Coupling an aerosol box model with one-dimensional flow: a tool for understanding observations of new particle formation events

Field observations of new particle formation and the subsequent particle growth are typically only possible at a fixed measurement location, and hence do not follow the temporal evolution of an air parcel in a Lagrangian sense. Standard analysis for determining formation and growth rates requires that the time-dependent formation rate and growth rate of the particles are spatially invariant; air parcel advection means that the observed temporal evolution of the particle size distribution at a fixed measurement location may not represent the true evolution if there are spatial variations in the formation and growth rates. Here we present a zero-dimensional aerosol box model coupled with one-dimensional atmospheric flow to describe the impact of advection on the evolution of simulated new particle formation events. Wind speed, particle formation rates and growth rates are input parameters that can vary as a function of time and location, using wind speed to connect location to time. The output simulates measurements at a fixed location; formation and growth rates of the particle mode can then be calculated from the simulated observations at a stationary point for different scenarios and be compared with the ‘true’ input parameters. Hence, we can investigate how spatial variations in the formation and growth rates of new particles would appear in observations of particle number size distributions at a fixed measurement site. We show that the particle size distribution and growth rate at a fixed location is dependent on the formation and growth parameters upwind, even if local conditions do not vary. We also show that different input parameters used may result in very similar simulated measurements. Erroneous interpretation of observations in terms of particle formation and growth rates, and the time span and areal extent of new particle formation, is possible if the spatial effects are not accounted for.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Constraining a North Atlantic Ocean general circulation model with chlorofluorocarbon observations

Measurements of anthropogenic tracers such as chlorofluorocarbons and tritium must be quantitatively combined with ocean general circulation models as a component of systematic model development. The authors have developed and tested an inverse method, using a Green's function, to constrain general circulation models with transient tracer data. Using this method chlorofluorocarbon-11 and -12 (CFC-11 and -12) observations are combined with a North Atlantic configuration of the Miami Isopycnic Coordinate Ocean Model with 4/3 degrees resolution. Systematic differences can be seen between the observed CFC concentrations and prior CFC fields simulated by the model. These differences are reduced by the inversion, which determines the optimal gas transfer across the air-sea interface, accounting for uncertainties in the tracer observations. After including the effects of unresolved variability in the CFC fields, the model is found to be inconsistent with the observations because the model/data misfit slightly exceeds the error estimates. By excluding observations in waters ventilated north of the Greenland-Scotland ridge (sigma (0) < 27.82 kg m(-3); shallower than about 2000 m), the fit is improved, indicating that the Nordic overflows are poorly represented in the model. Some systematic differences in the model/data residuals remain and are related, in part, to excessively deep model ventilation near Rockall and deficient ventilation in the main thermocline of the eastern subtropical gyre. Nevertheless, there do not appear to be gross errors in the basin-scale model circulation. Analysis of the CFC inventory using the constrained model suggests that the North Atlantic Ocean shallower than about 2000 m was near 20% saturated in the mid-1990s. Overall, this basin is a sink to 22% of the total atmosphere-to-ocean CFC-11 flux-twice the global average value. The average water mass formation rates over the CFC transient are 7.0 and 6.0 Sv (Sv = 10(6) m(3) s(-1)) for subtropical mode water and subpolar mode water, respectively.

Soilborne fungi and bacteria symbiotically associated with Steinernema spp. acting as biological agents against Fusarium wilt of tomato

An isolate of Gliocladium virens from disease affected soil in a commercial tomato greenhouse proved highly antagonistic to Fusarium oxysporum f.sp. lycopersici, used together with an isolate of the nematophagus fungus Verticillium chlamydosporium. Significant disease control was obtained when young mycelial preparation (on a food-base culture) of the G. virens together with V. chlamydosporium was applied in potting medium. Similar results were observed when a Trichoderma harzianum isolate was treated in combination with the V. chlamydosporium isolate. Most promising, in terms of minimizing the Fusarium wilt of tomato incidence, was also the effect of the bacteria associated with entomopathogenic nematodes (Steinernema spp.), Pseudomonas oryzihabitans and Xenorhabdus nematophilus.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

The incidence of oral Gram-negative bacteria in patients with Parkinson's disease

Background: Parkinson's disease is a common neurodegenerative disorder that affects an increasing number of older people every year. Dysphagia is not only a common feature, but one that results in poor nutrition and an increased risk of bronchopneumonia. Previous work has suggested that the oral flora is altered in patients with oral pathology. Methods: Fifty patients were assessed to quantify the incidence of oral Gram-negative bacteria. Results: Sixteen of the patients with Parkinson's disease were found to have six different Gram-negative bacilli in their oral cavities. The 20 different Gram-negative bacteria present were Escherichia coli (n=7), Klebsiella spp. (n=3), Kluyvera spp. (n=3), Serratia spp. (n=3), Proteus spp. (n=2) and Enterobacter spp. (n=2). We found that the oral cavity of 16 (32%) of the patients with Parkinson's disease was abnormally colonised with Gram-negative bacteria and that Gram-negative bacteria were more likely to occur in those patients in whom oromuscular dysfunction was present (88% vs. 21%; p<0.05). Conclusion: Further work is required to determine the association between oral flora and the pathogenic organisms found in aspiration pneumonia as well as work on innovative treatments to reduce oral Gram-negative bacteria in those patients at particular risk of aspiration pneumonia.

The core-periphery model with three regions and more

We determine the properties of the core-periphery model with three regions and compare our results with those of the standard 2-region model. The conditions for the stability of dispersion and concentration are established. As in the 2-region model, dispersion and concentration can be simultaneously stable. We show that the 3-region (2-region) model favours the concentration (dispersion) of economic activity. Furthermore, we provide some results for the n-region model. We show that the stability of concentration of the 2-region model implies that of any model with an even number of regions.

Bayesian estimation of the spatial Durbin error model with an application to voter turnout in the 2004 presidential election

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, we formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

Analyzing the persuasion context of the persuasive systems design model with the 3D-RAB model

Research into design methodology is one of the most challenging issues in the field of persuasive technology. However, the introduction of the Persuasive Systems Design model, and the consideration of the 3-Dimensional Re-lationship between Attitude and Behavior, offer to make persuasive technolo-gies more practically viable. In this paper we demonstrate how the 3-Dimensional Relationship between Attitude and Behavior guides the analysis of the persuasion context in the Persuasive System Design model. As a result, we propose a modification of the persuasion context and assert that the technology should be analyzed as part of strategy instead of event.