How Does Plastic Debris Make Its Way Into Ocean Garbage Patches?

Researchers in the U.S. and Germany decided to explore which pathways transport debris to the middle of the oceans, causing garbage patches, as well as the relative strengths of different subtropical gyres and how they influence long-term accumulation of debris. In Chaos, they report creating a model of the oceans’ surface dynamics from historical trajectories of surface buoys. Their model describes the probability of plastic debris being transported from one region to another.

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Temperature, Humidity, Wind Predict Second Wave of Pandemic

The “second wave” of the coronavirus pandemic has placed much blame on a lack of appropriate safety measures. However, due to the impacts of weather, research suggests two outbreaks per year are inevitable. Though face masks, travel restrictions, and social distancing guidelines help slow the number of new infections in the short term, the lack of climate effects incorporated into epidemiological models presents a glaring hole that can cause long-term effects. In Physics of Fluids, researchers discuss the impacts of these parameters.

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SARS-CoV-2 Transmission Model Offers Decision-Making Pathways for Safe School Opening

Can schools safely remain open or reopen during periods of significant community spread of COVID-19? According to predictions from a model of SARS-CoV-2 transmission in the school setting, yes – if appropriate precautions are followed both in school and in the community.

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App Developed at Rensselaer Can Help Guide COVID-19 Management on Any Campus

Leaders at Rensselaer Polytechnic Institute relied on a powerful algorithm, COVID Back-to-School – now freely available to the public – to determine that twice-weekly testing of all students would be the optimal regimen for keeping the infection rate on campus safely below 1% for any two-week period.

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Final GW Election Model Project Using Twitter Activity Forecasts Sizable Biden Victory

The final PEORIA Project election forecast from the George Washington University Graduate School of Political Management (GSPM) predicts former Vice President Joe Biden will win the electoral vote count for the 2020 presidential election.

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Interactions Within Larger Social Groups Can Cause Tipping Points in Contagion Flow

Contagion processes, such as opinion formation or disease spread, can reach a tipping point, where the contagion either rapidly spreads or dies out. When modeling these processes, it is difficult to capture this complex transition. In the journal Chaos, researchers studied the parameters of these transitions by including three-person group interactions in a contagion model called the susceptible-infected-susceptible model. In this model, an infected person who recovers from an infection can be reinfected.

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Galaxy Simulations Could Help Reveal Origins of Milky Way

Rutgers astronomers have produced the most advanced galaxy simulations of their kind, which could help reveal the origins of the Milky Way and dozens of small neighboring dwarf galaxies. Their research also could aid the decades-old search for dark matter, which fills an estimated 27 percent of the universe. And the computer simulations of “ultra-faint” dwarf galaxies could help shed light on how the first stars formed in the universe.

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Big Data Analytics Enables Scientists to Model COVID-19 Spread

Researchers will use big data analytics techniques to develop computational models to predict the spread of COVID-19. They will utilize forward simulation from a given patient and the propagation of the infection into the community; and backward simulation tracing a number of verified infections to a possible patient “zero.” The project also will provide quick and automatic contact tracing and leverages the researchers’ prior experience in modeling Ebola spread.

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Modeling COVID-19 Data Must Be Done With Extreme Care

As the virus causing COVID-19 began its devastating spread, an international team of scientists was alarmed by the lack of uniform approaches by various countries’ epidemiologists. Data modeling to predict the numbers of likely infections varied widely and revealed a high degree of uncertainty. In the journal Chaos, the group describes why modeling and extrapolating the evolution of COVID-19 outbreaks in near real time is an enormous scientific challenge that requires a deep understanding of the nonlinearities underlying the dynamics of epidemics.

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A New Way to Accurately Estimate COVID-19 Death Toll

A Rutgers engineer has created a mathematical model that accurately estimates the death toll linked to the COVID-19 pandemic in the United States and could be used around the world. The model, detailed in a study published in the journal Mathematics, predicted the death toll would eventually reach about 68,120 in the United States as a result of the SARS-CoV-2 coronavirus that causes COVID-19. That’s based on data available on April 28, and there was high confidence (99 percent) the expected death toll would be between 66,055 and 70,304.

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UW team illustrates the adverse impact of visiting ‘just one friend’ during COVID-19 lockdown

“What’s the harm in visiting just one friend?” A lot of people are asking that during times of social distancing. A new website illustrates how doing so would essentially reconnect most households in a community and provide conduits through which the COVID-19 virus could spread.

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Why Do So Many Pregnancies and In Vitro Fertilization Attempts Fail?

Scientists have created a mathematical model that can help explain why so many pregnancies and in vitro fertilization attempts fail. The Rutgers-led study, which may help to improve fertility, is published in the journal Proceedings of the National Academy of Sciences.

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US approaching peak of ‘active’ COVID-19 cases, strain on medical resources, new modeling shows

A new data-driven mathematical model of the coronavirus pandemic predicts that the United States will peak in the number of “active” COVID-19 cases on or around April 20, marking a critical milestone on the demand for medical resources.

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Lab researchers aid COVID-19 response in antibody, anti-viral research

Lawrence Livermore National Laboratory scientists are contributing to the global fight against COVID-19 by combining artificial intelligence/machine learning, bioinformatics and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs to combat the disease.

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When coronavirus is not alone

Interacting contagious diseases like influenza and pneumonia—and perhaps coronavirus too—follow the same complex spreading patterns as social trends, like the adoption of new slang or technologies. This new finding, published in Nature Physics, could lead to better tracking and intervention when multiple diseases spread through a population at the same time.

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Duchenne Muscular Dystrophy Diagnosis Improved by Simple Accelerometers

Testing for Duchenne muscular dystrophy can require specialized equipment, invasive procedures and high expense, but measuring changes in muscle function and identifying compensatory walking gait could lead to earlier detection. This week in Chaos, researchers present a relative coupling coefficient, which can be used to quantify the factors involved in the human gait and more accurately screen for the disorder. They measured movements of different parts of the body in test subjects, viewing the body as a kinematic chain.

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DOE to Provide $10 Million for New Research into Ecosystem Processes

The U.S. Department of Energy (DOE) announced a plan to provide $10 million for new observational and experimental studies aimed at improving the accuracy of today’s Earth system models. Research will focus on three separate types of environments—terrestrial, watershed, and subsurface—where current models fall short of providing fully accurate representation.

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LLNL leads multi-institutional team in modeling protein interactions tied to cancer

Computational scientists, biophysicists and statisticians from Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL) are leading a massive multi-institutional collaboration that has developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancers.

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Studying Ice to Understand Astrophysical Bodies

Understanding the formation and evolution of ice in astrophysical environments can provide information about the physical conditions encountered in space and the chemical similarities and differences between planetary and stellar systems. At the AVS 66th International Symposium and Exhibition, Edith Fayolle, an astrochemist at NASA’s Jet Propulsion Laboratory, will talk about how scientists are trying to understand properties of ice on astrophysical bodies, such as its formation, composition and sublimation — the process by which ice transitions directly into gas, without being in its liquid phase in between.

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Evolution of Catalysts, Real-World Applications

Electrocatalysts accelerate energy conversion, which is an integral component to many industrially important technologies, such as fuel cells. While many models show promising results to improving this approach, technologies to demonstrate a decrease in degradation to optimize performance are lacking. At the AVS 66th International Symposium and Exhibition, Serhiy Cherevko, a physicist at the Institute of Energy and Climate Research, will talk about the challenges facing current electrocatalysis techniques and possible analytical tools to optimize this approach for widespread commercialization.

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Analysis of Galileo’s Jupiter Entry Probe Reveals Gaps in Heat Shield Modeling

The entry probe of the Galileo mission to Jupiter entered the planet’s atmosphere in 1995 in fiery fashion, generating enough heat to cause plasma reactions on its surface. The data relayed about the burning of its heat shield differed from the effects predicted in fluid dynamics models, and new work examines what might have caused such a discrepancy.

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