Online promotion of research expertise self assessment

Helps institutions identify if their use of online channels in communicating information about the expertise of researchers within their organisation meets the needs of both business and wider communities, as well as the researchers themselves.

Online communication and promotion of research expertise report

Examining how universities use online channels to promote their research expertise and making recommendations for how institutions can improve this.

Presenting research expertise information online: a guide for higher education institutions

Helping HEIs plan the use of online channels to communicate information about the expertise of researchers within their institution so that it meets the needs of business and community users, as well researchers themselves

Promotion of research expertise online project report

An output from the online promotion of research expertise project:

Promotion of research expertise online: the self-assessment tool

Helping HEIs identify whether the way they use online channels to communicate information about the expertise of researchers within their institution meets the needs of: – Business and community users – Researchers themselves.

Case study: Engaging students and staff in online activities

Case study about how East Berkshire College is engaging student and staff in online activities to develop digital literacy skills.

Online Learning Readiness Tool Guide

A user guide for the Online Learning Readiness Tool, developed as part of the Jisc Scaling up online learning project.

Poster: Outputs from the Manchester Metropolitan University online distance learning think tank event

Poster from the overview of, and the emergent themes from, the Manchester Metropolitan University distance learning think tank event which took place on 10th July 2014 and aimed to provide a forum to explore and discuss some of the key issues for colleagues across the university involved in planning, setting-up and delivering such programmes at MMU.

Exploring your online behaviour

Personal e-safety audit

Online exercise for the design and simulation of PCR and PCR-RFLP experiments

[EN] Background: Polymerase Chain Reaction (PCR) and Restriction Fragment Length Polymorphism of PCR products (PCR-RFLP) are extensively used molecular biology techniques. An exercise for the design and simulation of PCR and PCR-RFLP experiments will be a useful educational tool. Findings: An online PCR and PCR-RFLP exercise has been create that requires users to find the target genes,compare them, design primers, search for restriction endonucleases, and finally to simulate the experiment. Each user of the service is randomly assigned a gene from Escherichia coli; to complete the exercise, users must design an experiment capable of distinguishing among E. coli strains. By applying the experimental procedure to all completely sequenced E. coli, a basic understanding of strain comparison and clustering can also be acquired. Comparison of results obtained in different experiments is also very instructive. Conclusions: The exercise is freely available at http://insilico.ehu.es/edu.

Learning and using taxonomies for visual and olfactory classification

Humans are able of distinguishing more than 5000 visual categories even in complex environments using a variety of different visual systems all working in tandem. We seem to be capable of distinguishing thousands of different odors as well. In the machine learning community, many commonly used multi-class classifiers do not scale well to such large numbers of categories. This thesis demonstrates a method of automatically creating application-specific taxonomies to aid in scaling classification algorithms to more than 100 cate- gories using both visual and olfactory data. The visual data consists of images collected online and pollen slides scanned under a microscope. The olfactory data was acquired by constructing a small portable sniffing apparatus which draws air over 10 carbon black polymer composite sensors. We investigate performance when classifying 256 visual categories, 8 or more species of pollen and 130 olfactory categories sampled from common household items and a standardized scratch-and-sniff test. Taxonomies are employed in a divide-and-conquer classification framework which improves classification time while allowing the end user to trade performance for specificity as needed. Before classification can even take place, the pollen counter and electronic nose must filter out a high volume of background “clutter” to detect the categories of interest. In the case of pollen this is done with an efficient cascade of classifiers that rule out most non-pollen before invoking slower multi-class classifiers. In the case of the electronic nose, much of the extraneous noise encountered in outdoor environments can be filtered using a sniffing strategy which preferentially samples the visensor response at frequencies that are relatively immune to background contributions from ambient water vapor. This combination of efficient background rejection with scalable classification algorithms is tested in detail for three separate projects: 1) the Caltech-256 Image Dataset, 2) the Caltech Automated Pollen Identification and Counting System (CAPICS) and 3) a portable electronic nose specially constructed for outdoor use.

Future of earthquake early warning : quantifying uncertainty and making fast automated decisions for applications

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications.

Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake.

To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.