What is the weakest topology in which feedback stability is robust?

Mathematical theorems in control theory are only of interest in so far as their assumptions relate to practical situations. The space of systems with transfer functions in ℋ∞, for example, has many advantages mathematically, but includes large classes of non-physical systems, and one must be careful in drawing inferences from results in that setting. Similarly, the graph topology has long been known to be the weakest, or coarsest, topology in which (1) feedback stability is a robust property (i.e. preserved in small neighbourhoods) and (2) the map from open-to-closed-loop transfer functions is continuous. However, it is not known whether continuity is a necessary part of this statement, or only required for the existing proofs. It is entirely possible that the answer depends on the underlying classes of systems used. The class of systems we concern ourselves with here is the set of systems that can be approximated, in the graph topology, by real rational transfer function matrices. That is, lumped parameter models, or those distributed systems for which it makes sense to use finite element methods. This is precisely the set of systems that have continuous frequency responses in the extended complex plane. For this class, we show that there is indeed a weaker topology; in which feedback stability is robust but for which the maps from open-to-closed-loop transfer functions are not necessarily continuous. © 2013 Copyright Taylor and Francis Group, LLC.

The further understanding of chain topology effect on the properties of single polymer in good solvent: Special behaviors of single tadpole chain

Chain topology strongly affects the static and dynamic properties of polymer melts and polymers in dilute solution. For different chain architectures, such as ring and linear polymers, the molecular size and the diffusion behavior are different. To further understand the chain topology effect on the static and dynamic properties of polymers, we focus on the tadpole polymer which consists of a cyclic chain attached with one or more linear tails. It is found that both the number and the length of linear tails play important roles on the properties of the tadpole polymers in dilute solution. For the tadpole polymers with fixed linear tail length and number, with increasing the degree of polymerization of tadpole polymers, a transition from linear-like to ring-like behavior is observed for both the static and dynamic properties.

Influence of molecular topology on the static and dynamic properties of single polymer chain in solution

The influence of molecular topology on the structural and dynamic properties of polymer chain in solution with ring structure, three-arm branched structure, and linear structure are studied by molecular dynamics simulation. At the same degree of polymerization (N), the ring-shaped chain possesses the smallest size and largest diffusion coefficient. With increasing N, the difference of the radii of gyration between the three types of polymer chains increases, whereas the difference of the diffusion coefficients among them decreases. However, the influence of the molecular topology on the static and the dynamic scaling exponents is small. The static scaling exponents decrease slightly, and the dynamic scaling exponents increase slightly, when the topology of the polymer chain is changed from linear to ring-shaped or three-arm branched architecture. The dynamics of these three types of polymer chain in solution is Zimm-like according to the dynamic scaling exponents and the dynamic structure factors.

STRUCTURE-PROPERTY RELATIONSHIP BETWEEN HALF-WAVE POTENTIALS OF ORGANIC-COMPOUNDS AND THEIR TOPOLOGY

A significant correlation was found between half-wave potentials of organic compounds and their topological indices, A(x1), A(x2), and A(x3). The simplicity of calculation of the index from the connectivity in the molecular skeleton, together with the significant correlation, indicates its practical value. Good results have been obtained by using them to predict the half-wave potentials of some organic compounds.

Unprecedented 5,5-connected (4(7)center dot 6(3)) (4(8)center dot 6(2)) structural topology: A lead biphosphonate with double layered structure

A new lead(II) phosphonate, Pb[(PO3)(2)C(OH)CH3]center dot H2O (1) was hydrothermally synthesized and characterized by IR, elemental analysis, UV, TGA, SEM, and single crystal X-ray diffraction analysis. X-ray crystallographic study showed that complex 1 has a two-dimensional double layered hybrid structure containing interconnected 4- and 12-membered rings and shows an unusual (5,5)-connected (4(7) . 6(3)) (4(8) .6(2)) topology. (C) 2008 Elsevier B.V. All rights reserved.

On the Emergence of Highly Variable Distributions in the Autonomous System Topology

Recent studies have noted that vertex degree in the autonomous system (AS) graph exhibits a highly variable distribution [15, 22]. The most prominent explanatory model for this phenomenon is the Barabási-Albert (B-A) model [5, 2]. A central feature of the B-A model is preferential connectivity—meaning that the likelihood a new node in a growing graph will connect to an existing node is proportional to the existing node’s degree. In this paper we ask whether a more general explanation than the B-A model, and absent the assumption of preferential connectivity, is consistent with empirical data. We are motivated by two observations: first, AS degree and AS size are highly correlated [11]; and second, highly variable AS size can arise simply through exponential growth. We construct a model incorporating exponential growth in the size of the Internet, and in the number of ASes. We then show via analysis that such a model yields a size distribution exhibiting a power-law tail. In such a model, if an AS’s link formation is roughly proportional to its size, then AS degree will also show high variability. We instantiate such a model with empirically derived estimates of growth rates and show that the resulting degree distribution is in good agreement with that of real AS graphs.

On the Interaction between Data Aggregation and Topology Control in Wireless Sensor Networks

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.

BRITE: Universal Topology Generation from a User's Perspective

Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the large-scale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. Unfortunately, developing a deep understanding of these issues has proven to be a challenging task, since it in turn involves solving difficult problems such as mapping the actual topology, characterizing it, and developing models that capture its emergent behavior. Consequently, even though there are a number of topology models, it is an open question as to how representative the topologies they generate are of the actual Internet. Our goal is to produce a topology generation framework which improves the state of the art and is based on design principles which include representativeness, inclusiveness, and interoperability. Representativeness leads to synthetic topologies that accurately reflect many aspects of the actual Internet topology (e.g. hierarchical structure, degree distribution, etc.). Inclusiveness combines the strengths of as many generation models as possible in a single generation tool. Interoperability provides interfaces to widely-used simulation and visualization applications such as ns and SSF. We call such a tool a universal topology generator. In this paper we discuss the design, implementation and usage of the BRITE universal topology generation tool that we have built. We also describe the BRITE Analysis Engine, BRIANA, which is an independent piece of software designed and built upon BRITE design goals of flexibility and extensibility. The purpose of BRIANA is to act as a repository of analysis routines along with a user–friendly interface that allows its use on different topology formats.

Sampling Biases in IP Topology Measurements

Considerable attention has been focused on the properties of graphs derived from Internet measurements. Router-level topologies collected via traceroute studies have led some authors to conclude that the router graph of the Internet is a scale-free graph, or more generally a power-law random graph. In such a graph, the degree distribution of nodes follows a distribution with a power-law tail. In this paper we argue that the evidence to date for this conclusion is at best insufficient. We show that graphs appearing to have power-law degree distributions can arise surprisingly easily, when sampling graphs whose true degree distribution is not at all like a power-law. For example, given a classical Erdös-Rényi sparse, random graph, the subgraph formed by a collection of shortest paths from a small set of random sources to a larger set of random destinations can easily appear to show a degree distribution remarkably like a power-law. We explore the reasons for how this effect arises, and show that in such a setting, edges are sampled in a highly biased manner. This insight allows us to distinguish measurements taken from the Erdös-Rényi graphs from those taken from power-law random graphs. When we apply this distinction to a number of well-known datasets, we find that the evidence for sampling bias in these datasets is strong.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.