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Jeff Bezos says he’ll give most of his money to charity, but will that do much to appease critics of his legacy?

A major vulnerability in a networking technology widely used in critical infrastructures such as spacecraft, aircraft, energy generation systems and industrial control systems was exposed by researchers at the University of Michigan and NASA.

It goes after a network protocol and hardware system called time-triggered ethernet, or TTE, which greatly reduces costs in high-risk settings by allowing mission-critical devices (like flight controls and life support systems) and less important devices (like passenger WiFi or data collection) to coexist on the same network hardware. This blend of devices on a single network arose as part of a push by many industries to reduce network costs and boost efficiency.

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It would usually be cheaper to use e-fuels derived from hydrogen — rather than H2 directly — due to system costs, academic paper explains.

From wearable gadgets to battery separators, the future of sustainable tech is starting to look like a mushroom. A team of researchers from the Institute of Experimental Physics in Linz have completed a proof-of-concept study, testing whether mycelium skin could substitute plastic in the production of soft electronics. The scientists used processed skin from the mushroom Ganoderma Lucidum – a saprophytic fungus native to some parts of Europe and China that grows naturally on dead hardwood.

This works by laying electronic components on the fungal skin through a process called physical vapor deposition, used to produce thin materials. The resulting electronic circuit has high thermal stability and can withstand thousands of bending cycles. The researchers say that combining conventional electronics with the biodegradable material could help reduce waste in the production of wearable electronics and sustainable battery separators, among other uses.

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Researchers control the speed of sound in an ultracold gas, mimicking features of a curved spacetime and reproducing quantum field behavior predicted in early Universe models.

Physical dynamical processes can be modelled with differential equations that may be solved with numerical approaches, but this is computationally costly as the processes grow in complexity. In a new approach, dynamical processes are modelled with closed-form continuous-depth artificial neural networks. Improved efficiency in training and inference is demonstrated on various sequence modelling tasks including human action recognition and steering in autonomous driving.

In a recent study, charged atoms, also known as ions, have been found to behave strangely during nuclear fusion reactions, in ways that scientists did not expect.

According to a paper published on November 14 in the journal Nature Physics, researchers at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory discovered that when deuterium and tritium ions, which are isotopes of hydrogen with one and two neutrons, respectively—are heated using lasers during laser-fusion experiments, there are more ions with higher energies than expected when a thermonuclear burn starts.

“The process of inertial confinement fusion (ICF) squeezes a small (1mm radius) capsule filled with a layer of frozen deuterium and tritium (isotopes of hydrogen) surrounding a volume of deuterium and tritium gas down to a radius of about 30 micrometers. In the process, these isotopes of hydrogen ionize and a plasma of electrons, deuterium and tritium nuclei [is the result],” Edward Hartouni, a physicist at NIF and a co-author of the paper, told Newsweek.

Meta introduces ‘Tulip,’ a binary serialization protocol supporting schema evolution. This simultaneously addresses protocol reliability and other issues and assists us with data schematization. Tulip has multiple legacy formats. Hence, it is used in Meta’s data platform and has seen a considerable increase in performance and efficiency. Meta’s data platform is made up of numerous heterogeneous services, such as warehouse data storage and various real-time systems exchanging large amounts of data and communicating among themselves via service APIs. As the number of AI and machine learning ML-related workloads in Meta’s system increase that use data for training these ML models, it is necessary to continually work on making our data logging systems efficient. The schematization of data plays a huge role in creating a platform for data at Meta’s scale. These systems are designed based on the knowledge that every decision and trade-off impacts reliability, data preprocessing efficiency, performance, and the engineer’s developer experience. Changing serialization formats for the data infrastructure is a big bet but offers benefits in the long run that make the platform evolve over time.

The Data Analytics Logging Library is present in the web tier and the internal services, and this is also responsible for logging analytical and operational data using Scribe-a durable message queuing system used by Meta. Data is read and ingested from Scribe, which also includes a data platform ingestion service and real-time processing systems. The data analytics reading library helps deserialize data and rehydrate it into a structured payload. Logging schemas are created, updated, and deleted every month by thousands of engineers at Meta, and these logging schema data flows in petabytes range each and every day over Scribe.

Schematization is necessary to ensure that any message logged in the past, present, or future, depending on the (de) serializer’s version, can be reliably (de)serialized at any time with the utmost fidelity and no data loss. Safe schema evolution via backward and forward compatibility is the name given to this characteristic. The article’s main focus lies on the on-wire serialization format used to encode the data that is finally processed by the data platform. Compared to the two serialization formats previously utilized, Hive Text Delimited and JSON serialization, the new encoding format is more efficient, requiring 40 to 85 percent fewer bytes and 50 to 90 percent fewer CPU cycles to (de)serialize data.

Why nuclear fusion may be the future of energy. Visit https://brilliant.org/undecided to sign up for free. And also, the first 200 people will get 20% off their annual premium membership. Fusion energy is considered by many as the holy grail for supplying all of our clean electricity needs. However, the old joke is that nuclear fusion is always 30 years away, no matter what advances or promises are made. But now there are several privately funded startups that are accelerating nuclear fusion development with the ultimate goal of commercializing electricity production much sooner than you might think possible. There’s a lot of interesting developments and news around these companies to sift through. What makes each of these companies’ fusion promises unique compared to what’s come before? And will they finally break that 30 year curse?

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