Divorce, dispersal and incest avoidance in the cooperatively breeding superb fairy-wren Malurus cyaneus

1. Between 1988 and 2001, we studied social relationships in the superb fairy-wren Malurus cyaneus (Latham), a cooperative breeder with male helpers in which extra-group fertilizations are more common than within-pair fertilizations. 2. Unlike other fairy-wren species, females never bred on their natal territory. First-year females dispersed either directly from their natal territory to a breeding vacancy or to a foreign 'staging-post' territory where they spent their first winter as a subordinate. Females dispersing to a foreign territory settled in larger groups. Females on foreign territories inherited the territory if the dominant female died, and were sometimes able to split the territory into two by pairing with a helper male. However, most dispersed again to obtain a vacancy. 3. Females dispersing from a staging post usually gained a neighbouring vacancy, but females gaining a vacancy directly from their natal territory travelled further, perhaps to avoid pairing or mating with related males. 4. Females frequently divorced their partner, although the majority of relationships were terminated by the death of one of the pair. If death did not intervene, one-third of pairings were terminated by female-initiated divorce within 1000 days. 5. Three divorce syndromes were recognized. First, females that failed to obtain a preferred territory moved to territories with more helpers. Secondly, females that became paired to their sons when their partner died usually divorced away from them. Thirdly, females that have been in a long relationship divorce once a son has gained the senior helper position. 6. Dispersal to avoid pairing with sons is consistent with incest avoidance. However, there may be two additional benefits. Mothers do not mate with their sons, so dispersal by the mother liberates her sons to compete for within-group matings. Further, divorcing once their son has become a breeder or a senior helper allows the female to start sons in a queue for dominance on another territory. Females that do not take this option face constraints on their ability to recruit more sons into the local neighbourhood.

Habitat-predator association and avoidance in rainbowfish (Melanotaenia spp.)

The ability to recall the location of a predator and later avoid it was tested in nine populations of rainbowfish (Melanotaenia spp.), representing three species from a variety of environments. Following the introduction of a model predator into a particular microhabitat, the model was removed, the arena rotated and the distribution of the fish recorded again. In this manner it could be determined what cues the fish relied on in order to recall the previous location of the predator model. Fish from all populations but one (Dirran Creek) were capable of avoiding the predator by remembering either the location and/or the microhabitat in which the predator was recently observed. Reliance on different types of visual cues appears to vary between populations but the reason for this variation remains elusive. Of the ecological variables tested (flow variability, predator density and habitat complexity), only the level of predation appeared to be correlated with the orientation technique employed by each population. There was no effect of species identity, which suggests that the habitat that each population occupies plays a strong role in the development of both predator avoidance responses and the cues used to track predators in the wild.

A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

Numerical Uncertainty and Its Implications

Copyright © 2014 António F. Rodrigues, Nuno O. Martins. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accordance of the Creative Commons Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intellectual property António F. Rodrigues, Nuno O. Martins. All Copyright © 2014 are guarded by law and by SCIRP as a guardian.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach.

Mathematical aspects of the Heisenberg uncertainty principle within local fractional Fourier analysis

In this paper, we discuss the mathematical aspects of the Heisenberg uncertainty principle within local fractional Fourier analysis. The Schrödinger equation and Heisenberg uncertainty principles are structured within local fractional operators.

Uncertainty assessment approach for composite structures based on global sensitivity indices

The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the maximum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the structural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.

Uncertainty analysis based on sensitivity applied to angle-ply composite structures

This article describes a finite element-based formulation for the statistical analysis of the response of stochastic structural composite systems whose material properties are described by random fields. A first-order technique is used to obtain the second-order statistics for the structural response considering means and variances of the displacement and stress fields of plate or shell composite structures. Propagation of uncertainties depends on sensitivities taken as measurement of variation effects. The adjoint variable method is used to obtain the sensitivity matrix. This method is appropriated for composite structures due to the large number of random input parameters. Dominant effects on the stochastic characteristics are studied analyzing the influence of different random parameters. In particular, a study of the anisotropy influence on uncertainties propagation of angle-ply composites is carried out based on the proposed approach.

From local to global importance measures of uncertainty propagation in composite structures

The influence of uncertainties of input parameters on output response of composite structures is investigated in this paper. In particular, the effects of deviations in mechanical properties, ply angles, ply thickness and on applied loads are studied. The uncertainty propagation and the importance measure of input parameters are analysed using three different approaches: a first-order local method, a Global Sensitivity Analysis (GSA) supported by a variance-based method and an extension of local variance to estimate the global variance over the domain of inputs. Sample results are shown for a shell composite laminated structure built with different composite systems including multi-materials. The importance measures of input parameters on structural response based on numerical results are established and discussed as a function of the anisotropy of composite materials. Needs for global variance methods are discussed by comparing the results obtained from different proposed methodologies. The objective of this paper is to contribute for the use of GSA techniques together with low expensive local importance measures.

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.