Strategy=======The overall aim of the Cambridge EDC is to improve the effectiveness and efficiency of engineering designers and design teams by undertaking research into the theories that will underpin the design methods of the future. These methods will be embodied in software tools, workbooks and publications that support the creation of reliable, high-quality, cost-effective products.Research Themes==============The EDC's is structured under the following research Themes: * Healthcare Design: Design for Patient Safety * Inclusive Design: Designing for the Older and Disabled Users (1) * Process Modelling: Modelling the Design Process * Change Management: Tracking Changes in Products * Design Practice: Understanding Practice * Engineering Knowledge: Capture, Storage and Retrival (1) * Computational Design: Integrated Optimisation Methods and Tools Note (1) These Themes receive zero or minimal support from the IMRC Block Grant.

In developed countries such as the UK, we spend 90% of our time indoors with approximately two thirds of this in our homes. Despite this fact, most air pollutant regulation focuses on the outdoor environment. There is increasing evidence that exposure to air pollution causes a range of health effects, but uncertainties on the causal effects of individual pollutants on specific health outcomes still exist partly due to crude exposure metrics. Nearly all studies of health effects to date have used measurements from fixed outdoor air pollution monitoring networks, a procedure that ignores the modification effects of indoor microenvironments where people spend most of their time. There are consequently large uncertainties surrounding human exposure to indoor air pollution, which means we are currently unable to identify the most effective solutions to design, operate and use our homes to minimise our exposure to air pollution within them. In the UK, there are virtually no data to quantify indoor air pollutant emissions, building-to-building variability of these, chemical speciation of indoor pollutants, ingress of outdoor pollution indoors or of indoor generated pollutants outdoors, or the social, economic or lifestyle factors that can lead to elevated pollutant exposures. Without a fundamental understanding of how indoor air pollution is caused, transformed and distributed in UK homes, research aiming to develop behavioural, technical or policy interventions may have little impact, or at worst be counterproductive. For example, energy efficiency measures are broadly designed to make buildings more airtight. However, given that the concentrations of many air pollutants are often higher indoors than outdoors, reducing ventilation rates may increase our exposure to air pollution indoors and to any potentially harmful effects of the resulting pollutant mixture. Further, if interventions are introduced without sufficient consideration of how occupants actually use and behave in a building, they may fail to achieve the desired effect. To understand and improve indoor air quality (IAQ), we must adopt a systems approach that considers both the home and the human. There is a particular paucity of data for the most deprived households in the UK. There is a facile assumption that poorer homes are likely to experience worse IAQ than better off households, although the reality may be considerably more nuanced. Lower quality housing may be leakier than more expensive homes allowing indoor emissions to escape more easily, whilst large, expensive town-houses converted to flats can be badly ventilated following poor retrofitting practices. Differences in cooking practices, smoking rates, internal building materials and the usage of solvent containing products indoors will also be subject to wide variations across populations and hence have differential effects on IAQ and pollutant exposure. In fact, differences in individual behaviour lead to large variations in indoor concentrations of air pollutants even for identical houses, typically driven by the frequency and diversity of personal care product use. The INGENIOUS project will provide a comprehensive understanding of indoor pollution in UK homes, including i) the key sources relevant to the UK ii) the variability between homes in an ethnically diverse urban city, with a focus on deprived areas (using the ongoing Born in Bradford cohort study) iii) the effects of pollutant transformation indoors to generate by-products that may adversely affect health iv) the drivers of behaviours that impact on indoor air pollution (v) recommendations for interventions to improve IAQ that we have co-designed and tested with community members.

SECURE is a network of statisticians, modellers and environmental scientists and our aim is to grow a shared vision of how to describe and quantify environmental change to assist in decision making. Understanding and forecasting environmental changes are crucial to the development of strategies to mitigate against the impacts of future events. Communications and decision making around environmental change are sometimes troubled by issues concerning the weight of evidence, the nature and size of uncertainties and how both are described. Evidence for environmental change comes from a number of sources, but key to this proposal is the optimal use of data (from observational, regulatory monitoring and earth observations platforms such as satellites and mobile sensors) and models (process and statistical). A robust and reliable evidence base is key in the decision making process, informed by powerful statistical models and the best data. This proposal will deliver the statistical tools to support decision making. Many environmental challenges related to change require statistical modelling and inferential tools to be developed to understand the drivers and system responses which may be direct or indirect and linked by feedback and lags. The character of environmental data is changing as new technologies (e.g. sensor networks offering high resolution data streams) are developed and become more widely accessible. Emerging sensor technology is able to deliver enhanced dynamic detail of environmental systems at unprecedented scale and . There is also an increasing public engagement with environmental science, through citizen science. Increasing use of citizen science observatories will present new statistical challenges, since the sampling basis of such observations will most likely be preferential and not directed, be of varying quality and collected with different effort. Fusion of the different streams of data will be challenging but essential in terms of informing society and regulators alike about change. Linkage of the different data sources, and the challenges of dealing with big data, in the environmental sphere lie in drawing together diverse, high-throughput data sources, analysing, aggregating and integrating the signals with models and then ultimately using the data-model system to address complex and shifting environmental change issues in support of decision making. Key to success lies in generating digestible outputs which can be disseminated and critiqued across academia, policy-makers and other stakeholders. In climate change, food security, ecosystem resilience, sustainable resource use, hazard warning and disaster management there are new high-volume data sources, including crowd sourced streams, which present problems and untapped opportunities around data management, synthesis, communication and real-time decision-support. Our research will involve: improving modelling and communication tools concerning uncertainty and variability, which are ubiquitous in many environmental data sources; developing and extending modelling capabilities to deal with multi-scale issues, specifically integrating over the different spatial and temporal scales of the data streams, and the derived timescales of model outputs; exploring the power and limitations of recent statistical innovations applied to environmental change issues and finally reflecting on new technologies for visualisation and communication.

Smart environments are designed to react intelligently to the needs of those who visit, live and work in them. For example, the lights can come on when it gets dark in a living room or a video exhibit can play in the correct language when a museum visitor approaches it. However, we lack intuitive ways for users without technical backgrounds to understand and reconfigure the behaviours of such environments, and there is considerable public mistrust of automated environments. Whilst there are tools that let users view and change the rules defining smart environment behaviours without having programming knowledge, they have not seen wide uptake beyond technology enthusiasts. One drawback of existing tools is that they pull attention away from the environment in question, requiring users to translate from real world objects to abstract screen-based representations of them. New programming tools that allow users to harness their understandings of and references to objects in the real world could greatly increase trust and uptake of smart environments. This research will investigate how users understand and describe smart environment behaviours whilst in situ, and use the findings to develop more intuitive programming tools. For example, a tool could let someone simply say that they want a lamp to come on when it gets dark, and point at it to identify it. Speech interfaces are now widely used in intelligent personal assistants, but the functionality is largely limited to issuing immediate commands or setting simple reminders. In reality, there are many challenges with using speech interfaces for programming tasks, and idealised interactions such as the lamp example are not at all simple, in reality. In many cases, research used to design programming interfaces for everyday users is carried out in research labs rather than in the real home or workplace settings, and the people invited to take part in design and evaluation studies are often university students or staff, or people with an existing interest or background in technology. These interfaces often fall down once taken away from the small set of toy usage scenarios in which they have been designed and tested and given to everyday users. This research investigates the challenges with using speech for programming, and evaluates ways to mitigate these challenges, including conversational prompts, use of gesture and proximity data to avoid ambiguity, and providing default behaviours that can be customised. In this project, we focus primarily on smart home scenarios, and we will carry out our studies in real domestic settings. Speech interfaces are increasingly being used in these scenarios, but there is no support for querying, debugging and alternating the behaviours through speech. We will recruit participants with no programming background, including older and disabled users, who are often highlighted as people who could benefit from smart home technology, but rarely included in studies of this sort. We will carry out interviews in people's homes to understand how they naturally describe rules for smart environments, taking into account speech, gesture and location. We will look for any errors or unclear elements in the rules they describe, and investigate how far prompts from researchers can help them to be able to express the rules clearly. We will also explore how far participants can customise default behaviours presented to them. This data will be used to allow us to create a conversational interface that harnesses the approaches that worked with human prompts, and test it in real world settings. Some elements of the system will be controlled by a human researcher, but the system will simulate the experience of interacting with an intelligent conversational interface. This will allow us to identify fruitful areas to pursue in developing fully functional conversational programming tools, which may also be useful in museums, education, agriculture and robotics.

This world-leading Centre for Doctoral Training in Bioenergy will focus on delivering the people to realise the potential of biomass to provide secure, affordable and sustainable low carbon energy in the UK and internationally. Sustainably-sourced bioenergy has the potential to make a major contribution to low carbon pathways in the UK and globally, contributing to the UK's goal of reducing its greenhouse gas emissions by 80% by 2050 and the international mitigation target of a maximum 2 degrees Celsius temperature rise. Bioenergy can make a significant contribution to all three energy sectors: electricity, heat and transport, but faces challenges concerning technical performance, cost effectiveness, ensuring that it is sustainably produced and does not adversely impact food security and biodiversity. Bioenergy can also contribute to social and economic development in developing countries, by providing access to modern energy services and creating job opportunities both directly and in the broader economy. Many of the challenges associated with realising the potential of bioenergy have engineering and physical sciences at their core, but transcend traditional discipline boundaries within and beyond engineering. This requires an effective whole systems research training response and given the depth and breadth of the bioenergy challenge, only a CDT will deliver the necessary level of integration. Thus, the graduates from the CDT in Bioenergy will be equipped with the tools and skills to make intelligent and informed, responsible choices about the implementation of bioenergy, and the growing range of social and economic concerns. There is projected to be a large absorptive capacity for trained individuals in bioenergy, far exceeding current supply. A recent report concerning UK job creation in bioenergy sectors concluded that there "may be somewhere in the region of 35-50,000 UK jobs in bioenergy by 2020" (NNFCC report for DECC, 2012). This concerned job creation in electricity production, heat, and anaerobic digestion (AD) applications of biomass. The majority of jobs are expected to be technical, primarily in the engineering and construction sectors during the building and operation of new bioenergy facilities. To help develop and realise the potential of this sector, the CDT will build strategically on our research foundation to deliver world-class doctoral training, based around key areas: [1] Feedstocks, pre-processing and safety; [2] Conversion; [3] Utilisation, emissions and impact; [4] Sustainability and Whole systems. Theme 1 will link feedstocks to conversion options, and Themes 2 and 3 include the core underpinning science and engineering research, together with innovation and application. Theme 4 will underpin this with a thorough understanding of the whole energy system including sustainability, social, economic public and political issues, drawing on world-leading research centres at Leeds. The unique training provision proposed, together with the multidisciplinary supervisory team will ensure that students are equipped to become future leaders, and responsible innovators in the bioenergy sector.