Performance of an ideal turbine in an inviscid shear flow

Although wind and tidal turbines operate in turbulent shear flow, most theoretical results concerning turbine performance, such as the well-known Betz limit, assume the upstream velocity profile is uniform. To improve on these existing results we extend the classical actuator disc model in this paper to investigate the performance of an ideal turbine in steady, inviscid shear flow. The model is developed on the assumption that there is negligible lateral interaction in the flow passing through the disc and that the actuator applies a uniform resistance across its area. With these assumptions, solution of the model leads to two key results. First, for laterally unbounded shear flow, it is shown that the normalised power extracted is the same as that for an ideal turbine in uniform flow, if the average of the cube of the upstream velocity of the fluid passing through the turbine is used in the normalisation. Second, for a laterally bounded shear flow, it is shown that the same normalisation can be applied, but allowance must also be made for the fact that non-uniform flow bypassing the turbine alters the background pressure gradient and, in turn, the turbines ‘effective blockage’ (so that it may be greater or less than the geometric blockage, defined as the ratio of turbine disc area to cross-sectional area of the flow). Predictions based on the extended model agree well with numerical simulations approximating the incompressible Euler equations. The model may be used to improve interpretation of model-scale results for wind and tidal turbines in tunnels/flumes, to investigate the variation in force across a turbine and to update existing theoretical models of arrays of tidal turbines

Reduced Phagocytic Capacity of Blood Monocyte/Macrophages in Tuberculosis Patients Is Further Reduced by Smoking.

Tuberculosis (TB) and tobacco use are two major alarming global health issues posing immense threats to human populations. Mycobacterium tuberculosis (MTB) by activation of macrophages could induce the sequences of cells activation and releases of inflammatory cytokines such as CXCL-8, Il-12 and TNF-α which in turn induces the immune system network. However no information is available on other activity of cells by MTB and smoking. In the current study we aimed to investigate the serum levels TNF-a, CXCL-8 and phagocytosis capacity in tuberculosis patients with and without smoking. 103 subjects entered the study including 61 new diagnosed pulmonary TB patients (23 smokers and 38 nonsmokers) and 42 control healthy subjects. The phagocytosis of fluorescein isothiocyanate dextran (FITC-dextran) in blood monocytes/macrophages through flowcytometry was assessed. Serum levels of TNF-a and CXCL-8 were analyzed by ELISA methods. A lower percentage of cells from TB patients who smoked [50.29% (43.4-57.2), p<0.01] took up FITC-dextran after 2h compared to non-smoking TB subjects [71.62% (69.2-74.1)] and healthy cases [97.45% (95.9-99.1). Phagocytic capacity was inversely correlated with cigarette smoking as measured by pack years (r=-0.73, p<0.001). The serum levels of TNF-a and CXCL-8 were significantly higher in the TB patients who smoked compared to the TB non-smoker group (p<0.001, p<0.01 respectively). Blood monocytes/macrophages from TB patients have reduced phagocytic capacity which is further reduced in TB patients who smoke. Smoking enhanced serum levels of TNF-a and CXCL-8 suggesting a greater imbalance between the proinflammatory and anti-inflammatory factors in these patients

Beta lives - some statistical perspectives on the capital asset pricing model

This note summarizes some technical issues relevant to the use of the idea of excess return in empirical modelling. We cover the case where the aim is to construct a measure of expected return on an asset and a model of the CAPM type is used. We review some of the problems and show examples where the basic CAPM may be used to develop other results which relate the expected returns on assets both to the expected return on the market and other factors

Measuring portfolio performance using a modified measure of risk

This paper reports the results of an investigation into the properties of a theoretical modification of beta proposed by Leland (1999) and based on earlier work of Rubinstein (1976). It is shown that when returns are elliptically symmetric, beta is the appropriate measure of risk and that there are other situations in which the modified beta will be similar to the traditional measure based on the capital asset pricing model. For the case where returns have a normal distribution, it is shown that the criterion either does not exist or reduces exactly to the conventional beta. It is therefore conjectured that the modified measure will only be useful for portfolios that have nonstandard return distributions which incorporate skewness. For such situations, it is shown how to estimate the measure using regression and how to compare the resulting statistic with a traditional estimated beta using Hotelling's test. An empirical study based on stocks from the FTSE350 does not find evidence to support the use of the new measure even in the presence of skewness.Journal of Asset Management (2007) 7, 388-403. doi:10.1057/palgrave.jam.225005

Inhibition of CD73 improves B cell-mediated anti-tumor immunity in a mouse model of melanoma.

CD73 is a cell surface enzyme that suppresses T cell-mediated immune responses by producing extracellular adenosine. Growing evidence suggests that targeting CD73 in cancer may be useful for an effective therapeutic outcome. In this study, we demonstrate that administration of a specific CD73 inhibitor, adenosine 5'-(α,β-methylene)diphosphate (APCP), to melanoma-bearing mice induced a significant tumor regression by promoting the release of Th1- and Th17-associated cytokines in the tumor microenvironment. CD8+ T cells were increased in melanoma tissue of APCP-treated mice. Accordingly, in nude mice APCP failed to reduce tumor growth. Importantly, we observed that after APCP administration, the presence of B cells in the melanoma tissue was greater than that observed in control mice. This was associated with production of IgG2b within the melanoma. Depletion of CD20+ B cells partially blocked the anti-tumor effect of APCP and significantly reduced the production of IgG2b induced by APCP, implying a critical role for B cells in the anti-tumor activity of APCP. Our results also suggest that APCP could influence B cell activity to produce IgG through IL-17A, which significantly increased in the tumor tissue of APCP-treated mice. In support of this, we found that in melanoma-bearing mice receiving anti-IL-17A mAb, the anti-tumor effect of APCP was ablated. This correlated with a reduced capacity of APCP-treated mice to mount an effective immune response against melanoma, as neutralization of this cytokine significantly affected both the CD8+ T cell- and B cell-mediated responses. In conclusion, we demonstrate that both T cells and B cells play a pivotal role in the APCP-induced anti-tumor immune response

The persistence landscape and some of its properties

Persistence landscapes map persistence diagrams into a function space, which may often be taken to be a Banach space or even a Hilbert space. In the latter case, it is a feature map and there is an associated kernel. The main advantage of this summary is that it allows one to apply tools from statistics and machine learning. Furthermore, the mapping from persistence diagrams to persistence landscapes is stable and invertible. We introduce a weighted version of the persistence landscape and define a one-parameter family of Poisson-weighted persistence landscape kernels that may be useful for learning. We also demonstrate some additional properties of the persistence landscape. First, the persistence landscape may be viewed as a tropical rational function. Second, in many cases it is possible to exactly reconstruct all of the component persistence diagrams from an average persistence landscape. It follows that the persistence landscape kernel is characteristic for certain generic empirical measures. Finally, the persistence landscape distance may be arbitrarily small compared to the interleaving distance.Comment: 18 pages, to appear in the Proceedings of the 2018 Abel Symposiu

The Theory of the Interleaving Distance on Multidimensional Persistence Modules

In 2009, Chazal et al. introduced $\epsilon$-interleavings of persistence modules. $\epsilon$-interleavings induce a pseudometric $d_I$ on (isomorphism classes of) persistence modules, the interleaving distance. The definitions of $\epsilon$-interleavings and $d_I$ generalize readily to multidimensional persistence modules. In this paper, we develop the theory of multidimensional interleavings, with a view towards applications to topological data analysis. We present four main results. First, we show that on 1-D persistence modules, $d_I$ is equal to the bottleneck distance $d_B$. This result, which first appeared in an earlier preprint of this paper, has since appeared in several other places, and is now known as the isometry theorem. Second, we present a characterization of the $\epsilon$-interleaving relation on multidimensional persistence modules. This expresses transparently the sense in which two $\epsilon$-interleaved modules are algebraically similar. Third, using this characterization, we show that when we define our persistence modules over a prime field, $d_I$ satisfies a universality property. This universality result is the central result of the paper. It says that $d_I$ satisfies a stability property generalizing one which $d_B$ is known to satisfy, and that in addition, if $d$ is any other pseudometric on multidimensional persistence modules satisfying the same stability property, then $d\leq d_I$. We also show that a variant of this universality result holds for $d_B$, over arbitrary fields. Finally, we show that $d_I$ restricts to a metric on isomorphism classes of finitely presented multidimensional persistence modules.Comment: Major revision; exposition improved throughout. To appear in Foundations of Computational Mathematics. 36 page

Persistent topology for natural data analysis - A survey

Natural data offer a hard challenge to data analysis. One set of tools is being developed by several teams to face this difficult task: Persistent topology. After a brief introduction to this theory, some applications to the analysis and classification of cells, lesions, music pieces, gait, oil and gas reservoirs, cyclones, galaxies, bones, brain connections, languages, handwritten and gestured letters are shown