Patients with complicated Pott's disease: Management in a rehabilitation department and functional prognosis

AbstractObjectiveThe objective is to study the rehabilitation management and to assess autonomy in daily life activities as well as walking recovery in patients with complicated Pott's disease.Patients and methodsRetrospective study in nine patients over a period of 8 years extending from 2000 to 2008, collated in the Department of Physical Medicine and Functional Rehabilitation, CHU Sahloul, Sousse, Tunisia.ResultsThe mean age of our patients was 43.8 years; sex ratio was 5/4. The spine involvement of tuberculosis was dorsal in seven cases, dorso-lumbar in one patient, and multiple (cervical, dorsal and lumbar) in one case. All patients were paraplegic with a neurological involvement of the bladder. They had prior antituberculosis chemotherapy for at least 8 months. Decompression surgery was performed in six cases. Two female patients presented disorders of spinal posture during treatment requiring surgical revision with osteosynthesis. All patients received additional rehabilitation care. Following a mean duration of hospitalisation in the Rehabilitation department of 47 days with twice-daily sessions of tailored physiotherapy, three patients remained in complete paraplegia, autonomous in wheel-chair and with vesical and sphincter incontinence. The measure of functional independence (MFI) was at admission/discharge 71/92.ConclusionRehabilitation takes an important place in the medico-surgical management in Pott's disease, to limite or compensate the disabilities and handicap related to this pathology

QCM Measurements of RH with Nanostructured Carbon-Based Materials: Part 2-Experimental Characterization

In this series of two papers, the humidity sensing of a carbon nanotube (CNT) network-based material is transduced and studied through quartz crystal microbalance (QCM) measurements. To this aim, quartzes functionalized with different amounts of sensing material were realized, exposed to different humidity levels, and characterized. In this second paper, the experimental results are presented and discussed. The sensing mechanisms are elucidated exploiting the theory presented in the first paper of this series. The presented results show that the investigated material functionalization induces a large response of QCM to humidity in terms of resonant frequency even at low RH levels, with a sensitivity of about 12 Hz/%RH (at RH < 30% and room temperature and 10 ug of deposited SWCNT solution) and an increase in sensitivity in the high RH range typical of nanostructured film. Regarding the response in terms of motional resistance, a large response is obtained only at intermediate and high humidity levels, confirming that condensation of water in the film plays an important role in the sensing mechanism of nanostructured materials

Big-Data Streaming Applications Scheduling Based on Staged Multi-Armed Bandits

Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively. � 2016 IEEE

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies