Monte Carlo selection of the bilateral contracts in the Italian power Exchange

2006 ◽  
Author(s):  
A. Berizzi ◽  
C. Bovo ◽  
M. Delfanti ◽  
M. S. Pasquadibisceglie
Keyword(s):  
Monte Carlo ◽  
Bilateral Contracts ◽  
Power Exchange ◽  
Selection Of

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 Multi-Attribute Group-Decision on a Construction Diversion Scheme for Hydropower Projects Based on Perception Utility

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3027 ◽  
Author(s):  
Dawei Liu ◽  
Zhigen Hu ◽  
Wencheng Guo
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Decision Problem ◽  
Group Decision ◽  
Regret Theory ◽  
Interval Numbers ◽  
Monte Carlo Simulation Technique ◽  
Construction Diversion ◽  
Attribute Weights ◽  
Selection Of

The multi-attribute and group-decision problem in the selection of a construction diversion scheme for large hydropower projects often involves multi-decision subjects and schemes. Each scheme includes multiple attributes, and the attribute values and weights are multi-attribute group decision-making problems with interval numbers. In this study, a new method for solving the multi-attribute group-decision problem is proposed by integrating regret theory, negotiation gathering theory, and the Monte Carlo simulation technique. Firstly, decision-makers’ comprehensive perception utility for each scheme is calculated based on the regret theory. Then, non-uniform and fuzzy opinion of different decision subjects are negotiated and gathered, and negotiation intervals of the attribute weights are calculated through group negotiation gathering theory. Moreover, fuzzy complementary judgment matrixes describing an excellent degree of the diversion schemes are obtained by conducting Monte Carlo simulations. Finally, the alternative diversion schemes are sorted in terms of their priorities, and the reliability of the sorting procedure is confirmed. The multi-attribute group-decision problem in the selection of a construction diversion scheme for Jinping I Hydropower Station is effectively solved by the proposed method. The proposed method is reliable and may significantly contribute to engineering decision-making.

scholarly journals Monte Carlo performance studies for the site selection of the Cherenkov Telescope Array

2017 ◽  
Vol 93 ◽  
pp. 76-85 ◽  
Author(s):  
T. Hassan ◽  
L. Arrabito ◽  
K. Bernlöhr ◽  
J. Bregeon ◽  
J. Cortina ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Performance Studies ◽  
Site Selection ◽  
Telescope Array ◽  
Cherenkov Telescope ◽  
Selection Of

Monte Carlo Atomistic Simulation of Polycrystalline Aluminum Deposition

1998 ◽  
Vol 514 ◽  
Author(s):  
J. Emiliano Rubio ◽  
Martín Jaraíz ◽  
Luis A. BaiIón ◽  
Juan Barbolla ◽  
M José López ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Atomistic Simulation ◽  
Thin Film Deposition ◽  
Film Deposition ◽  
Polycrystalline Thin Film ◽  
Columnar Growth ◽  
Polycrystalline Aluminum ◽  
Different Temperatures ◽  
Aluminum Deposition ◽  
Selection Of

ABSTRACTA new atomistic scheme for simulating polycrystalline thin film deposition based on a Monte Carlo approach has been developed. Simulations of polycrystalline aluminum deposition and annealing at different temperatures are presented. The time evolution of the film morphology for those temperatures is discussed. During deposition, columnar growth is observed at low temperatures. Grain growth takes place mainly during annealing. Faceting and selection of preferred crystal orientations (texture) is observed.

Noniterative Factor Analysis Estimators, With Algorithms for Subset and Instrumental Variable Selection

1991 ◽  
Vol 16 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Robert Cudeck
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Variable Selection ◽  
Instrumental Variable ◽  
Relative Performance ◽  
Monte Carlo Experiment ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
New Methods ◽  
Selection Of

Noniterative estimators of the unrestricted factor analysis model have been developed by, among others, Hägglund (1982) and Ihara and Kano (1986) that are consistent and very efficient computationally. Whereas each of these methods has several desirable properties, both require a subjective decision regarding the selection of subsets of variables that are needed to compute estimates of the parameters. An algorithm called PACE, based on an application of the sweep operator, is presented that automatically selects subsets of variables used for the Ihara-Kano estimator. A second algorithm initially presented by Du Toit (1986) is also described that automatically selects reference variables used in Hägglund’s Fabin estimators. A Monte Carlo experiment is reviewed that compares the relative performance of these estimators in addition to several others. Both new methods performed well in this experiment. Their relative merits on other criteria are discussed.

scholarly journals Selection of Heat Tolerant Lettuce (Lactuca sativa L.) Cultivars Grown in Deep Water Culture and Their Marketability

Horticulturae ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 50 ◽  
Author(s):  
Sydney C. Holmes ◽  
Daniel E. Wells ◽  
Jeremy M. Pickens ◽  
Joseph M. Kemble
Keyword(s):  
Monte Carlo ◽  
Deep Water ◽  
Strongly Correlated ◽  
Warm Climate ◽  
Cool Season ◽  
Water Culture ◽  
Excessive Heat ◽  
Lactuca Sativa L ◽  
Protective Structures ◽  
Selection Of

Lettuce is a cool season vegetable often produced in greenhouses and other protective structures to meet market demands. Greenhouses are being increasingly adopted in warm climate zones where excessive heat often leads to physiological disorders of lettuce, such as tipburn and premature bolting. Greenhouse lettuce growers in warm climates need cultivar recommendations that can help improve production without ignoring marketability. In the current study, eighteen lettuce cultivars were grown in deep water culture and evaluated for growth, bolting, and tipburn in a greenhouse in Auburn, AL, starting on 30 June and 19 August 2016. Based on the severity of bolting and tipburn, nine cultivars were then selected and evaluated on 17 November 2016 for sensory attributes and marketability by 50 untrained consumer panelists. Cultivars ‘Adriana’, ‘Aerostar’, ‘Monte Carlo’, ‘Nevada’, ‘Parris Island’, ‘Salvius’, ‘Skyphos’, and ‘Sparx’ were selected as having higher heat tolerance than cultivars ‘Bambi’, ‘Buttercrunch’ ‘Coastal Star’, ‘Flashy Trout Back’, ‘Green Forest’, ‘Green Towers’, ‘Jericho’, ‘Magenta’, and ‘Truchas’. Higher crispness, lower bitterness, higher overall texture, and higher overall flavor each correlated to higher marketability, regardless of cultivar, but the strongest predictor of marketability was overall flavor. Overall flavor and overall texture were more strongly correlated to marketability than bitterness and crispness, respectively, suggesting that broader sensory categories may better capture human sensory perceptions of lettuce than narrower categories. Cultivars ‘Aerostar’, ‘Monte Carlo’, ‘Nevada’, ‘Parris Island’, ‘Rex’, ‘Salvius’, and ‘Sparx’ performed well in a hot greenhouse and were preferred by consumers. This step-wise experiment could be an adaptable tool for determining highest performing cultivars under any given production constraint, without ignoring marketability.

Sign in / Sign up

Export Citation Format

Share Document