Massive star formation: Nurture, not nature

We investigate the physical processes which lead to the formation of massive stars. Using a numerical simulation of the formation of a stellar cluster from a turbulent molecular cloud, we evaluate the relevant contributions of fragmentation and competitive accretion in determining the masses of the more massive stars. We find no correlation between the final mass of a massive star, and the mass of the clump from which it forms. Instead, we find that the bulk of the mass of massive stars comes from subsequent competitive accretion in a clustered environment. In fact, the majority of this mass infalls onto a pre-existing stellar cluster. Furthermore, the mass of the most massive star in a system increases as the system grows in numbers of stars and in total mass. This arises as the infalling gas is accompanied by newly formed stars, resulting in a larger cluster around a more massive star. High-mass stars gain mass as they gain companions, implying a direct causal relationship between the cluster formation process, and the formation of higher-mass stars therein.Comment: 8 pages, accepted for publication in MNRAS. Version including hi-res colour postscript figure available at http://star-www.st-and.ac.uk/~sgv/ps/massnurt.ps.g

The WiggleZ Dark Energy Survey: the transition to large-scale cosmic homogeneity

We have made the largest-volume measurement to date of the transition to large-scale homogeneity in the distribution of galaxies. We use the WiggleZ survey, a spectroscopic survey of over 200,000 blue galaxies in a cosmic volume of ~1 (Gpc/h)^3. A new method of defining the 'homogeneity scale' is presented, which is more robust than methods previously used in the literature, and which can be easily compared between different surveys. Due to the large cosmic depth of WiggleZ (up to z=1) we are able to make the first measurement of the transition to homogeneity over a range of cosmic epochs. The mean number of galaxies N(<r) in spheres of comoving radius r is proportional to r^3 within 1%, or equivalently the fractal dimension of the sample is within 1% of D_2=3, at radii larger than 71 \pm 8 Mpc/h at z~0.2, 70 \pm 5 Mpc/h at z~0.4, 81 \pm 5 Mpc/h at z~0.6, and 75 \pm 4 Mpc/h at z~0.8. We demonstrate the robustness of our results against selection function effects, using a LCDM N-body simulation and a suite of inhomogeneous fractal distributions. The results are in excellent agreement with both the LCDM N-body simulation and an analytical LCDM prediction. We can exclude a fractal distribution with fractal dimension below D_2=2.97 on scales from ~80 Mpc/h up to the largest scales probed by our measurement, ~300 Mpc/h, at 99.99% confidence.Comment: 21 pages, 16 figures, accepted for publication in MNRA

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination ‘as a public health problem’ when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models’ predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020

Boundary Conformal Field Theory and Tunneling of Edge Quasiparticles in non-Abelian Topological States

We explain how (perturbed) boundary conformal field theory allows us to understand the tunneling of edge quasiparticles in non-Abelian topological states. The coupling between a bulk non-Abelian quasiparticle and the edge is due to resonant tunneling to a zero mode on the quasiparticle, which causes the zero mode to hybridize with the edge. This can be reformulated as the flow from one conformally-invariant boundary condition to another in an associated critical statistical mechanical model. Tunneling from one edge to another at a point contact can split the system in two, either partially or completely. This can be reformulated in the critical statistical mechanical model as the flow from one type of defect line to another. We illustrate these two phenomena in detail in the context of the nu=5/2 quantum Hall state and the critical Ising model. We briefly discuss the case of Fibonacci anyons and conclude by explaining the general formulation and its physical interpretation

Client Demand and its Effect on Audit Quality: Is it More Than Just Auditor Choice?

This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.This study investigates the role of the client and client demand and its effect on audit quality. By exploring client demand contexts, I investigate the first research question: does client demand for audit quality effect audit quality beyond the choice of auditor. Since the audited financial statements are the joint product of negotiations between the client and its external auditor (Antle and Nalebluff 1991, Francis 2011), it is important to consider the role of the client and a client's demand for audit quality. Based on previous research, I identify three contexts in which companies demand more or less audit quality. In particular, I express the demand for audit quality as a function of the level of the client's (1) operational complexity, (2) managerial influence, and/or (3) future external financing needs. For my second research question, I expand the analysis to consider the potential asymmetry in the effect of client demand on audit quality. I posit that the effect of high quality auditors is dependent on the demand of the client for audit quality. When client demand is high, the client will work with the high quality auditor to produce the audited financials. However, the efforts and contribution required from the client will be reduced, given that the higher quality auditor will act, in part, as a substitute for the client's effort. When client demand is low, the client will be less cooperative or will actively work against the auditor and the audit process. In conclusion, this study extends the audit quality literature by examining the effects of client demand on audit quality and examining whether these effects are asymmetric in nature. By examining the effects of client demand for audit quality beyond the choice of auditor, I provide empirical evidence as to the importance of client demand and its inclusion in audit quality research.Accountin