Synthesis of Safe, QoS Extendible, Application Specific Schedulers for Heterogeneous Real-Time Systems

We present a new scheduler architecture, which permits adding QoS (quality of service) policies to the scheduling decisions. We also present a new scheduling synthesis method which allows a designer to obtain a safe scheduler for a particular application. Our scheduler architecture and scheduler synthesis method can be used for heterogeneous applications where the tasks communicate through various synchronization primitives. We present a prototype implementation of this scheduler architecture and related mechanisms on top of an open-source OS (operating system) for embedded systems

Banking Permits: Economic Efficiency and Distributional Effects

Most analyses of the Kyoto flexibility mechanisms focus on the cost effectiveness of “where” flexibility (e.g. by showing that mitigation costs are lower in a global permit market than in regional markets or in permit markets confined to Annex 1 countries). Less attention has been devoted to “when” flexibility, i.e. to the benefits of allowing emission permit traders to bank their permits for future use. In the model presented in this paper, banking of carbon allowances in a global permit market is fully endogenised, i.e. agents may decide to bank permits by taking into account their present and future needs and the present and future decisions of all the other agents. It is therefore possible to identify under what conditions traders find it optimal to bank permits, when banking is socially optimal, and what are the implications for present and future permit prices. We can also explain why the equilibrium rate of growth of permit prices is likely to be larger than the equilibrium interest rate. Most importantly, this paper analyses the efficiency and distributional consequences of allowing markets to optimally allocate emission permits across regions and over time. The welfare and distributional effects of an optimal intertemporal emission trading scheme are assessed for different initial allocation rules. Finally, the impact of banking on carbon emissions, technological progress, and optimal investment decisions is quantified and the incentives that banking provides to accelerate technological innovation and diffusion are also discussed. Among the many results, we show that not only does banking reduce abatement costs, but it also increases the amount of GHG emissions abated in the short-term. It should therefore belong to all emission trading schemes under construction.Emission Trading, Banking, Welfare Distribution, Stabilisation Cost

Banking Permits: Economic Efficiency and Distributional Effects

Most analyses of the Kyoto flexibility mechanisms focus on the cost effectiveness of “where” flexibility (e.g. by showing that mitigation costs are lower in a global permit market than in regional markets or in permit markets confined to Annex 1 countries). Less attention has been devoted to “when” flexibility, i.e. to the benefits of allowing emission permit traders to bank their permits for future use. In the model presented in this paper, banking of carbon allowances in a global permit market is fully endogenised, i.e. agents may decide to bank permits by taking into account their present and future needs and the present and future decisions of all the other agents. It is therefore possible to identify under what conditions traders find it optimal to bank permits, when banking is socially optimal, and what are the implications for present and future permit prices. We can also explain why the equilibrium rate of growth of permit prices is likely to be larger than the equilibrium interest rate. Most importantly, this paper analyses the efficiency and distributional consequences of allowing markets to optimally allocate emission permits across regions and over time. The welfare and distributional effects of an optimal intertemporal emission trading scheme are assessed for different initial allocation rules. Finally, the impact of banking on carbon emissions, technological progress, and optimal investment decisions is quantified and the incentives that banking provides to accelerate technological innovation and diffusion are also discussed. Among the many results, we show that not only does banking reduce abatement costs, but it also increases the amount of GHG emissions abated in the short-term. It should therefore belong to all emission trading schemes under construction.Emission Trading, Banking

Banking Permits: Economic Efficiency and Distributional Effects

Most analyses of the Kyoto flexibility mechanisms focus on the cost effectiveness of “where” flexibility (e.g. by showing that mitigation costs are lower in a global permit market than in regional markets or in permit markets confined to Annex 1 countries). Less attention has been devoted to “when” flexibility, i.e. to the benefits of allowing emission permit traders to bank their permits for future use. In the model presented in this paper, banking of carbon allowances in a global permit market is fully endogenised, i.e. agents may decide to bank permits by taking into account their present and future needs and the present and future decisions of all the other agents. It is therefore possible to identify under what conditions traders find it optimal to bank permits, when banking is socially optimal, and what are the implications for present and future permit prices. We can also explain why the equilibrium rate of growth of permit prices is likely to be larger than the equilibrium interest rate. Most importantly, this paper analyses the efficiency and distributional consequences of allowing markets to optimally allocate emission permits across regions and over time. The welfare and distributional effects of an optimal intertemporal emission trading scheme are assessed for different initial allocation rules. Finally, the impact of banking on carbon emissions, technological progress, and optimal investment decisions is quantified and the incentives that banking provides to accelerate technological innovation and diffusion are also discussed. Among the many results, we show that not only does banking reduce abatement costs, but it also increases the amount of GHG emissions abated in the short-term. It should therefore belong to all emission trading schemes under construction.emission trading, banking

Methodological and empirical challenges in modelling residential location choices

The modelling of residential locations is a key element in land use and transport planning. There are significant empirical and methodological challenges inherent in such modelling, however, despite recent advances both in the availability of spatial datasets and in computational and choice modelling techniques. One of the most important of these challenges concerns spatial aggregation. The housing market is characterised by the fact that it offers spatially and functionally heterogeneous products; as a result, if residential alternatives are represented as aggregated spatial units (as in conventional residential location models), the variability of dwelling attributes is lost, which may limit the predictive ability and policy sensitivity of the model. This thesis presents a modelling framework for residential location choice that addresses three key challenges: (i) the development of models at the dwelling-unit level, (ii) the treatment of spatial structure effects in such dwelling-unit level models, and (iii) problems associated with estimation in such modelling frameworks in the absence of disaggregated dwelling unit supply data. The proposed framework is applied to the residential location choice context in London. Another important challenge in the modelling of residential locations is the choice set formation problem. Most models of residential location choices have been developed based on the assumption that households consider all available alternatives when they are making location choices. Due the high search costs associated with the housing market, however, and the limited capacity of households to process information, the validity of this assumption has been an on-going debate among researchers. There have been some attempts in the literature to incorporate the cognitive capacities of households within discrete choice models of residential location: for instance, by modelling households’ choice sets exogenously based on simplifying assumptions regarding their spatial search behaviour (e.g., an anchor-based search strategy) and their characteristics. By undertaking an empirical comparison of alternative models within the context of residential location choice in the Greater London area this thesis investigates the feasibility and practicality of applying deterministic choice set formation approaches to capture the underlying search process of households. The thesis also investigates the uncertainty of choice sets in residential location choice modelling and proposes a simplified probabilistic choice set formation approach to model choice sets and choices simultaneously. The dwelling-level modelling framework proposed in this research is practice-ready and can be used to estimate residential location choice models at the level of dwelling units without requiring independent and disaggregated dwelling supply data. The empirical comparison of alternative exogenous choice set formation approaches provides a guideline for modellers and land use planners to avoid inappropriate choice set formation approaches in practice. Finally, the proposed simplified choice set formation model can be applied to model the behaviour of households in online real estate environments.Open Acces

Stochastic utility-efficient programming of organic dairy farms

Opportunities to make sequential decisions and adjust activities as a season progresses and more information becomes available characterise the farm management process. In this paper, we present a discrete stochastic two-stage utility efficient programming model of organic dairy farms, which includes risk aversion in the decision maker’s objective function as well as both embedded risk (stochastic programming with recourse) and non-embedded risk (stochastic programming without recourse). Historical farm accountancy data and subjective judgements were combined to assess the nature of the uncertainty that affects the possible consequences of the decisions. The programming model was used within a stochastic dominance framework to examine optimal strategies in organic dairy systems in Norway

Conditions for duality between fluxes and concentrations in biochemical networks

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We also provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality. That is, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes

Stochastic Utility-Efficient Programming of Organic Dairy Farms

Opportunities to make sequential decisions and adjust activities as a season progresses and more information becomes available characterize the farm management process. In this paper, we present a discrete stochastic two-stage utility efficient programming model of organic dairy farms, which includes risk aversion in the decision maker's objective function as well as both embedded risk (stochastic programming with resource) and non-embedded risk (stochastic programming without recourse). Historical farm accountancy data and subjective judgments were combined to assess the nature of the uncertainty that affects the possible consequences of the decisions. The programming model was used within a stochastic dominance framework to examine optimal strategies in organic dairy systems in Norway.agriculture, risk analysis, stochastic programming, stochastic dominance, organic farming, Livestock Production/Industries, Q12, C61,

Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering

We address a new efficient robust optimisation approach to large-scale environmental image reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem-oriented robustification of the previously proposed Fused Bayesian-Regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-model uncertainties. Second, the modification of the Hopfield-type Maximum Entropy Neural Network (MENN) is proposed that enables such MENN to perform numerically the robustified FBR technique via computationally efficient iterative scheme. The efficiency of the aggregated robust regularised MENN technique is verified through simulation studies of enhancement of the real-world environmental images.CINVESTA