Effect of engine backpressure on the performance and emissions of a CI engine

This study investigated the effect of engine backpressure on the performance and emissions of a CI engine under different speed and load conditions. A 4-stroke single cylinder naturally aspirated direct injection (DI) diesel engine was used for the investigation with the backpressure of 0, 40, 60 and 80 mm of Hg at engine speed of 600, 950 and 1200 rpm. Two parameters were measured during the engine operation: one is engine performance (brake thermal efficiency and brake specific fuel consumption), and the other is the exhaust emissions (NOx, CO and odor). NOx and CO emission were measured by flue gas analyzer (IMR 1400). The engine backpressure produced by the flow regulating valve in the exhaust line was measured by Hg (mercury) manometer. The result showed that, the brake thermal efficiency and brake specific fuel consumption (bsfc) are almost unchanged with increasing backpressure up to 40 mm of Hg pressure for all engine speed and load conditions. The NOx emission became constant or a little decreased with increasing backpressure. The formation of CO was slightly higher with increase of load and back pressure at low engine speed condition. However, under high speed conditions, CO reduced significantly with increasing backpressure for all load conditions. The odor level was similar or a little higher with increasing backpressure for all engine speed and load conditions. Hence, backpressure up to a certain level is not detrimental for a CI engine.

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

A prime-boost immunisation regimen using recombinant BCG and Pr55 gag virus-like particle vaccines based on HIV type 1 subtype C successfully elicits Gag-specific responses in baboons

Mycobacterium bovis BCG is considered an attractive live bacterial vaccine vector. In this study, we investigated the immune response of baboons to a primary vaccination with recombinant BCG (rBCG) constructs expressing the gag gene from a South African HIV-1 subtype C isolate, and a boost with HIV-1 subtype C Pr55 gag virus-like particles (Gag VLPs). Using an interferon enzyme-linked immunospot assay, we show that although these rBCG induced only a weak or an undetectable HIV-1 Gag-specific response on their own, they efficiently primed for a Gag VLP boost, which strengthened and broadened the immune responses. These responses were predominantly CD8+ T cell-mediated and recognised similar epitopes as those targeted by humans with early HIV-1 subtype C infection. In addition, a Gag-specific humoral response was elicited. These data support the development of HIV-1 vaccines based on rBCG and Pr55 gag VLPs. © 2009 Elsevier Ltd. All rights reserved.