Comparing the Effects of Various Exercise Regimens on Anxiety Levels in College Aged Students

Generalized anxiety disorder is described as excessive and inappropriate worrying that is persistent and not restricted to particular circumstances. Anxiety has recently surpassed depression as the number one mental health disorder on college campuses. Multiple studies have shown that exercise reduces anxiety symptoms and feelings of stress. Many students enroll in physical activity courses due to college requirements, or as a way to engage in regular physical activity. On our campus, yoga and pilates are two of the most popular physical activity courses and require two 50-minute sessions each week. The purpose of this study was to determine whether participation in these courses has a significant impact in reducing anxiety in a traditional college population. We surveyed students enrolled in either a yoga or pilates class and compared their responses to a control group not enrolled in a physical activity course

MEMS 411: The Pill Cutter

The goal of this design project was to improve the current method of pill cutting for STL Hills Pharmacy. Pharmacists and other medical professionals cut hundreds of pills weekly. Their current methods can only cut one pill at a time with an error rate of 50%. Pills are often split without enough precision and need to be thrown out. Our project was designed to have a 10% maximum error rate, require 1/4 of the force, and split pills 4 times as fast

Orbital character effects in the photon energy and polarization dependence of pure C60 photoemission

Recent direct experimental observation of multiple highly-dispersive C$_{60}$ valence bands has allowed for a detailed analysis of the unique photoemission traits of these features through photon energy- and polarization-dependent measurements. Previously obscured dispersions and strong photoemission traits are now revealed by specific light polarizations. The observed intensity effects prove the locking in place of the C$_{60}$ molecules at low temperatures and the existence of an orientational order imposed by the substrate chosen. Most importantly, photon energy- and polarization-dependent effects are shown to be intimately linked with the orbital character of the C$_{60}$ band manifolds which allows for a more precise determination of the orbital character within the HOMO-2. Our observations and analysis provide important considerations for the connection between molecular and crystalline C$_{60}$ electronic structure, past and future band structure studies, and for increasingly popular C$_{60}$ electronic device applications, especially those making use of heterostructures

G99-1389 Cultural Practices to Improve Weed Control in Winter Wheat

This NebGuide explains the influence of cultural practices on weeds in winter wheat. Precipitation and temperature greatly influence crop and weed growth in the semiarid areas of the central Great Plains. Precipitation in Nebraska varies from 14 to 24 inches where fallow is practiced. The purpose of fallow is to control weeds and, when not cropping a field, to store water and stabilize winter wheat fields. With good prewheat-fallow techniques, sufficient soil moisture is usually available to establish winter wheat. Peak rainfall occurs in May and June during the pollination and grain-filling period of winter wheat. The latter part of June, July, and first part of August is the hottest period. High temperatures may cause stress to the wheat and weeds, reducing weed control if herbicides are applied after harvest

Statistical and systematic errors for gravitational-wave inspiral signals: A principal component analysis

Identifying the source parameters from a gravitational-wave measurement alone is limited by our ability to discriminate signals from different sources and the accuracy of the waveform family employed in the search. Here we address both issues in the framework of an adapted coordinate system that allows for linear Fisher-matrix type calculations of waveform differences that are both accurate and computationally very efficient. We investigate statistical errors by using principal component analysis of the post-Newtonian (PN) expansion coefficients, which is well conditioned despite the Fisher matrix becoming ill conditioned for larger numbers of parameters. We identify which combinations of physical parameters are most effectively measured by gravitational-wave detectors for systems of neutron stars and black holes with aligned spin. We confirm the expectation that the dominant parameter of the inspiral waveform is the chirp mass. The next dominant parameter depends on a combination of the spin and the symmetric mass ratio. In addition, we can study the systematic effect of various spin contributions to the PN phasing within the same parametrization, showing that the inclusion of spin-orbit corrections up to next-to-leading order, but not necessarily of spin-spin contributions, is crucial for an accurate inspiral waveform model. This understanding of the waveform structure throughout the parameter space is important to set up an efficient search strategy and correctly interpret future gravitational-wave observations.Comment: 16 pages, 7 figures, pdfLaTeX, improved presentation, matches published versio

Toward the Next Generation of Air Quality Monitoring Indicators

This paper introduces an initiative to bridge the state of scientific knowledge on air pollution with the needs of policymakers and stakeholders to design the "next generation" of air quality indicators. As a first step this initiative assesses current monitoring and modeling associated with a number of important pollutants with an eye toward identifying knowledge gaps and scientific needs that are a barrier to reducing air pollution impacts on human and ecosystem health across the globe. Four outdoor air pollutants were considered e particulate matter, ozone, mercury, and Persistent Organic Pollutants (POPs) e because of their clear adverse impacts on human and ecosystem health and because of the availability of baseline data for assessment for each. While other papers appearing in this issue will address each pollutant separately, this paper serves as a summary of the initiative and presents recommendations for needed investments to provide improved measurement, monitoring, and modeling data for policyrelevant indicators. The ultimate goal of this effort is to enable enhanced public policy responses to air pollution by linking improved data and measurement methods to decision-making through the development of indicators that can allow policymakers to better understand the impacts of air pollution and, along with source attribution based on modeling and measurements, facilitate improved policies to solve it. The development of indicators represents a crucial next step in this process

In Vivo Cross-Priming of MHC Class I–Restricted Antigens Requires the TAP Transporter

AbstractRecent in vitro evidence suggests two alternative mechanisms by which bone marrow–derived APCs may process exogenous antigens for presentation to CTL in vivo, a phenomenon termed cross-priming. Although in vitro studies have suggested that both TAP-dependent and TAP-independent pathways exist, we have now demonstrated an absolute requirement for a functional TAP for cross-priming to occur in vivo. Bone marrow chimeras reconstituted with marrow from TAP-defective donors develop functional CD8+ CTL, but have APCs with disrupted TAP function. In such chimeras, in vivo priming of naive CTL was observed when antigen was targeted to the ER in a TAP-independent fashion, but cross-priming could not be demonstrated. These results support the TAP-dependent mechanism of cross-priming

Can ChatGPT predict future interest rate decisions?

The public interest in Large Language Models (LLMs) and generative artificial intelligence (AI) has gained significant traction, with academic studies attempting to assess their application to a range of fields. In particular, the rise in Generative Pre-trained Transformer (GPT) models, in the form of ChatGPT has prompted a range of calls to examine beyond its use in writing and language generation; to test its predictive qualities and assess its abilities to process complex textual information. Our paper extends upon the examination of GPT model use in the monetary policy context. We set out to test the hypothesis that LLMs and GPT models can offer predictive qualities of future interest rate decisions through their textual processing and sentiment capabilities. Specifically, we use GPT-3.5 to evaluate and label the speech of every Bank of England Monetary Policy Committee (MPC) member based on linguistic expectations. We then model a preferred policy rate vote equation for each speech giving committee member (i) in each of their future rate decision windows (t+n). We find that ChatGPT can predict future interest rate decisions. Our results provide evidence for the potential of LLMs to help us better process latent human beliefs, make out-of-sample predictions and navigate possible models of rational expectations