Paisley, United Kingdom

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Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: ICT-14-2014 | Award Amount: 6.87M | Year: 2015

The proposed SELFNET project will design and implement an autonomic network management framework to achieve self-organizing capabilities in managing network infrastructures by automatically detecting and mitigating a range of common network problems that are currently still being manually addressed by network operators, thereby significantly reducing operational costs and improving user experience. SELFNET explores a smart integration of state-of-the-art technologies in Software-Defined Networks (SDN), Network Function Virtualization (NFV), Self-Organizing Networks (SON), Cloud computing, Artificial intelligence, Quality of Experience (QoE) and Next-generation networking to provide a novel intelligent network management framework that is capable of assisting network operators in key management tasks: automated network monitoring by the automatic deployment of NFV applications to facilitate system-wide awareness of Health of Network metrics to have more direct and precise knowledge about the real status of the network; autonomic network maintenance by defining high-level tactical measures and enabling autonomic corrective and preventive actions against existing or potential network problems. SELFNET is driven by use cases designed to address major network management problems including Self-protection capabilities against distributed cyber-attacks, Self-healing capabilities against network failures, and Self-optimization to dynamically improve the performance of the network and the QoE of the users. SELFNET is designed within this economic and business context to substantially reduce operational costs of network operators by automating a significant number of current labour-intensive network management tasks. Therefore, SELFNET directly addresses the Strand Network Management challenge highlighted by the EC.


MacKenzie A.,University of West of Scotland
Pharmacology and Therapeutics | Year: 2011

Angiotensin II, through activation of the angiotensin II-type 1 receptor, induces generation of inflammatory mediators in the blood vessel wall and as such plays an active role in the inflammation process. Direct stimulation of reactive oxygen species and nuclear factors seem to be key mechanisms through which this receptor induces inflammation. Inflammatory molecules are also known to modify endothelial cell function, especially endothelium-derived vasoactive agents, and inflammation is increasingly recognized as primary cause of major vascular disorders. There is accumulating evidence that stimulation of the type 1 angiotensin II receptor participates in vascular dysfunction by reducing activity of the endothelium-derived relaxants nitric oxide and hyperpolarizing factors. Furthermore activation of this angiotensin II receptor also enhances generation of endothelium-derived constricting factors, such as endothelin-1. This change in endothelial cell output not only impairs blood vessel relaxation but leads to pro-inflammatory and pro-coagulation conditions that are associated with disease initiation and progression. Pharmacological inhibitors of the angiotensin II pathway and the type 1 receptor subtype are in current clinical use for the treatment of hypertension. However evidence supports that these agents have a positive therapeutic benefit in other vascular pathologies with recognized inflammatory etiology, such as atherosclerosis. © 2010 Elsevier Inc. All rights reserved.


Grant
Agency: GTR | Branch: STFC | Program: | Phase: Training Grant | Award Amount: 149.50K | Year: 2016

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.


Grant
Agency: GTR | Branch: STFC | Program: | Phase: Research Grant | Award Amount: 581.34K | Year: 2016

Einsteins General Relativity predicts that dynamical systems in strong gravitational fields will emit vast amounts of energy in the form of gravitational waves (GW). These are ripples in the very fabric of spacetime that travel from their sources at the speed of light, carrying information about physical processes responsible for their emission. They are among the most elusive signals from the deepest reaches in the Universe. Experiments aimed at detecting them have been in development for several decades, and are now reaching sensitivities where detection is expected within a few years. The worldwide network of interferometric detectors includes the American advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO), the French-Italian-Dutch-Polish advanced Virgo and the German-UK GEO600 that are being enhanced with a new detector (KAGRA) under construction in Japan. The former detectors have all reached sensitivities close to their design goals and have taken the most sensitive data to date. Cooperation amongst different projects has enabled continuous data acquisition, with sensitivity to a wide range of sources and phenomena, over most of the sky. Modelling GW sources has allowed deeper searches and data from LIGO, Virgo, and GEO have increased our understanding of astronomical phenomena. For example, we have built accurate models to describe the dynamics of spinning black hole binaries for improving efficiency of detection and accuracy of parameter estimation, initiated studies on distinguishing models of the formation and evolution of compact binaries and supernovae, ruled out merging neutron star binary as progenitor of the gamma ray burst (GRB) GRB070201, and shown that less than 1% of the Crab pulsars radiated power is in GW. We are now entering a new era as advanced detectors begin their first phase of operation and within a few years will, we expect, routinely observe GW. The aLIGO detectors are based on the quasi-monolithic silica suspension concept developed in the UK for GEO600 and on the high power lasers developed by our German colleagues in GEO600. The AdV detector also uses a variant of the silica suspension technology. Further, KAGRA is being built with input on cryogenic bonding technology from the UK groups. The consortium groups have initiated and led searches for astronomical sources, thanks to funding support received since first data taking runs began 12 years ago. Key ingredients of several searches (accurate waveforms models, geometric formulation of data analysis to optimise searches, algorithms to search for generic bursts, Bayesian search and inference techniques) were developed at Cardiff and Glasgow. We propose a programme that leads to full exploitation of data from aLIGO and AdV, building on the analysis of data from the most recent LIGO/Virgo science runs and from GEO600 while the advanced detectors were under construction. In particular, we will refine waveform models and carry out deep and wide parameter space searches for coalescing binaries, GW emitted in coincidence with GRBs and supernovae, and continuous signals from rotating neutron stars. In parallel, we propose essential detector R&D. Detector sensitivity is mainly limited by thermal noise associated with the substrates of the mirrors, their reflective coatings, and their suspension elements, as well as by noise resulting from the quantum nature of the light used in sensing. Our research is targeted towards making innovative improvements in these areas, essential to maximize the astrophysical potential of GW observatories. We have major responsibilities for the silica suspensions in aLIGO, both in the US and for a possible 3rd aLIGO detector in India, and in the development of enhancements and upgrades to the aLIGO detectors in the areas of mirror coatings for low thermal noise, silicon substrates, room temperature and cryogenic suspensions and improved interferometer topologies to combat quantum noise.


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Knowledge Transfer Partnership | Award Amount: 49.80K | Year: 2016

To embed proton exchange membrane electrolyser design capability and develop an in-house hydrogen cell for use in the range of Hydrogen generator


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Knowledge Transfer Partnership | Award Amount: 90.45K | Year: 2015

To develop a novel zero waste process for removal of metal ions from distillery wastewater.


Grant
Agency: GTR | Branch: STFC | Program: | Phase: Research Grant | Award Amount: 88.59K | Year: 2015

This project is about building a cost-effective deformable mirror system suitable to incorporate into advanced laser manufacturing systems. Laser systems are now used or proposed for machining, polishing, and additive layer manufacture. Directing and shaping a laser beam under computer control allows multiple laser tools to be generated and enables highly complex structures to be manufactured, suitable for prototyping, low volume high value manufacture, and one-off components such as medical implants. Faster, more accurate, and cheaper adaptive optic components will increase the take-up in industry of these advanced laser manufacturing techniques. The key aspects of the fast beam shaping technology are (i) the mirror design, which is adaptable to miniaturisation and integration with sensing and drive electronics, and (ii) the technique of actuator extension sensing which reduces the effect of piezoelectric hysteresis enabling faster and more accurate mirror control. The development of the deformable mirror with extension sensing offers the potential for continuously-variable manipulation of the beam profile and spot size of a laser. For example in additive layer manufacturing, in which a laser beam is scanned under computer control to create a component by melting metal powder, it will be possible to compensate for off-axis distortions during scanning across the metal powder bed, or to use a larger or smaller spot for coarse or fine detail of the components structure. The aim in this project is to show the effectiveness and commercial value of the deformable mirror with extension sensing control. We will show the mirror working under control in the laboratory in a laser manufacturing application, namely additive layer manufacture. We will investigate in depth the market opportunity in advanced laser manufacturing, route to market for the technology, and the requirements for further investment.


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Knowledge Transfer Partnership | Award Amount: 85.82K | Year: 2015

To embed video codec expertise, in order to exploit market opportunities within the mobile device marketplace.


Grant
Agency: GTR | Branch: EPSRC | Program: | Phase: Research Grant | Award Amount: 290.45K | Year: 2016

Bone graft is regularly used in surgery (plastics, maxillofacial surgery and orthopaedics); bone is actually the second most grafted tissue after blood. Ideally the surgeon wishes to take bone from one area (donor site) to another area (recipient site) to support the operation they are performing. However, a patients own donor bone is in short supply and its removal can lead to complications in the donor site. This means the surgeon will often recourse to allograft - decellularised (and thus biologically inferior) - bone from other people. A third, and growing, option is synthetic graft. Synthetic graft can be made from biologically active materials, but is not viable and thus not yet as good as living bone. Our bioreactor, that supplies nanoscale kicks to cells in culture can be used to convert mesenchymal stem cells (the stem cells of the bone, simple to isolate from a patients iliac crest or fat tissue) to bone forming osteoblasts. It can achieve this with cells seeded into 3D environments such as gels or potentially synthetic graft materials. This thus allows us to envisage supply of living bone graft derived from a patients own cells. The ability to supply such materials would provide a new gold standard for bone grafting. In this project we will thus develop our bioreactor into a flexible platform for study of bone regeneration (which will also be of significant interest to many academic labs in the field) and provision of bone graft. Further to this vision of tissue engineered bone supply, there is also a big need in Pharma for relevant bone models to reduce use of both standard lab models that are very dissimilar to the in-body environment and animal testing which has large cost and ethical consideration. Our ability to produce 3D bone in the lab simply, reproducibly, at low cost and without need for chemical control of cell phenotype (we will just use the nanokicks) will provide an excellent model for testing of drugs for e.g. osteoporosis, osteogenesis imperfecta and other bone conditions. In this project, we will use our technique to study 3D bone formation in the lab and look at what metabolites, the basic building blocks of life, the cell use as they form bone. We will then identify bioactive metabolites and validate them in our bone mimics. Finally, we will test to see feasibility of applying nanokicks to humans to help treat e.g. spinal injury, slow bone repair and osteoporosis etc. We will move from mechanical nanokicks to acoustic nanokicks to achieve this.


Grant
Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 55.16K | Year: 2016

Abstract Low cost Hyperspectral Crop Camera (HCC). A consortium from a broad range of disciplines have come together to develop a revolutionary low cost crop camera that could potentially allow farmers to improve crop yield, use less fertiliser, use less pesticide and spot pests and diseases earlier. The project will be led and coordinated by Wideblue Limited - a developer and manufacturer of specialist cameras. The project will also call on the skills of the the James Hutton Institutes expertise in crop nutrition and monitoring, the University of Strathclydes Hyperspectral Imaging Centre, the University of the West of Scotlands Institute of Thin Films, Sensors and Imaging and Galloway & MacLeods intelligent agriculture division. Summary Farmers and horticulturists face varying difficulties that require experience and knowledge of their fields and crops, gained over many years. These difficulties include, but are not limited to: uneven growth/yield of their fields; inexact and estimated fertiliser application; uneven irrigation and local variations in pests/diseases/weeds. Additionally, the optimum harvest timing is still speculated and often inexact. Faced with numerous variables, farmers cannot avoid high variations in costs and crop yields from year to year. Tools to assist farmers to optimise e.g. fertiliser & water applications or early detection of disease will provide a useful diagnostic and management capability for optimum control of crop growth. Currently, solutions for these challenges do exist, however, current systems are large, heavy, not portable and as such are not readily deployable. They are also prohibitively expensive - typically £10,000 - £150,000 each - and are generally only suitable for use in airborne or satellite imaging applications or laboratory analysis. In effect, the current solutions available for the aforementioned agricultural challenges are limited to large scale farming and/ or high value crops. In these expensive systems, spectrometer scan or image of the crop is taken at visible and/or infrared wavelengths with analysis showing spectral image signature changes relating to crop growth conditions. The signatures of interest varies from plant to plant and from cause to cause. The colour of a crop (visible and IR) also changes as it approaches maturity, with spectrometer scans providing scientific information for informed management decisions in relation to crop hydration, fertiliser application, disease progression and harvesting. Hyperspectral Imaging (HSI) can capture these changes: HSI systems capture a large number of images of the scene, each at a different wavelength within some range determined by the sensor technology, to produce a so called hyperspetral data cube in which each pixel in the spatial domain contains a spectral profile of the object observed. For our application, this spectral information can be analysed to make decisions about the diagnostics/management of challenges in maximising crop yield. The proposed Hyperspectral Crop Camera (HCC) will be: low-cost, compact & portable, simple in operation and robust. A camera housing will contain the, sensor, battery and electronics to produce one small simple lightweight device. This device would be suitable for handheld use or potentially mountable in a low cost drone for local airborne analysis. HSI technology in farming and agriculture which can cost anything from £10k - £150k. Application of HCC can allow a farmer and/ or agriculturists to: - Save water by providing optimised or localised irrigation - Timely identify areas of pests/diseases/weeds for early intervention - Optimise use of fertiliser - Determine optimum harvest time and help increase crop yield - Improve evenness of crop yield across field area - Reduced man hours, manually surveying fields etc - Reduce need for technical agronomy training/knowledge

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