The effect of protons on E2V technologies L3Vision CCDs

The effect of different 10 MeV equivalent proton fluences on the performance of E2V Technologies (formerly Marconi applied technologies, formerly EEV) L3Vision Charge Coupled Devices (CCDs) was investigated. The first experimental radiation damage results of the L3Vision device are presented, with emphasis given to the analysis of damage to the gain register of the device. Changes in dark current and generation of bright pixels in the CCD image, store, readout register and gain register as a result of proton irradiation are reported and viewed in light of the potential use of the device in space-based applications

Proton induced leakage current in CCDs

The effect of different proton fluences on the performance of two E2V Technologies CCD47-20 devices was investigated with particular emphasis given to the analysis of 'random telegraph signal' (RTS) generation, bright pixel generation and induced changes in base dark current level. The results show that bright pixel frequency increases as the mean energy of the proton beam is increased, and that the base dark current level after irradiation scales with the level of ionization damage. For the RTS study, 500 pixels on one device were monitored over a twelve hour period. This data set revealed a number of distinct types of pixel change level fluctuation and a system of classification has been devised. Previously published RTS data is discussed and reviewed in light of the new data

Optimal schedule of home care visits for a health care center

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurse teams from Health Care Centers. Usually, the visits are manually planned and without computer support. The main goal of this work is to carry out the automatic schedule of home care visits, of one Portuguese Health Care Center, in order to minimize the time spent in all home care visits and, consequently, reduce the costs involved. The developed algorithms were coded in MatLab Software and the problem was efficiently solved, obtaining several schedule solutions of home care visits for the presented data. Solutions found by genetic and particle swarm algorithms lead to significant time reductions for both nurse teams and patients.This work has been supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT - Fundru;ao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Access to and use of clinical services and disease-modifying therapies by people with progressive multiple sclerosis in the United Kingdom

Background: According to current UK guidelines everyone with progressive MS should have access to an MS Specialist but levels of access and use of clinical services is unknown. Our objective was to investigate access to MS Specialists, use of clinical services and disease-modifying therapies (DMTs) by people with progressive MS in the United Kingdom. Methods: A UK wide, online survey was conducted via the UK MS Register. Inclusion criteria: age over 18 years, primary or secondary progressive MS and a member of the UK MS Register. Participants were asked about access to MS Specialists; recent clinical service use; receipt of regular review and current and previous DMT use. Participant demographics; quality of life and disease impact measures were supplied from the UK MS Register. Results: In total 1298 participants responded: 5% were currently taking DMT; 23% had previously taken DMT; and 95% reported access to an MS Specialist. Most utilised services were: MS Doctor/Nurse (50%), General Practitioner (45%), and Physiotherapist (40%). Seventy-four percent received a regular review although 37% received theirs less than annually. Current DMT use was associated with better quality of life but past DMT use was associated with poorer quality of life and higher impact of disease. Conclusions: Access to, and use of, MS Specialists was high. However a gap in service provision was highlighted in both receiving and frequency of regular reviews

Scheduling of home health care services based on multi-agent systems

Home Health Care (HHC) services are growing worldwide and, usually, the home care visits are manually planned, being a time and effort consuming task that leads to a non optimized solution. The use of some optimization techniques can significantly improve the quality of the scheduling solutions, but lacks the achievement of solutions that face the fast reaction to condition changes. In such stochastic and very volatile environments, the fast re-scheduling is crucial to maintain the system in operation. Taking advantage of the inherent distributed and intelligent characteristics of Multi-agent Systems (MAS), this paper introduces a methodology that combines the optimization features provided by centralized scheduling algorithms, e.g. genetic algorithms, with the responsiveness features provided by MAS solutions. The proposed approach was codified in Matlab and NetLogo and applied to a real-world HHC case study. The experimental results showed a significant improvement in the quality of scheduling solutions, as well as in the responsiveness to achieve those solutions.info:eu-repo/semantics/publishedVersio

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Role of the employment status and education of mothers in the prevalence of intestinal parasitic infections in Mexican rural schoolchildren

<p><b>Background:</b> Intestinal parasitic infections are a public health problem in developing countries such as Mexico. As a result, two governmental programmes have been implemented: a) "National Deworming Campaign" and b) "Opportunities" aimed at maternal care. However, both programmes are developed separately and their impact is still unknown. We independently investigated whether a variety of socio-economic factors, including maternal education and employment levels, were associated with intestinal parasite infection in rural school children.</p> <p><b>Methods:</b> This cross-sectional study was conducted in 12 rural communities in two Mexican states. The study sites and populations were selected on the basis of the following traits: a) presence of activities by the national administration of albendazole, b) high rates of intestinal parasitism, c) little access to medical examination, and d) a population having less than 2,500 inhabitants. A total of 507 schoolchildren (mean age 8.2 years) were recruited and 1,521 stool samples collected (3 per child). Socio-economic information was obtained by an oral questionnaire. Regression modelling was used to determine the association of socio-economic indicators and intestinal parasitism.</p> <p><b>Results:</b> More than half of the schoolchildren showed poliparasitism (52%) and protozoan infections (65%). The prevalence of helminth infections was higher in children from Oaxaca (53%) than in those from Sinaloa (33%) (p < 0.0001). Giardia duodenalis and Hymenolepis nana showed a high prevalence in both states. Ascaris lumbricoides, Trichuris trichiura and Entamoeba hystolitica/dispar showed low prevalence. Children from lower-income families and with unemployed and less educated mothers showed higher risk of intestinal parasitism (odds ratio (OR) 6.0, 95% confidence interval (CI) 1.6–22.6; OR 4.5, 95% CI 2.5–8.2; OR 3.3, 95% CI 1.5–7.4 respectively). Defecation in open areas was also a high risk factor for infection (OR 2.4, 95% CI 2.0–3.0).</p> <p><b>Conclusion:</b> Intestinal parasitism remains an important public health problem in Sinaloa (north-western Mexico) and Oaxaca (south-eastern Mexico). Lower income, defecation in open areas, employment status and a lower education level of mothers were the significant factors related to these infections. We conclude that mothers should be involved in health initiatives to control intestinal parasitism in Mexico.</p&gt

A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis