Left: An aerial photo showing (foreground) high
sequoia mortality in an area that had not experienced fire in many decades, and
(background) high sequoia survival in an area that had been treated with
prescribed fire in 2012.
Right: General Sherman, a giant sequoia and the largest tree in the world by
volume, towers over the rest of the forest in Sequoia National Park. These
trees can grow to over 300 feet tall, over 30 feet wide, live for more than
3,000 years, and are only naturally found[DDJ(SAERI1] on the western slopes of the Sierra Nevadas.National Park Service/Anthony Caprio (Left), Samuel
Grinis (Right)
National Park Service/Anthony Caprio (Left), Samuel
Grinis (Right)
A new study, funded by
NASA, found that giant sequoias were nearly four times more
likely to survive extreme wildfires in forests that have been managed
with prescribed burns, which decrease tree mortality.
Giant sequoias are a unique species,
only naturally found on the western slopes of the Sierra Nevada
mountain range in California. These trees, which can reach more
than 300 feet in height and more than 30 feet in diameter, are
keystones of the ecosystem and serve as carbon sinks, water filters,
and structural habitats for birds, insects, and small
mammals.
Recent wildfires have caused far more
deaths among giant sequoias than usual, threatening their
future. Prescribed, or “controlled” burns help the trees by thinning
underbrush, dead wood, and other dry fuels, reducing the likelihood of mortality
from high-intensity wildfires.
Researchers analyzed data on 26,403 giant sequoias across 19 groves in Sequoia and Kings
Canyon National Parks in the wake of the 2020 and 2021 fire seasons, when
a record 6.8 million acres burned across California. The
study was published July 11 in the journal Nature Communications.
During those two years,19% of the total mature giant sequoia
population across the range was destroyed.
The team combined lidar, stem maps,
high-resolution imagery, and modeling techniques to estimate giant sequoia
mortality in relation to forest management practices and found
that prescribed burns saved roughly 1,800 giant sequoias.
A forestry technician uses a drip torch to set a
prescribed fire near a grove of giant sequoias.
National Park Service/Anthony Caprio
While prescribed burns are widely
considered among the best ways to protect giant sequoias,
evidence to date has been primarily anecdotal. This study
showed the value of high-resolution remote sensing for species-specific conservation
efforts, with applications for old-growth forests worldwide.
A custom-fit wearable device using 3D
printing designed specifically for each individual. The device contains
built-in magnetic sensors that detect subtle muscle activity. Credit: Florida
Atlantic University
Most
prosthetic hands today still struggle with a fundamental problem: No two
amputees are the same, yet most devices are designed as if they are. That
mismatch makes natural, intuitive control difficult, often turning what should
feel like a seamless extension of the body into something that requires
constant learning and adjustment.
Even with advanced technology, users are
frequently left to interpret faint muscle signals that can shift with sweat,
skin changes or everyday movement—creating a gap between intention and control
that can be frustrating and, in some cases, lead people to abandon the device
altogether.
Researchers have made progress by
improving how muscle signals are interpreted, but the core challenge remains:
The signals are often unstable and hard to translate into natural movement.
To address this challenge, Erik
Engeberg, Ph.D., is leading research to shift the focus from standardized
devices to truly personalized systems that adapt to each individual. Engeberg
is a professor in FAU's College of Engineering and Computer Science, with
appointments in the Department of Ocean and Mechanical Engineering and the
Department of Biomedical Engineering. He is also a member of the FAU
Stiles-Nicholson Brain Institute and the FAU Center for Complex Systems within
the Charles E. Schmidt College of Science.
The process begins with a quick 3D scan of a
person's arm to capture its exact shape. That scan is then used to create a
custom-fit wearable device using 3D printing. Designed specifically for each
individual, the lightweight two-piece device fits comfortably around the
forearm without limiting movement. It also contains built-in magnetic sensors
that detect subtle muscle activity, allowing the system to capture movement
signals with greater accuracy. Credit: Florida Atlantic University
A custom sleeve reads intent
The approach begins with 3D
scanning a person's residual limb to create a custom 3D-printed wearable sleeve
embedded with soft, flexible magnetic sensors. These sensors sit comfortably
against the skin and capture subtle changes in muscle shape and pressure as the
user attempts hand and wrist movements, allowing the system to interpret intent
in real time.
The design is tailored to each
individual, with sensor arrays configured with either 18 or 24 modules
depending on limb size and anatomy and paired with an individualized artificial
intelligence model that learns each person's unique muscle patterns rather than
relying on a generalized data set.
Stable signals under repeated use
In testing with 10 participants,
including three upper-limb amputees, the system classified 19 hand and wrist
gestures in real time, translating intent into control of a dexterous robotic
hand. Results, published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, show the system performed consistently and reliably
under repeated use.
To assess durability, researchers
applied more than 7,500 robotic force cycles over several hours while precisely
measuring sensor response. The system showed a strong, stable relationship
between applied force and output, accurately capturing pressure without loss of
performance.
Even after thousands of cycles,
signals remained clear and stable, with strong separation between signal and
noise and only minor variation over time. Overall, the sensors showed no
meaningful drift or degradation, maintaining the accuracy, repeatability and
responsiveness essential for real-world prosthetic control.
"Prosthetic control is not
one-size-fits-all. Every individual brings a distinct movement signature shaped
by their anatomy, injury history and how their remaining muscles
function," said Engeberg, senior author. "If we want these systems to
truly work in everyday life, they have to be custom-fit. By combining
3D-printed wearable sensors with individualized AI models, we're moving closer
to prosthetic systems that can respond naturally and in real time to a person's
intent, rather than forcing users to adapt to the limitations of the
device."
No universal sensor setup
Findings also showed there is no
single best sensor configuration for all users. Some participants achieved
higher accuracy with fewer sensors, while others required more, with optimal
setups varying based on individual anatomy and differences in injury history
and remaining muscle function. In several cases, participants achieved more
than 90% accuracy across multiple gestures only when the sensor layout was
tailored to their residual muscles.
"Our results highlight that
prosthetic performance is highly dependent on how well sensor placement and
quantity are matched to the individual," said Engeberg. "This
suggests a future in which prosthetists can fine-tune sensor configurations
much like a prescription, balancing both function and comfort for each
user."
The research also produced a shared
data set from all participants, including amputees and non-amputees, providing
a valuable resource for the broader scientific community.
The scale of unmet need
"This work speaks to something
very practical: improving quality of life in a very direct way," said
Stella Batalama, Ph.D., dean of the College of Engineering and Computer
Science. "When we close the gap between engineering innovation and what
people actually need in their daily lives, especially for individuals who
depend on prosthetic devices for independence, the impact goes far beyond the
lab. It's about restoring function, confidence and the ability to engage with
the world more naturally."
In the United States alone, an
estimated 2.1 million people are living with limb loss, with around 185,000
amputations occurring each year. Globally, more than 50 million people are
affected, a number expected to grow due to diabetes, vascular disease, trauma
and conflict-related injuries. Upper-limb amputations are among the most
challenging to restore function because of the complexity of natural hand and
finger movement.
Study co-author is Wen-Yu
"Marty" Cheng, a graduate student and Ph.D. candidate in FAU's
College of Engineering and Computer Science.
