Finite elements/Taguchi method based procedure for the identification of the geometrical parameters significantly affecting the biomechanical behavior of a lumbar disc

The aim of this work is to show a quick and simple procedure able to identify the geometrical parameters of the intervertebral disc that strongly affect the behavior of the FEM model. First, we allocated a selection criterion for the minimum number of geometrical parameters that describe, with a good degree of approximation, a healthy human vertebra. Next, we carried out a sensitivity analysis using the ‘Taguchi orthogonal array’ to arrive at a quick identification of the parameters that strongly affect the behavior of the Fem model.     

Central Place Foraging in Nonpatchy Habitats

This paper deal with a model of optimal foraging in a habitat with arbitrary food distribution. It takes into account an arbitrary risk cost related to the distance to the animal's nest. Food acquisition and risk cost are accounted for in common units of fitness. The resulting problem is solved in the context of Calculus of Variations. The optimal duration of absence from the nest and the optimal spatial allocation of foraging time are obtained: the optimal strategy leads to separate the habitat into a region to exploit and a region to ignore. The definition of these two distinct regions depends on the relative importance of risk and food availability. With realistic risk costs, the resulting strategy indicates a highly selective behaviour when far from the nest, as observed in field studies. The model is also extended to take account of the need of returning to the nest to guard it or to feed the young.     

Rare species habitat suitability assessment and reliability evaluation of an expert-based model: A case study of the insectivorous plant Pinguicula crystallina Sibth. et Smith subsp. hirtiflora (Ten.) Strid (Lentibulariaceae)

The development of habitat suitability models requires a large amount of data which are rarely available. In this case, researchers need to get information on the ecological features of the studied species, based on the opinion of experts or on the literature, to construct a qualitative model. However, such models cannot be rigorously evaluated, as in most cases absence points are not available. In this paper, we assess the habitat suitability for a vulnerable insectivorous plant, Pinguicula crystallina Sibth. et Smith subsp. hirtiflora (Ten.) Strid (Lentibulariaceae) in the Campania region. Our aim was to develop an expert-based, presence-only model in support of possible conservation actions. Topographic and geological features of this species suggested by the literature were used in our model. Both the Boyce index and field surveys were chosen to evaluate the model's reliability. During field surveys, 31 absence sites and 1 new presence site were identified, and differences between sites with regard to water chemistry and quality were investigated, water being an element in the species habitat. Factors that affect reliability of the model, such as the lack of a large amount of information on the species and the limited spatial resolution of geographical information system data, are discussed.     

A simulation approach for power calculation in large cohort studies based on multistate models

Realistic power calculations for large cohort studies and nested case control studies are essential for successfully answering important and complex research questions in epidemiology and clinical medicine. For this, we provide a methodical framework for general realistic power calculations via simulations that we put into practice by means of an R‐based template. We consider staggered recruitment and individual hazard rates, competing risks, interaction effects, and the misclassification of covariates. The study cohort is assembled with respect to given age‐, gender‐, and community distributions. Nested case‐control analyses with a varying number of controls enable comparisons of power with a full cohort analysis. Time‐to‐event generation under competing risks, including delayed study‐entry times, is realized on the basis of a six‐state Markov model. Incidence rates, prevalence of risk factors and prefixed hazard ratios allow for the assignment of age‐dependent transition rates given in the form of Cox models. These provide the basis for a central simulation‐algorithm, which is used for the generation of sample paths of the underlying time‐inhomogeneous Markov processes. With the inclusion of frailty terms into the Cox models the Markov property is specifically biased. An “individual Markov process given frailty” creates some unobserved heterogeneity between individuals. Different left‐truncation‐ and right‐censoring patterns call for the use of Cox models for data analysis. p‐values are recorded over repeated simulation runs to allow for the desired power calculations. For illustration, we consider scenarios with a “testing” character as well as realistic scenarios. This enables the validation of a correct implementation of theoretical concepts and concrete sample size recommendations against an actual epidemiological background, here given with possible substudy designs within the German National Cohort.