Proposal for generalised supersymmetry Les Houches Accord for see-saw models and PDG numbering scheme

The SUSY Les Houches Accord (SLHA) 2 extended the first SLHA to include various generalisations of the Minimal Supersymmetric Standard Model (MSSM) as well as its simplest next-to-minimal version. Here, we propose further extensions to it, to include the most general and well-established see-saw descriptions (types I/II/III, inverse, and linear) in both an effective and a simple gauged extension of the MSSM framework. In addition, we generalise the PDG numbering scheme to reflect the properties of the particles. (c) 2012 Elsevier B.V. All rights reserved.

Membership models and the design of authentication protocols for MANETs

Authentication protocols are very much essential for secure communication in mobile ad hoc networks (MANETs). A number of authentication protocols for MANETs have been proposed in the literature which provide the basic authentication service while trying to optimize their performance and resource consumption parameters. A problem with most of these protocols is that the underlying networking environment on which they are applicable have been left unspecified. As a result, lack of specifications about the networking environments applicable to an authentication protocol for MANETs can mislead about the performance and the applicability of the protocol. In this paper, we first characterize networking environment for a MANET as its 'Membership Model' which is defined as a set of specifications related to the 'Membership Granting Server' (MGS) and the 'Membership Set Pattern' (MSP) of the MANET. We then identify various types of possible membership models for a MANET. In order to illustrate that while designing an authentication protocol for a MANET, it is very much necessary to consider the underlying membership model of the MANET, we study a set of six representative authentication protocols, and analyze their applicability for the membership models as enumerated in this paper. The analysis shows that the same protocol may not perform equally well in all membership models. In addition, there may be membership models which are important from the point of view of users, but for which no authentication protocol is available.

Quorum Sensing and Biofilm Formation in Mycobacteria: Role of c-di-GMP and Methods to Study This Second Messenger

Bacteria have evolved to survive the ever-changing environment using intriguing mechanisms of quorum sensing (QS). Very often, QS facilitates formation of biofilm to help bacteria to persist longer and the formation of such biofilms is regulated by c-di-GMP. It is a well-known second messenger also found in mycobacteria. Several methods have been developed to study c-di-GMP signaling pathways in a variety of bacteria. In this review, we have attempted to highlight a connection between c-di-GMP and biofilm formation and QS in mycobacteria and several methods that have helped in better understanding of c-di-GMP signaling. (c) 2014 IUBMB Life, 66(12):823-834, 2014

Models and algorithms for detection and tracking of coordinated groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time. ©2008 IEEE.

Models and algorithms for tracking of maneuvering objects using variable rate particle filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

Designing Of Partial Similarity Models And Evaluation Method In Polymer Flooding Experiment

Based on the scaling criteria of polymer flooding reservoir obtained in our previous work in which the gravity and capillary forces, compressibility, non-Newtonian behavior, absorption, dispersion, and diffusion are considered, eight partial similarity models are designed. A new numerical approach of sensitivity analysis is suggested to quantify the dominance degree of relaxed dimensionless parameters for partial similarity model. The sensitivity factor quantifying the dominance degree of relaxed dimensionless parameter is defined. By solving the dimensionless governing equations including all dimensionless parameters, the sensitivity factor of each relaxed dimensionless parameter is calculated for each partial similarity model; thus, the dominance degree of the relaxed one is quantitatively determined. Based on the sensitivity analysis, the effect coefficient of partial similarity model is defined as the summation of product of sensitivity factor of relaxed dimensionless parameter and its relative relaxation quantity. The effect coefficient is used as a criterion to evaluate each partial similarity model. Then the partial similarity model with the smallest effect coefficient can be singled out to approximate to the prototype. Results show that the precision of partial similarity model is not only determined by the number of satisfied dimensionless parameters but also the relative relaxation quantity of the relaxed ones.