Collective orientational variable

For laminate optimization, which we examined in Section 7.7, we have some strong temptations. We could include many design variables. We could talk about which fibers we would deal with out of a collection of those offered by various manufacturers. In addition, we could consider which matrix materials, what percentage of fibers and matrix that we deal with, what orientation of each of the fiber directions, and the thicknesses of the various laminae. All of those various factors are potential design variables, and, in order to treat them, you must have a fairly complicated optimization scheme to be able to achieve the objective of actually tailoring a laminate for specific design requirements. 

Many investigations of small particles or of other materials may involve the collection and analysis of diffraction patterns from very large numbers of individual specimen regions. For small metal particles, for example, it may not be sufficient to obtain diffraction patterns from just a few particles unless there is reason to believe that all particles are of the same composition, structure, orientation and size or unless these parameters are not of interest. More commonly, it is of interest to obtain statistics on the variability of these parameters. The collection of such... 

Source-dispersion and receptor-oriented models have a common physical basis. Both assume that mass arriving at a receptor (sampling site) from source j was transported with conservation of mass by atmospheric dispersion of source emitted material. From the source-dispersion model point of view, the mass collected at the receptor from source j, Mj, Is the dependent variable which Is equal to the product of a dispersion factor, Dj (which depends on wind speed, wind direction, stability, etc.) and an emission rate factor, Ej, 1. e. , ... 

Within this section, it will be assiuned that the LRA holds in the forms of both Eqs. (8) and (10). Thus no distinction will be made between Cfif) and Cl (i) and the subseript denoting the solute electronic state will be dropped. The dynamical variable AC that describes the solvation response is collective, dependent on the relative distances and orientations of the solute and of all V solvent moleeules in the system. In order to umavel how different types of dynam-ies eontribute to the time correlation of a collective variable such as 8AC, it is useful to introduce the corresponding velocity time correlation, which in this case is the solvation velocity ... 

Collections of Py-MS spectra were published [47], and also specific interpretation techniques were adapted to process Py-MS data, mainly developed for providing pertinent comparisons. Most techniques are oriented toward comparing multi-component fingerprint information. Therefore, the stability of the results (reproducibility) is an important quality that must be maintained when performing Py-MS work. It was shown [47] that variability of 1-3% in peak intensities can be noticed for replicates within 1 day of work and up to 10-11% in long term (one month). 

Fig. 14. Mossbauer data for transferrin in variable conditions and the calculation from the electronic model. Each spectrum on the right was calculated with the assumption of a small randomly oriented field acting on the electronic moment. The data on the right were collected in exactly zero applied field...

Additionally, evaluative data should be collected to assess the experiences of diverse staff and faculty. Climate surveys should be administered to all faculty and staff, with data collected carefully and confidentially, including demographic information— such as race, ethnicity, gender, sexual orientation, and ability status— so that findings can be disaggregated by key demographic variables. As with students, it is useful to conduct exit interviews with faculty and staff who leave the university, in order to learn about their experience of the institution with respect to diversity and inclusion (Wadsworth, 1999). 

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