Protecting Elders from Scams
Research done by the Understanding America Survey at the
University of Southern California shows that only 39 percent of Americans over
the age of 60 have a durable power of attorney, a legal document naming a trusted
person to manage finances if they can’t do it themselves. That can leave an
elderly person vulnerable to a host of financial disasters, including
unintentional money management mistakes, fraud, and scams.
To prevent these
misfortunes, U of M and Stanford researchers have released the Thinking Ahead
Roadmap. Funded by AARP, this free toolkit offers instructions to help people
select someone they trust to make financial decisions for them, if necessary.
“Problems with financial decision-making can appear many years before a
dementia diagnosis,” says the toolkit’s lead author, Marti DeLiema, a
gerontologist at the School of Social Work. “Even cognitively healthy older
adults may show declines in their financial decision-making abilities.”
Available both online or in print, the Roadmap includes a financial inventory
that users can download and fill out. The toolkit offers prompts elders can use
to overcome common issues, such as adult children who don’t want to acknowledge
that their parents are getting older or who are uncomfortable talking about
The Thinking Ahead Roadmap can be viewed and downloaded
The fiercely territorial red-headed woodpecker has been on
the decline in many parts of the United States. Thankfully, the population of
these “flying checkerboards” is thriving at the University of Minnesota’s Cedar
Creek Ecosystem Science Reserve, a biological field station north of the Twin
Cities in East Bethel, Minnesota, which has one of the largest tracts of oak
savanna in the state.
To learn more about woodpecker habitats, U of M
College of Food, Agricultural and Natural Resource Sciences researcher
Elena West leads a team of volunteers to study them. First, West attaches a
small tracking device to a woodpecker’s leg, a delicate and complicated task
that involves peanuts and a wire box. If she’s successful—not a given due to
the birds’ intelligence and feisty nature— her team is able to follow their
movements to better understand habitats and migration patterns; red-headed
woodpeckers are facultative migrants, so they migrate in some years and stay in
place in others.
West also gathers information about the birds’ nesting ecology
and behaviors by examining videos from trail cameras placed in the reserve.
These images are then posted on the Woodpecker Cavity Cam on Zooniverse, a
citizen science platform where volunteers can assist researchers as they work.
Interested in joining these efforts? Check out zooniverse.org/projects/elwest/woodpecker-cavity-cam
Algorithms and the Environment
From helping us choose the next binge-worthy TV series to
preventing us from backing into a parked car, algorithms have become integral
to modern life. Now, researchers are using these machine-learning tools to make
A team of researchers from the U of M, the
University of Pittsburgh, and the U.S. Geological Survey used algorithms to
more accurately predict river and stream temperatures, even when there was a
dearth of available data to analyze. They say these predictions can now be used
to determine a host of environmental factors, including the suitability of
aquatic habitats, evaporation rates, greenhouse gas exchange, and the
efficiency of thermoelectric energy production.
Researchers say that being able
to accurately predict water temperature and streamflow will help with a range
of decisions on waterways, including for resource managers, who use the data to
determine when and how much water to release from reservoirs to downstream
“These knowledge-guided machine learning techniques are fundamentally
more powerful than standard machine learning approaches and traditional
mechanistic models used by the scientific community to address environmental
problems,” says U of M computer scientist Vipin Kumar, whose lab in the College
of Science and Engineering develops such technologies.
This study was
originally published in the 2021 Society for Industrial and Applied Mathematics
(SIAM) International Conference on Data Mining proceedings.