Extreme heat has forced the cancellation of plans to discuss the impact of extreme heat.

The data for part of the equatorial Pacific shows it to be the largest warm deviation from the historical average for June since 1981.

POLITICO grilled multiple candidates on energy policy at an event Tuesday.

The European Commission’s original version of the grids package required grid operators to set aside 25 percent of unspent congestion revenues to finance cross-border projects to benefit broader connectivity.

In 2022, global production of construction materials accounted for more than 7 percent of total carbon emissions. But how many of those materials were truly necessary to build houses, buildings, and bridges?

A technique called topology optimization can design structures that reduce the amount of material used, in some cases by as much as 90 percent, which would represent a multi-gigaton reduction in building emissions. Unfortunately, topology optimization is mostly used by researchers for applications like 3D printing rather than by engineers designing at the scale of buildings and bridges.

That’s because topology optimization doesn’t create structures that can easily be built on time and budget, which are the things builders really care about.

Now MIT researchers have created a way to make topology optimization designs more buildable. Their framework, described in a new paper in Automation in Construction today, allows users to apply constraints to algorithmically generated structures to limit their complexity. For instance, the approach allows users to limit how many components meet at each point of their design and how small they want their smallest parts. It also builds on previous work by designing structures with multiple materials and taking into account materials’ properties to distribute load and specify part connections.

“There’s an interplay between the materials you’re using, the constructability of designs, and the optimization of the structure,” says senior author Josephine Carstensen, MIT’s Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering. “You need to be able to address all three at the same time. That’s what we tried to do here.”

The researchers used their approach to design steel, wood, and multimaterial truss structures that support loads in buildings and bridges, showing the carbon emissions associated with materials changed significantly when different constraints were applied. They hope their framework will move topology optimization closer to being used in real-world construction.

“In the literature, there’s sometimes been a disconnect between the carbon savings you can achieve on a computer and the realistic carbon savings you can achieve for built structures — especially when it comes to design technologies like topology optimization,” Carstensen says. “The problem lies in the lack of constructability of designs. These designs have been perceived as too difficult to make with conventional methods, so they are never even attempted. That’s what is exciting about our approach: We can add constraints so that you will never be in a situation where the design that comes out is too hard to make.”

Joining Carstensen on the paper is first author and civil and environmental engineering PhD student Zane Schemmer.

More buildable designs

Computer-based topology optimization has been around for decades. It uses computer programs to optimally distribute material in a given space, for instance creating the strongest possible structures at the lowest weight. The resulting designs are often complex, spider web-like structures that would be a challenge for even the most capable engineers to build.

“A big question Josephine and I were asking is why isn’t industry using it?” Schemmer recalls. “What are the obstacles that prevent industry from designing things more efficiently, and how can we fill the gaps between research and real life?”

In recent years, several researchers have developed ways to make topology optimization easier to use. For their study, Schemmer and Carstensen wanted to bring those approaches together and add new capabilities, like creating designs that use multiple materials, which has been another challenge in the field.

“A big aspect of sustainability going forward will be not only using less material, but also implementing materials efficiently based on considerations like where you are in the world, your access to materials, and each of their associated carbon costs,” Schemmer says.

To build their framework, they used a class of equations called mixed integer algorithms that help make binary decisions about things like materials and connections.

“You can’t have a part that’s 72 percent timber and 28 percent steel,” Schemmer says. “Instead, it says, ‘This truss or cable is going to be made out of this,’ and then based on that decision, how do we make sure all of these connections meet their strength standards?”

The system’s decisions also take into account material properties. For instance, steel struts can withstand compressive loads, but steel cables cannot. The model also has more realistic modeling of how parts connect than previous approaches.

“In 3D printing, the way things come together is easy,” Carstensen says. “In construction, that’s not the case. If you’re building with timber there’s a certain rule set, versus steel has a different rule set.”

Users can also decide how complex they want their design to be by specifying the maximum number of connections at each joint and the minimum angle between connected components. The model also creates minimum size limits for parts, further improving its constructability.

“It’s tough to give a contractor these complex, intricate designs because it’s going to be super difficult to build,” Schemmer says. “A lot of times contractors won’t pick up a project like that to begin with.”

The researchers compared structures designed with their approach to structures designed with conventional topology optimization, showing dramatic differences in final designs that transformed how the structures would be built. Using the Lockport “Upside-Down Bridge” near Buffalo, New York, as an example, they applied individual constraints, like a minimum angle on part connections or minimum part sizes, to the bridge’s truss design, to better understand how each constraint impacted final designs.

Finally, they made truss designs that used wood only, steel only, and combined wood and steel, showing how different projects offered tradeoffs with respect to environmental impact and constructability.

“We saw how the system knew that you could design a bridge of pure steel, but that might not be best from a carbon standpoint,” Schemmer says. “Or you could design a bridge out of purely timber, but that might not be the strongest. But these materials can work together, so you use timber for the carbon savings and steel where you need extra strength, and there’s a balance you can find in these structures.”

From research to industry

The researchers say their approach is more computationally intensive than some others, but they were able to use a MacBook Pro to run the programs in their experiment, and they believe it’s practical for most civil engineering firms.

“It’s computationally a little tougher to solve, but there’s a lot of tools coming out nowadays that make these problems a lot more feasible,” Schemmer says. “This approach has been avoided by industry in the past, but now we think it’s a practical way to solve problems dealing with variable constraints.”

If users have more computational resources, the researchers say their approach could work with a long list of materials and far bigger structures than homes, small buildings, and bridges.

Moving forward, Carstensen says the team plans to build scaled-down structures designed by the model to further validate its predictions. They also want to add constraints to their model to make it even more seamless for civil engineers to use when designing the world’s infrastructure.

“As a structural engineer by training, I was never taught how to design for low-carbon,” Schemmer says. “To tackle a problem as big as climate change, addressing the built environment is a great place to start. One of the most tangible things we can do is work at the layer of construction, at the design stage, because that’s a fundamental step that we can control. There’s a lot of decisions we make early on that lead us to use extra material we don’t need.”

The work was funded by the MIT Morningside Academy for Design.

At the recent AI and Society Forum at MIT, experts from across the Institute discussed the potential benefits and dangers of technological innovation on labor, the nature of work, civil discourse, election administration, and other topics.

The event featured individual research presentations and panel discussions, as well as a musical performance exploring the use of generative artificial intelligence in the arts.

The forum was co-organized by the School of Humanities, Arts, and Social Sciences (SHASS) and the Social and Ethical Responsibilities of Computing (SERC). It was presented in collaboration with two of MIT’s strategic initiatives: the MIT Generative AI Impact Consortium (MGAIC) and the MIT Human Insight Collaborative (MITHIC).

Agustín Rayo, the Kenan Sahin Dean of SHASS, and Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, provided opening remarks.

Rayo said bringing scholars from across MIT together was intentional because understanding AI’s impact requires expertise from disciplines throughout the Institute.

“Paying attention to the societal consequences of AI is not a departure from MIT’s mission; it’s a way of ensuring that our technical leadership has maximum impact,” Rayo said.

Huttenlocher added that computing and AI’s rapid growth makes it critical to support interdisciplinary conversations and research.

“Understanding where AI excels and where it falls short is essential not only to unlocking its benefits, but also to avoiding critical errors, overreliance, and unintended consequences,” Huttenlocher said.

Jobs and AI 

Held in the Tull Concert Hall in MIT’s Linde Music Building, the May 12 forum opened with a keynote presentation from economist David Autor, the Daniel (1972) and Gail Rubinfeld Professor in the MIT Department of Economics. Autor challenged the common narrative that AI will simply eliminate jobs by proposing instead that technology's impact depends on how it affects the scarcity and value of human expertise. 

“When I think about how technology interacts with the value of labor, I think about it in terms of how it changes the scarcity of expertise, whether it makes it more valuable or whether it makes it more of a commodity,” he said.

Autor said that what matters is whether automation removes routine supporting tasks or removes expert tasks. He argued that AI will likely create new specialized work, requiring proactive policies around worker training, wage insurance, and broader capital ownership.

A panel discussion followed, moderated by Rob Loughlin, a partner at McKinsey & Company, featuring experts from MIT discussing how work is changing and what it means for society. 

Daniela Rus, the MIT Panasonic Professor of Computer Science and director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), described excitement around ways AI could enhance the workplace.

“I’d like to imagine the robot as your friend and assistant, as someone who watches you and figures out how to help you as someone you can task at a high level,” she said. 

Still, Rus said, human judgment remains critical in decision-making.

“We could really think about co-work with the AI tools, but the role of the human as the decider, as the person with good judgment, as the person deciding the next step, whatever that is, remains super important,” she said.

David Mindell, professor of Aeronautics and Astronautics and the Dibner Professor of the History of Engineering and Manufacturing in the Program in Science, Technology, and Society, says the nature of work has constantly changed over the years, but “what matters is the new work.” 

“We need to be supporting individuals, the economy, professions, to constantly be creating the new work,” he said. “It’s absolutely imperative that we give the tools to the young people and let them do what they find creative and show us what the new work is going to be.”

Panelists also talked about the need to maintain safety standards, while also exploring ways to find efficiencies. Mindell used an example of cargo flights that require six pilots due to the length of the flight.

“We don’t know how to take that six number down to five yet, much less two, one, or zero. There's a lot of money behind solving that problem, but there's also a very rich system that has evolved to make those systems safe,” he said.

Sendhil Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science (EECS), described a vision of AI’s utility and growth that offers productivity improvements, but also cautioned, “I think it's very much worth differentiating productivity gains from things that actually drive long-term growth.”

Either way, Mullainathan said, it’s clear we’re entering a time of high variance with regard to AI’s impact on the workforce.

“If you said, ‘exactly how will organizations restructure?’ I don’t know. But is there going to be a lot of restructuring? It’s hard to believe there isn’t going to be a lot of restructuring. And in some sense, if we know that what we’re entering is a period of high variance, that itself is incredibly informative,” he said.

Democracy and AI

The day’s second session focused on AI technology and its impact on democracy. 

Chara Podimata, the Class of 1942 Career Development Assistant Professor and assistant professor of operations research and statistics in the MIT Sloan School of Management, presented her research on auditing large language models for bias in election information.

“Algorithms decide a lot of things about our lives right now,” she said. “With regard to chatbots and election information, if I take two people and they interact with the same chatbot … how will the chatbot respond? How will it personalize the information it gives to these people?”

A longitudinal study of 12 major models during the 2024 U.S. presidential election season found responses varied dramatically based on stated demographics and political leanings. Her research team is now working on a new audit of the 2026 U.S. midterm elections, using a redesigned survey with input from political science experts.

During a panel discussion moderated by Songyee Yoon, founder and managing partner at Principal Venture Partners and member of the MIT Corporation, experts raised concern about the potential for AI to erode democratic norms and processes, but also explored potential positive outcomes.

Bailey Flanigan, the Theodore T. Miller (1922) Career Development Professor in the Department of Political Science, who holds an MIT Schwarzman College of Computing shared position with EECS, said she’s skeptical of how some are applying AI as a tool that can get people to reach decisions or consensus more quickly.

“And there is a reason to think that this is nice because it is more efficient. It's easier. But it loses a lot of these procedural elements of democracy that are the rituals of how we come together and make decisions,” she said. “And I think it’s a mistake to forget about that when we start thinking about automation.”

Charles Stewart III, the Kenan Sahin (1963) Distinguished Professor of Political Science and founding director of the MIT Election Data and Science Lab, said one challenge is that governmental structures do not evolve at the same rate as technology.

Stewart said his biggest concern is the potential for AI to lead to chaos during and after elections.

“If and when things go wrong, they can go really bad, and really wrong. If an election is called into question, that can lead to violence,” Stewart said.

“We’ve already seen in the low-tech eras election results being manipulated. What worries me is what I’m going to observe this coming Election Day, and the Wednesday after, and if AI has helped to create irreversible disruptions to the election system,” he added.

Lily Tsai, the Ford Professor of Political Science and director and founder of the MIT Governance Lab (MIT GOV/LAB), said in many ways, AI runs against the democratic norms and commitments necessary for a healthy democracy.

“It is really important not just in terms of design principles, but the commitments of designers to be familiar with the values and principles that characterize what democracy is based on: agency, political equality, mutual respect, inclusion, and autonomy,” Tsai said.

Tsai also noted her research has shown some people are more comfortable interacting with machines. She described a “Socratic dialogue chatbot” her team designed that asks people to articulate the thinking behind their beliefs and positions.

“And that actually, interestingly, seems to moderate their policy position in the process,” Tsai said. “So there are absolutely examples of ways in which AI can have positive impacts on democracy. But it really is about designing with the right principles and evaluating them rigorously.”

Fable 5 is the supposed safe version of Anthropic’s Mythos Preview, with guardrails to ensure that it can’t be used to create cyberattacks.

Well, that restriction was bypassed within days.

The voluntary initiative was not presented to the industry ahead of time.

In declining to hear the case, the justices sidestepped a broader fight over limits on the power of Congress.

Every Democrat running for attorney general supports a lawsuit that Boulder has filed against the oil and gas industry. Republicans say it could threaten the state’s energy industry.

Utilities are leaning into a regulatory future free of carbon restrictions. But they are mindful of a potential future about-face.

The oil major said natural gas will fuel a proposed generating plant that would not initially be connected to the grid.

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.”