The Intersection of Articles 2 and 9

I. Standard Form Contracts II. Buyer in Ordinary Course; Prepaying Buyer III. Consignments IV. Seller\u27s Right to Reclaim Delivered Good

Using modelling to investigate the effectiveness of national surveillance monitoring aimed at detecting a Xylella fastidiosa outbreak in Scotland

Xylella fastidiosa is an important bacterial plant pathogen with a wide host range, causing significant economic impact in the agricultural and horticultural trades. Once restricted to the Americas, new severe outbreaks have recently been discovered in Italy, Spain and France, and to a lesser extent in other countries. Given the ever increasing global plant trade, the likelihood of this potentially devastating plant disease being introduced to novel locations, such as Scotland, is also increasing. Therefore, understanding the potential spread in novel locations is important for accurate risk assessment and mitigation strategies. The Scottish Plant Health Centre requested a preliminary exploration of this potential threat in a Scottish context, with a view of informing contingency planning, which we address here

Displacement and disparity representations in early vision

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.Includes bibliographical references (p. 211-220).by Steven James White.Ph.D

Real-Time Detection of Optical Transients with RAPTOR

Fast variability of optical objects is an interesting though poorly explored subject in modern astronomy. Real-time data processing and identification of transient celestial events in the images is very important for such study as it allows rapid follow-up with more sensitive instruments. We discuss an approach which we have developed for the RAPTOR project, a pioneering closed-loop system combining real-time transient detection with rapid follow-up. RAPTOR's data processing pipeline is able to identify and localize an optical transient within seconds after the observation. The testing we performed so far have been confirming the effectiveness of our method for the optical transient detection. The software pipeline we have developed for RAPTOR can easily be applied to the data from other experiments.Comment: 10 pages, 7 figures, to appear in SPIE proceedings vol. 484

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector long-distance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally—suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal

Spreading speeds for plant populations in landscapes with low environmental variation

Characterising the spread of biological populations is crucial in responding to both biological invasions and the shifting of habitat under climate change. Spreading speeds can be studied through mathematical models such as the discrete-time integro-difference equation (IDE) framework. The usual approach in implementing IDE models has been to ignore spatial variation in the demographic and dispersal parameters and to assume that these are spatially homogeneous. On the other hand, real landscapes are rarely spatially uniform with environmental variation being very important in determining biological spread. This raises the question of under what circumstances spatial structure need not be modelled explicitly. Recent work has shown that spatial variation can be ignored for the specific case where the scale of landscape variation is much smaller than the spreading population׳s dispersal scale. We consider more general types of landscape, where the spatial scales of environmental variation are arbitrarily large, but the maximum change in environmental parameters is relatively small. We find that the difference between the wave-speeds of populations spreading in a spatially structured periodic landscape and its homogenisation is, in general, proportional to ϵ2, where ϵ governs the degree of environmental variation. For stochastically generated landscapes we numerically demonstrate that the error decays faster than ϵ. In both cases, this means that for sufficiently small ϵ, the homogeneous approximation is better than might be expected. Hence, in many situations, the precise details of the landscape can be ignored in favour of spatially homogeneous parameters. This means that field ecologists can use the homogeneous IDE as a relatively simple modelling tool – in terms of both measuring parameter values and doing the modelling itself. However, as ϵ increases, this homogeneous approximation loses its accuracy. The change in wave-speed due to the extrinsic (landscape) variation can be positive or negative, which is in contrast to the reduction in wave-speed caused by intrinsic stochasticity. To deal with the loss of accuracy as ϵ increases, we formulate a second-order approximation to the wave-speed for periodic landscapes and compare both approximations against the results of numerical simulation and show that they are both accurate for the range of landscapes considered

Inventory and review of quantitative models for spread of plant pests for use in pest risk assessment for the EU territory

This report considers the prospects for increasing the use of quantitative models for plant pest spread and dispersal in EFSA Plant Health risk assessments. The agreed major aims were to provide an overview of current modelling approaches and their strengths and weaknesses for risk assessment, and to develop and test a system for risk assessors to select appropriate models for application. First, we conducted an extensive literature review, based on protocols developed for systematic reviews. The review located 468 models for plant pest spread and dispersal and these were entered into a searchable and secure Electronic Model Inventory database. A cluster analysis on how these models were formulated allowed us to identify eight distinct major modelling strategies that were differentiated by the types of pests they were used for and the ways in which they were parameterised and analysed. These strategies varied in their strengths and weaknesses, meaning that no single approach was the most useful for all elements of risk assessment. Therefore we developed a Decision Support Scheme (DSS) to guide model selection. The DSS identifies the most appropriate strategies by weighing up the goals of risk assessment and constraints imposed by lack of data or expertise. Searching and filtering the Electronic Model Inventory then allows the assessor to locate specific models within those strategies that can be applied. This DSS was tested in seven case studies covering a range of risk assessment scenarios, pest types and dispersal mechanisms. These demonstrate the effectiveness of the DSS for selecting models that can be applied to contribute to EFSA Plant Health risk assessments. Therefore, quantitative spread and dispersal modelling has potential to improve current risk assessment protocols and contribute to reducing the serious impacts of plant pests in Europe

A trait-based approach for predicting species responses to environmental change from sparse data : how well might terrestrial mammals track climate change?

Acknowledgements LS was supported by two STSMs by the COST Action ES1101 ”Harmonising Global Biodiversity Modelling“ (Harmbio), supported by COST (European Cooperation in Science and Technology). JMB and SMW were funded by CEH projects NEC05264 and NEC05100. JMJT and SCFP are grateful for the support of the Natural Environment Research Council UK (NE/J008001/1). LS, JAH and JMJT conceived the original idea. LS, JAH, JMB, TC & JMJT designed the study; LS collected the data; LS and TC performed the statistical analyses; LS conducted the integrodifference modelling assisted by JMB and SMW. LS conducted the individual-based modelling assisted by SCFP. LS led the writing supported by JMJT, JMB, SCFP, SMW, TC, JAH and GB.Peer reviewedPublisher PD

Speeding up the simulation of population spread models

1. Simulating spatially explicit population models to predict population spread allows environmental managers to make better-informed decisions. Accurate simulation requires high spatial resolution, which, using existing techniques, can require prohibitively large amounts of computational resources (RAM, CPU, etc). 2. We developed and implemented a novel algorithm for the simulation of integro-difference equations (IDEs) modelling population spread, including stage structure, which uses adaptive mesh refinement. 3. We measured the accuracy of the adaptive algorithm by comparing the results of simulations using the adaptive and a standard non-adaptive algorithm. The relative error of the population's spatial extent was low (<0·05) for a range of parameter values. Comparing efficiency, we found that our algorithm used up to 10 times less CPU time and RAM than the non-adaptive algorithm. 4. Our approach provides large improvements in efficiency without significant loss of accuracy, so it enables faster simulation of IDEs and simulation at scales and at resolutions that have not been previously feasible. As an example, we simulate the spread of a hypothetical species over the UK at a resolution of 25 m. We provide our implementation of the algorithm as a user-friendly executable application