Sentence examples for class aggregation from inspiring English sources

Spatial matrices are algorithms used to quantify spatial characteristics of patches, (discrete areas of relatively homogeneous environmental conditions), class (aggregation of same type of patches like grassland, woodland, bareland), or entire landscape mosaics (McGarigal et al. 2002).

We show that there is a consistent difference between the initial class aggregation level, and all other levels, which gradually broaden the forest definition and are characterized by very distinct ecological parameters, such as a higher mean patch size, increased levels of landscape connectivity and slightly more irregularly shaped patches.

The behavior of several landscape pattern metrics were tested against various pattern scenarios generated by neutral landscape models, including number of classes, scale-map extent, scale-resolution, class proportion, aggregation level RULE, and aggregation level SimMap.

One much-discussed class of aggregation rules violating independence is given by the premise-based rules.

Another prominent class of aggregation rules violating independence is given by the distance-based rules (Pigozzi 2006, building on Konieczny and Pino Pérez 2002; see also Miller and Osherson 2009).

This tool could help address one of the main challenges associated with the development of new therapeutics monoclonal antibody and also any other class of aggregation prone proteins.

We discuss both classes of aggregation in Sections 2.1.1 and 2.1.2, respectively.

Four main classes of aggregation functions are identified, and important subclasses are described and characterized as prototypes.

The probe is composed of a new class of aggregation-induced emission enhancement (AIEE) active dye 2 based on excited-state intramolecular proton transfer (ESIPT) and an acrylate group as the Cys recognition unit as well as the ESIPT blocking agent, which can be cut off by Cys from the probe in aqueous solution with mild conditions.

A welcomed side-effect of this work is that it enlarged the class of concrete aggregation procedures for judgment aggregation, until then limited mainly to premise and conclusion-based procedures.

Identification of Aggregation-Prone and Non-Aggregation-Prone Proteins in Each Structural Class We identified aggregation prone stretches in the proteins of the S group by using the TANGO algorithm (Fernandez-Escamilla et al., 2004 ) and aggregation prone stretches in the proteins of the U group by using the algorithm described by Michelitsch and Weissman (Michelitsch and Weissman, 2000 ).