1,093 research outputs found

    Smarter Simulation Placement of Kilonova Light Curve Models for Computationally Inexpensive Surrogate Model Creation

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    The first detected binary neutron star merger GW170817 allowed for the simultaneous detection of gravitational and electromagnetic waves which started the era of multi-messenger astrophysics. The existence of an electromagnetic counterpart to a compact object merger allowed for a significantly deeper analysis of the merger event and much tighter resultant constraints on existing physical models of neutron stars, nuclear physics, and the Universe itself. Multi-messenger analysis requires sophisticated source modeling. For the foreseeable future, demanding computational resource costs will result in a sparse availability of state-of-the-art neutron star merger light curve simulations. Astrophysical inference can proceed using an alternate approach of creating computationally cheaper surrogate models based on the aforementioned state-of-the-art simulations. The work presented here focuses on the creation and interpolation of a library of light curve simulations suitable for the generation of surrogate models capable of conveying useful astrophysical information. It addresses the necessity of switching from grid-based simulation placement to an error-maximization approach which identifies the least understood regions of parameter space. Interpolation is introduced as the connecting factor between the long-term goal of surrogate model creation and the new simulation placement mechanism. Finally, a discussion about the iterative process of simulation placement using interpolation outputs describes how each new simulation brings the library one step closer to serving as a surrogate model training set

    Cryptographically Privileged State Estimation With Gaussian Keystreams

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    The effects of valsartan on cardiac function and pro-oxidative parameters in the streptozotocin-induced diabetic rat heart

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    © 2020, University of Kragujevac, Faculty of Science. All rights reserved. Diabetes mellitus is a major risk factor for cardiovascular diseases, while cardiovascular diseases are a leading cause of morbidity and mortality worldwide. The renin–angiotensin– aldosterone system controls renal, cardiovascular, adrenal function and regulates fluid and electrolyte balance as well as blood pressure. Because of his role, inhibition of renin-angiotensin-aldosteron system is another therapy approach that reduces the risk of diabetes and cardiovascular disease. In this study, our goal was to evaluate effect of valsartan,as inhibitor of angiotensin II receptor type 1, on cardiac tissue and function, with focus on cardiodynamic and oxidative stress. The present study was carried out on 20 adult male Wistar albino rats (8 week old and with body masses of 180-200 g). Rats were divided randomly into 2 groups (10 animals per group). Healthy animals treated with 1 μM of valsartan and streptozotocin-induced diabetic animals perfused with 1 μM of valsartan 4 weeks after the induction of diabetes. Our results demonstrated that acute application of valsartan has different effect on cardiodynamics in rat heart of diabetic and healthy animals but did not improve cardiac function in hy-perglycemia-induced changes. A challenge for further investi-gations are studies with chronic or acute administration, alone or in combination with other angiotensin-converting-enzyme inhibitor in various models of diabetes

    Interpolated kilonova spectra models: necessity for a phenomenological, blue component in the fitting of AT2017gfo spectra

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    In this work, we present a simple interpolation methodology for spectroscopic time series, based on conventional interpolation techniques (random forests) implemented in widely-available libraries. We demonstrate that our existing library of simulations is sufficient for training, producing interpolated spectra that respond sensitively to varied ejecta parameter, post-merger time, and viewing angle inputs. We compare our interpolated spectra to the AT2017gfo spectral data, and find parameters similar to our previous inferences using broadband light curves. However, the spectral observations have significant systematic short-wavelength residuals relative to our models, which we cannot explain within our existing framework. Similar to previous studies, we argue that an additional blue component is required. We consider a radioactive heating source as a third component characterized by light, slow-moving, lanthanide-free ejecta with Mth=0.003 M⊙M_{\rm th} = 0.003~M_\odot, vth=0.05v_{\rm th} = 0.05c, and κth=1\kappa_{\rm th} = 1 cm2^2/g. When included as part of our radiative transfer simulations, our choice of third component reprocesses blue photons into lower energies, having the opposite effect and further accentuating the blue-underluminosity disparity in our simulations. As such, we are unable to overcome short-wavelength deficits at later times using an additional radioactive heating component, indicating the need for a more sophisticated modeling treatment.Comment: 11 pages, 7 figures, presenting at April APS session F13.0000

    Predicting the effect of chemical factors on the pH of crystallisation trials

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    In macromolecular crystallisation, success is often dependent on the pH of the experiment. However, little is known about the pH of reagents used and it is generally assumed that the pH of the experiment will closely match that of any buffering chemical in the solution. We use a large data set of experimentally measured solution pH values to show that this assumption can be very wrong and generate a model which can be used to successfully predict the overall solution pH of a crystallisation experiment. Further, we investigate the time dependence of the pH of some polyethylene glycol polymers widely used in protein crystallisation under different storage conditions

    Tools to Ease the Choice and Design of Protein Crystallisation Experiments

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    The process of macromolecular crystallisation almost always begins by setting up crystallisation trials using commercial or other premade screens, followed by cycles of optimisation where the crystallisation cocktails are focused towards a particular small region of chemical space. The screening process is relatively straightforward, but still requires an understanding of the plethora of commercially available screens. Optimisation is complicated by requiring both the design and preparation of the appropriate secondary screens. Software has been developed in the C3 lab to aid the process of choosing initial screens, to analyse the results of the initial trials, and to design and describe how to prepare optimisation screens
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