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Gene regulatory networks have an important role to study the behaviour of genes. By analysing these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its qualitative dynamical behaviour. René Thomas discrete modelling of gene regulatory network (GRN) is a well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN. We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs. As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra features i.e. CTL editor, parameters filtering and input/output files management. GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification, K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours and days of manual computation

The template of "white paper project" in CyberAgent, inc.

This LaTeX tutorial will introduce LaTeX, creating a .tex file, sections, body text, tables, figures, mathematical formulas, acknowledgements, and references.

devoir 1 ere annee niveu lycee

Las bases de datos relacionales han sido las herramientas por excelencia para el almacenamiento de la información en los sistemas informáticos. No obstante, las bases de datos NoSQL, como tendencia, han venido ganando espacio especialmente por la escalabilidad y velocidad en sus tiempos de respuestas. PostgreSQL ha incorporado algunas características de tipo NoSQL, como el almacenamiento efímero y el manejo de datos JSON; características que pueden aprovecharse para realizar acciones desde el gestor dándole mayor potencia. El objetivo de este artículo es evaluar, mediante toda la documentación encontrada, el comportamiento de las características NoSQL de PostgreSQL frente a un gestor NoSQL, comparandola con MongoDB, respecto a los tiempos de respuestas y dar a conocer las ventajas de uno con respecto al otro. Palabras Claves: Características NoSQL en PostgreSQL, MongoDB, PostgreSQL

Distributed system is a collection of independent systems which can communicate with each other by transferring massages. There are some major issues in distributed systems but we focus in this paper on fault tolerance. It is the system’s ability to work in the condition when there occur any type of some fault in the system, like failure in communication, hardware or resources. It is a very important issue in distributed system, in this paper we present a survey of different types of fault tolerance techniques and their comparison.

Deep learning is a fast growing field in tech that is often described to have limitless potential. This paper describes its history, why the explosion in popularity, and how it works. An example of classifying images of handwritten digits (MNIST) will be explored using a fully connected network and a convolutional neural network. Next, a brief description of the tools necessary for the reader to implement his or her own network. Finally, a view of the state of the art being developed by companies such as Google, Facebook, and Baidu.

Created using the Deedy CV/Resume XeLaTeX Template Version 1.0 (5/5/2014) This template has been downloaded from: http://www.LaTeXTemplates.com Original author: Debarghya Das (http://www.debarghyadas.com) With extensive modifications by: Vel (vel@latextemplates.com)

目的： 熟悉分析實驗數據的方法，測量並分析誤差。 學習有效數的處理。 了解螺旋測微器(Micrometer)的操作方式並用以測定微小物體的厚度或直徑等。 了解游標尺(Vernier caliper)的操作方式並用以測量塑膠管的深度及內、外徑。