The Sporting Image: A Personal Journey Utilising History to Develop Academic Inquiry and Creativity

In 1997, an optional third year undergraduate module, The Sporting Image, was developed for sports studies students in which they scrutinized the portrayal of sport in popular and high culture; including literature, film, TV, art and music. Fifteen years later, this module, now compulsory for Sports Journalism students, continues to examine the portrayal of sport and ways in which it has become an integral part of popular culture and resonates with values and standards specific in time and place. This paper describes the evolution of the module and its successes and failures in obliging both the lecturers and students to move outside of their comfort zones and engage with creative writing, poetry, music and the visual arts

The expression and assessment of emotions and internal states in individuals with severe or profound intellectual disabilities

The expression of emotions and internal states by individuals with severe or profound intellectual disabilities is a comparatively under-researched area. Comprehensive or standardised methods of assessing or understanding the emotions and internal states within this population, whose ability to communicate is significantly compromised, do not exist. The literature base will be discussed and compared to that within the general population. Methods of assessing broader internal states, notably depression, anxiety, and pain within severe or profound intellectual disabilities are also addressed. Finally, this review will examine methods of assessing internal states within genetic syndromes, including hunger, social anxiety and happiness within Prader-Willi, Fragile-X and Angelman syndrome. This will then allow for the identification of robust methodologies used in assessing the expression of these internal states, some of which may be useful when considering how to assess emotions within individuals with intellectual disabilities

Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included

Another dimension from LiDAR – Obtaining foliage density from full waveform data

LiDAR tells the user where surfaces are, not what they are. In this study we investigate the potential for waveform LiDAR to provide more information on the nature of the returns over forestry. Waveform LiDAR was acquired for ten Pinus radiata plots in a New Zealand plantation, along with comprehensive leaf area sampling in 2m vertical bands. The decay rate of each waveform peak was shown to be a useful tool for estimating foliage density, and has potential for identifying regions containing ground and understorey. Leaf Area Density (LAD) is an expression of foliage density per unit height, and a relationship between waveform decay rate and LAD was developed with an R2 of 56%. Incorporating the proportion of discrete LiDAR that fell in that band (which itself has an R2 of 50%) improves this model to explain 69% of the variation in LAD. This is a good result, especially given the costs and difficulties in measuring leaf area directly. As foliage density varies dramatically on a fine scale it was not possible to differentiate the nature of every single LiDAR return – but by averaging over a small area local variation in LAD could be easily mapped. Ground returns could be distinguished as having short decays, and broad leafed understorey typically had values between those of the canopy and ground, although surface roughness and slope make it impossible to robustly identify single returns. This study produced a useful model for estimating LAD in Pinus radiata which could easily be extended to other coniferous species

Resonant Removal of Exomoons During Planetary Migration

Jupiter and Saturn play host to an impressive array of satellites, making it reasonable to suspect that similar systems of moons might exist around giant extrasolar planets. Furthermore, a significant population of such planets is known to reside at distances of several Astronomical Units (AU), leading to speculation that some moons thereof might support liquid water on their surfaces. However, giant planets are thought to undergo inward migration within their natal protoplanetary disks, suggesting that gas giants currently occupying their host star's habitable zone formed further out. Here we show that when a moon-hosting planet undergoes inward migration, dynamical interactions may naturally destroy the moon through capture into a so-called "evection resonance." Within this resonance, the lunar orbit's eccentricity grows until the moon eventually collides with the planet. Our work suggests that moons orbiting within about 10 planetary radii are susceptible to this mechanism, with the exact number dependent upon the planetary mass, oblateness and physical size. Whether moons survive or not is critically related to where the planet began its inward migration as well as the character of inter-lunar perturbations. For example, a Jupiter-like planet currently residing at 1AU could lose moons if it formed beyond 5AU. Cumulatively, we suggest that an observational census of exomoons could potentially inform us on the extent of inward planetary migration, for which no reliable observational proxy currently exists.Comment: 6 Figures, Accepted for Publication in The Astrophysical Journa

Polarization spectroscopy of an excited state transition.

We demonstrate polarization spectroscopy of an excited state transition in room-temperature cesium vapor. An anisotropy induced by a circularly polarized pump beam on the D2 transition is observed using a weak probe on the 6P3/2→7S1/2 transition. At high pump power, a subfeature due to Autler-Townes splitting is observed that theoretical modeling shows is enhanced by Doppler averaging. Polarization spectroscopy provides a simple modulation–free signal suitable for laser frequency stabilization to excited state transitions

Charged Schrodinger Black Holes

We construct charged and rotating asymptotically Schrodinger black hole solutions of IIB supergravity. We begin by obtaining a closed-form expression for the null Melvin twist of a broad class of type IIB backgrounds, including solutions of minimal five-dimensional gauged supergravity, and identify the resulting five-dimensional effective action. We use these results to demonstrate that the near-horizon physics and thermodynamics of asymptotically Schrodinger black holes obtained in this way are essentially inherited from their AdS progenitors, and verify that they admit zero-temperature extremal limits with AdS_2 near-horizon geometries. Notably, the AdS_2 radius is parametrically larger than that of the asymptotic Schrodinger space.Comment: 22 pages, LaTe

Life History Variation in Migratory Salmonid Populations

Over the last 150 years, many of the native migratory salmonid populations in North America have declined or been extirpated, and their native habitats have been significantly altered. Life history variation within and among migratory fish populations plays an important role in their persistence when faced with changing habitat conditions. One of the most extreme life history events in salmonids is the movement from lotic to lentic habitats, a migration that can span long distances and different habitat types. Understanding the factors affecting migratory life histories expressed by individuals within a population play an important role in dynamics and habitat requirements of the whole population. Here, I investigate three primary factors that contribute to an individual fishes’ “decision” to migrate: genetics, environmental conditions, and individual body condition. In rainbow trout Oncorhynchus mykiss of the Shasta River, California we found distinct genetic structure among subpopulations in spatially separate habitats. Within one of those population segments we detected partial migration in which some individuals migrate, but others do not. We found that increased in daily mean water temperature were associated with upriver migration of adult coaster brook trout Salvelinus fontinalis in the Salmon Trout River, Michigan. In the Pilgrim River, Michigan we documented a previously unrecognized population of migratory brook trout. These results provide information critical to understanding the ecology of these at-risk populations and broaden our understanding of migratory behavior in general. The methodologies we developed to quantify movement data in the context of migratory life histories are applicable to other systems where further understanding of the drivers of migratory life history variation is needed

Predicting plant environmental exposure using remote sensing

Wheat is one of the most important crops globally with 776.4 million tonnes produced in 2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend £0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase which makes it difficult to detect before symptoms develop, after which point fungicide intervention is ineffective. In the second chapter of my thesis I use hyperspectral sensing and imaging techniques, analysed with machine learning to detect and predict symptomatic Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of symptomatic Z. tritici infection and could facilitate precision agriculture methods, to use in the subsequent growing season, that optimise fungicide use and increase yield. In the third chapter of my thesis, I develop a multispectral imaging system which can detect and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the nitrogen source applied. Currently, plants are treated with nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing, multispectral imaging, and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. This suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in plant leaf reflectance detectable via machine learning of multispectral data but not the naked eye. These results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture. In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral imaging and machine learning image analysis developed in the third chapter to detect asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of all stages of Z. tritici infection and could facilitate precision agriculture methods to be used during the current growing season that optimise fungicide use and increase yield.Open Acces