Lifeboat Foundation

In 4 billion years, when the Milky Way galaxy collides with the Andromeda Galaxy, the distance between the stars will be so vast that none of the 1.3 trillion stars are expected to collide.

In roughly 4 billion years, the Andromeda Galaxy and the Milky Way will collide, creating a new supergalaxy. This galactic merger will not result in stars colliding due to the vast distances between them, but the supermassive black holes at the centres of both galaxies will eventually merge. While the solar system might get flung farther from the galactic core, there’s also a chance it could be ejected entirely. Even though life on Earth would have ended by then due to the Sun’s increasing heat, this cosmic event would offer a stunning view of the changing night sky.

After reading the article, Reddit user Harry, with over +6.5k upvotes, commented: “It’s not direct collisions that are the issue. It’s the disruption to the normal gravitational systems and orbital paths. A planet that was in the goldilocks zone for liquid water and life could get affected by another passing star system enough to move it sufficiently out of its normal orbit to have planet changing effects.”

Discovering quark behavior: investigating the critical point in quantum chromodynamics.

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As NASA advances the Artemis II mission, significant strides are being made with the mobile launcher at Kennedy Space Center.

Recently moved back to the Vehicle Assembly Building after undergoing extensive testing and upgrades, the launcher is central to launching the next Moon rocket. Alongside, the crawler-transporter marked a historical milestone, reaching over 2,500 miles since its inception, reinforcing its title as the heaviest self-powered vehicle by Guinness World Records.

Artemis II mission progress at kennedy space center.

America PAC was created to support these key values: Secure Borders, Safe Cities, Sensible spending, Fair Justice System, Free Speech, Right to Self-Protection.

Scientists have discovered that cosmic filaments, the largest known structures in the universe, are rotating. These massive, twisting filaments of dark matter and galaxies stretch across hundreds of millions of light-years and play a crucial role in channeling matter to galaxy clusters. The finding challenges existing theories, as it was previously believed that rotation could not occur on such large scales. The research was confirmed through both computer simulations and real-world data, and it opens up new questions about how these giant structures acquire their spin.

After reading the article, a Reddit user named Kane gained more than 100 upvotes with this comment: “What if galaxy clusters are like neuron and glial clusters in a brain. And dark matter is basically the equivalent of a synapse. It connects galaxies and matter together and is responsible for sending quantum information back and forth like a signal chain.”

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The German scientist argues that information cannot be destroyed and, in principle, it is possible that a higher being, one day, in some way, could reassemble it and bring it back to life.

While machine learning methods can be used for accurate flow prediction in complex environments, such as for urban structures³⁰ or turbulent fields³¹, generalizing these approaches to domains of arbitrary size and complexity remains a challenging problem. One reason is that flows near and around obstacles depend on factors associated with the fluid (i.e., Reynolds number) or domain (i.e., boundary conditions), and fixing either of these conditions puts bounds on the validity of the estimated fields. Thus, if we seek broad applicability, then we should seek the fewest set of model restrictions that together provide the most accurate flow predictions. To this end, our approach has been to deconstruct certain types of domains into individual obstacles that each maintain some level of geometrical similarity, so that a single neural network model can be used to predict flows near all structural boundaries of the domain. Flows between these structural surfaces, at a scale on the order of the obstacle diameter, are predicted using a second neural network model in series with the first. Together, this serial-modeling approach allows for rapid prediction of flows in domains that can be represented by a disjoint set of structural elements. This type of domain is common, for example, in urban and periurban areas, wherein buildings conform to a common structural motif that affects ground-level velocity fields.

Another relevant length scale is the grid size used to digitize individual domains for read-in by the model. Thus, we investigated how flow patterns can be affected when this input resolution is varied. Although our choice of grid size is somewhat arbitrary, it is dense enough to capture variation in the relevant velocity fields near individual obstacles, but not so dense that producing a large enough cohort of CFD-generated training datasets becomes computationally intractable.

Our approach can also be trained to predict flows with a variable inlet velocity, which, in the case of urban wind flow prediction, permits model parameterization in terms of current meteorological conditions. In the specific case of aerial dispersion of chemicals throughout an urban environment, our predicted flows are considered as the advective field of a drift-diffusion model of molecular dispersion. This advection field plays a central role because concentration fluctuations decorrelate in relationship with the velocity fluctuations of the advection field, and spatial heterogeneity in the flow patterns is determined by the sequence of obstacles in the flow path.