The Cavaliers will kickstart indoor competition in 2026 at the Hokie Invitational in Blacksburg, Va. on Friday and Saturday, January 16-17.
Americans are now more likely to trust the American Medical Association than the Centers for Disease Control and Prevention when the two conflict on vaccine guidance.
The survey, created by APPC’s Annenberg Health and Risk…
Ashley Tisdale ignited a fire with her essay from The Cut entitled “Breaking Up With My Toxic Mom Group” released on New Year’s Day. In the short tome, the High School Musical Star attempted to anonymously chronicle…
Cambridge, MA (January 8, 2025) —Astronomers have completed the most comprehensive census of active galactic nuclei (AGN) to date, providing the clearest picture yet of the probability that galaxies of different sizes host active black…
Bipartisan Fiscal Forum (BFF) co-chairs Representatives Bill Huizenga (R-MI) and Scott Peters (D-CA), along with BFF Members, Representatives Lloyd Smucker (R-PA) and Mike Quigley (D-IL), introduced a resolution in the House yesterday calling on…
Minneapolis is once again the focus of debates about violence involving law enforcement after an Immigration and Customs Enforcement officer shot and killed Renee Nicole Good, a 37-year-old mother, in her car.
The incident quickly prompted…
Cambodia is not alone in facing capacity limitations in the production and timely release of key official statistics needed for data-driven policy decisions. This paper demonstrates that combining satellite-derived indicators (e.g., nighttime lights, NO₂ emissions, vegetation indices) with traditional high-frequency indicators in a machine learning framework significantly improves the accuracy of GDP nowcasts. Moreover, satellite data enables closer examination of subnational patterns, providing granular, near-real-time insights into economic activity. These findings highlight the potential of non-traditional approaches to complement conventional methods and strengthen macroeconomic surveillance in data-scarce environments.
Subject: Agricultural sector, Economic forecasting, Economic sectors, Health
Keywords: Agricultural sector, big data, machine learning, non-traditional data, nowcast, nowcasting, random forest, satellite, satellite data