Security threats in network coding-enabled mobile small cells

The recent explosive growth of mobile data traffic, the continuously growing demand for higher data rates, and the steadily increasing pressure for higher mobility have led to the fifth-generation mobile networks. To this end, network-coding (NC)-enabled mobile small cells are considered as a promising 5G technology to cover the urban landscape by being set up on-demand at any place, and at any time on any device. In particular, this emerging paradigm has the potential to provide significant benefits to mobile networks as it can decrease packet transmission in wireless multicast, provide network capacity improvement, and achieve robustness to packet losses with low energy consumption. However, despite these significant advantages, NC-enabled mobile small cells are vulnerable to various types of attacks due to the inherent vulnerabilities of NC. Therefore, in this paper, we provide a categorization of potential security attacks in NC-enabled mobile small cells. Particularly, our focus is on the identification and categorization of the main potential security attacks on a scenario architecture of the ongoing EU funded H2020-MSCA project “SECRET” being focused on secure network coding-enabled mobile small cells

Operating theatre related syncope in medical students: a cross sectional study

<p>Abstract</p> <p>Background</p> <p>Observing surgical procedures is a beneficial educational experience for medical students during their surgical placements. Anecdotal evidence suggests that operating theatre related syncope may have detrimental effects on students' views of this. Our study examines the frequency and causes of such syncope, together with effects on career intentions, and practical steps to avoid its occurrence.</p> <p>Methods</p> <p>All penultimate and final year students at a large UK medical school were surveyed using the University IT system supplemented by personal approach. A 20-item anonymous questionnaire was distributed and results were analysed using the Statistical Package for Social Sciences, version 15.0 (Chicago, Illinois, USA).</p> <p>Results</p> <p>Of the 630 clinical students surveyed, 77 responded with details of at least one near or actual operating theatre syncope (12%). A statistically significant gender difference existed for syncopal/near-syncopal episodes (male 12%; female 88%), p < 0.05. Twenty-two percent of those affected were graduate entry medical course students with the remaining 78% undergraduate. Mean age was 23-years (range 20 – 45). Of the 77 reactors, 44 (57%) reported an intention to pursue a surgical career. Of this group, 7 (9%) reported being discouraged by syncopal episodes in the operating theatre. The most prevalent contributory factors were reported as hot temperature (n = 61, 79%), prolonged standing (n = 56, 73%), wearing a surgical mask (n = 36, 47%) and the smell of diathermy (n = 18, 23%). The most frequently reported measures that students found helpful in reducing the occurrence of syncopal episodes were eating and drinking prior to attending theatre (n = 47, 61%), and moving their legs whilst standing (n = 14, 18%).</p> <p>Conclusion</p> <p>Our study shows that operating theatre related syncope among medical students is common, and we establish useful risk factors and practical steps that have been used to prevent its occurrence. Our study also highlights the detrimental effect of this on the career intentions of medical students interested in surgery. Based on these findings, we recommend that dedicated time should be set aside in surgical teaching to address this issue prior to students attending the operating theatre.</p

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

Background: Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. Methods: In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes. Results: Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI. Conclusion: This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making

Motion Planning for Continuous-Time Stochastic Processes: A Dynamic Programming Approach

We study stochasticmotion planning problems which involve a controlled process, with possibly discontinuous sample paths, visiting certain subsets of the state-space while avoiding others in a sequential fashion. For this purpose, we first introduce two basic notions of motion planning, and then establish a connection to a class of stochastic optimal control problems concerned with sequential stopping times. A weak dynamic programming principle (DPP) is then proposed, which characterizes the set of initial states that admit a control enabling the process to execute the desired maneuver with probability no less than some pre-specified value. The proposed DPP comprises auxiliary value functions defined in terms of discontinuous payoff functions. A concrete instance of the use of this novel DPP in the case of diffusion processes is also presented. In this case, we establish that the aforementioned set of initial states can be characterized as the level set of a discontinuous viscosity solution to a sequence of partial differential equations, for which the first one has a known boundary condition, while the boundary conditions of the subsequent ones are determined by the solutions to the preceding steps. Finally, the generality and flexibility of the theoretical results are illustrated on an example involving biological switches

A decomposition method for large scale MILPs, with performance guarantees and a power system application

All rights reserved. Lagrangian duality in mixed integer optimization is a useful framework for problem decomposition and for producing tight lower bounds to the optimal objective. However, in contrast to the convex case, it is generally unable to produce optimal solutions directly. In fact, solutions recovered from the dual may not only be suboptimal, but even infeasible. In this paper we concentrate on large scale mixed-integer programs with a specific structure that appears in a variety of application domains such as power systems and supply chain management. We propose a solution method for these structures, in which the primal problem is modified in a certain way, guaranteeing that the solutions produced by the corresponding dual are feasible for the original unmodified primal problem. The modification is simple to implement and the method is amenable to distributed computation. We also demonstrate that the quality of the solutions recovered using our procedure improves as the problem size increases, making it particularly useful for large scale problem instances for which commercial solvers are inadequate. We illustrate the efficacy of our method with extensive experimentations on a problem stemming from power systems

A decomposition method for large scale MILPs, with performance guarantees and a power system application

All rights reserved. Lagrangian duality in mixed integer optimization is a useful framework for problem decomposition and for producing tight lower bounds to the optimal objective. However, in contrast to the convex case, it is generally unable to produce optimal solutions directly. In fact, solutions recovered from the dual may not only be suboptimal, but even infeasible. In this paper we concentrate on large scale mixed-integer programs with a specific structure that appears in a variety of application domains such as power systems and supply chain management. We propose a solution method for these structures, in which the primal problem is modified in a certain way, guaranteeing that the solutions produced by the corresponding dual are feasible for the original unmodified primal problem. The modification is simple to implement and the method is amenable to distributed computation. We also demonstrate that the quality of the solutions recovered using our procedure improves as the problem size increases, making it particularly useful for large scale problem instances for which commercial solvers are inadequate. We illustrate the efficacy of our method with extensive experimentations on a problem stemming from power systems