Metapolybia wasps construct their nests on flat surfaces using plant materials, which they process into paper. For processing the pulp wasps need water, which is collected by water foragers, and it is transferred to pulp foragers indirectly via a "common stomach." The common stomach is formed by generalist wasps that can engage in water exchange and can store water in their crops. Our goal is to provide an alternative model for regulating task partitioning in construction behavior, focusing on worker connectivity instead of using threshold curves to model mechanisms of colony-level regulation. We propose that the existence of an information center and of a network of worker interactions, which establish sets of positive and negative feedbacks, allow collective regulation of colony-wide behaviors. Using a Stock and Flow modeling framework, we illustrate that the common stomach could serve both as a temporal storage for water and also as a source of information about the colony's current demands related to nest construction tasks. Our model predicts that assessing colony needs via individual interactions with the common stomach leads to a robust regulation of task partitioning in construction behavior. Using perturbation experiments in our simulations, we show that this emergent task allocation is able to dynamically adapt to perturbations of the environment and to changes in colony-level demands or population structure. Our model closely mimics and predicts the behavior of Metapolybia wasps, demonstrating that the regulation mechanism based on worker connectivity through a common stomach is a plausible hypothesis for the organization of collective behavior.

<p>Disertacijom su opisane postojeće tehnike redukcije dinamičkih modela koje se koriste u teoriji upravljanja i postojeće tehnike za redukciju dinamičkih modela i ekvivalentiranje elektroenergetskih sistema. Predložen je nov pristup na fizici problema zasnovanoj redukciji dinamičkog modela elektroenergetskog sistema korišćenjem teorije balansnih realizacija. Takođe se predlaže korišćenje aproksimativnih bisimulacionih relacija za redukciju dinamičkih modela elektroenergetskog sistema. Postojeće tehnike i predloženi pristupi i algoritmi su primenjeni za redukciju dinamičkih modela dva razmatrana test sistema.</p> / <p>Dissertation describes the existing dynamic model reduction techniques used in control theory and existing techniques that are used for the reduction (equivalencing) of power system dynamic models. A new approach to physics-based reduction of power system dynamic model based on the balanced realization theory is proposed. Use of approximate bisimulation relations for reduction of power system dynamic models is also proposed. Existing techniques and proposed approaches and algorithms are applied to reduce the dynamic models of two considered test systems.<br />&nbsp;</p>

In order for a medication treatment to be considered successful, several roles and functions along the pharmacological treatment chain must function and cooperate effectively. The chain can most easily be described as five transitions; diagnosis, prescription treatment, dispensing, drug use and finally results and follow-up. Unfortunately, there are many problems and inefficiencies in the pharmacological drug chain. Unfortunately, those who study medication errors and their solutions have focused on individual parts of the pharmacological treatment system. However, for this reason, this study aims to develop a general framework for system analysis of inefficiencies along the pharmacological treatment chain. Due to the size of the problem, this project focused on medication adherence. Adherence can be defined as to what extent the patient follows the medication treatment plan. Adherence has many known problems and difficulties, among other things, it has major financial consequences. It can also be difficult to measure compliance, and there is no recognized perfect method. A system dynamic model is a theoretical image of a real system or object, which is a model used to understand the nonlinear behavior of complex systems. These models are useful when considering interventions and their effects when there are complex relationships. The project started with a literature study, and then went into data collection. Here, a search design and refinements were designed to find relevant articles. Once the articles were selected, the data was compiled from the articles and the analysis began. Here, factors and effects on adherence were identified as well as other interesting information from the articles. When the information was compiled and analyzed, the system dynamic model was created. The model was then sent via email to experts in the field to validation and revise the model. During the data collection, 23 relevant articles were found, compiled into 38 factors associated with compliance. In addition to these factors, 8 were excluded because they were too disease-specific or too ambiguous in their effect of adherence. The various articles studied many different chronic diseases, but hypertension was the most common. How adherence was measured in the articles also varied greatly, however, some form of self-report or questionnaire was most common method used. Three out of seven experts responded to the sent-out model and provided valuable comments. Although these are not sufficient to validate the model, their views showed that a validation can be designed in this way. The model would have to be sent to a larger set of experts and stakeholders, but because these experts are recognized in their fields, it gave weight to the results even though they were few reviewers. With the support of the literature and the experts’ statement, it was concluded that this model provides a good foundation and structure to continue to build upon. In addition, the model has proven to have many key relationships and cornerstones with important and relevant factors. It is also concluded that it is possible to translate the model into quantitative patterns, which is based on the fact that the factor itself can be translated quantitatively. Overall, it is also finally concluded that the model created in this project could be of great use in future projects when working towards a framework for system analysis of inefficiencies along the pharmacological treatment chain.