scholarly journals A Look-ahead Monte Carlo Simulation Method for Improving Parental Selection in Trait Introgression

2020 ◽  
Author(s):  
Saba Moeinizade ◽  
Ye Han ◽  
Hieu Pham ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Monte Carlo ◽  
State Of The Art ◽  
Simulation Method ◽  
Multiple Trait ◽  
Look Ahead ◽  
Probability Of Success ◽  
Genetic Distribution ◽  
Selection Of ◽  
Trait Introgression

ABSTRACTMultiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches.

scholarly journals A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saba Moeinizade ◽  
Ye Han ◽  
Hieu Pham ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Monte Carlo ◽  
State Of The Art ◽  
Simulation Method ◽  
Multiple Trait ◽  
Look Ahead ◽  
Probability Of Success ◽  
Genetic Distribution ◽  
Selection Of ◽  
Trait Introgression

AbstractMultiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. Our objective is to select the most promising individuals for further backcrossing or selfing. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches. Our proposed selection method can assist breeders to efficiently design trait introgression projects.

scholarly journals Monte Carlo Simulation for Economic Analysis of Hydropower Pumped Storage Project in Nepal

2013 ◽  
Vol 12 ◽  
pp. 39-44 ◽  
Author(s):  
Kaspar Vereide ◽  
Leif Lia ◽  
Laras Ødegård
Keyword(s):  
Monte Carlo ◽  
Economic Analysis ◽  
Net Present Value ◽  
Simulation Method ◽  
Construction Costs ◽  
Energy And Environment ◽  
Pumped Storage ◽  
High Degree ◽  
Mc Method

Investments in hydropower pumped storage projects (PSP) are subjected to a high degree of uncertainty. In addition to normal uncertainties in hydropower schemes, the profit of a pumped storage scheme is dependent on the margin between power prices for buying and selling, which is difficult to predict without a power purchase agreement (PPA). A PSP without a PPA and without known construction costs requires quantification of the uncertainties in order to make qualified decisions before investing in such projects. This article demonstrates the advantages of using Monte Carlo (MC) simulations as a tool in the economic analysis of PSPs. The method has been tested on a case study, namely the Tamakoshi-3 Hydropower Project (HPP) in Nepal. The MC method is used to calculate the probability distribution of the net present value of installing reversible units in the Tamakoshi-3 HPP. The calculations show that PSPs may be profitable in Nepal, given a beneficial development of the power market. The MC method is considered to be a useful tool for economic analysis of PSPs. In this case study of installing reversible units in the Tamakoshi-3 HPP, there are many uncertainties, which the MC simulation method is able to quantify. Hydro Nepal; Journal of Water, Energy and Environment Vol. 12, 2013, January Page: 39-44DOI: http://dx.doi.org/10.3126/hn.v12i0.9031 Uploaded Date : 10/29/2013

Airstrip Site Selection in Coastal Labrador

1981 ◽  
Vol 35 (1) ◽  
pp. 17-25
Author(s):  
J. D. Innes ◽  
E. H. Smith ◽  
Allan Fiander
Keyword(s):  
Site Selection ◽  
Coastal Area ◽  
State Of The Art ◽  
Selection Process ◽  
Geometric Design ◽  
Digital Mapping ◽  
Data Requirements ◽  
Selection Of ◽  
Design Techniques

This paper examines a case study of the selection of nine airstrip sites in the coastal area of Labrador. The procedure used involved the procurement of better mapping for the site selection process. The benefits of this process are examined in the context of the data requirements for good airstrip selection. The site selection process is then examined utilizing state of the art digital mapping and computerized geometric design techniques.

Sediment source identification: a review and a case study in some Canadian streamsThis paper is one of a selection of papers in this Special Issue in honour of Professor M. Selim Yalin (1925–2007).

2009 ◽  
Vol 36 (10) ◽  
pp. 1622-1633 ◽  
Author(s):  
Bommanna G. Krishnappan ◽  
Patricia A. Chambers ◽  
Glenn Benoy ◽  
Joseph Culp
Keyword(s):  
Source Identification ◽  
State Of The Art ◽  
Control Strategies ◽  
Agricultural Watersheds ◽  
Sediment Source ◽  
Sediment Control ◽  
Adverse Impacts ◽  
Identification Techniques ◽  
Selection Of

The state-of-the-art of sediment source identification is reviewed in this paper. Sediment “fingerprinting” techniques using different “fingerprint” properties were examined. With these techniques, it is possible to identify potential sources of sediment transported in river systems. Such knowledge is useful for implementing sediment control strategies to limit sediment production from upland areas in a watershed as well as for developing guidelines for land use practices to minimize adverse impacts on surface and ground water resources in agricultural watersheds. Examples of sediment source identification techniques that were carried out in agricultural watersheds in different parts of the world were also included in the present review.

scholarly journals Picture Fuzzy ARAS Method for Freight Distribution Concept Selection

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1062 ◽  
Author(s):  
Stefan Jovčić ◽  
Vladimir Simić ◽  
Petr Průša ◽  
Momčilo Dobrodolac
Keyword(s):  
State Of The Art ◽  
Evaluation Criteria ◽  
Third Party ◽  
The Novel ◽  
Third Party Logistics ◽  
Concept Selection ◽  
The Czech Republic ◽  
The Third ◽  
Selection Of

Companies can perform their freight distribution in three different ways. The first concept, the in-house concept, represents the use of a company’s own resources and knowledge to organize transportation from the production to retailers or from the warehouse to customers. The opposite concept is to outsource distribution activities by hiring third-party logistics providers. The third concept represents a combination of the previous two. Although the arguments in favor of outsourcing can be found in the literature, an appropriate selection of a freight distribution concept is specific for each company and depends on many evaluation criteria and their symmetrical roles. This paper presents a methodology that can be used by companies that need to choose their freight distribution concept. An advanced extension of the Additive Ratio ASsessment (ARAS) method is developed to solve the freight distribution concept selection problem. To illustrate the implementation of the proposed methodology, a tire manufacturing company from the Czech Republic is taken as a case study. However, the proposed picture fuzzy ARAS method is general and can be used by any other company. To validate the novel picture fuzzy ARAS method, a comparative analysis with the nine existing state-of-the-art picture fuzzy multi-criteria decision-making methods is provided.

scholarly journals Evolutionary Convolutional Neural Network: a case study

2021 ◽  
Author(s):  
Amanda Lucas Pereira ◽  
Manoela Kohler ◽  
Marco Aurélio C. Pacheco
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Building Blocks ◽  
Research Field ◽  
Search Problem ◽  
Original Network ◽  
Selection Of

Most of the state-of-the-art Convolutional Neural Network (CNN) architectures are manually crafted by experts, usually with background knowledge from extent working experience in this research field. Therefore, this manner of designing CNNs is highly limited and many approaches have been developed to try to make this procedure more automatic. This paper presents a case study in tackling the architecture search problem by using a Genetic Algorithm (GA) to optimize an existing CNN Architecture. The proposed methodology uses VGG-16 convolutional blocks as its building blocks and each individual from the GA corresponds to a possible model built from these blocks with varying filter sizes, keeping fixed the original network architecture connections. The selection of the fittest individuals are done according to their weighted F1-Score when training from scratch on the available data. To evaluate the best individual found from the proposed methodology, the performance is compared to a VGG-16 model trained from scratch on the same data.

scholarly journals Navigating features: a topologically informed chart of electromyographic features space

2017 ◽  
Vol 14 (137) ◽  
pp. 20170734 ◽  
Author(s):  
Angkoon Phinyomark ◽  
Rami N. Khushaba ◽  
Esther Ibáñez-Marcelo ◽  
Alice Patania ◽  
Erik Scheme ◽  
...  
Keyword(s):  
State Of The Art ◽  
Feature Space ◽  
Classification Problem ◽  
Biological Variability ◽  
Class Separability ◽  
Similar Information ◽  
Set Identification ◽  
Optimal Feature ◽  
Selection Of

The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variability, even the same classification problem on different datasets can display variations in the respective optimal sets, casting doubts on the generalizability of relevant features. Here, we approach this problem by leveraging topological tools to create charts of features spaces. These charts highlight feature sub-groups that encode similar information (and their respective similarities) allowing for a principled and interpretable choice of features for classification and analysis. Using multiple electromyographic (EMG) datasets as a case study, we use this feature chart to identify functional groups among 58 state-of-the-art EMG features, and to show that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions. We find that these groups describe meaningful non-redundant information, succinctly recapitulating information about different regions of feature space. We then recommend representative features from each group based on maximum class separability, robustness and minimum complexity.

scholarly journals Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents

2018 ◽  
Author(s):  
Liang Hu ◽  
Songlei Jian ◽  
Longbing Cao ◽  
Qingkui Chen
Keyword(s):  
New Media ◽  
Categorical Data ◽  
State Of The Art ◽  
User Selection ◽  
Textual Data ◽  
Online Videos ◽  
Attraction Model ◽  
Different Levels ◽  
Selection Of

New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multilevel attraction model (MLAM) over the content features -- the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users' attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie.

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