3,287 research outputs found
Magic number behavior for heat capacities of medium sized classical Lennard-Jones clusters
Monte Carlo methods were used to calculate heat capacities as functions of
temperature for classical atomic clusters of aggregate sizes that were bound by pairwise Lennard-Jones potentials. The parallel
tempering method was used to overcome convergence difficulties due to
quasiergodicity in the solid-liquid phase-change regions. All of the clusters
studied had pronounced peaks in their heat capacity curves, most of which
corresponded to their solid-liquid phase-change regions. The heat capacity peak
height and location exhibited two general trends as functions of cluster size:
for to 36, the peak temperature slowly increased, while the peak
height slowly decreased, disappearing by ; for , a very small
secondary peak at very low temperature emerged and quickly increased in size
and temperature as increased, becoming the dominant peak by .
Superimposed on these general trends were smaller fluctuations in the peak
heights that corresponded to ``magic number'' behavior, with local maxima found
at and 49, and the largest peak found at . These
magic numbers were a subset of the magic numbers found for other cluster
properties, and can be largely understood in terms of the clusters' underlying
geometries. Further insights into the melting behavior of these clusters were
obtained from quench studies and by examining rms bond length fluctuations.Comment: 15 pages, 17 figures (PDF format
A computational study of 13-atom Ne-Ar cluster heat capacities
Heat capacity curves as functions of temperature were calculated using Monte
Carlo methods for the series of Ne_(13-n)Ar_n clusters (0 <= n <= 13). The
clusters were modeled classically using pairwise additive Lennard-Jones
potentials. The J-walking (or jump-walking) method was used to overcome
systematic errors due to quasiergodicity. Substantial discrepancies between the
J-walking results and those obtained using standard Metropolis methods were
found. Results obtained using the atom-exchange method, another Monte Carlo
variant for multi-component systems, also did not compare well with the
J-walker results. Quench studies were done to investigate the clusters'
potential energy surfaces. Only those Ne-Ar clusters consisting predominately
of either one or the other component had lowest energy isomers having the
icosahedral-like symmetry typical of homogeneous 13-atom rare gas clusters;
non-icosahedral structures dominated the lowest-energy isomers for the other
clusters. This resulted in heat capacity curves that were very much different
than that of their homogeneous counterpart. Evidence for coexistence behavior
different than that seen in homogenous clusters is also presented.Comment: 45 pages, 11 Figures, figures in .gif format files. Journal of
Chemical Physics, AIP ID number 513730JC
A Computational Study of Thirteen-atom Ar-Kr Cluster Heat Capacities
Heat capacity curves as functions of temperature were calculated using Monte
Carlo methods for the series of Ar_{13-n}Kr_n clusters (0 <= n <= 13). The
clusters were modeled classically using pairwise additive Lennard-Jones
potentials. J-walking (or jump-walking) was used to overcome convergence
difficulties due to quasiergodicity present in the solid-liquid transition
regions, as well as in the very low temperature regions where heat capacity
anomalies arising from permutational isomers were observed. Substantial
discrepancies between the J-walking results and the results obtained using
standard Metropolis Monte Carlo methods were found. Results obtained using the
atom-exchange method, another Monte Carlo variant designed for multi-component
systems, were mostly similar to the J-walker results. Quench studies were also
done to investigate the clusters' potential energy surfaces; in each case, the
lowest energy isomer had an icosahedral-like symmetry typical of homogeneous
thirteen-atom rare gas clusters, with an Ar atom being the central atom.Comment: 46 pages, 13 Figures combined in 2 .gif files, Journal of Chemical
Physics, AIP ID number 508646JC
Encouraging practitioners in infection prevention and control to publish: a cross-sectional survey
Aim: The aim of this cross-sectional survey was to determine the views of infection prevention and control practitioners (IPCPs) on publishing research.
Methods: A convenience sample was obtained by approaching delegates at the 2015 Infection Prevention Society conference and data was captured via a hand-held electronic device.
Findings: Of the 79 respondents most (83%) read Journal of Infection Prevention (JIP) and found it useful for informing their practice (72%). However, most (91%) had never published in JIP, and less than half (40%) published elsewhere. The main barrier to publication was not having work suitable for publication (38%). Support (37%), training in writing for publication (10%) and time (9%) were considered to be important facilitators in encouraging respondents to publish.
Discussion: Strategies that support IPCPs in developing their writing skills may encourage more IPCPs to disseminate evidence to support best practice by publishing their work in peer reviewed journals
Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms
The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises’ applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.info:eu-repo/semantics/acceptedVersio
Task scheduling characterisation in enterprise application integration
Cloud computing allows enterprises to incorporate applications and computational resources as services, and thus, enterprises can concentrate on their business processes, without concerning the development, configuration and maintenance of these applications and resources. Integration platforms are one of these services that allow enterprises to integrate applications in order to reduce the maintenance costs and operations of the integration of on-premises platforms. However, high performance on resources offered by the cloud, demands improvement in task scheduling of integration platforms. Our literature review has identified a lack of studies in the field of enterprise application integration, focusing on specificities and vulnerabilities of the task scheduling of integration processes. This is a pioneer work regarding the characterisation of the scheduling of tasks of integration processes. We propose a ranking according to their conceptual models and apply this ranking to five integration processes. Then, we have statistically analysed the influence of each component of their conceptual models on the performance of the execution of these integration processes. We characterise the task scheduling of integration processes and presented a mathematical equation for the makespan as a function of the components of this characterisation. This study can guide software engineers in the optimal task scheduling for integration processes, which can improve the performance runtime systems regarding using the computational resources and result in minimisation of costs of companies.info:eu-repo/semantics/acceptedVersio
Restriction of Late Cerebral Cortical Progenitors to an Upper-Layer Fate
AbstractEarly in development, neural progenitors in cerebral cortex normally produce neurons of several layers during successive cell divisions. The laminar fate of their daughters depends on environmental cues encountered just before mitosis. At the close of neurogenesis, however, cortical progenitors normally produce neurons destined only for the upper layers. To assess the developmental potential of these cells, upper-layer progenitors were transplanted into the cerebral cortex of younger hosts, in which deep-layer neurons were being generated. These studies reveal that late cortical progenitors are not competent to generate deep-layer neurons and are instead restricted to producing the upper layers
Reducing Quasi-Ergodic Behavior in Monte Carlo Simulations by J-Walking: Applications to Atomic Clusters
A method is introduced that is easy to implement and greatly reduces the systematic error resulting from quasi-ergodicity, or incomplete sampling of configuration space, in Monte Carlo simulations of systems containing large potential energy barriers. The method makes possible the jumping over these barriers by couplingn the usual Metropolis sampling to the Boltzmann distribution generated by another random walker at a higher temperature. the basic techniques are illustrated on some simple classical systems, beginning for heuristic purposes with a simple one-dimensional double well potential based on a quartic polynomial. the method\u27s suitability for typical multidimensional Monte Carlo systems is demonstrated by extending the double well potential to several dimensions, and then by applying the method to a multiparticle cluster system consisting of argon atoms bound by pairwise Lennard-Jones potentials. Remarkable improvements are demonstrated in the convergence rate for the cluster configuration energy, and especially for the heat capacity, at temperatures near the cluster melting transition region. Moreover, these improvements can be obtained even in the worst-case scenario where clusters are initialized from random configurations
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