Lifeboat Foundation

The World Health Organization (WHO) has endorsed a second malaria vaccine to protect children against the deadly disease, which killed 619,000 people in 2021.

Researchers say that the vaccine, known as R21, is easier to make than the first-approved malaria vaccine, called RTS, S, and will be cheaper per dose.

“There’s going to be enough of it to actually give out to children,” says Jackie Cook, a malaria researcher at the London School of Hygiene and Tropical Medicine.

Jets that develop along the walls of fluid-based thermal-energy-storage systems induce multiple flows that limit the devices’ ability to store energy.

Converting waste heat from renewable-energy technologies into electricity could reduce the need for fossil-fuel power stations—but only if that energy can be stored efficiently, for example, in a thermal battery. Researchers have partially solved this problem by designing batteries with vacuum insulation panels that reduce thermal leakage to the environment. But the useful energy available to the system can diminish even if environmental heat loss is reduced to zero. Now Christian Cierpka of the Technical University of Ilmenau, Germany, and colleagues have explored one such energy drain: mixing of hot and cold regions within a fluid-based energy-storage device [1]. The results could aid in the design of more-efficient thermal-energy-storage systems, potentially making such facilities useful as backups for intermittent renewable-energy sources.

The team studied mixing in a common thermal-energy-storage system in which a hot fluid reservoir sits atop a cold one. Between the reservoirs lies a transition layer with a temperature gradient across its width. The maximum energy output of such a battery depends on the temperature difference between the hot and cold reservoirs. Any drop in this difference will reduce the battery’s recoverable energy.

The more physicists use artificial intelligence and machine learning, the more important it becomes for them to understand why the technology works and when it fails.

The advent of ChatGPT, Bard, and other large language models (LLM) has naturally excited everybody, including the entire physics community. There are many evolving questions for physicists about LLMs in particular and artificial intelligence (AI) in general. What do these stupendous developments in large-data technology mean for physics? How can they be incorporated in physics? What will be the role of machine learning (ML) itself in the process of physics discovery?

Before I explore the implications of those questions, I should point out there is no doubt that AI and ML will become integral parts of physics research and education. Even so, similar to the role of AI in human society, we do not know how this new and rapidly evolving technology will affect physics in the long run, just as our predecessors did not know how transistors or computers would affect physics when the technologies were being developed in the early 1950s. What we do know is that the impact of AI/ML on physics will be profound and ever evolving as the technology develops.

The early failure of thin-walled cones under compression was thought to arise mainly from the presence of imperfections. A new model suggests otherwise.

A crystalline reflective coating being considered for future gravitational-wave detectors exhibits peculiar noise features at cryogenic temperatures.

Imagine you’re in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they’re always looking out for different things. If they’re both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.

Meet the Air-Guardian, a system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). As modern pilots grapple with an onslaught of information from multiple monitors, especially during critical moments, Air-Guardian acts as a proactive co-pilot; a partnership between human and machine, rooted in understanding attention.

But how does it determine attention, exactly? For humans, it uses eye-tracking, and for the neural system, it relies on something called “saliency maps,” which pinpoint where attention is directed. The maps serve as visual guides highlighting key regions within an image, aiding in grasping and deciphering the behavior of intricate algorithms. Air-Guardian identifies early signs of potential risks through these attention markers, instead of only intervening during safety breaches like traditional autopilot systems.

Like fingerprints, a firearm’s discarded shell casings have unique markings. This allows forensic experts to compare casings from a crime scene with those from a suspect’s gun. Finding and reporting a mismatch can help free the innocent, just as a match can incriminate the guilty.

But a new study from Iowa State University researchers reveals mismatches are more likely than matches to be reported as “inconclusive” in cartridge-case comparisons.

“Firearms experts are failing to report evidence that’s favorable to the defense, and it has to be addressed and corrected. This is a terrible injustice to innocent people who are counting on expert examiners to issue a report showing that their gun was not involved but instead are left defenseless by a report that says the result was inconclusive,” says Gary Wells, an internationally recognized pioneer and scholar in eyewitness memory research.

The tool — called the Cryosection Histopathology Assessment and Review Machine, or CHARM — studies images to quickly pick out the genetic profile of a kind of tumor called glioma, a process that currently takes days or weeks.

UCLA pediatric pulmonologist Mindy Ross, MD, MBA, MAS, talks about diagnosis and evaluation of chronic cough in children.