Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

Discriminant analysis for the prediction and classification of tick-borne infections in some dairy cattle herds at Dakahlia Governorate, Egypt

This study was undertaken to use the variable loadings in linear discriminant analysis (LDA) to determine the most important predictors for the discrimination of tick-borne diseases (TBDs), particularly babesiosis and anaplasmosis and predict the group membership from the predictors. In total, 163 cattle, from different localities at Dakahlia Governorate, Egypt, were investigated in 2012 and 2013 for the presence of TBDs. All cattle were clinically examined and a clinical index score was determined for each cow. Blood samples were also collected from each animal for adopting microscopy and diagnostic laboratory methods. Out of the examined cattle, 83 animals were acutely-ill (Babesia bovis and Anaplasma marginale were identified in 11 and 10 animals, respectively), while 80 cows were apparently healthy but having previous attacks of blood parasites (23 animals harbored anaplasma marginale (asymptomatic carriers)). The remained 119 animals were negative to TBDs. Fourteen animals were not survived and 149 cases were survived. As the result of the first LDA to discriminate babesiosis, anaplasmosis and negative to TBDs, 89.0% of animals were correctly classified; 78.8% (26/33) for anaplasma, 100% (11/11) for babesia infections, 90.8% (108/119) for negative to TBDs, respectively. The important predictors for the discrimination were oculonasal discharge, bloody feces, hemoglobinuria, bloody feces and respiratory rate. On the other hand, the second LDA discrimination showed high classification accuracy of 87.1% for the discrimination of survivors and non-survivors; 89.9% (134/149) for survivors and 57.1% (8/14) for non-survivors, while the important predictors included oculonasal discharge, recumbent posture and nervous sign

The 3XMM/SDSS Stripe 82 Galaxy Cluster Survey: Cluster catalogue and discovery of two merging cluster candidates

We present a galaxy cluster survey based on XMM-Newton observations that are located in Stripe 82 of the Sloan Digital Sky Survey (SDSS). The survey covers an area of 11.25 deg$^2$. The X-ray cluster candidates were selected as serendipitously extended detected sources from the third XMM-Newton serendipitous source catalogue (3XMM-DR5). A cross-correlation of the candidate list that comprises 94 objects with recently published X-ray and optically selected cluster catalogues provided optical confirmations and redshift estimates for about half of the candidate sample. We present a catalogue of X-ray cluster candidates previously known in X-ray and/or optical bands from the matched catalogues or NED. The catalogue consists of 54 systems with redshift measurements in the range of 0.05-1.19 with a median of 0.36. Of these, 45 clusters have spectroscopic confirmations as stated in the matched catalogues. We spectroscopically confirmed another 6 clusters from the available spectroscopic redshifts in the SDSS-DR12. The cluster catalogue includes 17 newly X-ray discovered clusters, while the remainder were detected in previous XMM-Newton and/or ROSAT cluster surveys. Based on the available redshifts and fluxes given in the 3XMM-DR5 catalogue, we estimated the X-ray luminosities and masses for the cluster sample. We also present the list of the remaining X-ray cluster candidates (40 objects) that have no redshift information yet in the literature. Of these candidates, 25 sources are considered as distant cluster candidates beyond a redshift of 0.6. We also searched for galaxy cluster mergers in our cluster sample and found two strong candidates for newly discovered cluster mergers at redshifts of 0.11 and 0.26. The X-ray and optical properties of these systems are presented.Comment: 17 pages, 12 figures, accepted for publication in A&A, revised version after language editin

The Nikolaevskiy equation with dispersion

The Nikolaevskiy equation was originally proposed as a model for seismic waves and is also a model for a wide variety of systems incorporating a neutral, Goldstone mode, including electroconvection and reaction-diffusion systems. It is known to exhibit chaotic dynamics at the onset of pattern formation, at least when the dispersive terms in the equation are suppressed, as is commonly the practice in previous analyses. In this paper, the effects of reinstating the dispersive terms are examined. It is shown that such terms can stabilise some of the spatially periodic traveling waves; this allows us to study the loss of stability and transition to chaos of the waves. The secondary stability diagram (Busse balloon) for the traveling waves can be remarkably complicated.Comment: 24 pages; accepted for publication in Phys. Rev.

Adherence to UK national guidance for discharge information: an audit in primary care

Aims: Poor communication of clinical information between healthcare settings is associated with patient harm. In 2008, the UK National Prescribing Centre (NPC) issued guidance regarding the minimum information to be communicated upon hospital discharge. This study evaluates the extent of adherence to this guidance and identifies predictors of adherence. Methods: This was an audit of discharge summaries received by medical practices in one UK primary care trust of patients hospitalized for 24 h or longer. Each discharge summary was scored against the applicable NPC criteria which were organized into: ‘patient, admission and discharge’, ‘medicine’ and ‘therapy change’ information. Results: Of 3444 discharge summaries audited, 2421 (70.3%) were from two teaching hospitals and 906 (26.3%) from three district hospitals. Unplanned admissions accounted for 2168 (63.0%) of the audit sample and 74.6% (2570) of discharge summaries were electronic. Mean (95% CI) adherence to the total NPC minimum dataset was 71.7% [70.2, 73.2]. Adherence to patient, admission and discharge information was 77.3% (95% CI 77.0, 77.7), 67.2% (95% CI 66.3, 68.2) for medicine information and 48.9% (95% CI 47.5, 50.3) for therapy change information. Allergy status, co-morbidities, medication history and rationale for therapy change were the most frequent omissions. Predictors of adherence included quality of the discharge template, electronic discharge summaries and smaller numbers of prescribed medicines. Conclusions: Despite clear guidance regarding the content of discharge information, omissions are frequent. Adherence to the NPC minimum dataset might be improved by using comprehensive electronic discharge templates and implementation of effective medicines reconciliation at both sides of the health interface

Handwritten Signature Verification using Deep Learning

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%