scholarly journals A note on increasing the limit of selection through selection within families

1974 ◽  
Vol 24 (2) ◽  
pp. 127-135 ◽  
Author(s):  
Leo Dempfle
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Population ◽  
Selection Criterion ◽  
Mass Selection ◽  
Breeding Scheme ◽  
Selection Limit ◽  
The Difference ◽  
Genetic Value ◽  
Theoretical Results ◽  
Equal Contribution

SUMMARYIt is shown that, in a finite population, by ensuring an equal contribution of offspring from each family we achieve a higher selection limit than by using mass selection, given that the correlation between selection criterion and additive genetic value is sufficiently high. The difference between the selection limits of the two schemes increases with more intense selection. The theoretical results were verified by Monte Carlo Simulation, and the influences of several factors were investigated. It is shown that such a breeding scheme might be useful in dairy cattle.

scholarly journals How informative is Wright's estimator of the number of genes affecting a quantitative character?

Genetics ◽  
1990 ◽  
Vol 126 (1) ◽  
pp. 235-247 ◽  
Author(s):  
Z B Zeng ◽  
D Houle ◽  
C C Cockerham
Keyword(s):  
Genetic Variance ◽  
Estimation Procedure ◽  
Expected Value ◽  
Allele Frequencies ◽  
Quantitative Character ◽  
Base Population ◽  
Single Population ◽  
Number Of Genes ◽  
Selection Limit ◽  
The Difference

Abstract S. Wright suggested an estimator, m, of the number of loci, m, contributing to the difference in a quantitative character between two differentiated populations, which is calculated from the phenotypic means and variances in the two parental populations and their F1 and F2 hybrids. The same method can also be used to estimate m contributing to the genetic variance within a single population, by using divergent selection to create differentiated lines from the base population. In this paper we systematically examine the utility and problems of this technique under the influences of unequal allelic effects and initial allele frequencies, and linkage, which are known to lead m to underestimate m. In addition, we examine the effects of population size and selection intensity during the generations of selection. During selection, the estimator m rapidly approaches its expected value at the selection limit. With reasonable assumptions about unequal allelic effects and initial allele frequencies, the expected value of m without linkage is likely to be on the order of one-third of the number of genes. The estimates suffer most seriously from linkage. The practical maximum expectation of m is just about the number of chromosomes, considerably less than the "recombination index" which has been assumed to be the upper limit. The estimates are also associated with large sampling variances. An estimator of the variance of m derived by R. Lande substantially underestimates the actual variance. Modifications to the method can ameliorate some of the problems. These include using F3 or later generation variances or the genetic variance in the base population, and replicating the experiments and estimation procedure. However, even in the best of circumstances, information from m is very limited and can be misleading.

scholarly journals Non-submodular model for group profit maximization problem in social networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jianming Zhu ◽  
Smita Ghosh ◽  
Weili Wu ◽  
Chuangen Gao
Keyword(s):  
Social Networks ◽  
Profit Maximization ◽  
Randomized Algorithm ◽  
Political Orientation ◽  
Submodular Functions ◽  
Maximization Problem ◽  
Modular Algorithm ◽  
The Difference ◽  
Group Enterprise ◽  
Theoretical Results

AbstractIn social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if $$\beta$$ β percent of users are influenced in the group. Enterprise will gain income from all influenced groups. Simultaneously, to propagate influence, enterprise needs pay advertisement diffusion cost. Group profit maximization (GPM) problem aims to pick k seeds to maximize the expected profit that considers the benefit of influenced groups with the diffusion cost. GPM is proved to be NP-hard and the objective function is proved to be neither submodular nor supermodular. An upper bound and a lower bound which are difference of two submodular functions are designed. We propose a submodular–modular algorithm (SMA) to solve the difference of two submodular functions and SMA is shown to converge to a local optimal. We present an randomized algorithm based on weighted group coverage maximization for GPM and apply sandwich framework to get theoretical results. Our experiments verify the efficiency of our methods.

Design-based and model-based inference in survey sampling: appreciating the difference

1998 ◽  
Vol 28 (10) ◽  
pp. 1429-1447 ◽  
Author(s):  
T G Gregoire
Keyword(s):  
Finite Population ◽  
Survey Sampling ◽  
Finite Population Sampling ◽  
Population Sampling ◽  
Model Based ◽  
The Difference

Model-based ideas in finite-population sampling have received renewed discussion in recent years.Their relationship to the classical ideas in sampling theorydo not appear to be universally well understood by samplers in applied disciplines such as forestry, and ecology more broadly.The two inferential paradigms are constrasted, andexplanations are supplemented with examples of discrete aswell as continuously distributed populations. The treatment of spatial structureis examined, also.

scholarly journals Breeding schemes in reindeer husbandry

Rangifer ◽  
2003 ◽  
Vol 23 (2) ◽  
pp. 45 ◽  
Author(s):  
Lars Rönnegård ◽  
J. A. Woolliams ◽  
Öje Danell
Keyword(s):  
Genetic Gain ◽  
Mass Selection ◽  
Effective Population ◽  
Selection Programme ◽  
Breeding Schemes ◽  
The Difference ◽  
Genetic Contributions ◽  
Parameter Values ◽  
Open Nucleus

The objective of the paper was to investigate annual genetic gain from selection (G), and the influence of selection on the inbreeding effective population size (Ne), for different possible breeding schemes within a reindeer herding district. The breeding schemes were analysed for different proportions of the population within a herding district included in the selection programme. Two different breeding schemes were analysed: an open nucleus scheme where males mix and mate between owner flocks, and a closed nucleus scheme where the males in non-selected owner flocks are culled to maximise G in the whole population. The theory of expected long-term genetic contributions was used and maternal effects were included in the analyses. Realistic parameter values were used for the population, modelled with 5000 reindeer in the population and a sex ratio of 14 adult females per male. The standard deviation of calf weights was 4.1 kg. Four different situations were explored and the results showed: 1. When the population was randomly culled, Ne equalled 2400. 2. When the whole population was selected on calf weights, Ne equalled 1700 and the total annual genetic gain (direct + maternal) in calf weight was 0.42 kg. 3. For the open nucleus scheme, G increased monotonically from 0 to 0.42 kg as the proportion of the population included in the selection programme increased from 0 to 1.0, and Ne decreased correspondingly from 2400 to 1700. 4. In the closed nucleus scheme the lowest value of Ne was 1300. For a given proportion of the population included in the selection programme, the difference in G between a closed nucleus scheme and an open one was up to 0.13 kg. We conclude that for mass selection based on calf weights in herding districts with 2000 animals or more, there are no risks of inbreeding effects caused by selection.

scholarly journals Design of preview controller for a type of discrete-time interconnected systems

2020 ◽  
Vol 53 (3-4) ◽  
pp. 719-729
Author(s):  
Hao Xie ◽  
Fucheng Liao ◽  
Usman ◽  
Jiamei Deng
Keyword(s):  
Discrete Time ◽  
Difference Operator ◽  
Interconnected Systems ◽  
Preview Control ◽  
Interconnected System ◽  
Lyapunov Function Method ◽  
System Equations ◽  
Error System ◽  
The Difference ◽  
Theoretical Results

This article proposes and studies a problem of preview control for a type of discrete-time interconnected systems. First, adopting the technique of decentralized control, isolated subsystems are constructed by splitting the correlations between the systems. Utilizing the difference operator to the system equations and error vectors, error systems are built. Then, the preview controller is designed for the error system of each isolated subsystem. The controllers of error systems of isolated subsystems are aggregated as a controller of the interconnected system. Finally, by employing Lyapunov function method and the properties of non-singular M-matrix, the guarantee conditions for the existence of preview controllers for interconnected systems are given. The numerical simulation shows that the theoretical results are effective.

scholarly journals On the Difference between Self-Assembling Process of Monomeric and Dimeric Surfactants with the Same Head to Tail Ratio: A Lattice Monte Carlo Simulation

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Reza Behjatmanesh-Ardakani ◽  
Maryam Farsad
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gemini Surfactants ◽  
Chain Structure ◽  
Single Chain ◽  
Lattice Monte Carlo ◽  
Self Assembling ◽  
Spacer Group ◽  
The Difference ◽  
Dimeric Surfactants

Experimental data show that gemini surfactants have critical micelle concentrations that are almost tenfold lower than the CMCs of single chain ones. It is believed that the spacer groups play an important role in this subject. Short hydrophilic or long hydrophobic spacers can reduce CMC dramatically. In this paper, self-assembling processes of double-chain and one-chain surfactants with the same head to tail ratio are compared. Dimeric chain structure is exactly double of single chain. In other words, hydrophilic-lyophilic balances of two chain models are the same. Two single chains are connected head-to-head to form a dimeric chain, without introducing extra head or tail beads as a spacer group. Premicellar, micellar, and shape/phase transition ranges of both models are investigated. To do this, lattice Monte Carlo simulation in canonical ensemble has been used. Results show that without introducing extra beads as spacer group, the CMC of (H3T3)2as a dimeric surfactant is much lower than the CMC of its similar single chain, H3T3. For dimeric case of study, it is shown that bolaform aggregates are formed.

Power Transformer Constants Affecting its Impulse Voltage Distribution

1977 ◽  
Vol 14 (1) ◽  
pp. 53-63
Author(s):  
A. M. El-Arabaty ◽  
Ezzat A. A. Mansour ◽  
Osama A. M. Said
Keyword(s):  
Power Transformer ◽  
Power Transformers ◽  
Stress Distributions ◽  
Voltage Distribution ◽  
Impulse Voltage ◽  
The Difference ◽  
Theoretical Results

This work deals with the modification of the known calculating formulae for power transformer constants used for impulse voltage distribution, presents the effect of transformer constants and their modifications on impulse voltage and stress distributions in power transformers, and compares experimental and theoretical results considering measures taken to minimize the difference between them.

scholarly journals A Bracketing Relationship between Difference-in-Differences and Lagged-Dependent-Variable Adjustment

2019 ◽  
Vol 27 (4) ◽  
pp. 605-615 ◽  
Author(s):  
Peng Ding ◽  
Fan Li
Keyword(s):  
Linear Models ◽  
Nonparametric Model ◽  
True Positive ◽  
Difference In Differences ◽  
Inverse Probability ◽  
Model Free ◽  
Lagged Dependent Variable ◽  
The Difference ◽  
Positive Effect ◽  
Theoretical Results

Difference-in-differences is a widely used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale-dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, Angrist and Pischke (2009) show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting. We provide three examples to illustrate the theoretical results with replication files in Ding and Li (2019).

Difference in the odor concentrations measured by the triangle odor bag method and dynamic olfactometry

2009 ◽  
Vol 59 (7) ◽  
pp. 1339-1342 ◽  
Author(s):  
H. Ueno ◽  
S. Amano ◽  
B. Merecka ◽  
J. Kośmider
Keyword(s):  
Selection Criterion ◽  
Control Law ◽  
Odor Control ◽  
Odor Concentration ◽  
Threshold Data ◽  
Selection Test ◽  
The Difference ◽  
The Individual ◽  
Individual Threshold ◽  
Dynamic Olfactometry

‘The triangle odor bag method’, which has been adopted for the offensive odor control law in Japan, and the dynamic olfactometry defined by EN 13725 have been compared. The odor concentration measured by the triangle odor bag method tends to be higher than that of the dynamic olfactometry in the forced choice mode, while well agreed in the Yes/No mode olfactometry when the panel is the same. The difference can be minimized by applying the panel selection criterion of EN13725 to the triangle odor bag method. The European panel selection test is useful to negate the difference in the measurement equipments although the criteria seem to be strict considering the individual threshold data of n-butanol.

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