The meshing of timing belt teeth in pulley grooves

The work described here has been carried out to obtain a better understanding of the tooth root cracking failure mode of timing belts. Previous work has demonstrated the close dependence of this on the tooth deflections of fully meshed teeth, generated by torque transmission, but has not considered the additional distortions generated in the partially meshed conditions at entry to and exit from a pulley groove. Approximate compatibility and constitutive equations are combined with a rigorous consideration of tooth equilibrium in partial meshing to show how bending moments are generated at both exit from a driven pulley and entry to a driving pulley. Experimentally determined belt lives correlate very well with a combined measure of fully meshed tooth strain and strain due to bending at entry or exit. The analysis also shows that this strain measure reduces with increasing belt tooth stiffness, confirming the importance of a high tooth stiffness for a long belt life. Tooth force variations through the partial meshing cycle have also been predicted and compared with measurements obtained from a special strain gauge instrumented pulley. A greater pulley rotation than is predicted is required for a belt tooth to seat in a pulley groove. There is room for improvement in the modellin

copanlisib monotherapy activity in relapsed or refractory indolent b cell lymphoma combined analysis from phase i and ii studies

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Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Modelling requirements for computer simulation of metal machining

Machining modelling efforts are summarised and some important shortcomings of these are presented. The reasons for these shortcomings have been investigated. The investigations are based on machining simulation research and accompanying experimental studies. The reasons are found to be mainly in the approaches taken towards modelling the mesh and boundary conditions, the flow stress of the workpiece material and the frictional properties at the interface between the chip and the cutting tool. The handlings of the extremely high temperature increases and the mechanism taking place during separation of the chip from the workpiece by the tool are also among the significant reasons. The aim of this paper is to present some solutions towards modelling these aspects of machining

Evaluating whole field irrigation performance using statistical inference of inter-furrow infiltration variation

Inter-furrow infiltration variability is often ignored during the evaluation and optimisation of furrow irrigation. Existing techniques for estimating infiltration parameters are generally too intensive in measurement and computation for routine application at the field scale and have therefore primarily been used to study the behaviour of single furrows. This paper identifies the inter-furrow infiltration variation within typical furrow-irrigated fields and determines that this variation can be adequately described using a log-normal distribution. A procedure to predict whole field furrow infiltration characteristics using minimum field measurements is then presented. The technique uses a single advance measurement for single furrows and the log-normal probability distribution to predict the statistical distribution of infiltration functions across the field based on the measured infiltration curve for one or more fully evaluated furrows. This technique also considers the infiltration curve over appropriate ranges of opportunity time. Simulations using the predicted infiltration parameters demonstrate more accurate estimates of the whole field efficiency and uniformity than those extrapolated from infiltration measurements on a limited number of furrows as sampled in a conventional evaluation