Indiana University-Purdue University Indianapolis (IUPUI) / Clear coats have been part of automotive field paint finishes for several decades. Originally a layer of paint with no pigment, they have evolved into a protective layer important to the appearance and longevity of the vehicle's finish. These clear coats have been studied previously using infrared spectroscopy and other spectroscopic techniques. Previous studies focused on either all the layers of an automobile finish or on chemometric analysis of clear coats using other analytical techniques. For this study, chemometric analysis was performed on preprocessed spectra averaged from five separate samples. Samples were analyzed on a Thermo-Nicolet Nexus 670 connected to a Continuμm™ FT-IR microscope. Two unsupervised chemometric techniques, Agglomerative Hierarchical Clustering (AHC) and Principal Component Analysis (PCA), were used to evaluate the data set. Discriminant analysis, a supervised technique, was evaluated using several known qualifiers; these included cluster group from AHC, make, model, and year. Although discriminant analysis confirmed the AHC and PCA results, no correlation to make, model, or year was indicated.

In der Arbeit wird eine Methodik einer zusammenhängenden Analyse und modellhaften Beschreibung der Verkehrssicherheit in städtischen Hauptstraßennetzen am Beispiel der Stadt Dresden entwickelt. Die dabei gewonnenen Modelle dienen der Abschätzung von Erwartungswerten von Unfallhäufigkeiten mit und ohne Personenschaden unter Berücksichtigung der Verkehrsbeteiligungsart. Die Grundlage bilden multivariate Regressionsmodelle auf Basis verallgemeinerter linearer Modelle (GLM). Die Verwendung verallgemeinerter Regressionsmodelle erlaubt eine Berücksichtigung von Verteilungen, die besser geeignet sind, den Unfallentstehungsprozess wiederzugeben, als die häufig verwendete Normalverteilung. Im konkreten Fall werden hierzu die Poisson-Verteilung sowie die negative Binomialverteilung verwendet. Um Effekte im Hauptverkehrsstraßennetz möglichst trennscharf abbilden zu können, werden vier grundsätzliche Netzelemente differenziert und das Netz entsprechend zerlegt. Unterschieden werden neben Streckenabschnitten und Hauptverkehrsknotenpunkten auch Annäherungsbereiche und Anschlussknotenpunkte. Die Kollektive der Knotenpunkte werden ferner in signalisierte und nicht-signalisierte unterteilt. Es werden zunächst Modelle unterschiedlicher Unfallkollektive getrennt für alle Kollektive der vier Netzelemente berechnet. Anschließend werden verschiedene Vorgehensweisen für eine Zusammenfassung zu Netzmodellen entwickelt. Neben der Verwendung verkehrstechnischer und infrastruktureller Größen als erklärende Variable werden in der Arbeit auch Kenngrößen zur Beschreibung der Umfeldnutzung ermittelt und im Rahmen der Regression einbezogen. Die Quantifizierung der Umfeldnutzung erfolgt mit Hilfe von Korrelations-, Kontingenz- und von Hauptkomponentenanalysen (PCA). Im Ergebnis werden Modelle präsentiert, die eine multivariate Quantifizierung erwarteter Unfallhäufigkeiten in Hauptverkehrsstraßennetzen erlauben. Die vorgestellte Methodik bildet eine mögliche Grundlage für eine differenzierte Sicherheitsbewertung verkehrsplanerischer Variantenabschätzungen. / A methodology is developed in order to predict the number of accidents within an urban main road network. The analysis was carried out by surveying the road network of Dresden. The resulting models allow the calculation of individual expectancy values for accidents with and without injury involving different traffic modes. The statistical modelling process is based on generalized linear models (GLM). These were chosen due to their ability to take into account certain non-normal distributions. In the specific case of accident counts, both the Poisson distribution and the negative binomial distribution are more suitable for reproducing the origination process than the normal distribution. Thus they were chosen as underlying distributions for the subsequent regressions. In order to differentiate overlaying influences, the main road network is separated into four basic elements: major intersections, road sections, minor intersections and approaches. Furthermore the major and minor intersections are additionally subdivided into signalised and non-signalised intersections. Separate models are calculated for different accident collectives for the various types of elements. Afterwards several methodologies for calculating aggregated network models are developed and analysed. Apart from traffic-related and infrastructural attributes, environmental parameters are derived taking into account the adjacent building structure as well as the surrounding land-use, and incorporated as explanatory variables within the regression. The environmental variables are derived from statistical analyses including correlation matrices, contingency tables and principal components analyses (PCA). As a result, a set of models is introduced which allows a multivariate calculation of expected accident counts for urban main road networks. The methodology developed can serve as a basis for a differentiated safety assessment of varying scenarios within a traffic planning process.

<p>Two-phase modeling and simulation capabilities are strongly dependent on the accuracy of flow regime identification methods. Flow regimes have traditionally been determined through visual observation, resulting in subjective classifications that are susceptible to inconsistencies and disagreements between researchers. Since the majority of two-phase flow studies have been concentrated around vertical and horizontal pipe orientations, flow patterns in inclined pipes are not well-understood. Moreover, they may not be adequately described by conventional flow regimes which were conceptualized for vertical and horizontal flows. Recent work has explored applying machine learning methods to vertical and horizontal flow regime identification to help remedy the subjectivity of classification. Such methods have not, however, been successfully applied to inclined flow orientations. In this study, two novel unsupervised machine learning methods are proposed: a modular configuration of multiple machine learning algorithms that is adaptable to different pipe orientations, and a second universal approach consisting of several layered algorithms which is capable of performing flow regime classification for data spanning multiple orientations. To support this endeavor, an experimental database is established using a dual-ring impedance meter. The signals obtained by the impedance meter are capable of conveying distinct features of the various flow patterns observed in vertical, horizontal, and inclined pipes. Inputs to the unsupervised learning algorithms consist of statistical measures computed from these signals. A novel conceptualization for flow pattern classification is developed, which maps three statistical parameters from the data to red, green, and blue primary color intensities. By combining the three components, a flow pattern map can be developed wherein similar colors are produced by flow conditions with like statistics, transforming the way flow regimes are represented on a flow regime map. The resulting dynamic RGB flow pattern map provides a physical representation of gradual changes in flow patterns as they transition from one regime to another. By replacing the static transition boundaries with physically informed, dynamic gradients between flow patterns, transitional flow patterns may be described with far greater accuracy. This study demonstrates the effectiveness of the proposed method in generating objective flow regime maps, providing a basis for further research on the characterization of two-phase flow patterns in inclined pipes. The three proposed methods are compared and evaluated against flow regime maps found in literature.</p>

BACKGROUND: Clinical facilities are essential components not only for health care delivery systems but also for health care education programs. The clinical learning environment is important in training the future workforce in healthcare. Respiratory therapy education programs face several issues with the need to prepare a proper learning environment in different clinical settings. PURPOSE: The purpose of this study was to determine the perceptions of respiratory therapy students on the learning environment of clinical facilities affiliated with a respiratory therapy program at an urban state university. METHODS: This study used an exploratory research design to evaluate the essential aspects of a clinical learning environment in respiratory therapy education. A self-reporting survey was utilized to gather data from 34 respiratory therapy students regarding their perception about the effectiveness of clinical facilities in respiratory therapy education. The researcher utilized The Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) evaluation scale that was developed by Sarrikoski et al. (2008). The CLES+T evaluation scale was adapted and modified after a written agreement from the author. The survey included three main domains, which are the clinical learning environment (18 items), the supervision relationship (15 items), and the role of clinical instructors (9 items). Thirty-two students participated in the survey with a response rate of 94.1%. RESULTS: Responses included two groups of students: the second year undergraduate (68.8%) and graduate students (31.3%), with 75% being female participants. The results obtained from the study indicated that both graduate and undergraduate respiratory therapy students gave high mean scores to the learning environment of the clinical facilities, supervisory relationship and the roles of clinical instructors. A statistically significant data was obtained pertaining to the difference of perceptions regarding the multi-dimensional learning between the graduate and undergraduate students. The graduate students evaluated that “the learning situation are multi-dimensional” more than the undergraduate students (p = 0.03). Findings of this study showed that female students had higher ratings than male students in all evaluations of clinical facilities. However, only one dimension of leadership style stating that “the effort of individual employees was appreciated” was statistically significant (p=0.03). The results stating, the presence of a significant percentage of the students with lack of successful private supervision and high percentage of failed supervisory relationship, are in contrast with the fact that clinical learning plays a vital role in the respiratory therapy education. It is also contrasting that majority of the students experienced team supervision, which is against the philosophy and principles of individualization. CONCLUSION: Since respiratory therapy is a practice-based profession, it is essential to integrate clinical education to respiratory care education. Gender and education level may impact students’ perceptions about the learning environment of clinical facilities. This study provides information about areas for improvement in clinical facilities affiliated with a respiratory care education program at an urban university.

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this study was to locate the most suitable blocks to plant trees within Indianapolis, Indiana’s Near Eastside Community (NESCO). LiDAR data were utilized, with 1.0 meter average post spacing, captured by the Indiana Statewide Imagery and LiDAR Program from March 13, 2011 to April 30, 2012, to conduct a covertype classification and identify blocks that have low canopies, high impervious surfaces and high surface temperatures. Tree plantings in these blocks can help mitigate the effects of the urban heat island effect. Using 2010 U.S. Census demographic data and the principal component analysis, block groups with high social vulnerability were determined, and tree plantings in these locations could help reduce mortality from extreme heat events. This study also determined high and low priority plantable space in order to emphasize plantable spaces with the potential to shade buildings; this can reduce cooling costs and the urban heat island, and it can maximize the potential of each planted tree.