First Results from HaloSat – A CubeSat to Study the Hot Galactic Halo

HaloSat is the first CubeSat for astrophysics funded by NASA\u27s Science Mission Directorate and is designed to map soft X-ray oxygen line emission across the sky in order to constrain the mass and spatial distribution of hot gas in the Milky Way. HaloSat will help determine if hot halos with temperatures near a million degrees bound to galaxies make a significant contribution to the cosmological budget of the normal matter (baryons). HaloSat was deployed from the International Space Station in July 2018 and began routine science operations in October 2018. We describe the on-orbit performance including calibration of the X-ray detectors and initial scientific results including an observation of a halo field and an observation of solar wind charge exchange emission from the helium-focusing cone

STUDENT PERSPECTIVES OF SUSTAINABLE TRANSPORTATION USE ON A COLLEGE CAMPUS

As climate change grows in relevance in society, attention has often been turned to large universities to look at what solutions can help minimize the impacts contributing to the devastation. Sustainable transportation is an alternative that can help combat these issues. This study specifically focuses on what sustainable transportation looks like at the University of Nebraska-Lincoln (UNL), and what barriers and motivations are present in the undergraduate students’ eyes. This was investigated through an open-ended qualitative survey administered to the undergraduate population of the UNL. This study asks how students make their mode decisions in relation to sustainable transportation methods, as well as what students think would encourage the campus population to use more sustainable methods to, from, and around campus. Results from the study indicate that factors influencing students’ sustainable transportation use are distance, weather, timing, and safety. Student ideas for how to encourage more sustainable transpiration use on campus fall into the themes of greater information/education, better infrastructure, and the introduction of incentives. These findings also acknowledge how while students are aware of their environmental impacts, they are not strongly inclined to take action on their own. This emphasizes the need for change at higher levels

ON COMMUTATIVE SPLITTING RINGS

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FCSAmerica Lead Generation

This project utilizes machine learning and data science techniques to build predictive models to generate leads for the Farm Credit Services sales team. Using publicly available data sets, we have developed models in the Python and R programming languages that can successfully predict high-value customers. These customers can then be mapped back to the company\u27s internal database and lead management software for utilization in the company\u27s operations. Through this work, we have proven the ability to apply machine learning and data modeling to the agriculture industry and extract useful and actionable insights from publicly available data

FCSAmerica Lead Generation

This project utilizes machine learning and data science techniques to build predictive models to generate leads for the Farm Credit Services sales team. Using publicly available data sets, we have developed models in the Python and R programming languages that can successfully predict high-value customers. These customers can then be mapped back to the company\u27s internal database and lead management software for utilization in the company\u27s operations. Through this work, we have proven the ability to apply machine learning and data modeling to the agriculture industry and extract useful and actionable insights from publicly available data

FCSAmerica Lead Generation

This project utilizes machine learning and data science techniques to build predictive models to generate leads for the Farm Credit Services sales team. Using publically available data sets, we have developed models in the Python and R programming languages that can successfully predict high-value customers. These customers can then be mapped back to the company\u27s internal database and lead management software for utilization in the company\u27s operations. Through this work, we have proven the ability to apply machine learning and data modeling to the agriculture industry and extract useful and actionable insights from publically available data