Increasing throughput in IEEE 802.11 by optimal selection of backoff parameters

Engineering and Physical Sciences Research Council. Grant Number: EP/G012628/

Organic indicators of alteration in the CR chondrites

A study of the organic components in the CR chondrite macromolecule in order to assess the role of pre-terrestrial alteration on the organic inventory

Analysis of Tagish Lake macromolecular organic material

Macromolecular material is, by far, the major organic component of meteorites. Flash pyrolysis GCMS has been used to investigate this organic component in Tagish Lake. It is more condensed, less susbtituted than Murchson

Dual isotopic composition of methane in Murchison meteorite

Dual isotopic composition (H and C) of methane extracted from a small sample of Murchison meteorite reveals a deuterium enrichment for this molecule, indicating the presence of interstellar hydrogen

Macromolecular organic acids in the Murchison meteorite

This study has detected bound organic acids within the Murchison meteorite organic macromolecule. Benzoic acid was the most abundant compound; other abundant compounds include C1 and C2 benzoic acids. Their origin and significance will be discussed

Organic Geochemistry of a Hydrocarbon-rich Calcarenite from the Chicxulub Scientific Drilling Program

The organic geochemistry of hydrocarbon-rich core material recovered by the CSDP is examined to establish whether hydrocarbons are associated with the migration and emplacement of organic matter by post-impact hydrothermal activity

Estimating Potential Ground and Surface Water Pollution from Land Application of Poultry Litter - II

In Arkansas, approximately 1 Tg of poultry (Gallus gallus domesticus) manure and litter is produced annually. These waste products are commonly applied to pastures as a soil amendment or fertilizer, but excessive application rates and poor management practices could result in nutrient contamination of ground and surface water. The purpose of this study was to: (1) assess the nutrient concentrations in poultry manure and (2) evaluate the nitrogen loss from land-applied poultry litter and manure due to ammonia volatilization and denitrification. Analyses for total Kjeldahl nitrogen (TKN), inorganic nitrogen (Ni), phosphorus (P), and potassium (K) were compared in 12 wet and dry hen manure samples. Drying the manure reduced the TKN from 57 to 40 g N/kg on a dry weight basis in wet and dry manure, respectively. The Ni in the manure was in the ammoniacal form with values of 19 and 2 g N/kg for wet and dry manure, respectively. The P and K levels were not influenced by drying the manure and had values of 24 and 21 g/kg, respectively. The results indicate that the nitrogen content of hen manure can be significantly reduced by drying the sample prior to analysis. In a 10-day laboratory study and an 11-day field study to evaluate ammonia volatilization from surface-applied hen manure, results indicated that 37% of the total nitrogen content of the manure was lost. The results indicated that a substantial amount of nitrogen in surface-applied poultry waste can be lost due to ammonia volatilization. Laboratory studies to evaluate denitrification in a Captina silt loam amended with 9 Mg/ha of poultry litter were conducted. When the soil was aerobically incubated for 168 h and then flooded for 66 h, the nitrate-nitrogen level decreased a net of 17 mg N/kg. The results indicated that, if the ammoniacal nitrogen in the litter is oxidized to nitrate under aerobic conditions and then the soil is flooded and available carbon is present, denitrification can occur rapidly. Results from these studies indicate that soil and environmental conditions playa critical role in determining the potential for nitrate pollution of ground and surface water when poultry manure and litter are surface-applied to pastures

Developing and validating a predictive model for stroke progression

<p><b>Background:</b> Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts.</p> <p><b>Methods:</b> Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p < 0.1) on univariate analysis were included in the multivariate model. Logistic regression was the technique employed using backward stepwise regression to drop the least significant variables (p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values.</p> <p><b>Results:</b> Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92)].</p> <p><b>Conclusion:</b> The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.</p&gt

A Raman spectroscopic study of carbon phases in impact melt rocks and breccias from the Gardnos impact structure, Norway

Raman spectroscopy suggests that the C was emplaced in at least two separate episodes into the impactites of the Gardnos impact structure