A Monte Carlo study of solutions of oppositely charged polyelectrolytes

The formation of complexes appearing in solutions containing oppositely charged polyelectrolytes has been investigated by Monte Carlo simulations using two different models. The polyions are described as flexible chains of 20 connected charged hard spheres immersed in a homogenous dielectric background representing water. The small ions are either explicitly included or their effect described by using a screened Coulomb potential. The simulated solutions contained 10 positively charged polyions with 0, 2, or 5 negatively charged polyions and the respective counterions. Two different linear charge densities were considered, and structure factors, radial distribution functions, and polyion extensions were determined. A redistribution of positively charged polyions involving strong complexes formed between the oppositely charged polyions appeared as the number of negatively charged polyions was increased. The nature of the complexes was found to depend on the linear charge density of the chains. The simplified model involving the screened Coulomb potential gave qualitatively similar results as the model with explicit small ions. Finally, owing to the complex formation, the sampling in configurational space is nontrivial, and the efficiency of different trial moves was examined.

Theory and generalization of Monte Carlo models of the BOLD signal source

The dependency of the blood oxygenation level dependent (BOLD) signal on underlying hemodynamics is not well understood. Building a forward biophysical model of this relationship is important for the quantitative estimation of the hemodynamic changes and neural activity underlying functional magnetic resonance imaging (fMRI) signals. We have developed a general model of the BOLD signal which can model both intra- and extravascular signals for an arbitrary tissue model across a wide range of imaging parameters. The model of the BOLD signal was instantiated as a look-up-table (LuT), and was verified against concurrent fMRI and optical imaging measurements of activation induced hemodynamics. Magn Reson Med, 2008. © 2008 Wiley-Liss, Inc.

Portmanteau model diagnostics and tests for nonlinearity: a comparative Monte Carlo study of two alternative methods

This paper employs an extensive Monte Carlo study to test the size and power of the BDS and close return methods of testing for departures from independent and identical distribution. It is found that the finite sample properties of the BDS test are far superior and that the close return method cannot be recommended as a model diagnostic. Neither test can be reliably used for very small samples, while the close return test has low power even at large sample sizes

Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution π by simulating a Markov chain with transition kernel P such that π is invariant under P. However, there are many situations for which it is impractical or impossible to draw from the transition kernel P. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace P by an approximation Pˆ. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how ’close’ the chain given by the transition kernel Pˆ is to the chain given by P . We apply these results to several examples from spatial statistics and network analysis.

Reducing noise associated with the Monte Carlo Independent Column Approximation for weather forecasting models

The Monte Carlo Independent Column Approximation (McICA) is a flexible method for representing subgrid-scale cloud inhomogeneity in radiative transfer schemes. It does, however, introduce conditional random errors but these have been shown to have little effect on climate simulations, where spatial and temporal scales of interest are large enough for effects of noise to be averaged out. This article considers the effect of McICA noise on a numerical weather prediction (NWP) model, where the time and spatial scales of interest are much closer to those at which the errors manifest themselves; this, as we show, means that noise is more significant. We suggest methods for efficiently reducing the magnitude of McICA noise and test these methods in a global NWP version of the UK Met Office Unified Model (MetUM). The resultant errors are put into context by comparison with errors due to the widely used assumption of maximum-random-overlap of plane-parallel homogeneous cloud. For a simple implementation of the McICA scheme, forecasts of near-surface temperature are found to be worse than those obtained using the plane-parallel, maximum-random-overlap representation of clouds. However, by applying the methods suggested in this article, we can reduce noise enough to give forecasts of near-surface temperature that are an improvement on the plane-parallel maximum-random-overlap forecasts. We conclude that the McICA scheme can be used to improve the representation of clouds in NWP models, with the provision that the associated noise is sufficiently small.

Effective interaction between monolayers of block copolymer compatiblizer in a polymer blend

The stability of ternary blends of two immiscible homopolymers and a block copolymer compatiblizer depends crucially on the effective interaction between the copolymermonolayers that form between the unlike homopolymer domains. Here, the interaction is calculated for blends involving A and B homopolymers of equal size with ABABdiblock copolymers of symmetric composition using both self-consistent field theory (SCFT) and strong-segregation theory (SST). If the homopolymers are larger than the copolymer molecules, an attractive interaction is predicted which would destroy the blend. This conclusion coupled with considerations regarding the elastic properties of the monolayer suggests that the optimum size of the homopolymer molecules is about 80% that of the copolymer molecule. A detailed examination of the theory demonstrates that the attraction results from the configurational entropy loss of the homopolymer molecules trapped between the copolymermonolayers. We conclude by suggesting how the monolayers can be altered in order to suppress this attraction and thus improve compatiblization.

Improving polymeric microemulsions with block copolymer polydispersity

Recent experiments have demonstrated that block copolymers are capable of stabilizing immiscible homopolymer blends producing bicontinuous microemulsion. The stability of these polymeric alloys requires the copolymer to form flexible, nonattractive monolayers along the homopolymer interfaces. We predict that copolymer polydispersity can substantially and simultaneously improve the monolayers in both of these respects. Furthermore, polydispersity should provide similar improvements in systems, such as colloidal suspensions and polymer/clay composites, that utilize polymer brushes to suppress attractive interactions.

The economic viability and potential of a novel poultry agroforestry system

Investigating agroforestry systems that incorporate poultry is warranted in Northern Europe as they may offer benefits including: improved welfare and use of range; reduced feed costs; price premia on products; reduced payback periods for forests; and, greater returns on investment. Free-range egg production accounts for 27% of the United Kingdom egg market and demand for outdoor broilers is increasing. No research has been conducted recently on the economic viability of agroforestry systems with poultry. An economic model was constructed to: assess economic viability of a broiler agroforestry system; and, investigate the sensitivity of economic performance to key factors and interactions, and identify those which warrant attention in research and management. The system modelled is a commercial trial established in Southern England in 2002 where deciduous trees were planted and broilers reared in six- or nine-week periods. The model uses Monte Carlo simulation and financial performance analyses run for a 120-year period. An Internal Rate of Return (IRR) of 15.5% is predicted for the six-week system which remains viable under a 'worst case' scenario (IRR of 12.6%). Factors which affect financial performance most (decreasing in magnitude) are prices achieved for broilers, costs of brooding houses, chicks, arks, feed and timber prices. The main anticipated effects of biological interactions on financial performance (increased ranging on feed conversion and excess nutrient supply on tree health) were not supported by analysis. Further research is particularly warranted on the welfare benefits offered by the tree component and its relation to price premia.

Variance estimation for systematic sampling from deliberately ordered populations

The systematic sampling (SYS) design (Madow and Madow, 1944) is widely used by statistical offices due to its simplicity and efficiency (e.g., Iachan, 1982). But it suffers from a serious defect, namely, that it is impossible to unbiasedly estimate the sampling variance (Iachan, 1982) and usual variance estimators (Yates and Grundy, 1953) are inadequate and can overestimate the variance significantly (Särndal et al., 1992). We propose a novel variance estimator which is less biased and that can be implemented with any given population order. We will justify this estimator theoretically and with a Monte Carlo simulation study.

Conserving marginal populations of the foodplant (Impatiens noli-tangere) of an endangered moth (Eustroma reticulatum) in a changing climate

Impatiens noli-tangere is scarce in the UK and probably only native to the Lake District and Wales. It is the sole food plant for the endangered moth Eustroma reticulattum. Significant annual fluctuations in the size of I. noli-tangere populations endanger the continued presence of E. reticulatum in the UK. In this study, variation in population size was monitored across native populations of L noli-tangere in the English Lake District and Wales. In 1998, there was a crash in the population size of all metapopulations in the Lake District but not of those found in Wales. A molecular survey of the genetic affinities of samples in 1999 from both regions and a reference population from Switzerland was performed using AFLP and ISSR analyses. The consensus UPGMA dendrogram and a PCO scatter plot revealed clear differentiation between the populations of L noli-tangere in Wales and those in the Lake District. Most of the genetic variation in the UK (H-T= 0.064) was partitioned between (G(ST) = 0.455) rather than within (H-S = 0.034) regions, inferring little gene flow occurs between regions. There was similar bias towards differentiation between metapopulations in Wales, again consistent with low levels of interpopulation gene flow. This contrasts with far lower levels of differentiation in the Lake District which suggests modest rates of gene flow may occur between populations. It is concluded that in the event of local extinction of sites or populations, reintroductions should be restricted to samples collected from the same region. We then surveyed climatic variables to identify those most likely to cause local extinctions. Climatic correlates of population size were sought from two Lake District metapopulations situated close to a meteorological station. A combination of three climatic variables common to both sites explained 81-84% of the variation in plant number between 1990 and 2001. Projected trends for these climatic variables were used in a Monte Carlo simulation which suggested an increased risk of I. noli-tangere population crashes by 2050 at Coniston Water. but not at Derwentwater. Implications of these findings for practical conservation strategies are explored. (C) 2003 Elsevier Ltd. All rights reserved.

Parallel performance and scalability experiments with the Danish Eulerian Model on the EPCC supercomputers

The Danish Eulerian Model (DEM) is a powerful air pollution model, designed to calculate the concentrations of various dangerous species over a large geographical region (e.g. Europe). It takes into account the main physical and chemical processes between these species, the actual meteorological conditions, emissions, etc.. This is a huge computational task and requires significant resources of storage and CPU time. Parallel computing is essential for the efficient practical use of the model. Some efficient parallel versions of the model were created over the past several years. A suitable parallel version of DEM by using the Message Passing Interface library (AIPI) was implemented on two powerful supercomputers of the EPCC - Edinburgh, available via the HPC-Europa programme for transnational access to research infrastructures in EC: a Sun Fire E15K and an IBM HPCx cluster. Although the implementation is in principal, the same for both supercomputers, few modifications had to be done for successful porting of the code on the IBM HPCx cluster. Performance analysis and parallel optimization was done next. Results from bench marking experiments will be presented in this paper. Another set of experiments was carried out in order to investigate the sensitivity of the model to variation of some chemical rate constants in the chemical submodel. Certain modifications of the code were necessary to be done in accordance with this task. The obtained results will be used for further sensitivity analysis Studies by using Monte Carlo simulation.

On the regulation of spatial externalities: coexistence between GM and conventional crops in the EU and the 'newcomer principle'

Pollen-mediated gene flow is one of the main concerns associated with the introduction of genetically modified (GM) crops. Should a premium for non-GM varieties emerge on the market, ‘contamination’ by GM pollen would generate a revenue loss for growers of non-GM varieties. This paper analyses the problem of pollen-mediated gene flow as a particular type of production externality. The model, although simple, provides useful insights into coexistence policies. Following on from this and taking GM herbicide-tolerant oilseed rape (Brassica napus) as a model crop, a Monte Carlo simulation is used to generate data and then estimate the effect of several important policy variables (including width of buffer zones and spatial aggregation) on the magnitude of the externality associated with pollen-mediated gene flow.

Real estate portfolio size and risk reduction

The reduction of portfolio risk is important to all investors but is particularly important to real estate investors as most property portfolios are generally small. As a consequence, portfolios are vulnerable to a significant risk of under-performing the market, or a target rate of return and so investors may be exposing themselves to greater risk than necessary. Given the potentially higher risk of underperformance from owning only a few properties, we follow the approach of Vassal (2001) and examine the benefits of holding more properties in a real estate portfolio. Using Monte Carlo simulation and the returns from 1,728 properties in the IPD database, held over the 10-year period from 1995 to 2004, the results show that increases in portfolio size offers the possibility of a more stable and less volatile return pattern over time, i.e. down-side risk is diminished with increasing portfolio size. Nonetheless, increasing portfolio size has the disadvantage of restricting the probability of out-performing the benchmark index by a significant amount. In other words, although increasing portfolio size reduces the down-side risk in a portfolio, it also decreases its up-side potential. Be that as it may, the results provide further evidence that portfolios with large numbers of properties are always preferable to portfolios of a smaller size.

Precisely wrong or roughly right? An evaluation of development viability appraisal modelling

Purpose – The purpose of this paper is to investigate the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Design/methodology/approach – Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. Findings – It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, that simple and simplistic models may produce similar outputs to more robust and disaggregated models. Evidence is found of equifinality in the outputs of a simple, aggregated model of development viability relative to more complex, disaggregated models. Originality/value – Development viability appraisal has become increasingly important in the planning system. Consequently, the theory, application and outputs from development appraisal are under intense scrutiny from a wide range of users. However, there has been very little published evaluation of viability models. This paper contributes to the limited literature in this area.

Precisely wrong or roughly right? An evaluation of development viability

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, that simple and simplistic models may produce similar outputs to more robust and disaggregated models.