Smart objects as building blocks for the internet of things

The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application

A national coastal erosion susceptibility model for Scotland

The upland nature of the Scottish landscape means that much of the social and economic activity has a coastal bias. The importance of the coast is further highlighted by the wide range of ecosystem services that coastal habitats provide. It follows that the threat posed by coastal erosion and flooding has the potential to have a substantial effect on the socioeconomic activity of the whole country. Currently, the knowledge base of coastal erosion is poor and this serves to hinder the current and future management of the coast. To address this knowledge gap, two interrelated models have been developed and are presented here: the Underlying Physical Susceptibility Model (UPSM) and the Coastal Erosion Susceptibility Model (CESM). The UPSM is generated within a GIS at a 50 m2 raster of national coverage, using data relating to ground elevation, rockhead elevation, wave exposure and proximity to the open coast. The CESM moderates the outputs of the UPSM to include the effects of sediment supply and coastal defence data. When validated against locations in Scotland that are currently experiencing coastal erosion, the CESM successfully identifies these areas as having high susceptibility. This allows the UPSM and CESM to be used as tools to identify assets inherently exposed to coastal erosion, areas where coastal erosion may exacerbate coastal flooding, and areas are inherently resilient to erosion, thus allow more efficient and effective management of the Scottish coast

A method for modelling coastal erosion risk: the example of Scotland

It is thought that 70% of beaches worldwide are experiencing erosion (Bird in Coastline changes: a global review, Wiley, Hoboken, 1985), and as global sea levels are rising and expected to accelerate, the management of coastal erosion is now a shared global issue. This paper aims to demonstrate a method to robustly model both the incidence of the coastal erosion hazard, the vulnerability of the population, and the exposure of coastal assets to determine coastal erosion risk, using Scotland as a case study. In Scotland, the 2017 Climate Change Risk Assessment for Scotland highlights the threat posed by coastal erosion to coastal assets and the Climate Change (Scotland) Act 2009 requires an Adaptation Programme to address the risks posed by climate change. Internationally, an understanding and adaption to coastal hazards is imperative to people, infrastructure and economies, with Scotland being no exception. This paper uses a Coastal Erosion Susceptibility Model (CESM) (Fitton et al. in Ocean Coast Manag 132:80–89. https://doi.org/10.1016/j.ocecoaman.2016.08.018 , 2016) to establish the exposure to coastal erosion of residential dwellings, roads, and rail track in Scotland. In parallel, the vulnerability of the population to coastal erosion, using a suite of indicators and Experian Mosaic Scotland geodemographic classification, is also presented. The combined exposure and vulnerability data are then used to determine coastal erosion risk in Scotland. This paper identifies that 3310 dwellings (a value of £524 m) are exposed to erosion, and the Coastal Erosion Vulnerability Index (CEVI) identifies 1273 of these are also considered to be highly vulnerable to coastal erosion, i.e. at high risk. Additionally, the CESM classified 179 km (£1.2 bn worth) of road and 13 km of rail track (£93 m to £2 bn worth) to be exposed. Identifying locations and assets that are exposed and at risk from coastal erosion is crucial for effective management and enables proactive, rather that reactive, decisions to be made at the coast. Natural hazards and climate change are set to impact most on the vulnerable in society. It is therefore imperative that we begin to plan, manage, and support both people and the environment in a manner which is socially just and sustainable. We encourage a detailed vulnerability analysis, such as the CEVI demonstrated here for Scotland, to be included within future coastal erosion risk research. This approach would support a more sustainable and long-term approach to coastal management decisions

The challenge of modelling nitrogen management at the field scale : simulation and sensitivity analysis of N2O fluxes across nine experimental sites using DailyDayCent

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In utero antihypertensive medication exposure and neonatal outcomes : A data linkage cohort study

We acknowledge the support from the Farr Institute @ Scotland. The Farr Institute @ Scotland is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates), the Wellcome Trust, (MRC grant number MR/K007017/1).Peer reviewedPublisher PD

The FAOSTAT database of greenhouse gas emissions from agriculture

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Educational and health outcomes of children and adolescents receiving antidepressant medication : Scotland-wide retrospective record linkage cohort study of 766 237 schoolchildren

Funding Health Data Research UK (grant reference number MR/S003800/1). Acknowledgements The study was sponsored by Health Data Research UK (www.hdruk.ac.uk), which is a joint investment led by the Medical Research Council, together with the National Institute for Health Research (England), the Chief Scientist Office (Scotland), Health and Care Research Wales, Health and Social Care Research and Development Division (Public Health Agency, Northern Ireland), the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the British Heart Foundation and Wellcome (grant reference number MR/S003800/1). The sponsor and funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication. This study formed part of a wider PhD thesis undertaken by the lead author within the University of Glasgow and was published in 2017. Certain sections of this paper appear in the thesis, which is accessible and downloadable from the following link: http://theses.gla.ac.uk/8594/1/2017flemingphd.pdf. Author Contributions J.P.P. had the original concept. All authors agreed the study design. D.C. and A.K. provided data and undertook record linkage. M.F. and D.F.M. undertook the statistical analyses. All authors interpreted the results. M.F. and J.P.P. drafted the manuscript and all other authors contributed revisions. All authors reviewed and approved the final version of the manuscript. M.F. is guarantor for the study. Approvals The authors applied for permission to access, link and analyse these data and undertook mandatory training in data protection, IT security and information governance. Therefore, the datasets generated and analysed during the study are not publicly available. The study was approved by the National Health Service Privacy Advisory Committee and covered by a data-processing agreement between Glasgow University and ISD, and a data-sharing agreement between Glasgow University and ScotXed. All data were linked by the Electronic Data Research and Innovation Service (eDRIS), part of NHS National Services Scotland. Ethics The NHS West of Scotland Research Ethics Service confirmed that formal NHS ethics approval was not required, since the study involved anonymized extracts of routinely collected data with an acceptably negligible risk of identification. Conflict of interest: None declaredPeer reviewedPublisher PD