*Public Policy Tipoffs Involving Arizona Newsletter for Thursday November 14, 2024 ( 4 items ) |
2024-25 NAIA Men's Basketball Coaches' Top 25 Poll - No. 1
KANSAS CITY, Missouri, Nov. 14 -- The National Association of Intercollegiate Athletics issued the following news release:
The Langston (Okla.) Lions continued to hold the top spot in the first regular season NAIA Men's Basketball Coaches' Top 25 Poll after receiving 18 first-place votes. College of Idaho held second, receiving one first-place vote and Indiana Wesleyan rounds out the top three. Arizona Christian and Georgetown (Ky.) both jumped a pair of spots to fourth and fifth, respectively.
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2024-25 NAIA Women's Basketball Coaches Top 25 Poll - No. 1
KANSAS CITY, Missouri, Nov. 14 -- The National Association of Intercollegiate Athletics issued the following news release:
Dordt (Iowa) remains at No. 1 in the latest edition of the 2024-25 NAIA Women's Basketball Top 25 Poll. Two teams jumped up in the poll by six spots: Southern Oregon, who is now in the Top 5, and Briar Cliff (Iowa), who went from No. 23 to No. 17. Arizona Christian, Marian (Ind.), Bethel (Tenn.) and Mid-America Christian (Okla.) joined the Top 25 this week.
Poll Methodolo
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Improving Water Conservation and Mitigating Arizona's Heat Island Effect
TUCSON, Arizona, Nov. 14 (TNSres) -- The University of Arizona issued the following news release:
The Arizona Board of Regents held a showcase Wednesday at the Arizona Experiment Station's Campus Agricultural Center in Tucson, featuring award-winning research from the University of Arizona and Arizona State University around new smart tree watering techniques. Hosting members of the local media, members of the U of A and ASU research team highlighted the results of the Smart Tree Watering Regen
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N.C. State: Machine Learning Predicts Highest-Risk Groundwater Sites to Improve Water Quality Monitoring
RALEIGH, North Carolina, Nov. 13 (TNSres) -- North Carolina State University issued the following news release:
An interdisciplinary team of researchers has developed a machine learning framework that uses limited water quality samples to predict which inorganic pollutants are likely to be present in a groundwater supply. The new tool allows regulators and public health authorities to prioritize specific aquifers for water quality testing.
This proof-of-concept work focused on Arizona and Nort
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