The total margin of exposure of ethanol and acetaldehyde for heavy drinkers consuming cider or vodka

Heavy drinkers in Scotland may consume 1600 g ethanol per week. Due to its low price, cider may be preferred over other beverages. Anecdotal evidence has linked cider to specific health hazards beyond other alcoholic beverages. To examine this hypothesis, nine apple and pear cider samples were chemically analysed for constituents and contaminants. None of the products exceeded regulatory or toxicological thresholds, but the regular occurrence of acetaldehyde in cider was detected. To provide a quantitative risk assessment, two collectives of exclusive drinkers of cider and vodka were compared and the intake of acetaldehyde was estimated using probabilistic MonteeCarlo type analysis. The cider consumers were found to ingest more than 200-times the amount of acetaldehyde consumed by vodka consumers. The margins of exposure (MOE) of acetaldehyde were 224 for the cider and over 220,000 for vodka consumers. However, if the effects of ethanol were considered in a cumulative assessment of the combined MOE, the effect of acetaldehyde was minor and the combined MOE for both groups was 0.3. We suggest that alcohol policy priority should be given on reducing ethanol intake by measures such as minimum pricing, rather than to focus on acetaldehyde.

The heavy metal content of skeletons from an ancient metalliferous polluted area in southern Jordan with particular reerence to bioaccumulation and human health

Pyatt, F.B., Pyatt, A.J., Walker, C., Sheen, T., Grattan, J.P, The heavy metal content of skeletons from an ancient metalliferous polluted area in southern Jordan with particular reerence to bioaccumulation and human health, Ecotoxicology & Environmental Safety 60, 13th August 2003, 295-300

Invertebrates of ancient heavy metal spoil and smelting tip sites in southern Jordan: Thier distribution and use as bioindicators of metalliferous pollution derived from ancient sources

Pyatt, B. Amos, D. Grattan, J. Pyatt, A. Terrell-Nield, C. Invertebrates of ancient heavy metal spoil and smelting tip sites in southern Jordan: Thier distribution and use as bioindicators of metalliferous pollution derived from ancient sources. Journal of Arid Environments. 2002. 52 pp 53-62

Vegetation and Topographic Control of Wind-blown Snow Distributions in Distributed and Aggregated Simulations for an Arctic Tundra Basin

Essery, RLH & JW, Pomeroy, (2004). Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations. Journal of Hydrometeorology, 5, 735-744.

Effects of prior heavy exercise on V(over dot)O-2 kinetics during heavy exercise are related to changes in muscle activity

Burnley, M., Doust, J.H., Ball, D. and Jones, A.M. (2002) Effects of prior heavy exercise on VO2 kinetics during heavy exercise are related to changes in muscle activity. Journal of Applied Physiology 93, 167-174. RAE2008

Time required for the restoration of normal heavy exercise Vo(2) kinetics following prior heavy exercise

Burnley, M., Doust, J. and Jones, A. (2006). Time required for the restoration of normal heavy exercise Vo(2) kinetics following prior heavy exercise. Journal of Applied Physiology. 101(5), pp.1320-1327 RAE2008

Electrons in feldspar II: a consideration of the influence of conduction band-tail states on luminescence processes

Poolton, Nigel; Ozanyan, K.B.; Wallinga, J.; Murray, A.S., (2002) 'Electrons in feldspar II: a consideration of the influence of conduction band-tail states on luminescence processes', Physics and Chemistry of Minerals 29(3) pp.217-225 RAE2008

The performance of models relating species geographical distributions to climate is independent of trophic level

Brian Huntley, Rhys E. Green, Yvonne C. Collingham, Jane K. Hill, Stephen G. Willis , Patrick J. Bartlein, Wolfgang Cramer, Ward J. M. Hagemeijer and Christopher J. Thomas (2004). The performance of models relating species geographical distributions to climate is independent of trophic level. Ecology Letters, 7(5), 417-426. Sponsorship: NERC (awards: GR9/3016, GR9/04270, GR3/12542, NER/F/S/2000/00166) / RSPB RAE2008

On the Relationship Between File Sizes, Transport Protocols, and Self-Similar Network Traffic

Recent measurements of local-area and wide-area traffic have shown that network traffic exhibits variability at a wide range of scales self-similarity. In this paper, we examine a mechanism that gives rise to self-similar network traffic and present some of its performance implications. The mechanism we study is the transfer of files or messages whose size is drawn from a heavy-tailed distribution. We examine its effects through detailed transport-level simulations of multiple TCP streams in an internetwork. First, we show that in a "realistic" client/server network environment i.e., one with bounded resources and coupling among traffic sources competing for resources the degree to which file sizes are heavy-tailed can directly determine the degree of traffic self-similarity at the link level. We show that this causal relationship is not significantly affected by changes in network resources (bottleneck bandwidth and buffer capacity), network topology, the influence of cross-traffic, or the distribution of interarrival times. Second, we show that properties of the transport layer play an important role in preserving and modulating this relationship. In particular, the reliable transmission and flow control mechanisms of TCP (Reno, Tahoe, or Vegas) serve to maintain the long-range dependency structure induced by heavy-tailed file size distributions. In contrast, if a non-flow-controlled and unreliable (UDP-based) transport protocol is used, the resulting traffic shows little self-similar characteristics: although still bursty at short time scales, it has little long-range dependence. If flow-controlled, unreliable transport is employed, the degree of traffic self-similarity is positively correlated with the degree of throttling at the source. Third, in exploring the relationship between file sizes, transport protocols, and self-similarity, we are also able to show some of the performance implications of self-similarity. We present data on the relationship between traffic self-similarity and network performance as captured by performance measures including packet loss rate, retransmission rate, and queueing delay. Increased self-similarity, as expected, results in degradation of performance. Queueing delay, in particular, exhibits a drastic increase with increasing self-similarity. Throughput-related measures such as packet loss and retransmission rate, however, increase only gradually with increasing traffic self-similarity as long as reliable, flow-controlled transport protocol is used.

Scheduling Flows with Unknown Sizes: Approximate Analysis

Previous studies have shown that giving preferential treatment to short jobs helps reduce the average system response time, especially when the job size distribution possesses the heavy-tailed property. Since it has been shown that the TCP flow length distribution also has the same property, it is natural to let short TCP flows enjoy better service inside the network. Analyzing such discriminatory system requires modification to traditional job scheduling models since usually network traffic managers do not have detailed knowledge about individual flows such as their lengths. The Multi-Level (ML) queue, proposed by Kleinrock, can b e used to characterize such system. In an ML queueing system, the priority of a flow is reduced as the flow stays longer. We present an approximate analysis of the ML queueing system to obtain a closed-form solution of the average system response time function for general flow size distributions. We show that the response time of short flows can be significantly reduced without penalizing long flows.

Sampling Biases in IP Topology Measurements

Considerable attention has been focused on the properties of graphs derived from Internet measurements. Router-level topologies collected via traceroute studies have led some authors to conclude that the router graph of the Internet is a scale-free graph, or more generally a power-law random graph. In such a graph, the degree distribution of nodes follows a distribution with a power-law tail. In this paper we argue that the evidence to date for this conclusion is at best insufficient. We show that graphs appearing to have power-law degree distributions can arise surprisingly easily, when sampling graphs whose true degree distribution is not at all like a power-law. For example, given a classical Erdös-Rényi sparse, random graph, the subgraph formed by a collection of shortest paths from a small set of random sources to a larger set of random destinations can easily appear to show a degree distribution remarkably like a power-law. We explore the reasons for how this effect arises, and show that in such a setting, edges are sampled in a highly biased manner. This insight allows us to distinguish measurements taken from the Erdös-Rényi graphs from those taken from power-law random graphs. When we apply this distinction to a number of well-known datasets, we find that the evidence for sampling bias in these datasets is strong.

Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.

Mining Anomalies Using Traffic Feature Distributions

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.

Heavy metals in the asteroid Asterias rubens and its prey Mytilus edulis: Seasonal and geographical variations in four selected North Sea biotopes

info:eu-repo/semantics/published