Urban Principles for Ecological Landscape Design and Maintenance: Scientific Fundamentals

Urban ecology is a rapidly developing scientific discipline with great relevance to sustainable city design and management. Though several frameworks have been proposed in the last 10 years, urban ecology, as yet, has no complete, mature theory. There are, however, general principles emerging that may facilitate the development of such a theory. In the meantime, these principles can serve as useful guides for ecological landscape design and maintenance. This paper aims to use the principles to conceptually frame a series of papers to follow in this special issue. The main ecological principles concerning cities are that: 1) Cities are ecosystems; 2) Cities are spatially heterogeneous; 3) Cities are dynamic; 4) Human and natural processes interact in cities; and 5) Ecological processes are still at work and are important in cities. The first three principles address the structure of cities and the change in structure through time. The remaining two principles focus on ecological processes in cities. We briefly summarize each of these principles and their roots in the ecological and design fields. Each principle points to ecological functions that can be translated into ecosystem services. Application of these principles to ecological landscape design and maintenance is discussed

A Bayesian-Influence Model for Error Probability Analysis of Combine Operations in Harvesting

Harvesting is one of the most important agricultural operations because it captures the value from the entire cropping season. In modern agriculture, grain harvesting has been mechanized through the combine harvester. A combine harvester enables highly productive crop harvesting. Combine harvesting performance depends on the highly variable skill of combine operators and associated operator error. An approach was developed to analyze the risk of the combine harvesting operation as it relates to operator error. Specifically, a risk analysis model was built based on a task analysis from operator interviews and estimates of the probability of operator error. This paper employs a Bayesian approach to assess risks in combine operation. This approach applies a Bayesian Belief Network to agriculture operations, which represents a new application for this risk analysis tool. Sensitivity analysis of different errors and operator skill levels was also performed. The preliminary results indicate that a reduction of human operator action errors can substantially improve the outcomes of the human-machine interaction