Abstract: A processing system including at least one processor may obtain a data set comprising a plurality of records, each record associating at least one feature value of at least one feature with a value of a target variable. The processing system may next segregate the plurality of records into a plurality of subsets based upon a range of values of the at least one feature and calculate a plurality of sub-volumes for the plurality of subsets, each sub-volume comprising a sum of the values of the target variable from records in a respective subset. The processing system may then generate a significance metric that is based on a difference between a highest sub-volume and a lowest sub-volume of the plurality of sub-volumes and select the at least one feature to train a classification model associated with the target variable, based upon the significance metric.

Abstract: Aspects of the subject disclosure may include, for example, dividing a feature range of a feature into a plurality of subsets that span the feature range, calculating an average target variable value for each subset of the plurality of subsets, resulting in a plurality of average target variable values, and estimating a measure of feature significance with respect to a target variable by determining a difference between a maximum average target variable value and a minimum average target variable value in the plurality of average target variable values. Other embodiments are disclosed.

Abstract: Improving configuration of routing areas within an area of interest can include obtaining input data including a list of common language location identifiers within a defined area of interest and identification of a number of routing areas to be created within the area of interest; creating a bounding rectangle around the area of interest represented by the list of common language location identifiers; generating, within the bounding rectangle, a uniformly distributed sequence point; determining, from within the list of common language location identifiers, if the uniformly distributed sequence point is located in a particular common language location identifier of the list of common language location identifiers; if so, adding, to a list of kernel common language location identifiers the uniformly distributed sequence point; and if not, discarding the uniformly distributed sequence point.

Abstract: An extracellular vesicle-containing sample can be processed using a device for isolating one or more subpopulations of the extracellular vesicles. The extracellular vesicle-containing sample is flowed through a flow chamber of the device under an applied fluid pressure, in which the device has one or more inlets and two or more outlets in fluid communication with one another via the flow chamber. The device has one or more filters in the flow chamber between the inlet(s) and at least one of the outlet(s). The extracellular vesicle-containing sample is flowed through the filter(s) in the flow chamber to sort the extracellular vesicles of extracellular vesicle-containing sample by size into two or more subpopulations of the extracellular vesicles. At least one of the subpopulations that has been sorted flows out of a corresponding one of the outlets. Surface marker-based exosome sorting using magnetic beads may be used after the size-based exosome isolation.