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The particle distributions of the energetic protons, which are the source of the EMIC waves, is assumed as a subtracted Maxwellian, which is a distribution function modeling the loss cone distribution (Baumjohann and Treumann 1997).
This similarity justifies the conventional approach to modeling the loss of stability of a uniform plastic flow as a local property, governed by the homogenized behavior of the dislocation ensemble.
This work extends these approaches to account for repeated admissions by explicitly modeling the loss of carriage following discharge.
The approach extends recent methodological developments by simultaneously allowing for imperfect screening sensitivity, incorporating ward- and patient-level covariates, and, by explicitly modeling the loss or acquisition of colonization between repeated admissions, accounting for longer-term dependencies resulting from repeated admissions of the same patients.
Utilizing analytical modeling, the loss factors of the core materials was determined based upon the measured structural loss factors for a frequency range up to 4000 Hz.
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These m observations are used to model the loss distribution for an individual cell of Table 1.
We develop some analytical models which can model the loss performance more accurately than the traditional methods.
Furthermore, this steel/concrete interface constitutive model can be extended to model the loss of bond properties due to corrosion.
Using measurements of residential broadband networks in the UK and Finland, we show that existing models for packet loss, such as the Gilbert model and simple hidden Markov models, do not effectively model the loss patterns seen in this environment.
Indeed, most reports of adult-onset disorders or late-onset neurodegenerative conditions have not been able to model the loss of neurons that is typical in human disease.
We use an exponential function to model the loss rate of neofunctionlization, i.e., μ t = αe− tα for 0 < α < 1.
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