940 resultados para second order statistics


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Quantitative monitoring of a mechanochemical reaction by Raman spectroscopy leads to a surprisingly straightforward second-order kinetic model in which the rate is determined simply by the frequency of reactive collisions between reactant particles.

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In this paper we investigate the first and second order characteristics of the received signal at the output ofhypothetical selection, equal gain and maximal ratio combiners which utilize spatially separated antennas at the basestation. Considering a range of human body movements, we model the model the small-scale fading characteristics ofthe signal using diversity specific analytical equations which take into account the number of available signal branchesat the receiver. It is shown that these equations provide an excellent fit to the measured channel data. Furthermore, formany hypothetical diversity receiver configurations, the Nakagami-m parameter was found to be close to 1.

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Radio-frequency (RF) impairments in the transceiver hardware of communication systems (e.g., phase noise (PN), high power amplifier (HPA) nonlinearities, or in-phase/quadrature-phase (I/Q) imbalance) can severely degrade the performance of traditional multiple-input multiple-output (MIMO) systems. Although calibration algorithms can partially compensate these impairments, the remaining distortion still has substantial impact. Despite this, most prior works have not analyzed this type of distortion. In this paper, we investigate the impact of residual transceiver hardware impairments on the MIMO system performance. In particular, we consider a transceiver impairment model, which has been experimentally validated, and derive analytical ergodic capacity expressions for both exact and high signal-to-noise ratios (SNRs). We demonstrate that the capacity saturates in the high-SNR regime, thereby creating a finite capacity ceiling. We also present a linear approximation for the ergodic capacity in the low-SNR regime, and show that impairments have only a second-order impact on the capacity. Furthermore, we analyze the effect of transceiver impairments on large-scale MIMO systems; interestingly, we prove that if one increases the number of antennas at one side only, the capacity behaves similar to the finite-dimensional case. On the contrary, if the number of antennas on both sides increases with a fixed ratio, the capacity ceiling vanishes; thus, impairments cause only a bounded offset in the capacity compared to the ideal transceiver hardware case.

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As is now well established, a first order expansion of the Hohenberg-Kohn total energy density functional about a trial input density, namely, the Harris-Foulkes functional, can be used to rationalize a non self consistent tight binding model. If the expansion is taken to second order then the energy and electron density matrix need to be calculated self consistently and from this functional one can derive a charge self consistent tight binding theory. In this paper we have used this to describe a polarizable ion tight binding model which has the benefit of treating charge transfer in point multipoles. This admits a ready description of ionic polarizability and crystal field splitting. It is necessary in constructing such a model to find a number of parameters that mimic their more exact counterparts in the density functional theory. We describe in detail how this is done using a combination of intuition, exact analytical fitting, and a genetic optimization algorithm. Having obtained model parameters we show that this constitutes a transferable scheme that can be applied rather universally to small and medium sized organic molecules. We have shown that the model gives a good account of static structural and dynamic vibrational properties of a library of molecules, and finally we demonstrate the model's capability by showing a real time simulation of an enolization reaction in aqueous solution. In two subsequent papers, we show that the model is a great deal more general in that it will describe solvents and solid substrates and that therefore we have created a self consistent quantum mechanical scheme that may be applied to simulations in heterogeneous catalysis.

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Arguably, the myth of Shakespeare is a myth of universality. Much has been written about the dramatic, thematic and ‘humanistic’ transference of Shakespeare’s works: their permeability, transcendence of cultures and histories, geographies and temporalities. Located within this debate is a belief that this universality, among other dominating factors, is founded upon the power and poeticism of Shakespeare’s language. Subsequently, if we acknowledge Frank Kermode’s assertion that “the life of the plays is the language” and “the secret (of Shakespeare’s works) is in the detail,” what then becomes of this myth of universality, and how is Shakespeare’s language ‘transferred’ across cultures? In Asian intercultural adaptations, language becomes the primary site of confrontation as issues of semantic accuracy and poetic affiliation abound. Often, the language of the text is replaced with a cultural equivalent or reconceived with other languages of the stage – song and dance, movement and music; metaphor and imagery consequently find new voices. Yet if myth is, as Roland Barthes propounds, a second-order semiotic system that is predicated upon the already constituted sign, here being language, and myth is parasitical on language, what happens to the myth of Shakespeare in these cultural re-articulations? Wherein lies the ‘universality’? Or is ‘universality’ all that it is – an insubstantial (mythical) pageant? Using Ong Keng Sen’s Search Hamlet (2002), this paper would examine the transference of myth and / as language in intercultural Shakespeares. If, as Barthes argues, myths are to be understood as metalanguages that adumbrate social hegemonies, intercultural imaginings of Shakespeare can be said to expose the hollow myth of universality yet in a paradoxical double-bind reify and reinstate this self-same myth.

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Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

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Recent experimental results definitively showed, for the first time, optical radiation mediated by the slow mode surface plasmon polariton of metal-oxide-metal tunnel junctions. Here, dispersion curves for this mode are calculated. They are consistent with first-order grating coupling to light at the energies of the experimental emission peaks. The curves are then used to analyze second-order and high-energy (> 2.35 eV) grating coupling of the polaritons to radiation. Finally, variation of slow mode damping as a function of energy is used to explain qualitatively the relative experimental peak emission intensities and the absence of radiation peaks above 2.35 eV.

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In this work, the removal of arsenic from aqueous solutions onto thermally processed dolomite is investigated. The dolomite was thermally processed (charred) at temperatures of 600, 700 and 800 degrees C for 1, 2, 4 and 8 h. Isotherm experiments were carried out on these samples over a wide pH range. A complete arsenic removal was achieved over the pH range studied when using the 800 degrees C charred dolomite. However, at this temperature, thermal degradation of the dolomite weakens its structure due to the decomposition of the magnesium carbonate, leading to a partial dissolution. For this reason, the dolomitic sorbent chosen for further investigations was the 8 h at 700 degrees C material. Isotherm studies indicated that the Langmuir model was successful in describing the process to a better extent than the Freundlich model for the As(V) adsorption on the selected charred dolomite. However, for the As(III) adsorption, the Freundlich model was more successful in describing the process. The maximum adsorption capacities of charred dolomite for arsenite and arsenate ions are 1.846 and 2.157 mg/g, respectively. It was found that both the pseudo first- and second-order kinetic models are able to describe the experimental data (R-2 > 0.980). The data suggest the charring process allows dissociation of the dolomite to calcium carbonate and magnesium oxide, which accelerates the process of arsenic oxide and arsenic carbonate precipitation. (C) 2014 Elsevier B.V. All rights reserved.

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This paper describes the results of non-linear elasto-plastic implicit dynamic finite element analyses that are used to predict the collapse behaviour of cold-formed steel portal frames at elevated temperatures. The collapse behaviour of a simple rigid-jointed beam idealisation and a more accurate semi-rigid jointed shell element idealisation are compared for two different fire scenarios. For the case of the shell element idealisation, the semi-rigidity of the cold-formed steel joints is explicitly taken into account through modelling of the bolt-hole elongation stiffness. In addition, the shell element idealisation is able to capture buckling of the cold-formed steel sections in the vicinity of the joints. The shell element idealisation is validated at ambient temperature against the results of full-scale tests reported in the literature. The behaviour at elevated temperatures is then considered for both the semi-rigid jointed shell and rigid-jointed beam idealisations. The inclusion of accurate joint rigidity and geometric non-linearity (second order analysis) are shown to affect the collapse behaviour at elevated temperatures. For each fire scenario considered, the importance of base fixity in preventing an undesirable outwards collapse mechanism is demonstrated. The results demonstrate that joint rigidity and varying fire scenarios should be considered in order to allow for conservative design.

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There is an emerging scholarship on the emotional bases of political opinion and behaviour and, in particular, the contrasting implications of two distinct negative emotions - anger and anxiety. I apply the insights in this literature to the previously unresearched realm of the emotional bases of voting in EU referendums. I hypothesise that anxious voters rely on substantive EU issues and angry voters rely on second-order factors relating to domestic politics (partisanship and satisfaction with government). Focusing on the case of Irish voting in the Fiscal Compact referendum, and using data from a representative sample of voters, I find support for the hypotheses and discuss the implications of the findings for our understanding of the emotional conditionality of EU referendum voting.