Consequently, in 1D representations where lateral fluxes are neglected, it is easy to overestimate vertical transport processes such as fluxes at the sediment/water interface.[16] One-dimensional approaches generally limit our ability to account for complex features of sediments.
These templates that can be represented by neurons acting as detectors for complex features of a sound could form the neural substrate for perceptual restoration in a similar way as the preexisting lexical representation affects restoration in humans [10].
These models help to identify key flow regimes for reservoir description and serve as checkbox to the derivative approach for better interpretation of complex features.
Modern evolutionary theory recognizes several reasons that may account for the loss of complex features (e.g., Jeffery 2005; Espinasa and Espinasa 2008), some of which involve direct natural selection, but none of which is based simply on disuse.
RS and GIS are among those geospatial tools, i.e., are important for analysis of voluminous hydrogeological data and for the simulation modeling of complex features (Watkins et al. 1997; Loague and Corwin 1998; Gogu et al. 2001; Gossel et al. 2004).
For example, the in vivo xenograft assay tests for several complex features of cancer cells, such as survival in hypoxic conditions, survival in nutrient-deprived conditions, and the ability to establish a vascular network, none of which are factors in the in vitro assays.
We conclude that these results reflect cortical processes that integrate successive acoustic events at different time scales for extracting complex features of natural sound.
The methodology used in this work is suitable for modeling complex features of reservoirs such as irregular boundaries, fractures, faults, inclined and distorted wells.
Rats can discriminate simple shapes visually, even if they are moved around, made smaller, or partially covered up; the strategy they use may help shed light on human brain mechanisms for discriminating complex features, such as faces.
We investigate if known extrinsic and intrinsic factors fully account for the complex features observed in recordings of human activity as measured from forearm motion in subjects undergoing their regular daily routine.
Fractals, which are self-similar patterns, provide convenient mathematical bases for describing complex features; this is one of the reasons that they find different applications.
