Magnetohydrodynamics (MHD) provides a convenient, programmable means for propelling liquids and controlling fluid flow in microfluidic devices without a need for mechanical pumps and valves. When the magnetic field is uniform and the electric field in the electrolyte solution is confined to a plane that is perpendicular to the direction of the magnetic field, the Lorentz body force is irrotational and one can define a "Lorentz" potential. Since the MHD-induced flow field under these circumstances is identical to that of pressure-driven flow, one can utilize the large available body of knowledge about pressure-driven flows to predict MHD flows and infer MHD flow patterns. In this note, we prove the equivalence between MHD flows and pressure-driven flows under certain conditions other than flow in straight conduits with rectangular crosssections. We determine the velocity profile and the efficiency of MHD pumps, accounting for current transport in the electrolyte solutions. Then, we demonstrate how data available for pressure driven flow can be utilized to study various MHD flows, in particular, in a conduit patterned with pillars such as may be useful for liquid chromatography and chemical reactors. Additionally, we examine the effect of interior obstacles on the electric current flow in the conduit and show the existence of a particular pillar geometry that maximizes the current. solution is confined to a plane that is perpendicular to the direction of the magnetic field, the Lorentz body force is irrotational and one can define a "Lorentz" potential. Since the MHDinduced flow field under these circumstances is identical to that of pressure-driven flow, one can utilize the large available body of knowledge about pressure-driven flows to predict MHD flows and infer MHD flow patterns. In this note, we prove the equivalence between MHD flows and pressure-driven flows under certain conditions other than flow in straight conduits with rectangular cross-sections. We determine the velocity profile and the efficiency of MHD pumps, accounting for current transport in the electrolyte solutions. Then, we demonstrate how data available for pressure driven flow can be utilized to study various MHD flows, in particular, in a conduit patterned with pillars such as may be useful for liquid chromatography and chemical reactors. Additionally, we examine the effect of interior obstacles on the electric current flow in the conduit and show the existence of a particular pillar geometry that maximizes the current.

We study theoretically magnetohydrodynamic (MHD) motion of a binary electrolyte in a concentric annulus subjected to a uniform, axial magnetic field. The annulus' cylindrical surfaces serve as electrodes. When a potential difference is imposed across the cylindrical electrodes, radial electric current flows in the solution and interacts with the axial magnetic field to induce a Lorentz body force that drives azimuthal fluid flow. When the annulus is infinitely long, a purely azimuthal flow (analogous to the classical Dean flow) is possible. We determine the velocity profile, ion concentration fields, and current density as functions of the electrodes' potential difference and study the linear stability of the azimuthal flow. Of particular interest is the effect of the ions' concentration fields on the centrifugal Dean instability. When the current is directed outwardly, electrochemical effects destabilize the flow, and the MHD flow loses stability at a Dean number much lower than its analogous, pressure driven flow. The supercritical flow consists of convective cells in the transverse plane. In contrast, when the current is directed inwardly, electrochemical effects stabilize the flow and the azimuthal flow is linearly stable for all Dean numbers. When the annulus is capped, purely azimuthal flow is no longer possible, and the flow in the annulus is always three-dimensional. In this case, the secondary flow is mostly driven by pressure gradients induced by the no-slip floor and ceiling. The intensity of the transverse convection depends then only weakly on the current's direction. Disciplines Fluid Dynamics | Physics Comments Qin, M. and Bau, H. H. (2012). Magnetohydrodynamic flow of a binary electrolyte in a concentric annulus.

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We evaluated the immunogenicity and protective ability of a chimpanzee replication-deficient adenovirus vectored COVID-19 vaccine (BV-AdCoV-1) expressing a stabilized pre-fusion SARS-CoV-2 spike glycoprotein in golden Syrian hamsters. Intranasal administration of BV-AdCoV-1 elicited strong humoral and cellular immunity in the animals. Furthermore, vaccination prevented weight loss, reduced SARS-CoV-2 infectious virus titers in the lungs as well as lung pathology and provided protection against SARS-CoV-2 live challenge. In addition, there was no vaccine-induced enhanced disease nor immunopathological exacerbation in BV-AdCoV-1-vaccinated animals. Furthermore, the vaccine induced cross-neutralizing antibody responses against the ancestral strain and the B.1.617.2, Omicron(BA.1), Omicron(BA.2.75) and Omicron(BA.4/5) variants of concern. These results demonstrate that BV-AdCoV-1 is potentially a promising candidate vaccine to prevent SARS-CoV-2 infection, and to curtail pandemic spread in humans.

Vous avez des questions?Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca.Abstract. This paper explores the idea of clustering partial preference relations as a means for agent prediction of users' preferences. Due to the high number of possible outcomes in a typical scenario, such as an automated negotiation session, elicitation techniques can provide only a sparse specification of a user's preferences. By clustering similar users together, we exploit the notion that people with common preferences over a given set of outcomes will likely have common interests over other outcomes. New preferences for a user can thus be predicted with a high degree of confidence by examining preferences of other users in the same cluster. Experiments on the MovieLens dataset show that preferences can be predicted independently with 70-80% accuracy. We also show how an error-correcting procedure can boost accuracy to as high as 98%.