Monitoring Infection Risk for Air and Soil Borne Fungal Plant Pathogens using Antibody and DNA Techniques and Mathematical Models describing Environmental Parameters

Economic losses resulting from disease development can be reduced by accurate and early detection of plant pathogens. Early detection can provide the grower with useful information on optimal crop rotation patterns, varietal selections, appropriate control measures, harvest date and post harvest handling. Classical methods for the isolation of pathogens are commonly used only after disease symptoms. This frequently results in a delay in application of control measures at potentially important periods in crop production. This paper describes the application of both antibody and DNA based systems to monitor infection risk of air and soil borne fungal pathogens and the use of this information with mathematical models describing risk of disease associated with environmental parameters.

Drawing in Duos: The Journeys and Intersections Collaboration Project Between Istanbul and Worcester

This paper focuses on a collaborative drawing project carried out between two undergraduate drawing courses, one in İstanbul and the other in Worcester, during the Fall 2015 semester. Duos -teams of two working remotely- took turns to work on the same drawing in correspondence for three months by means of communication and sharing the changing images on-line with one another. The project introduced a collaborative method of learning based on drawing, initiated cultural exchange and cooperation. The role of drawing in the project was critical due to the direct but gradual nature of transmitting meaning onto paper. Outcomes of this project consisted of a flexible and playful creation process for students making use of the element of chance. They sought alternate ways to finalize a drawing and experienced the benefits of artistic co-production. The project has the capacity to inspire artists, instructors and others interested in creative partnership in different disciplines and can be of value as an educational model.

A Parameter Estimation and Identifiability Analysis Methodology Applied to a Street Canyon Air Pollution Model

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPMr). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to succesfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.