Physical quality factors parameters

Influence of Freezing and Freeze-Drying Parameters on Physical Quality Factors 63... 

This section is devoted to the discussion and analysis of typical data showing the infiuence of the freezing and freeze-drying parameters on the main physical quality factors encountered during freeze-drying of dmgs. 

We note that by changing the physical parameters of the microcavity and organic material, as well as conditions for polariton excitation, one can influence the dynamics described above. One should also be aware that the evolution times are limited by the actual lifetimes r of small wavevector cavity polaritons. Long lifetimes r on the order of 10 ps can be achieved only in microcavities with high quality factors Q = cut. 

Power efficiency, defined as the ratio of the power delivered to load over the total power dissipation, is the most important parameter in designing power telemetry system. To optimize power efficiency under physical constraints, an accurate solution for power efficiency in terms of design parameters is desired. Earlier work in the literature provides power efficiency formulae based on linear models [106,107]. Although such linear models provide design insights, they are oversimplified for the purpose of optimization. In this section, an expression of the power efficiency is derived. Based on derivations, the optimal component values are obtained and the fundamental upper limit of the power telemetry is concisely represented as a function of the quality factor Q) and coupling coefficient. 

Critical process steps are usually determined by analyzing process parameters (factors in a process that are controllable and measurable) and their respective outcomes. Not all process parameters affect the quality and purity of APIs namely its impurity profile and physical characteristics. For validation purposes, manufacturers should identify, control, and monitor critical process parameters that may influence the critical quality attributes of the API. Process parameters unrelated to quality, such as variables controlled to minimize energy consumption or equipment use, need not be included in process validation. 

Reduction of the subjective values or criteria to a specified compound, group of compounds or physical factor is often a very difficult endeavor, but one by one each of the so-called "qualitative criteria" is being linked to the presence or absence of specific constituents. Sometimes only a single compound is involved, however, often the qualitative character is the result of additive or even synergistic interactions of two or more constituents. Thus, since there are so many different characteristics that can contribute to the total quality of any single food product, it is paramount that precise objective methodology be developed for each parameter. 

Within the last 25 years of X-ray spectroscopy on fusion devices, the theory of He-like ions has been developed to an impressive precision. The spectra can be modeled with deviations not more than 10% on all lines. For the modeling, only parameters with physical meaning and no additional approximation factors are required. Even the small effects due to recombination of H-like atoms, which contribute only a few percent to the line intensity, can be used to explain consistently the recombination processes and hence the charge state distribution in a hot plasma. The measurements on fusion devices such as tokamaks or stellarators allow the comparison to the standard diagnostics for the same parameters. As these diagnostics are based on different physical processes, they provide sensitive tests for the atomic physics used for the synthetic spectra. They also allow distinguishing between different theoretical approaches to predict the spectra of other elements within the iso-electronic series. The modeling of the X-ray spectra of astronomical objects or solar flares, which are now frequently explored by X-ray satellite missions, is now more reliable. In these experiments, the statistical quality of the spectra is limited due to the finite observation time or the lifetime of... 

Another factor to be taken into account is the degree of over determination, or the ratio between the number of observations and the number of variable parameters in the least-squares problem. The number of observations depends on many factors, such as the X-ray wavelength, crystal quality and size, X-ray flux, temperature and experimental details like counting time, crystal alignment and detector characteristics. The number of parameters is likewise not fixed by the size of the asymmetric unit only and can be manipulated in many ways, like adding parameters to describe complicated modes of atomic displacements from their equilibrium positions. Estimated standard deviations on derived bond parameters are obtained from the least-squares covariance matrix as a measure of internal consistency. These quantities do not relate to the absolute values of bond lengths or angles since no physical factors feature in their derivation. 

Another parameter that influences the overall properties of the bulk emulsion is the physical state of the lipid droplets in an emulsion (17, 19, 28-31). Crystallization of lipid droplets in emulsions can be either beneficial or detrimental to product quality. Margarine and butter, the most common water-in-oil emulsions in the food industry, are prepared by a controlled destabilization of oil-in-water emulsions containing partly crystalline droplets. The stability of dairy cream to mechanical agitation and temperature cycling depends on the nature and extent of crystallization in milk-fat globules. It should be noted that because the density of the phases can change as crystallization occurs, the rate at which milkfat droplets cream can be altered as droplets solidify. Emulsion manufacturers should therefore understand which factors influence the crystallization and melting of emulsified substances, and be aware of the effect that droplet phase transitions can have on the properties of emulsions. 

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