Automated Generation of Availability Models for SFCs: The case of Virtualized IP Multimedia Subsystem

Author(s):  
Mario Di Mauro ◽  
Giovanni Galatro ◽  
Maurizio Longo ◽  
Arcangelo Palma ◽  
Fabio Postiglione ◽  
...  
2010 ◽  
Vol 56 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Mosiuoa Tsietsi ◽  
Alfredo Terzoli ◽  
George Wells

Using JAIN SLEE as an Interaction and Policy Manager for Enabler-based Services in Next Generation Networks The IP Multimedia Subsystem is a telecommunications framework with a standard architecture for the provision of services. While the services themselves have not been standardised, standards do exist for basic technologies that can be re-used and aggregated in order to construct more complex services. These elements are called service capabilities by the 3GPP and service enablers by the OMA, both of which are reputable standards bodies in this area. In order to provide re-usability, there is a need to manage access to the service capabilities. Also, in order to build complex services, there is a further need to be able to manage and coordinate the interactions that occur between service capabilities. The 3GPP and the OMA have separately defined network entities that are responsible for handling aspects of these requirements, and are known as a service capability interaction manager (SCIM) and a policy enforcer respectively. However, the internal structure of the SCIM and the policy enforcer have not been standardised by the relevant bodies. In addition, as the SCIM and the policy enforcer have been defined through complementary yet separate processes, there is an opportunity to unify efforts from both bodies. This paper builds on work and standards defined by the bodies, and proposes the design of an interaction manager with features borrowed from both the SCIM and the policy enforcer. To help validate the design, we have identified a platform known as JAIN SLEE which we believe conforms to the model proposed, and we discuss how JAIN SLEE can be used to implement our ideas.


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>


Kerntechnik ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. 196-205
Author(s):  
V.-P. Tran ◽  
H.-N. Tran ◽  
A. Yamamoto ◽  
T. Endo
Keyword(s):  

Author(s):  
Mario Di Mauro ◽  
Giovanni Galatro ◽  
Maurizio Longo ◽  
Fabio Postiglione ◽  
Marco Tambasco

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