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      Q&A: Can the world change course on climate?

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      In this ongoing series on climate issues, MIT faculty, students, and alumni in the humanistic fields share perspectives that are significant for solving climate change and mitigating its myriad social and ecological impacts. Nazli Choucri is a professor of political science and an expert on climate issues, who also focuses on international relations and cyberpolitics. She is the architect and director of the Global System for Sustainable Development, an evolving knowledge networking system centered on sustainability problems and solution strategies. The author and/or editor of 12 books, she is also the founding editor of the MIT Press book series “Global Environmental Accord: Strategies for Sustainability and Institutional Innovation.” Q: The impacts of climate change — including storms, floods, wildfires, and droughts — have the potential to destabilize nations, yet they are not constrained by borders. What international developments most concern you in terms of addressing climate change and its myriad ecological and social impacts?

      A: Climate change is a global issue. By definition, and a long history of practice, countries focus on their own priorities and challenges. Over time, we have seen the gradual development of norms reflecting shared interests, and the institutional arrangements to support and pursue the global good. What concerns me most is that general responses to the climate crisis are being framed in broad terms; the overall pace of change remains perilously slow; and uncertainty remains about operational action and implementation of stated intent. We have just seen the completion of the 26th meeting of states devoted to climate change, the United Nations Climate Change Conference (COP26). In some ways this is positive. Yet, past commitments remain unfulfilled, creating added stress in an already stressful political situation. Industrial countries are uneven in their recognition of, and responses to, climate change. This may signal uncertainty about whether climate matters are sufficiently compelling to call for immediate action. Alternatively, the push for changing course may seem too costly at a time when other imperatives — such as employment, economic growth, or protecting borders — inevitably dominate discourse and decisions. Whatever the cause, the result has been an unwillingness to take strong action. Unfortunately, climate change remains within the domain of “low politics,” although there are signs the issue is making a slow but steady shift to “high politics” — those issues deemed vital to the existence of the state. This means that short-term priorities, such as those noted above, continue to shape national politics and international positions and, by extension, to obscure the existential threat revealed by scientific evidence. As for developing countries, these are overwhelmed by internal challenges, and managing the difficulties of daily life always takes priority over other challenges, however compelling. Long-term thinking is a luxury, but daily bread is a necessity. Non-state actors — including registered nongovernmental organizations, climate organizations, sustainability support groups, activists of various sorts, and in some cases much of civil society — have been left with a large share of the responsibility for educating and convincing diverse constituencies of the consequences of inaction on climate change. But many of these institutions carry their own burdens and struggle to manage current pressures. The international community, through its formal and informal institutions, continues to articulate the perils of climate change and to search for a powerful consensus that can prove effective both in form and in function. The general contours are agreed upon — more or less. But leadership of, for, and by the global collective is elusive and difficult to shape. Most concerning of all is the clear reluctance to address head-on the challenge of planning for changes that we know will occur. The reality that we are all being affected — in different ways and to different degrees — has yet to be sufficiently appreciated by everyone, everywhere. Yet, in many parts of the world, major shifts in climate will create pressures on human settlements, spur forced migrations, or generate social dislocations. Some small island states, for example, may not survive a sea-level surge. Everywhere there is a need to cut emissions, and this means adaptation and/or major changes in economic activity and in lifestyle.The discourse and debate at COP26 reflect all of such persistent features in the international system. So far, the largest achievements center on the common consensus that more must be done to prevent the rise in temperature from creating a global catastrophe. This is not enough, however. Differences remain, and countries have yet to specify what cuts in emissions they are willing to make.Echoes of who is responsible for what remains strong. The thorny matter of the unfulfilled pledge of $100 billion once promised by rich countries to help countries to reduce their emissions remained unresolved. At the same time, however, some important agreements were reached. The United States and China announced they would make greater efforts to cut methane, a powerful greenhouse gas. More than 100 countries agreed to end deforestation. India joined the countries committed to attain zero emissions by 2070. And on matters of finance, countries agreed to a two-year plan to determine how to meet the needs of the most-vulnerable countries. Q: In what ways do you think the tools and insights from political science can advance efforts to address climate change and its impacts?A: I prefer to take a multidisciplinary view of the issues at hand, rather than focus on the tools of political science alone. Disciplinary perspectives can create siloed views and positions that undermine any overall drive toward consensus. The scientific evidence is pointing to, even anticipating, pervasive changes that transcend known and established parameters of social order all across the globe.That said, political science provides important insight, even guidance, for addressing the impacts of climate change in some notable ways. One is understanding the extent to which our formal institutions enable discussion, debate, and decisions about the directions we can take collectively to adapt, adjust, or even depart from the established practices of managing social order.If we consider politics as the allocation of values in terms of who gets what, when, and how, then it becomes clear that the current allocation requires a change in course. Coordination and cooperation across the jurisdictions of sovereign states is foundational for any response to climate change impacts.We have already recognized, and to some extent, developed targets for reducing carbon emissions — a central impact from traditional forms of energy use — and are making notable efforts to shift toward alternatives. This move is an easy one compared to all the work that needs to be done to address climate change. But, in taking this step we have learned quite a bit that might help in creating a necessary consensus for cross-jurisdiction coordination and response.Respecting individuals and protecting life is increasingly recognized as a global value — at least in principle. As we work to change course, new norms will be developed, and political science provides important perspectives on how to establish such norms. We will be faced with demands for institutional design, and these will need to embody our guiding values. For example, having learned to recognize the burdens of inequity, we can establish the value of equity as foundational for our social order both now and as we recognize and address the impacts of climate change.

      Q: You teach a class on “Sustainability Development: Theory and Practice.” Broadly speaking, what are goals of this class? What lessons do you hope students will carry with them into the future?A: The goal of 17.181, my class on sustainability, is to frame as clearly as possible the concept of sustainable development (sustainability) with attention to conceptual, empirical, institutional, and policy issues.The course centers on human activities. Individuals are embedded in complex interactive systems: the social system, the natural environment, and the constructed cyber domain — each with distinct temporal, special, and dynamic features. Sustainability issues intersect with, but cannot be folded into, the impacts of climate change. Sustainability places human beings in social systems at the core of what must be done to respect the imperatives of a highly complex natural environment.We consider sustainability an evolving knowledge domain with attendant policy implications. It is driven by events on the ground, not by revolution in academic or theoretical concerns per se. Overall, sustainable development refers to the process of meeting the needs of current and future generations, without undermining the resilience of the life-supporting properties, the integrity of social systems, or the supports of the human-constructed cyberspace.More specifically, we differentiate among four fundamental dimensions and their necessary conditions:

      (a) ecological systems — exhibiting balance and resilience;(b) economic production and consumption — with equity and efficiency;(c) governance and politics — with participation and responsiveness; and(d) institutional performance — demonstrating adaptation and incorporating feedback.The core proposition is this: If all conditions hold, then the system is (or can be) sustainable. Then, we must examine the critical drivers — people, resources, technology, and their interactions — followed by a review and assessment of evolving policy responses. Then we ask: What are new opportunities?I would like students to carry forward these ideas and issues: what has been deemed “normal” in modern Western societies and in developing societies seeking to emulate the Western model is damaging humans in many ways — all well-known. Yet only recently have alternatives begun to be considered to the traditional economic growth model based on industrialization and high levels of energy use. To make changes, we must first understand the underlying incentives, realities, and choices that shape a whole set of dysfunctional behaviors and outcomes. We then need to delve deep into the driving sources and consequences, and to consider the many ways in which our known “normal” can be adjusted — in theory and in practice. Q: In confronting an issue as formidable as global climate change, what gives you hope?  A: I see a few hopeful signs; among them:The scientific evidence is clear and compelling. We are no longer discussing whether there is climate change, or if we will face major challenges of unprecedented proportions, or even how to bring about an international consensus on the salience of such threats.Climate change has been recognized as a global phenomenon. Imperatives for cooperation are necessary. No one can go it alone. Major efforts have and are being made in world politics to forge action agendas with specific targets.The issue appears to be on the verge of becoming one of “high politics” in the United States.Younger generations are more sensitive to the reality that we are altering the life-supporting properties of our planet. They are generally more educated, skilled, and open to addressing such challenges than their elders.However disappointing the results of COP26 might seem, the global community is moving in the right direction.None of the above points, individually or jointly, translates into an effective response to the known impacts of climate change — let alone the unknown. But, this is what gives me hope.

      Interview prepared by MIT SHASS CommunicationsEditorial, design, and series director: Emily HiestandSenior writer: Kathryn O’Neill More

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      Q&A: More-sustainable concrete with machine learning

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      As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

      Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

      Q: What applications does concrete have, and what properties make it a preferred building material?

      Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

      Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

      Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

      While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

      Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

      Q: Why is this problem important to address now, at this point in history?

      Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

      Q: How are you planning to address this problem to produce better concrete?

      Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

      Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

      Q: What kinds of AI and computational techniques are you employing for this?

      Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

      Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

      We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

      Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

      Q: How do you balance cost-benefit trade-offs?

      Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

      Q: What kinds of challenges do you face in this work?

      Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

      Q: What do you hope to achieve through this work?

      Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

      Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

      Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

      This research is supported by the MIT-IBM Watson AI Lab. More

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      The reasons behind lithium-ion batteries’ rapid cost decline

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      Lithium-ion batteries, those marvels of lightweight power that have made possible today’s age of handheld electronics and electric vehicles, have plunged in cost since their introduction three decades ago at a rate similar to the drop in solar panel prices, as documented by a study published last March. But what brought about such an astonishing cost decline, of about 97 percent?

      Some of the researchers behind that earlier study have now analyzed what accounted for the extraordinary savings. They found that by far the biggest factor was work on research and development, particularly in chemistry and materials science. This outweighed the gains achieved through economies of scale, though that turned out to be the second-largest category of reductions.

      The new findings are being published today in the journal Energy and Environmental Science, in a paper by MIT postdoc Micah Ziegler, recent graduate student Juhyun Song PhD ’19, and Jessika Trancik, a professor in MIT’s Institute for Data, Systems and Society.

      The findings could be useful for policymakers and planners to help guide spending priorities in order to continue the pathway toward ever-lower costs for this and other crucial energy storage technologies, according to Trancik. Their work suggests that there is still considerable room for further improvement in electrochemical battery technologies, she says.

      The analysis required digging through a variety of sources, since much of the relevant information consists of closely held proprietary business data. “The data collection effort was extensive,” Ziegler says. “We looked at academic articles, industry and government reports, press releases, and specification sheets. We even looked at some legal filings that came out. We had to piece together data from many different sources to get a sense of what was happening.” He says they collected “about 15,000 qualitative and quantitative data points, across 1,000 individual records from approximately 280 references.”

      Data from the earliest times are hardest to access and can have the greatest uncertainties, Trancik says, but by comparing different data sources from the same period they have attempted to account for these uncertainties.

      Overall, she says, “we estimate that the majority of the cost decline, more than 50 percent, came from research-and-development-related activities.” That included both private sector and government-funded research and development, and “the vast majority” of that cost decline within that R&D category came from chemistry and materials research.

      That was an interesting finding, she says, because “there were so many variables that people were working on through very different kinds of efforts,” including the design of the battery cells themselves, their manufacturing systems, supply chains, and so on. “The cost improvement emerged from a diverse set of efforts and many people, and not from the work of only a few individuals.”

      The findings about the importance of investment in R&D were especially significant, Ziegler says, because much of this investment happened after lithium-ion battery technology was commercialized, a stage at which some analysts thought the research contribution would become less significant. Over roughly a 20-year period starting five years after the batteries’ introduction in the early 1990s, he says, “most of the cost reduction still came from R&D. The R&D contribution didn’t end when commercialization began. In fact, it was still the biggest contributor to cost reduction.”

      The study took advantage of an analytical approach that Trancik and her team initially developed to analyze the similarly precipitous drop in costs of silicon solar panels over the last few decades. They also applied the approach to understand the rising costs of nuclear energy. “This is really getting at the fundamental mechanisms of technological change,” she says. “And we can also develop these models looking forward in time, which allows us to uncover the levers that people could use to improve the technology in the future.”

      One advantage of the methodology Trancik and her colleagues have developed, she says, is that it helps to sort out the relative importance of different factors when many variables are changing all at once, which typically happens as a technology improves. “It’s not simply adding up the cost effects of these variables,” she says, “because many of these variables affect many different cost components. There’s this kind of intricate web of dependencies.” But the team’s methodology makes it possible to “look at how that overall cost change can be attributed to those variables, by essentially mapping out that network of dependencies,” she says.

      This can help provide guidance on public spending, private investments, and other incentives. “What are all the things that different decision makers could do?” she asks. “What decisions do they have agency over so that they could improve the technology, which is important in the case of low-carbon technologies, where we’re looking for solutions to climate change and we have limited time and limited resources? The new approach allows us to potentially be a bit more intentional about where we make those investments of time and money.”

      “This paper collects data available in a systematic way to determine changes in the cost components of lithium-ion batteries between 1990-1995 and 2010-2015,” says Laura Diaz Anadon, a professor of climate change policy at Cambridge University, who was not connected to this research. “This period was an important one in the history of the technology, and understanding the evolution of cost components lays the groundwork for future work on mechanisms and could help inform research efforts in other types of batteries.”

      The research was supported by the Alfred P. Sloan Foundation, the Environmental Defense Fund, and the MIT Technology and Policy Program. More

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      in Energy

      Design’s new frontier

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      In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

      “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

      Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

      In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

      Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

      Computational design

      Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

      “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

      For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

      To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

      Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

      Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

      “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

      Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

      Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

      “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

      Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

      Personal fabrication

      Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

      “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

      Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

      First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

      To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

      “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

      Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

      Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

      “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

      Computational fluid dynamics and participatory design

      Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

      As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

      “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

      After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

      The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

      Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

      “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

      These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

      Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

      Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

      The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

      Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

      “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

      While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

      Designing intelligent systems

      When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

      Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

      “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

      For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

      To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

      “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

      Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

      “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

      To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

      In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

      “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

      Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

      Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

      “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

      Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

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      in Energy

      At UN climate change conference, trying to “keep 1.5 alive”

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      After a one-year delay caused by the Covid-19 pandemic, negotiators from nearly 200 countries met this month in Glasgow, Scotland, at COP26, the United Nations climate change conference, to hammer out a new global agreement to reduce greenhouse gas emissions and prepare for climate impacts. A delegation of approximately 20 faculty, staff, and students from MIT was on hand to observe the negotiations, share and conduct research, and launch new initiatives.

      On Saturday, Nov. 13, following two weeks of negotiations in the cavernous Scottish Events Campus, countries’ representatives agreed to the Glasgow Climate Pact. The pact reaffirms the goal of the 2015 Paris Agreement “to pursue efforts” to limit the global average temperature increase to 1.5 degrees Celsius above preindustrial levels, and recognizes that achieving this goal requires “reducing global carbon dioxide emissions by 45 percent by 2030 relative to the 2010 level and to net zero around mid-century.”

      “On issues like the need to reach net-zero emissions, reduce methane pollution, move beyond coal power, and tighten carbon accounting rules, the Glasgow pact represents some meaningful progress, but we still have so much work to do,” says Maria Zuber, MIT’s vice president for research, who led the Institute’s delegation to COP26. “Glasgow showed, once again, what a wicked complex problem climate change is, technically, economically, and politically. But it also underscored the determination of a global community of people committed to addressing it.”

      An “ambition gap”

      Both within the conference venue and at protests that spilled through the streets of Glasgow, one rallying cry was “keep 1.5 alive.” Alok Sharma, who was appointed by the UK government to preside over COP26, said in announcing the Glasgow pact: “We can now say with credibility that we have kept 1.5 degrees alive. But, its pulse is weak and it will only survive if we keep our promises and translate commitments into rapid action.”

      In remarks delivered during the first week of the conference, Sergey Paltsev, deputy director of MIT’s Joint Program on the Science and Policy of Global Change, presented findings from the latest MIT Global Change Outlook, which showed a wide gap between countries’ nationally determined contributions (NDCs) — the UN’s term for greenhouse gas emissions reduction pledges — and the reductions needed to put the world on track to meet the goals of the Paris Agreement and, now, the Glasgow pact.

      Pointing to this ambition gap, Paltsev called on all countries to do more, faster, to cut emissions. “We could dramatically reduce overall climate risk through more ambitious policy measures and investments,” says Paltsev. “We need to employ an integrated approach of moving to zero emissions in energy and industry, together with sustainable development and nature-based solutions, simultaneously improving human well-being and providing biodiversity benefits.”

      Finalizing the Paris rulebook

      A key outcome of COP26 (COP stands for “conference of the parties” to the UN Framework Convention on Climate Change, held for the 26th time) was the development of a set of rules to implement Article 6 of the Paris Agreement, which provides a mechanism for countries to receive credit for emissions reductions that they finance outside their borders, and to cooperate by buying and selling emissions reductions on international carbon markets.

      An agreement on this part of the Paris “rulebook” had eluded negotiators in the years since the Paris climate conference, in part because negotiators were concerned about how to prevent double-counting, wherein both buyers and sellers would claim credit for the emissions reductions.

      Michael Mehling, the deputy director of MIT’s Center for Energy and Environmental Policy Research (CEEPR) and an expert on international carbon markets, drew on a recent CEEPR working paper to describe critical negotiation issues under Article 6 during an event at the conference on Nov. 10 with climate negotiators and private sector representatives.

      He cited research that finds that Article 6, by leveraging the cost-efficiency of global carbon markets, could cut in half the cost that countries would incur to achieve their nationally determined contributions. “Which, seen from another angle, means you could double the ambition of these NDCs at no additional cost,” Mehling noted in his talk, adding that, given the persistent ambition gap, “any such opportunity is bitterly needed.”

      Andreas Haupt, a graduate student in the Institute for Data, Systems, and Society, joined MIT’s COP26 delegation to follow Article 6 negotiations. Haupt described the final days of negotiations over Article 6 as a “roller coaster.” Once negotiators reached an agreement, he says, “I felt relieved, but also unsure how strong of an effect the new rules, with all their weaknesses, will have. I am curious and hopeful regarding what will happen in the next year until the next large-scale negotiations in 2022.”

      Nature-based climate solutions

      World leaders also announced new agreements on the sidelines of the formal UN negotiations. One such agreement, a declaration on forests signed by more than 100 countries, commits to “working collectively to halt and reverse forest loss and land degradation by 2030.”

      A team from MIT’s Environmental Solutions Initiative (ESI), which has been working with policymakers and other stakeholders on strategies to protect tropical forests and advance other nature-based climate solutions in Latin America, was at COP26 to discuss their work and make plans for expanding it.

      Marcela Angel, a research associate at ESI, moderated a panel discussion featuring John Fernández, professor of architecture and ESI’s director, focused on protecting and enhancing natural carbon sinks, particularly tropical forests such as the Amazon that are at risk of deforestation, forest degradation, and biodiversity loss.

      “Deforestation and associated land use change remain one of the main sources of greenhouse gas emissions in most Amazonian countries, such as Brazil, Peru, and Colombia,” says Angel. “Our aim is to support these countries, whose nationally determined contributions depend on the effectiveness of policies to prevent deforestation and promote conservation, with an approach based on the integration of targeted technology breakthroughs, deep community engagement, and innovative bioeconomic opportunities for local communities that depend on forests for their livelihoods.”

      Energy access and renewable energy

      Worldwide, an estimated 800 million people lack access to electricity, and billions more have only limited or erratic electrical service. Providing universal access to energy is one of the UN’s sustainable development goals, creating a dual challenge: how to boost energy access without driving up greenhouse gas emissions.

      Rob Stoner, deputy director for science and technology of the MIT Energy Initiative (MITEI), and Ignacio Pérez-Arriaga, a visiting professor at the Sloan School of Management, attended COP26 to share their work as members of the Global Commission to End Energy Poverty, a collaboration between MITEI and the Rockefeller Foundation. It brings together global energy leaders from industry, the development finance community, academia, and civil society to identify ways to overcome barriers to investment in the energy sectors of countries with low energy access.

      The commission’s work helped to motivate the formation, announced at COP26 on Nov. 2, of the Global Energy Alliance for People and Planet, a multibillion-dollar commitment by the Rockefeller and IKEA foundations and Bezos Earth Fund to support access to renewable energy around the world.

      Another MITEI member of the COP26 delegation, Martha Broad, the initiative’s executive director, spoke about MIT research to inform the U.S. goal of scaling offshore wind energy capacity from approximately 30 megawatts today to 30 gigawatts by 2030, including significant new capacity off the coast of New England.

      Broad described research, funded by MITEI member companies, on a coating that can be applied to the blades of wind turbines to prevent icing that would require the turbines’ shutdown; the use of machine learning to inform preventative turbine maintenance; and methodologies for incorporating the effects of climate change into projections of future wind conditions to guide wind farm siting decisions today. She also spoke broadly about the need for public and private support to scale promising innovations.

      “Clearly, both the public sector and the private sector have a role to play in getting these technologies to the point where we can use them in New England, and also where we can deploy them affordably for the developing world,” Broad said at an event sponsored by America Is All In, a coalition of nonprofit and business organizations.

      Food and climate alliance

      Food systems around the world are increasingly at risk from the impacts of climate change. At the same time, these systems, which include all activities from food production to consumption and food waste, are responsible for about one-third of the human-caused greenhouse gas emissions warming the planet.

      At COP26, MIT’s Abdul Latif Jameel Water and Food Systems Lab announced the launch of a new alliance to drive research-based innovation that will make food systems more resilient and sustainable, called the Food and Climate Systems Transformation (FACT) Alliance. With 16 member institutions, the FACT Alliance will better connect researchers to farmers, food businesses, policymakers, and other food systems stakeholders around the world.

      Looking ahead

      By the end of 2022, the Glasgow pact asks countries to revisit their nationally determined contributions and strengthen them to bring them in line with the temperature goals of the Paris Agreement. The pact also “notes with deep regret” the failure of wealthier countries to collectively provide poorer countries $100 billion per year in climate financing that they pledged in 2009 to begin in 2020.

      These and other issues will be on the agenda for COP27, to be held in Sharm El-Sheikh, Egypt, next year.

      “Limiting warming to 1.5 degrees is broadly accepted as a critical goal to avoiding worsening climate consequences, but it’s clear that current national commitments will not get us there,” says ESI’s Fernández. “We will need stronger emissions reductions pledges, especially from the largest greenhouse gas emitters. At the same time, expanding creativity, innovation, and determination from every sector of society, including research universities, to get on with real-world solutions is essential. At Glasgow, MIT was front and center in energy systems, cities, nature-based solutions, and more. The year 2030 is right around the corner so we can’t afford to let up for one minute.” More

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      in Environment

      MIT collaborates with Biogen on three-year, $7 million initiative to address climate, health, and equity

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      MIT and Biogen have announced that they will collaborate with the goal to accelerate the science and action on climate change to improve human health. This collaboration is supported by a three-year, $7 million commitment from the company and the Biogen Foundation. The biotechnology company, headquartered in Cambridge, Massachusetts’ Kendall Square, discovers and develops therapies for people living with serious neurological diseases.

      “We have long believed it is imperative for Biogen to make the fight against climate change central to our long-term corporate responsibility commitments. Through this collaboration with MIT, we aim to identify and share innovative climate solutions that will deliver co-benefits for both health and equity,” says Michel Vounatsos, CEO of Biogen. “We are also proud to support the MIT Museum, which promises to make world-class science and education accessible to all, and honor Biogen co-founder Phillip A. Sharp with a dedication inside the museum that recognizes his contributions to its development.”

      Biogen and the Biogen Foundation are supporting research and programs across a range of areas at MIT.

      Advancing climate, health, and equity

      The first such effort involves new work within the MIT Joint Program on the Science and Policy of Global Change to establish a state-of-the-art integrated model of climate and health aimed at identifying targets that deliver climate and health co-benefits.

      “Evidence suggests that not all climate-related actions deliver equal health benefits, yet policymakers, planners, and stakeholders traditionally lack the tools to consider how decisions in one arena impact the other,” says C. Adam Schlosser, deputy director of the MIT Joint Program. “Biogen’s collaboration with the MIT Joint Program — and its support of a new distinguished Biogen Fellow who will develop the new climate/health model — will accelerate our efforts to provide decision-makers with these tools.”

      Biogen is also supporting the MIT Technology and Policy Program’s Research to Policy Engagement Initiative to infuse human health as a key new consideration in decision-making on the best pathways forward to address the global climate crisis, and bridge the knowledge-to-action gap by connecting policymakers, researchers, and diverse stakeholders. As part of this work, Biogen is underwriting a distinguished Biogen Fellow to advance new research on climate, health, and equity.

      “Our work with Biogen has allowed us to make progress on key questions that matter to human health and well-being under climate change,” says Noelle Eckley Selin, who directs the MIT Technology and Policy Program and is a professor in the MIT Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences. “Further, their support of the Research to Policy Engagement Initiative helps all of our research become more effective in making change.”

      In addition, Biogen has joined 13 other companies in the MIT Climate and Sustainability Consortium (MCSC), which is supporting faculty and student research and developing impact pathways that present a range of actionable steps that companies can take — within and across industries — to advance progress toward climate targets.

      “Biogen joining the MIT Climate and Sustainability Consortium represents our commitment to working with member companies across a diverse range of industries, an approach that aims to drive changes swift and broad enough to match the scale of the climate challenge,” says Jeremy Gregory, executive director of the MCSC. “We are excited to welcome a member from the biotechnology space and look forward to harnessing Biogen’s perspectives as we continue to collaborate and work together with the MIT community in exciting and meaningful ways.”

      Making world-class science and education available to MIT Museum visitors

      Support from Biogen will honor Nobel laureate, MIT Institute professor, and Biogen co-founder Phillip A. Sharp with a named space inside the new Kendall Square location of the MIT Museum, set to open in spring 2022. Biogen also is supporting one of the museum’s opening exhibitions, “Essential MIT,” with a section focused on solving real-world problems such as climate change. It is also providing programmatic support for the museum’s Life Sciences Maker Engagement Program.

      “Phil has provided fantastic support to the MIT Museum for more than a decade as an advisory board member and now as board chair, and he has been deeply involved in plans for the new museum at Kendall Square,” says John Durant, the Mark R. Epstein (Class of 1963) Director of the museum. “Seeing his name on the wall will be a constant reminder of his key role in this development, as well as a mark of our gratitude.”

      Inspiring and empowering the next generation of scientists

      Biogen funding is also being directed to engage the next generation of scientists through support for the Biogen-MIT Biotech in Action: Virtual Lab, a program designed to foster a love of science among diverse and under-served student populations.

      Biogen’s support is part of its Healthy Climate, Healthy Lives initiative, a $250 million, 20-year commitment to eliminate fossil fuels across its operations and collaborate with renowned institutions to advance the science of climate and health and support under-served communities. Additional support is provided by the Biogen Foundation to further its long-standing focus on providing students with equitable access to outstanding science education. More

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      in Security

      A robot that finds lost items

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      A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.

      Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.

      The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.

      Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.

      While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.

      “This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab.

      Co-authors include research assistant Tara Boroushaki, the lead author; electrical engineering and computer science graduate student Isaac Perper; research associate Mergen Nachin; and Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering. The research will be presented at the Association for Computing Machinery Conference on Embedded Networked Senor Systems next month.

      Play video

      Sending signals

      RFusion begins searching for an object using its antenna, which bounces signals off the RFID tag (like sunlight being reflected off a mirror) to identify a spherical area in which the tag is located. It combines that sphere with the camera input, which narrows down the object’s location. For instance, the item can’t be located on an area of a table that is empty.

      But once the robot has a general idea of where the item is, it would need to swing its arm widely around the room taking additional measurements to come up with the exact location, which is slow and inefficient.

      The researchers used reinforcement learning to train a neural network that can optimize the robot’s trajectory to the object. In reinforcement learning, the algorithm is trained through trial and error with a reward system.

      “This is also how our brain learns. We get rewarded from our teachers, from our parents, from a computer game, etc. The same thing happens in reinforcement learning. We let the agent make mistakes or do something right and then we punish or reward the network. This is how the network learns something that is really hard for it to model,” Boroushaki explains.

      In the case of RFusion, the optimization algorithm was rewarded when it limited the number of moves it had to make to localize the item and the distance it had to travel to pick it up.

      Once the system identifies the exact right spot, the neural network uses combined RF and visual information to predict how the robotic arm should grasp the object, including the angle of the hand and the width of the gripper, and whether it must remove other items first. It also scans the item’s tag one last time to make sure it picked up the right object.

      Cutting through clutter

      The researchers tested RFusion in several different environments. They buried a keychain in a box full of clutter and hid a remote control under a pile of items on a couch.

      But if they fed all the camera data and RF measurements to the reinforcement learning algorithm, it would have overwhelmed the system. So, drawing on the method a GPS uses to consolidate data from satellites, they summarized the RF measurements and limited the visual data to the area right in front of the robot.

      Their approach worked well — RFusion had a 96 percent success rate when retrieving objects that were fully hidden under a pile.

      “Sometimes, if you only rely on RF measurements, there is going to be an outlier, and if you rely only on vision, there is sometimes going to be a mistake from the camera. But if you combine them, they are going to correct each other. That is what made the system so robust,” Boroushaki says.

      In the future, the researchers hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements. This would enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting.

      Beyond its potential industrial uses, a system like this could even be incorporated into future smart homes to assist people with any number of household tasks, Boroushaki says.

      “Every year, billions of RFID tags are used to identify objects in today’s complex supply chains, including clothing and lots of other consumer goods. The RFusion approach points the way to autonomous robots that can dig through a pile of mixed items and sort them out using the data stored in the RFID tags, much more efficiently than having to inspect each item individually, especially when the items look similar to a computer vision system,” says Matthew S. Reynolds, CoMotion Presidential Innovation Fellow and associate professor of electrical and computer engineering at the University of Washington, who was not involved in the research. “The RFusion approach is a great step forward for robotics operating in complex supply chains where identifying and ‘picking’ the right item quickly and accurately is the key to getting orders fulfilled on time and keeping demanding customers happy.”

      The research is sponsored by the National Science Foundation, a Sloan Research Fellowship, NTT DATA, Toppan, Toppan Forms, and the Abdul Latif Jameel Water and Food Systems Lab. More

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      in Environment

      Study: Global cancer risk from burning organic matter comes from unregulated chemicals

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      Whenever organic matter is burned, such as in a wildfire, a power plant, a car’s exhaust, or in daily cooking, the combustion releases polycyclic aromatic hydrocarbons (PAHs) — a class of pollutants that is known to cause lung cancer.

      There are more than 100 known types of PAH compounds emitted daily into the atmosphere. Regulators, however, have historically relied on measurements of a single compound, benzo(a)pyrene, to gauge a community’s risk of developing cancer from PAH exposure. Now MIT scientists have found that benzo(a)pyrene may be a poor indicator of this type of cancer risk.

      In a modeling study appearing today in the journal GeoHealth, the team reports that benzo(a)pyrene plays a small part — about 11 percent — in the global risk of developing PAH-associated cancer. Instead, 89 percent of that cancer risk comes from other PAH compounds, many of which are not directly regulated.

      Interestingly, about 17 percent of PAH-associated cancer risk comes from “degradation products” — chemicals that are formed when emitted PAHs react in the atmosphere. Many of these degradation products can in fact be more toxic than the emitted PAH from which they formed.

      The team hopes the results will encourage scientists and regulators to look beyond benzo(a)pyrene, to consider a broader class of PAHs when assessing a community’s cancer risk.

      “Most of the regulatory science and standards for PAHs are based on benzo(a)pyrene levels. But that is a big blind spot that could lead you down a very wrong path in terms of assessing whether cancer risk is improving or not, and whether it’s relatively worse in one place than another,” says study author Noelle Selin, a professor in MIT’s Institute for Data, Systems and Society, and the Department of Earth, Atmospheric and Planetary Sciences.

      Selin’s MIT co-authors include Jesse Kroll, Amy Hrdina, Ishwar Kohale, Forest White, and Bevin Engelward, and Jamie Kelly (who is now at University College London). Peter Ivatt and Mathew Evans at the University of York are also co-authors.

      Chemical pixels

      Benzo(a)pyrene has historically been the poster chemical for PAH exposure. The compound’s indicator status is largely based on early toxicology studies. But recent research suggests the chemical may not be the PAH representative that regulators have long relied upon.   

      “There has been a bit of evidence suggesting benzo(a)pyrene may not be very important, but this was from just a few field studies,” says Kelly, a former postdoc in Selin’s group and the study’s lead author.

      Kelly and his colleagues instead took a systematic approach to evaluate benzo(a)pyrene’s suitability as a PAH indicator. The team began by using GEOS-Chem, a global, three-dimensional chemical transport model that breaks the world into individual grid boxes and simulates within each box the reactions and concentrations of chemicals in the atmosphere.

      They extended this model to include chemical descriptions of how various PAH compounds, including benzo(a)pyrene, would react in the atmosphere. The team then plugged in recent data from emissions inventories and meteorological observations, and ran the model forward to simulate the concentrations of various PAH chemicals around the world over time.

      Risky reactions

      In their simulations, the researchers started with 16 relatively well-studied PAH chemicals, including benzo(a)pyrene, and traced the concentrations of these chemicals, plus the concentration of their degradation products over two generations, or chemical transformations. In total, the team evaluated 48 PAH species.

      They then compared these concentrations with actual concentrations of the same chemicals, recorded by monitoring stations around the world. This comparison was close enough to show that the model’s concentration predictions were realistic.

      Then within each model’s grid box, the researchers related the concentration of each PAH chemical to its associated cancer risk; to do this, they had to develop a new method based on previous studies in the literature to avoid double-counting risk from the different chemicals. Finally, they overlaid population density maps to predict the number of cancer cases globally, based on the concentration and toxicity of a specific PAH chemical in each location.

      Dividing the cancer cases by population produced the cancer risk associated with that chemical. In this way, the team calculated the cancer risk for each of the 48 compounds, then determined each chemical’s individual contribution to the total risk.

      This analysis revealed that benzo(a)pyrene had a surprisingly small contribution, of about 11 percent, to the overall risk of developing cancer from PAH exposure globally. Eighty-nine percent of cancer risk came from other chemicals. And 17 percent of this risk arose from degradation products.

      “We see places where you can find concentrations of benzo(a)pyrene are lower, but the risk is higher because of these degradation products,” Selin says. “These products can be orders of magnitude more toxic, so the fact that they’re at tiny concentrations doesn’t mean you can write them off.”

      When the researchers compared calculated PAH-associated cancer risks around the world, they found significant differences depending on whether that risk calculation was based solely on concentrations of benzo(a)pyrene or on a region’s broader mix of PAH compounds.

      “If you use the old method, you would find the lifetime cancer risk is 3.5 times higher in Hong Kong versus southern India, but taking into account the differences in PAH mixtures, you get a difference of 12 times,” Kelly says. “So, there’s a big difference in the relative cancer risk between the two places. And we think it’s important to expand the group of compounds that regulators are thinking about, beyond just a single chemical.”

      The team’s study “provides an excellent contribution to better understanding these ubiquitous pollutants,” says Elisabeth Galarneau, an air quality expert and PhD research scientist in Canada’s Department of the Environment. “It will be interesting to see how these results compare to work being done elsewhere … to pin down which (compounds) need to be tracked and considered for the protection of human and environmental health.”

      This research was conducted in MIT’s Superfund Research Center and is supported in part by the National Institute of Environmental Health Sciences Superfund Basic Research Program, and the National Institutes of Health. More