Distributed Contact and Identity Management

Contact management is a twofold problem involving a local and global level where the separation between them is rather fuzzy. Locally, users need to deal with contact management, which refers to a local need to store, organize, maintain up to date, and find information that will allow them contacting or reaching other people, organizations, etc. Globally, users deal with identity management that refers to peers having multiple identities (i.e., profiles) and the need of staying in control of them. In other words, they should be able to manage what information is shared and with whom. We believe many existing applications try to deal with this problem looking only at the data level and without analyzing the underlying complexity. Our approach focus on the complex social relations and interactions between users, identifying three main subproblem: (i) management of identity, (ii) search, and (iii) privacy. The solution we propose concentrates on the models that are needed to address these problems. In particular, we propose a Distributed Contact Management System (DCM System) that: Models and represents the knowledge of peers about physical or abstract objects through the notion of entities that can be of different types (e.g., locations, people, events, facilities, organizations, etc.) and are described by a set of attributes; By representing contacts as entities, allows peers to locally organize their contacts taking into consideration the semantics of the contact’s characteristics; By describing peers as entities allows them to manage their different identities in the network, by sharing different views of themselves (showing possibly different in- formation) with different people. The contributions of this thesis are, (i) the definition of a reference architecture that allows dealing with the diversity in relation with the partial view that peers have of the world, (ii) an approach to search entities based on identifiers, (iii) an approach to search entities based on descriptions, and (iv) the definition of the DCM system that instantiates the previously mentioned approaches and architecture to address concrete usage scenarios

Custom made EIT electrode belt with 32 electrodes.

<p>(A) Each electrode comprised of 4 x 4 gold plated blunt pins with sufficient length to reach through the hair coat. (B) The electrodes did not cause any discomfort to conscious dogs.</p

Averaged functional EIT images showing the regression coefficient (Pearson R) for each pixel that significantly correlated with the reference respiratory signal.

<p>Pixels with red colour positively correlate with respiration (respiratory signals) but those with blue colour inversely correlate. Inverse respiratory signals occupy only a few pixels on the sides of the images and these are most likely artefacts. Unlike in horses, there is no indication that abdominal gas pockets would affect thoracic EIT images in dogs. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183340#pone.0183340.g001" target="_blank">Fig 1</a> for more explanation.</p

Curve of change in impedance (ΔZ) over the respiratory cycle of a representative horse 24 hours before anaesthesia (baseline) and one hour after recovering from anaesthesia.

<p>Breath holding is defined as the percentage of the total breath length (<i>t</i>_breath) in which impedance remained higher than 50% (<i>t</i>_above50) of the maximum change in impedance during inspiration. Inspiratory time = <i>t</i>insp; time to reach 50% of maximum impedance change during inspiration = <i>t</i>to50.</p

Results of EIT and arterial blood gas measurements (mean ± SD) in 6 horses for the first 6 hours (t1-t6) after recovery from anaesthesia.

<p>ROI = Region of interest 1–4; <i>t</i>fill_r = Filling time for each region (1_dors, 2_dors, 3_dors, 4_mid, 5_vent, 6_vent, 7_vent); <i>t</i>infl_r = Inflation period for each region (1_dors-7_vent); RR = Respiratory rate; VT_EIT = Tidal volume as calculated from the EIT signal; PaCO₂ = Arterial partial pressure of carbon dioxide; PaO₂ = Arterial partial pressure of oxygen. Values are percentages unless otherwise stated.</p

Horses Auto-Recruit Their Lungs by Inspiratory Breath Holding Following Recovery from General Anaesthesia

<div><p>This study evaluated the breathing pattern and distribution of ventilation in horses prior to and following recovery from general anaesthesia using electrical impedance tomography (EIT). Six horses were anaesthetised for 6 hours in dorsal recumbency. Arterial blood gas and EIT measurements were performed 24 hours before (baseline) and 1, 2, 3, 4, 5 and 6 hours after horses stood following anaesthesia. At each time point 4 representative spontaneous breaths were analysed. The percentage of the total breath length during which impedance remained greater than 50% of the maximum inspiratory impedance change (breath holding), the fraction of total tidal ventilation within each of four stacked regions of interest (ROI) (distribution of ventilation) and the filling time and inflation period of seven ROI evenly distributed over the dorso-ventral height of the lungs were calculated. Mixed effects multi-linear regression and linear regression were used and significance was set at p<0.05. All horses demonstrated inspiratory breath holding until 5 hours after standing. No change from baseline was seen for the distribution of ventilation during inspiration. Filling time and inflation period were more rapid and shorter in ventral and slower and longer in most dorsal ROI compared to baseline, respectively. In a mixed effects multi-linear regression, breath holding was significantly correlated with PaCO₂ in both the univariate and multivariate regression. Following recovery from anaesthesia, horses showed inspiratory breath holding during which gas redistributed from ventral into dorsal regions of the lungs. This suggests auto-recruitment of lung tissue which would have been dependent and likely atelectic during anaesthesia.</p></div