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Many process models in the chemical industry involve discrete phenomena superimposed on continuous system dynamics.
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SE has introduced many process improvement models and techniques, and Software Development Life Cycle SDLCC) models, with some variabilities and commonalties.
The processes are described in terms of finite state machines (FSMs) and their composition is achieved via a set of composition operators, common to many process algebra models.
PEMs complement the many ecophysiological process models that simulate carbon exchange [13].
In this context, this study investigates the feasibility of using the normalised difference vegetation index (NDVI) derived from remote sensing data to provide indirect estimates of: (1) the leaf area index (LAI), which is a key-variable of many crop process models; and (2) crop coefficients, which represent the ratio of actual (AET) to reference (ET0) evapotranspiration.
PEMs complement the many ecophysiological process models that simulate carbon exchange [ 13].
Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for northern forest ecosystems.
Many process-based models have been developed, ranging in complexity from the simple light use efficiency approach (Monteith and Moss 1977) through to more sophisticated models such as 3-PG (Landsberg and Waring 1997) and models that link carbon, water and nitrogen flows in the trees and soil (Kirschbaum 1999; Battaglia et al. 2004).
Whilst many process-based models have been tested for accuracy by evaluating or validating against observed data, few have dealt with the complexity of the incorporated procedures to estimate uncertainties associated with model predictions or the sensitivity of these predictions to input factors in a systematic, inter-model comparison fashion.
In particular, impulsive fractional evolution equations are used to describe many practical dynamical systems in many evolutionary processes models.
Impulsive fractional differential equations are used to describe many practical dynamical systems in many evolutionary processes models.
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