Character and OTU stability in five taxonomic groups

The character and OTU stability of classifications based on UPGMA clustering and maximum parsimony (MP) trees were compared for 5 datasets (families of angiosperms, families of orthopteroid insects, species of the fish genusIctalurus, genera of the salamander family Salamandridae, and genera of the frog family Myobatrachidae). Stability was investigated by taking different sized random subsamples of OTUs or characters, computing UPGMA clusters and an MP tree, and then comparing the resulting trees with those based on the entire dataset. Agreement was measured by two consensus indices, that of Colless, computed from strict consensus trees, and Stinebrickner's 0.5-consensus index. Tests of character stability generally showed a monotone decrease in agreement with the standard as smaller sets of characters are considered. The relative success of the two methods depended upon the dataset. Tests of OTU stability showed a monotone decrease in agreement for UPGMA as smaller sets of OTUs are considered. But for MP, agreement decreased and then increased again on the same scale. The apparent superiority of UPGMA relative to MP with respect to OTU stability depended upon the dataset. Considerations other than stability, such as computer efficiency or accuracy, will also determine the method of choice for classifications.     

General systems: A basis for knowledge engineering

General systems formalizations of expert knowledge play a foundational role in the process of knowledge engineering of intelligent systems. Canonical definitions of systems (knowledge structures) which have been arrived at by a crossdisciplinary inductive modeling process are shown to be the basis for the modeling of certain generic categories of expert reasoning processes which are of current interest in knowledge engineering. The methodology for transforming the representation of knowledge expressed in general systems formalizations to the representation of that knowledge in expert systems is also developed. In particular, transformations from general systems formalizations into frames are presented.     

Modelling peak electricity demand

This paper estimates a forecasting equation for the hourly peak electricity demand one day in the future. The models incorporate deterministic influences such as holidays, stochastic influences such as average loads by building bivariate models, and exogenous influences such as the weather which is given a careful non-linear formulation. Out-of-sample comparisons are made using an additional year of data.