BALTIMORE, MD, March 5, 2025 – INFORMS, the world’s largest association for professionals and students in operations research (O.R.), AI, analytics and data science, proudly announces the three distinguished finalists for the 2025 UPS George D. Smith Prize. This prestigious award honors institutions that excel in preparing students for impactful careers in the rapidly evolving O.R., analytics and data science fields.
Contentious minerals deal has sparked a war of words between Trump and Zelensky, and could be announced later this week
Americans have probably heard the word “tariffs” more in the past month than in the past four years — and for good reason. Tariffs are central to President Donald Trump’s economic playbook, despite opposition from mainstream economists and trade experts.
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A new study in the INFORMS journal Operations Research on the conflict in Syria using existing attrition models, found that certain factors were lacking, leading to the creation of a new predictive model for multilateral war, which is called the Lanchester multiduel.
Three researchers have conducted a study of war, specifically the current conflict in Syria that's been raging since 2011, to arrive at the creation of a new predictive model for multilateral war, which is called the Lanchester multiduel. The research, published in the August edition of the INFORMS journal Operations Research, is titled "The Attrition Dynamics of Multilateral War."
CATONSVILLE, MD, September 12, 2018 – New research has found a way to identify extremists, such as those associated with the terrorist group ISIS, by monitoring their social media accounts, and can identify them even before they post threatening content.
CATONSVILLE, MD, September 10, 2018 – Three researchers have conducted a study of war, specifically the current conflict in Syria that’s been raging since 2011, to arrive at the creation of a new predictive model for multilateral war, which is called the Lanchester multiduel.
A patient goes into the emergency room for a broken toe, is given a series of standardized tests, has their data fed into an algorithm, and—though they haven’t mentioned feeling depressed or having any suicidal thoughts—the machine identifies them as at high risk of suicide in the next week. Though the patient didn’t ask for help, medical professionals must now broach the subject of suicide and find some way of intervening. This scenario, where an actionable diagnosis comes not from a doctor’s evaluation or family member’s concern, but an algorithm, is an imminent reality.
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