3– 6 Meissner et al 5 have performed the most extensive analysis to date regarding the cost of such intravenous PCA errors as incorrect flow rates, missing decimal places, dosage strength errors (μg versus mg), and malfunctions of the intravenous PCA device itself.
All of the algorithms, which include MAS5 (Microarray Suite), RMA (Robust Multichip Average), GC-RM A(Robust Multichip Average with adjustment for GC content of probes) and PLIER (Probe Logarithmic Intensity Error), have strengths and weaknesses, and none was designed to take advantage of the unique strengths afforded by a time series [ 30].
All of the algorithms, which include MAS5 (Microarray Suite), RMA (Robust Multichip Average), GC-RM A(Robust Multichip Average with adjustment for GC content of probes) and PLIER (Probe Logarithmic Intensity Error), have strengths and weaknesses [ 46, 47].
This extra a priori knowledge can be identified as a definite strength of error concealment compared to speech enhancement.
The strength of error computed between the given image and the original (reference) image is weighted based on known features of the HVS.
Finally, we discuss commonalities and differences between both approaches, identify a particular strength of error concealment in general, and provide possible research directions for speech enhancement.
We intentionally omit other typical channel and link context data related to signal strength and error rate as we focus on stationary scenarios with no terminal mobility.
This article translates and extends Becker (1968) from public law enforcement to private litigation by examining optimal legal system design in a model with private suits, signals of case strength, court error, and two types of primary behavior: harmful acts that may be deterred and benign acts that may be chilled.
The VMC variables that correlated significantly with the right limb's clinical deficit were, in a descending order of correlation strength: Directional error during tracing (R = 0.53), frequency of tracking Interruptions (R = 0.50), tracking speed (R = −0.49), % lost tracking time (R = 0.47) and directional error during tracking (R = 0.42).
Both methods proved successful in predicting tensile strength with errors of 3% and 2.55% for RSM and ANN, respectively.
