Lifeboat Foundation

Recently, scientists made groundbreaking detections that allowed them that gravity does not act instantaneously as Newton thought, instead it propagates at the speed of light.

Neil Cornish, a physicist at Montana State University said, “The speed of gravity, like the speed of light, is one of the fundamental constants in the Universe. Until the advent of gravitational wave astronomy, we had no way to directly measure the speed of gravity.”

In the course of recent months, physicists have gained exceptionally fast ground in bouncing the speed of gravity utilizing gravitational wave perceptions. Earlier, the first LIGO detections of gravitational waves constrained the speed of gravity suggests 50% of the speed of light.

A 100% renewable energy future cannot ignore geothermal as part of the mix.

Modelling only looked at solar and wind rather than considering the baseload capability of geothermal as a renewable energy solution.

Classifying celestial objects is a long-standing problem. With sources at near unimaginable distances, sometimes it’s difficult for researchers to distinguish between objects such as stars, galaxies, quasars or supernovae.

Instituto de Astrofísica e Ciências do Espaço’s (IA) researchers Pedro Cunha and Andrew Humphrey tried to solve this classical problem by creating SHEEP, a machine-learning algorithm that determines the nature of astronomical sources. Andrew Humphrey (IA & University of Porto, Portugal) comments: “The problem of classifying celestial objects is very challenging, in terms of the numbers and the complexity of the universe, and artificial intelligence is a very promising tool for this type of task.”

The first author of the article, now published in the journal Astronomy & Astrophysics, Pedro Cunha, a Ph.D. student at IA and in the Dept. of Physics and the University of Porto, says, “This work was born as a side project from my MSc thesis. It combined the lessons learned during that time into a unique project.”

This March, we, a group of educators, scientists, and psychologists started an educational non-profit (501 c3) Earthlings Hub, helping kids in refugee camps and evacuated orphanages. We are getting lots of requests for help, and are in urgent need to raise funds. If you happen to have any connections to educational and humanitarian charities, or if your universities or companies may be interested in providing some financial support to our program, we would really appreciate that! Please share with everyone who might be able to offer help or advice.

Our advisory board includes NASA astronaut Greg Chamitoff, Professor Uri Wilensky, early math educator Maria Droujkova, AI visionary Joscha Bach, and others.

Support Us The Earthlings Hub works with a fiscal sponsor Blue Marble Space. CREDIT CARD & PAYPAL Please contact us if you would like to via other means, such as checks, stocks, cryptocurrency, or using your Donor Advised Fund: [email protected]

“We’ve spent a lot of time in education to help people understand that just like an automobile extends your capabilities in the physical domain, artificial intelligence extends your abilities within the data domain and the information domain,” the general said Wednesday.

AI and its traces can be found across the Pentagon and its many enclaves and alcoves. The department has for years recognized its value as well, describing the tech in a 2018 strategy as rapidly changing businesses, industries and military threats. More can be done, Groen said.

Continue reading “AI less about ‘killer robots,’ more about Pentagon transformation, Groen says” »

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Papers referenced in the video:
Inter-and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults.
https://pubmed.ncbi.nlm.nih.gov/32023264/

Continue reading “Resting Heart Rate And Heart Rate Variability: What’s Optimal, 4-Year Progress” »

Limited mileage range and rare mineral mining have been sticking points for electric vehicle batteries, but Tesla is working on a design to last 100 years.

This week’s image from the Hubble Space Telescope shows two galaxies that are a hotbed of star formation. The pair, known together as Arp 303 or individually as IC 563 on the bottom right and IC 564 on the top left, are located 275 million light-years away. They are in the dim constellation of Sextans, named after the astronomical instrument used to measure the position of stars.

The image below was captured by two Hubble instruments during two separate observations. The two observations were combined to show both visible light data and data from the infrared part of the spectrum.

“The image holds data from two separate Hubble observations of Arp 303,” Hubble scientists write. “The first used Hubble’s Wide Field Camera 3 (WFC3) to study the pair’s clumpy star-forming regions in infrared light. Galaxies like IC 563 and IC 564 are very bright at infrared wavelengths and host many bright star-forming regions.

Just as it’s hard to understand a conversation without knowing its context, it can be difficult for biologists to grasp the significance of gene expression without knowing a cell’s environment. To solve that problem, researchers at Princeton Engineering have developed a method to elucidate a cell’s surroundings so that biologists can make more meaning of gene expression information.

The researchers, led by Professor of Computer Science Ben Raphael, hope the new system will open the door to identifying rare cell types and choosing cancer treatment options with new precision. Raphael is the senior author of a paper describing the method published May 16 in Nature Methods.

The basic technique of linking gene expression with a cell’s environment, called spatial transcriptomics (ST), has been around for several years. Scientists break down tissue samples onto a microscale grid and link each spot on the grid with information about gene expression. The problem is that current computational tools can only analyze spatial patterns of gene expression in two dimensions. Experiments that use multiple slices from a single tissue sample—such as a region of a brain, heart or tumor—are difficult to synthesize into a complete picture of the cell types in the tissue.