A Northumbria University physicist has been awarded more than half a million pounds to develop artificial intelligence which will protect the Earth from devastating space storms. Activity from the Sun such as solar eruptions, known as Coronal Mass Ejections, results in plasma being fired towards Earth at supersonic speeds, which can result in serious disruption to power and communication systems. With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011. Northumbria's Dr Andy Smith has recently been awarded a Research Fellowship from the Natural Environment Research Council (NERC) to explore how physics-inspired machine learning could be used to forecast space weather more accurately and predict serious space storms. During the Next Generation, Physics-Inspired AI for Space Weather Forecasting project, Dr Smith and his team will analyse huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

A feasibility study investigating how intelligence-based technologies can be used to connect predictive maintenance software to stock management software has received over £250,000 from UKRI's Industrial Strategy Challenge Fund (ISCF) 'Made Smarter innovation challenge' to evaluate emerging smart technologies. The collaborative research project driven by supply chain management company The NBT Group, Northumbria University and industrial software developers Senseye will provide outcomes and knowledge for what could be'a game-changer' in the Industry 4.0 journey – by increasing productivity, up-skilling work roles and effecting more economically, socially and environmentally sustainability. Toby Bridges, Executive Chair at The NBT Group, said: "NBT's ambition is to utilise automation and Industry 4.0 thinking in all its activities, generating more and better jobs within our business and drive operating efficiencies for our clients and suppliers," said Toby Bridges, Executive Chair at The NBT Group. "This funding allows us to work closely with a leading predicative maintenance company in Senseye and Northumbria University's Global Operations and Supply Chain Competitiveness (GLOPSCO) research interest group to widen our thinking on how we integrate our own supply chain technologies into other systems and technologies across the manufacturing plant." Initially the study will assess the use of intelligence – based systems to connect NBT Group's stock management software to Senseye's predictive maintenance software - a platform that allows prediction of upcoming issues with machinery so that failures can be avoided.

The capability to perform facial analysis from video sequences has significant potential to positively impact in many areas of life. One such area relates to the medical domain to specifically aid in the diagnosis and rehabilitation of patients with facial palsy. With this application in mind, this paper presents an end-to-end framework, named 3DPalsyNet, for the tasks of mouth motion recognition and facial palsy grading. 3DPalsyNet utilizes a 3D CNN architecture with a ResNet backbone for the prediction of these dynamic tasks. Leveraging transfer learning from a 3D CNNs pre-trained on the Kinetics data set for general action recognition, the model is modified to apply joint supervised learning using center and softmax loss concepts. 3DPalsyNet is evaluated on a test set consisting of individuals with varying ranges of facial palsy and mouth motions and the results have shown an attractive level of classification accuracy in these task of 82% and 86% respectively. The frame duration and the loss function affect was studied in terms of the predictive qualities of the proposed 3DPalsyNet, where it was found shorter frame duration's of 8 performed best for this specific task. Centre loss and softmax have shown improvements in spatio-temporal feature learning than softmax loss alone, this is in agreement with earlier work involving the spatial domain.

It's a question that has plagued humanity through the ages: What moves make all the difference when you hit the dance floor? Researchers have found that the sexiest dancers move their hips a lot, and shift their arms and thighs independently of one another. Scientists suggest that there could be evolutionary benefits to the dance moves we enjoy most. For example, they suggest that hip swing may be an explicitly feminine trait that shows fertility and youth. The ability to move limbs independently of one another could reveal good motor control, and hence healthy genes for procreation.