1,475 Projects, page 1 of 295
Immunities, jurisdictional gaps and a lack of dispute resolution mechanisms result in the UN and its personnel almost never being held accountable for criminal actions. One of the most egregious of those crimes is sexual violence perpetrated by personnel within peacekeeping operations. Horrifying cases of such crimes - committed by individuals deployed to protect locals and at the very least to 'do no harm' - have been uncovered within peacekeeping operations in countries as diverse as Cambodia, Liberia, the Congo and Bosnia, among many others. This has to change. Many academic and civil society projects have sought to address the issue from a broad range of angles and disciplines (e.g. public health, law, gender, conflict and security, international relations, history, amongst others). This work, while commendable in its intentions, has been largely disparate and therefore ineffective. Some collaborations between individuals and/or organisations have taken place, but largely in the context of broader, tangentially related projects on topics such as peacekeeping, gender, international law, and female reproductive health, amongst others. The Sexual Violence and Peacekeeping Network will overcome these limitations by bringing academics and practitioners from the civil society sector together in a spirit of conversation and collaboration. It will thereby achieve a deeper understanding of sexual violence and peacekeeping, its causes, consequences and the best ways in which to tackle the problem. Its broad spectrum of participants - from network partners in the Kofi Annan Peacekeeping Training Centre (Ghana) and AIDS-Free World (US/Canada) alongside academics from the University of the Free State (South Africa), University of Canterbury (New Zealand) and a range of UK universities - will ensure that the network and its outputs harness the variety of existing perspectives and approaches to sexual violence and peacekeeping.
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 www.rcuk.ac.uk/StudentshipTerminology. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.
To develop a suitable platform which integrates existing and new applications to provide the technical systems required for rolling out a new franchise business model.
Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemes and (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical, bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.
Our increasing reliance upon space-based technologies (communications, location-finding, etc) means that predicting the "Space Weather" of Earth's Radiation Belts is very desirable. The high-energy electron flux within Earth's Outer Radiation Belt is highly variable on timescales of hours to days, but we do not yet know the major factors that control this variability. It is clear that important solar wind transient features, such as coronal mass ejections and stream interaction regions can alter the size and strength of the Outer Radiation Belt, but the changes are difficult to predict. Spacecraft anomalies including electrostatic discharges and single-event upsets are related to increases in the density of high-energy electrons in the spacecraft environment. The Outer Radiation Belt spans the distance from around 2.5 Earth radii from the centre of the Earth to beyond geosynchronous orbit, at 6.6 Earth radii, but until recently there have been few opportunities to scientifically sample the high-energy electron environment, principally because it is so hazardous to spacecraft and electronic instruments. Physics-based numerical models of the Outer Radiation Belt are in their infancy. It is a significant challenge to reduce the complex plasma physics of collisionless, relativistic electron dynamics to a numerical system that can be solved on the necessary timescales. This project will provide valuable insight into the most important physical processes linking the solar wind the Outer Radiation Belt that can be used to build such a physics-based model in the future. There are two key novelties in the planned project. First, we will use data from the NASA Van Allen Probes mission to provide information on the variability of electron flux across a range of different distances from the Earth. This mission (launched in 2013) will provide the project with at least four years of in-situ space plasma data in unprecedented detail and in multiple locations simultaneously throughout the Outer Radiation Belt. Previous attempts to investigate the relationship between solar wind variability and Radiation Belt fluxes have been restricted to locations near geosynchronous orbit due to a lack of in situ data from other locations. Spacecraft data from the solar wind and from the Van Allen Probes will be combined to investigate how variability in the solar wind relates to variability in the Earth's Radiation Belts and whether there are repeatable patterns in both that may be predicted successfully. Second, cutting-edge machine learning techniques will be used to investigate the relationship between solar wind variability and the electron flux and which sets of parameters best predict future radiation belt conditions. Machine-learning techniques can be used to find repeatable patterns in empirical data and then build them into predictive models. Solar wind parameters such as speed, number density and magnetic field orientation all contribute to changes in the Earth's magnetosphere, and especially in the Outer Radiation Belts, but they also exhibit inter-dependencies due to the physics of the solar wind. We will begin by studying patterns in the solar wind variability, where techniques will be developed for time series at a single point in space. Later, these techniques will be employed to build models of the variability of electron flux over a range of distances from the Earth, based upon inputs from the solar wind data. Carefully interpreted, machine-learning techniques allow us to determine those parameters that most influence the changing electron flux and provide indispensable clues for the physical mechanisms by which they exert that influence. By judiciously interpreting the results from machine-learning algorithms in the framework of space plasma physics, it is hoped to gain new insight into how the solar wind controls the variability of the whole Outer Radiation Belt.