Reflection using the derivability conditions

Reflection principles are a way to build non-conservative true extensions of a theory. However the application of a reflection principle needs a proof predicate, and the effort needed to provide this is so great as to be not really practical. We look at a possible way to avoid this effort by using, instead of a proof predicate, a predicate defined using only necessary `modal' properties. Surprisingly, we can produce powerful non-conservative extensions this way. But a reflection principle based on such a predicate is essentially weaker, and we also consider its limitations

Analyzing complex functional brain networks: fusing statistics and network science to understand the brain

Complex functional brain network analyses have exploded over the last eight years, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.Comment: Statistics Surveys, In Pres

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.

Spitzer observations of extragalactic H II regions - III. NGC 6822 and the hot star, H II region connection

Using the short-high module of the Infrared Spectrograph on the Spitzer Space Telescope, we have measured the [S IV] 10.51, [Ne II] 12.81, [Ne III] 15.56, and [S III] 18.71-micron emission lines in nine H II regions in the dwarf irregular galaxy NGC 6822. These lines arise from the dominant ionization states of the elements neon (Ne$^{++}$, Ne$^+$) and sulphur (S$^{3+}$, S$^{++}$), thereby allowing an analysis of the neon to sulphur abundance ratio as well as the ionic abundance ratios Ne$^+$/Ne$^{++}$ and S$^{3+}$/S$^{++}$. By extending our studies of H II regions in M83 and M33 to the lower metallicity NGC 6822, we increase the reliability of the estimated Ne/S ratio. We find that the Ne/S ratio appears to be fairly universal, with not much variation about the ratio found for NGC 6822: the median (average) Ne/S ratio equals 11.6 (12.2$\pm$0.8). This value is in contrast to Asplund et al.'s currently best estimated value for the Sun: Ne/S = 6.5. In addition, we continue to test the predicted ionizing spectral energy distributions (SEDs) from various stellar atmosphere models by comparing model nebulae computed with these SEDs as inputs to our observational data, changing just the stellar atmosphere model abundances. Here we employ a new grid of SEDs computed with different metallicities: Solar, 0.4 Solar, and 0.1 Solar. As expected, these changes to the SED show similar trends to those seen upon changing just the nebular gas metallicities in our plasma simulations: lower metallicity results in higher ionization. This trend agrees with the observations.Comment: 22 pages, 13 figures. To be published in MNRAS. reference added and typos fixed. arXiv admin note: text overlap with arXiv:0804.0828, which is paper II by Rubin et al. (2008

Monitoring bioaerosol and odour emissions from composting facilities - WR1121

Government policy requires that valuable resources should be recovered and recycled from biodegradable waste. A successful and growing organics recycling industry delivers this policy with composting being one of the principal technologies deployed to process suitable feedstock such as garden and food waste. Composting inevitably generates bioaerosols – particulate matter comprising cells or cellular components that are released into the air as a result of disturbance of composting feedstock or the processing of final product. Exposure to bioaerosols has the potential to be harmful to human and animal health. The Environment Agency adopts a precautionary and risk-based approach to the regulation of composting facilities which was developed on the basis of research by Wheeler et al. (2001) and which has been updated as new evidence has become available. The Environment Agency also requires site operators to monitor bioaerosols around their facilities using methods specified in a standard protocol which relies upon classical microbiology methods which are tried and tested but which are labour-intensive, slow and offer only a snapshot view of a highly dynamic system. A recent IOM review commissioned by Defra (Searl, 2009) on exposure-response relationships for bioaerosol emissions from waste treatment processes identified significant gaps in knowledge of exposure to bioaerosols and recommended that more research was needed into alternatives to viable microbial monitoring such as priority biomarkers (notably endotoxin) and potential surrogates such as particulate matter. The IOM review also concluded that there is a lack of information to support the development of appropriate stand-off distances. The overall aim of this project was to provide evidence on bioaerosol production, dispersion and potential exposures from composting facilities in support of future developments in policy and regulation of biowaste facilities. The objectives were: (i) to undertake a comprehensive set of standard and novel bioaerosol measurements at representative composting sites to assess comparability between different methods and also to measure spatial and temporal variations; and (ii) to determine the odour emissions and then compare these with bioaerosol emissions to see if odour is a marker of significant bioaerosol exposure. Standard (AfOR, 2009) and novel (CEN filter method, endotoxin, glucan, qPCR, real-time particulates) bioaerosols measurements were taken on a minimum of three to a maximum of six occasions over a twelve month period at four different composting facilities in England. The composting facilities were selected to represent sites of varying sizes (tonnages) and to allow a comparison of bioaerosol concentrations at standard open windrow sites versus a fully-contained site. Additional supporting information was collected including meteorological data at the time of sampling, observation of site operations and measurements of odour at one of the sites. Supporting bioaerosol and odour dispersion modelling was conducted at the site where the odour measurements were made. The spatial trend of bioaerosol concentrations described by Wheeler et al., (1991) and upon which EA regulatory policy is based was broadly corroborated by this dataset. Excursions above the EA acceptable levels at or beyond 250m from source were rare. Bioaerosol concentrations at the enclosed site were generally lower than at the open windrow sites. There was no evidence of a seasonal pattern in bioaerosol concentrations at any of the sites whereas between-sampling day variations were apparent. The cause(s) of these variations were not identified. No consistent relationship was observed between the concentration of bioaerosols measured by the two AfOR standard methods. The two methods displayed certain strengths and weakness in different situations. The IOM sampling device proved to be better suited to situations where high bioaerosol concentrations were encountered (close to source); the Andersen proving to be more effective in the lower concentration range typically found upwind of a site or at distance downwind from source. The higher volume filtration device tested in this project (referred to as the CEN method) produced data that did not consistently match either of the AfOR standard methods. This device demonstrated greater sensitivity than the IOM filter method but suffered drawbacks associated with its weight and a lack of ease of use in the field. Endotoxin concentrations were normally below the level recommended by the Dutch Expert Committee on Occupational Safety but occasional exceedances of this standard were detected at the larger open windrow sites. The majority of glucan measurements were below a widely referred to 10ng/m3 threshold. Significantly elevated concentrations were detected at one of the larger open windrow sites. The dynamic range of the qPCR method is wider (4-5-log) than either of the AfOR and the CEN methods. It is also quicker to carry out and has the potential for automation. The results from the qPCR method are mainly higher than standard AfOR methods, as the method does not distinguish viable and non-viable spores. The spatial distribution of Aspergillus fumigatus spores (by qPCR) along sampling transects, gives similar results compared to AfOR (and CEN) methods. Real time particle detection showed that both TSP and PM10 are correlated to Aspergillus fumigatus spore concentration. No consistent relationship was observed between odour and bioaerosol concentrations (although this was a limited dataset). The envelope of modelled (back-extrapolated) bioaerosol emission rates straddles several orders of magnitude. Distinguishing the influences of meteorological conditions on this variability was not possible. It was not possible to predict bioaerosol or odour emission rates with confidence. This continues to hamper confidence in modelling of odours and bioaerosols from open windrow facilities. The findings of this research have implications for the current standard monitoring protocol which should be reviewed accordingly. The findings of this multi-site survey accord with existing regulatory policy and are supportive of the general trend towards enclosed facilities. Notwithstanding this, continuing research is needed to enhance the database on emission from bioaerosol and odour abatement technologies (e.g. biofilters); to determine the cause(s) of occasional bioaerosol peaks from open facilities; to improve exposure assessments through better modelling protocols; and to link enhanced exposure information to future health impact studies