Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

Towards cost-sensitive adaptation: when is it worth updating your predictive model?

Our digital universe is rapidly expanding,more and more daily activities are digitally recorded, data arrives in streams, it needs to be analyzed in real time and may evolve over time. In the last decade many adaptive learning algorithms and prediction systems, which can automatically update themselves with the new incoming data, have been developed. The majority of those algorithms focus on improving the predictive performance and assume that model update is always desired as soon as possible and as frequently as possible. In this study we consider potential model update as an investment decision, which, as in the financial markets, should be taken only if a certain return on investment is expected. We introduce and motivate a new research problem for data streams ? cost-sensitive adaptation. We propose a reference framework for analyzing adaptation strategies in terms of costs and benefits. Our framework allows to characterize and decompose the costs of model updates, and to asses and interpret the gains in performance due to model adaptation for a given learning algorithm on a given prediction task. Our proof-of-concept experiment demonstrates how the framework can aid in analyzing and managing adaptation decisions in the chemical industry.

Physico-chemical controls on phosphorus cycling in two lowland streams. Part 2 - The sediment phase

This article investigates the temporal and spatial controls on sediment-phosphorus (P) dynamics in two contrasting sub-catchments of the River Kennet, England. Suspended sediment (collected under representative flow conditions) and size-fractionated bedload (collected weekly for one year) from the Rivers Lambourn and Enborne was analysed for a range of physico-chemical determinands. Total P concentrations were highest in the most mobile fractions of sediment: suspended sediment, fine silt and clay and organic matter (mean concentrations of 1758, 1548 and 1440 mug P g(-1) dry sediment, respectively). Correlation analysis showed significant relationships between total P and total iron (n = 110), total manganese (n = 110), organic matter (n = 110) and specific surface area (n = 28) in the Lambourn (r(2) 0.71, 0.68, 0.62 and 0.52, respectively) and between total P and total iron (n = 110), total manganese (n = 110) and organic matter (n = 110) in the Enborne (r(2) 0.74, 0.85 and 0.68, respectively). These data highlight the importance of metal oxyhydroxide adsorption of P on fine particulates and organic matter. However, high total P concentrations in the granule gravel and coarse sand size fraction during the summer period (mean concentration 228 mug P g(-1) dry sediment) also highlight the role of calcite co-precipitation on P dynamics in the Lambourn. P to cation ratios in Lambourn sediment indicated that fine silt and clay and granule gravel and coarse sand size fractions were potential sources of P release to the water column during specific periods of the summer and autumn. In the Enborne, however, only the granule gravel and coarse sand size fraction had high ratios and a slow, constant release of P was observed. In addition, scanning electron microscopy work confirmed the association of P with calcite in the Lambourn and P with iron on clay particles in the Enborne. The study highlighted the importance of the chemical and physical properties of the sediment in influencing the mechanisms controlling P storage and release within river channels. (C) 2004 Elsevier B.V. All rights reserved.

Phosphorus dynamics and export in streams draining micro-catchments: Development of empirical models

Annual total phosphorus (TP) export data from 108 European micro-catchments were analyzed against descriptive catchment data on climate (runoff), soil types, catchment size, and land use. The best possible empirical model developed included runoff, proportion of agricultural land and catchment size as explanatory variables but with a low explanation of the variance in the dataset (R-2 = 0.37). Improved country specific empirical models could be developed in some cases. The best example was from Norway where an analysis of TP-export data from 12 predominantly agricultural micro-catchments revealed a relationship explaining 96% of the variance in TP-export. The explanatory variables were in this case soil-P status (P-AL), proportion of organic soil, and the export of suspended sediment. Another example is from Denmark where an empirical model was established for the basic annual average TP-export from 24 catchments with percentage sandy soils, percentage organic soils, runoff, and application of phosphorus in fertilizer and animal manure as explanatory variables (R-2 = 0.97).

Bayesian sample size for exploratory clinical trials incorporating historical data

This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence either that an experimental treatment is better than a control or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving 'non-informative' prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p(0). Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored. Copyright (C) 2007 John Wiley & Sons, Ltd.

Predicting the arrival of high-speed solar wind streams at Earth using the STEREO Heliospheric Imagers

High-speed solar wind streams modify the Earth's geomagnetic environment, perturbing the ionosphere, modulating the flux of cosmic rays into the Earth atmosphere, and triggering substorms. Such activity can affect modern technological systems. To investigate the potential for predicting the arrival of such streams at Earth, images taken by the Heliospheric Imager (HI) on the STEREO-A spacecraft have been used to identify the onsets of high-speed solar wind streams from observations of regions of increased plasma concentrations associated with corotating interaction regions, or CIRs. In order to confirm that these transients were indeed associated with CIRs and to study their average properties, arrival times predicted from the HI images were used in a superposed epoch analysis to confirm their identity in near-Earth solar wind data obtained by the Advanced Composition Explorer (ACE) spacecraft and to observe their influence on a number of salient geophysical parameters. The results are almost identical to those of a parallel superposed epoch analysis that used the onset times of the high-speed streams derived from east/west deflections in the ACE measurements of solar wind speed to predict the arrival of such streams at Earth, assuming they corotated with the Sun with a period of 27 days. Repeating the superposed epoch analysis using restricted data sets demonstrates that this technique can provide a timely prediction of the arrival of CIRs at least 1 day ahead of their arrival at Earth and that such advanced warning can be provided from a spacecraft placed 40° ahead of Earth in its orbit.

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.