Investigation of squeeze-film dampers

Squeeze film dampers are a means of curing instabilities in rotating shaft assemblies. Their efficiency depends very much on the condition of the oil, which in turn depends on inlet and outlet arrangements, on damper geometry and on the flexibility of the rotor and surrounding structure. Rig investigations in which structural flexibility is included experimentally are discussed. Comparisons are made between measured and predicted results

Modelling Interdependent Cascading Failures in Real World Complex Networks using a Functional Dependency Model

Infrastructure systems are becoming increasingly complex and interdependent. As a result our ability to predict the likelihood of large-scale failure of these systems has significantly diminished and the consequence of this is that we now have a greatly increased risk of devastating impacts to society. Traditionally these systems have been analysed using physically-based models. However, this approach can only provide information for a specific network and is limited by the number of scenarios that can be tested. In an attempt to overcome this shortcoming, many studies have used network graph theory to provide an alternative analysis approach. This approach has tended to consider infrastructure systems in isolation, but has recently considered the analysis of interdependent networks through combination with percolation theory. However, these studies have focused on the analysis of synthetic networks and tend to only consider the topology of the system. In this paper we develop a new analysis approach, based upon network theory, but accounting for the hierarchical structure and functional dependency observed in real world infrastructure networks. We apply this method to two real world networks, to show that it can be used to quantify the impact that failures within an electricity network have upon a dependent water network

Confirming what we know: Understanding questionable research practices in intro physics labs

Many institutions are changing the focus of their introductory physics labs from verifying physics content towards teaching students about the skills and nature of science. As instruction shifts, so too will the ways students approach and behave in the labs. In this study, we evaluated students' lab notes from an early activity in an experimentation-focused lab course. We found that about 30% of student groups (out of 107 groups at three institutions) recorded questionable research practices in their lab notes, such as subjective interpretations of results or manipulating equipment and data. The large majority of these practices were associated with confirmatory goals, which we suspect stem from students' prior exposure to verification labs. We propose ways for experimentation-focused labs to better engage students in the responsible conduct of research and authentic scientific practice.Comment: 4 pages, 4 figure

Encrypted statistical machine learning: new privacy preserving methods

We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and na\"{i}ve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.Comment: 39 page