Automatic, around-the-clock blood pressure measurements have increased our understanding of hypertension in humans. Patients with essential hypertension display patterns similar to those observed in nonnotensive subjects, whereas those with secondary hypertension frequently show abnormal circadian rhythms characterized by a failure to reduce blood pressure at night. We have modeled this situation in rats. Nonnotensive Wistar-Kyoto and Sprague-Dawley rats, spontaneously hypertensive rats, and rats made hypertensive by transgenic implantation of the mouse salivary gland renin gene (TGR 27) underwent chronic implantation of a device that telemetrically monitored their blood pressures, heart rates, and motor activities. In either nonnotensive or hypertensive rats, motor activity peaked during the dark phase, indicating that animals from all strains were nocturnal. In both nonnotensive and spontaneously hypertensive rats, the 24-hour blood pressure and heart rate profiles showed peak values during the rats' active phase at night, ie, between midnight and 3 AM. In the transgenic rats, on the other hand, blood pressure values were at maximum during the day around noon, when the rats were in their resting phase. The heart rate of the transgenic rats nevertheless still peaked around midnight. These data suggest that nonnotensive rats and those with primary and secondary hypertension display circadian rhythms of blood pressure and heart rate analogous to those observed in nonnotensive and primary or secondary hypertensive humans, respectively. The TGR(mRen-2)27 strain may be a useful model with which to investigate the mechanisms responsible for alterations in circadian rhythms of blood pressure and heart rate in forms of secondary hypertension. (Hypertension 1993;22:97-101) KEY WORDS • blood pressure • heart rate • circadian rhythms • telemetry • rats, inbred SHR • animals, transgenic A mbulatory 24-hour blood pressure measurements / \ show promise in supplanting casual readings in A. \ . the diagnosis of arterial hypertension. In secondary forms of hypertension, the normal pattern of circadian variation is frequently disturbed, in that the nocturnal decrease in blood pressure fails to occur.

A new program is presented for nonlinear fitting of data from pharmacological and chronobiological investigations. It contains functions for calculating data from ligand-binding studies and competition experiments, for the analysis of dose-response curves, for pharmacokinetic calculations, and for cosine analysis of harmonic and overlapping rhythms. In addition, it is possible to implement general equations by the user. The program allows data exchange with most spreadsheet, database, and graphics presentation programs, and accepts data from two widely used ambulatory 24-h blood-pressure monitoring systems. The fitting procedure uses the Marquardt-Levenberg algorithm. It calculates the weighted or the unweighted fit together with a great variety of statistics for estimation of goodness of fit. A graphics module permits graphical presentation of the fitted curve. Moreover, fitting of data to different models can be compared for the most likely fit and model discrimination statistics for improvement of further experiments are provided. To demonstrate the chronobiological application of the fitting program PHARMFIT, the analysis of telemetric heart rate data from rats is presented.

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Within the ENIAC project "IMPROVE", new algorithms for virtual metrology and predictive maintenance are being developed to substantially enhance efficiency in European semiconductor manufacturing. The consortium comprises important IC manufacturers in Europe, solution providers, and research institutions. A major objective of the project is to make these new APC methods applicable in the existing fab systems of the IC manufacturers which widely differ in the automation infrastructure. A novel framework architecture for integration of the new control paradigms was researched and a software for implementation of the framework was developed. This paper describes the technical details and results of the framework development, implementation, and test

This paper presents a comparison of three algorithm types (Bayesian Networks, Random Forest and Linear Regression) for Predictive Maintenance on an implanter system in semiconductor manufacturing. The comparison studies are executed using a Virtual Equipment which serves as a testing environment for prediction algorithms prior to their implementation in a semiconductor manufacturing plant (fab). The Virtual Equipment uses input data that is based on historical fab data collected during multiple filament failure cycles. In an automated study, the input data is altered systematically, e.g. by adding noise, drift or maintenance effects, and used for predictions utilizing the created Predictive Maintenance models. The resulting predictions are compared to the actual time-to-failure and to each other. Multiple analysis methods are applied, resulting in a performance table