scholarly journals Predicting soccer penalty success: an optimality model

2019 ◽  
Author(s):  
Andrew Hunter
Keyword(s):  
Optimality Model

Decision and Optimization for Preventive Replacement Contents of Ship Equipment with Multistate System

2015 ◽  
Vol 741 ◽  
pp. 594-598
Author(s):  
Jia Liu ◽  
Jian Wei Lv ◽  
Jing Bo Yan
Keyword(s):  
Heuristic Algorithm ◽  
Pareto Optimality ◽  
Case Analysis ◽  
Replacement Cost ◽  
Preventive Replacement ◽  
Optimality Model ◽  
Multistate System ◽  
Replacement Time

To scientifically analyze the preventive replacement contents of ship equipment with multiple performance states during sea service, this paper builds a Pareto optimality model for replacement contents based on mission reliability, replacement time and replacement cost, brings forward a heuristic algorithm to constantly eliminate non-inferior solution and impossible solution, and presents a case analysis to verify accuracy of model and operability of algorithm.

scholarly journals How Fast Should an Animal Run When Escaping? An Optimality Model Based on the Trade-Off Between Speed and Accuracy

2015 ◽  
pp. icv091 ◽  
Author(s):  
Rebecca Wheatley ◽  
Michael J. Angilletta ◽  
Amanda C. Niehaus ◽  
Robbie S. Wilson
Keyword(s):  
Trade Off ◽  
Model Based ◽  
Optimality Model ◽  
Speed And Accuracy

The Joint Evolution of Diapause and Insecticide Resistance: A Test of an Optimality Model

Ecology ◽  
1995 ◽  
Vol 76 (5) ◽  
pp. 1497-1505 ◽  
Author(s):  
Y. Carrière ◽  
D. A. Roff ◽  
J.-P. Deland
Keyword(s):  
Insecticide Resistance ◽  
Optimality Model ◽  
Joint Evolution

The influence of carbon costs and benefits on predicted vegetation behaviour along a precipitation gradient using the Vegetation Optimality Model

2020 ◽  
Author(s):  
Remko Nijzink ◽  
Jason Beringer ◽  
Lindsay Hutley ◽  
Stan Schymanski
Keyword(s):  
Root System ◽  
Leaf Area ◽  
Water Use ◽  
Vegetation Cover ◽  
Total Carbon ◽  
Limiting Factors ◽  
Rainfall Gradient ◽  
Area Index ◽  
Carbon Costs ◽  
Optimality Model

<p>Vegetation properties such as rooting depths and vegetation cover play a key role in coupling ecological and hydrological processes. These properties are however highly variable in space and/or time and their parametrization generally poses challenges for terrestrial biosphere models (Whitley et al., 2016). Models often use static values for dynamic vegetation properties or prescribe values based on observations, such as remotely sensed leaf area index. Here, vegetation optimality provides a way forward in order to predict such vegetation properties and their response to environmental change (Schymanski et al., 2015).</p><p>In this study, we explore the utility of a combined water-vegetation model, the Vegetation Optimality Model (VOM, Schymanski et al., 2009), to predict vegetation properties such as rooting depths, foliage cover, photosynthetic capacity and water use strategies. The VOM schematizes perennial trees and seasonal grasses each as a single big leaf with an associated root system and optimizes leaf and root system properties in order to maximize the Net Carbon Profit, i.e. the difference between the total carbon taken up by photosynthesis and all the carbon costs related to the construction and maintenance of the plant organs involved. The VOM was applied along the North-Australian Tropical Transect, which consists of six savanna sites equipped with flux towers along a strong rainfall gradient between 500 and 1700 mm per year. The multi-annual half-hourly measurements of evaporation and CO<sub>2</sub>-assimilation at the different sites were used here to evaluate the model.</p><p>The VOM produced similar or better results than more traditional models even though it requires much less information about site-specific vegetation properties. However, we found a persistent bias in the predicted vegetation cover. More detailed numerical experiments revealed a likely misrepresentation of the foliage costs in the model, which are based on a linear relation between leaf area and fractional vegetation cover. This finding, and the already favourable comparison with traditional models, implies that optimization of vegetation properties for Net Carbon Profit is a very promising approach for predicting the soil-vegetation-atmosphere exchange of water and carbon in complex ecosystems such as savannas.</p><p><strong>References<br></strong>Schymanski, S.J., Roderick, M.L., Sivapalan, M., 2015. Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2 concentrations. AoB PLANTS 7, plv060. https://doi.org/10.1093/aobpla/plv060</p><p>Schymanski, S.J., Sivapalan, M., Roderick, M.L., Hutley, L.B., Beringer, J., 2009. An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resources Research 45. https://doi.org/10.1029/2008WR006841</p><p>Whitley, R., Beringer, J., Hutley, L.B., Abramowitz, G., De Kauwe, M.G., Duursma, R., Evans, B., Haverd, V., Li, L., Ryu, Y., Smith, B., Wang, Y.-P., Williams, M., Yu, Q., 2016. A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas. Biogeosciences 13, 3245–3265. https://doi.org/10.5194/bg-13-3245-2016</p>

Use of an optimality model to solve the immunological puzzle of concomitant infection

Parasitology ◽  
2001 ◽  
Vol 122 (S1) ◽  
pp. S61-S64 ◽  
Author(s):  
A. L. GRAHAM
Keyword(s):  
Immune Response ◽  
Immune Responses ◽  
Weighted Average ◽  
Single Species ◽  
Average Response ◽  
T Helper ◽  
Concomitant Infection ◽  
Optimality Model ◽  
Different Types ◽  
Infectious Organism

Immunological data indicate that different subsets of T-helper cells work best against different types of infection. Concomitant infection of a host may thus impose either conflicting or synergistic immune response requirements, depending upon the extent to which the component optimal immune responses differ. Drawing upon empirically-determined optimal responses to single-species infections, an optimality model is here used to generate testable hypotheses for optimal responses to concomitant infection. The model is based upon the principle that the joint immune response will minimize divergence from each of the optima for single-species infections, but that it will also be weighted by the importance of mounting the correct response against each infectious organism. The model thus predicts a weighted average response as the optimal response to concomitant infection. Data on concomitant infection of murine hosts by the parasites Schistosoma mansoni and Toxoplasma gondii will provide the first test of the optimality model. If the weighted average hypothesis holds true, then there are no emergent immunological properties of concomitant infections and we may be able to understand immune responses to concomitant infection directly via our understanding of single-species infections.

How (not) to test an optimality model

1994 ◽  
Vol 9 (7) ◽  
pp. 265-267 ◽  
Author(s):  
Steven Hecht Orzack ◽  
Elliott Sober
Keyword(s):  
Optimality Model

scholarly journals Step-wise modifications of the Vegetation Optimality Model

2021 ◽  
Author(s):  
Remko Christiaan Nijzink ◽  
Jason Beringer ◽  
Lindsay Beaumont Hutley ◽  
Stanislaus Josef Schymanski
Keyword(s):  
Soil Texture ◽  
Optimality Theory ◽  
Input Data ◽  
Optimization Approach ◽  
Flux Tower ◽  
Optimality Model ◽  
Free Drainage ◽  
The Mean ◽  
Conventional Models ◽  
Modified Soil

Abstract. The Vegetation Optimality Model (VOM, Schymanski et al., 2009, 2015) is an optimality-based, coupled water-vegetation model that predicts vegetation properties and behaviour based on optimality theory, rather than calibrating vegetation properties or prescribing them based on observations, as most conventional models do. In order to determine wheter optimality theory can alleviate common shortcomings of conventional models, as identified in a previous model inter-comparison study along the North Australian Tropical Transect (NATT) (Whitley et al., 2016), a range of updates to previous applications of the VOM have been made for increased generality and improved comparability with conventional models. To assess in how far the updates to the model and input data would have affected the original results, we implemented them one-by-one while reproducing the analysis of Schymanski et al. (2015). The model updates included extended input data, the use of variable atmospheric CO2-levels, modified soil properties, implementation of free drainage conditions, and the addition of grass rooting depths to the optimized vegetation properties. A systematic assessment of these changes was carried out by adding each individual modification to the original version of the VOM at the flux tower site of Howard Springs, Australia. The analysis revealed that the implemented changes affected the simulation of mean annual evapo-transpiration (ET) and gross primary productivity (GPP) by no more than 20 %, with the largest effects caused by the newly imposed free drainage conditions and modified soil texture. Free drainage conditions led to an underestimation of ET and GPP, whereas more fine-grained soil textures increased the water storage in the soil and resulted in increased GPP. Although part of the effect of free drainage was compensated for by the updated soil texture, when combining all changes, the resulting effect on the simulated fluxes was still dominated by the effect of implementing free drainage conditions. Eventually, the relative error for the mean annual ET, in comparison with flux tower observations, changed from an 8.4 % overestimation to an 10.2 % underestimation, whereas the relative errors for the mean annual GPP stayed similar with a change from 17.8 % to 14.7 %. The sensitivity to free drainage conditions suggests that a realistic representation of groundwater dynamics is very important for predicting ET and GPP at a tropical open-forest savanna site as investigated here. The modest changes in model outputs highlighted the robustness of the optimization approach that is central to the VOM architecture.

Supplementary material to "Step-wise modifications of the Vegetation Optimality Model"

2021 ◽  
Author(s):  
Remko Christiaan Nijzink ◽  
Jason Beringer ◽  
Lindsay Beaumont Hutley ◽  
Stanislaus Josef Schymanski
Keyword(s):  
Optimality Model ◽  
Supplementary Material
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