General Solution to Two-Dimensional Nonslipping JKR Model with a Pulling Force in an Arbitrary Direction

In this paper, a generalized JKR model is investigated, in which an elastic cylinder adhesively contacts with an elastic half space and the contact region is assumed to be perfect bonding. An external pulling force is acted on the cylinder in an arbitrary direction. The contact area changes during the pull-off process, which can be predicted using the dynamic Griffith energy balance criterion as the contact edge shifts. Full coupled solution with an oscillatory singularity is obtained and analyzed by numerical calculations. The effect of Dundurs' parameter on the pull-off process is analyzed, which shows that a nonoscillatory solution can approximate the general one under some conditions, i.e., larger pulling angle (pi/2 is the maximum value), smaller a/R or larger nondimensional parameter value of Delta gamma/E*R. Relations among the contact half width, the external pulling force and the pulling angle are used to determine the pull-off force and pull-off contact half width explicitly. All the results in the present paper as basic solutions are helpful and applicable for experimenters and engineers.

Non-Slipping Jkr Model For Transversely Isotropic Materials

A generalized plane strain JKR model is established for non-slipping adhesive contact between an elastic transversely isotropic cylinder and a dissimilar elastic transversely isotropic half plane, in which a pulling force acts on the cylinder with the pulling direction at an angle inclined to the contact interface. Full-coupled solutions are obtained through the Griffith energy balance between elastic and surface energies. The analysis shows that, for a special case, i.e., the direction of pulling normal to the contact interface, the full-coupled solution can be approximated by a non-oscillatory one, in which the critical pull-off force, pull-off contact half-width and adhesion strength can be expressed explicitly. For the other cases, i.e., the direction of pulling inclined to the contact interface, tangential tractions have significant effects on the pull-off process, it should be described by an exact full-coupled solution. The elastic anisotropy leads to an orientation-dependent pull-off force and adhesion strength. This study could not only supply an exact solution to the generalized JKR model of transversely isotropic materials, but also suggest a reversible adhesion sensor designed by transversely isotropic materials, such as PZT or fiber-reinforced materials with parallel fibers. (c) 2007 Elsevier Ltd. All rights reserved.

An R package for permutations, Mallows and Generalized Mallows models

[EN]Probability models on permutations associate a probability value to each of the permutations on n items. This paper considers two popular probability models, the Mallows model and the Generalized Mallows model. We describe methods for making inference, sampling and learning such distributions, some of which are novel in the literature. This paper also describes operations for permutations, with special attention in those related with the Kendall and Cayley distances and the random generation of permutations. These operations are of key importance for the efficient computation of the operations on distributions. These algorithms are implemented in the associated R package. Moreover, the internal code is written in C++.

Temperatura atmosférica e internações hospitalares por doenças cardíacas no município do Rio de Janeiro.

Os efeitos das temperaturas elevadas na saúde humana representam um problema de grande magnitude na saúde pública. A temperatura atmosférica e a poluição do ar são fatores de risco para as doenças crônicas não transmissíveis, em particular as doenças isquêmicas do coração. O estudo teve como objetivo analisar a associação entre a temperatura atmosférica e internações hospitalares por doenças cardíacas isquêmicas no município do Rio de Janeiro entre os anos de 2009 e 2013. Utilizaram-se modelos de séries temporais, via modelos aditivos generalizados, em regressão de Poisson, para testar a hipótese de associação. Como variáveis de controle de confusão foram utilizadas as concentrações de poluentes atmosféricos (ozônio e material particulado) e umidade relativa o ar; utilizou-se método de defasagem simples e distribuída para avaliar o impacto da variação de 1oC nas internações hospitalares diárias. No modelo de defasagem simples foram encontradas associações estatisticamente significativas para as internações por DIC no dia concorrente a exposição ao calor, tanto para a temperatura média quanto para a máxima. No modelo de defasagem distribuída polinomial, essa associação foi observada com 1 e 2 dias de defasagem e no efeito acumulado tanto para a temperatura média quanto para a máxima. Ao estratificarmos por faixa etária, as associações para as internações por DIC e exposição ao calor não foram estatisticamente significativas no modelo de defasagem simples para as temperaturas média e máxima. Em contrapartida, no modelo de defasagem distribuída polinomial, a correlação entre internações por DIC e exposição ao calor foi observada na faixa de 30 a 60 anos no efeito acumulado para a temperatura média; e com defasagem de 1 e 2 dias para 60 anos ou mais de idade para a temperatura média. Estes resultados sugerem associação positiva entre as internações hospitalares por doença cardíaca isquêmica e temperatura na cidade do Rio de Janeiro. Os resultados do presente estudo fornecem informações para o planejamento de investimentos de áreas urbanas climatizadas e para a preparação dos hospitais para receber emergências relacionadas aos efeitos de calor que é uma das consequências mais importantes das mudanças climáticas.

Bioenergetic and ecological consequences of diet variability in Atlantic croaker Micropogonias undulatus

Atlantic croaker Micropogonias undulatus is a commercially and ecologically important bottom-associated fish that occurs in marine and estuarine systems from Cape Cod, MA to Mexico. I documented the temporal and spatial variability in the diet of Atlantic croaker in Chesapeake Bay and found that in the summer fish, particularly bay anchovies Anchoa mitchilli, make up at least 20% of the diet of croaker by weight. The use of a pelagic food source seems unusual for a bottom-associated fish such as croaker, but appears to be a crepuscular feeding habit that has not been previously detected. Thus, I investigated the bioenergetic consequences of secondary piscivory to the distribution of croaker, to the condition of individuals within the population and to the ecosystem. Generalized additive models revealed that the biomass of anchovy explained some of the variability in croaker occurrence and abundance in Chesapeake Bay. However, physical factors, specifically temperature, salinity, and seasonal dynamics were stronger determinants of croaker distribution than potential prey availability. To better understand the bioenergetic consequences of diet variability at the individual level, I tested the hypothesis that croaker feeding on anchovies would be in better condition than those feeding on polychaetes using a variety of condition measures that operate on multiple time scales, including RNA:DNA, Fulton's condition factor (K), relative weight (Wr), energy density, hepatosomatic index (HSI), and gonadosomatic index (GSI). Of these condition measures, several morphometric measures were significantly positively correlated with each other and with the percentage (by weight) of anchovy in croaker diets, suggesting that the type of prey eaten is important in improving the overall condition of individual croaker. To estimate the bioenergetic consequences of diet variability on growth and consumption in croaker, I developed and validated a bioenergetic model for Atlantic croaker in the laboratory. The application of this model suggested that croaker could be an important competitor with weakfish and striped bass for food resources during the spring and summer when population abundances of these three fishes are high in Chesapeake Bay. Even though anchovies made up a relatively small portion of croaker diet and only at certain times of the year, croaker consumed more anchovy at the population level than striped bass in all simulated years and nearly as much anchovy as weakfish. This indicates that weak trophic interactions between species are important in understanding ecosystem processes and should be considered in ecosystem-based management.

Fitting host-parasitoid models with CV² > 1 using hierarchical generalized linear models

The powerful general Pacala-Hassell host-parasitoid model for a patchy environment, which allows host density–dependent heterogeneity (HDD) to be distinguished from between-patch, host density–independent heterogeneity (HDI), is reformulated within the class of the generalized linear model (GLM) family. This improves accessibility through the provision of general software within well–known statistical systems, and allows a rich variety of models to be formulated. Covariates such as age class, host density and abiotic factors may be included easily. For the case where there is no HDI, the formulation is a simple GLM. When there is HDI in addition to HDD, the formulation is a hierarchical generalized linear model. Two forms of HDI model are considered, both with between-patch variability: one has binomial variation within patches and one has extra-binomial, overdispersed variation within patches. Examples are given demonstrating parameter estimation with standard errors, and hypothesis testing. For one example given, the extra-binomial component of the HDI heterogeneity in parasitism is itself shown to be strongly density dependent.

Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

Modelling the future biogeography of North Atlantic zooplankton communities in response to climate change

Advances in habitat and climate modelling allow us to reduce uncertainties of climate change impacts on species distribution. We evaluated the impacts of future climate change on community structure, diversity, distribution and phenology of 14 copepod species in the North Atlantic. We developed and validated habitat models for key zooplankton species using continuous plankton recorder (CPR) survey data collected at mid latitudes of the North Atlantic. Generalized additive models (GAMs) were applied to relate the occurrence of species to environmental variables. Models were projected to future (2080–2099) environmental conditions using coupled hydroclimatix–biogeochemical models under the Intergovernmental Panel on Climate Change (IPCC) A1B climate scenario, and compared to present (2001–2020) conditions. Our projections indicated that the copepod community is expected to respond substantially to climate change: a mean poleward latitudinal shift of 8.7 km per decade for the overall community with an important species range variation (–15 to 18 km per decade); the species seasonal peak is expected to occur 12–13 d earlier for Calanus finmarchicus and C. hyperboreus; and important changes in community structure are also expected (high species turnover of 43–79% south of the Oceanic Polar Front). The impacts of the change expected by the end of the century under IPCC global warming scenarios on copepods highlight poleward shifts, earlier seasonal peak and changes in biodiversity spatial patterns that might lead to alterations of the future North Atlantic pelagic ecosystem. Our model and projections are supported by a temporal validation undertaken using the North Atlantic climate regime shift that occurred in the 1980s: the habitat model built in the cold period (1970–1986) has been validated in the warm period (1987–2004).

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

A new statistical framework for the quantification of covariate associations with species distributions

Identifying processes that shape species geographical ranges is a prerequisite for understanding environmental change. Currently, species distribution modelling methods do not offer credible statistical tests of the relative influence of climate factors and typically ignore other processes (e.g. biotic interactions and dispersal limitation). We use a hierarchical model fitted with Markov Chain Monte Carlo to combine ecologically plausible niche structures using regression splines to describe unimodal but potentially skewed response terms. We apply spatially explicit error terms that account for (and may help identify) missing variables. Using three example distributions of European bird species, we map model results to show sensitivity to change in each covariate. We show that the overall strength of climatic association differs between species and that each species has considerable spatial variation in both the strength of the climatic association and the sensitivity to climate change. Our methods are widely applicable to many species distribution modelling problems and enable accurate assessment of the statistical importance of biotic and abiotic influences on distributions.

Study of baleen whales’ ecology and interaction with maritime traffic activities to support management of a complex socio-ecological system

Thesis written in co-mentorship with Robert Michaud.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

A hands-on approach for fitting long-term survival models under the GAMLSS framework

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model, Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the rth moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin er al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214-223]. The usefulness of these models is illustrated in a Simulation study and in applications to three real data sets. (C) 2009 Elsevier B.V. All rights reserved.