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Business environments, in which any modern Manufacturing Enterprise (ME) operates, have grown significantly in complexity and are changing faster than ever before. It follows that designing a flexible manufacturing system to achieve a set of strategic objectives involves making a series of complex decisions over time. Therefore manufacturing industry needs improved knowledge about likely impacts of making different types of change in MEs and improved modelling approaches that are capable of providing a systematic way of modelling change impacts in complex business processes; prior to risky and costly change implementation projects. An ability to simulate the execution of process instances is also needed to control, animate and monitor simulated flows of multiple products through business processes; and thereby to assess impacts of dynamic distributions and assignments of multiple resource types during any given time period. Further more this kind of modelling capability needs to be integrated into a single modelling framework so as to improve its flexibility and change coordination. Such a modelling capability and framework should help MEs to achieve successfully business process re-engineering, continuous performance development and enterprise re-design. This thesis reports on the development of new modelling constructs and their innovative application when used together with multiple existing modelling approaches. This enables human and technical resource systems to be described, specified and modelled coherently and explicitly. In turn this has been shown to improve the design of flexible, configurable and re-usable manufacturing resource systems, capable of supporting decision making in agile manufacturing systems. A newly conceived and developed Role-Based Modelling Methodology (R-BMM) was proposed during this research study. Also the R-BMM was implemented and tested by using it together with three existing modelling approaches namely (1) extended Enterprise Modelling, (2) dynamic Causal Loop Diagramming and (3) Discrete Event Simulation Modelling (via software PlantSimulation ®). Thereby these three distinct modelling techniques were deployed in a new and coherent way. The new R-BMM approach to modelling manufacturing systems was designed to facilitate: (1) Graphical Representation (2) Explicit Specification and (3) Implementation Description of Resource systems. Essentially the approach enables a match between suitable human and technical resource systems and well defined models of processes and workflows. Enterprise Modelling is used to explicitly define functional and flexibility competencies that need to be possessed by suitable role holders. Causal Loop Diagramming is used to reason about dependencies between different role attributes. The approach was targeted at the design and application of simulation models that enable relative performance comparisons (such as work throughput, lead-time and process costs) to be made and to show how performance is affected by different role decompositions and resourcing policies. The different modelling techniques are deployed via a stepwise application of the R-BMM approach. Two main case studies were carried out to facilitate methodology testing and methodology development. The chosen case company possessed manufacturing characteristics required to facilitate testing and development; in terms of significant complexity and change with respect to its products and their needed processing structures and resource systems. The first case study was mainly designed to illustrate an application, and benefits arising from application, of the new modelling approach. This provided both qualitative and quantitative results analysis and evaluation. Then with a view to reflecting on modelling methodology testing and to address a wider scope manufacturing problem, the second case study was designed and applied at a different level of abstraction, to further test and verify the suitability and re-usability of the methodology. Through conceiving the new R-BMM approach, to create, analyse and assess the utility of sets of models, this research has proposed and tested enhancements to current means of realising reconfigurable and flexible production systems.

This thesis will consider the following assumptions which are based on a few insights about the artic climate: (1)the artic climate can be characterised by a growing season called summer and a dormat season called winter (2)in the summer season growing conditions are reasonably favourable and species are more likely to compete for plentiful resources (3)in the winter season there would be no further growth and the plant populations would instead by subjected to fierce weather events such as storms which is more likely to lead to the destruction of some or all of the biomass. Under these assumptions, is it possible to find those change in the environment that might cause mutualism (see section 1.9.2) from competition (see section 1.9.1) to change? The primary aim of this thesis to to provide a prototype simulation of growth of two plant species in the artic that: (1)take account of different models for summer and winter seasons (2)permits the effects of changing climate to be seen on each type of plant species interaction.

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No description available.

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The climate is changing and the rate of this change is expected to increase. In the 20th century global surface temperatures rose by 0.6 (±0.2) K. Based on current model predictions, and economic forecasts, global temperature increases of 1.4 to 5.8 K are expected over the period 1990 – 2100. One of the main drivers for this temperature increase is the build up of CO2 in the atmosphere which has been increasing since pre-industrial times. Pre-industrial concentrations of CO2 were bounded between 180 ppm and 300 ppm, however the current concentrations of 380 ppm are far in excess of these bounds. Further more, forecasts indicates that a further doubling in the next century is a distinct possibility. However making predictions about the future climate is difficult. Predicting the trajectory that the climate will take uses assumptions of economic growth, technological advances and ecological and physical processes. If we are to make informed decisions regarding the future of the planet, we have to account not only for future anthropogenic emissions and land use, but we also have to identify the response of the Earth system. By its very nature the Earth is immensely complex; processes, interactions and feedbacks exist which operate on vastly different spatial and temporal scales. Each of these processes has an associated level of uncertainty. This uncertainty propagates through models and the processes and feedbacks they simulate. One of our jobs as environmental scientists is to quantify and then reduce these uncertainties. Consequently it is critical to quantify the interactions of the land-surface and the atmosphere. The role of the land-surface is critical to the response of the Earth’s climate. All general circulation models and regional scale models need representations of the land-surface. A lot of the work concerning the land-surface aims to determine the land-surface partitioning of energy, the evapotranspiration of water and if the land-surface is a sink or a source of CO2. To do achieve this we need to understand (1) the underlying processes governing the response of the land-surface, (2) the response of these processes to perturbations from climate change and humans, (3) the temporal and spatial heterogeneity in these processes, and (4) the feedbacks that land-surface processes have with the climate. In this thesis I use a coupled atmosphere-biosphere model to show current understanding of the carbon, water and energy dynamics of the biosphere and the atmosphere to be consistent with both PBL and stand-based measurements. I then use the CAB model to investigate the strength of different feedbacks between the atmosphere and biosphere. Finally the model is then used in a Monte Carlo Bayesian inversion scheme to invert atmospheric measurements to infer information about surface parameters.

The ability to learn multiple languages simultaneously is a fundamental human linguistic capacity. Yet there has been little attempt to explain this in evolutionary terms. Perhaps one reason for this lack of attention is the idea that monolingualism is the default, most basic state and so needs to be explained before considering bilingualism. When thinking about bilingualism in this light, a paradox appears: Intuitively, learning two languages is harder than learning one, yet bilingualism is prevalent in the world. Previous explanations for linguistic diversity involve appeals to adaptation for group resistance to freeriders. However, the first statement of the paradox is a property of individuals, while the second part is a property of populations. This thesis shows that the properties of cultural transmission mean that the link between individual learning and population-level phenomena can be complex. A simple Bayesian model shows that just because learning one language is easier than two, it doesn't mean that monolingualism will be the most prevalent property of populations. Although this appears to resolve the paradox, by building models of bilingual language evolution the complexity of the problem is revealed. A bilingual is typically defined as an individual with "native-like control of two languages" (Bloomfield, 1933, p. 56), but how do we define a native speaker? How do we measure proficiency? How do we define a language? How can we draw boundaries between languages that are changing over large timescales and spoken by populations with dynamic structures? This thesis argues that there is no psychological reality to the concept of discrete, monolithic, static `languages' - they are epiphenomena that emerge from the way individuals use low-level linguistic features. Furthermore, dynamic social structures are what drives levels of bilingualism. This leads to a concrete definition of bilingualism: The amount of linguistic optionality that is conditioned on social variables. However, integrating continuous variation and dynamic social structures into existing top-down models is difficult because many make monolingual assumptions. Subsequently, introducing bilingualism into these models makes them qualitatively more complicated. The assumptions that are valid for studying the general processes of cultural transmission may not be suitable for asking questions about bilingualism. I present a bottom-up model that is specifically designed to address the bilingual paradox. In this model, individuals have a general learning mechanism that conditions linguistic variation on semantic variables and social variables such as the identity of the speaker. If speaker identity is an important conditioning factor, then `bilingualism' emerges. The mechanism required to learn one language in this model can also learn multiple languages. This suggests that the bilingual paradox derives from focussing on the wrong kind of question. Rather than having to explain the ability to learn multiple languages simultaneously as an adaptation, we should be asking how and why humans developed a flexible language learning mechanism. This argument coincides with a move in the field of bilingualism away from asking `how are monolinguals and bilinguals different?' to `how does the distribution of variation affect the way children learn?'. In this case, while studies of language evolution look at how learning biases affect linguistic variation, studies of bilingualism look at how linguistic variation affects learning biases. I suggest that the two fields have a lot to offer each other.