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Relevant bibliographies by topics / Multiruolo

Academic literature on the topic 'Multiruolo'

Author: Grafiati

Published: 11 March 2023

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Journal articles

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Journal articles on the topic "Multiruolo"

1

Walker, Brandon S., Lauren N. Pearson, and Robert L. Schmidt. "An Analysis of Multirules for Monitoring Assay Quality Control." Laboratory Medicine 51, no. 1 (June 27, 2019): 94–98. http://dx.doi.org/10.1093/labmed/lmz038.

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AbstractBackgroundMultirules are often employed to monitor quality control (QC). The performance of multirules is usually determined by simulation and is difficult to predict. Previous studies have not provided computer code that would enable one to experiment with multirules. It would be helpful for analysts to have computer code to analyze rule performance.ObjectiveTo provide code to calculate power curves and to investigate certain properties of multirule QC.MethodsWe developed computer code in the R language to simulate multirule performance. Using simulation, we studied the incremental performance of each rule and determined the average run length and time to signal.ResultsWe provide R code for simulating multirule performance. We also provide a Microsoft Excel spreadsheet with a tabulation of results that can be used to create power curves. We found that the R4S and 10x rules add very little power to a multirule set designed to detect shifts in the mean.ConclusionQC analysts should consider using a limited-rule set.

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2

Carey, R. Neill. "Implementation of Multirule Quality Control Procedures." Laboratory Medicine 20, no. 6 (June 1, 1989): 393–99. http://dx.doi.org/10.1093/labmed/20.6.393.

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3

Blum, A. S. "Computer evaluation of statistical procedures, and a new quality-control statistical procedure." Clinical Chemistry 31, no. 2 (February 1, 1985): 206–12. http://dx.doi.org/10.1093/clinchem/31.2.206.

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Abstract I describe a program for definitive comparison of different quality-control statistical procedures. A microcomputer simulates quality-control results generated by repetitive analytical runs. It applies various statistical rules to each result, tabulating rule breaks to evaluate rules as routinely applied by the analyst. The process repeats with increasing amounts of random and systematic error. Rate of false rejection and true error detection for currently popular statistical procedures were comparatively evaluated together with a new multirule procedure described here. The nature of the analyst's response to out-of-control signals was also evaluated. A single-rule protocol that is as effective as the multirule protocol of Westgard et al. (Clin Chem 27:493, 1981) is reported.

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4

Payra, Swagata, and Manju Mohan. "Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool." Advances in Meteorology 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/456065.

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The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

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5

Lunetzky, Ellen S., and George S. Cembrowski. "Performance Characteristics of Bull’s Multirule Algorithm for the Quality Control of Multichannel Hematology Analyzers." American Journal of Clinical Pathology 88, no. 5 (November 1, 1987): 634–38. http://dx.doi.org/10.1093/ajcp/88.5.634.

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6

Eggert, Arthur A., James O. Westgard, Patricia L. Barry, and Kenneth A. Emmerich. "Implementation of a multirule, multistage quality control program in a clinical laboratory computer system." Journal of Medical Systems 11, no. 6 (December 1987): 391–411. http://dx.doi.org/10.1007/bf00993007.

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7

Parvin, C. A. "Comparing the Power of Quality-Control Rules to Detect Persistent Systematic Error." Clinical Chemistry 38, no. 3 (March 1, 1992): 358–63. http://dx.doi.org/10.1093/clinchem/38.3.358.

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Abstract A simulation approach that allows direct estimation of the power of a quality-control rule to detect error that persists until detection is used to compare and evaluate the error detection capabilities of a group of quality-control rules. Two persistent error situations are considered: a constant shift and a linear trend in the quality-control mean. A recently proposed "moving slope" quality-control test for the detection of linear trends is shown to have poor error detection characteristics. A multimean quality-control rule is introduced to illustrate the strategy underlying multirule procedures, which is to increase power without sacrificing response rate. This strategy is shown to provide superior error detection capability when compared with other rules evaluated under both error situations.

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8

Cembrowski, George S., Carol C. Patrick, and Cynthia A. Sentigar. "Use of a Multirule Control Chart for the Quality Control of PT and aPTT Analyses." Laboratory Medicine 20, no. 6 (June 1, 1989): 418–21. http://dx.doi.org/10.1093/labmed/20.6.418.

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9

Seungwoo Kim, Youngwan Cho, and Mignon Park. "A multirule-base controller using the robust property of a fuzzy controller and its design method." IEEE Transactions on Fuzzy Systems 4, no. 3 (1996): 315–27. http://dx.doi.org/10.1109/91.531773.

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10

Parvin, C. A. "New insight into the comparative power of quality-control rules that use control observations within a single analytical run." Clinical Chemistry 39, no. 3 (March 1, 1993): 440–47. http://dx.doi.org/10.1093/clinchem/39.3.440.

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Abstract The error detection characteristics of quality-control (QC) rules that use control observations within a single analytical run are investigated. Unlike the evaluation of QC rules that span multiple analytical runs, most of the fundamental results regarding the performance of QC rules applied within a single analytical run can be obtained from statistical theory, without the need for simulation studies. The case of two control observations per run is investigated for ease of graphical display, but the conclusions can be extended to more than two control observations per run. Results are summarized in a graphical format that offers many interesting insights into the relations among the various QC rules. The graphs provide heuristic support to the theoretical conclusions that no QC rule is best under all error conditions, but the multirule that combines the mean rule and a within-run standard deviation rule offers an attractive compromise.

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