Synthetic biology is an emerging field that integrates and applies engineering design methods to biological systems. Its aim is to make biology an "engineerable" science. Over the years, biologists and engineers alike have abstracted biological systems into functional models that behave similarly to electric circuits, thus the creation of the subfield of genetic circuits. Mathematical models have been devised to simulate the behavior of genetic circuits in silico. Most models can be classified into deterministic and stochastic models. The work in this dissertation is for stochastic models. Although ordinary differential equation (ODE) models are generally amenable to simu- late genetic circuits, they wrongly assume that a system's chemical species vary continuously and deterministically, thus making erroneous predictions when applied to highly stochastic systems. Stochastic methods have been created to take into account the variability, un- predictability, and discrete nature of molecular populations. The most popular stochastic method is the stochastic simulation algorithm (SSA). These methods provide a single path of the overall pool of possible system's behavior. A common practice is to take several inde- pendent SSA simulations and take the average of the aggregate. This approach can perform iv well in low noise systems. However, it produces incorrect results when applied to networks that can take multiple modes or that are highly stochastic. Incremental SSA or iSSA is a set of algorithms that have been created to obtain ag- gregate information from multiple SSA runs. The marginal probability density evolution (MPDE) algorithm is a subset of iSSA which seeks to reveal the most likely "qualitative" behavior of a genetic circuit by providing a marginal probability function or statistical enve- lope for every species in the system, under the appropriate conditions. MPDE assumes that species are statistically independent given the rest of the system. This assumption is satisfied by some systems. However, most of the interesting biological systems, both synthetic and in nature, have correlated species forming conservation laws. Species correlation imposes con- straints in the system that are broken by MPDE. This work seeks to devise a mathematical method and algorithm to correct conservation constraints errors in MPDE. Furthermore, it aims to identify these constraints a priori and efficiently deliver a trustworthy result faithful to the true behavior of the system.

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The present study focuses on students at two Swedish secondary schools and the pronunciation errors that are the most prominent during reception and production of specific speech sounds. The primary focus of this degree paper is to establish whether or not certain speech sounds such as e.g. the /tʃ/ sound, which do not occur in the Swedish language in initial position are difficult or not and whether or not they act as an obstacle for Swedish students learning English as their L2. The aim was to establish which specific pronunciation errors that occurred in the L2 language classroom. Since this was the aim, primarily quantitative studies were carried out at two secondary schools in southern Sweden. The results from the four different tests show that the tested Swedish L2 students seem to have a greater difficulty with speech sounds placed in initial position than in final position of a specific word. According to this degree paper this is due to the fact that the Swedish language does not have an equivalent to the difficult speech sound in initial position, therefore making it difficult and often resulting in negative transfer from the L1. Furthermore, the English sounds that posed the biggest problems for the students were ones that sometimes can be found in the Swedish language. These sounds were very similar to native sounds creating a challenge for the Swedish students when perceiving and producing the English sounds. However, it was shown that when these sounds were presented in a context, they proved to be less challenging for the students to receive and produce. Moreover, although the syllabus only mentions that communication should be functional, there still needs to be an element of focus on form in order to become a proficient language user.

Indiana University-Purdue University Indianapolis (IUPUI) / The ability to improve the safety of patient care delivery is dependent on the safety culture, or the norms surrounding reactions following an error, the learning that takes place, and the proactive strategies in place to prevent future errors. While measurement of patient safety culture is now common in the United States (US) using instrument specifically developed for US healthcare organizations, no measurements of safety culture had been conducted at Hamad Medical Corporation in the State of Qatar, a Middle Eastern country; nor were valid or reliable instruments available. The purpose of this study was to assess registered nurses’ perceptions of the safety culture in the units where they provide nursing care at Hamad Medical Corporation using a modified version of the Agency for Healthcare Research and Quality (AHRQ) patient safety culture an instrument (Hospital Survey of Patient Safety Culture). Eight hundred surveys were distributed to all randomly-selected nurses from eight targeted clinical services with a response rate of 57%. Survey results were compared with those from US hospitals using the original AHRQ survey. Ranking of subscales for this study in terms of strengths and areas needing improvement were almost identical to the ordering of US hospital results, with teamwork within units ranked highest and indicating a strength; and the subscale non-punitive response to error the lowest and indicating an area for improvement. Positive response rates in terms of safety culture for this study were generally lower on most subscales compared to the US results and may reflect the intensity of patient safety improvement activity in the US over the last eight years in response to the Institute of Medicine’s report on medical errors in 1999. Results from this study provide a baseline measurement for safety culture at Hamad Medical Corporation and beginning adaptation of an instrument that can be used in other Middle Eastern healthcare organizations in the future.

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This document only includes an excerpt of the corresponding thesis or dissertation. To request a digital scan of the full text, please contact the Ruth Lilly Medical Library's Interlibrary Loan Department (rlmlill@iu.edu).

The estimation of standard errors is essential to statistical inference. Statistical variability is inherent within data, but is usually of secondary interest; still, some options exist to deal with this variability. One approach is to carefully model the covariance structure. Another approach is robust estimation. In this approach, the covariance structure is estimated from the data. White (1980) introduced a biased, but consistent, robust estimator. Long et al. (2000) added an adjustment factor to White's estimator to remove the bias of the original estimator. Through the use of simulations, this project compares restricted maximum likelihood (REML) with four robust estimation techniques: the Standard Robust Estimator (White 1980), the Long estimator (Long 2000), the Long estimator with a quantile adjustment (Kauermann 2001), and the empirical option of the MIXED procedure in SAS. The results of the simulation show small sample and asymptotic properties of the five estimators. The REML procedure is modelled under the true covariance structure, and is the most consistent of the five estimators. The REML procedure shows a slight small-sample bias as the number of repeated measures increases. The REML procedure may not be the best estimator in a situation in which the covariance structure is in question. The Standard Robust Estimator is consistent, but it has an extreme downward bias for small sample sizes. The Standard Robust Estimator changes little when complexity is added to the covariance structure. The Long estimator is unstable estimator. As complexity is introduced into the covariance structure, the coverage probability with the Long estimator increases. The Long estimator with the quantile adjustment works as designed by mimicking the Long estimator at an inflated quantile level. The empirical option of the MIXED procedure in SAS works well for homogeneous covariance structures. The empirical option of the MIXED procedure in SAS reduces the downward bias of the Standard Robust Estimator when the covariance structure is homogeneous.

Being a competent writer is an important skill in academic education. However, second language (L2) writers often struggle to be linguistically and lexically competent. This project explored the most frequent linguistic writing errors made by 343 English as a second language (ESL) students when Dynamic Written Corrective Feedback (WCF) was applied as an instructional methodology. These errors were also classified by language groups based on the students' first language (L1). These students were enrolled in an intensive English program at the English Language Center (ELC), Brigham Young University. The first languages of these students were Spanish, Korean, Portuguese, Chinese, Japanese, and Russian. The students' writing samples were collected to compile the most frequent linguistic error types. The results of this project show that the most frequent linguistic errors for ESL students are spelling, word choice, determiner, preposition, singular/plural, and word form. Among these errors, spelling and word choice were the most common errors for all ESL students no matter their L1. The principal aim of this project was to take the data collected in the error analysis and create a booklet to be used as a reference guide to frequent ESL linguistic writing errors. With this booklet, teachers should be more aware of frequent errors to better assist their students since this could help them anticipate some of the linguistic difficulties that L2 learners may encounter. This booklet could also help L2 learners attain writing linguistic competence.

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features.