Fog paradigm for local energy management systems

Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform. The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments

Phenoloxidase activity acts as a mosquito innate immune response against infection with semliki forest virus

Several components of the mosquito immune system including the RNA interference (RNAi), JAK/STAT, Toll and IMD pathways have previously been implicated in controlling arbovirus infections. In contrast, the role of the phenoloxidase (PO) cascade in mosquito antiviral immunity is unknown. Here we show that conditioned medium from the Aedes albopictus-derived U4.4 cell line contains a functional PO cascade, which is activated by the bacterium Escherichia coli and the arbovirus Semliki Forest virus (SFV) (Togaviridae; Alphavirus). Production of recombinant SFV expressing the PO cascade inhibitor Egf1.0 blocked PO activity in U4.4 cell- conditioned medium, which resulted in enhanced spread of SFV. Infection of adult female Aedes aegypti by feeding mosquitoes a bloodmeal containing Egf1.0-expressing SFV increased virus replication and mosquito mortality. Collectively, these results suggest the PO cascade of mosquitoes plays an important role in immune defence against arboviruses

HlSRB, a Class B Scavenger Receptor, Is Key to the Granulocyte-Mediated Microbial Phagocytosis in Ticks

Ixodid ticks transmit various pathogens of deadly diseases to humans and animals. However, the specific molecule that functions in the recognition and control of pathogens inside ticks is not yet to be identified. Class B scavenger receptor CD36 (SRB) participates in internalization of apoptotic cells, certain bacterial and fungal pathogens, and modified low-density lipoproteins. Recently, we have reported on recombinant HlSRB, a 50-kDa protein with one hydrophobic SRB domain from the hard tick, Haemaphysalis longicornis. Here, we show that HlSRB plays vital roles in granulocyte-mediated phagocytosis to invading Escherichia coli and contributes to the first-line host defense against various pathogens. Data clearly revealed that granulocytes that up-regulated the expression of cell surface HlSRB are almost exclusively involved in hemocyte-mediated phagocytosis for E. coli in ticks, and post-transcriptional silencing of the HlSRB-specific gene ablated the granulocytes' ability to phagocytose E. coli and resulted in the mortality of ticks due to high bacteremia. This is the first report demonstrating that a scavenger receptor molecule contributes to hemocyte-mediated phagocytosis against exogenous pathogens, isolated and characterized from hematophagous arthropods

Modelling Energy Demand Response Using Long-Short Term Memory Neural Networks

We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions