A Classroom\u27s Evolution

Based on the four texts that we read in Social Foundations of Music Education, I took some of the main points and concepts from each of these books and incorporated them into an original poetic monologue. The main question I was trying to answer was: How should teachers as transformative intellectuals navigate through the current educational system in the age of accountability to pursue equity among, in, and through education? Teachers must work to completely defy the stereotypical boundaries of education and inspire students to become investigators in the world, both in and out of the classroom

A Zero-Inflated Box-Cox Normal Unipolar Item Response Model for Measuring Constructs of Psychopathology

This research introduces a latent class item response theory (IRT) approach for modeling item response data from zero-inflated, positively skewed, and arguably unipolar constructs of psychopathology. As motivating data, the authors use 4,925 responses to the Patient Health Questionnaire (PHQ-9), a nine Likert-type item depression screener that inquires about a variety of depressive symptoms. First, Lucke’s log-logistic unipolar item response model is extended to accommodate polytomous responses. Then, a nontrivial proportion of individuals who do not endorse any of the symptoms are accounted for by including a nonpathological class that represents those who may be absent on or at some floor level of the latent variable that is being measured by the PHQ-9. To enhance flexibility, a Box-Cox normal distribution is used to empirically determine a transformation parameter that can help characterize the degree of skewness in the latent variable density. A model comparison approach is used to test the necessity of the features of the proposed model. Results suggest that (a) the Box-Cox normal transformation provides empirical support for using a log-normal population density, and (b) model fit substantially improves when a nonpathological latent class is included. The parameter estimates from the latent class IRT model are used to interpret the psychometric properties of the PHQ-9, and a method of computing IRT scale scores that reflect unipolar constructs is described, focusing on how these scores may be used in clinical contexts

Convergence of the homology spectral sequence of a cosimplical space

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1995.Includes bibliographical references (p. 30-31).by Brooke E. Shipley.Ph.D

A Comparative Life Cycle Assessment between a Metered Dose Inhaler and Electric Nebulizer

Life cycle assessment (LCA) evaluates the environmental impact of a product based on the materials and processes used to manufacture the item as well as the item’s use and disposal. The objective of this LCA was to evaluate and compare the environmental impact of a metered dose inhaler, specifically the Proventil® HFA inhaler (Merk & Co., Inc., Kenilworth, NJ, USA), and an electric nebulizer, specifically the DeVilbiss Pulmo-Aide® nebulizer (DeVilbiss, Port Washington, NY, USA). GaBi LCA software was used to model the global warming potential (GWP) of each product by using substantiated data and well-justified assumptions for the components, manufacturing, assembly, and use of both devices. The functional unit used to model each device was one dose of the active drug, albuterol sulfate. The inhaler’s GWP, 0.0972 kg CO2-eq, was greater than the nebulizer’s even when uncertain parameters were varied ±100x. During the use phase ofa the inhaler, which accounted for approximately 96% of the inhaler’s total GWP, HFA 134a is used as a propellant to deliver the drug. The total GWP for the electric nebulizer was 0.0294 kg CO2-eq assuming that the mouthpiece was cleaned in a dishwasher, while it was 0.0477 kg CO2-eq when the nebulizer mouthpiece was hand washed between uses. The GWP breakeven scenario between dishwashing and hand washing occurred when the mouthpiece accounted for 10% of the dishwasher load

Kernel methods for detecting coherent structures in dynamical data

We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (RKHS) operators associated with dynamical systems. In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score. As a result, we show that coherent sets of particle trajectories can be computed by kernel CCA. We demonstrate the efficiency of this approach with several examples, namely the well-known Bickley jet, ocean drifter data, and a molecular dynamics problem with a time-dependent potential. Finally, we propose a straightforward generalization of dynamic mode decomposition (DMD) called coherent mode decomposition (CMD). Our results provide a generic machine learning approach to the computation of coherent sets with an objective score that can be used for cross-validation and the comparison of different methods

Long-term variation in distribution of sunspot groups

We studied the relation between the distribution of sunspot groups and the Gleissberg cycle. As the magnetic field is related to the area of the sunspot groups, we used area-weighted sunspot group data. On the one hand, we confirm the previously reported long-term cyclic behaviour of the sum of the northern and southern sunspot group mean latitudes, although we found a somewhat longer period (P~104 years). We introduced the difference between the ensemble average area of sunspot groups for the two hemispheres, which turns out to show similar behaviour. We also investigated a further aspect of the Gleissberg cycle where while in the 19th century the consecutive Schwabe cycles are sharply separated from each other, one century later the cycles overlap each other more and more.Comment: 4 page

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

The clustering of data into physically meaningful subsets often requires assumptions regarding the number, size, or shape of the subgroups. Here, we present a new method, simultaneous coherent structure coloring (sCSC), which accomplishes the task of unsupervised clustering without a priori guidance regarding the underlying structure of the data. sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters. To achieve this, we obtain a set of orthogonal coordinates along which dissimilarity in the dataset is maximized from a generalized eigenvalue problem based on the pairwise dissimilarity between the data points to be clustered. This sequence of bifurcations produces a binary tree representation of the system, from which the number of clusters in the data and their interrelationships naturally emerge. To illustrate the effectiveness of the method in the absence of a priori assumptions, we apply it to three exemplary problems in fluid dynamics. Then, we illustrate its capacity for interpretability using a high-dimensional protein folding simulation dataset. While we restrict our examples to dynamical physical systems in this work, we anticipate straightforward translation to other fields where existing analysis tools require ad hoc assumptions on the data structure, lack the interpretability of the present method, or in which the underlying processes are less accessible, such as genomics and neuroscience