FollowMe: A Bigraphical Approach

In this paper we illustrate the use of modelling techniques using bigraphs to specify and refine elementary aspects of the FollowMe framework. This framework provides the seamless migration of bi-directional user interfaces for users as they navigate between zones within an intelligent environment

Towards FollowMe User Profiles for Macro Intelligent Environments

We envision an Ambient Intelligent Environment as an environment with technology embedded within the framework of that environment to help enhance an users experience in that environment. Existing implementations , while working effectively, are themselves an expensive and time consuming investment. Applying the same expertise to an environment on a monolithic scale is very inefficient, and thus, will require a different approach. In this paper, we present this problem, propose theoretical solutions that would solve this problem, with the guise of experimentally verifying and comparing these approaches, as well as a formal method to model the entire scenario

Status of GEO 600

The German-British laser-interferometric gravitational wave detector GEO 600 is in its 13th year of operation since its first lock in 2001. After participating in science runs with other first generation detectors, GEO 600 has continued collecting data as an astrowatch instrument with a duty cycle of 62% during the time when the other detectors have gone offline to undergo substantial upgrades. Less invasive upgrades to demonstrate advanced technologies and improve the GEO 600 sensitivity at high frequencies as part of the GEO-HF program have additionally been carried out in parallel to data taking. We report briefly on the status of GEO 600

An interference-aware virtual clustering paradigm for resource management in cognitive femtocell networks

Femtocells represent a promising alternative solution for high quality wireless access in indoor scenarios where conventional cellular system coverage can be poor. They are randomly deployed by the end user, so only post deployment network planning is possible. Furthermore, this uncoordinated deployment creates severe interference to co-located femtocells, especially in dense deployments. This paper presents a new architecture using a generalised virtual cluster femtocell (GVCF) paradigm, which groups together FAP into logical clusters. It guarantees severely interfering and overlapping femtocells are assigned to different clusters. Since each cluster operates on different band of frequencies, the corresponding virtual cluster controller only has to manage its own FAPs, so the overall system complexity is low. The performance of the GVCF algorithm is analysed from both a resource availability and cluster number perspective. Simulation results conclusively corroborate the superior performance of the GVCF model in interference mitigation, particularly in high density FAP scenarios

Expression of TLR-2 in hepatocellular carcinoma is associated with tumour proliferation, angiogenesis and Caspase-3 expression

Aims: Unlike other Toll-like receptors (TLRs), the role of toll like receptor 2 (TLR-2) in the pathogenesis of chronic liver disease and hepatocellular carcinoma (HCC) is not well studied. We, therefore, set out to investigate the expression of TLR-2 in different chronic liver disease states along with other markers of cell death, cellular proliferation and tissue vascularisation Methods and results: Immunohistochemistry was performed on liver tissue microarrays comprising hepatitis, cirrhosis and HCC patient samples using antibodies against TLR-2, Ki-67, Caspase-3 and VEGF. This was done in order to characterise receptor expression and translocation, apoptosis, cell proliferation and vascularisation. Cytoplasmic TLR-2 expression was found to be weak in 5/8 normal liver cases, 10/19 hepatitis cases and 8/21 cirrhosis patients. Moderate to strong TLR-2 expression was observed in some cases of hepatitis and cirrhosis. Both, nuclear and cytoplasmic TLR-2 expression was present in HCC with weak intensity in 11/41 cases, and moderate to strong staining in 19/41 cases. Eleven HCC cases were TLR-2 negative. Surprisingly, both cytoplasmic and nuclear TLR-2 expression in HCC were found to significantly correlate with proliferative index (r = 0.24 and 0.37), Caspase-3 expression (r = 0.27 and 0.38) and vascularisation (r = 0.56 and 0.23). Further, nuclear TLR-2 localisation was predominant in HCC, whereas cytoplasmic expression was more prevalent in hepatitis and cirrhosis. Functionally, treatment of HUH7 HCC cells with a TLR-2 agonist induced the expression of cellular proliferation and vascularisation markers CD34 and VEGF. Conclusions: Our results demonstrate a positive correlation between the expression of TLR-2 and other markers of proliferation and vascularisation in HCC which suggests a possible role for TLR-2 in HCC pathogenesi

Physical interventions to interrupt or reduce the spread of respiratory viruses: systematic review

Objective To review systematically the evidence of effectiveness of physical interventions to interrupt or reduce the spread of respiratory viruses

A Novel Approach for Predicting Monthly Water Demand by Combining Singular Spectrum Analysis with Neural Networks

Valid and dependable water demand prediction is a major element of the effective and sustainable expansion of municipal water infrastructures. This study provides a novel approach to quantifying water demand through the assessment of climatic factors, using a combination of a pretreatment signal technique, a hybrid particle swarm optimisation algorithm and an artificial neural network (PSO-ANN). The Singular Spectrum Analysis (SSA) technique was adopted to decompose and reconstruct water consumption in relation to six weather variables, to create a seasonal and stochastic time series. The results revealed that SSA is a powerful technique, capable of decomposing the original time series into many independent components including trend, oscillatory behaviours and noise. In addition, the PSO-ANN algorithm was shown to be a reliable prediction model, outperforming the hybrid Backtracking Search Algorithm BSA-ANN in terms of fitness function (RMSE). The findings of this study also support the view that water demand is driven by climatological variables

Forecasting the impact of diabetes mellitus on tuberculosis disease incidence and mortality in India.

Background: In context of the rapidly expanding diabetes mellitus (DM) epidemic in India and slowly declining tuberculosis (TB) incidence, we aimed to estimate the past, current, and future impact of DM on TB epidemiology. Methods: An age-structured TB-DM dynamical mathematical model was developed and analyzed to assess the DM-on-TB impact. The model was calibrated using a literature review and meta-analyses. The DM-on-TB impact was analyzed using population attributable fraction metrics. Sensitivity analyses were conducted by accommodating less conservative effect sizes for the TB-DM interactions, by factoring the age-dependence of the TB-DM association, and by assuming different TB disease incidence rate trajectories. Results: In 1990, 11.4% (95% uncertainty interval (UI) = 6.3%-14.4%) of new TB disease incident cases were attributed to DM. This proportion increased to 21.9% (95% UI = 12.1%-26.4%) in 2017, and 33.3% (95% UI = 19.0%-44.1%) in 2050. Similarly, in 1990, 14.5% (95% UI = 9.5%-18.2%) of TB-related deaths were attributed to DM. This proportion increased to 28.9% (95% UI = 18.9%-34.1%) in 2017, and 42.8% (95% UI = 28.7%-53.1%) in 2050. The largest impacts originated from the effects of DM on TB disease progression and infectiousness. Sensitivity analyses suggested that the impact could be even greater. Conclusions: The burgeoning DM epidemic is predicted to become a leading driver of TB disease incidence and mortality over the coming decades. By 2050, at least one-third of TB incidence and almost half of TB mortality in India will be attributed to DM. This is likely generalizable to other Asian Pacific countries with similar TB-DM burdens. Targeting the impact of the increasing DM burden on TB control is critical to achieving the goal of TB elimination by 2050

Efficient Congestion Minimisation by Successive Load Shifting in Multilayer Wireless Networks

Congestion in wireless networks is one of the major causes of system inefficiency, and with router load being the main contributor to overall network traffic flow, congestion is very dependent on the level of router load and how it can be effectively managed. This paper presents a novel low-complexity Successive Load Shifting (SLS) technique for intelligently shifting router load between network routers by predicting the probability of congestion occurrences in the network and exploiting the topology to reassign load to minimise congestion. Crucially, SLS does not compromise the data rate in avoiding congestion and is able to be seamlessly embedded into existing protocols with only a small increase incurred in system overheads. The performance of SLS has been extensively tested and critically evaluated using the widely adopted TCP and UDP protocols, with results confirming both significant throughput gains and superior packet loss performance