Does Corporate Inversion Lead to Tax Savings?

Corporate Inversio

The Simulated Ocular and Whole-body Distribution of Natural Sunlight to Kiteboarders: A High Risk Case of UVR Exposure for Athletes Utilizing Water Surfaces in Sport [Not yet published 14/01/20 MC]

Kiteboarding is an aquatic sporting discipline that has not yet been considered in the literature to date in terms of solar ultraviolet radiation (UVR) measurement. Kiteboarders need to look upward and are placed obliquely relative to the horizon when towed behind an overhead kite over a reflective water surface. This research defines the typical body surface orientation of a kiteboarder in motion through video vector analysis and demonstrates the potential risk to ocular and skin surface damage through practical measurement of solar UVR using a manikin model. Video analysis of 51 kiteboarders were made to construct skeletal wireframes showing the surface orientation of the leg, thigh, spine, humerus, lower arm and head of a typical kiteboarder. Solar UVR dosimeter measurements made using a manikin model demonstrate that the vertex and anterior surfaces of the knee, lower leg, and lower humerus received 89%, 90%, 80% and 63% of the available ambient UVR respectively for a typical kiteboarder who is tilted back more than 15o from vertical while in motion. Ocular (periorbital) exposures ranged from 56 to 68% of ambient. These new findings show that the anterior skin surfaces of kiteboarders and the eye are at elevated risk of solar UVR damage

Solar blue light radiation enhancement during mid to low solar elevation periods under cloud affected skies

Solar blue-violet wavelengths (380 nm - 455 nm) are at the high energy end of the visible spectrum, referred to as ‘high energy visible’ (HEV). Both chronic and acute exposure to these wavelengths have been often highlighted as causes of concern with respect to ocular health. The sun is the source of HEV which reaches the Earth’s surface either directly or after scattering by the atmosphere and clouds. This research has investigated the effect of clouds on HEV for low solar elevation (solar zenith angles between 60° and 80°), simulating time periods when potential ocular exposure in global populations are high during the early morning and late afternoon. The enhancement of ‘bluing’ of the sky due to the influence of clouds was found to increase significantly with the amount of cloud. A method is presented for calculating HEV irradiance from the more commonly measured global solar radiation (300 – 3,000 nm) for all cases when clouds do and do not obscure the sun. The method, when applied to global solar radiation data correlates well with measured HEV within the solar zenith angle range 60° and 80° (R2 = 0.94, MBE = -1.63%, MABE = 10.3% and RMSE = 14.6%). The technique can be used to develop repeatable HEV hazard evaluations for human ocular health applications

Evaluation of shade profiles while walking in urban environments: A case study from inner suburban Sydney, Australia

Precise shade distributions at the street level are an area of research of increasing importance to provide complete and high spatial and temporal resolutions of the amount and effectiveness of shade. Temporal shade distributions and profiles were evaluated for an inner Sydney tree-lined suburban street at different times of the day using an electronic sun journal (ESJ), providing detailed profiles of shade availability for various times of the day to provide very detailed street-level shade profiles and distributions that are often not included in shade audit methods and models. Further profiles were developed of streets adjoining shopfronts and public parks. Distributions of dense, light and no shade areas were calculated, revealing that tree canopy shade area during the middle of the day is considerably less effective and more prone to gaps than at other times. Distributions calculated using the ESJ were compatible with the paper-based shade auditing with less than 10% variation, whilst the ESJ has revealed a greater resolution of detail of gaps in the shade, thus records a higher amount of areas of no shade. The ESJ is a robust, low cost and portable tool that can efficiently and quickly produce shade profiles during walks in an urban environment, such as streetscapes

Electronic Sun Journal Versus Self-report Sun Diary: A Comparison of Recording Personal Sunlight Exposure Methods

This research compared personal sunlight exposure times monitored electronically within suburban Australian environments against self-report paper journals for determining the timing and total duration of individual exposure to daily solar radiation. A total of 90 Electronic Sun Journal (ESJ) daily readings and self-report timing and duration estimates of exposure for weekend and weekdays were compared. A Wilcoxon ranked sign test showed a significant difference (V = 157, p < 0.001) between the duration of exposure recorded electronically and the duration of exposure that was self-reported in a diary. There was also found to be a statistically significant difference between total exposure time measured using both methods for weekends (V = 10, p < 0.001) and weekdays (V = 87, p < 0.001). General trends in outdoor exposure timing confirmed that the most frequent daily exposures received over the weekend occurred between one and two hours earlier than the most frequent exposures received on weekdays. This preliminary research found that exposure durations as recorded by the ESJ were longer on the weekends compared to weekdays (W = 402, p < 0.001) and confirmed that the ESJ is a viable alternative to self-reporting diaries

Synthesis of 3-D coronal-solar wind energetic particle acceleration modules

1. Introduction Acute space radiation hazards pose one of the most serious risks to future human and robotic exploration. Large solar energetic particle (SEP) events are dangerous to astronauts and equipment. The ability to predict when and where large SEPs will occur is necessary in order to mitigate their hazards. The Coronal-Solar Wind Energetic Particle Acceleration (C-SWEPA) modeling effort in the NASA/NSF Space Weather Modeling Collaborative [Schunk, 2014] combines two successful Living With a Star (LWS) (http://lws. gsfc.nasa.gov/) strategic capabilities: the Earth-Moon-Mars Radiation Environment Modules (EMMREM) [Schwadron et al., 2010] that describe energetic particles and their effects, with the Next Generation Model for the Corona and Solar Wind developed by the Predictive Science, Inc. (PSI) group. The goal of the C-SWEPA effort is to develop a coupled model that describes the conditions of the corona, solar wind, coronal mass ejections (CMEs) and associated shocks, particle acceleration, and propagation via physics-based modules. Assessing the threat of SEPs is a difficult problem. The largest SEPs typically arise in conjunction with X class flares and very fast (\u3e1000 km/s) CMEs. These events are usually associated with complex sunspot groups (also known as active regions) that harbor strong, stressed magnetic fields. Highly energetic protons generated in these events travel near the speed of light and can arrive at Earth minutes after the eruptive event. The generation of these particles is, in turn, believed to be primarily associated with the shock wave formed very low in the corona by the passage of the CME (injection of particles from the flare site may also play a role). Whether these particles actually reach Earth (or any other point) depends on their transport in the interplanetary magnetic field and their magnetic connection to the shock

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems

Understanding continent-wide variation in vulture ranging behavior to assess feasibility of Vulture Safe Zones in Africa: Challenges and possibilities

Protected areas are intended as tools in reducing threats to wildlife and preserving habitat for their long-term population persistence. Studies on ranging behavior provide insight into the utility of protected areas. Vultures are one of the fastest declining groups of birds globally and are popular subjects for telemetry studies, but continent-wide studies are lacking. To address how vultures use space and identify the areas and location of possible vulture safe zones, we assess home range size and their overlap with protected areas by species, age, breeding status, season, and region using a large continent-wide telemetry datasets that includes 163 individuals of three species of threatened Gyps vulture. Immature vultures of all three species had larger home ranges and used a greater area outside of protected areas than breeding and non-breeding adults. Cape vultures had the smallest home range sizes and the lowest level of overlap with protected areas. Rüppell\u27s vultures had larger home range sizes in the wet season, when poisoning may increase due to human-carnivore conflict. Overall, our study suggests challenges for the creation of Vulture Safe Zones to protect African vultures. At a minimum, areas of 24,000 km2 would be needed to protect the entire range of an adult African White-backed vulture and areas of more than 75,000 km2 for wider-ranging Rüppell\u27s vultures. Vulture Safe Zones in Africa would generally need to be larger than existing protected areas, which would require widespread conservation activities outside of protected areas to be successful

Cloud segmentation property extraction from total sky image repositories using Python

Acquiring the reflectance, radiance and related structural cloud properties from repositories of historical sky images can be a challenging and a computationally intensive task, especially when performed manually or by means of non-automated approaches. In this paper, a quick and efficient, self-adaptive Python tool for the acquisition and analysis of cloud segmentation properties that is applicable to images from all-sky image repositories is presented and a case study demonstrating its usage and the overall efficacy of the technique is demonstrated. The proposed Python tool aims to build a new data extraction technique and to improve the accessibility of data to future researchers, utilizing the freely available libraries in the Python programming language with the ability to be translated into other programming languages. After development and testing of the Python tool in determining cloud and whole sky segmentation properties, over 42,000 sky images were analysed in a relatively short time of just under 40 minutes, with an average execution time of about 0.06 seconds to complete each image analysis