Jablotron 100-Based Corporation Access System

Bakalářská práce se zabývá problematikou návrhu a realizace přístupového systému podniku s elektronickým zabezpečovacím systémem Jablotron 100 a jeho propojením s jinými systémy. Byl navržen a naprogramován můstek s API rozhraním pro ovládání systému Jablotron 100. Tento můstek byl použit pro realizaci webového uživatelského rozhraní k ovládání přístupového systému. Dále byl vytvořen koncept získávání dat docházky ze systému Jablotron 100.This bachelor thesis deals with the design and implementation of an enterprise access control system based on the electronic security system Jablotron 100 and its connection with external systems. The bridge with an API interface for controlling the Jablotron 100-based corporation access system has been designed and programmed. This bridge was used to implement the web user interface to control the access system. Furthermore, the concept of collecting attendance data from the Jablotron 100-based system was created.

Additional file 1: of Structural identifiability of cyclic graphical models of biological networks with latent variables

Identifiability Preservation by Matrix Reduction. This file contains the theoretical justification for the proposed identifiability matrix reduction operations. (PDF 92 kb

Generalized Ordinary Differential Equation Models

<div><p>Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method. Supplementary materials for this article are available online.</p></div

Additional file 2: of Structural identifiability of cyclic graphical models of biological networks with latent variables

Proof of Theorem 1. This file includes the details of theoretical derivation of Theorem 1. (PDF 62 kb

Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

<div><p>Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.</p></div

Real experimental data between 0 to 5 days.

<p>Real experimental data between 0 to 5 days.</p

Optimum parameter for each sampling model.

<p>Optimum parameter for each sampling model.</p

Parameters and variables definitions for agent based model.

<p>Parameters and variables definitions for agent based model.</p

The summary table of ARE values for model 41×9.

<p>The summary table of ARE values for model 41×9.</p