This paper deals with the creation of the tool for chip design in UML. The intention of this work is to present the news in the UML language version 2.0, that can be possibly used for modeling of embedded systems. Furthermore, it deals with the possibility and method of modeling in the Eclipse environment and it focuses on the Eclipse Modeling Framework. This work explains the principle of developing of graphical editors based on GMF used fully by developing tool. Finally, it discusses the chosen solution.

With complex systems, monolithic models become impractical and it becomes necessary to model them through subsystems and components. Unless these components and subsystems are structured, exploiting them in a methodical manner to develop a control logic for them also becomes complex. In the previous research, to characterize the input/output behavior of discrete state interacting, systems a condition language framework was defined and algorithms that can automatically generate a controller given the system model and the desired specification using this framework were presented. Though this framework and the control algorithms are ideally suited to simple systems, representation of components with large state spaces requires a more refined approach. In this thesis, we present the modelling framework namely Color condition systems, that compactly represent components with large state spaces. We also present algorithms that can automatically generate a controller that consists of a set of action type taskblocks, given the system model and the desired specification described using color condition systems. The modelling framework and the working of the algorithms are illustrated using figures and comments on the possible ways of optimizing the algorithms are also quoted. Finally, in the appendix, we also present the approach that can be taken to implement a few parts of the algorithm.

<p dir="ltr">In recent years, probabilistic graphical models have emerged as a powerful framework for understanding complex dependencies in multivariate data, offering a structured approach to tackle uncertainty and model complexity. These models have revolutionized the way we interpret the interplay between variables in various domains, from genetics to social network analysis. Inspired by the potential of probabilistic graphical models to provide insightful data analysis while addressing the challenges of high-dimensionality and computational efficiency, this dissertation introduces two novel methodologies that leverage the strengths of graphical models in high-dimensional settings. By integrating advanced inference techniques and exploiting the structural advantages of graphical models, we demonstrate how these approaches can efficiently decode complex data patterns, offering significant improvements over traditional methods. This work not only contributes to the theoretical advancements in the field of statistical data analysis but also provides practical solutions to real-world problems characterized by large-scale, complex datasets.</p><p dir="ltr">Firstly, we introduce a novel Bayesian hybrid method for learning the structure of Gaus- sian Bayesian Networks (GBNs), addressing the critical challenge of order determination in constraint-based and score-based methodologies. By integrating a permutation matrix within the likelihood function, we propose a technique that remains invariant to data shuffling, thereby overcoming the limitations of traditional approaches. Utilizing Cholesky decompo- sition, we reparameterize the log-likelihood function to facilitate the identification of the parent-child relationship among nodes without relying on the faithfulness assumption. This method efficiently manages the permutation matrix to optimize for the sparsest Cholesky factor, leveraging the Bayesian Information Criterion (BIC) for model selection. Theoretical analysis and extensive simulations demonstrate the superiority of our method in terms of precision, recall, and F1-score across various network complexities and sample sizes. Specifically, our approach shows significant advantages in small-n-large-p scenarios, outperforming existing methods in detecting complex network structures with limited data. Real-world applications on datasets such as ECOLI70, ARTH150, MAGIC-IRRI, and MAGIC-NIAB further validate the effectiveness and robustness of our proposed method. Our findings contribute to the field of Bayesian network structure learning by providing a scalable, efficient, and reliable tool for modeling high-dimensional data structures.</p><p dir="ltr">Secondly, we introduce a Bayesian methodology tailored for Gaussian Graphical Models (GGMs) that bridges the gap between GBNs and GGMs. Utilizing the Cholesky decomposition, we establish a novel connection that leverages estimated GBN structures to accurately recover and estimate GGMs. This innovative approach benefits from a theoretical foundation provided by a theorem that connects sparse priors on Cholesky factors with the sparsity of the precision matrix, facilitating effective structure recovery in GGMs. To assess the efficacy of our proposed method, we conduct comprehensive simulations on AR2 and circle graph models, comparing its performance with renowned algorithms such as GLASSO, CLIME, and SPACE across various dimensions. Our evaluation, based on metrics like estimation ac- curacy and selection correctness, unequivocally demonstrates the superiority of our approach in accurately identifying the intrinsic graph structure. The empirical results underscore the robustness and scalability of our method, underscoring its potential as an indispensable tool for statistical data analysis, especially in the context of complex datasets.</p>

Event history analysis is the most prevalent modeling technique used to model event occurrence with longitudinal data (Cox & Oakes, 1984; Menard, 1991; Singer & Willett, 1993, 2003). An alternative is to model longitudinal data within the SEM framework, known as latent variable growth modeling (McArdle, 1988; Meredith & Tisak, 1990), which can provide a more robust framework. Whether or not a student remains in college presents an appropriate context within which to examine the modeling of event occurrence with longitudinal data. The purpose of the study was to compare event history and latent growth modeling as for predicting change in college student perseverance, with college student persistence literature serving as the framework. Students are defined as having persevered if they have earned hours and the end of the semester rather than if they are enrolled at the beginning of the semester, which is the traditional definition of persistence.The population for the study was the 2001 and 2002 cohorts of first-time, full-time freshmen at a large mid-Atlantic urban research university. Stopouts and transfer students were excluded. Data was analyzed for the first five semesters for each cohort. The results showed that parameter estimates were quite consistent across model type and time frame and were mostly consistent with previous research. No one method outperformed the others in terms of predicting correct classification. Using event history analysis with the structural equation modeling framework, however, appeared to be a very promising alternative to event history analysis with logistic regression since one can model error term and examine the differential effects of predictors at each time period. Finally, while latent growth modeling did not outperform the other methods in predictive classification, the study demonstrated it can be used for event occurrence analysis to test more complex theories.

Domain Specific Modeling (DSM) aims to increase productivity of software development by raising the level of abstraction beyond code concepts and using domain concepts. By providing a generative model-driven tooling component and runtime support, the Eclipse Graphical Modeling Framework (GMF) aims to simplify the creation of diagram editors for specific domains based on a series of model creation and transformation steps. GMF leverages the Eclipse Modeling Framework (EMF) and the Eclipse Graphical Editing Framework (GEF) to allow the graphical modeling of Domain Specific Languages (DSL). A Domain Specific Language (DSL) is developed specifically for a specific task and specific domain. In this research, the State Machine Compiler (SMC) represents the specific domain for which a DSL in a form of a diagram editor is developed using GMF. SMC is an open source Java tool allowing generation of state pattern classes from textual descriptions of state machines. The main objective of this research is to describe the use of GMF, highlight potential pitfalls and identify strengths and weaknesses of GMF based on certain criteria. To be able to feed the SMC diagrams created with the editor into SMC, a Java Emitter Templates (JET) transformation is used to transform SMC model instances into textual format expected by SMC. / Thesis (Master, Computing) -- Queen's University, 2011-04-14 18:58:08.797

The adage A picture is worth a thousand words conveys the notion that acomplex concept can be understood with just a single picture. Thus visualisingdata allows users to absorb and use large amounts of data quickly.Although textual programming is widely used, it is not best suited for allsituations. Some of these situations require a graphical way to programdata. This thesis investigates the dierent modeling frameworks available withinthe Eclipse ecosystem that allow the reuse of existing XML schema modelsand the creation as well as editing of diagnostic methods. The chosenframeworks were used to build a graphical editor that allows users to create,edit and use diagnostic methods graphically.

The Architecture of integrated Information Systems (ARIS) is a technical space that is widely used in the area of business process management. The reuse of ARIS models in other working contexts is offered by ARIS-specific import and export interfaces. Nevertheless, the interoperability with other technical spaces is limited. In this paper, we explore ARIS language definition concepts and relate them to the Eclipse Modeling Framework (EMF). We describe an ARIS to EMF bridge which provides transformations of ARIS modeling languages and ARIS models to the EMF environment. Our bridge shows similarities and differences between the two approaches and provides technical interoperability that e.g. enables the processing of ARIS models in EMF-supporting tools (e.g. ATLAS Transformation Language).

Immediate treatment is of extreme importance for stroke patients. However, providing fast enough treatment for stroke patients is far from trivial, mainly due to logistical challenges and difficulties in diagnosing the correct stroke type. One way to reduce the time to treatment is to use so-called Mobile Stroke Units (MSUs), which allows to diagnose and provide treatment for stroke patients already at the patient scene. A well-designed stroke transport policy is vital to improve the access to treatment for stroke patients. Simulation and mathematical optimization are useful approaches for assessing and optimizing stroke transport policies, without endangering the health of the patients. The main purpose of this thesis is to contribute to improving the situation for stroke patients and to reducing the social impacts of stroke. The aim is to study how to use simulation and optimization to achieve improved analysis and planning of prehospital stroke care. In particular, we focus on assessing the potential use of MSUs in a geographic area. In this thesis, optimization is used to identify the optimal locations of MSUs, and simulation is used to assess different stroke transport policies, including MSU locations. The results of this thesis aim to support public health authorities when making decisions in the prehospital stroke care domain. In order to fulfill the aim of this thesis, we develop and analyze a number of different simulation and optimization models. First, we propose a macro-level simulation model, an average time to treatment estimation model, used to estimate the expected time to treatment for different parts of a geographic region. Using the proposed model, we generate two different MSU scenarios to explore the potential benefits of employing MSUs in Sweden’s southern healthcare region (SHR).   Second, we present an optimization model to identify the best placement of MSUs while making a trade-off between the efficiency and equity perspectives, providing maximum population coverage and equal service for all patients, respectively. The trade-off function used in the model makes use of the concepts of weighted average time to treatment to model efficiency and the time difference between the expected time to treatment for different geographical areas to model equity. In a scenario study applied in the SHR, we evaluate our optimization model by comparing the current situation with three MSU scenarios, including 1, 2, and 3 MSUs. Third, we present a micro-level discrete event simulation model to assess stroke transport policies, including MSUs, allowing us to model the behaviors of individual entities, such as patients and emergency vehicles, over time. We generate a synthetic set of stroke patients using a Poisson distribution, used as input in a scenario study. Finally, we present a modeling framework with reusable components, which aims to facilitate the construction of discrete event simulation models in the emergency medical services domain. The framework consists of a number of generic activities, which can be used to represent healthcare chains modeled in the form of flowcharts. As the framework includes activities and policies modeled on the general level, the framework can be used to create models only by providing input data and a care chain specification. We evaluate the framework by using it to build a model for simulating EMS activities related to the complex case of acute stroke. / <p>Note: The papers are not included in the fulltext online.</p>

Enterprises are growing in complexity due to numerous interactions within and outside the enterprise. Enterprise modeling addresses this issue of complexity by helping to structure it. A review of the literature indicates several issues in the field of enterprise modeling need to be addressed. First, the terms related to enterprise modeling have numerous definitions, each one focusing on different aspects. These definitions are analyzed and a comprehensive definition is provided. Next, enterprise modeling methodologies and enterprise modeling frameworks in the literature focus on different views when modeling an enterprise, thus making it difficult for an enterprise to choose the framework that best fits their needs. In order to resolve this, an enterprise modeling framework is designed that attempts to incorporate all of the views of an enterprise. This framework is then extended, by taking into account various models and functionalities provided in enterprise modeling software packages.

Economic input-output (I-O) and watershed models provide useful results but when seeking to integrate these systems, the structural, spatial, and temporal differences between these models must be carefully considered. To reconcile these differences, a hydrologic-economic modeling framework is designed to couple an economic model with a watershed model. A physically constrained, I-O model, RCOT, is used to represent the economic system in this framework because it provides sectoral detail for a regional economy and calculates physical resource quantities used by these sectors. Uniquely, it also allows for technology options for all sectors and minimizes the resource use based on environmental constraints imposed by the watershed, which adds complexity to the representation of the economic system and its interactions with the watershed system. To represent the watershed system in this framework, the Hydrological Simulation Program-Fortran (HSPF) is used. An HSPF model has been calibrated to represent the hydrological processes of Cedar Run Watershed by the Occoquan Watershed Monitoring Laboratory (OWML). Thus, the capabilities of this framework are demonstrated using strategic scenarios developed to examine future development patterns that may occur within Fauquier County, northern Virginia, and its local basin, Cedar Run Watershed. The scenarios evaluate both the downstream and seasonal impacts on water flow and nitrogen concentration within the watershed, and the changes made within the economic system in response to these impacts. For these scenarios, the most efficient solution is the one that minimizes the use of resource inputs within the economic sectors, including developed land, water withdrawn, and applied nitrogen, which in turn inform watershed health. The scenario results demonstrate that this coupled hydrologic-economic modeling framework can overcome the spatial differences of the individual models and can capture the interactions between watershed and economic systems at a temporal resolution that expands the types of questions one can address beyond those that can be analyzed using these models separately. / Doctor of Philosophy / Water is an essential commodity for human survival, a necessary resource for many industries, and a crucial indicator of environmental health. Rising human populations have created stress on the natural supply of water resources while corresponding economic activities have contributed to the deterioration in water quality. Therefore, it is essential to identify pathways for addressing water use and contamination while also supporting economic progress to achieve sustainable development. The region of study is Fauquier County, located in northern Virginia, USA. This county has a long association with agricultural production, but it has been experiencing development pressure due to its proximity to Washington DC (50 km southwest). Within Fauquier County lies Cedar Run Watershed (498 km2), a sub-basin of Occoquan Watershed (1,515 km2). Occoquan Watershed drains into the Occoquan Reservoir, which is a drinking water source for close to two million residents in northern Virginia. The motivation of this research is to design a coupled modeling framework that allows insight to be gained into the interactions that occur between watershed and economic systems. This framework is then used to evaluate how changes in economic activities will cause changes in water use and contamination levels within Cedar Run Watershed and vice versa. By designing strategic scenarios to provide implications about future development patterns that may occur in the region, changes can be anticipated, and conclusions can be reached.