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Universities are undergoing historic change, from the sharp downward shift in government funding to widespread demands to document performance. At the University of California Berkeley, this led to an operational change effort unlike any the university had ever attempted, dubbed Operational Excellence. The authors describe their experiences designing and leading this change effort, with emphasis on practical advice for similar efforts at other universities.

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.

Here we present several refinements to a model of feedback control for the suppression of epileptic seizures. We utilize a stochastic partial differential equation (SPDE) model of the human cortex. First, we verify the strong convergence of numerical solutions to this model, paying special attention to the sharp spatial changes that occur at electrode edges. This allows us to choose appropriate step sizes for our simulations; because the spatial step size must be small relative to the size of an electrode in order to resolve its electrical behavior, we are able to include a more detailed electrode profile in the simulation. Then, based on evidence that the mean soma potential is not the variable most closely related to the measurement of a cortical surface electrode, we develop a new model for this. The model is based on the currents flowing in the cortex and is used for a simulation of feedback control. The simulation utilizes a new control algorithm incorporating the total integral of the applied electrical potential. Not only does this succeed in suppressing the seizure-like oscillations, but it guarantees that the applied signal will be charge-balanced and therefore unlikely to cause cortical damage.

A quantitative approximation of the cortical surface potential from measured scalp surface potential data is developed. The derivation is based on a local Taylor series expansion (TSE) in the surface normal coordinate. Analytical and numerical results for the four shell spherical head model show that the TSE method improves the spatial deblurring of the surface Laplacian method. The inclusion of the biharmonic term, the extension to other geometries, and the application to electrocorticogram measurements are discussed. Index Terms--Cortical imaging, high-resolution ECoG, high-resolution EEG, spatial deblurring, surface Laplacian.

A model is developed for the response of a micromachined, rotary gyroscope subject to a general input motion. The governing equations are formulated for weakly nonlinear oscillations of the rotor, suspended above the moving substrate via elastic beams. The method of multiple scales is used to separate the slow and fast responses. This approach allows quick computation of the long-term behavior of the rotor without the need to integrate over fast oscillations. The power of the model to evince cross-coupling errors is demonstrated through examples. [1184] Index Terms--Cross-coupling, gyroscope, micromachined, multiple scales, parametric excitation.