A Source of Error in College Attrition Studies

1964 ◽  
Vol 38 (1) ◽  
pp. 60 ◽  
Author(s):  
Bruce K. Eckland
Keyword(s):  
College Attrition ◽  
Source Of Error

Meteorological Measurement Systems

2001 ◽  
Author(s):  
Fred V. Brock ◽  
Scott J. Richardson
Keyword(s):  
Dynamic Performance ◽  
Measurement Systems ◽  
Sensor Performance ◽  
Performance Specifications ◽  
Meteorological Measurement ◽  
Design Type ◽  
Static Sensitivity ◽  
And Function ◽  
Logical Functions ◽  
Source Of Error

This book treats instrumentation used in meteorological surface systems, both on the synoptic scale and the mesoscale, and the instrumentation used in upper air soundings. The text includes material on first- and second-order differential equations as applied to instrument dynamic performance, and required solutions are developed. Sensor physics are emphasized in order to explain how sensors work and to explore the strengths and weaknesses of each design type. The book is organized according to sensor type and function (temperature, humidity, and wind sensors, for example), though several unifying themes are developed for each sensor. Functional diagrams are used to portray sensors as a set of logical functions, and static sensitivity is derived from a sensor's transfer equation, focusing attention on sensor physics and on ways in which particular designs might be improved. Sensor performance specifications are explored, helping to compare various instruments and to tell users what to expect as a reasonable level of performance. Finally, the text examines the critical area of environmental exposure of instruments. In a well-designed, properly installed, and well-maintained meteorological measurement system, exposure problems are usually the largest source of error, making this chapter one of the most useful sections of the book.

Cholesterol in Serum and Lipoprotein Fractions

1956 ◽  
Vol 2 (3) ◽  
pp. 145-159 ◽  
Author(s):  
Joseph T Anderson ◽  
Ancel Keys
Keyword(s):  
Human Serum ◽  
Total Cholesterol ◽  
Filter Paper ◽  
Room Temperature ◽  
Paper Electrophoresis ◽  
Cholesterol Content ◽  
Intraindividual Variability ◽  
Detailed Data ◽  
The Stability ◽  
Source Of Error

Abstract 1. Methods are described for the separation, by paper electrophoresis and by cold ethanol, of α- and β-lipoproteins in 0.1 ml. of serum, with subsequent analysis of cholesterol in the separated portions. 2. It is shown that both methods of separation yield separated fractions containing substantially the same amounts of cholesterol. 3. Detailed data are given on the errors of measurement for total cholesterol and for cholesterol in the separated lipoprotein fractions. 4. Studies are reported on the stability of cholesterol in stored serum and on paper electrophoresis strips. It is shown that simple drying on filter paper causes no change in cholesterol content and yields a product that is stable for many weeks at ordinary room temperature. 5. The sources of variability in human serum cholesterol values are examined and it is shown that spontaneous intraindividual variability is a much greater source of error than the errors of measurement with these methods.

scholarly journals Error estimation on extracorporeal trajectory determination from body scans

2021 ◽  
Author(s):  
F. Riva ◽  
U. Buck ◽  
K. Buße ◽  
R. Hermsen ◽  
E. J. A. T. Mattijssen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Soft Tissue ◽  
Supine Position ◽  
Post Mortem ◽  
Surface Scanning ◽  
3D Data ◽  
Gravitational Pull ◽  
Scanned Images ◽  
Source Of Error ◽  
Trajectory Determination

AbstractThis study explores the magnitude of two sources of error that are introduced when extracorporeal bullet trajectories are based on post-mortem computed tomography (PMCT) and/or surface scanning of a body. The first source of error is caused by an altered gravitational pull on soft tissue, which is introduced when a body is scanned in another position than it had when hit. The second source of error is introduced when scanned images are translated into a virtual representation of the victim’s body. To study the combined magnitude of these errors, virtual shooting trajectories with known vertical angles through five “victims” (live test persons) were simulated. The positions of the simulated wounds on the bodies were marked, with the victims in upright positions. Next, the victims were scanned in supine position, using 3D surface scanning, similar to a body’s position when scanned during a PMCT. Seven experts, used to working with 3D data, were asked to determine the bullet trajectories based on the virtual representations of the bodies. The errors between the known and determined trajectories were analysed and discussed. The results of this study give a feel for the magnitude of the introduced errors and can be used to reconstruct actual shooting incidents using PMCT data.

scholarly journals Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Artifact Reduction ◽  
Interferometric Synthetic Aperture Radar ◽  
Spatial Properties ◽  
Model Free ◽  
Weather Model ◽  
Phase Variations ◽  
Source Of Error ◽  
Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

Isotype controls in phenotyping and quantification of microparticles: A major source of error and how to evade it

2008 ◽  
Vol 122 (5) ◽  
pp. 691-700 ◽  
Author(s):  
Arne Trummer ◽  
Christiane De Rop ◽  
Andreas Tiede ◽  
Arnold Ganser ◽  
Roswith Eisert
Keyword(s):  
Source Of Error
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