scholarly journals Preventing Catastrophic Failure of Microfibrillar Adhesives in Compliant Systems Based on Statistical Analysis of Adhesive Strength

2021 ◽  
Vol 13 (16) ◽  
pp. 19422-19429
Author(s):  
René Hensel ◽  
Jonathan Thiemecke ◽  
Jamie A. Booth
Keyword(s):  
Statistical Analysis ◽  
Adhesive Strength ◽  
Catastrophic Failure ◽  
Compliant Systems

scholarly journals Adhesion Strength of Wood Based Composites Coated with Cellulosic and Polyurethane Paints

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Tuncer Dilik ◽  
Seda Erdinler ◽  
Ender Hazır ◽  
Hüseyin Koç ◽  
Salim Hiziroglu
Keyword(s):  
Statistical Analysis ◽  
Layer Thickness ◽  
Adhesion Strength ◽  
Adhesive Strength ◽  
Coating Layer ◽  
Medium Density Fiberboard ◽  
Medium Density ◽  
Testing Method ◽  
Number Of Layers ◽  
Coating Layer Thickness

The objective of this study was to determine adhesive strength of particleboard and medium density fiberboard (MDF) finished with two types of paints. Samples were coated using cellulosic and polyurethane based paints. Adhesion strength and coating layer thickness of each sample were measured using pull-off testing method and PosiTector equipment, respectively. The highest adhesion strength value of 3.62 MPa was found for MDF samples coated with paint. Based on the statistical analysis type of substrate significantly influenced overall adhesion strength of the samples while type and layer thickness of paint as well as number of layers applied to the surface of specimens have not affected significantly adhesion resistance of the panels used in this work. It appears that pull-off test can effectively be used to determine and evaluate adhesion strength of the samples considered in the experiments.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Intraindividual Variability in Development Within and Between Individuals

2001 ◽  
Vol 6 (3) ◽  
pp. 187-193 ◽  
Author(s):  
John R. Nesselroade
Keyword(s):  
Statistical Analysis ◽  
Research Design ◽  
Intraindividual Variability ◽  
Factor Analytic

A focus on the study of development and other kinds of changes in the whole individual has been one of the hallmarks of research by Magnusson and his colleagues. A number of different approaches emphasize this individual focus in their respective ways. This presentation focuses on intraindividual variability stemming from Cattell's P-technique factor analytic proposals, making several refinements to make it more tractable from a research design standpoint and more appropriate from a statistical analysis perspective. The associated methods make it possible to study intraindividual variability both within and between individuals. An empirical example is used to illustrate the procedure.

Review of Basic FORTRAN for Statistical Analysis.

1967 ◽  
Vol 12 (9) ◽  
pp. 467-467
Author(s):  
JOHN C. LOEHLIN
Keyword(s):  
Statistical Analysis
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