Functional forecasting: University of Rhode Island team uses Homeland Security exercises to evaluate storm decision support tool as part of Katrina lookback issue

URI’s CHAMP tool and expertise highlighted in series, '20 years after Hurricane Katrina: Lessons learned and implemented in flood risk management'

University of Rhode Island

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URI offers tools to meet Rhode Island’s resilience and emergency management needs: CHAMP predicted flooding on a Newport street during a 2022 nor’easter.

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Credit: URI CHAMP

In a new paper in the Journal of Coastal and Riverine Flood Risk, a team from the University of Rhode Island discusses the novel application of Homeland Security exercises to evaluate emergency managers’ use of their simulation support tools to improve response to major coastal storms such as Hurricane Katrina.

They ran their models based on Hurricane Henri, which hit the northeastern U.S. in 2021, but the paper was inspired by a 20-year lookback at Katrina and the damage it wrought on New Orleans. User feedback and observation data were used to inform real-world activation protocols and guide ongoing development of CHAMP (Coastal Hazards Analysis, Modeling, and Prediction).

For one of the paper’s lead authors, Samuel Adams, a marine affairs Ph.D. candidate in URI’s Marine Affairs Coastal Resilience Lab — and also URI’s Emergency Management Director — considering the impact of Katrina is not only an academic exercise, it’s personal.

A native of New Orleans, Adams came to Rhode Island for college, but says, “NOLA will always be home.” His parents’ home was so damaged, he was there for a week after the storm dealing with their house while the city was evacuated. Adams was working as a firefighter and EMT at the time in Bristol, R.I.

“That experience was the primary catalyst that led to my career in emergency management, and ultimately inspired me to pursue my Ph.D. in this area,” Adams comments. “I looked around me and knew there had to be a better way.”

Adams joined with his colleagues in marine affairs and Graduate School of Oceanography professor Isaac Ginis, who helped develop a hurricane-ocean model for the National Hurricane Center / NOAA when Katrina hit. National Geographic magazine featured his team’s work in its special edition on Hurricane Katrina and the National Science Foundation funded his team’s development of the educational website hurricanescience.org, in collaboration with the Louisiana State Museum, now widely used by educators around the country.

Today, Ginis, Adams and their URI colleagues in Marine Affairs and Rhode Island Sea Grant are looking ahead to make sure southeastern New England is not caught off guard, as New Orleans was. They say that tools like URI’s CHAMP can help emergency managers and simulation experiences can help mainstream their use; such programs were not available 20 years ago, but they are now — and Rhode Island is leading in this area. URI even exported the program to coastal Connecticut recently and could do the same for other regions. In Rhode Island, CHAMP is funded by the state to conduct real-time forecasting of all tropical and extratropical (nor’easters) storms affecting the Ocean State.

To help emergency managers here become more comfortable and knowledgeable about the programs’ capabilities, URI’s team utilized the U.S. Department of Homeland Security’s Homeland Security Exercise and Evaluation Program (HSEEP), the universally accepted standard for emergency management exercises in the United States and a template familiar to emergency management practitioners.

Practice as you play

Working with Ginis and his colleagues in professor Austin Becker’s Marine Affairs Coastal Resilience Lab, Adams’ team presented the results of two HSEEP-based exercises they ran. The team used 2021’s Hurricane Henri as their model storm, one that the CHAMP program actually forecast in real time that year for RIEMA. Since then, they have forecast several nor’easters in real time.

URI’s team designed exercises for local emergency managers to deploy CHAMP in response to a tropical storm striking Rhode Island, with realistic, plausible impacts predicted. The exercises were held at URI’s Narragansett Bay Campus and the Rhode Island Emergency Management Agency headquarters in Cranston, and focused on the entire State of Rhode Island. The east-west orientation of Rhode Island’s coastline, and the north-south orientation of Narragansett Bay, leaves 21 of 39 municipalities exposed to storm surge, making the “Ocean State” uniquely vulnerable to the effects of tropical cyclones.

“These kind of simulation-and-consequence prediction tools have the potential to transform the practice of emergency managers,” says Adams, noting that sophisticated tools like those found at URI can geolocate vulnerabilities and simulate the effects of weather hazards including storms that local communities may not have experienced in the past.

Some municipalities found that CHAMP’s models showed their towns being cut off by flood waters at certain points in a storm. For example, the towns of Bristol and Warren, located on a peninsula, would become an island cut off from emergency services and unable to evacuate. This created an additional imperative to make early decisions about evacuating vulnerable populations and implementing contingency plans to maintain essential services until outside help would reach them.

“Climate change is showing us that we can no longer depend on past storms as indicators of future risk,” Adams says. “We need better decision support tools that can help us anticipate and prepare for eventualities that haven’t happened previously. If we fail to do so, we are going to experience ‘surprise’ events like Katrina, or Helene in Western North Carolina, with increasing frequency and escalating destruction.”

This work was supported by the U.S. Dept of Homeland Security, using data from the URI Marine Affairs Coastal Resilience Lab.

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Journal

Journal of Coastal and Riverine Flood Risk

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Practice as You Play: Using Homeland Security Exercise and Evaluation Program (HSEEP) Exercises to Evaluate a Storm Decision Support Tool

 

DNA provides a solution to our enormous data storage problem

ASU researchers show how molecular structures can store large volumes of data while providing powerful encryption

Arizona State University

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An illustration shows a strand of engineered DNA passing through a nanoscale sensor, where its physical structure can be decoded as digital information. DNA nanostructures could one day serve as ultra-dense carriers of digital information and advance the field of data encryption.

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Credit: Jason Drees for the Biodesign Institute at ASU

Since the dawn of the computer age, researchers have wrestled with two persistent challenges: how to store ever-increasing reams of data and how to protect that information from unintended access.

Now, researchers with Arizona State University’s Biodesign Institute and their colleagues offer a surprising answer. In a pair of new studies, they show how DNA, the molecule of life, can be harnessed to faithfully store enormous volumes of data and provide powerful encryption.

The findings, appearing in the journals Advanced Functional Materials and Nature Communications respectively, provide a nature-inspired alternative to silicon-based storage and encryption solutions. They could help reshape the design of future microelectronic and molecular information systems for a broad range of applications. 

“For decades, information technology has relied almost entirely on silicon,” said Hao Yan, a Regents Professor in the School of Molecular Sciences and director of the Biodesign Center for Molecular Design and Biomimetics at Arizona State University. “What we’re showing here is that biological molecules, specifically DNA, can be engineered to store and protect information in fundamentally new ways. By treating DNA as an information platform rather than just a genetic material, we can begin to rethink how data is stored, read and secured at the nanoscale.”

Yan, along with researchers Chao Wang, associate professor in the School of Electrical, Computer and Energy Engineering, and Rizal Hariadi, associate professor in the Department of Physics, worked together to lead the projects. 

Big data, tiny molecule

As the world generates tremendous volumes of digital information, today’s storage technologies are struggling to keep up. The first study demonstrates a new way to store information using DNA — not by analyzing genetic letters, but by interpreting DNA’s physical shape.

DNA is appealing because it can store massive amounts of information in a tiny physical volume, and because it can remain stable for astonishingly long periods. (In 2022, researchers recovered DNA fragments from Greenland sediments dating back roughly 2 million years.)

The new research describes the design and construction of tiny DNA structures that act like physical letters in an alphabet, each carrying a piece of information. As the structures pass through a microscopic sensor, machine learning software records and analyzes subtle electrical signals. Then, the system can translate the data back into readable words and short messages with high accuracy.

The approach offers a powerful alternative to more traditional DNA data storage methods that rely on slow and expensive DNA sequencing. In contrast, the new technique is faster, cheaper and more scalable.

The work points toward a future where DNA could serve as an ultra-dense, long-lasting and secure medium for data storage. It could be useful for archiving massive amounts of information — from scientific records to cultural data — using very little space and energy. It also demonstrates a powerful bridge between synthetic biology and semiconductor technology, opening the door to new kinds of molecular information systems that go beyond conventional electronics.

Locking down information at the molecular level

While the first study focuses on how DNA can store information efficiently, the second explores how DNA nanostructures could also help protect information through encryption.

In this work, the researchers design intricate DNA origami structures — folded arrangements of DNA strands that form precise two- and three-dimensional shapes. Instead of storing data simply as bits or letters, information is encoded in the arrangement and pattern of these nanoscale structures. This creates a kind of molecular code that is difficult to interpret without the correct tools and reference patterns.

To read the encrypted information, the team uses an advanced form of super-resolution microscopy that can visualize individual DNA structures at extremely high precision. Machine learning software then analyzes thousands of molecular images, grouping similar patterns and translating them back into the original message. Without the correct decoding framework, the patterns are essentially meaningless, adding a layer of built-in security.

The approach greatly increases the number of possible molecular codes that can be created, making unauthorized decoding far more difficult. It also allows information to be packed into three-dimensional DNA structures, which adds even more complexity and security to each molecular key.

“In these studies, our team brings together complementary approaches, including DNA nanotechnology, super-resolution optical imaging, high-speed electronic readout and machine learning, to interrogate DNA nanostructures across multiple spatial and temporal scales,” Wang said. "This integrated strategy helps us better understand the behavior and function of DNA nanostructures. 

"This is a very good example of doing research at the intersection of semiconductor technology and biology. In this emerging field, more applications, from advanced biosensing to programmable nanodevices, remain to be explored.”

Bringing storage and security together at the molecular scale

Together, the two studies show how DNA can function not only as a compact storage medium, but also as a platform for secure information handling at the nanoscale. One technique emphasizes fast, electronic-style readout of molecular information, while the other demonstrates how molecular patterns themselves can serve as encrypted carriers of data.

DNA-based systems could one day support ultra-dense storage for scientific data, medical records or cultural archives. Molecular encryption could provide new ways to secure sensitive information in environments where conventional electronics struggle, such as extreme temperatures, radiation or long-term preservation.

The research highlights a growing convergence between biology, materials science, computation and electronics. By treating DNA as both a biological molecule and an information platform, researchers are opening new ways to store, protect and access data at scales far smaller and potentially far more durable than today’s digital devices.

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Journal

Nature Communications

DOI

10.1038/s41467-025-66338-y 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

High-speed 3D DNA PAINT and unsupervised clustering for unlocking 3D DNA origami cryptography

POSTMODERN ALCHEMY

Simulations and experiments meet: Machine learning predicts the structures of gold nanoclusters

University of Jyväskylä - Jyväskylän yliopisto

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Atomistic snapshots describing how two thiolate-protected gold nanoclusters of 144 gold atoms each coalesce producing a single larger cluster matching a size that previously has been synthesized. 

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Credit: Maryam Sabooni Asre Hazer, University of Jyväskylä.

Researchers at University of Jyväskylä (Finland) advance understanding of gold nanocluster behavior at elevated temperatures using machine learning-based simulations. This information is crucial in the design of nanomaterials so that their properties can be modified for use in catalysis and other technological applications.

Thiolate protected gold nanoclusters are hybrid nanomaterials with promising applications in nanomedicine, bioimaging and catalysis. However, understanding how these nanoclusters behave under elevated temperatures, which is critical for their use, has remained largely unexplored due to the prohibitive computational cost of traditional simulation methods. 

Record-long simulations of gold nanoclusters

Researchers at the University of Jyväskylä have successfully employed machine learning-driven simulations to investigate the thermal dynamics of Au₁₄₄(SR)₆₀, one of the most well-studied gold nanoclusters. Using a recently developed atomic cluster expansion (ACE) potential trained on extensive density functional theory data, the researchers conducted molecular dynamics simulations extending up to 0.12 microseconds. This is approximately five orders of magnitude longer than what is feasible with conventional quantum chemical methods.

"This work opens new possibilities for understanding how ligand-protected metal nanoclusters behave under realistic operating conditions," says lead author Dr. Maryam Sabooni Asre Hazer. "Through this work, we can observe in atomistic detail how these clusters transform, fragment, and even merge at elevated temperatures over timescales that are relevant for experimental conditions."

Layer-by-layer thermal transformations revealed

The study revealed that thermal effects induce structural changes in a layer-by-layer fashion, starting from the outermost gold-thiolate protective shell. At temperatures between 300 and 550 K, the researchers observed the spontaneous formation of polymer-like chains and ring structures of gold-thiolate units, which can dynamically detach and reattach to the cluster surface. The remaining cluster compositions closely matched those observed in experimental studies, demonstrating the accuracy of the machine learning potential.

"What's particularly exciting is that we can now see how gold atoms migrate between different layers of the cluster and how the surface restructures under thermal stress," explains Dr. Sabooni Asre Hazer. "These processes are directly relevant to understanding why thermally treated gold nanoclusters become effective catalysts."

Gold clusters joined together in the simulation

In an even more remarkable finding, the researchers successfully simulated the complete coalescence of two Au₁₄₄(SR)₆₀ clusters at 550 K. The fusion process produced a larger cluster with composition Au₂₃₉(SR)₆₉, strikingly similar to a gold nanocluster previously synthesized experimentally. 

"The merged cluster exhibited a twinned face-centered cubic metal core structure, matching the symmetry determined from experimental X-ray diffraction data," says Dr. Sabooni Asre Hazer.

Opening new avenues for nanomaterials research

The methodology enables detailed atomistic studies of processes that were previously inaccessible to computational investigation, including cluster-cluster interactions, catalytic activation mechanisms, thermal stability, and inter-particle reactions.

"Our results provide fundamental insights into how ligand-protected nanoclusters behave as they transition toward larger nanoparticles," explains Professor Hannu Häkkinen, who supervised the research. "This knowledge is instrumental for the rational design of nanomaterials with tailored functionalities for catalysis and other applications.", he continues. 

The research was published in Nature Communications. The publication was recognized as an Editors' Highlight in the Inorganic and Physical Chemistry section of Nature Communications.

The work was supported by the Research Council of Finland and the European Research Council (ERC) through the Advanced Grant project DYNANOINT. Computational resources on supercomputers Puhti and Mahti were provided by the Finnish national supercomputing center CSC. 

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Journal

Nature Communications

DOI

10.1038/s41467-025-67700-w 

Method of Research

Imaging analysis

Subject of Research

Not applicable

Article Title

Thermal dynamics and coalescence of Au144(SR)60 clusters from a machine-learned potential

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