SCI-FI-TEK

Commercial fusion power plant closer to reality following research breakthrough

AFTER SEVENTY YEARS

Cambridge University Press

Facebook

WeChatBlueskyMessageWhatsAppEmail

 

image: 

Type One Energy employees at the Bull Run Fossil Plant, soon to be home to the prototype Infinity One

view more 

Credit: Type One Energy

Successfully harnessing the power of fusion energy could lead to cleaner and safer energy for all – and contribute substantially to combatting the climate crisis. Towards this goal, Type One Energy has published a comprehensive, self-consistent, and robust physics basis for a practical fusion pilot power plant.  

This groundbreaking research is presented in a series of six peer-reviewed scientific papers in a special issue of the prestigious Journal of Plasma Physics (JPP), published by Cambridge University Press. 

The articles serve as the foundation for the company’s first fusion power plant project, which Type One Energy is developing with the Tennessee Valley Authority utility in the United States.  

Alex Schekochihin, Professor of Theoretical Physics at the University of Oxford and Editor of the JPP, spoke with enthusiasm about this development: 

“JPP is very proud to provide a platform for rigorous peer review and publication of the papers presenting the physics basis of the Infinity Two stellarator — an innovative and ground-breaking addition to the expanding family of proposed fusion power plant designs.  

“Fusion science and technology are experiencing a period of very rapid development, driven by both public and private enthusiasm for fusion power. In this environment of creative and entrepreneurial ferment, it is crucial that new ideas and designs are both publicly shared and thoroughly scrutinised by the scientific community — Type One Energy and JPP are setting the gold standard for how this is done (as we did with Commonwealth Fusion Systems 5 years ago for their SPARC physics basis).” 

The new physics design basis for the pilot power plant is a robust effort to consider realistically the complex relationship between challenging, competing requirements that all need to function together for fusion energy to be possible.  

This new physics solution also builds on the operating characteristics of high-performing stellarator fusion technology – a stellarator being a machine that uses complex, helical magnetic fields to confine the plasma, thereby enabling scientists to control it and create suitable conditions for fusion. This technology is already being used with success on the world’s largest research stellarator, the Wendelstein 7-X, located in Germany, but the challenge embraced by Type One Energy’s new design is how to scale it up to a pilot plant. 

Building the future of energy 

Functional fusion technology could offer limitless clean energy. As global energy demands increase and energy security is front of mind, too, this new physics design basis comes at an excellent time.  

Christofer Mowry, CEO of Type One Energy, is cognisant of the landmark nature of his company’s achievement and proud of its strong, real-world foundations. 

“The physics basis for our new fusion power plant is grounded in Type One Energy’s expert knowledge about reliable, economic, electrical generation for the power grid. We have an organisation that understands this isn’t only about designing a science project.” 

This research was developed collaboratively between Type One Energy and a broad coalition of scientists from national laboratories and universities around the world. Collaborating organisations included the US Department of Energy, for using their supercomputers, such as the exascale Frontier machine at Oak Ridge National Laboratory, to perform its physics simulations. 

While commercial fusion energy has yet to move from theory into practice, this new research marks an important and promising milestone. Clean and abundant energy may yet become reality.  

---

Journal

Journal of Plasma Physics

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Physics basis of the Infinity Two fusion power plant

Article Publication Date

27-Mar-2025

 

Drone experiment reveals how Greenland ice sheet is changing

University of Colorado at Boulder

Facebook

XLinkedInWeChatBlueskyMessageWhatsAppEmail

 

image: 

A pneumatically launched drone bound for collecting air samples for isotopic analysis at EastGRIP, Greenland.

view more 

Credit: Ole Zeising/Alfred-Wegener-Institute

For the first time, researchers have collected detailed measurements of water vapor high above the surface of the Greenland ice sheet. Their research, aided by a custom-designed drone, could help scientists improve ice loss calculations in rapidly warming polar regions. 
 
“We will be able to understand how water moves in and out of Greenland in the next few years,” said first author Kevin Rozmiarek, a doctoral student at the Institute of Arctic and Alpine Research (INSTAAR) at CU Boulder. “As a major freshwater reservoir, we need to understand how Greenland’s environment is going to change in the future.” 
 
The findings were published March 14 in JGR Atmospheres.  
 
According to the National Oceanic and Atmospheric Administration (NOAA), Greenland lost about 55 gigatons of ice and snow between fall 2023 and fall 2024. The island is shedding ice for the 28th year in a row, and scientists estimate that it has lost more than 5 trillion tons of ice since 1992. 
  
The Greenland ice sheet contains about 8% of the planet’s freshwater, and its meltwater could contribute significantly to rising sea levels, changing ocean circulation and ecosystems worldwide.  
 
The majority of ice loss comes from large ice chunks breaking off from glaciers and the melting of surface ice and snow. Sublimation, the process of solids turning into gases without turning into liquids first, may also play a role. Prior studies have suggested that in some parts of Greenland, about 30% of summer surface snow could sublimate to water vapor. 

Tracking water in the sky
 
It is unclear where the water vapor goes, said Rozmiarek. Some might fall back down as snow or recondense on the surface later, but some could leave Greenland’s water system entirely. 
 
Collecting air samples in the Arctic is an expensive and technically challenging task, because it traditionally involves flying a plane to the middle of an ice sheet in harsh weather and carrying air samples back to the laboratory.    
 
Rozmiarek and his team overcame the challenges by loading air sampling equipment on a large drone with a 10-foot wingspan. 

Throughout the summer of 2022, the team flew the drone 104 times from the East Greenland Ice-Core Project camp—managed by the University of Copenhagen— in the island’s interior. The drone collected air samples at different heights of up to nearly 5,000 feet above the ground. 
 
The team aimed to look into the type of hydrogen and oxygen atoms in the air’s water vapor.  Water molecules from different sources contain distinct combinations of hydrogen and oxygen. Scientists call these variations in isotopes.  
 
“Isotopes are water’s fingerprints. By following these fingerprints, we can trace back to the source where the water vapor came from,” Rozmiarek said. Scientists have collected high-quality data on the source of water in Greenland, including water that flows from the tropics, and the sink, which is the surface snow on the Greenland ice sheet. “But we don’t know much about the isotopic composition of water in motion, which is the vapor between the source and sink,” he added. 

When the team compared their drone-based measurements with an existing computer simulation that models the Arctic water cycle, they found the simulation underestimated the amount of precipitation that fell on Greenland. By incorporating the isotopic data observed in the simulation, the model rendered an accurate prediction of how water moves over Greenland.  
 
“It’s really important to be able to predict what’s going to happen to Greenland in the warming world as accurately as possible,” Rozmiarek said. “We demonstrated how useful water vapor isotope data is by successfully improving an existing model.” 

Melting ice sheet
 
About 125,000 years ago, when Earth was warmer than preindustrial levels, Greenland was covered by a significantly smaller ice sheet, and the sea level was as much as 19 feet higher than today. As the planet continues to warm, the Greenland ice sheet could see dramatic changes and even shrink to its size back then, Rozmiarek said. 
 
The Greenland ice sheet contains a massive amount of freshwater, and that water, if leaving the system, could lead to significant increases in global sea level. The United Nations estimated that rising sea levels caused by climate change currently impact 1 billion people worldwide.  
 
Rozmiarek hopes to return to Greenland and other parts of the Arctic to conduct more flights and gather additional data.  
 
“It’s like we just figured out how to discover fingerprints at a crime scene. This is a concrete step forward in understanding where water is going and where it is coming from in this important system at a time when we need it most,” he said. 

---

Journal

Journal of Geophysical Research Atmospheres

DOI

10.1029/2024JD042719 

 

Research Alert: UC San Diego medical students support tattoo removal for adults impacted by the justice system

University of California - San Diego

Facebook

XLinkedInWeChatBluesky
Email

A study by University of California San Diego School of Medicine described a novel medical student service-learning initiative that provides free laser tattoo removal services for justice-impacted adults reintegrating into the community. Medical students who expressed a desire to address social justice issues also benefit by receiving hands-on experience in dermatology. In fact, 100% of students wanted to provide medical care for justice-impacted adults and 83% were interested in dermatology training for a future career.

The initiative — which takes place at the UC San Diego Clean Slate Free Tattoo Removal Program — has been well-received by both students and clinical teams. It enhances medical education, social justice engagement, and patient care in underserved communities. The structured mentorship model allows for continued growth and sustainability of the initiative. Medical students participate in pre- and post-procedure tasks, procedure support and mentoring of incoming students.

The research was led by Victoria D. Ojeda, Ph.D., a professor at the School of Medicine and the Herbert Wertheim School of Public Health & Human Longevity Science. The findings were published on March 13, 2025 at the Lasers in Surgery and Medicine Journal.

# # #

---

Journal

Lasers in Surgery and Medicine

DOI

10.1002/lsm.70005 

Disclaimer: AAAS and EurekAl

 

Mapping the Earth’s crops

National Center for Supercomputing Applications

Facebook

XLinkedInMessageWhatsAppEmail

As agricultural research continues to become more entwined with technology, smart farming – a phrase that encompasses research computing tools that help farmers to better address issues like crop disease, drought and sustainability – has quickly become a ubiquitous term in Ag labs across the country. The availability of NCSA resources like Delta for researchers, both nationally and on the University of Illinois Urbana-Champaign (U. of I.) campus, has fostered a hotbed of cutting-edge research projects in the agricultural domain.

Yi-Chia Chang, a Ph.D. student at the U. of I., focuses his research on machine learning (ML) and remote sensing. His most recent research, published in arXiv and accepted to IEEE IGARSS 2025, concerns crop mapping.

Imagine you’re a farmer, and you’re planning what to grow this season. You may want to know what crop would be most valuable to grow. If you’re a policymaker, you might want to know if there would be a shortage of a particular crop and incentivize farmers to grow it through subsidies. To do this, you’d have to know what’s currently growing to make those decisions – that’s where crop mapping comes into play.

Crop mapping uses satellite imagery to create a map of all the crop types in a particular region. Crop maps are essential tools when it comes to monitoring crops and regional food supplies, and these maps help when farmers are planning which crops to plant in a growing season. The maps can also help with smart farming – using these crop maps applications can monitor growth, precipitation conditions, yield predictions and even disease.

All these tools are great for farmers, but they also help at a larger scale as well, helping policymakers and organizations determine how much food and what types are being produced in a given area. Machine learning is an essential component when it comes to keeping these crop maps up-to-date. In the U.S. alone, there are millions of acres of farmland to analyze, label and map. There aren’t enough experts to analyze and keep up with data to create up-to-date, accurate crop maps, so training machines to scan satellite images and label crops is far more efficient and useful.

Researchers have had great success training machines to recognize not only crops but many other elements of farming from satellite imagery. They’ve created accurate models for crop mapping in well-researched regions like the U.S. However, there has been little research on how well these models work in new geographic areas, especially in regions where data is lacking. This raises concerns about "geospatial bias," meaning models trained on data from well-developed countries may not perform well in less-developed regions.

Our research will enable better-informed agricultural systems for policymakers and stakeholders to support global food security.

–Yi-Chia Chang, University of Illinois

Chang’s study, which was inspired by his team’s previous related research published in NeurIPS 2023 proceedings, looks at how popular Earth observation models work when applied to new regions, particularly in agriculture, where differences in farming practices and uneven data availability make it harder to transfer knowledge between areas. To do this, Chang chose four major cereal grains – maize, soybean, rice and wheat – and then tested three widely-used pre-trained models and compared their performance on data they had seen before (in-distribution) versus data from new regions (out-of-distribution).

The results showed that models pre-trained on satellite images like Sentinel-2 (SSL4EO-S12) performed better than those pre-trained on general image datasets like ImageNet.

“By harmonizing crop type datasets across five continents, we found that foundation models pre-trained on full spectral bands of Sentinel-2 perform better for crop-type mapping,” said Chang. “Our research also shows that training with out-of-distribution data can boost performance when the in-distribution data is scarce. In the long run, we still hope to acquire larger and more balanced labeled datasets since those can help achieve the best crop-type mapping results. I am excited to see how foundation models and transfer learning can benefit food security.”

Chang’s work has been fully integrated with TorchGeo, an open-source library for geospatial machine learning, so future research can easily develop further based on his results. As his team looks ahead, they plan to build upon the results of this study and apply their methodology to new smart-farming models.

“Our future work will focus on expanding crop-type datasets and developing agriculture-specific pre-trained models,” said Chang. “We will also establish benchmarks for agricultural applications of foundation models, such as crop-type mapping and crop-yield prediction, bridging the gap between GeoAI and food security solutions.”

Chang’s work required massive amounts of storage and compute power to complete. GPUs were necessary for the machine-learning aspect of the project to be completed in a timely manner, but a lot of space was also needed for all that satellite imagery.

HPC resources significantly accelerate the machine learning workflows using GPUs, reducing model training time from hours on CPUs to minutes on GPUs. Additionally, the large data-storage allocation enables us to efficiently manage the training datasets, pre-trained weights and model outputs in the cluster.

–Yi-Chia Chang, University of Illinois

Chang has experience using research computing. Prior to this project, he utilized the campus cluster hosted by a research group led by Arindam Banerjee, a professor of computer science at U. of I. Even with his previous experience with high-performance computing (HPC), Chang was happy to report that moving his project onto Delta was relatively simple.

“My experience using Delta has been smooth and user-friendly. The admin staff was responsive, approving token exchange for GPU hours and storage allocations within a few days. The technical staff efficiently helped with troubleshooting. I’d like to send a special thanks to Brett Bode for helping to allocate over 50 TB of storage for satellite imagery.”

For more information about getting an allocation on Delta, University of Illinois researchers can make a request through Illinois Computes. For larger allocations or for researchers from outside of the U. of. I, visit the ACCESS allocations page to request time on Delta or DeltaAI.

---

ABOUT DELTA AND DELTAAI
NCSA’s Delta and DeltaAI are part of the national cyberinfrastructure ecosystem through the U.S. National Science Foundation ACCESS program. Delta (OAC 2005572) is a powerful computing and data-analysis resource combining next-generation processor architectures and NVIDIA graphics processors with forward-looking user interfaces and file systems. The Delta project partners with the Science Gateways Community Institute to empower broad communities of researchers to easily access Delta and with the University of Illinois Division of Disability Resources & Educational Services and the School of Information Sciences to explore and reduce barriers to access. DeltaAI (OAC 2320345) maximizes the output of artificial intelligence and machine learning (AI/ML) research. Tripling NCSA’s AI-focused computing capacity and greatly expanding the capacity available within ACCESS, DeltaAI enables researchers to address the world’s most challenging problems by accelerating complex AI/ML and high-performance computing applications running terabytes of data. Additional funding for DeltaAI comes from the State of Illinois.

New geospatial intelligence methodology makes land use management more accurate and faster

A technique developed by researchers was tested in the Brazilian state of Mato Grosso and more accurately delineated areas of natural vegetation and agricultural production by crop type; the results showed 95% accuracy in mapping

News Release 27-Mar-2025
Fundação de Amparo à Pesquisa do Estado de São Paulo

Facebook

XLinkedInWeChatBlueskyMessageWhatsAppEmail

 

image: 

The researchers applied the new methodology in Mato Grosso using data from the 2016/2017 strategic harvest 

view more 

Credit: Research Progress and Challenges of Agricultural Information Technology

Researchers from São Paulo State University (UNESP), at its Tupã campus in Brazil, have developed and tested a new geospatial intelligence methodology that can contribute more quickly and accurately to land use management and territorial planning projects. With this tool, it was possible to precisely delineate areas of Amazon rainforest, Cerrado vegetation (the Brazilian savannah-like biome), pastures and agricultural crops in a double-cropping system, something that can provide support for public policies aimed at agricultural production and environmental conservation.

By combining data cube architecture (ready for analysis), disseminated in Brazil through the Brazil Data Cube project, led by the National Institute for Space Research (INPE), and the Geobia (Geographic Object-Based Image Analysis) approach, the scientists were able to identify vegetation and double cropping – for example, soy and corn – over the course of a harvest in the state of Mato Grosso. They used time series of satellite images from NASA’s Modis (Moderate Resolution Imaging Spectroradiometer) sensor.

The results showed that the proposed combination, coupled with machine learning (artificial intelligence) algorithms, achieved 95% mapping accuracy.

Geobiology is a technique that allows satellite images to be processed using segmentations that group similar pixels into geo-objects and study their characteristics, such as shape, texture, and reflectance. In many cases, this allows for a more realistic interpretation. Data cubes, on the other hand, store information in dimensions – time and place – making it easier to aggregate and visualize information related to a specific location in a specific time period, such as crop areas in a harvest year.

Currently, mapping uses pixel image analysis in isolation, which ends up creating edge problems with blurring in some areas. “Scientific work has highlighted spectral confusion in border zones between different land uses as an area for improvement. So we decided to segment the images and evaluate the geographical object as the minimum unit of analysis, rather than the pixel. It’s as if the image were broken down and classified according to each piece. In this way, we were able to reduce recurring edge errors and accurately identify the targets, even with moderate spatial resolution,” Michel Eustáquio Dantas Chaves, professor at the Faculty of Science and Engineering of UNESP and corresponding author of the article, told Agência FAPESP.

Chaves has been using data cube architecture for several years to develop tools that contribute to analyses focused on the advancement of the agricultural frontier, especially in the Cerrado.

According to the professor, the methodology can be replicated to evaluate images from other Earth observation satellites, such as Landsat and Sentinel, which provide data for scientific studies, mapping and monitoring. Images from both are now being processed by the team coordinated by the professor.

The article describing the methodology was published in the special issue Research Progress and Challenges of Agricultural Information Technology of the scientific journal AgriEngineering. The study was supported by FAPESP through three projects (21/07382-2, 23/09903-5 and 24/08083-7).

Application in practice

Mato Grosso leads national grain production with 31.4% of the country’s total, followed by the states of Paraná (12.8%) and Rio Grande do Sul (11.8%). The state is expected to reach 97.3 million tons in the 2024/2025 harvest, an increase of 4.4% over the previous harvest, according to the National Supply Company (CONAB). Almost half of this production (46.1 million tons) is expected to be soybeans.

In addition, Mato Grosso is one of the most biodiverse states in the country, containing parts of three of Brazil’s six biomes. Around 53% of its territory is in the Amazon, 40% in the Cerrado and 7% in the Pantanal.

Due to this heterogeneity of land uses and vegetation types in the territory, the researchers applied the new methodology in Mato Grosso using data from the 2016/2017 strategic harvest, in which Brazil produced 115 million tons of soybeans, of which 30.7 million tons were in the state. Land use classifications were associated with agricultural land (fallow-cotton, soybean-cotton, soybean-corn, soybean-fallow, soybean-millet and soybean-sunflower), as well as sugarcane crops, urban areas and water bodies.

The results showed an overall accuracy of 95%, demonstrating the potential of the approach to provide mapping that optimizes forest and agricultural land delineation. “Since the approach manages to identify the targets in a consistent manner, the methodology can be applied to the estimation of areas within the same harvest, favoring productivity estimates; in territorial planning actions and anything that deals with land use and land cover for decision-making,” explains Chaves about the application of the tool.

The professor explains that the methodology also makes it possible to analyze disturbances in forests and other types of natural vegetation. “It’s quicker to detect deforestation than degradation. This method allowed us to detect these variations more quickly.”

In the article, the scientists pay tribute to Professor Ieda Del’Arco Sanches, a remote sensing researcher at INPE who died in January. “This article is a way of thanking her for her teachings and following her legacy. Ieda always worked to accurately assess the Earth’s surface and to treat the data ethically and responsibly, showing how they can contribute to the construction of public policies,” adds Chaves.

About FAPESP

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the state of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration.

---

Journal

AgriEngineering

DOI

10.3390/agriengineering7010019 

Article Title

Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes

 

DESANTISLAND

Rideshare data reveal discriminatory policing for speeding in Florida

High-frequency location data show that race affects citations and fines for speeding

Summary author: Walter Beckwith

American Association for the Advancement of Science (AAAS)

Facebook

XLinkedInWeChatBlueskyMessageWhatsAppEmail

Using data on more than 220,000 individuals on the Lyft rideshare platform, researchers report that drivers of color are significantly more likely to receive speeding tickets than white drivers, and to face steeper fines, even when traveling at identical speeds. Racial profiling by law enforcement is a pressing social issue in the United States. Previous research analyzing police and judicial records suggests that racial and ethnic minorities face disproportionately higher rates of searches, fines, force, detentions, and incarceration compared to white civilians. However, research on racial bias in policing has long been hindered by data limitations and challenging analyses. For example, to demonstrate racial bias in policing, researchers must compare officer treatment of minority and white civilians under identical circumstances, while also controlling for all other factors in a police-civilian encounter that might explain enforcement disparities. These so-called “all-else-equal” scenarios are scarce in policing research.

 

Leveraging high-frequency GPS location data from the rideshare platform Lyft, Pradhi Aggarwal and colleagues overcome some of these challenges and estimate the effect of racial profiling on citations and fines for speed violations. The analysis encompassed 222,838 Lyft drivers operating in Florida from 2017 to 2020. Lyft drivers use a smartphone application that transmits precise location and speed data to Lyft’s system at 10-second intervals, providing researchers with detailed, real-time driving information. Aggarwal et al. then matched this dataset with Florida’s government records for speeding violations, detailing traffic stops and driver’s license information for those involved. The authors found that minority drivers are significantly more likely to be cited for speeding and pay higher fines than white drivers, even after controlling for factors like driving speed, location, vehicle characteristics, and other relevant variables. The findings show that minority drivers are 24% to 33% more likely to be cited during a traffic stop and they pay 23% to 34% higher fines, compared to white drivers. Moreover, the analysis revealed no significant differences in accident or re-offense rates across White versus minority drivers, suggesting that policing bias – rather than driver behavior – drives these disparities. “Aggarwal et al. have provided a template for using recent technological advances to overcome some of the most challenging obstacles impeding policing research,” write Dean Knox and Jonathan Mummolo in a related Perspective.

 

For reporters interested in trends, a 2021 Science study led by Knox, and using a dataset on daily patrols of officers in the Chicago Police Department, reported that Black officers used force less often than white officers during a three-year period studied.

---

Journal

Science

DOI

10.1126/science.adp5357 

Article Title

High-frequency location data show that race affects citations and fines for speeding

Article Publication Date

28-Mar-2025