312 research outputs found

    Chemical Kinetic Models for HCCI and Diesel Combustion

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    Predictive engine simulation models are needed to make rapid progress towards DOE's goals of increasing combustion engine efficiency and reducing pollutant emissions. These engine simulation models require chemical kinetic submodels to allow the prediction of the effect of fuel composition on engine performance and emissions. Chemical kinetic models for conventional and next-generation transportation fuels need to be developed so that engine simulation tools can predict fuel effects. The objectives are to: (1) Develop detailed chemical kinetic models for fuel components used in surrogate fuels for diesel and HCCI engines; (2) Develop surrogate fuel models to represent real fuels and model low temperature combustion strategies in HCCI and diesel engines that lead to low emissions and high efficiency; and (3) Characterize the role of fuel composition on low temperature combustion modes of advanced combustion engines

    Variabilidad de rindes de trigo y fertilización nitrogenada

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    p.115-122En la primera parte del trabajo se presenta un modelo teórico de respuesta del cultivo de trigo a una serie de factores de producción. El objetivo del mismo es permitir la estimación del valor esperado de rinde, E (Y), y la varianza de rinde, V (Y), para cualquier nivel de uso de insumo. En la segunda parte se usa el modelo anterior para analizar el proceso de respuesta del cultivo de trigo al uso de fertilizante nitrogenado. Se utilizan datos experimentales correspondientes a la zona triguera II. Una función polinomial es ajustada a datos de ensayos llevados a cabo en la zona II Norte y en la zona II Sur. Una prueba estadística para detectar la presencia de heterocedasticidad (el Test de Park) permite estimar la influencia del nivel de fertilización nitrogenada sobre la variabilidad de rindes. Se concluye que (1) las dosis óptimas son considerablemente (30-50 por ciento) mayores en la zona II surque en la II Norte, (2) las diferencias (entre zonas) de dosis óptima son mayores cuanto menores son las relaciones de precio fertilizante-trigo y (3) la utilización de fertilizante nitrogenado no parece aumentad el riesgo económico de producción

    Adsorption of 2,2 '-dithiodipyridine as a tool for the assembly of silver nanoparticles

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    Silver nanostructured thin films stabilized by 2,2’-dithiodipyridine (2dtpy) were prepared. The Ag nanoparticles were obtained by treating the complex [Ag(2dtpy)]NO3 with NaBH4 in a methanol–toluene mixture. The films were transferred to borosilicate glass slips by a dip-coating method and were found to consist of Ag nanoparticles possibly linked via 2dtpy molecules. Surface-enhanced Raman scattering (SERS) studies have offered the possibility of investigating the adsorption modes of 2dtpy at the Ag nanoparticle surfaces in the fil

    Geometrical Effect in 2D Nanopores

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    A long-standing problem in the application of solid-state nanopores is the lack of the precise control over the geometry of artificially formed pores compared to the well-defined geometry in their biological counterpart, that is, protein nanopores. To date, experimentally investigated solid-state nanopores have been shown to adopt an approximately circular shape. In this Letter, we investigate the geometrical effect of the nanopore shape on ionic blockage induced by DNA translocation using triangular h-BN nanopores and approximately circular molybdenum disulfide (MoS2) nanopores. We observe a striking geometry-dependent ion scattering effect, which is further corroborated by a modified ionic blockage model. The well-acknowledged ionic blockage model is derived from uniform ion permeability through the 2D nanopore plane and hemisphere like access region in the nanopore vicinity. On the basis of our experimental results, we propose a modified ionic blockage model, which is highly related to the ionic profile caused by geometrical variations. Our findings shed light on the rational design of 2D nanopores and should be applicable to arbitrary nanopore shapes.This work was financially supported by the European Research Council (grant 259398, PorABEL), by a Swiss National Science Foundation (SNSF) Consolidator grant (BIONIC BSCGI0_157802), by SNSF Sinergia grant 147607 ... The work performed in Cambridge was supported by the EPSRC Cambridge NanoDTC, EP/L015978/1. The work performed in UIUC was supported by grants from Oxford Nanopore Technology and the Seeding Novel Interdisciplinary Research Program of the Beckman Institute. The UIUC authors gratefully acknowledge also supercomputer time provided through the Extreme Science and Engineering Discovery Environment (XSEDE) grant MCA93S028 and by the University of Illinois at Urbana-Champaign on the TAUB cluster

    Staff training to improve participant recruitment into surgical randomised controlled trials : A feasibility study within a trial (SWAT) across four host trials simultaneously

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    The PROMoting THE Use of SWATs (PROMETHEUS) programme was funded by the Medical Research Council (MRC) [grant number MR/R013748/1]. The DISC host trial is funded by the Health Technology Assessment Programme (Grant Ref: 15/102/04). IntAct is funded by the Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership (Grant Ref: 14/150/62). The EME Programme is funded by the MRC and NIHR, with contributions from the CSO in Scotland and Health and Care Research Wales and the HSC R&D Division, Public Health Agency in Northern Ireland. PROFHER-2 is funded by the Health Technology Assessment Programme (Grant Ref: 16/73/03). START: REACTS is funded by the NIHR Evaluation, Trials and Studies Co-ordinating Centre (NETSCC); Grant Codes: 16/61/18. The development of the training intervention was funded by the MRC Network of Hubs for Trials Methodology Research (MR/L004933/1- R53) and supported by the MRC ConDuCT-II Hub (Collaboration and innovation for Difficult and Complex randomized controlled Trials In Invasive procedures - MR/K025643/1). The online version of the training intervention was funded by the NIHR and is hosted on the NIHR Learn platform (https://learn.nihr.ac.uk/course/view.php?id=385). It is based on the face-to face GRANULE training course funded by the Bowel Disease Research Foundation in collaboration with the University of Birmingham, University of Bristol and former MRC ConDuCT-II Hub. This work was part-funded by the Wellcome Trust [ref: 204829] through the Centre for Future Health (CFH) at the University of York. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the MRC or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.Peer reviewedPublisher PD

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

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 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
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