I analyzed the Hextech-Crafting in a Stochastic Simulation with 10 Million Players and found that you're going to make more Riot Points than you spend (in value) and thats without counting Champs. I've made some assumptions, which can be found down in the paper itself for those interested. For everyone else: If you're trying to maximize your RP-Net-Worth stack up on Hextech Chests.
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.
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
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.
In this document we focus on modifying the Linux Kernel through memory and scheduler parameters. The main objective is to study the performance of a computer during the execution of AIO-Stress Benchmark. It was necessary to run the test several times since three of the parameter mentioned in this project were modified 5 times. After completing the test, the results were displayed on graphs, showing that all the variables have a noticeable influence on the performance of the computer.