Thermodynamic interpretation of the scaling of the dynamics of supercooled liquids

The recently discovered scaling law for the relaxation times, tau=f(T,V^g), where T is temperature and V the specific volume, is derived by a revision of the entropy model of the glass transition dynamics originally proposed by Avramov [I. Avramov, J. Non-Cryst. Solids 262, 258 (2000).]. In this modification the entropy is calculated by an alternative route, while retaining the approximation that the heat capacity is constant with T and P. The resulting expression for the variation of the relaxation time with T and V is shown to accurately fit experimental data for several glass-forming liquids and polymers over an extended range encompassing the dynamic crossover. From this analysis, which is valid for any model in which the relaxation time is a function of the entropy. we find that the scaling exponent g can be identified with the Gruneisen constant.Comment: 24 pages, 7 figure

Stationary probability density of stochastic search processes in global optimization

A method for the construction of approximate analytical expressions for the stationary marginal densities of general stochastic search processes is proposed. By the marginal densities, regions of the search space that with high probability contain the global optima can be readily defined. The density estimation procedure involves a controlled number of linear operations, with a computational cost per iteration that grows linearly with problem size

Discovery of functionally selective C5aR2 ligands: novel modulators of C5a signalling.

The complement cascade is comprised of a highly sophisticated network of innate immune proteins that are activated in response to invading pathogens or tissue injury. The complement activation peptide, C5a, binds two seven transmembrane receptors, namely the C5a receptor 1 (C5aR1) and C5a receptor 2 (C5aR2, or C5L2). C5aR2 is a non-G-protein-signalling receptor whose biological role remains controversial. Some of this controversy arises owing to the lack of selective ligands for C5aR2. In this study, a library of 61 peptides based on the C-terminus of C5a was assayed for the ability to selectively modulate C5aR2 function. Two ligands (P32 and P59) were identified as functionally selective C5aR2 ligands, exhibiting selective recruitment of β-arrestin 2 via C5aR2, partial inhibition of C5a-induced ERK1/2 activation and lipopolysaccharide-stimulated interleukin-6 release from human monocyte-derived macrophages. Importantly, neither ligand could induce ERK1/2 activation or inhibit C5a-induced ERK1/2 activation via C5aR1 directly. Finally, P32 inhibited C5a-mediated neutrophil mobilisation in wild-type, but not C5aR2(-/-) mice. These functionally selective ligands for C5aR2 are novel tools that can selectively modulate C5a activity in vitro and in vivo, and thus will be valuable tools to interrogate C5aR2 function.Immunology and Cell Biology advance online publication, 17 May 2016; doi:10.1038/icb.2016.43

Positive Semidefiniteness and Positive Definiteness of a Linear Parametric Interval Matrix

We consider a symmetric matrix, the entries of which depend linearly on some parameters. The domains of the parameters are compact real intervals. We investigate the problem of checking whether for each (or some) setting of the parameters, the matrix is positive definite (or positive semidefinite). We state a characterization in the form of equivalent conditions, and also propose some computationally cheap sufficient\,/\,necessary conditions. Our results extend the classical results on positive (semi-)definiteness of interval matrices. They may be useful for checking convexity or non-convexity in global optimization methods based on branch and bound framework and using interval techniques

Implementing Parallel Differential Evolution on Spark

[Abstract] Metaheuristics are gaining increased attention as an efficient way of solving hard global optimization problems. Differential Evolution (DE) is one of the most popular algorithms in that class. However, its application to realistic problems results in excessive computation times. Therefore, several parallel DE schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastruc- tures. However, with the emergence of Cloud Computing, new program- ming models, like Spark, have appeared to suit with large-scale data processing on clouds. In this paper we investigate the applicability of Spark to develop parallel DE schemes to be executed in a distributed environment. Both the master-slave and the island-based DE schemes usually found in the literature have been implemented using Spark. The speedup and efficiency of all the implementations were evaluated on the Amazon Web Services (AWS) public cloud, concluding that the island- based solution is the best suited to the distributed nature of Spark. It achieves a good speedup versus the serial implementation, and shows a decent scalability when the number of nodes grows.[Resumen] Las metaheurísticas están recibiendo una atención creciente como técnica eficiente en la resolución de problemas difíciles de optimización global. Differential Evolution (DE) es una de las metaheurísticas más populares, sin embargo su aplicación en problemas reales deriva en tiempos de cómputo excesivos. Por ello se han realizado diferentes propuestas para la paralelización del DE, en su mayoría utilizando infraestructuras e interfaces de programación paralela tradicionales. Con la aparición de la computación en la nube también se han propuesto nuevos modelos de programación, como Spark, que permiten manejar el procesamiento de datos a gran escala en la nube. En este artículo investigamos la aplicabilidad de Spark en el desarrollo de implementaciones paralelas del DE para su ejecución en entornos distribuidos. Se han implementado tanto la aproximación master-slave como la basada en islas, que son las más comunes. También se han evaluado la aceleración y la eficiencia de todas las implementaciones usando el cloud público de Amazon (AWS, Amazon Web Services), concluyéndose que la implementación basada en islas es la más adecuada para el esquema de distribución usado por Spark. Esta implementación obtiene una buena aceleración en relación a la implementación serie y muestra una escalabilidad bastante buena cuando el número de nodos aumenta.[Resume] As metaheurísticas están recibindo unha atención a cada vez maior como técnica eficiente na resolución de problemas difíciles de optimización global. Differential Evolution (DE) é unha das metaheurísticas mais populares, ainda que a sua aplicación a problemas reais deriva en tempos de cómputo excesivos. É por iso que se propuxeron diferentes esquemas para a paralelización do DE, na sua maioría utilizando infraestruturas e interfaces de programación paralela tradicionais. Coa aparición da computación na nube tamén se propuxeron novos modelos de programación, como Spark, que permiten manexar o procesamento de datos a grande escala na nube. Neste artigo investigamos a aplicabilidade de Spark no desenvolvimento de implementacións paralelas do DE para a sua execución en contornas distribuidas. Implementáronse tanto a aproximación master-slave como a baseada en illas, que son as mais comúns. Tamén se avaliaron a aceleración e a eficiencia de todas as implementacións usando o cloud público de Amazon (AWS, Amazon Web Services), tirando como conclusión que a implementación baseada en illas é a mais acaida para o esquema de distribución usado por Spark. Esta implementación obtén unha boa aceleración en relación á implementación serie e amosa unha escalabilidade bastante boa cando o número de nos aumenta.Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RXunta de Galicia; GRC2013/055Xunta de Galicia; R2014/04

Constant-time solution to the Global Optimization Problem using Bruschweiler's ensemble search algorithm

A constant-time solution of the continuous Global Optimization Problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Bruschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed.Comment: 14 pages, 0 figure

Feasibility and dominance rules in the electromagnetism-like algorithm for constrained global optimization

This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetismlike (ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature

Self-scheduling of wind-thermal systems using a stochastic MILP approach

In this work a stochastic (Stoc) mixed-integer linear programming (MILP) approach for the coordinated trading of a price-taker thermal (Ther) and wind power (WP) producer taking part in a day-ahead market (DAM) electricity market (EMar) is presented. Uncertainty (Uncer) on electricity price (EPr) and WP is considered through established scenarios. Thermal units (TU) are modelled by variable costs, start-up (ST-UP) technical operating constraints and costs, such as: forbidden operating zones, minimum (Min) up/down time limits and ramp up/down limits. The goal is to obtain the optimal bidding strategy (OBS) and the maximization of profit (MPro). The wind-Ther coordinated configuration (CoConf) is modelled and compared with the unCoConf. The CoConf and unCoConf are compared and relevant conclusions are drawn from a case study

Effect of entropy on the dynamics of supercooled liquids: New results from high pressure data

We show that for arbitrary thermodynamic conditions, master curves of the entropy are obtained by expressing S(T,V) as a function of TV^g_G, where T is temperature, V specific volume, and g_G the thermodynamic Gruneisen parameter. A similar scaling is known for structural relaxation times,tau = f(TV^g); however, we find g_G < g. We show herein that this inequality reflects contributions to S(T,V) from processes, such as vibrations and secondary relaxations, that do not directly influence the supercooled dynamics. An approximate method is proposed to remove these contributions, S_0, yielding the relationship tau = f(S-S_0).Comment: 10 pages 7 figure

Mechanical Relaxation in Glasses and at the Glass Transition

The Gilroy-Phillips model of relaxational jumps in asymmetric double-well potentials, developed for the Arrhenius-type secondary relaxations of the glass phase, is extended to a formal description of the breakdown of the shear modulus at the glass transition, the flow process.Comment: 13 pages, 11 figures, 49 ref