Intractable Epilepsy in Children

ObjectiveEpilepsy is a common disorder affecting approximately 1% of the population. It is estimated that about 20- 30% of the patients become refractory to proper medical therapies. Such cases are often termed intractable. Intractable epilepsy (IE) is a serious condition in children, leading to significant impairment in quality of life, as well as behavioral and psychiatric problems.In this review, we tried to define intractability, mention the causes of intractable epilepsy and its predictive factors in children, and outline the management and various treatments of intractable epilepsy.

Trust Challenges in Reusing Open Source Software: An Interview-based Initial Study

Open source projects play a significant role in software production. Most of the software projects reuse and build upon the existing open source projects and libraries. While reusing is a time and cost-saving strategy, some of the key factors are often neglected that create vulnerability in the software system. We look beyond the static code analysis and dependency chain tracing to prevent vulnerabilities at the human factors level. The literature lacks a comprehensive study of the human factors perspective on the issue of trust in reusing open source projects. We performed an interview-based initial study with software developers to get an understanding of the trust issue and limitations among the practitioners. We outline some of the key trust issues in this paper and lay out the first steps toward the trustworthy reuse of software.Comment: To appear in Proceedings of 26th ACM International Systems and Software Product Line Conference - Volume

Sodium Valproate and Phenobarbitol: Weight Complications of Treatment in Epileptic Children

Objective The aim of this study was to evaluate and compare the effects of Na Valproate and Phenobarbital on changes in the weight of epileptic patients following treatment for their condition using the drugs mentioned.Materials and methodsSixty epileptics were assigned into two groups of 30 patients each, the case and controls. The diagnosis was made on the basis of the International League Against Epilepsy (ILAE) characteristics. BMI was defined. In the case group, the patients received 20mg/kg/day of Na Valproate, while the 30 controls received 5mg/kg/day of Phenobarbital for 6 months. Using the Mc Nemar and Chi-2 tests, BMI changes were compared after 6 months between the groups. Fisher's exact test was used to evaluate the role of age, sex, and primary weight on the weight increase due to Na Valproate usage.ResultsThere were no specific changes in age, sex, primary BMI and fatness between the 2 groups; in the case group, 20 patients(66.7%) and in the controls 4(13.3%) gained weight (PConclusionThe results indicate that epileptic children, aged over 10 years, and those who are overweight have more chances of gaining weight or becoming fatter, following treatment with Na Valproate. Further studies investigating the issue are warranted

Repository for Reusing Artifacts of Artificial Neural Networks

Artificial Neural Networks (ANNs) replaced conventional software systems in various domains such as machine translation, natural language processing, and image processing. So, why do we need an repository for artificial neural networks? Those systems are developed with labeled data and we have strong dependencies between the data that is used for training and testing our network. Another challenge is the data quality as well as reuse-ability. There we are trying to apply concepts from classic software engineering that is not limited to the model, while data and code haven't been dealt with mostly in other projects. The first question that comes to mind might be, why don't we use GitHub, a well known widely spread tool for reuse, for our issue. And the reason why is that GitHub, although very good in its class is not developed for machine learning appliances and focuses more on software reuse. In addition to that GitHub does not allow to execute the code directly on the platform which would be very convenient for collaborative work on one project.Comment: tool paper https://github.com/ghofrani85/RAN2 7 page

Conceptualization and software development of a simulation environment for probabilistic safety assessments of radioactive waste repositories

Uncertainty and sensitivity analysis of complex simulation models are prominent issues, both in scientific research and education. ReSUS (Repository Simulation, Uncertainty propagation and Sensitivity analysis) is an integrated platform to perform such analysis with numerical models that simulate the THMC (Thermal Hydraulical Mechanical and Chemical) coupled processes via different programs, in particular in the context of safety assessments for radioactive waste repositories. This thesis presents the idea behind the software platform ReSUS and its working mechanisms. Apart from the idea and the working mechanisms, the thesis describes applications related to the safety assessment of radioactive waste disposal systems. In this thesis, previous simulation tools (including the preceding version of ReSUS) are analyzed in order to provide a comprehensive view of the state of the art. In comparison to this state, a more sophisticated software tool is developed here, which provides features which are not offered by previous simulation tools. To achieve this objective, the software platform ReSUS provides a framework for handling probabilistic data uncertainties using deterministic external simulation tools, thus enhancing uncertainty and sensitivity analysis. This platform performs probabilistic simulations of various models, in particular THMC coupled processes, using stand-alone deterministic simulation software tools. The complete software development process of the ReSUS Platform is discussed in this thesis. ReSUS components are developed as libraries, which are capable of being linked to other code implementations. In addition, ASCII template files are used as means for uncertainty propagation into the input files of deterministic simulation tools. The embedded input sampler and analysis tools allow for sensitivity analysis in several kinds of simulation designs. The novelty of the ReSUS platform consists in the flexibility to assign external stand-alone software tools (regardless of source code availability) to probabilistic simulations. Furthermore, the ReSUS platform implements a simple scientific workflow management system to handle the data and process flow between several external simulation programs automatically, thus allowing for the creation of chains of simulation models. In addition, the multilayer architecture of ReSUS platform provides a number of different interfaces to connect with other tools in the field of uncertainty and sensitivity analysis.Unsicherheits- und Sensitivitätsanalysen von komplexen Simulationsmodellen sind sowohl in der Forschung als auch in der Lehre bedeutende Themen. ReSUS (Repository Simulation, Uncertainty propagation and Sensitivity analysis) ist eine integrierte Plattform für solche Analysen unter Nutzung von numerischen Modellen bzw. Programmen zur Simulation von Thermo-Hydraulisch-Mechanisch- Chemisch (THMC) gekoppelten Prozessen, insbesondere in Zusammenhang mit Sichrheitsanalysen für Endlager radioaktiver Abfälle. Die vorliegende Arbeit stellt die Idee hinter ReSUS sowie die Arbeitsmechanismen vor. Zudem erfolgt die Beschreibung einer beispielhaften Anwendung mit Bezug zur Sicherheitsbewertung von Endlagern für radioaktive Abfälle. In der vorliegenden Arbeit wird darüber hinaus ein umfassender Überblick über weitere gängige einschlägige Simulation- und Analysestools (einschließlich der Vorgängerversion von ReSUS) und damit über den Stand von Wissenschaft und Technik gegeben. Die Entwicklung des Software-Tools ReSUS orientiert sich an diesem Stand und soll, im Vergleich zu diesen Programmen, ergänzende Funktionalität zur Verfügung stellen. Diese Software-Plattform ermöglicht dahingehend die probabilistische Behandlung von Datenunsicherheiten durch die Nutzung deterministischer Fremdcodes, und bietet damit verbesserte Möglichkeiten zur Unsicherheits- und Sensitivitätsanalyse. Mit der Plattform können durch die Nutzung eigenständiger deterministischer Software-Tools unterschiedliche Modelle probabilistisch simuliert werden, insbesondere THMC-gekoppelten Prozesse. Der gesamte Software-Entwicklungsprozess der ReSUS-Plattform wird in der vorliegenden Arbeit diskutiert. Die ReSUS-Komponenten wurden als Bibliotheken entwickelt, die eine Verbindung mit anderen Code- Implementierungen ermöglichen. Darüber hinaus werden ASCII-Datei-Templates verwendet, um die Informationen über die Unsicherheiten zwischen den jeweiligen einzelnen Simulationen bzw. Fremdcodes zu übertragen. Es können Ketten von Simulationsmodellen angelegt und ausgeführt werden. Die eingebetteten Stichprobenerzeuger und Analyse-Tools ermöglichen Sensitivitätsanalysen auf verschiedene Arten von Simulation-Designs. Das Novum der ReSUS Plattform besteht zusammenfassend in deren Flexibilität, ursprünglich geschlossen arbeitende Stand-alone-Software-Werkzeuge (auch unabhängig von der Quellcode- Verfügbarkeit) in globale (probabilistische) Simulationen zu integrieren. Darüber hinaus bietet die ReSUS Plattform ein einfaches System, welches ein automatisches, wissenschaftliches Workflow- Management des Daten- und Prozessablaufs zwischen den zugewiesen Simulationsprogrammen ermöglicht. Zusätzlich bietet die Mehrschichtarchitektur der ReSUS Plattform eine Reihe von verschiedenen Schnittstellen zu anderen Werkzeugen im Bereich der Unsicherheits- und Sensitivitätsanalyse