scholarly journals Human body prediction of size and shape: a hormonal framework

2019 ◽  
Author(s):  
Jeroen van Vugt
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Human Body ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Future Research ◽  
Least Squares Regression ◽  
Size And Shape ◽  
Log Normal ◽  
Log Normal Distribution

ABSTRACTTo achieve high prediction accuracy with minimal inputs from online retail respondents, a method was developed and tested to predict the size and shape of the human body in 3D using a hormonal framework. The prediction method is based on geometric morphometrics, image analysis, and kernel partial least squares regression. The inputs required are answers to three closed-ended questions and a passport photo. Prediction accuracy was tested with the 3D body scan dataset of the Civilian American and European Surface Anthropometry Resource project. Results from the test dataset showed that approximately 82% of the error expectations of landmarks followed a log-normal distribution with an expectation of 8.816 mm and standard deviation of 1.180 mm. The remaining 18% of the error expectations of landmarks followed a log-normal distribution with an expectation of 18.454 mm and standard deviation of 8.844 mm, which may herald future research. Benchmarked with another method, the proposed method features much less input. In addition to high accuracy, the method in this paper allows for visualisation of results as real-size meshes in millimeters.

Variance of ventilation during exercise

2001 ◽  
Vol 90 (6) ◽  
pp. 2151-2156 ◽  
Author(s):  
Kenneth C. Beck ◽  
Theodore A. Wilson
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Ventilation Distribution ◽  
Cycle Exercise ◽  
Expired Gas ◽  
Highly Correlated ◽  
Log Normal ◽  
Male Subjects ◽  
Log Normal Distribution ◽  
Ventilation And Perfusion

Expired gas concentrations were measured during a multibreath washin of He in one female and seven male subjects at rest (seated) and during cycle exercise at work rates of 70–210 W. In a computational model, the ventilation distribution was represented as a log-normal distribution with standard deviation (ςV˙); values of ςV˙ were obtained by fitting the output of the model to the data. At rest, ςV˙ was 0.89 ± 0.18; during exercise, ςV˙ was 0.60 ± 0.13, independent of the level of exercise. These values for the width of the functional ventilation distribution at the scale of the acinus are approximately two times larger than those obtained from anatomic measurements in animals at a scale of 1 cm3. The values for ςV˙, together with data from the literature on the width of the functional ventilation-perfusion distribution, show that ventilation and perfusion are highly correlated at rest, in agreement with anatomic data. The structural sources of nonuniform ventilation and perfusion and of the correlation between them are unknown.

Statistical Characteristics of Inter-Particle/Void Distance for Particulate Composites

2006 ◽  
Vol 312 ◽  
pp. 105-110
Author(s):  
Nam Ho Kim ◽  
Ho Sung Kim
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Gaussian Distribution ◽  
Three Dimensional ◽  
Statistical Characteristics ◽  
Particulate Composites ◽  
Void Size ◽  
Three Dimensional Models ◽  
Log Normal ◽  
Log Normal Distribution

Two dimensional statistical characteristics of inter particle/void distance (ID) for various particle/void and dispersion types are studied in relation with toughening of plastics using computer generated three dimensional models. Particle/void size groups adopted were of log-normal distribution. Particles/voids were dispersed at uniform-random. It was found that IDs are (a) of approximately Gaussian distribution but; (b) not of Gaussian distribution for particle/void sizes of bimodal log-normal distribution (created by mixing of two groups of articles/voids). It was also found that the degree of ID uniformity, which can be represented by the inverse of the coefficient of variation, for a single group of log-normally sized particles/voids is not sensitive to standard deviation of particle/void size. Mixing effect on ID characteristics using two groups of log-normally distributed particles/voids with similar mean particle/void diameters was simulated. It was found that, when a significant amount (36 vol %) of particles/voids of a small mean and standard deviation of ID, was mixed with a group of particles/voids of a large mean and standard deviation of ID, mean and standard deviation of ID for the mixture were not substantially lower than those of the group of particles/voids of the large mean and standard deviation of ID. It was also found that the degree of ID uniformity for the mixture of the two groups were lower than those of individual groups, indicating that the mixing has deleterious effect on toughening.

scholarly journals A Universal Model for the Log-Normal Distribution of Elasticity in Polymeric Gels and Its Relevance to Mechanical Signature of Biological Tissues

Biology ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 64
Author(s):  
Arnaud Millet
Keyword(s):  
Elastic Modulus ◽  
Normal Distribution ◽  
Biological Tissues ◽  
Force Microscopy ◽  
Polymeric Gels ◽  
Atomic Force ◽  
Clear Explanation ◽  
Log Normal ◽  
Log Normal Distribution

The mechanosensitivity of cells has recently been identified as a process that could greatly influence a cell’s fate. To understand the interaction between cells and their surrounding extracellular matrix, the characterization of the mechanical properties of natural polymeric gels is needed. Atomic force microscopy (AFM) is one of the leading tools used to characterize mechanically biological tissues. It appears that the elasticity (elastic modulus) values obtained by AFM presents a log-normal distribution. Despite its ubiquity, the log-normal distribution concerning the elastic modulus of biological tissues does not have a clear explanation. In this paper, we propose a physical mechanism based on the weak universality of critical exponents in the percolation process leading to gelation. Following this, we discuss the relevance of this model for mechanical signatures of biological tissues.

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