The post-pandemic lecture : views from academic staff across the UK

COVID-19 forced the closure of UK universities. One effect of this was a change in how lectures, and their recordings, were made and used. In this research, we aimed to address two related research questions. Firstly, we aimed to understand how UK universities replaced in-person lectures and, secondly, to establish what academic staff believed the post-pandemic lecture would look like. In a mixed-methods study, we collected anonymous quantitative and qualitative data from 87 academics at 36 UK institutions. Analysis revealed that respondents recognised the value and importance of interactive teaching and indicated that the post-pandemic lecture would and should make greater use of this. Data also revealed positive views of lecture capture, in contrast to pre-pandemic studies, and demonstrated that staff recognised their value for those who were unable to attend, or who had specific learning differences. However, staff also recognised the value of asynchronous lecture videos within a blended or flipped approach. This study provides evidence that the pandemic has engendered changes in attitudes and practices within UK higher education that are conducive to educational reform

Machine learning at the interface of structural health monitoring and non-destructive evaluation

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’

Scaling of the localization length in linear electronic and vibrational systems with long-range correlated disorder

The localization lengths of long-range correlated disordered chains are studied for electronic wavefunctions in the Anderson model and for vibrational states. A scaling theory close to the band edge is developed in the Anderson model and supported by numerical simulations. This scaling theory is mapped onto the vibrational case at small frequencies. It is shown that for small frequencies, unexpectateley the localization length is smaller for correlated than for uncorrelated chains.Comment: to be published in PRB, 4 pages, 2 Figure

Foundations of population-based SHM, Part I : homogeneous populations and forms

In Structural Health Monitoring (SHM), measured data that correspond to an extensive set of operational and damage conditions (for a given structure) are rarely available. One potential solution considers that information might be transferred, in some sense, between similar systems. A population-based approach to SHM looks to both model and transfer this missing information, by considering data collected from groups of similar structures. Specifically, in this work, a framework is proposed to model a population of nominally-identical systems, such that (complete) datasets are only available from a subset of members. The SHM strategy defines a general model, referred to as the population form, which is used to monitor a homogeneous group of systems. First, the framework is demonstrated through applications to a simulated population, with one experimental (test-rig) member; the form is then adapted and applied to signals recorded from an operational wind farm

Probabilistic inference for structural health monitoring: new modes of learning from data

In data-driven structural health monitoring (SHM), the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labeling to describe the measurements is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive while accommodating for missing information in the training data—such that new information can be included if it becomes available. By reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modeling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals, including semisupervised learning, active learning, and multitask learning

Autonomous ultrasonic inspection using bayesian optimisation and robust outlier analysis

The use of robotics is beginning to play a key role in automating the data collection process in Non Destructive Testing (NDT). Increasing the use of automation quickly leads to the gathering of large quantities of data, which makes it inefficient, perhaps even infeasible, for a human to parse the information contained in them. This paper presents a solution to this problem by making the process of NDT data acquisition an autonomous one as opposed to an automatic one. In order to achieve this, the robotic data acquisition task is treated as an optimisation problem, where one seeks to find locations with the highest indication of damage. The resulting algorithm combines damage detection technology from the field of data-driven Structural Health Monitoring (SHM) with novel ideas in uncertainty quantification which enable the optimisation routine to be probabilistic. The algorithm is sequential; a decision is made at every iteration regarding the next optimal physical location for making an observation. This is achieved by modelling a two-dimensional field of novelty indices across a part/structure which is derived from a robust outlier analysis procedure. The value of this autonomous approach is that the output is not only measured data, but the most desirable information from an NDT inspection – the probability that a component contains damage. Furthermore, the algorithm also minimises the number of observations required, thus minimising the time and cost of data gathering

Models of quintessence coupled to the electromagnetic field and the cosmological evolution of alpha

We study the change of the effective fine structure constant in the cosmological models of a scalar field with a non-vanishing coupling to the electromagnetic field. Combining cosmological data and terrestrial observations we place empirical constraints on the size of the possible coupling and explore a large class of models that exhibit tracking behavior. The change of the fine structure constant implied by the quasar absorption spectra together with the requirement of tracking behavior impose a lower bound of the size of this coupling. Furthermore, the transition to the quintessence regime implies a narrow window for this coupling around $10^{-5}$ in units of the inverse Planck mass. We also propose a non-minimal coupling between electromagnetism and quintessence which has the effect of leading only to changes of alpha determined from atomic physics phenomena, but leaving no observable consequences through nuclear physics effects. In doing so we are able to reconcile the claimed cosmological evidence for a changing fine structure constant with the tight constraints emerging from the Oklo natural nuclear reactor.Comment: 13 pages, 10 figures, RevTex, new references adde

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

Summary: 1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data

Susceptibility and dilution effects of the kagome bi-layer geometrically frustrated network. A Ga-NMR study of SrCr_(9p)Ga_(12-9p)O_(19)

We present an extensive gallium NMR study of the geometrically frustrated kagome bi-layer compound SrCr_(9p)Ga_(12-9p)O_(19) (Cr^3+, S=3/2) over a broad Cr-concentration range (.72<p<.95). This allows us to probe locally the kagome bi-layer susceptibility and separate the intrinsic properties due to the geometric frustration from those related to the site dilution. Our major findings are: 1) The intrinsic kagome bi-layer susceptibility exhibits a maximum in temperature at 40-50 K and is robust to a dilution as high as ~20%. The maximum reveals the development of short range antiferromagnetic correlations; 2) At low-T, a highly dynamical state induces a strong wipe-out of the NMR intensity, regardless of dilution; 3) The low-T upturn observed in the macroscopic susceptibility is associated to paramagnetic defects which stem from the dilution of the kagome bi-layer. The low-T analysis of the NMR lineshape suggests that the defect can be associated with a staggered spin-response to the vacancies on the kagome bi-layer. This, altogether with the maximum in the kagome bi-layer susceptibility, is very similar to what is observed in most low-dimensional antiferromagnetic correlated systems; 4) The spin glass-like freezing observed at T_g=2-4 K is not driven by the dilution-induced defects.Comment: 19 pages, 19 figures, revised version resubmitted to PRB Minor modifications: Fig.11 and discussion in Sec.V on the NMR shif

Workgroup emotional intelligence: Scale development and relationship to team process effectiveness and goal focus

Over the last decade, ambitious claims have been made in the management literature about the contribution of emotional intelligence to success and performance. Writers in this genre have predicted that individuals with high emotional intelligence perform better in all aspects of management. This paper outlines the development of a new emotional intelligence measure, the Workgroup Emotional Intelligence Profile, Version 3 (WEIP-3), which was designed specifically to profile the emotional intelligence of individuals in work teams. We applied the scale in a study of the link between emotional intelligence and two measures of team performance: team process effectiveness and team goal focus. The results suggest that the average level of emotional intelligence of team members, as measured by the WEIP-3, is reflected in the initial performance of teams. In our study, low emotional intelligence teams initially performed at a lower level than the high emotional intelligence teams. Over time, however, teams with low average emotional intelligence raised their performance to match that of teams with high emotional intelligence