Dual-process models of persuasion (e.g., Heuristic Systematic Model) contrast the use of heuristics with systematic information processing. However, a great deal of attention is increasingly being devoted to the interplay between the two types of processing. We propose a multistage view that builds on dual-process models of persuasion but emphasizes the interplay between processing modes. Accor...

In this paper maximum likelihood step-change-point estimators of the location parameter, the out-of-control sample, and the out-of-control stage are developed for autocorrelated multistage processes. To do this, the multistage process and the concept of change detection are first discussed. Then, a time-series model of the process is presented. Assuming step changes in the location parameter of...

Unsaturated, nitrogenated and sulfured compounds may reach undesirable levels in lubricant base oils, requiring hydrotreatment (HDT) at high temperatures and pressures. HDT processes are well known for their high capital and operational costs due to the use of hydrogen, compressors and multistage heterogeneous reactors. Process costs are thus highly dependent on the applied conditions. An overs...

The nature of value and how R&D/technology organizations provide value are considered. Value is defined in terms of three dimensions: quality, productivity, and innovation. This framework provides the basis for formulation of value strategies in terms of where value is to be provided, how it will be provided, enterprise designs that support these intentions, benchmarking to identify gaps, and s...

Cumulant-based inverse filter criteria (IFC) using secondand higher order statistics (HOS) proposed by Tugnait et al. have been widely used for blind deconvolution of discrete-time multi-input multi-output (MIMO) linear time-invariant systems with non-Gaussian measurements through a multistage successive cancellation procedure, but the deconvolved signals turn out to be an unknown permutation o...

Multistage stochastic optimization leads to NLPs over scenario trees that become extremely large when many time stages or fine discretizations of the probability space are required. Interior-point methods are well suited for these problems if the arising huge, structured KKT systems can be solved efficiently, for instance, with a large scenario tree but a moderate number of variables per node. ...