The “Mountain Laboratory” of Nature—A Largely Unexplored Mine of Information: Synthesis of the Book

2021 ◽  
Vol 9 ◽  
Author(s):  
Birgit Böhmdorfer-McNair ◽  
Wolfgang Huf ◽  
Reinhard Strametz ◽  
Michael Nebosis ◽  
Florian Pichler ◽  
...  

A version of the Institute for Safe Medication Practices (ISMP) questionnaire adapted to the Austrian inpatient setting was used to sample the estimates of a group of experts regarding the level of medication safety in a level II hospital. To synthesize expert opinions on a group level reproducibly, classical Delphi method elements were combined with an item weight and performance weight decision-maker. This newly developed information synthesis method was applied to the sample dataset to examine method applicability. Method descriptions and flow diagrams were generated. Applicability was then tested by creating a synthesis of individual questionnaires. An estimate of the level of medication safety in an Austrian level II hospital was, thus, generated. Over the past two decades, initiatives regarding patient safety, in general, and medication safety, in particular, have been gaining momentum. Questionnaires are state of the art for assessing medication practice in healthcare facilities. Acquiring consistent data about medication in the complex setting of a hospital, however, has not been standardized. There are no publicly available benchmark datasets and, in particular, there is no published method to reliably synthesize expertise regarding medication safety on an expert group level. The group-level information synthesis method developed in this study has the potential to synthesize information about the level of medication safety in a hospital setting more reliably than unstructured approaches. A medication safety level estimate for a representative Austrian level II hospital was generated. Further studies are needed to establish convergence characteristics and benchmarks for medication safety on a larger scale.


2001 ◽  
Author(s):  
J. Brown ◽  
M. A. Franchek

Abstract Presented in this paper is a model-based method for assessing suspension and motor health of washing machines. The diagnosis of the suspension and motor health is achieved by processing the measured dynamics of the washtub. In particular, an online adapted lumped parameter model of the washtub is used to estimate key suspension and motor parameters. These parameters are identified from the measured displacement of the washtub due to a motor pulse input prior to the beginning of the wash cycle. Comparing the estimated values to design values assesses both suspension and motor health. Included are simulation results to validate the parameter estimation process.


1997 ◽  
Vol 10 (2) ◽  
pp. 227-239
Author(s):  
Wan-Ju Bo ◽  
Jue-Min Xie ◽  
Guan-Shou Lou

Author(s):  
Patrick J. Buehler ◽  
Matthew A. Franchek ◽  
Imad Makki

Presented in this paper is an information synthesis (IS) approach for the mass air flow (MAF) sensor diagnosis on internal combustion engines. An information synthesis solution is attractive for diagnostics since the algorithm automatically calibrates itself, reduces the number of false detections and compresses a large amount of engine health information into the model coefficients. There are three primary parts to information synthesis diagnostics. First, an IS model is used to predict the MAF sensor output based on the engine operating condition. The inputs to this IS model include the throttle position sensor (TPS) and the engine speed sensor information. The second part concerns an online adaptation process that is used to reduce the errors between the IS model output and the actual MAF sensor output. Finally the adapted model coefficients are used to diagnose the sensor as well as identify the source for changes in the sensor characteristics. This proposed solution is experimentally tested and validated on a Ford 4.6 L V-8 fuel injected engine. The specific MAF sensor faults to be identified include sensor bias and a leak in the intake manifold.


1991 ◽  
Vol 156 (6) ◽  
pp. 286-291 ◽  
Author(s):  
Marlene R. Ventura ◽  
Frances Crosby ◽  
Mary Jane Feldman

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