Body shadowing mitigation using differentiated LOS/NLOS channel models for RSSI-based Monte Carlo personnel localization

Research into localization has produced a wealth of algorithms and techniques to estimate the location of wireless network nodes, however the majority of these schemes do not explicitly account for non-line of sight conditions. Disregarding this common situation reduces their accuracy and their potential for exploitation in real world applications. This is a particular problem for personnel tracking where the user's body itself will inherently cause time-varying blocking according to their movements. Using empirical data, this paper demonstrates that, by accounting for non-line of sight conditions and using received signal strength based Monte Carlo localization, meter scale accuracy can be achieved for a wrist-worn personnel tracking tag in a 120 m indoor office environment. © 2012 IEEE.

Testing the Prebisch–Singer hypothesis since 1650: Evidence from panel techniques that allow for multiple breaks

In this paper, we re-examine two important aspects of the dynamics of relative primary commodity prices, namely the secular trend and the short run volatility. To do so, we employ 25 series, some of them starting as far back as 1650 and powerful panel data stationarity tests that allow for endogenous multiple structural breaks. Results show that all the series are stationary after allowing for endogenous multiple breaks. Test results on the Prebisch–Singer hypothesis, which states that relative commodity prices follow a downward secular trend, are mixed but with a majority of series showing negative trends. We also make a first attempt at identifying the potential drivers of the structural breaks. We end by investigating the dynamics of the volatility of the 25 relative primary commodity prices also allowing for endogenous multiple breaks. We describe the often time-varying volatility in commodity prices and show that it has increased in recent years.

Accuracy of LightCycler SeptiFast for the detection and identification of pathogens in the blood of patients with suspected sepsis: a systematic review and meta-analysis

Purpose: There is an urgent need to develop diagnostic tests to improve the detection of pathogens causing life-threatening infection (sepsis). SeptiFast is a CE-marked multi-pathogen real-time PCR system capable of detecting DNA sequences of bacteria and fungi present in blood samples within a few hours. We report here a systematic review and meta-analysis of diagnostic accuracy studies of SeptiFast in the setting of suspected sepsis.

Methods: A comprehensive search strategy was developed to identify studies that compared SeptiFast with blood culture in suspected sepsis. Methodological quality was assessed using QUADAS. Heterogeneity of studies was investigated using a coupled forest plot of sensitivity and specificity and a scatter plot in receiver operator characteristic space. Bivariate model method was used to estimate summary sensitivity and specificity.

Results: From 41 phase III diagnostic accuracy studies, summary sensitivity and specificity for SeptiFast compared with blood culture were 0.68 (95 % CI 0.63–0.73) and 0.86 (95 % CI 0.84–0.89) respectively. Study quality was judged to be variable with important deficiencies overall in design and reporting that could impact on derived diagnostic accuracy metrics.

Conclusions: SeptiFast appears to have higher specificity than sensitivity, but deficiencies in study quality are likely to render this body of work unreliable. Based on the evidence presented here, it remains difficult to make firm recommendations about the likely clinical utility of SeptiFast in the setting of suspected sepsis.

The association between glucose lowering drug use and mortality among breast cancer patients with type 2 diabetes.

This study assessed the association between glucose-lowering drug (GLD) use, including metformin, sulphonylurea derivatives and insulin, after breast cancer diagnosis and breast cancer-specific and all-cause mortality. 1763 breast cancer patients, diagnosed between 1998 and 2010, with type 2 diabetes were included. Cancer information was retrieved from English cancer registries, prescription data from the UK Clinical Practice Research Datalink and mortality data from the Office of National Statistics (up to January 2012). Time-varying Cox regression models were used to calculate HRs and 95 % CIs for the association between GLD use and breast cancer-specific and all-cause mortality. In 1057 patients with diabetes before breast cancer, there was some evidence that breast cancer-specific mortality decreased with each year of metformin use (adjusted HR 0.88; 95 % CI 0.75–1.04), with a strong association seen with over 2 years of use (adjusted HR 0.47; 95 % CI 0.26–0.82). Sulphonylurea derivative use for less than 2 years was associated with increased breast cancer-specific mortality (adjusted HR 1.70; 95 % CI 1.18–2.46), but longer use was not (adjusted HR 0.94; 95 % CI 0.54–1.66). In 706 patients who developed diabetes after breast cancer, similar patterns were seen for metformin, but sulphonylurea derivative use was strongly associated with cancer-specific mortality (adjusted HR 3.64; 95 % CI 2.16–6.16), with similar estimates for short- and long-term users. This study provides some support for an inverse association between, mainly long-term, metformin use and (breast cancer-specific) mortality. In addition, sulphonylurea derivative use was associated with increased breast cancer-specific mortality, but this should be interpreted cautiously, as it could reflect selective prescribing in advanced cancer patients.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.