Interviewing children and young people with learning disabilities: guidelines for researchers and multi-professional practice

We argue that it is important for researchers and service providers to not only recognize the rights of children and young people with learning disabilities to have a ‘voice’, but also to work actively towards eliciting views from all. A set of guidelines for critical self-evaluation by those engaged in systematically collecting the views of children and young people with learning disabilities is proposed. The guidelines are based on a series of questions concerning: research aims and ethics (encompassing access/gatekeepers; consent/assent; confidentiality/anonymity/secrecy, recognition, feedback and ownership; and social responsibility) sampling, design and communication

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

Enchanting resilience: relations of care and people-place connections in agriculture

The practices and decision-making of contemporary agricultural producers are governed by a multitude of different, and sometimes competing, social, economic, regulatory, environmental and ethical imperatives. Understanding how they negotiate and adapt to the demands of this complex and dynamic environment is crucial in maintaining an economically and environmentally viable and resilient agricultural sector. This paper takes a socio-cultural approach to explore the development of social resilience within agriculture through an original and empirically grounded discussion of people-place connections amongst UK farmers. It positions enchantment as central in shaping farmers' embodied and experiential connections with their farms through establishing hopeful, disruptive and demanding ethical practices. Farms emerge as complex moral economies in which an expanded conceptualisation of the social entangles human and non-human actants in dynamic and contextual webs of power and responsibility. While acknowledging that all farms are embedded within broader, nested levels, this paper argues that it is at the micro-scale that the personal, contingent and embodied relations that connect farmers to their farms are experienced and which, in turn, govern their capacity to develop social resilience.

Gendered struggles over land: shifting inheritance practices among the Serer in rural Senegal

Although women's land rights are often affirmed unequivocally in constitutions and international human rights conventions in many African countries, customary practices usually prevail on the ground and often deny women's land inheritance. Yet land inheritance often goes unnoticed in wider policy and development initiatives to promote women's equal access to land. This paper draws on feminist ethnographic research among the Serer ethnic group in two contrasting rural communities in Senegal. Through analysis of land governance, power relations and 'technologies of the self', this article shows how land inheritance rights are contingent on the specific effects of intersectionality in particular places. The contradictions of legal pluralism, greater adherence to Islam and decentralisation led to greater application of patrilineal inheritance practices. Gender, religion and ethnicity intersected with individuals' marital position, status, generation and socio-ecological change to constrain land inheritance rights for women, particularly daughters, and widows who had been in polygamous unions and who remarried. Although some women were aware that they were legally entitled to inherit a share of the land, they tended not to 'demand their rights'. In participatory workshops, micro-scale shifts in women's and men's positionings reveal a recognition of the gender discriminatory nature of customary and Islamic law and a desire to 'change with the times'. While the effects of 'reverse' discourses are ambiguous and potentially reinforce prevailing patriarchal power regimes, 'counter' discourses, which emerged in participatory spaces, may challenge customary practices and move closer to a rights-based approach to gender equality and women's land inheritance.

Continental and global scale flood forecasting systems

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

Fast and slow precipitation responses to individual climate forcers: a PDRMIP multi-model study

Precipitation is expected to respond differently to various drivers of anthropogenic climate change. We present the first results from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where nine global climate models have perturbed CO2, CH4, black carbon, sulfate, and solar insolation. We divide the resulting changes to global mean and regional precipitation into fast responses that scale with changes in atmospheric absorption and slow responses scaling with surface temperature change. While the overall features are broadly similar between models, we find significant regional intermodel variability, especially over land. Black carbon stands out as a component that may cause significant model diversity in predicted precipitation change. Processes linked to atmospheric absorption are less consistently modeled than those linked to top-of-atmosphere radiative forcing. We identify a number of land regions where the model ensemble consistently predicts that fast precipitation responses to climate perturbations dominate over the slow, temperature-driven responses.

The global-scale impacts of climate change: the QUEST-GSI project

This paper introduces the special issue of Climatic Change on the QUEST-GSI project, a global-scale multi-sectoral assessment of the impacts of climate change. The project used multiple climate models to characterise plausible climate futures with consistent baseline climate and socio-economic data and consistent assumptions, together with a suite of global-scale sectoral impacts models. It estimated impacts across sectors under specific SRES emissions scenarios, and also constructed functions relating impact to change in global mean surface temperature. This paper summarises the objectives of the project and its overall methodology, outlines how the project approach has been used in subsequent policy-relevant assessments of future climate change under different emissions futures, and summarises the general lessons learnt in the project about model validation and the presentation of multi-sector, multi-region impact assessments and their associated uncertainties to different audiences.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.