LaTeX templates and examples — Two-column

This paper presents instructions for preparing papers for the IFMBE Proceedings series. The template is intended to guide the authors in preparing the electronic version of their paper. Only papers prepared according to these instructions will be published in both, the paper and online version of the IFMBE proceedings. In this abstract section you should provide an abstract of your paper, not longer than 300 words.

Con el auge de los sistemas de bases de datos NoSQL los cuales implementan modelos de datos diferentes al relacional como son las bases de datos documentales o de grafos, ha surgido el concepto de Persistencia Políglota. Ésta sostiene que debido a la gran variedad y cantidad de representación de los datos, y los diversos servicios que pueden dar las aplicaciones hoy en día; es necesario el uso de más de un tipo de sistema de almacenamiento para ser capaz de cubrir de forma eficiente todas las necesidades de la aplicación que use dicho sistema. En este trabajo se busca dar una idea general de las Aplicaciones de Persistencia Políglota describiendo las posibles arquitecturas que hacen uso de las bases de datos NoSQL y su funcionamiento, se estudian algunos casos de éxito y se lleva a cabo un caso de estudio usando MongoDB y Neo4j.

Lista de exercício com gabarito

El presente documento es una guía amigable de LaTeX para la redacción de protocolos de investigación con algunas sugerencias de como debe ser presentada y redactada la propuesta. Esta guía actualmente es utilizada por el departamento posgrado de ingeniería electrónica del Instituto Tecnológico de Morelia.

This paper describes the use of the LaTeX qjrms4.cls class file for setting papers for the Quarterly Journal of the Royal Meteorological Society

These instructions give you guidelines for preparing papers for IFAC technical meetings. Please use this document as a template to prepare your manuscript. For submission guidelines, follow instructions on paper submission system as well as the event website.

6 page conference paper submission format, for the Cognitive Science Society Conference (COGSCI) as downloadable here.

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

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