Diagnostic des mouvements anormaux du nouveau-né

RésuméLe nouveau-né est prédisposé à des phénomènes moteurs de sémiologie apparemment proche mais dont les mécanismes sousjacents et les étiologies peuvent être radicalement différents. Une analyse sémiologique fine, aidée au besoin par un enregistrement EEG vidéo, doit permettre de distinguer trémulations, myoclonies et convulsions qui sont les plus fréquents. Il ne faut pas méconnaître des manifestations motrices plus rares comme l’hyperekplexie et la myotonie. Le contexte de survenue, le caractère isolé ou associé à d’autres mouvements anormaux ou à des anomalies de l’examen clinique sont les éléments essentiels de l’orientation étiologique. Une démarche diagnostique précise est nécessaire pour éviter le traitement abusif de manifestations bénignes. Summary The newborn infant is prone to motor phenomena of various physiological mechanisms and pathological significance whereas they can share close clinical patterns. A detailed clinical analysis, that should be supported by a video EEG recording, is necessary. That may help to distinguish myoclonus, jitteriness or seizures. Some rare phenomenom such as hyperekplexia or myotonia have also to be known. The pregnancy and birth history, the clinical examination and the search for association of various motor phenomena give essential clues for the diagnosis. Misdiagnosing non epileptic phenomona as seizures has to be avoided as it leads to unnecessary anticonvulsivant therapy with potential harmful effects

BUCKLING ANALYSIS OF THE INDUSTRIAL FACTORY MODEL BY FINITE ELEMENT METHOD

Buckling is a subject that has been discussed for a long time, however, it still be studied and developed due to its practicality. The following article introduces two methods that are used to solve the problems involving buckling of the beam, shell and solid with an I shape cross-section having different cases of boundary load. The theory which is used in this article is Euler's formula and Eurocode 3 standard. The analytical results by ANSYS commercial software are compared with the theoretical results and results from Eurocode 3 standard. The authors based on the reliability of the calculating results to simulate buckling of the industrial factory model with different cases of load conditions. The simulating results show a general view of buckling cases

Tweeting the storm: A SCCT approach to NPOs’ Twitter communications during Hurricane Matthew

University of Minnesota M.A. thesis. May 2017. Major: Mass Communication. Advisor: Amy O'Connor. 1 computer file (PDF); v, 81 pages.Hurricane Matthew, one of recent history’s most devastating natural disasters, had a severe impact on parts of the Southeastern U.S. and Haiti. This research looked at how four non-profit organizations, The American Red Cross, The Salvation Army USA, Hope for Haiti, and World Vision Haiti, used Twitter to communicate crisis response strategies with the public. Guided by the SCCT, this study implemented a qualitative textual analysis of the organizations’ Tweets in the pre-crisis, crisis, and post-crisis phases of the disaster. The research findings indicated a disconnect between theoretical response recommendations and Twitter communication. Recommendations for practical implications of this research included a need for greater consideration, on the part of practitioners, organizations, and others involved in crisis communication, of SCCT response recommendations, Twitter as a unique and growing communication outlet, and target audience of response strategies and crisis communication

THE ANALYSIS OF FLUID DYNAMICS OF WAVE POWER STATION WITH WELLS TURBIN BY CFD

Natural energy such as wind, wave and other natural vibrations is one of the high potential renewable energy sources. The Wells turbine is based on the use of bidirectional turbines, which act as axial-flow self-rectifying turbines that employs a symmetrical blade profile and rotating unidirectionally in reciprocating airflows generated by the air chamber to extract energy from vibrations. These topics have been extensively studied both numerically and experimentally such as research on the parameters of the effects of structure, angle of attack, blade shape, etc. In this paper, numerical simulation is carried out using commercially available tool Fluent for fluid dynamics analysis and focus on oscillating predictions, with particular attention to the behavior of the flow. Based on the Numerical Wave Tank (NWT) model is simulated in a two dimensional used in this model, which is constructed mainly based on the spatially averaged Navier Stokes equation with the k-ε model for simulating the turbulence and modeled with Volume of Fluid (VOF). Axial-flow turbines system and future development as well as the proposed limitations will be discussed in detail

An enhanced nodal gradient finite element for non-linear heat transfer analysis

The present work is devoted to the analysis of non-linear heat transfer problems using the recent development of consective-interpolation procedure. Approximation of temperature is enhanced by taking into account both the nodal values and their averaged nodal gradients, which results in an improved finite element model. The novel formulation possesses many desirable properties including higher accuracy and higher-order continuity, without any change of the total number of degrees of freedom. The non-linear heat transfer problems equation is linearized and iteratively solved by the Newton-Raphson scheme. To show the accuracy and efficiency of the proposed method, several numerical examples are hence considered and analyzed

Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.Comment: 9 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis