UNDERSTANDING AND ADDRESSING THE ECOLOGICAL IMPACTS OF FLOW IMPOUNDMENT FOR RIVER SYSTEMS IN NORTHERN ENGLAND

Many rivers have undergone flow modification by impoundments to provide services such as water supply and hydropower. There is an established consensus that typical modified flow regimes do not sufficiently cater to the needs of downstream ecosystems, due to species having adapted to natural flow conditions. This may lead to changes in the biodiversity and functional composition of ecosystems, potentially compromising water quality and other river system services. More must be done to understand the relationship between flow and in-stream ecology, in order to mitigate the impacts of flow modification. The development of efficient methods of ecology-flow assessment is vital in order to meet current and future legislation, whilst considering other stakeholders and maintaining the resilience of the local water supply. This thesis combines statistical approaches applied to public datasets, and combined ecological-hydraulic modelling at a case study site, to propose environmental flow regimes. Univariate and multivariate analyses were performed on flow and macroinvertebrate sampling data from sites across northern England. The mean annual frequency of high flow events was identified as a particularly influential driver of functional composition and biodiversity metrics. Field data was gathered and a hydraulic-ecological model was also developed for a selected case study site in order to predict the responses of selected indicator species to flow. Spatial and temporal distributions of habitat quality with respect to flow were generated, allowing the impacts of various flow inputs to be assessed. These findings were integrated in order to generate recommended flow regimes for the case study site. It was demonstrated that the proposed regimes met or improved upon ecological metrics relative to impoundment outflow data, whilst also conserving significant quantities of water. Outcomes from this research demonstrate the potential of habitat suitability models, supplemented by knowledge of ecological-flow relationships, to inform environmental flow design decisions

Is Internet Gaming Legal in Canada: A Look at Starnet

The development of the Internet has kindled many new business opportunities in the online environment. Despite the recent slump in online business growth and popularity, one line of online business is generating profit and growing at a rapid rate: the business of online gaming. The legality of such businesses is questionable in Canada and there are few gaming cases to assist Canadian lawyers. The following analysis must be considered in light of the dearth of jurisprudence in this area and should not be considered legal advice. This area of the law is in flux and developments may be unpredictable. When you are feeling in the dark, even a flickering candle is welcome. Therefore, a recent British Columbia online gaming prosecution, in which the accused pled guilty, is worthy of study. Though resolved by a guilty plea with little judicial reasoning, the case provides some guidance in this largely unmapped area. It confirms that in certain circumstances, there can be criminal liability in Canada for running an online gaming operation. Online gaming ventures will have to consider several factors and be mindful of how their ventures are structured in order to avoid prosecution in Canada and conform to Canadian laws

Fidelity and plausibility of bimanual interaction in mixed reality

When human actors interact with virtual objects the result is often not convincing to a third party viewer, due to incongruities between the actor and object positions. In this study we aim to quantify the magnitude and impact of the errors that occur in a bimanual interaction, that is when an actor attempts to move a virtual object by holding it between both hands. A three stage framework is presented which firstly captures the magnitude of these interaction errors, then quantifies their effect on the relevant third party audience, and thirdly assesses methods to mitigate the impact of the errors. Findings from this work show that the degree of error was dependent on the size of the virtual object and also on the axis of the hand placement with respect to the axis of the interactive motion. In addition, actor hand placement outside and away from the object surface was found to affect the visual plausibility considerably more than when the actor's hands were within the object boundaries. Finally, a method for automatic adaptation of the object size to match the distance between the actor's hands gave a significant improvement in the viewers' assessment of the scene plausibility. © 1995-2012 IEEE

Estimation of hourly near surface air temperature across Israel using an ensemble model

Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies

Estimation of hourly near surface air temperature across Israel using an ensemble model

Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies

Titania-doped tantala/silica coatings for gravitational-wave detection

Reducing thermal noise from optical coatings is crucial to reaching the required sensitivity in next generation interferometric gravitational-wave detectors. Here we show that adding TiO2 to Ta2O5 in Ta2O5/SiO2 coatings reduces the internal friction and in addition present data confirming it reduces thermal noise. We also show that TiO2-doped Ta2O5/SiO2 coatings are close to satisfying the optical absorption requirements of second generation gravitational-wave detectors

Exploring the dynamics of compliance with community penalties

In this paper, we examine how compliance with community penalties has been theorized hitherto and seek to develop a new dynamic model of compliance with community penalties. This new model is developed by exploring some of the interfaces between existing criminological and socio-legal work on compliance. The first part of the paper examines the possible definitions and dimensions of compliance with community supervision. Secondly, we examine existing work on explanations of compliance with community penalties, supplementing this by drawing on recent socio-legal scholarship on private individuals’ compliance with tax regimes. In the third part of the paper, we propose a dynamic model of compliance, based on the integration of these two related analyses. Finally, we consider some of the implications of our model for policy and practice concerning community penalties, suggesting the need to move beyond approaches which, we argue, suffer from compliance myopia; that is, a short-sighted and narrowly focused view of the issues

Stillbirths and neonatal deaths among 18 942 women with postpartum hemorrhage: Analysis of perinatal outcomes in the WOMAN trial.

OBJECTIVE: To describe the rates and risk factors for stillbirth and pre-discharge neonatal mortality (PDNM), and impact on quality of life (QoL) among women with postpartum hemorrhage (PPH). METHODS: A secondary analysis was conducted of the WOMAN trial, which evaluated the use of tranexamic acid for PPH and collected infant outcome data to assess drug safety. The analysis was restricted to singletons (n=18 942). Overall and country-level rates of stillbirth and PDNM were calculated. Multilevel logistic regression models examined the association of stillbirth and PDNM with selected risks, and the association of mother's QoL at discharge after stillbirth or PDNM. RESULTS: For women with PPH, the rate of stillbirths was 104.42 per 1000 births (n=1978) and the rate of PDNM was 15.56 per 1000 live births (n=264). Cesarean delivery, increasing blood loss, maternal complications, and maternal death were strongly associated with these adverse outcomes. Women with stillbirth and PDNM were significantly more likely to report poorer QoL. CONCLUSION: Women with PPH experience an extremely high rate of stillbirth and slightly elevated PDNM, which is associated with markers of the severity of their condition and impacts on their QoL

Mirror coating solution for the cryogenic Einstein telescope

Planned, cryogenic gravitational-wave detectors will require improved coatings with a strain thermal noise reduced by a factor of 25 compared to Advanced LIGO. In this article, we present investigations of HfO2 doped with SiO2 as a new coating material for future detectors. Our measurements show an extinction coefficient of k=6×10−6 and a mechanical loss of ϕ=3.8×10−4 at 10,K, which is a factor of 2 below that of SiO2, the currently used low refractive-index coating material. These properties make HfO2 doped with SiO2 ideally suited as a low-index partner material for use with a-Si in the lower part of a multimaterial coating. Based on these results we present a multimaterial coating design which, for the first time, can simultaneously meet the strict requirements on optical absorption and thermal noise of the cryogenic Einstein Telescope

Substituting carbohydrate at lunch for added protein increases fat oxidation during subsequent exercise in healthy males

Context How pre-exercise meal composition influences metabolic and health responses to exercise later in the day is currently unclear. Objective Examine the effects of substituting carbohydrate for protein at lunch on subsequent exercise metabolism, appetite, and energy intake. Methods Twelve healthy males completed three trials in randomized, counterbalanced order. Following a standardized breakfast (779 ± 66 kcal; ∼08:15), participants consumed a lunch (1186 ± 140 kcal; ∼13:15) containing either 0.2 g·kg-1 carbohydrate and ∼2 g·kg-1 protein (LO-CARB), 2 g·kg-1 carbohydrate and ∼0.4 g·kg-1 protein (HI-CARB), or fasted (FAST). Participants later cycled at ∼60% V̇O2peak for 1 h (∼16:15) and post-exercise ad-libitum energy intake was measured (∼18:30). Substrate oxidation, subjective appetite, and plasma concentrations of glucose, insulin, non-esterified fatty acids (NEFA), peptide YY (PYY), glucagon-like peptide-1 (GLP-1), and acylated ghrelin (AG) were measured for 5 h post-lunch. Results Fat oxidation was greater during FAST (+11.66 ± 6.63 g) and LO-CARB (+8.00 ± 3.83 g) than HI-CARB (p < 0.001), with FAST greater than LO-CARB (+3.67 ± 5.07 g; p < 0.05). NEFA were lowest in HI-CARB and highest in FAST, with insulin demonstrating the inverse response (all p < 0.01). PYY and GLP-1 demonstrated a stepwise pattern, with LO-CARB greatest and FAST lowest (all p < 0.01). AG was lower during HI-CARB and LO-CARB versus FAST (p < 0.01). Energy intake in LO-CARB was lower than FAST (-383 ± 233 kcal; p < 0.001) and HI-CARB (-313 ± 284 kcal; p < 0.001). Conclusion Substituting carbohydrate for protein in a pre-exercise lunch increased fat oxidation, suppressed subjective and hormonal appetite, and reduced post-exercise energy intake