Indiana University-Purdue University Indianapolis (IUPUI) / This qualitative study was successful in utilization of grounded theory methodology to ascertain nurses’ decision-making processes following their awareness of having made a medical error, as well as how and/or if they corrected and reported the error. Significant literature documents the existence of medical errors; however, this unique study interviewed thirty nurses from adult intensive care units seeking to discover through a detailed interview process their individual stories and experiences, which were then analyzed for common themes. Common themes led to the development of a theoretical model of thought processes regarding error reporting when nurses made an error. Within this theoretical model are multiple processes that outline a shared, time-orientated sequence of events nurses encounter before, during, and after an error. One common theme was the error occurred during a busy day when they had been doing something unfamiliar. Each nurse expressed personal anguish at the realization she had made an error, she sought to understand why the error happened and what corrective action was needed. Whether the error was reported on or told about depended on each unit’s expectation and what needed to be done to protect the patient. If there was no perceived patient harm, errors were not reported. Even for reported errors, no one followed-up with the nurses in this study. Nurses were left on their own to reflect on what had happened and to consider what could be done to prevent error recurrence. The overall impact of the process of and the recovery from the error led to learning from the error that persisted throughout her nursing career. Findings from this study illuminate the unique viewpoint of licensed nurses’ experiences with errors and have the potential to influence how the prevention of, notification about and resolution of errors are dealt with in the clinical setting. Further research is needed to answer multiple questions that will contribute to nursing knowledge about error reporting activities and the means to continue to improve error-reporting rates

Injection molding is one of the most commonly applied processing methods for plastic components. Heat transfer coefficient (HTC), which describes the heat conducting ability of the interface between a polymer and cavity wall, significantly influences the temperature distribution of a polymer and mold during injection molding and thus affects the process and quality of plastic products. This thesis focuses on HTC under diverse processing situations. On the basis of the heat conducting principle, a theoretical model for calculating HTC was presented. Injection mold specially used for measuring and calculating HTC was designed and fabricated. Experimental injection studies under different processing conditions, especially different surface roughness, were performed for acquiring necessary temperature data. The heat quantity across the interface and HTC between a polymer and cavity wall was calculated on the basis of experimental results. The influence of surface roughness on HTC during injection molding was investigated for the first time. The factors influencing the HTC were analyzed on the basis of the factor weight during injection molding. Subsequently FEM (Finite element method) simulations were carried out with observed and preset value of HTC respectively and the relative crystallinity and part density were obtained. In the comparison between results from simulation and experiment, the result calculated with observed HTC shows better agreement with actually measured value, which can verify the reliability and precision of the injection molding simulation with observed HTC. The results of this thesis is beneficial for understanding the heat transfer process comprehensively, predicting temperature distribution, arranging cooling system, reducing cycle time and improving precision of numerical simulation. / Das Spritzgießen ist eines der am häufigsten angewandten Verarbeitungsverfahren zur Herstellung von Kunststoffkomponenten. Der Wärmedurchgangskoeffizient (WDK), welcher den Wärmeübergang zwischen Kunststoff und Werkzeugwand beschreibt, beeinflusst während des Spritzgießens maßgeblich die Temperaturverteilung im Bauteil und dem Werkzeug und folglich den Prozess und die Qualität der Kunststoffprodukte. Der Inhalt dieser Arbeit beschäftigt sich mit dem WDK unter verschiedenen Prozessbedingungen. Auf Grundlage des Wärmeleitungsprinzips wurde ein theoretisches Modell für die Berechnung des WDK vorgestellt. Es wurde dazu ein Spritzgießwerkzeug konstruiert und hergestellt, welches Messungen zur späteren Berechnung des WDK ermöglicht. Praktische Spritzgießversuche unter verschiedenen Prozessbedingungen, insbesondere unterschiedlicher Oberflächenrauheit, wurden für die Erfassung der erforderlichen Temperaturdaten durchgeführt. Auf Grundlage der experimentellen Ergebnisse wurde der Wärmeübergang zwischen dem Polymer und der Werkzeugwand berechnet. Der Einfluss der Oberflächenrauhigkeit auf den WDK wurde hierbei zum ersten Mal untersucht. Auf Grundlage des Bauteilgewichtes wurden anschließend die Faktoren, die den WDK beeinflussen, berechnet. Des Weiteren wurden FEM-Simulationen (Finite Element Methode) mit dem gemessenen und dem voreingestellten WDK durchgeführt und daraus der Kristallinitätsgrad und die Bauteildichte gewonnen. Der Vergleich zwischen den realen Ergebnissen und der Simulation zeigt, dass die Berechnungen mit dem gemessenen WDK eine bessere Übereinstimmung mit den realen Werten aufweist, was die Zuverlässigkeit und Präzision der Spritzgusssimulation bestätigt. Die Ergebnisse dieser Arbeit tragen zum umfassenden Verständnis des Wärmeübergangs im Spritzgießprozess, zur Vorhersage der Temperaturverteilung, zur Auslegung des Kühlsystems, zur Reduzierung der Zykluszeit und zur Verbesserung der Genauigkeit der numerischen Simulation bei.

Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or EBF. This model is descriptive, in that it can describe and compare learning algorithms. Thus the EBF is capable of modeling both effective and ineffective learning algorithms. We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of the EBF to deal with non-deterministic outcomes, and with utility functions other than 0-1 loss. Finally, we modify the EBF to create a "prescriptive" learning model, meaning that, instead of describing existing algorithms, our model defines how learning should optimally take place. We call the resulting model the Unified Bayesian Decision Theoretical Model, or the UBDTM. WE show that this model can serve as a cohesive theory and framework in which a broad range of questions can be analyzed and studied. Such a broadly applicable unified theoretical framework is one of the major missing ingredients of machine learning theory. Using the UBDTM, we concentrate on supervised learning and empirical function optimization. We then use the UBDTM to reanalyze many important theoretical issues in Machine Learning, including No-Free-Lunch, utility implications, and active learning. We also point forward to future directions for using the UBDTM to model learnability, sample complexity, and ensembles. We also provide practical applications of the UBDTM by using the model to train a Bayesian variation to the CMAC supervised learner in closed form, to perform a practical empirical function optimization task, and as part of the guiding principles behind an ongoing project to create an electronic and print corpus of tagged ancient Syriac texts using active learning.