The influence of test anxiety on memory

The purpose of this study was to investigate the relationship between test anxiety and memory among a college population (N = 42). Specifically, the goal was to ascertain whether text anxiety had a measurable effect on memory, which was represented by scores on the Nelson-Denny reading comprehension subtest. Participants were divided into a stressed group (N = 22) and a non-stressed group (N = 20) in order to compare scores from test-takers with anxiety to those who do not. It was hypothesized that (a) test anxiety would have a significant impact on test results, (b) the non-stressed group would score higher than the stressed group, and (c) the stressed-group’s memory capabilities, and thus test performance, would be reduced as a result of the test anxiety created by the environment. Results indicated that the difference between the two groups were not significant

A Content Neutral Public Nudity Ordinance That Satisfies the \u3cem\u3eO\u27Brien\u3c/em\u3e Test May Require Erotic Dancers to Wear G-Strings and Pasties without Violating Their First Amendment Right of Freedom of Expression: \u3cem\u3eCity of Erie v. Pap\u27s A.M.\u3c/em\u3e

The Supreme Court of the United States held that a content-neutral ordinance, aimed at combating the negative secondary effects of public nudity, satisfied the O\u27Brien standard for restrictions on symbolic speech and was, therefore, a constitutionally permissible restriction on freedom of expression. City of Erie v. Pap\u27s A.M., 529 U.S. 277 (2000)

Love\u27s Old Sweet Song

Published by The Columbian Conservatory of Music, a score for piano, No. 55.https://digitalcommons.jsu.edu/lib_ac_special_edwardianscores/1038/thumbnail.jp

Effects of Zucchini Fat Substitution on Palatability in Brownies: A Possible Solution to Heart Healthy Desserts

Description: In 2017-2018, 42.2% of the adult population living in the US was obese (BMI ≥ 30 kg/m2). This increases risk for cardiovascular diseases, such as coronary artery disease (CAD), which is the top cause of death in the United States. In addition, 18.2 million adults in the US are living with CAD. People with CAD are encouraged to reduce dietary saturated fat and cholesterol in a DASH diet-style of eating. Substituting fat in recipes is difficult due to its important role in the cooking process, such as, heat transfer, tenderizing, and emulsification. Desserts, such as brownies, are traditionally high in saturated fats. Zucchini is a nutrient-dense squash that can be used as a fat-substitute in baked goods. This study concluded that zucchini brownies, resulting in reduced saturated fat, cholesterol, and calories, may be a palatable alternative to traditional brownies. These zucchini brownies align more closely with a heart healthy diet.https://griffinshare.fontbonne.edu/fcs-424-2020/1000/thumbnail.jp

From National Populism to National Corporatism: The Case of Bolivia (1952-1970)

Analyzes the experience of Bolivia with an experiment in a populist resolution of its socioeconomic problems from 1952 to 1970. Objectives of national populist ideology; Factors that lead to the failure of Movimiento Nacionalista Revolucionario in Bolivia to achieve its revolutionary goals: Resurgence of private sector in mining and petroleum

Friends in High Places

We demonstrate that personal connections amongst U.S. politicians have a significant impact on Senate voting behavior. Networks based on alumni connections between politicians are consistent predictors of voting behavior. We estimate sharp measures that control for common characteristics of the network, as well as heterogeneous impacts of a common network characteristic across votes. We find that the effect of alumni networks is close to 60% as large as the effect of state-level considerations. The network effects we identify are stronger for more tightly linked networks, and at times when votes are most valuable. We show that politicians use school ties as a mechanism to engage in vote trading ("logrolling"), and that alumni networks help facilitate the procurement of discretionary earmarks

Exploiting UML dynamic object modeling for the visualization of C++ programs

In this paper we present an approach to modeling and visualizing the dynamic interactions among objects in a C++ application. We exploit UML diagrams to expressively visualize both the static and dynamic properties of the application. We make use of a class diagram and call graph of the application to select the parts of the application to be modeled, thereby reducing the number of objects and methods under consideration with a concomitant reduction in the cognitive burden on the user of our system. We use aspects to insert probes into the application to enable profiling of the interactions of objects and methods and we visualize these interactions by providing sequence and communication diagrams for the parts of the program under consideration. We complement our static selectors with dynamic selectors that enable the user to further filter objects and methods from the sequence and communication diagrams, further enhancing the cognitive economy of our system. A key feature of our approach is the provision for dynamic interaction with both the profiler and the application. Interaction with the profiler enables filtering of methods and objects. Interaction with the application enables the user to supply input to the application to provide direction and enhance comprehension or debugging

Misvaluing Innovation

We demonstrate that a firm’s ability to innovate is predictable, persistent, and relatively simple to compute, and yet the stock market ignores the implications of past successes when valuing future innovation. We show that two firms that invest the exact same in research and development (R&D) can have quite divergent, but predictably divergent, future paths. Our approach is based on the simple premise that while future outcomes associated with R&D investment are uncertain, the past track records of firms may give insight into their potential for future success. We show that a long-short portfolio strategy that takes advantage of the information in past track records earns abnormal returns of roughly 11% per year. Importantly, these past track records also predict divergent future real outcomes in patents, patent citations, and new product innovations

Playing Favorites: How Firms Prevent the Revelation of Bad News

We explore a subtle but important mechanism through which firms manipulate their information environments. We show that firms control information flow to the market through their specific organization and choreographing of earnings conference calls. Firms that “cast” their conference calls by disproportionately calling on bullish analysts tend to underperform in the future. Firms that call on more favorable analysts experience more negative future earnings surprises and more future earnings restatements. A long-short portfolio that exploits this differential firm behavior earns abnormal returns of up to 101 basis points per month. Further, firms that cast their calls have higher accruals leading up to call, barely exceed/meet earnings forecasts on the call that they cast, and in the quarter directly following their casting tend to issue equity and have significantly more insider selling

Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models

This paper describes how to perform classification of complex, high-dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between-function correlations. Classification is performed through training the model treating class as one of the fixed effects, and then predicting on the test data using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the method we present can be applied to any FMM-based method, we provide details for two specific Bayesian approaches: the Gaussian, wavelet-based functional mixed model (G-WFMM) and the robust, wavelet-based functional mixed model (R-WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features and complex nonstationarities in the functions. The R-WFMM allows potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a pancreatic cancer mass spectroscopy data set and compared with some other recently developed functional classification methods