Direct search techniques for the optimal design of biomechanical devices are computationally intensive requiring many iterations before converging to a global solution. This, along with the incorporation of environmental variables such as multiple loading conditions and bone properties, makes direct search techniques infeasible. In this study, we introduced new methods that are based on the sta...

The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using vario...

F&forward artificial neural networks (ANNs) that are tralned with the back-propagation algorithm are a useful tool for modelling environmental systems. They have almady been successfully used to model salinity, nutrient concentrations, air pollution, and algal growth. These successes, coupled with their suitability for modellmg complex systems, have resulted in an increase in their popularity a...

The productivity of greenhouses highly depends on the environmental conditions of crops, such as temperature and humidity. The control and monitoring might need large sensor networks, and as a consequence, mobile sensory systems might be a more suitable solution. This paper describes the application of a heterogeneous robot team to monitor environmental variables of greenhouses. The multi-robot...

The distribution of a species is a complex expression of its ecological and evolutionary history and integrating population genetic, environmental, and ecological data can provide new insights into the effects of the environment on the population structure of species. Previous work demonstrated strong patterns of genetic differentiation in natural populations of the hermaphroditic nematode Pris...

This paper proposes a general formulation of a nonparametric frontier model introducing external environmental factors that might influence the production process but are neither inputs nor outputs under the control of the producer. A representation is proposed in terms of a probabilistic model which defines the data generating process. Our approach extends the basic ideas from Cazals, Florens ...

OBJECTIVES Develop statistical methods for survival models to indirectly adjust hazard ratios of environmental exposures for missing risk factors. METHODS A partitioned regression approach for linear models is applied to time to event survival analyses of cohort study data. Information on the correlation between observed and missing risk factors is obtained from ancillary data sources such as...

In 2002, the construction industry had the undesirable distinction of having two of the most dangerous occupations in the United States, with fatalities among structural steel workers at 58.2 fatalities per 100,000 workers (4 highest rate) and construction laborers experiencing fatalities at the rate of 27.7 fatalities per 100,000 workers (9 highest rate). In addition to the costs associated wi...

Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway ve...

The continuous p-median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach to ED is computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p-median for datasets of South African animals. We test...