The technology emerged from an unexpected direction. Plant Armor began as an effort to make military uniforms resistant to mosquito bites and to improve comfort for soldiers wearing heavy body armor, according to co-author R. Michael Roe, William Neal Reynolds Distinguished Professor at NC State. The team’s original goal was a cloth that could sit against a soldier’s chest and make protective gear more wearable. Through iterative design and testing, that same class of knitted spacer fabrics proved remarkably effective at excluding insects from crops, a pivot that Roe described as impossible to predict at the outset. The version examined in the new study, Plant Armor Gen 2, is patented by NC State and licensed for commercial development by the university.

What distinguishes Plant Armor from conventional row covers is its three-dimensional architecture. Rather than a flat, single-layer textile, the fabric is a knitted spacer construction with multiple layers separated by an internal gap, creating a porous but tortuous pathway that insects cannot navigate. Laboratory bioassays demonstrated absolute exclusion of aphids, small sap-sucking pests that are among the most damaging and difficult-to-control insects in strawberry production. At the same time, the open, porous structure permits sunlight, rain and air to pass through to the plants beneath, meaning the cover functions less like a greenhouse wall and more like a selective filter that admits what crops need and blocks what harms them.

The yield results were striking. In tunnel field studies, covered strawberry plants produced 3.56 times more harvestable fruits than uncovered controls, with both the number and the total weight of fruit significantly higher. Equally important was what the researchers did not find: no differences in vegetative plant biomass between covered and uncovered plots, and no evidence of the shade-avoidance response the team had feared. Gabriel Olawuyi, a graduate research assistant at NC State and lead author of the paper, explained that the team expected the seemingly opaque fabric to reduce light availability and trigger a physiological shift in which plants divert energy from fruiting toward stem and leaf growth. Instead, measurements showed the fabric did not impede light access at all, and fruit production increased substantially.

Light is only part of the story. The cover also produced a consistent warming effect across the three seasons over which strawberry plants develop, from fall establishment through spring harvest. Olawuyi noted that plants require a certain amount of accumulated heat, calculated as growing degree-days, to progress through each developmental stage, including flowering and fruiting. By trapping warmth while remaining porous to air and water, the fabric appears to have delivered the thermal accumulation plants need at each phenological stage more optimally than the open field environment, allowing covered bushes to reach the fruiting stage earlier than their uncovered counterparts and to produce far more fruit once they arrived.

The microclimate effects were otherwise restrained. Despite the consistent temperature increase under the cover, relative humidity did not differ significantly between covered and uncovered plots, an important finding because elevated humidity under protective covers can promote fungal diseases and fruit rot in strawberries. Preliminary measurements of rhizosphere bacterial colony-forming units, an indicator of soil microbial communities around the roots, also showed no significant differences between covered and uncovered plots, suggesting the fabric did not disrupt the below-ground biology on which strawberry plants depend. Together, these results indicate that the cover modifies the above-canopy environment in targeted ways rather than wholesale.

Beyond yield, the insect-exclusion function carries implications for pesticide use. Strawberries are notoriously vulnerable to a range of arthropod pests, and conventional management relies heavily on chemical insecticides that raise concerns about residues, pollinator health, resistance development and regulatory pressure. By physically preventing insects from reaching plants, Plant Armor could reduce the need for traditional pesticides in agriculture, Olawuyi said. A non-chemical barrier that also boosts production, rather than merely protecting it, would be an unusual combination in crop protection, where physical exclusion methods such as netting and row covers typically involve trade-offs in light, ventilation or labor.

The study builds on the research group’s earlier work with the same class of materials. In previous experiments, the first 3-D spacer fabric constructed as a crop cover for tunnel field application increased cabbage vegetative growth 2.93-fold and proved resistant to insect penetration. What remained untested was whether such a cover could enhance fruit production specifically, since fruiting crops respond differently to modified light and temperature environments than leafy vegetables. Strawberries presented a demanding test case: they grow across three seasons, their flowers and fruit are traditionally protected from freezing with single-layer textiles, and their yield depends on a delicate balance of light, heat and pollination conditions.

The research was a collaborative effort spanning three NC State colleges: the College of Natural Resources, the Wilson College of Textiles and the College of Agriculture and Life Sciences. In addition to Olawuyi and Roe, the author team included James Clothier, Matthew Bertone, Grayson Cave, Reuben Garshong, Andre West, Loganathan Ponnusamy and Clyde Sorenson. Funding came from the North Carolina Agricultural Foundation and from the Research Capacity Fund (HATCH) administered by the U.S. Department of Agriculture’s National Institute of Food and Agriculture. The authors reported no known competing financial interests, though the patented Gen 2 fabric is licensed by the university for commercial development, with West and Roe among the named inventors.

The researchers caution that on-farm testing is now needed to determine how well the textile performs across different crop species, geographic locations and commercial production systems. Tunnel trials, while rigorous, do not capture the full variability of open-field agriculture, and strawberries grown in different climates, soils and management regimes may respond differently to the cover’s warming and exclusion effects. Still, the combination of a 3.56-fold yield increase, absolute aphid exclusion in bioassays, unchanged humidity and soil biology, and a fabric light enough to drape over plants positions Plant Armor as a rare crop-protection technology that appears to add productivity while subtracting chemical inputs. If on-farm results hold, a cloth originally conceived for soldiers’ body armor could find its largest audience in the strawberry fields of North Carolina and beyond.

Subject of Research: A knitted 3-D spacer fabric crop cover that increases strawberry fruit production and prevents insect feeding

Article Title: How a new textile fabric could triple production in strawberry plants

Article References: How a new textile fabric could triple production in strawberry plants. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Plant Armor, strawberries, textile crop cover, 3-D spacer fabric, insect exclusion, aphids, crop yield, growing degree-days, pesticide reduction, North Carolina State University, agriculture, protected cultivation

Actuators are the components of a machine that generate force or torque, and in conventional robotic design they are the primary currency of mobility. A robot that can translate in three dimensions and reorient itself typically requires at least six independently controlled actuators to achieve that full range of motion. Jie Yin, corresponding author of the paper and a professor of mechanical and aerospace engineering at NC State, explains that the feather star robot achieves the same degrees of freedom with a fraction of that hardware. The key, he says, is intelligent design: by shaping the robot’s body so that its structure and its interaction with the surrounding water govern its behavior, the researchers dramatically reduced the input required from computers or human operators. The robot’s capabilities emerge from its geometry and material properties rather than from an elaborate control system.

The inspiration for the design came from feather stars, crinoid echinoderms that can move in any direction or hold themselves stationary in the water column by coordinating the movements of their flexible, feather-like limbs. The robot distills that biological principle into a remarkably simple architecture: four elastic wings protrude from a central disk that houses the two actuators. Each wing is monostable, a term from mechanics describing a structure that can be bent out of shape but will always snap back to a single preferred resting position. This snap-through behavior is the heart of the robot’s locomotion strategy, converting smooth actuator input into rapid, impulsive wing motion that pushes effectively against the water.

The robot’s repertoire is organized into three distinct modes, each produced by a different pattern of actuator activation. When both actuators are switched on, all four wings snap downward simultaneously; when the actuators are turned off, the monostable wings snap back up. Rapidly cycling the actuators on and off makes the robot flap all four wings in quick succession, driving it upward through the water column. Switching the actuators off entirely stops the flapping and lets the robot descend. And by flapping the wings slowly rather than rapidly, the robot can hover in place, a behavior the researchers have nicknamed jellyfish mode, since the slow pulsing motion and stationary hovering recall the swimming style of a jellyfish.

Horizontal travel relies on an even more economical trick. To move forward or backward, the researchers activate only one of the two actuators. This causes a single wing to flutter, functioning like a tailfin and pushing the robot in the opposite direction. The team calls this fish mode, and it means that steering and translation along the horizontal plane require no additional hardware whatsoever. The third mode, rotor mode, comes from alternating rapidly between the two actuators, which sets the robot spinning on its axis. By combining jellyfish mode, fish mode, and rotor mode, the operators can maneuver the robot through all three dimensions, climbing, descending, translating, hovering, and turning with a machine that carries only two sources of actuation.

The project grew out of an earlier effort that illustrates exactly what mechanical intelligence is meant to solve. Haitao Qing, first author of the paper and a postdoctoral researcher at UC Berkeley who began the project as a Ph.D. student at NC State, previously helped create a soft aquatic robot inspired by the manta ray. That robot could move quickly through the water, but it lacked three-dimensional maneuverability. The goal of the new work was to design a robot with far greater agility without resorting to unduly complicated mechanisms. The feather star concept delivered that agility by shifting the burden from actuator count to structural design, letting the elastic wings and their snap-through dynamics generate a rich set of behaviors from minimal input.

The practical implications of reducing actuator count extend well beyond elegance. Every actuator on an underwater robot adds mass, wiring, power draw, and a potential point of failure, and in marine environments where maintenance is difficult and expensive, simplicity translates directly into reliability and endurance. A two-actuator platform is lighter, easier to seal against water intrusion, cheaper to manufacture, and simpler to control than a six-actuator equivalent. Because the robot’s modes are selected by simple activation patterns rather than complex coordinated control of many motors, the computational demands are correspondingly modest, which matters for small autonomous vehicles with limited onboard processing and battery capacity.

The researchers also demonstrated concrete uses for the robot. In tests, the machines were shown exploring underwater spaces while carrying a camera, and lifting objects underwater, either operating alone or working in concert with other robots. That cooperative capability hints at a future in which fleets of inexpensive, simply actuated soft robots swarm through shipwrecks, aquaculture pens, coral reefs, or submerged infrastructure, performing inspection and light manipulation tasks that would currently require larger, costlier, and more fragile vehicles. Qing describes the design as a versatile platform for integrating with other technologies across a range of underwater applications, and notes that the team’s future directions include developing a fully wireless version of the robot, freeing it from the tether that currently connects it to its power and control source.

Qing also extends an open invitation to other disciplines. The team members are mechanical engineers, he notes, and they have produced a novel design for aquatic robotics; they would welcome collaborations with experts in other fields to explore what the platform might accomplish in practice. That collaborative posture is fitting, because the underlying idea, mechanical intelligence, is relevant far beyond one robot. It suggests a broader design principle in which morphology, elasticity, and environmental interaction substitute for actuation and computation, a lesson that could inform everything from search-and-rescue vehicles to biomedical devices that must navigate constrained, fluid-filled spaces.

The paper, titled Minimal-Actuation Feather Star Inspired Soft Swimmers for Multimodal 3D Maneuverability, was published on October 7 in Science Advances. In addition to Yin and Qing, the co-authors are Caizhi Zhou and Haoze Sun, both Ph.D. students at NC State; Yuanhang Zhu of the University of Virginia and the University of California, Riverside; and Jiacheng Guo, Haibo Dong, and Daniel Quinn of the University of Virginia. The research was supported by the National Science Foundation under grants 2126072 and 2329674, and by the Office of Naval Research under MURI grant N00014-22-1-2616. For a field that has often equated capability with complexity, the feather star robot makes a quietly radical argument: sometimes the smartest machine is the one whose body already knows what to do.

Subject of Research: A feather star-inspired soft underwater robot achieving 3D maneuverability with two actuators through mechanical intelligence

Article Title: Feather star-inspired underwater robot gets 3D maneuverability from only two actuators

Article References: Feather star-inspired underwater robot gets 3D maneuverability from only two actuators. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: soft robotics, feather star, underwater robot, mechanical intelligence, actuators, bioinspired design, marine robotics, Science Advances, North Carolina State University, 3D maneuverability, monostable wings, swimming robot

The research team, led by undergraduate researcher Trent Ford, recruited 95 participants from North Carolina State University for a randomized, four-treatment, within-subjects experiment. Each participant experienced every combination of two landscapes and two soundscapes: a forest setting versus a gray, urban-like space, each paired with either recorded birdsong or recorded traffic noise. The design allowed the researchers to isolate the independent contributions of what participants saw and what they heard, rather than treating nature exposure as a single, undifferentiated experience.

Physiological stress was quantified with two well-established cardiovascular markers: heart rate and heart rate variability, the latter measured as the root mean square of successive differences between heartbeats, known as RMSSD. Heart rate is a straightforward index of cardiovascular arousal, while heart rate variability reflects the activity of the autonomic nervous system, with higher variability generally indicating greater parasympathetic engagement and a body in a state of recovery rather than vigilance. Each experimental session began with five minutes of silent control, followed by ten minutes of the assigned recorded soundscape, and every participant listened to each recording in both settings.

The statistical analysis relied on linear mixed models with a random intercept to account for repeated measures within each participant, and fixed effects for soundscape, landscape, their interaction, age, and gender. The results were clear on both fronts. Forest exposure was associated with a reduced heart rate relative to the gray space, with a coefficient of 0.65 and a p-value of 0.007. Birdsong exposure increased heart rate variability relative to traffic noise, with a coefficient of -1.38 and a p-value of 0.049. Crucially, the interaction between soundscape and landscape was not significant for either outcome, with all interaction p-values exceeding 0.16, meaning the two effects did not depend on each other.

That absence of an interaction is the scientific heart of the paper. The researchers had anticipated that physiological stress might only decrease among participants who simultaneously enjoyed a natural space and natural sounds, but the data told a different story. Time in a forested landscape and listening to birdsong each produced independent, positive effects on well-being. In the authors’ framing, visual and auditory nature cues appear to support physiological recovery through partially independent pathways: forest landscapes are associated with reduced cardiovascular arousal, while birdsong soundscapes are associated with increased autonomic recovery.

Lead author Trent Ford described the surprise directly. Across the four treatments, the team expected that physiological stress might only decrease for participants who were both in a natural space and listening to birdsong, he said, but instead they found that time in a forested landscape and listening to birdsong each had independent, positive effects on well-being. The finding complements a substantial body of previous research, much of it conducted in laboratory settings, showing that exposure to nature and birdsong improves mental and physiological health. This study extends that work outdoors, with real landscapes and wearable heart rate monitors, and focuses on a population, college students, that faces a well-documented mental health crisis potentially exacerbated by isolation from nature.

The practical implications reach well beyond campus greenspaces. Ford noted that providing access to forested landscapes where birdsong, rather than traffic noise, predominates would be the ideal scenario, but the research suggests urban planners have more options. When there is not room for trees, planners can design stress-reducing landscapes with traffic-calming measures that shift the soundscape balance away from traffic and toward birdsong. Conversely, when traffic noise is difficult to abate directly, additional tree cover may be beneficial by itself. In other words, the two levers of green infrastructure, visual and acoustic, can be pulled separately depending on the constraints of a given site.

For cities where land is scarce and noise is pervasive, this decoupling is more than an academic nuance. A pocket park beside a busy arterial road may still lower the heart rates of its visitors simply because it contains trees, even if traffic drones in the background. A treeless plaza on a quiet street may still aid autonomic recovery if its soundscape is dominated by birds rather than engines. Traffic-calming interventions, from speed humps to reduced speed limits, are already common tools of urban design, and this research gives them a new justification: they do not merely improve safety, they may actively reshape the acoustic environment in ways that measurably relax the human nervous system.

The study, titled Exposure to Forests and Birdsong May Reduce Physiological Stress Among College Students, was published on September 2, 2026, in Frontiers in Bird Science, with the DOI 10.3389/fbirs.2026.1884835. The co-author team includes Payton DeMay, J. Aaron Hipp, Lincoln R. Larson, Hannah M. Desrochers, Jackson Webb, Iris Howell, Made Ayu Pratiwi, Kushagra Meshram, JD Kluttz, Erin Foy, Griff Barlow, Caitlin McDade, Ivana Mali, Christopher E. Moorman and M. Nils Peterson of NC State University, along with Christian J. Bourke of the University of Georgia. The authors report no conflicts of interest, and the research was released as a peer-reviewed publication through North Carolina State University.

As with any single experiment, questions remain about how these effects generalize across ages, cultures, and environments beyond a university campus, and about how long-lasting the measured recovery might be. But the core message is unusually actionable. Nature’s benefits are not an all-or-nothing package requiring pristine wilderness. A canopy of trees and a chorus of birds each carry their own measurable physiological signature, and even modest, targeted interventions, a few more trees here, a quieter street there, can begin to deliver them. For a world in which most people live in cities and stress-related illness is rising, that is a finding worth listening to, ideally with birdsong in the background.

Subject of Research: Independent physiological stress-reduction effects of forest landscape exposure and birdsong soundscapes in college students

Article Title: Forest landscapes and birdsong soundscapes have independent physical health benefits

Article References: Forest landscapes and birdsong soundscapes have independent physical health benefits. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: birdsong, forest landscapes, physiological stress, heart rate variability, urban planning, green infrastructure, autonomic nervous system, college students, Frontiers in Bird Science, soundscape, nature exposure, North Carolina State University

The key insight lies in the chemistry of the eggshell itself. Eggshells are composed of roughly 95 percent calcium carbonate, a compound that metallurgists normally value in its processed forms. Calcium carbonate and calcium oxide are widely used industrial materials, and they play a particularly important role in alloy manufacturing, where calcium is added to magnesium to improve its mechanical properties. Traditionally, however, those calcium materials must first be manufactured from mined ore through a complex, energy-hungry process before they can be introduced into a molten or semi-solid metal. The NC State team, led by corresponding author Bharat Gwalani, an assistant professor of materials science and engineering, asked a deceptively simple question: why not let the eggshell supply the calcium directly, converting it into its useful chemical forms during the alloy-making step itself?

That question led to a proof-of-concept demonstration built around a solid-state processing technique known as friction stir extrusion. The method borrows its physics from friction stir welding, a process long used in aerospace manufacturing to join metals without melting them. In the NC State version, the researchers begin with a cylindrical block of magnesium and drill a series of evenly spaced holes into it. Those holes are then packed with finely ground eggshell powder. The loaded block is placed inside a steel cylinder, and a steel mandrel with a hole running through its center is lowered into the assembly. The mandrel functions much like a pestle in a mortar, pressing down on the magnesium block while spinning at 300 rotations per minute.

What happens next is a carefully choreographed sequence of mechanical and chemical events occurring almost simultaneously. As the spinning mandrel presses downward, the eggshell powder is mixed into the surrounding magnesium matrix. The intense friction generated between the eggshell particles and the metal produces enough heat to drive a chemical conversion: the calcium carbonate breaks down into calcium oxide and nascent calcium. Those reactive calcium species then combine with the magnesium to form Mg2Ca, a high-strength intermetallic compound that acts as a reinforcing phase within the alloy. Finally, the sustained downward pressure of the mandrel forces the newly formed magnesium alloy through the central hole, extruding it as a finished rod. In a single continuous operation, the process mixes, reacts, alloys, and shapes the material.

The resulting alloys are stronger and harder than unmodified magnesium, and they retain the property that makes magnesium so attractive to engineers in the first place: an exceptional strength-to-weight ratio. Magnesium is the lightest structural metal in common use, and alloys reinforced with calcium-containing phases are prized in applications where every gram matters, including consumer electronics housings, automotive components, and aerospace equipment. The same combination of biocompatibility and mechanical performance also makes magnesium alloys candidates for biomedical implants, where the metal can even degrade safely in the body over time. By demonstrating that eggshell-derived calcium can deliver these reinforcements, the researchers opened a path to producing such materials from a waste stream that is abundant, cheap, and continuously renewed.

The advantages extend well beyond the mechanical properties of the final product. Gwalani emphasizes that the approach offers fewer processing steps, a reliable and sustainable supply chain, and dramatically lower energy consumption, because the energy-intensive conversion of ore into calcium carbonate or calcium oxide is eliminated entirely. Eggshells are inexpensive by any industrial standard, and their calcium carbonate is already in a refined, biogenic form that the friction stir process can convert in situ. In effect, the technique collapses what would normally be two separate industrial chains, mining and processing on one side and alloy manufacturing on the other, into a single circular workflow that starts at the breakfast table and ends with a structural metal rod.

The study, titled Circular Manufacturing of Mg–Eggshell Composites: Transforming Biogenic Waste into Functional Reinforcements, was led by first author Aniruddha Malakar, a former postdoctoral researcher at NC State who is now at the Pacific Northwest National Laboratory. Co-authors include Fu-Yun Tsai, a postdoctoral researcher at NC State, along with doctoral students Md Jasim Uddin, Charles Perkins and Caleb Schenck, and former visiting scholar M. R. Gaur. The collaboration also drew on expertise from Pacific Northwest National Laboratory, where X. Li, Julian Escobar and T. Wang contributed, as well as X. Ma of the City University of Hong Kong and J. Jain of the Indian Institute of Technology Delhi. The work was supported by the Office of Naval Research Global under grant N00014-23-1-2758 and by the Pacific Northwest Research Laboratory, and the authors report no conflicts of interest.

Perhaps the most significant implication of the work is that the technique is not confined to magnesium and eggshells. Earlier in the same year, the researchers demonstrated that friction stir extrusion could be used to produce magnetic composites by grinding samarium-cobalt (SmCo5) magnetic powder into scrap aluminum. Taken together, the two demonstrations suggest a general-purpose platform for embedding functional powders, whether biogenic waste or recycled industrial scrap, directly into metal matrices without the need for high-temperature melting or multi-stage chemical processing. That flexibility positions friction stir extrusion as a candidate technology for circular manufacturing, in which waste materials from one sector become raw inputs for another.

Scalability remains the central question for any laboratory demonstration, but the researchers argue that the process is inherently suited to scale-up. Friction stir extrusion is a continuous process by design, producing extruded rods rather than discrete samples, and the equipment involved, a rotating mandrel and a containment cylinder, is mechanically simple compared with the furnaces and chemical reactors required for conventional calcium production. Because the reaction heat is generated by friction rather than by external heating, energy input is localized and efficient. Gwalani describes the approach as a scalable, energy-efficient, and environmentally responsible way to produce magnesium-based composites from biogenic waste materials, a framing that aligns with growing industrial pressure to decarbonize metallurgy.

The broader context makes the demonstration timely. Magnesium alloy production, like most primary metal manufacturing, carries a substantial energy and emissions footprint, and the calcium compounds used to strengthen these alloys add their own environmental burden through mining, calcination, and transport. Diverting even a fraction of the food industry’s eggshell waste into alloy production would simultaneously reduce landfill volumes and displace ore-derived calcium materials. For now, the NC State result stands as a striking proof of concept: a material most people throw away after breakfast can be spun, pressed, and chemically transformed into a structural alloy fit for aircraft, automobiles, and medical devices. It is a vivid illustration of how rethinking waste streams, and the chemistry that connects them to manufacturing, can reshape the materials economy one eggshell at a time.

Subject of Research: Using powdered eggshell waste as a sustainable calcium source to produce reinforced magnesium alloys via friction stir extrusion

Article Title: Researchers use eggshells (yes, eggshells) to make stronger, lighter metal alloys

Article References: Researchers use eggshells (yes, eggshells) to make stronger, lighter metal alloys. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: magnesium alloys, eggshells, calcium carbonate, friction stir extrusion, biogenic waste, circular manufacturing, materials science, sustainable metallurgy, aerospace materials, biomedical implants, North Carolina State University, Mg2Ca

“In the first 48 hours of a major disaster like a hurricane, responders show up from all over the country to help and are often operating in an information vacuum,” says Brandon McConnell, co-author of the study and an associate research professor in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering. The model was built specifically to address that vacuum, giving operational planners a data-driven starting point for deciding where to send boats, helicopters, and ground teams first, rather than relying on intuition or waiting for distress calls to accumulate.

The framework, described in a paper published open access in the International Journal of Disaster Risk Reduction under the title “Anticipating Household Rescue Demand in Hurricanes Using Socio-Demographic Data and Machine Learning,” rests on a straightforward but powerful premise: not all households face the same probability of needing rescue when a hurricane strikes. Decades of disaster research have identified factors that make people more vulnerable during such events, including physical disabilities that limit mobility, fewer financial resources that make evacuation difficult, lack of access to a vehicle, advanced age, and housing located in areas prone to flooding. The research team translated these established vulnerability factors into a predictive computational model.

Technically, the model draws on two complementary streams of publicly available federal data. U.S. Census data provide a fine-grained picture of the socio-demographic composition of each census tract, allowing the model to identify communities where a large share of residents are likely to have trouble evacuating in advance of a storm. National Flood Insurance Program data supply information on which areas face the greatest risk of flooding, indicating where those less-mobile populations are most likely to become trapped by rising water. By combining these inputs, the model produces a ranked map of expected rescue demand across an affected region before responders arrive on the scene.

A distinctive feature of the project is the perspective of its first author. Patrick Leavitt, who began the work while a graduate student at NC State, is an active-duty Coast Guard officer, and his first-hand experience with emergency response operations shaped how the team approached the problem. Rather than designing an abstract academic exercise, the researchers built the framework around the practical constraints responders face: the need for fast computation, the availability of data in real time, and the reality that every area will eventually be checked, but the order in which areas are searched can mean the difference between life and death for people trapped in attics or on rooftops.

“Specifically, we developed a predictive modeling framework to identify census tracts where residents are most likely to require rescue,” says Ben Rachunok, corresponding author of the paper and an assistant professor in NC State’s Fitts Department. “Responders will ultimately look in every area, but which areas are most likely to have people who require rescuing? If we can predict that, we can prioritize search efforts in those areas.” That prioritization logic is what distinguishes the tool from existing hazard maps, which typically show where flooding will occur but not where the intersection of flooding and human vulnerability will generate the greatest demand for rescue.

To test the framework, the researchers conducted a case study focused on Hurricane Harvey, the Category 4 storm that struck Texas in 2017 and caused catastrophic flooding across the Houston metropolitan area. Harvey is a particularly valuable test case because it produced one of the largest urban rescue operations in American history, with thousands of water rescues carried out by the Coast Guard, first responders, and volunteer rescuers. The team fed regional Census data and National Flood Insurance Program data into their model to generate predictions of which areas would be most likely to have residents trapped by floodwaters, and then compared those predicted high-priority zones against publicly available records of where rescues actually took place.

The comparison showed that the model performed well in identifying the areas where rescue demand concentrated. “Our framework did pretty well – it should be useful for responders in practice,” says Rachunok. “It’s not perfect, but even this version would be helpful – and we can put in the work to make it even better.” The researchers emphasize that the model is an initial version, and that its accuracy should improve with better data and continued refinement. Importantly, the model is computationally lightweight: it does not take long to run, which means it could be executed while responders are still deploying, handing them a prioritized list of search areas as soon as they arrive in the disaster zone.

“Being able to achieve these results with this initial version suggests we’re optimistic about its utility if we fine-tune the tool – particularly in instances where we have access to better data,” says McConnell. The team also sees applications beyond the immediate response phase. According to Leavitt, the research could support emergency managers during the planning phases of disaster preparedness, helping them develop response plans and design exercises that reflect realistic patterns of rescue demand. Because the model relies on data that are already collected and publicly available, jurisdictions could use it in advance of hurricane season to identify their most vulnerable communities and pre-position resources accordingly.

The researchers say they are open to collaborating with emergency management and disaster response leaders to improve the model itself and to make the tool more user-friendly for practical field use. As climate change increases the intensity of Atlantic hurricanes and coastal populations continue to grow, the demand for rapid, well-targeted search and rescue operations is only expected to rise. A predictive framework that turns census data and flood risk information into an actionable rescue priority map offers a glimpse of how data science can be woven into the earliest, most chaotic hours of disaster response – when every minute of saved search time can translate directly into lives saved.

Subject of Research: A predictive mathematical model that prioritizes census tracts for post-hurricane search and rescue operations using socio-demographic and flood risk data.

Article Title: New tool helps responders ID highest-risk areas for post-hurricane rescue efforts

Article References: New tool helps responders ID highest-risk areas for post-hurricane rescue efforts. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: hurricane, search and rescue, emergency response, flood risk, machine learning, census data, National Flood Insurance Program, Hurricane Harvey, disaster preparedness, Coast Guard, North Carolina State University, vulnerability mapping

The research, published in the Journal of Geophysical Research: Machine Learning and Computation, introduces a technique called Sparse Discrete Empirical Interpolation Method, or S-DEIM. It was developed by Mohammad Farazmand, associate professor of mathematics at NC State, together with graduate student Louisa Ebby and a team of undergraduate researchers from institutions across the United States. According to the authors, the method reconstructs high-resolution global sea surface temperature fields from as few as 100 in situ observations, a sampling density that amounts to just 0.2 percent of the full spatial grid. Even with so little direct information, more than 90 percent of the S-DEIM estimates landed within one degree Celsius of the true values in the team’s benchmark tests.

The problem the researchers set out to solve is one that oceanographers and climate scientists have wrestled with for decades. Federal agencies such as the National Oceanic and Atmospheric Administration have long relied on combinations of complicated differential equations to estimate temperatures across the vast unmonitored stretches of ocean. These model-based approaches are rigorous, but they are computationally demanding and depend on physical assumptions that may not hold perfectly across every ocean basin and season. Meanwhile, the explosive growth of machine learning has produced an alternative family of tools that can learn patterns directly from data, but at a steep price: deep neural networks often require hours of training on powerful hardware and enormous quantities of data before they can make a single useful prediction.

Somewhere in between sits the Discrete Empirical Interpolation Method, an established technique that the new work builds upon. Rather than modeling the ocean purely from physical first principles, DEIM specifies a basis, essentially a compact library of spatial patterns that jointly encode the structure of the temperature field being estimated. Given a handful of actual measurements, the method selects which of these patterns to activate and with what weights, producing a full-field estimate from sparse data. The approach is elegant and efficient, but it has a well-known weakness. When the available observations are truly sparse, as they typically are in the open ocean, the estimates it produces degrade considerably, because the method struggles to determine which patterns best explain a scattering of disconnected data points.

The NC State team’s insight was to bring historical information to bear on precisely this weakness. S-DEIM augments the classical framework with a so-called kernel vector, a quantity for which no closed-form mathematical formula exists, estimated instead from the long historical record of observations. In practice, the reconstruction produced by S-DEIM consists of two complementary terms. The first is computed from instantaneous in situ measurements using empirical interpolation, anchoring the estimate to what sensors are actually reporting right now. The second is learned from the historical time series using recurrent neural networks, which are particularly well suited to capturing how patterns in the data evolve over time. The marriage of the two allows the method to lean on decades of accumulated knowledge about ocean behavior while still respecting the fresh, if sparse, observations streaming in.

To train and test the method, the researchers used NOAA’s weekly high-resolution sea surface temperature dataset spanning 1989 through 2021, a record covering more than three decades of global ocean variability. The final year of the record, from January 2022 through January 2023, was withheld from the models entirely and reserved as a blind test. The team then asked S-DEIM, the classical DEIM method and a high-performing convolutional neural network to predict the sea surface temperatures for that unseen year, and compared their outputs against the actual historical data. This head-to-head design provided a rigorous measure of how each technique would perform under realistic conditions, where the future is genuinely unknown and the data available is sparse.

The results were striking. S-DEIM proved roughly 40 percent more accurate than DEIM, a substantial leap over the method it directly extends. More surprisingly, it also edged out the convolutional neural network, delivering estimates about 2 percent more accurate than the best AI model in the comparison. The efficiency gap was even more dramatic. Training the recurrent neural network at the heart of S-DEIM took approximately one minute, a one-time offline step, whereas the convolutional neural network required an hour and a half to train. Once trained, S-DEIM generates its full reconstructions in less than a second, making the approach practical for operational settings where forecasts must be produced continuously and quickly.

The method also displayed a robustness that matters greatly for real-world deployment. Sensor networks in the ocean are rarely arranged optimally; instruments drift, fail and are deployed wherever ships happen to travel. When the researchers distributed the sensors randomly rather than in favorable positions, the reconstruction error deteriorated by only 1 to 2 percent, suggesting that S-DEIM does not depend on carefully engineered measurement placements to deliver its accuracy. That resilience, combined with its computational thrift, makes the method attractive for agencies monitoring the ocean with limited and unevenly distributed instrumentation, and it opens the door to assimilating streaming observations in near real time.

The implications extend in two directions at once. In the short term, accurate and rapidly computed sea surface temperature fields feed directly into weather forecasting, where ocean conditions influence storm tracks, intensity and precipitation patterns on timescales of days to weeks. In the longer term, the same fields underpin climate models that track how the ocean absorbs and redistributes heat over decades. A tool that can deliver high-resolution temperature reconstructions from a sliver of the usual data, at a fraction of the computational cost, could meaningfully lower the barrier to both endeavors. The work also grew out of a National Science Foundation supported Research Experience for Undergraduates, with co-authors Cassidy All of the University of Colorado Boulder, Kevin Ho of Mississippi State University, Maya Magnuski of Bard College and Christopher Nicolaides of Indiana University contributing to the study alongside the NC State team.

Farazmand and his colleagues emphasize that this is not the end of the road. The team hopes to continue improving the accuracy of S-DEIM, and the framework’s flexibility suggests room for refinement, from richer historical models to better handling of measurement noise. For now, the study makes a compelling case that when data is scarce, a thoughtfully designed hybrid of classical interpolation and lightweight learning can beat brute-force deep learning on its own terms. In a field where every degree matters and every observation counts, S-DEIM offers a reminder that sometimes the smartest algorithm is not the biggest one, but the one that knows how to make the most of very little.

Subject of Research: A sparse-data interpolation method for rapidly reconstructing global sea surface temperatures from limited in situ observations.

Article Title: New method estimates sea surface temps quickly and accurately

Article References: New method estimates sea surface temps quickly and accurately. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: sea surface temperature, S-DEIM, data assimilation, machine learning, recurrent neural networks, NOAA, climate modeling, oceanography, sparse data, empirical interpolation, weather forecasting, North Carolina State University