9,743 research outputs found
Small-angle approximation to the transfer of narrow laser beams in anisotropic scattering media
The broadening and the signal power detected of a laser beam traversing an anisotropic scattering medium were examined using the small-angle approximation to the radiative transfer equation in which photons suffering large-angle deflections are neglected. To obtain tractable answers, simple Gaussian and non-Gaussian functions for the scattering phase functions are assumed. Two other approximate approaches employed in the field to further simplify the small-angle approximation solutions are described, and the results obtained by one of them are compared with those obtained using small-angle approximation. An exact method for obtaining the contribution of each higher order scattering to the radiance field is examined but no results are presented
Analytic modeling of aerosol size distributions
Mathematical functions commonly used for representing aerosol size distributions are studied parametrically. Methods for obtaining best fit estimates of the parameters are described. A catalog of graphical plots depicting the parametric behavior of the functions is presented along with procedures for obtaining analytical representations of size distribution data by visual matching of the data with one of the plots. Examples of fitting the same data with equal accuracy by more than one analytic model are also given
Mrpl35, A Mitospecific Component of Mitoribosomes, Plays A Key Role in Cytochrome \u3cem\u3eC\u3c/em\u3e Oxidase Assembly
Mitoribosomes perform the synthesis of the core components of the oxidative phosphorylation (OXPHOS) system encoded by the mitochondrial genome. We provide evidence that MrpL35 (mL38), a mitospecific component of the yeast mitoribosomal central protuberance, assembles into a subcomplex with MrpL7 (uL5), Mrp7 (bL27), and MrpL36 (bL31) and mitospecific proteins MrpL17 (mL46) and MrpL28 (mL40). We isolated respiratory defective mrpL35 mutant yeast strains, which do not display an overall inhibition in mitochondrial protein synthesis but rather have a problem in cytochrome coxidase complex (COX) assembly. Our findings indicate that MrpL35, with its partner Mrp7, play a key role in coordinating the synthesis of the Cox1 subunit with its assembly into the COX enzyme and in a manner that involves the Cox14 and Coa3 proteins. We propose that MrpL35 and Mrp7 are regulatory subunits of the mitoribosome acting to coordinate protein synthesis and OXPHOS assembly events and thus the bioenergetic capacity of the mitochondria
Kepler-91b: a planet at the end of its life. Planet and giant host star properties via light-curve variations
The evolution of planetary systems is intimately linked to the evolution of
their host star. Our understanding of the whole planetary evolution process is
based on the large planet diversity observed so far. To date, only few tens of
planets have been discovered orbiting stars ascending the Red Giant Branch.
Although several theories have been proposed, the question of how planets die
remains open due to the small number statistics. In this work we study the
giant star Kepler-91 (KOI-2133) in order to determine the nature of a
transiting companion. This system was detected by the Kepler Space Telescope.
However, its planetary confirmation is needed. We confirm the planetary nature
of the object transiting the star Kepler-91 by deriving a mass of and a planetary radius of
. Asteroseismic analysis produces a
stellar radius of and a mass of
. We find that its eccentric orbit
() is just away
from the stellar atmosphere at the pericenter. Kepler-91b could be the previous
stage of the planet engulfment, recently detected for BD+48 740. Our
estimations show that Kepler-91b will be swallowed by its host star in less
than 55 Myr. Among the confirmed planets around giant stars, this is the
planetary-mass body closest to its host star. At pericenter passage, the star
subtends an angle of , covering around 10% of the sky as seen from
the planet. The planetary atmosphere seems to be inflated probably due to the
high stellar irradiation.Comment: 21 pages, 8 tables and 11 figure
Causal connectivity of evolved neural networks during behavior
To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics
Activity Recognition and Prediction in Real Homes
In this paper, we present work in progress on activity recognition and
prediction in real homes using either binary sensor data or depth video data.
We present our field trial and set-up for collecting and storing the data, our
methods, and our current results. We compare the accuracy of predicting the
next binary sensor event using probabilistic methods and Long Short-Term Memory
(LSTM) networks, include the time information to improve prediction accuracy,
as well as predict both the next sensor event and its mean time of occurrence
using one LSTM model. We investigate transfer learning between apartments and
show that it is possible to pre-train the model with data from other apartments
and achieve good accuracy in a new apartment straight away. In addition, we
present preliminary results from activity recognition using low-resolution
depth video data from seven apartments, and classify four activities - no
movement, standing up, sitting down, and TV interaction - by using a relatively
simple processing method where we apply an Infinite Impulse Response (IIR)
filter to extract movements from the frames prior to feeding them to a
convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201
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A new method for the reproducible generation of polymorphs: two forms of sulindac with very different solubilities
Polymorphism of drugs has been the subject of intense interest in the pharmaceutical industry for over forty years. Although identical in chemical composition, polymorphs differ in bioavailability, solubility, dissolution rate, chemical and physical stability, melting point, colour, filterability, density, flow properties, and many other properties. The difference in solubility is particularly important for pharmaceuticals, as it can affect drug efficacy, bioavailability and safety. Despite significant investment in processes to find all the possible polymorphs of active pharmaceutical ingredients (APIs), new polymorphs can suddenly appear without warning. Polymorphs tend to convert spontaneously from less stable to more stable forms, and, therefore, it is best to discover and characterize the stable form as early as possible. Ideally the most stable polymorph will be found while the drug candidate is still in the discovery process, so that this is the form used for subsequent testing. The most stable polymorph will be the least soluble and solubility may be a limiting factor in the efficacy of the API. Despite the huge importance of polymorphism in the properties of materials, however, there is no method that can produce all the stable polymorphs of a compound, or even one that can provide confidence that the most stable polymorph has been obtained. Here we describe a new method, `potentiometric cycling for polymorph creation (PC)2', which is able to generate the most stable polymorph in aqueous solution. This new method has been applied to sulindac, a non-steroidal anti-inflammatory drug, which also shows promise in anticancer treatment, producing two polymorphs of this API, including a new more stable one. By adjusting the conditions, this method is able to produce either polymorph exclusively.</jats:p
Towards virtual machine energy-aware cost prediction in clouds
Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers. Furthermore, predicting the future cost of Cloud services can help the service providers to offer the suitable services to the customers that meet their requirements. This paper introduces an Energy-Aware Cost Prediction Framework to estimate the total cost of Virtual Machines (VMs) by considering the resource usage and power consumption. The VMs’ workload is firstly predicted based on an Autoregressive Integrated Moving Average (ARIMA) model. The power consumption is then predicted using regression models. The comparison between the predicted and actual results obtained in a real Cloud testbed shows that this framework is capable of predicting the workload, power consumption and total cost for different VMs with good prediction accuracy, e.g. with 0.06 absolute percentage error for the predicted total cost of the VM
The Kinetic Sunyaev-Zel'dovich Effect from Radiative Transfer Simulations of Patchy Reionization
We present the first calculation of the kinetic Sunyaev-Zel'dovich (kSZ)
effect due to the inhomogeneous reionization of the universe based on detailed
large-scale radiative transfer simulations of reionization. The resulting sky
power spectra peak at l=2000-8000 with maximum values of
l^2C_l~1\times10^{-12}. The peak scale is determined by the typical size of the
ionized regions and roughly corresponds to the ionized bubble sizes observed in
our simulations, ~5-20 Mpc. The kSZ anisotropy signal from reionization
dominates the primary CMB signal above l=3000. This predicted kSZ signal at
arcminute scales is sufficiently strong to be detectable by upcoming
experiments, like the Atacama Cosmology Telescope and South Pole Telescope
which are expected to have ~1' resolution and ~muK sensitivity. The extended
and patchy nature of the reionization process results in a boost of the peak
signal in power by approximately one order of magnitude compared to a uniform
reionization scenario, while roughly tripling the signal compared with that
based upon the assumption of gradual but spatially uniform reionization. At
large scales the patchy kSZ signal depends largely on the ionizing source
efficiencies and the large-scale velocity fields: sources which produce photons
more efficiently yield correspondingly higher signals. The introduction of
sub-grid gas clumping in the radiative transfer simulations produces
significantly more power at small scales, and more non-Gaussian features, but
has little effect at large scales. The patchy nature of the reionization
process roughly doubles the total observed kSZ signal for l~3000-10^4 compared
to non-patchy scenarios with the same total electron-scattering optical depth.Comment: 14 pages, 13 figures (some in color), submitted to Ap
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