Humanity can farm more food from the seas to help feed the planet while shrinking mariculture’s negative impacts on biodiversity, according to new research led by the University of Michigan.
There is a catch, though: We need to be strategic about it.
The findings are published in the journal Nature Ecology & Evolution.
What exercises can future astronauts on long-term missions to the Moon or Mars conduct to help mitigate the effects of cartilage damage resulting from microgravity? This is what a recent study published in npj Microgravity hopes to address as an international team of researchers investigated the health benefits of future astronauts performing jumping workouts during long-duration space missions. This study holds the potential to help astronauts, mission planners, and the public better understand the risks and strategies for long-duration space missions, especially as human exploration expands to the Moon and Mars.
“Think about sending somebody on a trip to Mars, they get there, and they can’t walk because they developed osteoarthritis of the knees or the hips and their joints don’t function,” said Dr. Marco Chiaberge, who is a research scientist at Johns Hopkins University and lead author of the study. “Astronauts also perform spacewalks often. They serviced the Hubble Space Telescope five times, and in the future, they will need to spend more time in space and the Moon, where we will build larger telescopes to explore the universe and where they will need to stay as healthy as possible.”
For the study, the researchers conducted a nine-week study with mice to ascertain the benefits of jumping exercises three times a week compared to limited movement regarding cartilage growth and sustainability. In the end, the researchers found that not only did the mice who participated in jumping exercises exhibit a 26 percent increase in cartilage compared to 14 percent reduction for the non-movement mice, but the jumping mice also displayed 110 percent thicker cartilage. Additionally, the jumping mice were found to exhibit 15 percent greater bone mineral density due to the jumping exercises.
Researchers at North Carolina State University have demonstrated a new technique that uses light to tune the optical properties of quantum dots—making the process faster, more energy-efficient and environmentally sustainable—without compromising material quality.
The findings are published in the journal Advanced Materials.
“The discovery of quantum dots earned the Nobel Prize in chemistry in 2023 because they are used in so many applications,” says Milad Abolhasani, corresponding author of a paper on the work and ALCOA Professor of Chemical and Biomolecular Engineering at NC State. “We use them in LEDs, solar cells, displays, quantum technologies and so on. To tune their optical properties, you need to tune the bandgap of quantum dots—the minimum energy required to excite an electron from a bound state to a free-moving state—since this directly determines the color of light they emit.
A dual-site electrocatalyst is developed to greatly enhance methanol production from CO2 reduction via a cascade process, taking advantage of molecular-scale CO spillover.
Researchers at NIMTE have turned metal corrosion into a tool for efficient biomass upgrading, achieving high HMF-to-BHMF conversion rates with a CoCuMW/CF electrode. Their findings offer a low-cost, sustainable solution for bio-based chemical production.
A research team led by Prof. Jian Zhang from the Ningbo Institute of Materials Technology and Engineering (NIMTE) of the Chinese Academy of Sciences (CAS) has harnessed metal corrosion to develop high-performance electrodes, facilitating the efficient and cost-effective upgrading of bio-based 5-hydroxymethylfurfural (HMF). Their findings were published in Chem Catalysis.
While corrosion is typically associated with material degradation and economic loss, researchers are now investigating its potential for advantageous applications, particularly in biomass upgrading.
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
Re relying on a weather app to predict next week’s temperature. How do you know you can trust its forecast? Scientists use statistical and physical models to make predictions about everything from weather to air pollution. But checking whether these models are truly reliable is trickier than it seems—especially when the locations where we have validation data don Traditional validation methods struggle with this problem, failing to provide consistent accuracy in real-world scenarios. In this work, researchers introduce a new validation approach designed to improve trust in spatial predictions. They define a key requirement: as more validation data becomes available, the accuracy of the validation method should improve indefinitely. They show that existing methods don’t always meet this standard. Instead, they propose an approach inspired by previous work on handling differences in data distributions (known as “covariate shift”) but adapted for spatial prediction. Their method not only meets their strict validation requirement but also outperforms existing techniques in both simulations and real-world data.
By refining how we validate predictive models, this work helps ensure that critical forecasts—like air pollution levels or extreme weather events—can be trusted with greater confidence.
A new evaluation method assesses the accuracy of spatial prediction techniques, outperforming traditional methods. This could help scientists make better predictions in areas like weather forecasting, climate research, public health, and ecological management.
In today’s AI news, Backed by $200 million in funding, Scott Wu and his team at Cognition are building an AI tool that could potentially disintegrate the whole industry, at a $2 Billion valuation. Devin is an autonomous AI agent that, in theory, writes the code itself—no people involved—and can complete entire projects typically assigned to developers.
In other advancements, OpenAI is changing how it trains AI models to explicitly embrace “intellectual freedom … no matter how challenging or controversial a topic may be,” the company says in a new policy. OpenAI is releasing a significantly expanded version of its Model Spec, a document that defines how its AI models should behave — and is making it free for anyone to use or modify.
Then, xAI, the artificial intelligence company founded by Elon Musk, is set to launch Grok 3 on Monday, Feb. 17. According to xAI, this latest version of its chatbot, which Musk describes as “scary smart,” represents a major step forward, improving reasoning, computational power and adaptability. Grok 3’s development was accelerated by its Colossus supercomputer, which was built in just eight months, powered by 100,000 Nvidia H100 GPUs.
And, large language models can learn complex reasoning tasks without relying on large datasets, according to a new study by researchers at Shanghai Jiao Tong University. Their findings show that with just a small batch of well-curated examples, you can train an LLM for tasks that were thought to require tens of thousands of training instances.
S new o1 model, which focuses on slower, more deliberate reasoning — much like how humans think — in order to solve complex problems. ” + Then, join Turing Award laureate Yann LeCun—Chief AI Scientist at Meta and Professor at NYU—as he discusses with Link Ventures’ John Werner, the future of artificial intelligence and how open-source development is driving innovation. In this wide-ranging conversation, LeCun explains why AI systems won’t “take over” but will instead serve as empowering assistants.
This process, which cannot be understood satisfactorily by classical physics alone, occurs constantly in green plants and other photosynthetic organisms, such as photosynthetic bacteria. However, the exact mechanisms have still not been fully elucidated. Hauer and first author Erika Keil see their study as an important new basis in the effort to clarify how chlorophyll, the pigment in leaf green, works.
Applying these findings in the design of artificial photosynthesis units could help to utilize solar energy with unprecedented efficiency for electricity generation or photochemistry.
Scientists develop a working prototype of a quantum battery, promising ultra-fast charging and potential applications in solar energy.
Attosecond time-resolved experiments have revealed the increasing importance of electronic correlations in the collective plasmon response as the size of the system decreases to sub-nm scales.
The study, published in the journal Science Advances, was led by the University of Hamburg and DESY as part of a collaboration with Stanford, SLAC National Accelerator Laboratory, Ludwig-Maximilians-Universität München, Northwest Missouri State University, Politecnico di Milano and the Max Planck Institute for the Structure and Dynamics of Matter.
Plasmons are collective electronic excitations that give rise to unique effects in matter. They provide a means of achieving extreme light confinement, enabling groundbreaking applications such as efficient solar energy harvesting, ultrafine sensor technology, and enhanced photocatalysis.