An Empirical Evaluation Of User Satisfaction With A School Nursing Information System

The adoption of a school nursing information system is considered one of the most efficient ways in which to document health records as well as monitor health conditions electronically. However, despite the importance of computerized health records in school nursing practice, few studies have examined user satisfaction of a school nursing information system. The aim of this study is to investigate the critical factors effecting school nurses’ satisfaction with a school nursing information system Utilizing a survey approach, questionnaires are distributed to nurses working in a primary or high school which introduces a new school nursing information system. The findings show several factors, including perceived usefulness, perceived of ease of use, training and workload are significant with user satisfaction. These results suggest that school nursing information system designers should comprehensively understand users’ demands and perceptions about the system, which will further facilitate user satisfaction, decrease their workload, and ultimately enhance job performance

SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or "speckles", to extract single hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high data rate facilities like European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments

Meijer Renewable Energy Strategy

As one of the largest energy consumers in the Midwest, Meijer is seeking opportunities to decrease its fossil fuel based electricity consumption. The SEAS MS project team worked to identify and evaluate opportunities to expand Meijer's renewable energy portfolio by understanding the current state of energy consumption, benchmarking Meijer with their competition, and developing a tailored renewable energy strategy to meet Meijer's sustainability goals. The scope of this project included understanding the baseline of Meijer's electricity consumption, evaluating on-site generation opportunities, evaluating off-site procurement opportunities, and developing a pre-development guide tool to be used to analyze specific projects before making investments into project development. The project resulted in a per year prioritization list that considered the economic benefit of potential projects at every store site given annual budgetary constraints and project NPVs, an analysis of off-site Power Purchase Agreement opportunities, a comprehensive renewable energy strategy for on-site and off-site renewable energy generation, and an excel based pre-development guide to further analyze specific projects.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/143156/1/Meijer Renewable Energy Strategy_326.pd