Computational Investigation of Inlet Distortion at High Angles of Attack

Lip separation is one of the primary sources of inlet distortion, which can result in a loss in fan stability. High angles of incidence are one of several critical causes of lip separation. There have been many studies into inlet performance at high incidence, including the resulting distortion levels when lip separation occurs. However, the vast majority of these investigations have been carried out experimentally, with little in the way of computational results for inlet performance at high incidence. The flow topology within an inlet when lip separation has occurred is also not well understood. This work aims to demonstrate a suitable model for the prediction of inlet flows at high incidence using ANSYS CFX, looking at both the performance of the inlet and the separated flow topology within the inlet. The attenuating effect of the fan is also investigated, with particular emphasis on the flow redistribution ahead of the fan. The results show that the model used is suitable for predicting inlet performance in adverse operating conditions, showing good agreement with experimental results. In addition, the attenuation of the distortion by the fan is also captured by the numerical model.

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

This paper introduces the discrete choice model-paradigm of Random Regret Minimisation (RRM) to the field of health economics. The RRM is a regret-based model that explores a driver of choice different from the traditional utility-based Random Utility Maximisation (RUM). The RRM approach is based on the idea that, when choosing, individuals aim to minimise their regret–regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. Analysing data from a discrete choice experiment on diet, physical activity and risk of a fatal heart attack in the next ten years administered to a sample of the Northern Ireland population, we find that the combined use of RUM and RRM models offer additional information, providing useful behavioural insights for better informed policy appraisal.

Impact of Metric Selection on Wireless DeAuthentication DoS Attack Performance

DeAuthentication Denial of Service attacks in Public Access WiFi operate by exploiting the lack of authentication of management frames in the 802.11 protocol. Detection of these attacks rely almost exclusively on the selection of appropriate thresholds. In this work the authors demonstrate that there are additional, previously unconsidered, metrics which also influence DoS detection performance. A method of systematically tuning these metrics to optimal values is proposed which ensures that parameter choices are repeatable and verifiable.

The Forgiving Graph: a distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick “repairs,” which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions, without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes v and w whose distance would have been l in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most l log n in the actual graph, where n is the total number of vertices seen so far. Similarly, at any point, a node v whose degree would have been d in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our distributed data structure, which we call the Forgiving Graph, has low latency and bandwidth requirements. The Forgiving Graph improves on the Forgiving Tree distributed data structure from Hayes et al. (2008) in the following ways: 1) it ensures low stretch over all pairs of nodes, while the Forgiving Tree only ensures low diameter increase; 2) it handles both node insertions and deletions, while the Forgiving Tree only handles deletions; 3) it requires only a very simple and minimal initialization phase, while the Forgiving Tree initially requires construction of a spanning tree of the network.

Man-in-the-Middle Attack Test-bed Investigating Cyber-security Vulnerabilities in Smart Grid SCADA Systems

The increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in the Smart Grid has exposed them to a wide range of cyber-security issues, and there are a multitude of potential access points for cyber attackers. This paper presents a SCADA-specific cyber-security test-bed which contains SCADA software and communication infrastructure. This test-bed is used to investigate an Address Resolution Protocol (ARP) spoofing based man-in-the-middle attack. Finally, the paper proposes a future work plan which focuses on applying intrusion detection and prevention technology to address cyber-security issues in SCADA systems.

The Forgiving Graph:A distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick "repairs," which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions,without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes v and w whose distance would have been - in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most - log n in the actual graph, where n is the total number of vertices seen so far. Similarly, at any point, a node v whose degreewould have been d in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our distributed data structure, which we call the Forgiving Graph, has low latency and bandwidth requirements. The Forgiving Graph improves on the Forgiving Tree distributed data structure from Hayes et al. (2008) in the following ways: 1) it ensures low stretch over all pairs of nodes, while the Forgiving Tree only ensures low diameter increase; 2) it handles both node insertions and deletions, while the Forgiving Tree only handles deletions; 3) it requires only a very simple and minimal initialization phase, while the Forgiving Tree initially requires construction of a spanning tree of the network. © Springer-Verlag 2012.

A pilot study of a family focused, psychosocial intervention with war-exposed youth at risk of attack and abduction in north-eastern Democratic Republic of Congo

Rural communities in the Haut-Uele Province of northern Democratic Republic of Congo live in constant danger of attack and/or abduction by units of the Lord's Resistance Army operating in the region. This pilot study sought to develop and evaluate a community-participative psychosocial intervention involving life skills and relaxation training and Mobile Cinema screenings with this war-affected population living under current threat. 159 war-affected children and young people (aged 7-18) from the villages of Kiliwa and Li-May in north-eastern DR Congo took part in this study. In total, 22% of participants had been abduction previously while 73% had a family member abducted. Symptoms of post-traumatic stress reactions, internalising problems, conduct problems and pro-social behaviour were assessed by blinded interviewers at pre- and post-intervention and at 3-month follow-up. Participants were randomised (with an accompanying caregiver) to 8 sessions of a group-based, community-participative, psychosocial intervention (n=79) carried out by supervised local, lay facilitators or a wait-list control group (n=80). Average seminar attendance rates were high: 88% for participants and 84% for caregivers. Drop-out was low: 97% of participants were assessed at post-intervention and 88% at 3 month follow-up. At post-test, participants reported significantly fewer symptoms of post-traumatic stress reactions compared to controls (Cohen's d=0.40). At 3 month follow up, large improvements in internalising symptoms and moderate improvements in pro-social scores were reported, with caregivers noting a moderate to large decline in conduct problems among the young people. Trial Registration clinicalTrials.gov, Identifier: NCT01542398.

PRECYSE: Cyber-attack Detection and Response for Industrial Control Systems

In this short paper, we present an integrated approach to detecting and mitigating cyber-attacks to modern interconnected industrial control systems. One of the primary goals of this approach is that it is cost effective, and thus whenever possible it builds on open-source security technologies and open standards, which are complemented with novel security solutions that address the specific challenges of securing critical infrastructures.