extreme-value distribution of Type I [2]