Determination of the magnetic moment by QCD sum rules

The magnetic moment of the Λ hyperon is calculated using the QCD sum-rule approach of Ioffe and Smilga. It is shown that μΛ has the structure μΛ=(2/3(eu+ed+4es)(eħ/2MΛc)(1+δΛ), where δΛ is small. In deriving the sum rules special attention is paid to the strange-quark mass-dependent terms and to several additional terms not considered in earlier works. These terms are now appropriately incorporated. The sum rule is analyzed using the ratio method. Using the external-field-induced susceptibilities determined earlier, we find that the calculated value of μΛ is in agreement with experiment.

Gluon-field contribution in QCD sum rules for the magnetic moments of the nucleons

The Wilson coefficient corresponding to the gluon-field strength GμνGμν is evaluated for the nucleon current correlation function in the presence of a static external electromagnetic field, using a regulator mass Λ to separate the high-momentum part of the Feynman diagrams. The magnetic-moment sum rules are analyzed by two different methods and the sensitivity of the results to variations in Λ are discussed.

Determination of baryon magnetic moments from QCD sum rules

The magnetic moment μB of a baryon B with quark content (aab) is written as μB=4ea(1+δB)eħ/2cMB, where ea is the charge of the quark of flavor type a. The experimental values of δB have a simple pattern and have a natural explanation within QCD. Using the ratio method, the QCD sum rules are analyzed and the values of δB are computed. We find good agreement with data (≊10%) for the nucleons and the Σ multiplet while for the cascade the agreement is not as good. In our analysis we have incorporated additional terms in the operator-product expansion as compared to previous authors. We also clarify some points of disagreement between the previous authors. External-field-induced correlations describing the magnetic properties of the vacuum are estimated from the baryon magnetic-moment sum rules themselves as well as by independent spectral representations and the results are contrasted.

Mechanism of adsorption effects on electrode kinetics: Part I: Selection rules in the case of low coverage by molecular surfactants

It is shown that the effect of adsorption of inert molecules on electrode reaction rates is completely accounted for, by introducing into the rate equation, adsorption-induced changes in both the effective electrode area as well as in the electrostatic potential at the reaction site with an additional term for the noncoulombic interaction between the reactant and the adsorbate. The electrostatic potential at the reaction site due to the adsorbed layer is calculated using a model of discretely-distributed molecules in parallel orientation when adsorbed on the electrode with an allowance for thermal agitation. The resulting expression, which is valid for the limiting case of low coverages, is used to predict the types of molecular surfactants that are most likely to be useful for acceleration and inhibition of electrode reactions.

Temperature independent mixing rules to correlate the solubilities of antibiotics and anti-inflammatory drugs in SCCO2

The accurate experimental determination of the solubilities of antibiotics and anti-inflammatory drugs in supercritical fluids (SCFs) and correlations are essential for the development of supercritical technologies for the pharmaceuticals industry. In this work, the solubilities of penicillinG, penicillinV, flurbiprofen, ketoprofen, naproxen, ibuprofen, aspirin and diflunisal in supercritical carbon dioxide (SCCO2) were correlated using Peng-Robinson equation of state (PR EOS) with the modified Kwak and Mansoori mixing rules (mKM) and with Bartle model. The ability of mKM rules was compared against the conventional mixing rules of van der Waals in correlating the solubilities. In the present model, vapor pressure was considered as an adjustable parameter along with binary interactions parameters. In the proposed model, the constants used in the mixing rule, and vapor pressure expression coefficients are temperature independent. The optimization of these constants with experimental data gives binary interaction parameters along with vapor pressure correlations. Sublimation enthalpies were estimated with both the models compared with literature reported experimental values.

Generic Routing Rules and a Scalable Access Enhancement for the Network-on-Chip RECONNECT

RECONNECT is a Network-on-Chip using a honeycomb topology. In this paper we focus on properties of general rules applicable to a variety of routing algorithms for the NoC which take into account the missing links of the honeycomb topology when compared to a mesh. We also extend the original proposal [5] and show a method to insert and extract data to and from the network. Access Routers at the boundary of the execution fabric establish connections to multiple periphery modules and create a torus to decrease the node distances. Our approach is scalable and ensures homogeneity among the compute elements in the NoC. We synthesized and evaluated the proposed enhancement in terms of power dissipation and area. Our results indicate that the impact of necessary alterations to the fabric is negligible and effects the data transfer between the fabric and the periphery only marginally.

Ising pyrochlore magnets: Low-temperature properties, "ice rules," and beyond

Pyrochlore magnets are candidates for what Harris et al. [Phys. Rev. Lett. 79, 2554 (1997)] call "spin-ice" behavior. We present theoretical simulations of relevance for the pyrochlore family R2Ti2O7 (R = rare earth) supported by magnetothermal measurements on selected systems. Ey considering long-ranged dipole-dipole as well as short-ranged superexchange interactions, we get three distinct behaviors: (i) an ordered doubly degenerate state, (ii) a highly disordered state with a broad transition to paramagnetism, and (iii) a partially ordered state with a sharp transition to paramagnetism. Closely corresponding behavior is seen in the real compounds.

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

Generalized frequent episodes in Event Sequences

Discovering patterns in temporal data is an important task in Data Mining. A successful method for this was proposed by Mannila et al. [1] in 1997. In their framework, mining for temporal patterns in a database of sequences of events is done by discovering the so called frequent episodes. These episodes characterize interesting collections of events occurring relatively close to each other in some partial order. However, in this framework(and in many others for finding patterns in event sequences), the ordering of events in an event sequence is the only allowed temporal information. But there are many applications where the events are not instantaneous; they have time durations. Interesting episodesthat we want to discover may need to contain information regarding event durations etc. In this paper we extend Mannila et al.’s framework to tackle such issues. In our generalized formulation, episodes are defined so that much more temporal information about events can be incorporated into the structure of an episode. This significantly enhances the expressive capability of the rules that can be discovered in the frequent episode framework. We also present algorithms for discovering such generalized frequent episodes.

A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Shear sum rules at finite chemical potential

We derive sum rules which constrain the spectral density corresponding to the retarded propagator of the T-xy component of the stress tensor for three gravitational duals. The shear sum rule is obtained for the gravitational dual of the N = 4 Yang-Mills, theory of the M2-branes and M5-branes all at finite chemical potential. We show that at finite chemical potential there are additional terms in the sum rule which involve the chemical potential. These modifications are shown to be due to the presence of scalars in the operator product expansion of the stress tensor which have non-trivial vacuum expectation values at finite chemical potential.

Reducing convergence times of self-propelled swarms via modified nearest neighbor rules

Vicsek et al. proposed a biologically inspired model of self-propelled particles, which is now commonly referred to as the Vicsek model. Recently, attention has been directed at modifying the Vicsek model so as to improve convergence properties. In this paper, we propose two modification of the Vicsek model which leads to significant improvements in convergence times. The modifications involve an additional term in the heading update rule which depends only on the current or the past states of the particle's neighbors. The variation in convergence properties as the parameters of these modified versions are changed are closely investigated. It is found that in both cases, there exists an optimal value of the parameter which reduces convergence times significantly and the system undergoes a phase transition as the value of the parameter is increased beyond this optimal value. (C) 2012 Elsevier B.V. All rights reserved.