Revealing the prior austenite grain boundaries from a martensitic structure is well known to be very difficult and dependent on the chemical composition and the thermomechanical processing of the steel. In the present study, four different chemical etching reagents and additional thermal etching have been conducted for thermomechanical simulated tool steels Orvar Supreme and Stavax ESR. The etching results have been characterized using light optical microscopy and electron backscattered diffraction. The obtained results show that saturated aqueous picric acid, oxalic and sodium bisulfite based acid reveals prior austenite grain boundaries well for Orvar Supreme. For Stavax ESR, only aqueous CrO3-NaOH-picric acid provides good results in revealing the prior austenite grain boundaries. Thermal etching shows good potential and if conducted properly, thermal etching is a good alternative to the chemical reagents from a health- and environmental perspective.

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No description available.

No description available.

No description available.

Creating a conducive environment for healthy eating can be achieved by empowering consumers with the necessary information to make informed nutritional choices. As a result, nutritional labeling has become increasingly imperative in assisting individuals in their daily purchasing decisions. Although research suggests that front-of-pack labeling is an effective means of informing consumers about healthier food options, no single labeling system has proven successful in this regard. The absence of a definitive labeling system ideal for all purposes makes it challenging to determine the most effective one. This uncertainty has led to a gap in the literature, which provided an opportunity for further research to examine the various labels and related concepts and factors. This dissertation aimed to fill the gap in the literature by studying two popular front-of-pack labeling systems: The Health Star Rating system and Nutri-Score. Four research chapters have been included to achieve this objective. The study commenced with a systematic review of the Health Star Rating label in the literature. The next chapters examined the impact of front-of-pack nutrition labels on consumers' food choices and preferences. The research investigated how the presence or absence of a label influences consumer decision-making and how individual differences play a role in interacting with these nutrition labels. Finally, the performance of the nutrition labels is examined in the context of existing beliefs and attitudes. This last study provided a fresh perspective on the effect of Nutri-Score on food choices by exploring its impact on consumers’ prior beliefs, intuitive thinking, analytical processing, and judgment of healthiness. In conclusion, the body of literature reviewed and the experimental data gathered in this thesis suggest that food labels are effective at influencing consumer choice; however, due to this, special caution must be exercised due to the risk that they could be used more as a marketing tool than as a genuine aid to informed choice.

This study examined how learners construct textbase and situation model knowledge in hypertext computer-based learning environments (CBLEs) and documented the influence of specific self-regulated learning (SRL) tactics, prior knowledge, and characteristics of the learner on posttest knowledge scores from exposure to a hypertext. A sample of 160 undergraduate education majors completed measures of prior knowledge, goal orientation, intrinsic motivation, self-efficacy to self-regulate learning, and a demographic survey. They were trained in the use of nStudy, a learning environment designed to facilitate self-regulated learning from web-based media including hypertext and to trace learners' actions while they learned online. Learners completed a 20-minute study session learning about Attention Deficit Hyperactivity Disorder and a posttest to assess changes in knowledge scores. Results indicate that employment of individual SRL tactics including tendency to highlight was found to be associated with increased posttest knowledge scores across learners. Goal orientation and prior knowledge also significantly predicted posttest knowledge scores in regression models. These findings can be used to inform the design and use of hypertext in order to individualize computer-based instruction and maximize knowledge acquisition for students, based upon their individual characteristics. / Educational Psychology

The available literature supports the fact that some students experience difficulty learning object-oriented design (OOD) principles. Previously explored predictors of OOD learning difficulties include student characteristics (cognitive activities, self-efficacy), teaching methodologies (teacher centered, course complexity), and student experiences (prior programming experience). Yet, within an extensive body of literature devoted to OOD, two explanations of student difficulty remain largely unexplored: (1) varying conceptualizations of the underlying principles/strategies of OOD, and (2) preparedness or readiness to learn OOD. This research also investigated the extent to which individual differences impacted DRAS and OOD performance. The individual difference measures of interest in this study included college grade point average, prior programming experience, cognitive abilities (spatial orientation, visualization, logical reasoning, flexibility, perceptual style), and design readiness. In addition, OOD performance was measured using two constructs: course grade (exams, labs, programs, overall), and a specially constructed design task. Participants selected from the CS2 course from two southeastern state universities were used within this study, resulting in a sample size of 161 (School A, n = 76; School B, n = 85). School A is a mid-sized comprehensive university and School B is a large research-intensive university. If was found that the schools significantly differed on all measures of prior computer science experience and cognitive abilities. Path analysis was conducted to determine which individual differences were related to design readiness and OOD performance. In summary, this research identified that instructors can not ignore individual differences when teaching OOD. It was found that the cognitive ability visualization, prior OO experience, and overall college grade point average should be considered when teaching OOD. As it stands, without identifying specific teaching strategies used at the schools within this study, this research implies that OOD may require a certain level of practical computer experience before OOD is introduced into the curriculum. The cognitive ability visualization was found to have a significant indirect relationship with overall course grade through the mediating variable design readiness. Further, the results suggest that the DRAS may serve as a viable instrument in identifying successful OOD students as well as students that require supplemental OOD instruction. / Ph. D.

Immunological heterogeneity is the norm in most free-living vertebrate populations, creating a diverse and challenging landscape for pathogens to replicate and transmit. This dissertation work sought to determine sources of immunological heterogeneity, as well as the consequences of this heterogeneity on pathogen fitness and evolution. A major source of heterogeneity in free-living host populations is the degree of exposure to a pathogen, as well as a host's history of exposure to a pathogen, which can create variation in standing immunity. We sought to determine the role of exposure heterogeneity on host susceptibility and immunity to secondary infection, and the influence of this heterogeneity on pathogen fitness and virulence evolution in a wild bird-bacterium system. We first determined that exposure level has a significant effect on host susceptibility to infection, severity of disease and infection, as well as immunity produced to secondary infection. Subsequently, we tested whether exposure history, and the immunity formed from this previous exposure, altered the within-host fitness advantage to virulent pathogens. We determined that previous low-level repeat exposure, which wild hosts likely encounter while foraging, produces a within-host environment which greatly favors more virulent pathogens. While within-host processes are vital for understanding and interpreting the evolutionary pressures on a pathogen, the ultimate metric of pathogen fitness is transmission. We therefore tested whether exposure history altered the transmission potential of a host and whether prior host exposure selected for more virulent pathogens. The transmission potential of a host significantly decreased with previous exposure, and high levels of previous exposure selected for more virulent pathogens. While we anticipated selection to be strongest at low-levels of exposure based on our previous results, we found here that high doses of prior exposure resulted in the strongest transmission advantage to virulence. This study also provided insight into the nuanced nature of transmission, which our results indicate is determined both by the degree of within-host pathogen replication as well as host disease severity. Together, our findings underscore the importance of exposure level and exposure history in natural populations in determining susceptibility, immunity and pathogen virulence evolution. / Doctor of Philosophy / Infectious diseases disrupt and threaten all life on this planet. To better anticipate and understand why some diseases are more harmful than others, it is vital that we consider the natural variability that exists in animal populations. A major source of variation in populations that experience disease is exposure level to a pathogen, as well as the history of exposure to a pathogen, which can alter an individual’s protection against future exposures. We sought to determine the role of exposure level on the likelihood of an individual contracting an infection, their protection from future infections, and the influence this has on pathogen evolution in a wild bird-bacterium system. We determined that exposure level has a significant effect on the likelihood an individual has of becoming infected, how severe the infection became, as well as how protected they were from future infections. Subsequently, we tested whether exposure history, and the immunity formed from previous exposure, altered the ability of pathogen strains that cause different levels of harm to replicate. We determined that previous low-level exposure, which hosts likely encounter in the wild, creates a level of immunity that favors more harmful strains of the pathogen. While understanding what happens within a host is important, the ultimate metric for predicting whether more or less harmful types of pathogens will persist is the ability of each pathogen type to spread from one host to another. We therefore tested whether exposure history altered the spread potential of a host and whether previous exposure preferentially favored the spread of more harmful pathogens. The spread potential of a host was much lower if that host had previously been exposed to the pathogen, and high levels of previous exposure in hosts only allowed the more harmful pathogen types to spread. We also found that a host’s spread potential was the result of both how much pathogen they had in their body, as well as how inflamed their affected tissues were. Together, our results indicate that natural variation in prior exposure to pathogens, which is common in all animal populations, including humans, can favor more harmful pathogen types.

<p dir="ltr">It has been shown that formative assessments can lead to improvements in the learning process. Cognitive Diagnostic Models (CDMs) are a powerful formative assessment tool that can be used to provide individuals with valuable information regarding skill mastery in educational settings. These models provide each student with a ``skill mastery profile'' that shows the level of mastery they have obtained with regard to a specific set of skills. These profiles can be used to help both students and educators make more informed decisions regarding the educational process, which can in turn accelerate learning for students. However, despite their utility, these models are rarely used with small sample sizes. One reason for this is that these models are often complex, containing many parameters that can be difficult to estimate accurately when working with a small number of observations. This work aims to contribute to and expand upon previous work to make CDMs more accessible for a wider range of educators and students.</p><p dir="ltr">There are three main small sample statistical problems that we address in this work: 1) accurate estimation of the population distribution of skill mastery profiles, 2) accurate estimation of additional model parameters for CDMs as well as improved classification of individual skill mastery profiles, and 3) improved selection of an appropriate CDM for each item on the assessment. Each of these problems deals with a different aspect of educational measurement and the solutions provided to these problems can ultimately lead to improvements in the educational process for both students and teachers. By finding solutions to these problems that work well when using small sample sizes, we make it possible to improve learning in everyday classroom settings and not just in large scale assessment settings.</p><p dir="ltr">In the first part of this work, we propose novel algorithms for estimating the population distribution of skill mastery profiles for a popular CDM, the Deterministic Inputs Noisy ``and'' Gate (DINA) model. These algorithms borrow inspiration from the concepts behind popular machine learning algorithms. However, in contrast to these methods, which are often used solely for prediction, we illustrate how the ideas behind these methods can be adapted to obtain estimates of specific model parameters. Through studies involving simulated and real-life data, we illustrate how the proposed algorithms can be used to gain a better picture of the distribution of skill mastery profiles for an entire population students, but can do so by only using a small sample of students from that population. </p><p dir="ltr">In the second part of this work, we introduce a new method for regularizing high-dimensional CDMs using a class of Bayesian shrinkage priors known as catalytic priors. We show how a simpler model can first be fit to the observed data and then be used to generate additional pseudo-observations that, when combined with the original observations, make it easier to more accurately estimate the parameters in a complex model of interest. We propose an alternative, simpler model that can be used instead of the DINA model and show how the information from this model can be used to formulate an intuitive shrinkage prior that effectively regularizes model parameters. This makes it possible to improve the accuracy of parameter estimates for the more complex model, which in turn leads to better classification of skill mastery. We demonstrate the utility of this method in studies involving simulated and real-life data and show how the proposed approach is superior to other common approaches for small sample estimation of CDMs.</p><p dir="ltr">Finally, we discuss the important problem of selecting the most appropriate model for each item on assessment. Often, it is not uncommon in practice to use the same CDM for each item on an assessment. However, this can lead to suboptimal results in terms of parameter estimation and overall model fit. Current methods for item-level model selection rely on large sample asymptotic theory and are thus inappropriate when the sample size is small. We propose a Bayesian approach for performing item-level model selection using Reversible Jump Markov chain Monte Carlo. This approach allows for the simultaneous estimation of posterior probabilities and model parameters for each candidate model and does not require a large sample size to be valid. We again demonstrate through studies involving simulated and real-life data that the proposed approach leads to a much higher chance of selecting the best model for each item. This in turn leads to better estimates of item and other model parameters, which ultimately leads to more accurate information regarding skill mastery. </p>