General systems perspective on achieving continuous synergy in a project organisation

Studies of construction labour productivity have revealed that limited predictability and multi-agent social complexity make long-range planning of construction projects extremely inaccurate. Fire-fighting, a cultural feature of construction project management, social and structural diversity of involved permanent organizations, and structural temporality all contribute towards relational failures and frequent changes. The main purpose of this paper is therefore to demonstrate that appropriate construction planning may have a profound synergistic effect on structural integration of a project organization. Using the general systems theory perspective it is further a specific objective to investigate and evaluate organizational effects of changes in planning and potentials for achieving continuous project-organizational synergy. The newly developed methodology recognises that planning should also represent a continuous, improvement-leading driving force throughout a project. The synergistic effect of the process planning membership duality fostered project-wide integration, eliminated internal boundaries, and created a pool of constantly upgrading knowledge. It maintained a creative environment that resulted in a number of process-related improvements from all parts of the organization. As a result labour productivity has seen increases of more than 30%, profits have risen from an average of 12% to more than 18%, and project durations have been reduced by several days.

A comparison of energy use in conventional and integrated arable farming systems in the UK

The LINK Integrated Farming Systems (LINK-IFS) Project (1992-1997) was setup to compare conventional and integrated arable farming systems (IAFS), concentrating on practical feasibility and economic viability, but also taking into account the level of inputs used and environmental impact. As part of this, an examination into energy use within the two systems was also undertaken. This paper presents the results from that analysis. The data used is from the six sites within the LINK-IFS Project, spread through the arable production areas of England and from the one site in Scotland, covering the 5 years of the project. The comparison of the energy used is based on the equipment and inputs used to produce I kg of each crop within the conventional and integrated rotations, and thereby the overall energy used for each system. The results suggest that, in terms of total energy used, the integrated system appears to be the most efficient. However, in terms of energy efficiency, energy use per kilogram of output, the results are less conclusive. (C) 2003 Elsevier Science B.V. All rights reserved.

Modelling complex systems for project planning - A semiotics motivated method

A large and complex IT project may involve multiple organizations and be constrained within a temporal period. An organization is a system comprising of people, activities, processes, information, resources and goals. Understanding and modelling such a project and its interrelationship with relevant organizations are essential for organizational project planning. This paper introduces the problem articulation method (PAM) as a semiotic method for organizational infrastructure modelling. PAM offers a suite of techniques, which enables the articulation of the business, technical and organizational requirements, delivering an infrastructural framework to support the organization. It works by eliciting and formalizing (e. g. processes, activities, relationships, responsibilities, communications, resources, agents, dependencies and constraints) and mapping these abstractions to represent the manifestation of the "actual" organization. Many analysts forgo organizational modelling methods and use localized ad hoc and point solutions, but this is not amenable for organizational infrastructures modelling. A case study of the infrared atmospheric sounding interferometer (IASI) will be used to demonstrate the applicability of PAM, and to examine its relevancy and significance in dealing with the innovation and changes in the organizations.

The application of implant technology for cybernetic systems

Objective: To assess the usefulness, compatibility, and long-term operability of a microelectrode array into the median nerve of the left arm of a healthy volunteer, including perception of feedback stimulation and operation of an instrumented prosthetic hand. Setting: The study was carried out from March 14 through June 18, 2002, in England and the United States. Results: The blindfolded subject received feedback information, obtained from force and slip sensors on the prosthetic hand, and subsequently used the implanted device to control the hand by applying an appropriate force to g rip an unseen object. Operability was also demonstrated remotely via the Internet, with the subject in New York, NY, and the prosthetic hand in Reading, England. Finally, the subject was able to control an electric wheelchair, via decoded signals from the implant device, to select the direction of travel by opening and closing his hand. The implantation did not result in infection or any perceivable loss of hand sensation or motion control. The implant was finally extracted because of mechanical fatigue of the percutaneous connection. Further testing after extraction has not indicated any measurable long-term defects in the subject. Conclusions: This implant may allow recipients to have abilities they would otherwise not possess. The response to stimulation improved considerably during the trial, suggesting that the subject learned to process the incoming information more effectively.

A compositional framework for hardware/software co-design

We describe a compositional framework, together with its supporting toolset, for hardware/software co-design. Our framework is an integration of a formal approach within a traditional design flow. The formal approach is based on Interval Temporal Logic and its executable subset, Tempura. Refinement is the key element in our framework because it will derive from a single formal specification of the system the software and hardware parts of the implementation, while preserving all properties of the system specification. During refinement simulation is used to choose the appropriate refinement rules, which are applied automatically in the HOL system. The framework is illustrated with two case studies. The work presented is part of a UK collaborative research project between the Software Technology Research Laboratory at the De Montfort University and the Oxford University Computing Laboratory.

Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

The north Atlantic marine oundary layer experiment (NAMBLEX): Overview of the campaign held at Mace Head, Ireland, in summer 2002

The North Atlantic Marine Boundary Layer Experiment (NAMBLEX), involving over 50 scientists from 12 institutions, took place at Mace Head, Ireland (53.32° N, 9.90° W), between 23 July and 4 September 2002. A wide range of state-of-the-art instrumentation enabled detailed measurements of the boundary layer structure and atmospheric composition in the gas and aerosol phase to be made, providing one of the most comprehensive in situ studies of the marine boundary layer to date. This overview paper describes the aims of the NAMBLEX project in the context of previous field campaigns in the Marine Boundary Layer (MBL), the overall layout of the site, a summary of the instrumentation deployed, the temporal coverage of the measurement data, and the numerical models used to interpret the field data. Measurements of some trace species were made for the first time during the campaign, which was characterised by predominantly clean air of marine origin, but more polluted air with higher levels of NOx originating from continental regions was also experienced. This paper provides a summary of the meteorological measurements and Planetary Boundary Layer (PBL) structure measurements, presents time series of some of the longer-lived trace species (O3, CO, H2, DMS, CH4, NMHC, NOx, NOy, PAN) and summarises measurements of other species that are described in more detail in other papers within this special issue, namely oxygenated VOCs, HCHO, peroxides, organo-halogenated species, a range of shorter lived halogen species (I2, OIO, IO, BrO), NO3 radicals, photolysis frequencies, the free radicals OH, HO2 and (HO2+Σ RO2), as well as a summary of the aerosol measurements. NAMBLEX was supported by measurements made in the vicinity of Mace Head using the NERC Dornier-228 aircraft. Using ECMWF wind-fields, calculations were made of the air-mass trajectories arriving at Mace Head during NAMBLEX, and were analysed together with both meteorological and trace-gas measurements. In this paper a chemical climatology for the duration of the campaign is presented to interpret the distribution of air-mass origins and emission sources, and to provide a convenient framework of air-mass classification that is used by other papers in this issue for the interpretation of observed variability in levels of trace gases and aerosols.

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).

Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism

Over the last 25years, "mindblindness" (deficits in representing mental states) has been one of the primary explanations behind the hallmark social-communication difficulties in autism spectrum conditions (ASC). However, highlighting neural systems responsible for mindblindness and their relation to variation in social impairments has remained elusive. In this study we show that one of the neural systems responsible for mindblindness in ASC and its relation to social impairments is the right temporo-parietal junction (RTPJ). Twenty-nine adult males with ASC and 33 age and IQ-matched Controls were scanned with fMRI while making reflective mentalizing or physical judgments about themselves or another person. Regions of interest within mentalizing circuitry were examined for between-group differences in activation during mentalizing about self and other and correlations with social symptom severity. RTPJ was the only mentalizing region that responded atypically in ASC. In Controls, RTPJ was selectively more responsive to mentalizing than physical judgments. This selectivity for mentalizing was not apparent in ASC and generalized across both self and other. Selectivity of RTPJ for mentalizing was also associated with the degree of reciprocal social impairment in ASC. These results lend support to the idea that RTPJ is one important neural system behind mindblindness in ASC. Understanding the contribution of RTPJ in conjunction with other neural systems responsible for other component processes involved in social cognition will be illuminating in fully explaining the hallmark social-communication difficulties of autism.