Lifeboat Foundation

COPENHAGEN, June 14 (Reuters) — Swedish electric self-driving truck company Einride expects to reduce CO₂ emissions in Norway by 2,100 tonnes over the coming three years as it partners up with Scandinavia’s leading postal service, PostNord, the company said on Wednesday.

Norway has the world’s highest number of electric vehicles per head of population and aims for all heavy vehicles to be zero-emission by 2040, potentially cutting CO₂ emissions by 4.4 million tonnes or nearly 9% of the country’s annual emissions.

“Given Norway’s pioneering work in electrifying passenger vehicles, it’s only logical that they should take a leading role in the electrification of heavy-duty freight as well,” Einride CEO Robert Falck said.

Dan this made me think of you.

‘Benchmark’ experiment suggests quantum computers could have useful real-world applications within two years.

The search for extraterrestrial life in our solar system just got more exciting. A team of scientists including Southwest Research Institute’s Dr. Christopher Glein has discovered new evidence that the subsurface ocean of Saturn’s moon Enceladus contains a key building block for life. The team directly detected phosphorus in the form of phosphates originating from the moon’s ice-covered global ocean using data from NASA’s Cassini mission. Cassini explored Saturn and its system of rings and moons for over 13 years.

“In 2020 (published in 2022), we used geochemical modeling to predict that phosphorus should be abundant in Enceladus’ ocean,” said Glein, a leading expert in extraterrestrial oceanography. He is a co-author of a paper in the journal Nature describing this research. “Now, we have found abundant phosphorus in plume ice samples spraying out of the subsurface ocean.”

The Cassini spacecraft discovered Enceladus’ subsurface liquid water and analyzed samples in a plume of ice grains and gases erupting into space from cracks in the moon’s icy surface. Analysis of a class of salt-rich ice grains by Cassini’s Cosmic Dust Analyzer showed the presence of sodium phosphates. The team’s observational results, together with laboratory analogue experiments, suggest that phosphorus is readily available in Enceladus’ ocean as phosphates.

The technology can help cut through buzzwordy “solutions” and serve as a shortcut for jumpstarting creativity.

Fibonacci numbers are seen in the natural structures of various plants, such as the florets in sunflower heads, areoles on cacti stems, and scales in pine cones. [HackerBox] has developed a Fibonacci Spiral LED Badge to bring this natural phenomenon to your electronics.

To position each of the 64 addressable LEDs within the PCB layout, [HackerBox] computed the polar (r,θ) coordinates in a spreadsheet according to the Vogel model and then converted them to rectangular (x, y) coordinates. A little more math translates the points “off origin” into the center of the PCB space and scale them out to keep the first two 5 mm LEDs from overlapping. Finally, the LED coordinates were pasted into the KiCad PCB design file.

Continue reading “Math You Can Wear: Fibonacci Spiral LED Badge” »

Explore how AI is revolutionizing the music industry, from creating original songs to altering how we discover and listen to music & even posing questions about copyright.

Researchers have created a small device that “sees” and creates memories in a similar way to humans, in a promising step towards one day having applications that can make rapid, complex decisions such as in self-driving cars.

The neuromorphic invention is a single chip enabled by a sensing element, doped indium oxide, that’s thousands of times thinner than a human hair and requires no external parts to operate.

RMIT University engineers in Australia led the work, with contributions from researchers at Deakin University and the University of Melbourne.

Type 2 diabetes is a significant global health concern, affecting millions of individuals worldwide. The disease is associated with numerous complications, as well as an increased risk of premature mortality. Recent research conducted by the University of Sydney has shed light on the potential of physical activity in preventing the onset of type 2 diabetes, even in individuals with a high genetic risk for the disease [1]. This study underscores the importance of exercise as a key strategy for chronic disease prevention and offers promising news for individuals seeking to reduce their risk of developing type 2 diabetes.

Longevity. Technology: The worldwide burden of type 2 diabetes is substantial, and the disease carries significant implications for public health. Type 2 diabetes is associated with various complications, including cardiovascular diseases, kidney problems and nerve damage. Moreover, individuals with type 2 diabetes often experience a shortened lifespan and reduced healthspan due to the increased risk of developing other chronic conditions. The study’s findings add to the clarion call for effective prevention strategies that alleviate this burden on individuals, families and healthcare systems worldwide.

The research, published in the British Journal of Sports Medicine, involved 59,325 adults enrolled in the UK Biobank project. Participants wore accelerometers on their wrists to measure their physical activity levels and the researchers also considered genetic markers associated with a higher risk of type 2 diabetes. The study followed the participants for up to seven years to assess their health outcomes.

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.