Physical Vapor Growth of Rubrene

Rubrene is a novel organic semiconductor, with many interesting electronic properties. Among organic semiconductors, rubrene has the highest carrier mobility, with values reaching 40 cm2/(V S). These electronic properties, however, are only applicable for crystallized and pure forms of rubrene. Physical vapor transport of rubrene is capable of purifying and crystallizing rubrene powder. This is accomplished by heating the impure sample and subjecting the sublimated vapors with a constant flow of Ar gas along a temperature gradient. We report efforts to build and test a physical vapor growth system for rubrene

Self-Organized Criticality in Protein Folding Simulations with AMBER Parameters

AMBER (Assisted Model Building with Energy Refinement) is a force field that allows for the simulation of proteins. Basic proteins were folded by a Monte-Carlo algorithm parameterized by AMBER on MATLAB. Simulations exhibited self-organized critical (SOC) behavior and are shown to be stable throughout the simulations by use of pairwise distance matrixes. Frequency shifting is explored as a possible explanation of the mechanisms behind structure inducing probes (SIPs) in the solid phase synthesis of proteins. This also provides a mechanistic link to many other SOC systems

The Effects of Discrete Emotions on Risky Decision Making

Contrary to the dominant view that generally equates feelings with poor thinking, converging evidence indicates that decisions – including those involving risk – are influenced by affective experiences. Research, however, is limited to studies on undifferentiated, global positive versus negative mood states; less is known about the influence of discrete emotions. The purpose of this research was to extend the affect-cognition literature by (a) examining the effects of discrete emotions varying along the dimensions of valence and arousal, and (b) identifying the systematic ways that discrete emotions underlie risky decision making. We used a set of emotion-laden IAPS images to elicit and compare the impact of incidental emotions on risky decision making. One hundred and twenty-two undergraduate students were randomly assigned to one of the four affective conditions: excitement, contentment, fear, and sadness. Following the emotion induction procedure, participants completed the Choice Dilemmas Questionnaire (CDQ) to assess their risk-taking propensity. Results indicated an interaction effect between valence and arousal for positive emotions, such that excited participants were significantly more risky in their decision making compared to contented participants. The discussion focuses on the theoretical and practical health implications of these findings. We recommend that future research capitalize on the insights gained from emotion research and use it favorably to improve decision making under risk

Fionn and Ailbhe's riddles between Ireland and Scotland

No abstract available

Shakespeare’s Scottish play in Scottish Gaelic

No abstract available

Beyond Standard Assumptions - Semiparametric Models, A Dyadic Item Response Theory Model, and Cluster-Endogenous Random Intercept Models

In most statistical analyses, quantitative education researchers often make simplifying assumptions regarding the manner in which their data was generated in order to answer some of these questions. These assumptions can help to reduce the complexity of the problem, and allow the researcher to describe their data using a simpler, and often times more interpretable, statistical model. However, making some of these assumptions when they are not true can lead to biased estimates and misleading answers. While the standard sets of assumptions associated with commonly-used statistical models are usually sufficient in a wide range of contexts, it will always be beneficial for education researchers to understand what they are, when they are reasonable, and how to modify them if necessary. This dissertation focuses on three of the most common models used in quantitative education research (viz. parametric models like Linear Models (LMs), Item Response Theory (IRT) models, and Random-Intercept Models (RIMs)), discusses the standard sets of assumptions that accompany these models, and then describes related models with less stringent sets of assumptions. In each of the following three chapters, we either explicitly unpack existing models that are useful but are currently still uncommon in the field of education research, or propose novel models and/or estimation strategies for these models. We begin in Chapter 1 with a common parametric model known as the Gaussian LM, and use it as a scaffold to better understand semiparametric models and their estimation. We begin by reviewing how the coefficients of the Gaussian LM are usually estimated using Maximum Likelihood (ML) or Least-Squares (LS). We then introduce the notion of an $m$-estimator as well as that of a Regular Asymptotically Linear estimator, and show how they relate to the ML estimator. In particular, we introduce the notion of influence functions/curves and discuss their geometry together with concepts such as Hilbert spaces and tangent spaces. We then demonstrate, concretely, how to derive the so-called efficient influence function under the Gaussian LM, and show that it is precisely the influence function of the ML and (Ordinary) LS estimators. This shows that the ML estimator (at least under the Gaussian LM) is efficient. Using the foundation built, we move on from the Gaussian LM by relaxing both the assumption that the residuals are normally distributed, as well as the assumption that they have a constant variance, and define this as the Heteroskedastic Linear Model. Unlike the Gaussian LM, this is a semiparametric model. Where possible, we make use of intuition and analogous results from the parametric setting to help describe the workflow for obtaining an efficient estimator for the coefficients of the Heteroskedastic Linear Model. In particular, we derive the nuisance tangent space for this semiparametric model, and use it to obtain the efficient influence function for our model. We then show how to use the efficient influence function to obtain an efficient estimator (which happens to be the Weighted LS estimator) from the (Ordinary) LS estimator via a one-step approach as well as an estimating equations approach. We then conclude by directing readers to more advanced material, including references on more modern approaches to estimating more general semiparametric models such as Targeted Maximum Likelihood Estimation. In Chapter 2, we focus on a class of measurement models known as Item Response Theory models which are useful for measuring latent traits of a subject based on the subject's response to items. We relax the condition that the responses are only a result of the individual's latent trait (and possibly an external rater), and propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors, perceptions, etc.) of each individual (actor) made within the context of a dyad formed with another individual (partner). Examples of its use in education include the assessment of collaborative problem solving among students, or the evaluation of intra-departmental dynamics among teachers. The dIRT model generalizes both Item Response Theory models for measurement and the Social Relations Model for dyadic data. Here, the responses of an actor when paired with a partner are modeled as a function of not only the actor's inclination to act and the partner's tendency to elicit that action, but also the unique relationship of the pair, represented by two directional, possibly correlated, interaction latent variables. We discuss generalizations such as accommodating triads or larger groups, but focus on demonstrating the key idea in the dyadic case. We show that estimation may be performed using Markov-chain Monte Carlo implemented in \texttt{Stan}, making it straightforward to extend the dIRT model in various ways. Specifically, we show how the basic dIRT model can be extended to accommodate latent regressions, random effects, distal outcomes. We perform a simulation study that demonstrates that our estimation approach performs well. In the absence of educational data of this form, we demonstrate the usefulness of our proposed approach using speed-dating data instead, and find new evidence of pairwise interactions between participants, describing a mutual attraction that is inadequately characterized by individual properties alone.Finally, in Chapter 3, we consider the often implicit assumption made when estimating the coefficients of structural Random Intercept Models (RIMs) that covariates at all levels do not co-vary with the random intercepts. A violation of this assumption (called cluster-level endogeneity) leads to inconsistent estimates when using standard estimation procedures. For two-level RIMs with such endogeneity, Hausman and Taylor (HT) devised a consistent multi-step instrumental variable estimator using only internal instruments. We, instead, approach this problem by explicitly modeling the endogeneity using a Structural Equation Model (SEM). In this chapter, we compare, through simulation, the HT and SEM estimators, and evaluate their asymptotic and finite sample properties. We show that the SEM approach is also flexible enough to deal with different exchangeability assumptions for the covariates (e.g., whether the correlations between pairs of all units in a cluster are the same) and investigate how these exchangeability assumptions affect finite sample properties of the HT estimator. For the simulations, we propose a new procedure for generating cluster- and unit-level covariates and random intercepts with a fully flexible covariance structure. We also compare our approach to another common approach known as Multilevel Matching using data from the High School and Beyond survey

Experimental study and numerical modelling of woven fabric kenaf fiber composites hybrid adhesively bonded-bolted joints

Couple with natural fiber composite parts, hybrid joints provide better joint strength than using separate joints. There are limited studies on structures response and strength prediction work on hybrid joints that limits its applicability. The aim of present study is to conduct experimental datasets on woven fabric kenaf fiber reinforced polymer (KFRP) and carbon fiber reinforced polymer (CFRP) composite hybrid joints under quasi-static testing and to carry out the strength prediction works subsequently by implementing physically-based traction-separation constitutive law. Testing series investigated includes variation of joint types, normalized W/d = 2 to 5, reinforcing fiber composites, lay-up types, plate thickness and bolt loads. Experimental observations and bearing stress at failures were conducted, the datasets were then used as validation works in FEA modelling. All KFRP hybrid joint series demonstrated net-tension failure mode associated to stress concentration at the vicinity of notch tip. Initially, strength prediction works were attempted by implementing various numerical approaches and fully XFEM techniques was adopted to all series as it provides promising results with better physically representation and less computational time. Good agreements between experimental datasets and predicted bearing stress at failure were found in KFRP hybrid joints with average discrepancy of less than 23%. It was found that combinations of thicker and cross-ply lay-up gives the best prediction of less than 2 % (where experimental datasets and FEA output were given as 201 N/mm2 and 198 N/mm2 respectively) due to better repetitive lay-up with implementation of smeared-out properties. Less significant effects from bolt loads and reinforcing fibers were found for both joint types. It can be concluded that fully XFEM technique able to provide as a unified prediction tools in hybrid joints of most composite materials with reasonable agreements