Numerical simulation of the airflow–rivulet interaction associated with the rain-wind induced vibration phenomenon

Rain-wind induced vibration is an aeroelastic phenomenon that occurs on the inclined cables of cable-stayed bridges. The vibrations are believed to be caused by a complicated nonlinear interaction between rivulets of rain water that run down the cables and the wind loading on the cables due to the unsteady aerodynamic flow field. Recent research at the University of Strathclyde has been to develop a numerical method to simulate the influence of the external air flow on the rivulet dynamics and vice versa, the results of which can be used to assess the importance of the water rivulets on the instability. The numerical approach for the first time couples a Discrete Vortex Method solver to determine the external flow field and unsteady aerodynamic loading, and a pseudo-spectral solver based on lubrication theory to model the evolution and growth of the water rivulets on the cable surface under external loading. The results of the coupled model are used to assess the effects of various loading combinations, and importantly are consistent with previous full scale and experimental observations of rain-wind induced vibration, providing new information about the underlying physical mechanisms of the instability

Relic High Frequency Gravitational waves from the Big Bang, and How to Detect Them

We show conditions for HFGW detection, employing an entropy concept written up by Jack Ng, and Steinhard's proceedure for reconstructing tensorial representations for relic HFGW from the onset of the big bang. The issue of the reality of gravitons as a measurable physical object which was raised by Rothman in 2006 is indirectly answered via a proceedure obtained from Weinberg's 1972 book on gravitation, and all the methodologies so obtained are referenced with respect to engineering specifications of the Li-Baker HFGW detector. In addition, the document also refers to entanglement entropy, and its possible aid in refining measurement predictions. Finally, commentary about HFGW and relic neutrino physics data sets is included, with regards to inflationary model candidatesComment: 15 pages, 1 table, 1 figure. Covers two AIP conference proceeding entries. Pages 1-9 correspond to one ias-spes Huntsville, Alabama February 2009 conference paper on the formalism of HFGW analysis, and pages 9-13 correspond to Neutrino physics-HFGW data set comparison, in terms of different inflationary potential candidate

Gravitinos, the Lithium problem, and DM production: Is there a corresponding neutrino physics linkage?

Studies are cited indicating that gravitino production acts as a natural upper bound to Li6 and Li7 levels, based on what happens after hadronic decay of relic 1 TeV into 100 GeV gravitinos at 1000 s. after the Big Bang. The produced gravitinos contribute a large fraction of required dark matter density. Whether or not gravitinos can be linked to neutrino production depends on which model of dark matter (DM) is assumed or used. A model presented by the author in 2008 links DM of about 100 GeV -- based on a phenomenological Lagrangian creating different Neutrino masses without SUSY -- with a dark matter candidate of about 100 GeV. This may tie in 100 GeV gravitinos with neutrino physics.Comment: 2 pages, no figures. New article in a sequel of DM applications articles. Conference entry to Rencontres De Moriond, for the Cosmology meeting, February 2009, to be published late 2009 by the Gioi company of Vietna

An evaluation of the transport reactor

Imperial Users onl

Parallelism and divergence in immune responses: a comparison of expression levels in two lakes

Question: How do immune phenotypes differ between infected and uninfected wild individuals, and is the effect the same in different populations? Organisms: Threespine stickleback (Gasterosteus aculeatus) from two lake populations on the island of North Uist, Scotland, sampled in May 2015. Methods: For each fish, we recorded length, sex, reproductive status, condition, and parasitic infection. We measured the expression levels of eight genes that act as key markers of immune system function using qPCR, and then examined the relationship between measured factors and immune gene expression profiles within each population. Conclusions: Populations differed significantly in their immune gene expression profiles. Within each population, multiple factors, including condition, reproductive status, and Schistocephalus solidus infection levels, were found to correlate with expression levels of different arms of the immune system

A Bayesian space–time model for clustering areal units based on their disease trends

Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC) 3 ) algorithm. The effectiveness of the (MC) 3 algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk

Practical classification of different moving targets using automotive radar and deep neural networks

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed