Martin Ehler and Karlheinz Gröchenig
Math. Comp. 93 (), 2885-2919.
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Ole Løseth Elvetun and Bjørn Fredrik Nielsen
Math. Comp. 93 (), 2811-2836.
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Thomas O. Gallouët, Andrea Natale and Gabriele Todeschi
Math. Comp. 93 (), 2769-2810.
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Zhaohui Fu, Tao Tang and Jiang Yang
Math. Comp. 93 (), 2745-2767.
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Katherine Baker, Lehel Banjai and Mariya Ptashnyk
Math. Comp. 93 (), 2711-2743.
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Selim Esedoḡlu, Jiajia Guo and David Li
Math. Comp. 93 (), 2679-2710.
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Constantin Carle and Marlis Hochbruck
Math. Comp. 93 (), 2611-2641.
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David M. Williams and Qingguo Hong
Math. Comp. 93 (), 2587-2609.
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Buyang Li, Zongze Yang and Zhi Zhou
Math. Comp. 93 (), 2557-2586.
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Math's connection with cooking extends beyond the mathematical constant that sounds like a dessert. For example, using differential equations to model fluid flow and heat transfer, research teams have found how spaghetti curls as it's cooked, how to rotate a pan to make the perfect crepe (thin pancake), and the temperature setting to get the perfect steak. Mathematics helps understand cooking, and parallels it in that following a recipe can lead to good results, but asking questions like "What if we tried this?" can lead to a masterpiece. Eugenia Cheng talks about the mathematics of cooking and baking.
Math is often described as the science of patterns, which makes it a natural subject to help in the study of the underlying causes of patterns found in nature, for example, bands of vegetation that often occur on gently sloped terrains in certain near-desert ecosystems worldwide. We are starting to learn more about these bands' common properties by using mathematical models built on data, such as rainfall totals and the curvature of the terrain. Mary Silber talks about these mathematical models of vegetation bands.
Math may sometimes seem as if it's comprised of countless meaningless unconnected exercises, but in reality, it's much more. It's figuring out how to do something, and, even better, why something works the way it does. The math you're doing now can open doors for you so that you can answer deep questions yourself about a subject or idea that you're interested in. Give those questions a shot and perhaps someday also help others solve their problems. Five mathematicians (Alexander Diaz-Lopez, Trachette Jackson, Francis Su, Erika Tatiana Camacho, and Deanna Haunsperger) talk about what mathematics means to them.
Angela Robinson explains the math behind the next generation of cryptographic algorithms. Whenever you log in to a website, send an email, or make an online purchase, you're counting on your data being sent securely, without hackers being able to crack the code. Our standard cryptographic systems hinge on mathematical problems that stump present-day computers, like finding the prime factors of a very large number. But in the coming decades, powerful quantum computers are expected to be able to rapidly solve some such problems, threatening the security of our online communications. To develop new methods that can withstand even the most sophisticated quantum computer, cryptographers are using a wide range of mathematical tools, many of which were originally developed without any real-life applications in mind.
Tim Chumley explains the connections between random billiards and the science of heat and energy transfer. If you've ever played billiards or pool, you've used your intuition and some mental geometry to plan your shots. Mathematicians have gone a step further, using these games as inspiration for new mathematical problems. Starting from the simple theoretical setup of a single ball bouncing around in an enclosed region, the possibilities are endless. For instance, if the region is shaped like a stadium (a rectangle with semicircles on opposite sides), and several balls start moving with nearly the same velocity and position, their paths in the region soon differ wildly: chaos. Mathematical billiards even have connections to thermodynamics, the branch of physics dealing with heat, temperature, and energy transfer.
Malena Espanol explains how she and others use linear algebra to correct blurry images. Imagine snapping a quick picture of a flying bird. The image is likely to come out blurry. But thanks to mathematics, you might be able to use software to improve the photo. Scientists often deal with blurry pictures, too. Linear algebra and clever numerical methods allow researchers to fix imperfect photos in medical imaging, astronomy, and more. In a computer, the pixels that make up an image can be represented as a column of numbers called a vector. Blurring happens when the light meant for each pixel spills into the adjacent pixels, changing the numbers in a way that can be mathematically represented as an enormous matrix. But knowing that matrix is not enough if you want to reconstruct the original (non-blurry) image.
Karen Rios Soto explains how mathematics illuminates the link between air pollution from motor vehicle emissions and asthma. Air pollution causes the premature deaths of an estimated seven million people each year, and it makes life worse for all of us. People with asthma can experience chest tightness, coughing or wheezing, and difficulty breathing when triggered by air pollution. One major source is gas- and diesel-powered cars and trucks, which emit "ultrafine" particles less than 0.1 micrometers across. That's about the width of the virus that causes COVID-19, so tiny that these particles are not currently regulated by the US Environmental Protection Agency. Yet ultrafine particles can easily enter your lungs and be absorbed into your bloodstream, causing health issues such as an asthma attack or even neurodegenerative diseases. Mathematics can help us understand the extent of the problem and how to solve it.
Imelda Flores Vazquez from Econometrica, Inc. explains how economists use mathematics to evaluate the efficacy of health care policies. When a hospital or government wants to adjust their health policies — for instance, by encouraging more frequent screenings for certain diseases — how do they know whether their program will work or not? If the service has already been implemented elsewhere, researchers can use that data to estimate its effects. But if the idea is brand-new, or has only been used in very different settings, then it's harder to predict how well the new program will work. Luckily, a tool called a microsimulation can help researchers make an educated guess.
Dr. Stan Wagon of Macalester College discusses the mathematics behind rolling a square smoothly. In 1997, inspired by a square wheel exhibit at The Exploratorium museum in San Francsico, Dr. Stan Wagon enlisted his neighbor Loren Kellen in building a square-wheeled tricycle and accompanying catenary track. For years, you could ride the tricycle at Macalester College in St. Paul, Minnesota. The National Museum of Mathematics in New York now also has square-wheeled tricycles that can be ridden around a circular track. And more recently, the impressive Cody Dock Rolling Bridge was built using rolling square mathematics by Thomas Randall-Page in London.
Dr. Valentina Wheeler of University of Wollongong, Australia, shares how her work influences efforts to understand wildfires and red blood cells. In Australia, where bushfires are a concern year-round, researchers have long tried to model these wildfires, hoping to learn information that can help with firefighting policy. Mathematician Valentina Wheeler and colleagues began studying a particularly dangerous phenomenon: When two wildfires meet, they create a new, V-shaped fire whose pointed tip races along to catch up with the two branches of the V, moving faster than either of the fires alone. This is exactly what happens in a mathematical process known as mean curvature flow. Mean curvature flow is a process in which a shape smooths out its boundaries over time. Just as with wildfires, pointed corners and sharp bumps will change the fastest.
Dr. Outi Tervo of Greenland Institute for Natural Resources, shares how mathematics helps recommend speed limits for marine vessels, which benefits narwhals and Inuit culture. Narwhals "can only be found in the Arctic," said Outi Tervo, a senior scientist at GINR. "These species are going to be threatened by climate change more than other species that can live in a bigger geographical area." The collaboration has already lobbied on behalf of the narwhals to reduce the level of sea traffic in their habitat, after using mathematical analysis to identify how noise from passing boats changes the narwhals' foraging behavior.
We are extremely excited to announce on behalf of the American Society for Biochemistry and Molecular Biology (ASBMB) that the Journal of Biological Chemistry (JBC), Molecular & Cellular Proteomics (MCP), and the Journal of Lipid Research (JLR) will be published as fully open-access journals beginning in January 2021. This is a landmark decision that will have huge impact for readers and authors. As many of you know, many researchers have called for journals to become open access to facilitate scientific progress, and many funding agencies across the globe are either already requiring or considering a requirement that all scientific publications based on research they support be published in open-access journals. The ASBMB journals have long supported open access, making the accepted author versions of manuscripts immediately and permanently available, allowing authors to opt in to the immediate open publication of the final version of their paper, and endorsing the goals of the larger open-access movement (1). However, we are no longer satisfied with these measures. To live up to our goals as a scientific society, we want to freely distribute the scientific advances published in JBC, MCP, and JLR as widely and quickly as possible to support the scientific community. How better can we facilitate the dissemination of new information than to make our scientific content freely open to all?For ASBMB journals and others who have contemplated or made the transition to publishing all content open access, achieving this milestone generally requires new financial mechanisms. In the case of the...
Soun-Hi Kwon
Trans. Amer. Math. Soc. 377 (), 6021-6022.
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Tsz-Kiu Aaron Chow and Yangyang Li
Trans. Amer. Math. Soc. 377 (), 5993-6020.
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Yinji Li, Zhiwei Wang and Xiangyu Zhou
Trans. Amer. Math. Soc. 377 (), 5947-5992.
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Alexandra Kjuchukova, Allison N. Miller, Arunima Ray and Sümeyra Sakallı
Trans. Amer. Math. Soc. 377 (), 5905-5946.
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Otto Overkamp
Trans. Amer. Math. Soc. 377 (), 5863-5903.
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Guido De Philippis, Nicola Fusco and Massimiliano Morini
Trans. Amer. Math. Soc. 377 (), 5787-5835.
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Daniel Le, Bao V. Le Hung, Stefano Morra, Chol Park and Zicheng Qian
Trans. Amer. Math. Soc. 377 (), 5749-5786.
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Kazuhiro Ishige, Paolo Salani and Asuka Takatsu
Trans. Amer. Math. Soc. 377 (), 5705-5748.
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Carolyn S. Gordon and Michael R. Jablonski
Trans. Amer. Math. Soc. 377 (), 5673-5704.
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Ming-Lun Hsieh and Shunsuke Yamana
Trans. Amer. Math. Soc. 377 (), 5617-5672.
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Jong In Han and Sijong Kwak
Trans. Amer. Math. Soc. 377 (), 5561-5581.
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Nathan Brownlowe, Alcides Buss, Daniel Gonçalves, Jeremy B. Hume, Aidan Sims and Michael F. Whittaker
Trans. Amer. Math. Soc. 377 (), 5513-5560.
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Song Dai and Qiongling Li
Trans. Amer. Math. Soc. 377 (), 5445-5481.
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Ángel D. Martínez
Trans. Amer. Math. Soc. 377 (), 5411-5444.
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Noga Alon, Anurag Bishnoi, Shagnik Das and Alessandro Neri
Trans. Amer. Math. Soc. 377 (), 5389-5410.
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Daniel López Neumann
Trans. Amer. Math. Soc. 377 (), 5361-5387.
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