The Russian invasion of Ukraine has highlighted the depth of Europe’s dependence on Russian natural gas imports. The REPowerEU plan, released by the European Commission, maps a European Union-wide pathway to reduce Russian natural imports by two-thirds by the end of 2022, with the complete elimination of Russian gas by 2027. Overall, there are only four main policy levers in the short term: increase pipeline gas and liquified natural gas imports from non-Russian sources, reduce gas demand in heating and industry, reduce gas-fired electricity generation and adopt flexible gas storage targets.

A research team including Binghamton University engineer Neha Patankar has created a power sector model for Europe and a gas network model to analyze multiple feasible pathways to secure full independence from Russian natural gas. Patankar is an operations research analyst with a research focus on energy system and power system modeling. Her research aims to provide insights for constructing a robust decision under parametric and structural uncertainty. Her research also includes the evaluation of low-carbon technologies and the effects of policy scenarios on energy systems.

The team modeled European natural gas and electricity systems to assess the feasibility and potential impact of a near-complete embargo on imports of Russian natural gas to Europe beginning in October.

It found that Europe can eliminate reliance on Russian natural gas by augmenting REPowerEU plans with a temporary boost in coal and recalibrated gas storage.

“All successful gas independence pathways increase reliance on coal-fired electricity generation but reduce overall greenhouse gas emissions due to offsets from lower gas demand,” said Patankar, assistant professor in Binghamton University’s Department of Systems Science and Industrial Engineering.

To be successful these actions will have to be sustained for the next two winters, Patankar said.

“Increasing renewables, electrification of heating and liquified natural gas imports will steadily replace the current policy levers and maintain Europe’s long-term energy transition,” she said.

Contributing to this research were Michael Lau, a second-year PhD student in the Department of Mechanical and Aerospace Engineering, Princeton University; Wilson Ricks, a fourth-year PhD student in the Department of Mechanical and Aerospace Engineering, Princeton University; Jesse Jenkins, an assistant professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment.

The commentary article, “Europe’s way out: Tools to rapidly eliminate imports of Russian natural gas,” was published in Joule.

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Materials provided by Binghamton University. Note: Content may be edited for style and length.

IAV is responsible for 250,000-500,000 deaths per year worldwide. When IAV infects its host, an immunological response composed of a series of molecular processes begins. IAV can infect several different species, and physiological and genetic differences among these species can contribute to different host responses, although some responses are shared.

“Identifying key defense processes and key regulators in multiple species can facilitate the development of treatments for IAV in humans,” said Bin Zhang, PhD, Director of the Center for Transformative Disease Modeling, Willard T.C. Johnson Research Professor of Neurogenetics, and Professor of Genetics and Genomic Sciences at Icahn Mount Sinai, who led the study.

The study used RNA sequencing to analyze gene expression over time in cells and tissues collected from IAV-infected humans, ferrets, and mice, identifying multiple key defense processes specific to tissues and species. One gene found to play a key role in immunological defense mechanisms against IAV across all species was TDRD7, whichencodes a Tudor domain-containing protein, a type of protein shown to be involved in epigenetic regulation. In light of this discovery, the researchers conducted subsequent experiments inhibiting the function of TDRD7, resulting in an increase of virus replication in IAV-infected models.

“Identifying both common and species-specific responses to influenza is essential in developing effective therapies for the flu and can help inform future research of other respiratory infections, such as COVID-19,” said Christian Forst, PhD, Assistant Professor of Genetics and Genomic Sciences, and Microbiology, at Icahn Mount Sinai and a first author of the study.

Co-first authors on the research team also include Laura Martin-Sancho, PhD, Staff Scientist at Scripps Research, California, and Shashank Tripathi, PhD, Assistant Professor, Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore.

Senior authors on the research team also include Adolfo García-Sastre, PhD, Irene and Dr. Arthur M. Fishberg Professor of Medicine and Director of the Global Health and Emerging Pathogens Institute at Icahn Mount Sinai in New York, and Elodie Ghedin, PhD, Chief of the Systems Genomics Section and Deputy Chief of the Laboratory of Parasitic Diseases at the National Institute of Allergy and Infectious Diseases, National Institutes of Health.

This study was supported by the National Institute of Allergy and Infectious Disease (NIAID) of the National Institutes of Health (NIH) under award numbers R21AI149013, U01AI111598, U19AI106754, U19AI135972, U19AI142733, and U19AI168631 and NIAID-funded Center for Research on Influenza Pathogenesis (CRIP) and Center of Excellence for Influenza Research and Response (CEIRR, contract #75N93021C00014).

The algorithms are unique in that they take a holistic approach to action anticipation, combining visual data — for example, where an athlete is located on the court — with information that is more implicit, like an athlete’s specific role on the team.

“Computer vision can interpret visual information such as jersey color and a player’s position or body posture,” said Silvia Ferrari, the John Brancaccio Professor of Mechanical and Aerospace Engineering, who led the research. “We still use that real-time information, but integrate hidden variables such as team strategy and player roles, things we as humans are able to infer because we’re experts at that particular context.”

Ferrari and doctoral students Junyi Dong and Qingze Huo trained the algorithms to infer hidden variables the same way humans gain their sports knowledge — by watching games. The algorithms used machine learning to extract data from videos of volleyball games, and then used that data to help make predictions when shown a new set of games.

The results were published Sept. 22 in the journal ACM Transactions on Intelligent Systems and Technology, and show the algorithms can infer players’ roles — for example, distinguishing a defense-passer from a blocker — with an average accuracy of nearly 85%, and can predict multiple actions over a sequence of up to 44 frames with an average accuracy of more than 80%. The actions included spiking, setting, blocking, digging, running, squatting, falling, standing and jumping.

Ferrari envisions teams using the algorithms to better prepare for competition by training them with existing game footage of an opponent and using their predictive abilities to practice specific plays and game scenarios.

Ferrari has filed for a patent and is now working with the Big Red men’s hockey team to further develop the software. Using game footage provided by the team, Ferrari and her graduate students, led by Frank Kim, are designing algorithms that autonomously identify players, actions and game scenarios. One goal of the project is to help annotate game film, which is a tedious task when performed manually by team staff members.

“Our program places a major emphasis on video analysis and data technology,” said Ben Russell, director of hockey operations for the Cornell men’s team. “We are constantly looking for ways to evolve as a coaching staff in order to better serve our players. I was very impressed with the research Professor Ferrari and her students have conducted thus far. I believe that this project has the potential to dramatically influence the way teams study and prepare for competition.”

Beyond sports, the ability to anticipate human actions bears great potential for the future of human-machine interaction, according to Ferrari, who said improved software can help autonomous vehicles make better decisions, bring robots and humans closer together in warehouses, and can even make video games more enjoyable by enhancing the computer’s artificial intelligence.

“Humans are not as unpredictable as the machine learning algorithms are making them out to be right now,” said Ferrari, who is also associate dean for cross-campus engineering research, “because if you actually take into account all of the content, all of the contextual clues, and you observe a group of people, you can do a lot better at predicting what they’re going to do.”

The research was supported by the Office of Naval Research Code 311 and Code 351, and commercialization efforts are being supported by the Cornell Office of Technology Licensing.

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Materials provided by Cornell University. Original written by Syl Kacapyr, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length.