The vast majority of problems that lie at the forefront of science are governed by mathematical equations that cannot be solved exactly. In the modern era, large-scale numerical computation and data analysis are powerful tools, but many questions still elude brute-force computation. For complex multi-scale and multi-parameter systems, it is often necessary to apply key reductions dependent on the smallness or largeness of certain parameters. The application of these reductions is called asymptotic analysis; these methods have the power to dramatically simplify complex systems to their salient features, extract key mechanisms, and provide details in regions where numerics and experiments fail. As noted by Crighton [1] "[the] design of computational or experimental schemes without the guidance of asymptotic information is wasteful at best, and dangerous at worst, because of the possible failure to identify crucial (stiff) features..." Some of the most challenging problems relate to the prediction of exponentially small effects that are invisible to traditional asymptotic analysis and often mistakenly considered as negligible. In some cases, these effects may correspond to some observable feature, such as an oscillation or wave in the system; in other cases, they may be largely non-observable, but instead serve to determine whether certain solutions are permissible. Over the last few decades, there has been an appreciation for the ubiquity of problems where exponentially-small effects are paradoxically important -- these problems can be found in studies related to dendritic crystal growth, viscous fluid flow, water waves, quantum tunneling, geophysics, and more. There are significant mathematical and computational challenges for the study of exponentially small terms. For example, the traditional mathematical techniques that exist, developed in the early 20th century, are usually insufficient. Exponential asymptotics is the name given to the set of specialised techniques that have been developed over the last two decades for these problems. In the last few years, some of the most significant applications of exponential asymptotics have related to the development of theory for free-surface flows. This includes the study of (i) water waves produced by gravity-driven flows past slow-moving full-bodied ships; (ii) solitary waves in a fluid of finite depth including both gravity and capillary effects; and (iii) viscous flows where bubbles or fingers are produced at an interface. These problems all involve crucial exponentially small effects. Despite the above successes, a significant bottleneck has emerged in numerous studies in the area: the majority of existing exponential asymptotic techniques are limited to ordinary differential equations where, for instance, only a one-dimensional fluid interface is considered. Many of the spectacular successes of exponential asymptotics that have emerged in the last two decades have analogues in higher-dimensional space or in time-dependent formulations, where the system is governed by partial differential equations. However, the standard techniques in exponential asymptotics are not easily adapted to study such situations. The most recent preliminary work on seeking extensions of the theory has shown that the likely avenue for progress lies with combining analytical methods with computational and data-driven approaches---hence a hybrid numerical-asymptotic approach to exponential asymptotics. The development of these methodologies, and the subsequent applications to multi-dimensional problems in fluid mechanics forms the main thrust of this project. [1] Crighton, D. G. (1994). Asymptotics--an indispensable complement to thought, computation and experiment in applied mathematical modelling. In Proc. 7th Eur. Conf. on Math. Industry (ECMI), Montecatini (pp. 3-19).

This project will investigate the drivers of eating behaviour that occur during a prolonged period of overconsumption (excess intake of calories). Overconsumption is important as a major cause of weight (re)gain and obesity. The type of society which exists in many developed countries is said to represent an 'obesigenic' environment. This type of environment facilitates a high consumption of food (as well as encouraging sedentariness) and generates rapid weight gain that leads to obesity. The obesigenic environment 'offers' the possibility for people to overeat. People are able to eat too much of some foods because of excessive activation of hedonic (pleasure related) processes, or because of a defect in homeostatic processes. Firstly this means that people will eat more because of elevated sensations of pleasure during eating or heightened motivation to obtain a looked-for food. These (hedonic) processes are termed 'liking' & 'wanting'. Secondly, people will eat more because their physiological systems fail to shut off eating quickly (leading to large meals) or because food fails to suppress their hunger after eating. These last two processes are called 'satiation' and 'satiety'. The pleasure of eating can be divided into two components /'liking' & 'wanting'. Although these terms often occur together, they are quite different. Sometimes we do not have a strong wanting for foods that we like a lot; at other times we have a strong wanting for foods that are not especially liked (e.g. potatoes/food staples). Importantly, we have developed procedures that measure both the liking & wanting of foods. It is not known if overconsumption results from an increase in liking for certain foods, or from an increase in wanting for those foods. We will identify the types of foods selected during a prolonged period of overeating and whether this is driven to a greater degree by increased liking or wanting. At the same time it is important to be able to measure the actual changes in processes that control meal size (satiation) and which lead to the reduction of hunger after eating (satiety). We will identify which aspect of eating plays the major role in allowing overeating /a large meal size, or weak suppression of hunger. This will inform us how to use specific foods to control these two aspects of eating. It is important to be able to relate changes in sensations and behavior to underlying physiological processes. This means measuring chemicals in the blood that are known to be involved in appetite control. Some of these chemicals are thought to be involved mainly in hunger (ghrelin) or in satiety (GLP1, CCK) or in both hunger/satiety, liking & wanting (leptin). We will therefore assess the particular ways in which these signals influence overconsumption. Generating overconsumption in the long term leads to a gain in weight which may never be lost again and could impair health. We have therefore developed a 'safe' model of overconsumption that has arisen from a BBSRC project just finished. When overweight and obese people volunteer for a 12 week programme of supervised daily exercise (of fixed energy expenditure) some individuals lose weight and others do not. However, independent of weight loss all volunteers show decreases in heart rate, blood pressure, and an increase in fitness (key to becoming healthy). The reason behind this variability in response is that the poor responders who do not lose weight have increased their food intake to negate the energy lost. This increase can be interpreted as overconsumption and amounts to ~290 kcal/day. In absence of exercise this would lead to a dramatic weight increase of more than 6kg over a year. Therefore we can use this 'safe' form of overconsumption to examine changes in underlying behavioral drivers /liking & wanting, satiation and satiety/ and their association with signalling peptides. This provides a relevant long term method for investigating the drivers of food behaviour.

Access to reliable and readily available estimates of the consequence of different land use and management practices on greenhouse gas (GHG) emissions is a prerequisite for successful implementation of land use-based GHG mitigation strategies. Moreover, this information is needed at the level at which management decisions are actually made – at the field scale – and thus information systems must be: 1) easily and universally available, 2) usable by non-experts, 3) employ state-of-the-art technology and 4) be easily aggregated to larger scales. Our overall project aim is to develop and deploy a state-of-the-art system for full greenhouse gas (GHG) accounting, operational at the scale of an individual entity (e.g., farm, livestock operation). The system will be web-based, free and accessible by anyone having an internet connection. Key attributes of the system will include: 1) use of advance methods, including well-validated process-based models that are run in real-time at high spatial resolution, using site-specific data on soil properties, climate and land use and management practices; 2) flexibility, so that users can select, were appropriate, country-specific methods and emission parameters; 3) user-friendly design, making it possible for land managers and others, without specialized knowledge of GHG emission processes to use the system, in their native language; and 4) information on uncertainty, based on robust statistical methods. An important goal of the consortium will be to disseminate and promote the uptake of the COMET-Global system, including engagement and outreach to farmer organizations, environmental groups, governmental agencies and other stakeholders in each of the partner countries, as well as other researchers working on GHG mitigation in the land use sector. The proposed project directly addresses Themes and Topics described in the FACCEJPI Call Announcement, specifically Themes 1 (Improved GHG methodologies) and 2 (Study of mitigation options), with the focus being at the individual farm-scale. It also address Topic 1 (GHG emission from agricultural sources) and Topic 2 (GHG removals), by virtue of providing a full GHG analysis at the farm-scale. Further, the consortium objectives align well with objectives in the Global Research Alliance towards harmonized methods for GHG emission estimation and to activities elsewhere within FACCEJPI (e.g. MACSUR), as well as the national priorities relating to GHG mitigation in each of the partner countries. The system development will leverage an existing comprehensive web-based tool, COMET-Farm, operational in the US. In addition to implementing spatial data (climate, soil, land management) and country-specific emission factors and methods for non-soil GHG emissions, two widely used process-based models, RothC and ECOSSE, will be incorporated along with the DayCent model for estimating soil GHG emissions. The user interface will be provided with multi-lingual capabilities (English, French, Spanish, German and Italian) to provide maximum convenience on the part of a multinational user community.

This fellowship will develop a new generation of a real-time model based control framework required for engineers to manage and control the real-time operations of a heterogeneous intelligent traffic system through Active Traffic Management (ATM) programs. In general, an ATM program, also known as managed lanes or smart lanes, is a scheme for improving traffic flow and reducing congestion on motorways. It makes use of automatic systems and human intervention to manage traffic flow and ensure the safety of road users. Information and communication technologies (ICT) have transformed many aspects of business, society and government, from healthcare to education and the economy. ICT are now in the early stages of transforming transportation systems by integrating sensors (remote sensing and positioning), control units (traffic signals, message signs) and automatic technologies with microchips to enable them to communicate with each other through wireless technologies. In many developed countries, particularly Japan and South Korea, the deployment of ICT in ATM programs has led to significant improvement of traffic network performance such as reduced congestion, increased traffic safety, enhanced environmental quality (e.g. reduced CO2) and a more reliable service to the road user. It is expected that in the coming 5 to 10 years ICT will considerably progress worldwide so that intelligent equipped vehicles, in which the driving tasks are shifted from the driver to the vehicle through autonomous vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, will make up a significant share of the traffic flow. In V2V communication, the leading equipped vehicle will issue information of its current speed, driving manoeuvre (e.g. acceleration or deceleration), etc. to further upstream vehicles while in V2I communication, the equipped vehicle will exchange information with roadside intelligent devices and receive commands from such devices for consequent driving activities. A considerable proportion of intelligent vehicles in traffic flow will create intelligent traffic networks containing a mixed composition of non-equipped (or manual) and equipped vehicles. Such traffic flow system is defined as a heterogeneous intelligent traffic system. This proposal will seek solutions for an improved ATM program to monitor and control more efficiently intelligent traffic networks. In principle, the traffic control problem for heterogeneous intelligent traffic networks is highly complex, which is characterized by the interactions between non-equipped vehicles and various types of equipped vehicles and by the interaction between equipped vehicles and the roadside intelligent devices, as well as by the interplay between different control strategies for different types of vehicles. The proposed research will tackle such complex issues and bring in a new real-time model-based intelligent traffic control framework using real-life data collected from multiple sources (loop detectors, remote sensing, mobile phones, floating cars, etc. ). The new model will predict in the short term the traffic congestion patterns (i.e. the transitions between free-flow, congestion or stop-and-go jams) and investigate the true causes of such congestion which occurs in a heterogeneous intelligent traffic network. Based on the traffic states predicted from the real-time model, a sequence of immediate control actions will be established for different types of vehicles (equipped and non-equipped) in order to reduce congestion, travel time and air pollution.