Genetic programming of conventional features to detect seizure precursors

This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure precursors. Evidence suggests that seizure precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: (1) genetically programmed features; (2) features selected via GP; (3) forward sequentially selected features; and (4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.     

Coordinating production and distribution of jobs with bundling operations

We consider an integrated scheduling and distribution model in which jobs completed by two different machines must be bundled together for delivery. The objective is to minimize the sum of the delivery cost and customers' waiting costs. Such a model not only attempts to coordinate the job schedules on both machines, but also aims to coordinate the machine schedules with the delivery plan. Polynomial-time heuristics and approximation schemes are developed for the model with only direct shipments as well as the general model with milk-run deliveries.     

The incompatibility of knowledge regimes: consequences of the material world for cross-domain work

In this paper, we argue that successful integration of knowledge across work domains in the short-term can mask the generation of long-term consequences. We explore a setting, the introduction of environmental considerations into semiconductor manufacturing, where the eventual adoption of common measurement artifacts and associated practices enabled knowledge integration, but failed to address significant underlying consequences. Drawing from observational, interview, and archival data we develop an understanding of the work practices of the Tech and EnviroTech groups as structured by the material world and broader collective conventions. We introduce the concept of knowledge regime to outline the differences in knowledge across these work domains. More specifically, we find that differences in the causal specificity and developmental time horizon of knowledge and the measurement artifacts that result contribute to the relative power of one knowledge regime over another. Understanding these sources of incompatibility provides insight into the design requirements of information systems as boundary objects for knowledge integration, but also specifies the potential limits to any design effort.