A q-Analog of Dual Sequences with Applications

In the present paper combinatorial identities involving q-dual sequences or polynomials with coefficients q-dual sequences are derived. Further, combinatorial identities for q-binomial coefficients(Gaussian coefficients), q-Stirling numbers and q-Bernoulli numbers and polynomials are deduced.Comment: 14 page

A Criterion for Comparing Measurement Results and Determining Conformity with Specifications

In this paper a new criterion for comparing measurement results and determining conformity with specifications is proposed, which essentially is a strategy of estimating the empirical relationships of objects. Comparing with traditional methods given in GUM: 2008 and ISO 14253-1, this criterion improves the resolution of comparison by reducing the sizes of the coverage intervals to be compared. Interval order (a binary relation) is used for comparing the coverage intervals of the measurand and represents the empirical relations. The systematic effects of measurement are classified into two types: monotonic and non-monotonic effects, so that, without correcting the monotonic effects, a biased measurand can be specified to represent the empirical relations. Thereby the uncertainty components arising from the monotonic effects can be removed from the combined uncertainty. A strategy is given for determining the relationships among measurement results and specification limits. An example is given to demonstrate the application of the criterion

'BioNessie(G) - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations

The Architecture of a Novel Weighted Network: Knowledge Network

Networked structure emerged from a wide range of fields such as biological systems, World Wide Web and technological infrastructure. A deeply insight into the topological complexity of these networks has been gained. Some works start to pay attention to the weighted network, like the world-wide airport network and the collaboration network, where links are not binary, but have intensities. Here, we construct a novel knowledge network, through which we take the first step to uncover the topological structure of the knowledge system. Furthermore, the network is extended to the weighted one by assigning weights to the edges. Thus, we also investigate the relationship between the intensity of edges and the topological structure. These results provide a novel description to understand the hierarchies and organizational principles in knowledge system, and the interaction between the intensity of edges and topological structure. This system also provides a good paradigm to study weighted networks.Comment: 5 figures 11 page

BioNessie - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations