Bicomponents and the robustness of networks to failure

A common definition of a robust connection between two nodes in a network such as a communication network is that there should be at least two independent paths connecting them, so that the failure of no single node in the network causes them to become disconnected. This definition leads us naturally to consider bicomponents, subnetworks in which every node has a robust connection of this kind to every other. Here we study bicomponents in both real and model networks using a combination of exact analytic techniques and numerical methods. We show that standard network models predict there to be essentially no small bicomponents in most networks, but there may be a giant bicomponent, whose presence coincides with the presence of the ordinary giant component, and we find that real networks seem by and large to follow this pattern, although there are some interesting exceptions. We study the size of the giant bicomponent as nodes in the network fail, using a specially developed computer algorithm based on data trees, and find in some cases that our networks are quite robust to failure, with large bicomponents persisting until almost all vertices have been removed.Comment: 5 pages, 1 figure, 1 tabl

Gathering evidence of benefits: a structured approach from the JISC Managing Research Data Programme

The work of the Jisc Managing Research Data programme is – along with the rest of the UK higher education sector – taking place in an environment of increasing pressure on research funding. In order to justify the investment made by Jisc in this activity – and to help make the case more widely for the value of investing time and money in research data management – projects and the programme as a whole must be able to clearly express the resultant benefits to the host institutions and to the broader sector. This paper describes a structured approach to the measurement and description of benefits provided by the work of these projects for the benefit of funders, institutions and researchers. We outline the context of the programme and its work; discuss the drivers and challenges of gathering evidence of benefits; specify benefits as distinct from aims and outputs; present emerging findings and the types of metrics and other evidence which projects have provided; explain the value of gathering evidence in a structured way to demonstrate benefits generated by work in this field; and share lessons learned from progress to date

Random graphs with clustering

We offer a solution to a long-standing problem in the physics of networks, the creation of a plausible, solvable model of a network that displays clustering or transitivity -- the propensity for two neighbors of a network node also to be neighbors of one another. We show how standard random graph models can be generalized to incorporate clustering and give exact solutions for various properties of the resulting networks, including sizes of network components, size of the giant component if there is one, position of the phase transition at which the giant component forms, and position of the phase transition for percolation on the network.Comment: 5 pages, 2 figure

Addressing data management training needs: a practice based approach from the UK

In this paper, we describe the current challenges to the effective management and preservation of research data in UK universities, and the response provided by the JISC Managing Research Data programme. This paper will discuss, inter alia, the findings and conclusions from data management training projects of the first iteration of the programme and how they informed the design of the second, paying particular attention to initiatives to develop and embed training materials

Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons

In this paper, we consider the problem of computing parameters of an objective function for a discrete-time optimal control problem from state and control trajectories with active control constraints. We propose a novel method of inverse optimal control that has a computationally efficient online form in which pairs of states and controls from given state and control trajectories are processed sequentially without being stored or processed in batches. We establish conditions guaranteeing the uniqueness of the objective-function parameters computed by our proposed method from trajectories with active control constraints. We illustrate our proposed method in simulation.Comment: 10 pages, 4 figures, Accepted for publication in Automatic

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

Commercial operation of unmanned aerial vehicles (UAVs) would benefit from an onboard ability to sense and avoid (SAA) potential mid-air collision threats. In this paper we present a new approach for detection of aircraft below the horizon. We address some of the challenges faced by existing vision-based SAA methods such as detecting stationary aircraft (that have no relative motion to the background), rejecting moving ground vehicles, and simultaneous detection of multiple aircraft. We propose a multi-stage, vision-based aircraft detection system which utilises deep learning to produce candidate aircraft that we track over time. We evaluate the performance of our proposed system on real flight data where we demonstrate detection ranges comparable to the state of the art with the additional capability of detecting stationary aircraft, rejecting moving ground vehicles, and tracking multiple aircraft

Computer programs for estimating civil aircraft economics

Computer programs for calculating airline direct operating cost, indirect operating cost, and return on investment were developed to provide a means for determining commercial aircraft life cycle cost and economic performance. A representative wide body subsonic jet aircraft was evaluated to illustrate use of the programs

S-Nitrosoglutathione reduces asymptomatic embolization after carotid angioplasty

Background: The major complication of carotid angioplasty is embolic stroke, which may occur after balloon inflation and deflation or in the early postintervention period. Platelet adhesion and aggregation to the angioplasty site with subsequent embolization seems to plays a major role in early postangioplasty embolization and stroke. During this period, asymptomatic embolic signals can be detected in patients by transcranial Doppler ultrasound despite aspirin and heparin treatment. S-Nitrosoglutathione (GSNO) is a nitric oxide donor that appears to have relative platelet specificity. We evaluated its effectiveness in reducing embolization after carotid angioplasty. Methods and results: Sixteen patients undergoing carotid angioplasty and stenting for symptomatic 70% internal carotid artery stenosis were randomized in a double-blind manner to GSNO or placebo given after surgery for 90 minutes. All patients were pretreated with aspirin and given heparin for 24 hours after the procedure. Transcranial Doppler recordings were made from the ipsilateral middle cerebral artery for 1 hour before treatment and at 0 to 3, 6, and 24 hours after treatment. GSNO resulted in a rapid reduction in the frequency of embolic signals of 95% at 0 to 3 hours and 100% at 6 hours (P=0.007 and P=0.01 versus placebo, respectively). In the placebo group, 2 patients experienced ipsilateral stroke after the angioplasty. No cerebrovascular events occurred in the GSNO group. Conclusions: S-Nitrosoglutathione was highly effective in rapidly reducing the frequency of embolic signals after endovascular treatment for symptomatic high-grade carotid stenosis

Optical properties of concentrated dispersions

Apparatus and methods have been developed to measure the diffuse transmittance T and reflectance R of multiple scattering, concentrated, colloidal dispersions. The variation of R and T with pathlength, wavelength, and concentration has been investigated for non-spherical particles in concentrated dispersions, over a range of pH and surfactant concentrations. Measurements of diffuse transmittance and reflectance required large corrections to be made £->r the presence of any specular interfaces i.e. windows. These corrections were minimised by developing a bifurcated fibre optic bundle reflectance method, which allowed R and T to be measured at volume fractions up to at least 0.3. Using magnetic, acoustic and shear fields to align the non-spherical kaolinite particles changes In R and T were measured at volume fractions upto 0.3. The amplitude of the changes and the relaxation of the changes Induced by the applied fields were measured. The amplitude of the change was found to vary strongly with pH and surfactant concentration. For any particular face diameter platelet, the amplitude of the change followed closely the flocculation process, and was sensitive to the mode of particle-particle aggregation, e.g. face-face, or face-edge. The amount of surfactant per unit mass of kaolinite required to stabilise dispersions Is found to vary with particle size and concentration. This showed that information about particle orientation can be obtained through multiple scattering systems when subjected to an aligning field. Kubelka-Munk two flux theory was used to relate R and T to the diffuse flux scattering parameter S. A simple theory was developed relating S to the size shape and orientation of the non-spherical particles, hence allowing the particle orientation to be determined for any aligning field The insight Into particle behaviour given by the optical method Is superior to that given by rheology alone, which does not provide an unambiguous measure of the mode of particle alignment