The human rights case comes nearly a year after the International Court of Justice found that countries have a legal duty to tackle climate change.

FEMA hasn't developed floodplain maps in many less-populated areas, including some Michigan counties that recently experienced unprecedented flooding.

Extreme feels-like temperatures, heat stress days and tropical nights have all become dramatically more frequent, long and severe over the past six decades, a study finds.

The missive was signed by Modernisation Fund recipients Bulgaria, Croatia, Czechia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia.

The Rhine is one of Europe’s most important trade arteries, with petroleum products accounting for more than a fifth of cargo transported on it in 2024.

A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.

The chip allows small autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments in real-time using only about as much power as a single LED. A robot could use such a map to plan a collision-free path to reach its goal.

Typically, generating such thorough maps requires power-hungry systems and a great deal of memory to build and store 3D representations of the obstacles in a robot’s environment.

The MIT researchers took a different approach by combining an extremely efficient mapping algorithm with specialized hardware designed to accelerate its workload, which minimizes memory and power consumption. 

This system-on-a-chip consumes only about 6 milliwatts of power, a fraction of the power required by other systems. 

This low-power operation could also make the chip well-suited for lightweight augmented reality headsets that can be worn for extended periods, for applications like educational medical simulation or detailed repair and assembly work.

“This paper showcases a key example of how you can leverage co-design of the algorithm and hardware to really push energy efficiency. While there has been a lot of work looking into compact 3D maps, what stands out about this work is that it also ensures that the process to generate those maps is as efficient as possible. Our chip allows you to store very large maps in a very small space, and do it in a very energy efficient manner,” says Vivienne Sze, a professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory of Electronics (RLE), and senior author of a paper on the chip.

She is joined on the paper by co-lead authors and MIT graduate students Zih-Sing Fu and Peter Zhi Xuan Li as well as Sertac Karaman, a professor of aeronautics and astronautics and the director of LIDS. The work was recently presented at the IEEE Very Large-Scale Integrated Circuits Symposium.

A more compact map

For a robot, generating a 3D map that includes the obstacles in its environment usually demands a lot of power because it must store images captured by its camera, and process all the 3D pixels in each image multiple times.

Instead of representing the environment using 3D pixels, which are cubes called voxels, the MIT researchers utilized a technique that maps the obstacles in space using ellipsoid blobs called Gaussians. 

The size, shape, and thickness of these ellipsoids can be smoothly adapted, so they match the shape of curved objects more efficiently than if one uses rigid, cube-shaped voxels. 

Importantly, the map captures the obstacles and free space around the robot, and together these let the robot plan a safe, collision-free path. Mapping obstacles and free space with voxels typically consumes a lot of memory, which makes traditional methods power-hungry. Because Gaussians can flexibly fit the geometry, a single elongated ellipsoid can represent a region that would take many voxels, so occupied surfaces and free space are captured far more compactly.

For their new system-on-a-chip, called Gleanmer, the researchers employed an algorithm their lab developed called GMMap that efficiently generates a 3D map of the robot’s environment using Gaussians to represent obstacles. 

With traditional approaches, a robot would need to load and process each depth image several times to adjust the size and shape of the ellipsoids. The system would usually construct Gaussians by comparing all the pixels in an image to each other. But the amount of memory and power needed to do this remains too high for many edge devices.

To solve this problem, the MIT researchers invented a technique that can generate highly accurate Gaussians from depth images with only one pass, after which they can discard the images, so the chip never has to store an entire image at once. 

Instead of comparing each pixel to every other pixel in the 3D image, their algorithm assumes that nearby pixels belong in the same Gaussian, so it only needs to compare each pixel to its neighbors.

“At any point in time, we only need to store a few pixels in memory, which significantly reduces the memory footprint our algorithm requires,” Li says.

Leveraging co-design

But as the robot moves through the space, it usually sees the same object from different viewpoints. When it generates Gaussians, some will overlap because they represent the same object. This can make the 3D map too large to store on an edge device.

Fusing overlapping Gaussians makes the map more compact, but doing so typically requires the algorithm to process many raw pixels stored in memory. The researchers developed a novel technique to perform this fusion process directly on overlapping Gaussians, without needing to revisit the original pixels. Since Gaussians are more compact than pixels, this significantly reduces memory and power requirements.

The same principle runs through their algorithm — most computations operate directly on compact Gaussians rather than the original pixels, enabling energy efficiency.

The researchers exploit this principle to design a chip that keeps the Gaussians it is actively working on within small, fast on-chip memory right beside the computational units. This is only possible because the Gaussian map is so compact.

The Gaussians the robot needs to work on next are waiting in the on-chip memory units, so they don’t need to be fetched from more distant, power-hungry, off-chip storage. 

“By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” Fu explains.

They tested the system-on-a-chip by reconstructing a range of diverse, pre-existing 3D environments. The chip can also reconstruct obstacles and free space directly from live data streamed from an iPhone camera.

Gleanmer generated detailed 3D maps in real-time while consuming about 6 milliwatts of power. It required only about 2.5 percent of the power that the best existing chip for map construction would need. 

By reusing compact Gaussians along the path as it plans, the chip lets a robot chart a safe trajectory using only about 20 percent of the energy it would otherwise need.

“We reduce the memory consumption by making sure the algorithm is efficient. Then we accelerate the workload that is performed by that efficient algorithm, so in the end, our chip is as efficient as possible,” Li says.

The researchers plan to further improve energy efficiency by moving the processing units on the chip closer to the sensors that gather environmental data. They could also explore additional applications, such as the use of Gaussians to represent schematics. This could help AI systems reason about complex blueprints more efficiently.

“Real-time 3D mapping has been the missing piece for small autonomous systems. A drone inspecting a pipeline or a pair of AR glasses navigating a room both need to understand the space around them — instantly, continuously, and at almost no power cost. Gleanmer makes that possible for the first time in a chip you can hold between your fingers,” says Karaman.

This work is supported, in part, by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel. 

Nature Climate Change, Published online: 23 June 2026; doi:10.1038/s41558-026-02678-x

Soil carbon is a critical component of the terrestrial carbon sink and is impacted by the total belowground carbon allocation (TBCA). This study uses a long-term multifactor grassland experiment to show that elevated temperature and CO2 increased the TBCA over time, modulated by drought and N addition.

Early mornings in the halls of Building 68 feature the sounds of rolling wheels on big metal carts, the rattling of glassware, the whooshing of faucets, and the clanking of autoclaves. 

These aren’t the sounds of researchers at work, but rather those of keeping the labs sterilized and stocked with the sundries of research: pipette tips, test tubes, flasks, petri dishes, and more.

Orchestrating this sunrise cacophony and the staff that undertakes it is Karen O’Leary, lab associate and acting supervisor in the Glassware Sterilization Facility, also known as the “kitchen.” 

Thanks, in part, to O’Leary’s proactivity and hard work, the kitchen staff were recently recognized with an MIT Excellence Award in 2025 for exceptional contributions in service of the community. 

“My goal is to get the scientists everything they need to do their research,” O’Leary says. “I’m good at what I do.” 

O’Leary admits she did not always possess such confidence. In almost 40 years at MIT, O’Leary has grown into this critical role for the department, and the department itself has evolved, moving into a brand-new building and away from previously standard practices like submerging equipment in acid for sterilization. 

From rookie to running the show

On Sept. 7, 1987, Karen O’Leary joined the MIT community as a staff member for the first time. The 18-year-old was fresh from vocational high school, where she studied cosmetology but felt too shy to pursue that as a career. She was also nervous about joining a research institution.

“When I started, I didn’t even know what a beaker was,” she recalls. 

Too embarrassed to admit in her interview that she couldn’t remember her brand-new home phone number, “I just made one up.” Fortunately, this didn’t prevent her from getting the job, where she worked under the mentorship of Thelma Watkins, who would retire in 1996 after 21 years at MIT. Watkins was critical for instilling a good work ethic and boosting O’Leary’s confidence. 

“She taught me to show up every day, and work hard, and laugh,” O’Leary says.

Even now, O’Leary continues to bring joy to that daily diligence, for herself and for her staff.

“Karen is always on top of things,” says longtime friend and fellow Lab Associate AnnMarie Budhai. “She doesn’t refuse work and always goes above and beyond.” 

Facilities and Operations Manager Cesar Duarte says that O’Leary’s long tenure, support, and knowledge have been invaluable as he transitioned into his role in Building 68 starting in 2023.

“Karen is one of those people who makes everything around her run more smoothly and more pleasantly,” Duarte says. 

Better, faster, safer

Although some might consider it drudgery, O’Leary says that washing glassware is her favorite task. 

“I like that when I wash, I can see the job is complete at the end of the day,” she says. 

Although washing glassware is a perennial task, safety and efficiency have come a long way in the past 38 years. More-effective autoclaves and dishwashers have eliminated steps like steaming to dissolve agar solvents before autoclaving, and scrubbing individual test tubes before washing.

O’Leary was working for the department in 2011 when Building 68 piloted a new approach to MIT’s management of regulated medical waste (RMW), such as petri dishes, blood, and needles — the new system, which is cheaper and produces less waste, is now used by all departments at MIT that produce RMW.

“EHS [the Environment, Health and Safety Office] has come really far — I’m glad we got away from acid,” O’Leary notes of the bygone era of submerging glass pipettes for sterilization. “Back then, no one knew of a better way.” 

Other tasks include cleaning velvets, which are used for replicating bacterial colonies on petri dishes, and pouring agar plates. 

“Everyone knows how to do almost every job, so we can take turns doing different tasks,” O’Leary says. “If you get sick, there’s always someone to cover.”

All in the family

For O’Leary, kinship with MIT has spanned generations. O’Leary was raised in Weymouth, Massachusetts, by a father who worked at MIT as a supervisor in the sheet metal shop. Having raised children of her own, now grown, O’Leary came to greatly appreciate the flexibility her job has granted her.

“I’ve had great work-family balance here,” she says. Even though she’s often at work more than an hour before the researchers that the kitchen serves, “The hours are great, and with MIT Health right across the street, it was easy to take everyone to doctors’ appointments.” 

She’s also gained a chosen family at MIT, spending breaks at work taking long walks along the Charles River, “talking about anything and everything” with colleagues like Budhai and Lab Aide Janet Katin. 

“We really grew up together,” she says. 

Working at MIT has provided O’Leary with support and community, and she’d like to pay it forward. In addition to strolling with colleagues, she hits the gym to help maintain the energy required for her highly active work. 

“I don’t like sitting around,” she says.

In addition to maintaining her stamina at work, she hopes that taking care of herself will keep her actively involved if she ever has grandchildren, and enable her to help neighborhood kids when she someday retires.

“I owe a lot to MIT,” she says. “I have been allowed to work hard and get satisfaction and have been appreciated and given space to care for my family.”

O’Leary returns this care to the Department of Biology in spades.

“It’s an understatement to say that Biology is lucky to have her,” says Duarte. “Karen’s overflowing energy, attention to detail, and care for the Biology research community are nothing short of amazing.”

I haven’t thought about the privacy issues surrounding professional athletes and wearables.

Wearables present serious privacy issues for “Average Joe” consumers, who are entrusting tech companies to safely store and protect their biometric data. Imagine the stakes for a professional athlete, whose entire livelihood could be affected by a single biometric data point. To give one of many realistic hypotheticals: a basketball player has a terrible game, and the coach wonders if they showed up to the gym hungover. The coach has access to the player’s wearable data, and checks to see when they went to sleep, as well as what their heart rate looked like during the night. Should the player have been out partying before a game? No. Should the coach be able to surveil them? Definitely not...

The bank's goal of steering 45 percent of its financing toward climate projects is being attacked by the White House before it expires at the end of June.

The budget agreement would levy a new, capped tax on the industry's power usage, while preserving its existing tax breaks.

A proposal to send treated water from the oil patch to farmland is drawing pushback from multiple sources.

The future of climate negotiations is uncertain as focus shifts from discussing treaties to actually implementing them.

When possible, relocation should happen before crises occur, the principles state, “with access to skills training, fair employment opportunities, and social integration.”

A lot of the country is witnessing rising day and nighttime temperatures and longer summer months in part due to climate change, and workers say the heat inside factories is unbearable at times.

The tech sector says only carbon-emitting gas plants are reliable enough today to power the EU’s AI goals.

Nature Climate Change, Published online: 22 June 2026; doi:10.1038/s41558-026-02670-5

The authors assess global changes in heat stress since 1950, considering daytime extremes, nocturnal heat and compound day–night events. They show multidimensional intensification, with an increased frequency of extremes and expanded spatial and temporal footprints of heat stress on humans.

Nature Climate Change, Published online: 22 June 2026; doi:10.1038/s41558-026-02657-2

The authors assess 133 avoided deforestation projects for their ability to safeguard forest ecological integrity. While some projects maintain integrity, most show variable outcomes, with mixed, negligible or negative effects relative to matched controls.

Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets.

Another news article.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Companies working at the frontier of aerospace, energy, and computing are constantly looking for new materials to improve performance. But in order to understand how those materials will actually behave once they’re inside rockets or on computer chips, companies first have to make the material and then test it. That’s because even the most powerful simulation techniques struggle to model the complex chemical arrangements in most of today’s solid materials. The problem adds costs and time to materials innovation.

Now a team of MIT researchers has created a way to accurately model the behavior of metals, regardless of the complexity of their chemical arrangement. At the center of the approach are machine-learning models that make simulations of materials faster and more accurate. The researchers improved those models by building training datasets that capture the diversity of atomic environments in chemically disordered materials.

In a new paper in Sciences Advances, the researchers showed their approach could be used to accurately predict material properties for a diverse group of metal alloys under a range of conditions. They also showed how the approach could be used to develop new materials, especially in scenarios where experimentation is expensive.

“The focus of the paper is metallic alloys, which is the field I work in, but this could be adapted to other types of materials, like semiconductors,” says senior author Rodrigo Freitas, MIT’s TDK Career Development Professor in Materials Science and Engineering. “This is not specific to any one application — you could use this approach to create new sustainable steels, new materials for aerospace, and more. That’s what makes this exciting.”

Joining Freitas on the paper are first author Killian Sheriff PhD ’26; MIT PhD students Daniel Xiao and Yifan Cao; and University of Sheffield Senior Lecturer Lewis R. Owen.

Modeling metals

Material properties are mostly determined by the internal arrangement of their chemical elements. Even if two materials have the same mix of chemical elements, different chemical arrangements can make the difference between a brittle material and one that deforms without breaking.

Capturing that distinction requires simulating materials atom by atom. To do that, researchers rely on models that describe how atoms interact with each other. Over the last two decades, machine learning has become the most accurate way to build those models. Such models work well when the chemical arrangements inside materials follow highly ordered patterns, but that’s not the case with most solid materials, whose atomic chemical arrangements are disordered and vary from one region to another.

“The real challenge in our field is modelling these chemically disordered phases,” Freitas says. “Chemical disorder means there’s a huge variety of local chemical environments, which is hard for the machine-learning model to learn. This is a problem because every single metal we use in practice is chemically disordered.”

The problem comes down to a lack of representative training data for those atom-by-atom simulations. The current leading approach for creating such data works by brute force, often requiring more than 100,000 hours of computation to create the training data for a single material. Even then, it does not transfer well when researchers change the material’s composition.

In previous work, Freitas’ group had developed a way to measure the chemical complexity of solid materials by analyzing the frequency and spacing of tiny groups of atoms. For this study, the researchers used that capability to build better training datasets. They used a mathematical approach known as information theory to generate training datasets that capture a wider variety of local chemical environments inside disordered materials. The method works by swapping out atoms from samples to reduce repetition and expose the model to chemical environments it might otherwise miss.

“We kept optimizing the training set so it captured as many different local environments as possible,” Freitas says. “If the same kind of environment showed up many times, we replaced redundant examples with ones the model hadn’t seen before. That makes the training set much more informative because each example adds something new.”

When trained on the researchers’ datasets, the models predicted material properties more accurately than models trained using random sampling or another popular sampling method.

“The starting point for all these atom-by-atom simulations is: Are you able to accurately describe the chemical bond between atoms?” Freitas explains. “If not, it can still teach you about materials in general, but it doesn’t tell you what will happen to specific materials in the real world. This approach makes the simulations high fidelity in terms of their chemistry, to better reflect what’s happening to materials.”

The researchers applied their technique to create machine-learning training datasets for a group of chemically diverse metal alloys. Using a set of machine-learning models, they showed the models trained on their datasets are more accurate than much larger models created by companies like Google and Microsoft.

“We got to a point where we were convinced it worked without using these expensive brute-force methods,” Freitas says. “I told Killian, ‘This is a good paper. But if you can show that simulations with these models can now accurately predict useful materials properties, then it becomes a very good paper.’ Killian took that to heart and tested this as widely as he could.”

Sheriff worked with Xiao and Cao to test the approach across different alloys and properties. The team also drew on Owen’s experimental data to compare the simulations against real measurements of atomic ordering in alloys.

From the lab to industry

The method works, in part, by capturing hidden patterns in the sample data. The researchers describe the patterns in the paper as “subtle energetic biases toward certain local chemical configurations.”

Those small energetic differences matter because they determine which phases form in an alloy, how those phases change with temperature and composition, and ultimately which properties the material will have. As one test, Daniel Xiao led simulations showing that the team’s models could predict phase diagrams that closely matched experimental data. Phase diagrams map which phases are stable across different temperatures and chemical compositions, and they are a central tool for designing and processing alloys.

“Phase diagrams are one of the main ways people connect materials modeling to real processing decisions,” Freitas says. “If you are welding, casting, or heat-treating an alloy, you need to know which phases are likely to form under different conditions. Our goal is to make these kinds of predictions accurate enough, and accessible enough, that they become part of how people design materials.”

The researchers are now using the approach to study how changing an alloy’s composition affects mechanical properties and radiation tolerance, with the goal of designing materials that remain strong and damage-tolerant in harsh environments. They are also working to make the method easier to use with the kinds of tools and workflows materials engineers already rely on.

“Industry isn’t going to change the way they do things if what you’re creating doesn’t fit into their existing operating procedures,” Freitas says. “The goal is to make these predictions useful in the places where materials decisions are actually made.”

The research was supported by the U.S. Air Force Office of Scientific Research.