On the use of headline indices to link environmental quality and income at the level of the nation state

This paper deconstructs the relationship between the Environmental Sustainability Index (ESI) and national income. The ESI attempts to provide a single figure which encapsulates environmental sustainability' for each country included in the analysis, and this allied with a 'league table' format so as to name and shame bad performers, has resulted in widespread reporting within the popular presses of a number of countries. In essence, the higher the value of the ESI then the more 'environmentally sustainable' a country is deemed to be. A logical progression beyond the use of the ESI to publicise environmental sustainability is its use within a more analytical context. Thus an index designed to simplify in order to have an impact on policy is used to try and understand causes of good and bad performance in environmental sustainability. For example the creators of the ESI claim that ESI is related to GDP/capita (adjusted for Purchasing Power Parity) such that the ESI increases linearly with wealth. While this may in a sense be a comforting picture, do the variables within the ESI allow for alternatives to the story, and if they do then what are the repercussions for those producing such indices for broad consumption amongst the policy makers, mangers, the press, etc.? The latter point is especially important given the appetite for such indices amongst non-specialists, and for all their weaknesses the ESI and other such aggregated indices will not go away. (C) 2007 Elsevier Ltd. All rights reserved.

Intercomparison of aircraft instruments on board the C-130 and Falcon-20 over southern Germany during EXPORT2000

In the summer 2000 EXPORT aircraft campaign (European eXport of Precursors and Ozone by long-Range Transport), two comprehensively instrumented research aircraft measuring a variety of chemical species flew wing tip to wing tip for a period of one and a quarter hours. During this interval a comparison was undertaken of the measurements of nitrogen oxide (NO), odd nitrogen species (NOy), carbon monoxide (CO) and ozone (O3). The comparison was performed at two different flight levels, which provided a 10-fold variation in the concentrations of both NO (10 to 1000 parts per trillion by volume (pptv)) and NOy (200 to over 2500 pptv). Large peaks of NO and NOy observed from the Falcon 20, which were at first thought to be from the exhaust of the C-130, were also detected on the 4 channel NOxy instrument aboard the C-130. These peaks were a good indication that both aircraft were in the same air mass and that the Falcon 20 was not in the exhaust plume of the C-130. Correlations and statistical analysis are presented between the instruments used on the two separate aircraft platforms. These were found to be in good agreement giving a high degree of correlation for the ambient air studied. Any deviations from the correlations are accounted for in the estimated inaccuracies of the instruments. These results help to establish that the instruments aboard the separate aircraft are reliably able to measure the corresponding chemical species in the range of conditions sampled and that data collected by both aircraft can be co-ordinated for purposes of interpretation.

A link between low-frequency mesoscale eddy variability around Madagascar and the large-scale Indian Ocean variability

A connection is shown to exist between the mesoscale eddy activity around Madagascar and the large-scale interannual variability in the Indian Ocean. We use the combined TOPEX/Poseidon-ERS sea surface height (SSH) data for the period 1993–2003. The SSH-fields in the Mozambique Channel and east of Madagascar exhibit a significant interannual oscillation. This is related to the arrival of large-scale anomalies that propagate westward along 10°–15°S in response to the Indian Ocean dipole (IOD) events. Positive (negative) SSH anomalies associated to a positive (negative) IOD phase induce a shift in the intensity and position of the tropical and subtropical gyres. A weakening (strengthening) results in the intensity of the South Equatorial Current and its branches along east Madagascar. In addition, the flow through the narrows of the Mozambique Channel around 17°S increases (decreases) during periods of a stronger and northward (southward) extension of the subtropical (tropical) gyre. Interaction between the currents in the narrows and southward propagating eddies from the northern Channel leads to interannual variability in the eddy kinetic energy of the central Channel in phase with the one in the SSH-field.

Improvement of link failure restoration utilising MPLS as a means to maintain QoS

The availability of a network strongly depends on the frequency of service outages and the recovery time for each outage. The loss of network resources includes complete or partial failure of hardware and software components, power outages, scheduled maintenance such as software and hardware, operational errors such as configuration errors and acts of nature such as floods, tornadoes and earthquakes. This paper proposes a practical approach to the enhancement of QoS routing by means of providing alternative or repair paths in the event of a breakage of a working path. The proposed scheme guarantees that every Protected Node (PN) is connected to a multi-repair path such that no further failure or breakage of single or double repair paths can cause any simultaneous loss of connectivity between an ingress node and an egress node. Links to be protected in an MPLS network are predefined and an LSP request involves the establishment of a working path. The use of multi-protection paths permits the formation of numerous protection paths allowing greater flexibility. Our analysis will examine several methods including single, double and multi-repair routes and the prioritization of signals along the protected paths to improve the Quality of Service (QoS), throughput, reduce the cost of the protection path placement, delay, congestion and collision.

The SAFEE on-board threat detection system

Under the framework of the European Union Funded SAFEE project(1), this paper gives an overview of a novel monitoring and scene analysis system developed for use onboard aircraft in spatially constrained environments. The techniques discussed herein aim to warn on-board crew about pre-determined indicators of threat intent (such as running or shouting in the cabin), as elicited from industry and security experts. The subject matter experts believe that activities such as these are strong indicators of the beginnings of undesirable chains of events or scenarios, which should not be allowed to develop aboard aircraft. This project aimes to detect these scenarios and provide advice to the crew. These events may involve unruly passengers or be indicative of the precursors to terrorist threats. With a state of the art tracking system using homography intersections of motion images, and probability based Petri nets for scene understanding, the SAFEE behavioural analysis system automatically assesses the output from multiple intelligent sensors, and creates. recommendations that are presented to the crew using an integrated airborn user interface. Evaluation of the system is conducted within a full size aircraft mockup, and experimental results are presented, showing that the SAFEE system is well suited to monitoring people in confined environments, and that meaningful and instructive output regarding human actions can be derived from the sensor network within the cabin.

Link between DOC in near surface peat and stream water in an upland catchment

Hydrologic transport of dissolved organic carbon (DOC) from peat soils may differ to organo-mineral soils in how they responded to changes in flow, because of differences in soil profile and hydrology. In well-drained organo-mineral soils, low flow is through the lower mineral layer where DOC is absorbed and high flow is through the upper organic layer where DOC is produced. DOC concentrations in streams draining organo-mineral soils typically increase with flow. In saturated peat soils, both high and low flows are through an organic layer where DOC is produced. Therefore, DOC in stream water draining peat may not increase in response to changes in flow as there is no switch in flow path between a mineral and organic layer. To verify this, we conducted a high-resolution monitoring study of soil and stream water at an upland peat catchment in northern England. Our data showed a strong positive correlation between DOC concentrations at − 1 and − 5 cm depth and stream water, and weaker correlations between concentrations at − 20 to − 50 cm depth and stream water. Although near surface organic material appears to be the key source of stream water DOC in both peat and organo-mineral soils, we observed a negative correlation between stream flow and DOC concentrations instead of a positive correlation as DOC released from organic layers during low and high flow was diluted by rainfall. The differences in DOC transport processes between peat and organo-mineral soils have different implications for our understanding of long-term changes in DOC exports. While increased rainfall may cause an increase in DOC flux from peat due to an increase in water volume, it may cause a decrease in concentrations. This response is contrary to expected changes in DOC exports from organo-mineral soils, where increase rainfall is likely to result in an increase in flux and concentration.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Linear-wavelet models applied to the identification of a two-link manipulator

This paper shows that a wavelet network and a linear term can be advantageously combined for the purpose of non linear system identification. The theoretical foundation of this approach is laid by proving that radial wavelets are orthogonal to linear functions. A constructive procedure for building such nonlinear regression structures, termed linear-wavelet models, is described. For illustration, sim ulation data are used to identify a model for a two-link robotic manipulator. The results show that the introduction of wavelets does improve the prediction ability of a linear model.