29,536 research outputs found
q-Differential equations for q-classical polynomials and q-Jacobi-Stirling numbers
No full textWe introduce, characterise and provide a combinatorial interpretation for the so-called q-Jacobi–Stirling numbers.
This study is motivated by their key role in the (reciprocal) expansion of any power of a second order
q-differential operator having the q-classical polynomials as eigenfunctions in terms of other even order operators,
which we explicitly construct in this work. The results here obtained can be viewed as the q-version of
those given by Everitt et al. and by the first author, whilst the combinatorics of this new set of numbers is a
q-version of the Jacobi–Stirling numbers given by Gelineau and the second author
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Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Tobin's Q and Financial Policy
No full textRecent research in macroeconomics has emphasized the importance of linking the financial and real sectors and the need for working with optimizing models. Tobin’s Q model of investment would appear to provide a framework that can satisfy these two criteria. In contrast to the original presentation of the Q model, the formal development has not recognized that the firm actively participates in a number of financial markets; in this broader context, we show that Q is likely to be an uninformative and possibly misleading signal for investment expenditures . We then endeavor to turn this negative theoretical result to positive advantage in resolving a number of empirical problems with Q models, but the modifications dictated by the theory receive little support from the data.
MULTIFRACTAL q RÉNYI DIMENSIONS OF POLISH SPACES FOR q < 1
No full text In earlier work the second author investigated the multifractal q Rényi dimensions of Polish spaces for q ≥ 1. In this paper we complement those results by investigating the multifractal q Rényi dimensions of Polish spaces for q < 1. </jats:p
Physical origin of Davydov splitting and resonant Raman spectroscopy of Davydov components in multilayer MoTe2
No full textWe systematically study the high-resolution and polarized Raman spectra of multilayer (ML) MoTe2. The layer-breathing (LB) and shear (C) modes are observed in the ultralow-frequency region, which are used to quantitatively evaluate the interlayer coupling in ML MoTe2 based on the linear chain model, in which only the nearest interlayer coupling is considered. The Raman spectra on three different substrates verify the negligible substrate effect on the phonon frequencies of ML MoTe2. Ten excitation energies are used to measure the high-frequency modes of N-layer MoTe2 (NL MoTe2; N is an integer). Under the resonant excitation condition, we observe N-dependent Davydov components in ML MoTe2, originating from the Raman-active A(1)' (A(1g)(2)) modes at similar to 172 cm(-1). More than two Davydov components are observed in NL MoTe2 for N > 4 by Raman spectroscopy. The N-dependent Davydov components are further investigated based on the symmetry analysis. A van der Waals model only considering the nearest interlayer coupling has been proposed to well understand the Davydov splitting of high-frequency A(1)' (A(1g)(2)) modes. The different resonant profiles for the two Davydov components in 3L MoTe2 indicate that proper excitation energy of similar to 1.8 - 2.2 eV must be chosen to observe the Davydov splitting in ML MoTe2. Our work presents a simple way to identify layer number of ultrathin MoTe2 flakes by the corresponding number and peak position of Davydov components. Our work also provides a direct evidence from Raman spectroscopy of how the nearest van der Waals interactions significantly affect the frequency of the high-frequency intralayer phonon modes in multilayer MoTe2 and expands the understanding on the lattice vibrations and interlayer coupling of transition metal dichalcogenides and other two-dimensional materials.National Basic Research Program of China [2013CB921901, 2012CB932703]; National Natural Science Foundation of China [11225421, 11434010, 11474277, 61125402, 51172004, 11474007]SCI(E)[email protected]; [email protected]
A Computationally Efficient Method for Estimating Multi-Model Process Sensitivity Index
No full textIdentification of important processes of a hydrologic system is critical for improving process-based hydrologic modeling. To identify important processes while jointly considering parametric and model uncertainty, Dai et al. (2017), https://doi.org/10.1002/2016WR019715, developed a multi-model process sensitivity index. Numerical evaluation of the index using a brute force Monte Carlo (MC) simulation is computationally expensive, because it requires a nested structure of parameter sampling and the number of model simulations is on the order of N-2 (N being the number of parameter samples). To reduce computational cost, we develop a new method (here denoted as quasi-MC for brevity) that uses triple sets of parameter samples (generated using quasi-MC sequence) to remove the nested structure of parameter sampling in a theoretically rigorous way. The quasi-MC method reduces the number of model simulations from the order of N-2 to 2N. The performance of the method is assessed against the brute force MC approach and the recent binning method developed by Dai et al. (2017), https://doi.org/10.1002/2016WR019715, through two synthetic cases of groundwater flow and solute transport modeling. Due to its rigorous theoretical foundation, the quasi-MC method overcomes the limitations imposed by the inherently empirical nature of the binning method. We find that the quasi-MC method outperforms both the brute force MC and the binning method in terms of computational requirements and theoretical aspects, thus strengthening its potential for the assessment of process sensitivity indices subject to various sources of uncertainty
Developing Industrial Multi-Agent Systems (Invited Paper)
No full textThe development and deployment of multi-agent systems in real world settings raises a number of important research issues and problems which must be overcome if Distributed AI (DAI) is to become a widespread solution technology. Work undertaken in the context of the ARCHON project has provided a number of important insights into these issues. By providing an in depth analysis of ARCHON’s electricity transportation management application, this paper draws together many of the experiences obtained when building one of the world's first operational DAI systems
The ARCHON System and its Applications
No full textARCHON™ (ARchitecture for Cooperative Heterogeneous ON-line systems) was Europe’s largest project in the area of Distributed Artificial Intelligence (DAI). It devised a general-purpose architecture, software framework, and methodology which has been used to support the development of DAI systems in a number of real world industrial domains. Some examples of the applications to which it has been successfully applied include: electricity distribution and supply, electricity transmission and distribution, control of a cement kiln complex, control of a particle accelerator, and control of a robotics application. The type of cooperating community that it supports has a decentralised control regime and individual problem solving agents which are large grain, loosely coupled, and semi-autonomous. This paper will tackle a broad range of issues related to the application of ARCHON technology to industrial applications. Firstly, it gives the rationale for a DAI approach to industrial applications and highlights the characteristics which typify this important domain. Secondly, the ARCHON framework is detailed - with a special emphasis being placed upon the implementation architecture. Thirdly, a brief resumee and status report of the main applications is presented. Finally, the lessons learned and the future plans are presented
A novel instrument based upon extremely high Q-value surface acoustic wave resonator array and neural network
No full textAn instrument comprising an array of surface acoustic wave (SAW) resonator and a neural network system capable of recognizing odours was developed. The system can distinguish gases with five SAW resonators. To make the sensor noise low, high Q-value SAW resonators were fabricated. To our knowledge, seldom has paper reported that a SAW resonator with metal reflector in this frequency band has such a high Q-factor. (C) 2000 Elsevier Science S.A. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000088378500034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Chemistry, AnalyticalElectrochemistryInstruments & InstrumentationSCI(E)EICPCI-S(ISTP)
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