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In-brief analysis

October 3, 2025

The value of all energy trade between the United States and Mexico was estimated to be $57 billion in 2024, down from nearly $72 billion in 2023, according to data from the U.S. Census Bureau. A combination of lower petroleum output from Mexico and lower fuel prices, particularly for petroleum products that make up the bulk of the cross-border energy trade between the two countries, drove most of the decrease.

Energy trade value represents the total value of energy imports and exports between two countries and is driven by commodity volumes and prices. Most of the energy trade value between the United States and Mexico comes from U.S. exports of refined petroleum products to Mexico—$37 billion in 2024—which accounted for 64% of the total energy value traded between the two countries.

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A powerful new AI tool called Diag2Diag is revolutionizing fusion research by filling in missing plasma data with synthetic yet highly detailed information. Developed by Princeton scientists and international collaborators, this system uses sensor input to predict readings other diagnostics can’t capture, especially in the crucial plasma edge region where stability determines performance. By reducing reliance on bulky hardware, it promises to make future fusion reactors more compact, affordable, and reliable.

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Synopsis

Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology.  

The National

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Synopsis

The Mathematical Biology Program supports research in all areas of mathematical sciences with relevance to the biological sciences. Successful proposals must demonstrate mathematical innovation, biological relevance and significance, and strong integration between mathematics and biology.

Some projects of interest to the Mathematical Biology Program may include development of mathematical theories, methodologies, and tools traditionally seen in other disciplinary programs within the Division of Mathematical Sciences. In general, if a proposal is appropriate for review by more than one NSF program, it is advisable to contact the program officers handling each program to determine when and where the proposal should be submitted and to facilitate the review process.

The Mathematical Biology Program regularly seeks joint reviews of proposals with programs in the Directorates of Biological Sciences and other relevant programs. Investigators are encouraged to discuss their project with program officers in relevant areas to

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