Behavioral changes associated with a population density decline in the facultatively social red fox

Understanding the causal mechanisms promoting group formation in carnivores has been widely investigated, particularly how fitness components affect group formation. Population density may affect the relative benefits of natal philopatry versus dispersal. Density effects on individual behavioral strategies have previously been studied through comparisons of different populations, where differences could be confounded by between-site effects. We used a single population of red foxes (Vulpes vulpes) in the city of Bristol, UK, that underwent a natural perturbation in density to compare key changes in 1) group structure, 2) within-group relatedness, 3) mating system, 4) dispersal, and 5) dominance attainment. At high densities (19.6-27.6 adults km(-2)), group sex ratios were equal and included related and unrelated individuals. At low densities (4.0-5.5 adults km(-2)), groups became female biased and were structured around philopatric females. However, levels of within-group relatedness were unchanged. The genetic mating patterns changed with no instances of multiple-paternity litters and a decline in the frequency of extrapair litters of cubs from <= 77% to <= 38%. However, the number of genetically monogynous groups did not differ between periods. Dispersal was male biased at both high and low densities. At high density, most dominant males in the study groups appeared to have gained dominance after dispersing, but natal philopatry was an equally successful strategy at low density; conversely, most dominant females were philopatric individuals at both high and low densities. These results illustrate how density may alter behavioral strategies such as mating patterns and how this, in turn, alters group structure in a single population.

Activity patterns of urban red foxes (Vulpes vulpes) reduce the risk of traffic-induced mortality

Traffic collisions can be a major source of mortality in wild populations, and animals may be expected to exhibit behavioral mechanisms that reduce the risk associated with crossing roads. Animals living in urban areas in particular have to negotiate very dense road networks, often with high levels of traffic flow. We examined traffic-related mortality of red foxes (Vulpes vulpes) in the city of Bristol, UK, and the extent to which roads affected fox activity by comparing real and randomly generated patterns of movement. There were significant seasonal differences in the number of traffic-related fox deaths for different age and sex classes; peaks were associated with periods when individuals were likely to be moving through unfamiliar terrain and would have had to cross major roads. Mortality rates per unit road length increased with road magnitude. The number of roads crossed by foxes and the rate at which roads were crossed per hour of activity increased after midnight when traffic flow was lower. Adults and juveniles crossed 17% and 30% fewer roads, respectively, than expected from randomly generated movement. This highly mobile species appeared to reduce the mortality risk of minor category roads by changing its activity patterns, but it remained vulnerable to the effects of larger roads with higher traffic flows during periods associated with extraterritorial movements.

Extreme gender biased post-fledging brood division in the toc-toc

The possibility that parents of one sex may preferentially invest in offspring of a certain sex raises profound evolutionary questions about the relative worth of sons and daughters to their mothers and fathers. Post-fledging brood division-in which cacti parent feeds a different subset of offspring-has been well documented in birds. However, a lack of empirical evidence that this may be based oil offspring sex, combined with the theoretical difficulty of explaining such an interaction, has led researchers to consider a gender bias in post-fledging brood division highly unlikely. Here we show that in the toc-toc, Foudia sechellarum, postfledging brood division is extreme and determined by sex; where brood composition allows, male parents exclusively provision male fledglings, whereas female parents provision female fledglings. This is the first study to provide unambiguous evidence, based on molecular sexing, that sex-biased post-fledging brood division can occur in birds. Male and female parents provisioned at the same rate and neither offspring nor parent survival appeared to be affected by the sex of the parent or offspring, respectively. The current hypotheses predicting advantages for brood division and preferential care for one specific type of offspring are discussed in the light of our results.

Equivalent noise level generated by drilling onto the ossicular chain as measured by laser Doppler vibrometry: A temporal bone study

Background: Inadvertent drilling on the ossicular chain is one of the causes of sensorineural hearing loss (HL) that may follow tympanomastoid surgery. A high-frequency HL is most frequently observed. It is speculated that the HL is a result of vibration of the ossicular chain resembling acoustic noise trauma. It is generally considered that using a large cutting burr is more likely to cause damage than a small diamond burr. Aim: The aim was to investigate the equivalent noise level and its frequency characteristics generated by drilling onto the short process of the incus in fresh human temporal bones. Methods and Materials: Five fresh cadaveric temporal bones were used. Stapes displacement was measured using laser Doppler vibrometry during short drilling episodes. Diamond. and cutting burrs of different diameters were used. The effect of the drilling on stapes footplate displacement was compared with that generated by an acoustic signal. The equivalent noise level (dB sound pressure level equivalent [SPL eq]) was thus calculated. Results: The equivalent noise levels generated ranged from 93 to 125 dB SPL eq. For a 1-mm cutting burr, the highest equivalent noise level was 108 dB SPL eq, whereas a 2.3-mm cutting burr produced a maximal level of 125 dB SPL eq. Diamond burrs generated less noise than their cutting counterparts, with a 2.3-mm diamond burr producing a highest equivalent noise level of 102, dB SPL eq. The energy of the noise increased at the higher end of the frequency spectrum, with a 2.3-mm cutting burr producing a noise level of 105 dB SPL eq at 1 kHz and 125 dB SPL eq at 8 kHz. In contrast, the same sized diamond burr produced 96 dB SPL eq at 1 kHz and 99 dB at 8 kHz. Conclusion:This study suggests that drilling on the ossicular chain can produce vibratory force that is analogous with noise levels known to produce acoustic trauma. For the same type of burr, the larger the diameter, the greater the vibratory force, and for the same size of burr, the cutting burr creates more vibratory force than the diamond burr. The cutting burr produces greater high-frequency than lower-frequency vibratory energy.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

A forward-constrained regression algorithm for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

A-optimality orthogonal forward regression algorithm using branch and bound

In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S. Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Designing Hospital Bedside Systems for use by Patients with Visual Impairments

This paper describes the development of an interface to a hospital portal system for information, communication and entertainment such that it can be used easily and effectively by all patients regardless of their age, disability, computer experience or native language. Specifically, this paper reports on the work conducted to ensure that the interface design took into account the needs of visually impaired users.