Fibers and materials of medical application

Efferent sorption methods of organism detoxication (by medical trend) are presented. Recently, specialists have shown their keen interest in the problem of treating exogenous and endogenous intoxications. This was stipulated by the growing production and accumulation of chemical products for industrial, agricultural, and domestic needs. To solve this problem the industrial production of carbon fibrous adsorbents was developed and implemented at NII Chimvolokno in St. Petersburg. A description of the carbon fibers is given. Also, application of modern composite materials for manufacturing compression-distraction apparatus used for setting fractured bones is described

Lateral imaging of the superconducting vortex lattice using Doppler-modulated scanning tunneling microscopy

By spatially mapping the Doppler effect of an in-plane magnetic field on the quasiparticle tunneling spectrum, we have laterally imaged the vortex lattice in superconducting 2H-NbSe2. Cryomagnetic scanning tunneling spectroscopy was performed at 300 mK on the ab-surface oriented parallel to the field H. Conductance images at zero bias show stripe patterns running along H, with the stripe separation varying as H^-0.5. Regions of higher zero-bias conductance show lower gap-edge conductance, consistent with spectral redistribution by spatially-modulated superfluid momentum. Our results are interpreted in terms of the interaction between vortical and screening currents, and demonstrate a general method for probing subsurface vortices.Comment: 3 pages, 3 figures, to appear in Applied Physics Letter

New tools of the trade? The potential and pitfalls of ’Machine Learning’ and ’DAGs’ to model origin-destination data

This paper explores the potential for emerging methods Machine Learning and Directed Acyclic Graphs (DAGs) to be applied to transport modelling at the origin-destination (OD) level. OD data is inherently spatial and is complex, due to the multitude of ways of allocating geographic attributes to the OD pairs (e.g. buffers and intersections with geographic representations of OD data generated using straight desire lines, shortest path algorithms or probabilistic routing). This makes their analysis an interesting geocomputational challenge, seldom tackled by geographers. The application of Machine Learning and DAG methods, developed in other fields, to this geographical data holds great potential to improve the ability to infer causality in mode split from OD data. However, there are also pitfalls to using these methods which can be black boxes, even if the code is open source, if the analyst does not understand what they are doing with the data. Based on the work we discuss ways to ensure new methods in the field are used wisely and set-out next steps for our own research

The Radial Orbit Instability in Collisionless N-Body Simulations

Using a suite of self-gravitating, collisionless N-body models, we systematically explore a parameter space relevant to the onset and behavior of the radial orbit instability (ROI), whose strength is measured by the systemic axis ratios of the models. We show that a combination of two initial conditions, namely the velocity anisotropy and the virial ratio, determines whether a system will undergo ROI and exactly how triaxial the system will become. A third initial condition, the radial shape of the density profile, plays a smaller, but noticeable role. Regarding the dynamical development of the ROI, the instability a) begins after systems collapse to their most compact configuration and b) evolves fastest when a majority of the particles have radially anisotropic orbits while there is a lack of centrally-concentrated isotropic orbits. We argue that this is further evidence that self-reinforcing torques are the key to the onset of the ROI. Our findings support the idea that a separate orbit instability plays a role in halting the ROI.Comment: accepted for publication in ApJ. 9 figures in emulateapj styl