Computer implementation of Mason\u27s rule and software development of stochastic petri nets

A symbolic performance analysis approach for discrete event systems can be formulated based on the integration of Petri nets and Moment Generating Function concepts [1-3]. The key steps in the method include modeling a system with arbitrary stochastic Petri nets (ASPN), generation of state machine Petri nets with transfer functions, derivation of equivalent transfer functions, and symbolic derivation of transfer functions to obtain the performance measures. Since Mason\u27s rule can be used to effectively derive the closed-form transfer function, its computer implementation plays a very important role in automating the above procedure. This thesis develops the computer implementation of Mason\u27s rule (CIMR). The algorithms and their complexity analysis are also given. Examples are used to illustrate CIMR method\u27s application for performance evaluation of ASPN and linear control systems. Finally, suggestions for future software development of ASPN are made

A tutorial on optimization for multi-agent systems

Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization. © 2013 The Author.Peer Reviewe

An Iteration on the Horizon Simulation Framework to Include .NET and Python Scripting

Modeling and Simulation is a crucial element of the aerospace engineering design pro- cess because it allows designers to thoroughly test their solution before investing in the resources to create it. The Horizon Simulation Framework (HSF) v3.0 is an aerospace modeling and simulation tool that allows the user to verify system level requirements in the early phases of the design process. A low fidelity model of the system that is created by the user is exhaustively tested within the built-in Day-in-the-Life simulator to provide useful information in the form of failed requirements, system bottle necks and leverage points, and potential schedules of operations. The model can be stood up quickly with Extended Markup Language (XML) input files or can be customly created with Python Scripts that interact with the framework at runtime. The goal of the work presented in this thesis is to progress HSF from v2.3 to v3.0 in order to take advantage of current software development technologies. This includes converting the codebase from C++ and Lua scripting to C♯ and Python Scripting. The particulars of the considerations, benefits, and implementation of the new framework are discussed in detail. The simulation data and performance run time of the new framework were compared to that of the old framework. The new framework was found to produce similar data outputs with a faster run time

Preditcting Treatment Outcome Using Interpretable Models for Patients with Head and Neck Cancer

Head and neck cancer accounts for around 3 % of cancers worldwide, resulting in many deaths each year. The increasing number of patients receiving a cancer diagnosis increases the demand for accurate diagnosis and effective treatment. Intra-tumor heterogeneity is said to be one of the issues in cancer therapy, an issue that needs to be solved. Radiomics pave the way for extracting features based on the shape, size, and texture of the entire tumor. Radiomics extracts features from tumors based on the gray levels in a medical image. The process of radiomics is intended to capture texture and heterogeneity in the tumor that would be impossible to deduce from a simple tumor biopsy. Feature extraction by radiomics has been proven to enrich clinical datasets with valuable features that positively impact the performance of predictive models. This thesis investigates the use of clinical and radiomics features for predicting treatment outcomes of head and neck cancer patients using interpretable models. The radiomics algorithm extracts first-order statistical, shape, and texture features from PET and CT images of each patient. The 139 patients in the training dataset were from Oslo University Hospital (OUS), whereas the 99 patients in the test set were from the MAASTRO clinic in the Netherlands. All the clinical features, together with the radiomics features, counted 388 features in total. Feature selection through the repeated elastic net technique (RENT) was performed to exclude irrelevant features from the dataset. Seven different tree-based machine learning algorithms were fitted to the data, and the performance was validated by the accuracy, ROC AUC, Matthews correlation coefficient, F1 score for class 1, and F1 score for class 0. The models were tested on the external MAASTRO dataset, and the overall best-performing models were interpreted. On the external dataset from the MAASTRO clinic, the highest-performing models obtained an MCC of 0.37 for OS prediction and 0.44 for DFS prediction. For both OS and DFS, the highest predictions were made on only the clinical data. Transparency in machine learning models greatly benefits decision-makers in clinical settings, as every prediction can be reasoned for. Predicting treatment outcomes for head and neck patients is highly possible with interpretable models. To determine if the methods used in this thesis are suited for predicting treatment outcomes for head and neck cancer patients, it is necessary to test the methods and models on more datasets

Computing paths and cycles in biological interaction graphs

<p>Abstract</p> <p>Background</p> <p>Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.</p> <p>Results</p> <p>We first review algorithms for the enumeration of paths and cycles and show that these algorithms are superior to a recently proposed enumeration approach based on elementary-modes computation. The main part of this work deals with the computation of shortest positive/negative paths, an NP-complete problem for which only very few algorithms are described in the literature. We propose extensions and several new algorithm variants for computing either exact results or approximations. Benchmarks with various concrete biological networks show that exact results can sometimes be obtained in networks with several hundred nodes. A class of even larger graphs can still be treated exactly by a new algorithm combining exhaustive and simple search strategies. For graphs, where the computation of exact solutions becomes time-consuming or infeasible, we devised an approximative algorithm with polynomial complexity. Strikingly, in realistic networks (where a comparison with exact results was possible) this algorithm delivered results that are very close or equal to the exact values. This phenomenon can probably be attributed to the particular topology of cellular signaling and regulatory networks which contain a relatively low number of negative feedback loops.</p> <p>Conclusion</p> <p>The calculation of shortest positive/negative paths and cycles in interaction graphs is an important method for network analysis in Systems Biology. This contribution draws the attention of the community to this important computational problem and provides a number of new algorithms, partially specifically tailored for biological interaction graphs. All algorithms have been implemented in the <it>CellNetAnalyzer </it>framework which can be downloaded for academic use at <url>http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html</url>.</p