Child protection in the UK relies heavily on risk prediction, an area of growing interest in the UK since the late 1980s (Browne & Saqi 1988, Creighton 1992). It is generally taken as an axiom that child abuse can and should be detected via risk prediction to identify vulnerable and risky families whose children may become abused or neglected. The purpose of identifying such families at an early stage is to target early intervention towards them to reduce the risk of abuse. To service this need, individual local authorities commission algorithmic risk prediction systems from profit making providers. The question this proposed project addresses is whether such systems are 'fit for purpose' given the concerning longitudinal data showing poor accuracy in child protection outcomes and an unacceptably high number of false positives and false negatives in risk prediction. This concern was recently highlighted by the President of the Family Division (Munby 2016). This proposed project addresses the issue by looking at the possibilities for a new method of predicting risk in a more realistic way that provides a better means for child protection systems to be supported by them, rather than have to work potentially inaccurate data. It sets out a new and transformative means of collating, assessing and extracting consistent information from previous studies and testing them in a consistent and reliable way. The potential exists for scoping a new system which moves algorithmic risk prediction into new territory; existing systems do not 'learn' from these errors so the technology stalls at the stage of algorithmic prediction rather than developing into evidenced-based, reliable and responsive artificial intelligence (AI). The key research questions/objectives are: - What is a normalised confidence limit(s) in existing risk prediction studies in child protection; - To develop a new method of calculating risk, and design for its application in child protection; - To assess the possibility of designing a model for a new, GDPR-compliant, AI model of risk prediction suitable for use in pre- and post-proceedings child protection work. This study's methodology is transformative, bringing together a mix of traditional and pioneering methods. Each stage of the methodology has been assessed for the level of potential transformation in either its approach and/or outcome. The team will start the proposed project by creating the first, comprehensive and re-usable database of previous relevant studies. The creative and new methods employed by the rest of the study is higher risk, but if successful will yield a correspondingly high reward. Having created the database of studies, the team will analyse their characteristics, size, scope and methods to apply a consistent means of calculating their power ratio, creating a comparative analysis including strengths, weaknesses and confidence limits. These results will be analysed using Bayesian statistics in the context of Eggleston's work in respect of the use of probability in fact finding processes (Eggleston 1983). Bayesian networks provide a novel means of establishing criteria for weighting of evidence for social and technical problems including reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, finding explanations for datastreams, and helping systems to analyse processes over time. Used in this context, we will provide a consistent measure of confidence across risk-factors and measure of their evidential probity. This core transformative element of our methods will enable scoping of a risk prediction system to take account of strengths and weaknesses, including identifying gaps, providing a reliable legal indicator to courts as to the appropriate weighting as a project outcome.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Converting biomass waste to bio-products will simultaneously provide a route to waste-disposal, and a process for the production of useful, economically attractive products. Within all the products derived from biomass waste, liquid hydrocarbon transport fuels are promising for the UK to meet its 2020 renewable energy target of providing 10% of its transport fuel from renewable sources. They will help to tackle the challenges of climate change and the ever-increasing fuel demand. The current waste-to-liquid technologies, however, are facing main problems of high production cost and technical uncertainty. To address these problems, we will develop a breakthrough technology in this project. This novel technology will co-produce liquid transport bio-fuel and one value-added bio-chemical. By doing this, high economic profits will be expected when comparing with conventional liquid bio-fuel plants. The co-production system will additionally benefit to the reduction of the biofuel's high oxygen content, which is known as the main source that leads to poor stability, immiscibility and low calorific value of the produced fuel. The integrated production system will be designed and evaluated within this project, with the involvement of three universities (Queen's University Belfast-QUB, Aston University-AU, and North China Electric Power University-NCEPU), three academics, one PDRA, and two PhDs (one is funded by QUB, the other is funded by NCEPU). The project is also highly industrial geared by directly involvement of two UK-based companies: Hirwaun Energy Ltd, who will provide a pilot scale biomass pyrolysis reactor for results validation, and Green Lizard Technologies Ltd, who will provide suggestions on the technology scale-up. Through the development of this innovative technology, high national impact will be realised to achieve the UK's 2020 Renewable Energy targets through the conversion of over 16 million tonnes per year of the UK's lignocellulosic biomass into advanced fuel together with value-added co-products. It will also have a positive impact on the UK's target of reducing carbon dioxide emissions and increasing the use of renewable materials.

POLYmer-COntrolled Mesocrystal Production (POLYCOMP) aims to develop an intimate understanding of the underlying mechanisms of mesocrystal formation. This in turn will lead to the development of new mesocrystals with controlled morphologies and thus optimised properties. Mesocrystals have only very recently been described and are best viewed as an entirely new class of material. As such these unique substances have the potential to revolutionise materials/devices containing inorganic components. Applications are myriad and include building materials, such as concrete, with vastly greater compression strengths (in theory at least, the heights of concrete buildings could be increased from 500m to 15km!), solar cells with far higher solar harvesting efficiencies, new biomimetic materials, e.g. for use in joint replacement procedures, and electronic devices where size-dependent nanoparticle-like properties, e.g. superparamagnetism, are retained in macroscopic-sized materials enabling easier manufacture of components such as computer memory, quantum dot-based LEDs, etc. Currently approaches to mesocrystal formation are somewhat ad hoc and these kinds of application remain largely unachievable. The principle underlying reason for this is that mesocrystal formation processes are often still too poorly understood. POLYCOMP will remove this bottleneck to mesocrystal exploitation by focusing directly on developing a generic understanding of mesocrystal formation processes. Such an approach is thus clearly directly relevant to the EU’s mission to advance knowledge and technology in areas such as construction, electronics and energy.

In the literature, there are two groups of research contributing to the adoption of RFID. One is related to the technical aspects, such as enhanced security tags, increased tracking range, and authentication protocols. The other is associated with the applications, which provide greater contribution to potential adoption. This area of research explores RFID applications in manufacturing, inbound/outbound logistics, warehousing, and many more. However, a crucial aspect of research which is not currently being investigated is the exploration of extensive use of real-time RFID data to improve and add substantial values to the business operations, e.g. optimising distribution routes/network responding to dynamic changing environment. This area of research is vital as it will result in greater operational costs to be reduced and system efficiency to be enhanced, and leads to a more promising investment to achieving higher returns. This area of investigation forms the key aim of this proposal and contributes to the justification required for decisions for RFID adoption. The findings of the proposed research will also highlight to the RFID implementers that the technology is not merely for track and trace purposes, but is used as a way to achieve improved economic competitiveness. This research proposal is to investigate how distribution network in outbound logistics can be efficiently modelled, reconfigured, integrated, and optimised dynamically in response to changes in the market, the production and at any stage in the supply chain. To facilitate this research work, it is proposed to adopt the concept of multi-agent systems and intelligently integrate with RFID technology. The central part of this research is to develop a dynamic integrated agent-based control system to enable distribution routes to be dynamically modelled. In response to changes, alternative route configurations can be generated and evaluated by the optimisation strategy/methodology developed (i.e. operational level optimisation). In addition, a global level optimisation across the supply chain sectors will also take place simultaneously to ensure a smooth, efficient flow of operations in the supply chain. This will enhance the responsiveness of the supply chain operations coping with dynamic changes efficiently and cost-effectively. With the optimised distribution network it will help to reduce CO2 emissions and as a result, promoting greener supply chain to support the UK government's Carbon Reduction Strategy for Transport (introduced in July 2009). Two industrial partners (Carton Edge and ATMS), the Centre of RF Applications and Testing at University of Hong Kong Science and Technology, and the RFID vendor IdentifyRFID, each with its expertise and interests in RFID, will support this research. These partners will play a significant role in this project, contributing to the project trials and output dissemination. They are able to provide resources to support the development and validate the methodology proposed. The end product of this research will then be used to assess the company's operations and they will receive a thorough assessment of the operational performance, as well as an evaluation of ways to maximise flexibility, agility, and efficiency of the operations to achieve economic competitiveness. The research will also identify how RFID-enabled operations can help them to add value to their customer services. ATMS, which develops RFID-enabled logistics software, has shown interests in exploiting the potential take-up of the end product of this research to a commercial exposure. Last but not least, invitations for workshops and academic visits to other research centres/institutions will play an important role for raising research awareness and for research collaboration. It is envisaged that the strengthened connection through this research will serve as a strong ground for partnership in the future research grants.