So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Data Mining Practical Machine Learning Tools and Techniques

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Principles of Plant Breeding

Principles of Plant Breeding.

Response of hygroscopicity to heat treatment and its relation to durability of thermally modified wood

A one-dimensional mathematical model to describe the transport phenomena during continuous radio frequency/vacuum (RF/V) drying of thick lumber was developed from general conservation equations. When drying at temperatures near the boiling point, as in RF/V drying, the effect of the gas phase pressure gradient on moisture transfer within the solid can be very important. The controlling resistances and transport mechanisms are discussed in detail. In addition, capillary transport in RF/V drying is discussed and its effect is compared with convective drying. The model provides a relatively fast and efficient way to simulate vacuum drying behavior assisted by dielectric heating. As an example, the governing heat and mass transfer equations, including consideration of internal heat generation and the effect of gas phase pressure gradient, are derived and solved in a one-dimensional system using a finite volume method. The effect of changes of the most important parameters on the predictions of the model is al...

Radio frequency vacuum drying of wood. i. mathematical model

Several machine learning models were used to predict interior spruce wood density using data from open-pollinated progeny testing trial. The data set consists of growth (height and diameter which were used to estimate individual tree volume) and wood quality (wood density determined by X-ray densitometry, resistance to drilling, and acoustic velocity) attributes for a total of 1146 trees growing on comparable sites in interior British Columbia. Various machine learning models were developed for estimating wood density. The multilayer feed-forward artificial neural networks and gene expression programming provided the highest predictability as compared to the other methods tested, including those based on classical multiple regression which was considered as the comparisons benchmark. The utilization of machine learning models as a credible method for estimating wood density using available growth data as an indirect method for determining trees wood density is expected to become increasingly helpful to forest managers and tree breeders.

Machine learning use in predicting interior spruce wood density utilizing progeny test information

Radio-frequency/vacuum drying of softwood : drying of thick western red cedar with constant electrode voltage

Abstract The stiffness and strength, modulus of elasticity (MOE) and modulus of rupture (MOR), as well as density, moisture content, microfibril angle and diffraction pattern coefficient of variation of azimuthal intensity profile (ICV) was determined for 259 small clear specimens. These samples represent 38 old- and second-growth western hemlock (Tsuga heterophylla) trees harvested from several sites in coastal British Columbia, Canada. The data were analyzed by classic statistical regression techniques to reveal interrelations among the mechanical properties and the inherent wood properties. Simultaneously, the predictive power of artificial neural networks was evaluated with the same data set by employing several optimization techniques. Regression analysis of wood density and the flexural strength properties resulted in R2 of 0.172 and 0.332 for MOE and MOR, respectively. The most efficient network model proved to be far superior demonstrating correlation coefficients with models for MOE ranging between 0.693 and 0.750, and the corresponding MOR models ranging between 0.438 and 0.561 in all testing phases. It is apparent that neural networks have the potential and capacity to self-train and become powerful adaptive systems that can predict the strength and stiffness of wood samples. The neural network analysis also revealed the importance level of each independent variable on both MOE and MOR properties.

Stavros Avramidis

Papers

Response of hygroscopicity to heat treatment and its relation to durability of thermally modified wood

Radio frequency vacuum drying of wood. i. mathematical model

Machine learning use in predicting interior spruce wood density utilizing progeny test information

Radio-frequency/vacuum drying of softwood : drying of thick western red cedar with constant electrode voltage

Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.)