Lifeboat Foundation

Tesla has made significant strides in improving its supply chain sustainability and human rights practices, as per a new study by environmental group Lead the Charge. This was highlighted by the fact that the electric vehicle giant jumped from ninth to third place in the group’s annual Auto Supply Chain Leaderboard and Report.

Lead the Charge’s Auto Supply Chain Leaderboard and Report analyzes publicly available data from 18 of the industry’s leading automotive manufacturers. The study also provides rankings of automakers’ efforts to eliminate emissions, environmental harms, and human rights violations from their supply chains. In a press release, Lead the Charge noted that its study comes at a crucial time as industry experts are calling for automakers to foster a cleaner supply chain.

Tesla is among the automakers that stood out in Lead the Charge’s study. As per the environmental group, Tesla achieved the largest score increase among its peers in the study, with the company jumping from ninth to third place. Tesla was also the only company to make improvements across all eight of the study’s indicator categories.

Apple struggled to build its high tech car, burning billions of dollars while constantly changing its project’s direction.

Imagine what it must have been like, as it was for so long throughout human history and prehistory, to look up at the wonders of the night sky in ignorance: not knowing what you were seeing or where any of it came from. All you could behold with your eyes were those glittering points of light in the sky: the Moon, the planets, the stars, a few deep-sky objects (or nebulae), and the tapestry of the Milky Way, with no way of knowing what they were made of, where they came from, or what any of it meant.

Today, the story is very different. Nearly all of the night sky objects we can see with our naked eye are objects present within the Milky Way galaxy. A few of those deep-sky objects turn out to be galaxies, with trillions of more galaxies — including small, faint, and ultra-distant ones — observable with superior tools. These galaxies all expand away from one another, with more distant objects expanding at greater speeds than nearer ones.

The expanding Universe swiftly led to the idea of the Big Bang, which was then confirmed and validated. The Big Bang was then modified to include an even earlier stage known as cosmic inflation, which preceded and set up the Big Bang’s initial conditions. That’s the current status of our understanding of the beginning as of today, in early 2024. Here are the biggest questions, both answered and unanswered, that we still have about the earliest phases of our Universe.

String theory in inflation.

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The purpose of this work is to investigate how several inflationary and bouncing scenarios can be realized by imperfect fluids. We shall use two different theoretical frameworks, namely classical cosmology and Loop Quantum Cosmology (LQC) (see where the derivation of the Hamiltonian in LQC was firstly derived to yield the modified Friedman equation, and also see for a recent derivation of the effective Hamiltonian in LQC, which was derived by demanding repulsive gravity, as in Loop Quantum Gravity). In both cases we shall investigate which imperfect fluid can realize various inflationary and bouncing cosmology scenarios. The inflationary cosmology and bouncing cosmology are two alternative scenarios for our Universe evolution. In the case of inflation, the Universe starts from an initial singularity and accelerates at early times, while in the case of the bouncing cosmology, the Universe initially contracts until it reaches a minimum radius, and then it expands again. With regards to inflation, we shall be interested in four different inflationary scenarios, namely the intermediate inflation, the Starobinsky inflation, and two constant-roll inflation scenarios. With regards to bouncing cosmologies, we shall be interested in realizing several well studied bouncing cosmologies, and particularly the matter bounce scenario, the superbounce scenario and the singular bounce.

As we already mentioned we shall use two theoretical frameworks, that of classical cosmology and that of LQC. After presenting the reconstruction methods for realizing the various cosmologies with imperfect fluids, we proceed to the realization of the cosmologies by using the reconstruction methods. In the case of classical cosmology, we will calculate the power spectrum of primordial curvature perturbations, the scalar-to-tensor ratio and the running of the spectral index for all the aforementioned cosmologies, and we compare the results to the recent Planck data. The main outcome of our work is that, although the cosmological scenarios we study in this paper are viable in other modified gravity frameworks, these are not necessarily viable in all the alternative modified gravity descriptions. As we will demonstrate, in some cases the resulting imperfect fluid cosmologies are not compatible at all with the observational data, and in some other cases, there is partial compatibility.

We need to note that the perturbation aspects in LQC are not transparent enough and assume that there are no non-trivial quantum gravitational modifications arising due to presence of inhomogeneities. As it was shown in, a consistent Hamiltonian framework does not allow this assumption to be true. The perturbations issues that may arise in the context of the present work, are possibly more related to some early works in LQC, so any calculation of the primordial power spectrum should be addressed as we commented above.

The main researchers working on LK99-like room temperature and room pressure superconductors are in China and South Korea. There have been reports that the China researchers have successfully reproduced the weak magnetic effects indicating Meissner effect. The original South Korean team will present video evidence at the American Physical Society conference on Monday, March 4. There are new peer reviewed research paper, new preprint papers and patents being worked upon but it is unclear when those will be released.

Proof of a full and strong Meissner effect would be definitive evidence for superconductivity. The Meissner effect is a fundamental property of superconductors that distinguishes them from ordinary conductors. It refers to the complete expulsion of magnetic fields from the interior of a superconducting material when it transitions from its normal state to the superconducting state. The expulsion of magnetic fields is the result of a remarkable phenomenon known as perfect diamagnetism.

In a superconducting state, the material exhibits zero electrical resistance, allowing the unimpeded flow of electric current. When a magnetic field is applied to a superconductor, it generates circulating currents on the surface of the material. These currents create an opposing magnetic field that exactly cancels out the applied field, resulting in the expulsion of the magnetic field from the interior of the superconductor.

Japan’s ambitious moonshot to develop fault-tolerant computers by 2050 has a clear goal, but it remains uncertain which technology will win out.

A new advance by Stanford engineers could lead to particle accelerators being widely available in science, medicine, and industry.

Stanford researchers are getting closer to building a tiny electron accelerator based on “accelerator-on-a-chip” technology with broad potential applications in studying physics as well as medical and industrial uses.

The researchers have demonstrated that a silicon dielectric laser accelerator, or DLA, can now both speed up and confine electrons, creating a focused beam of high-energy electrons. “If the electrons were microscopic cars, it’s as if, for the first time, we’re steering and we have our foot on the gas,” said Payton Broaddus, PhD ’23 in electrical engineering and the lead author on a paper published on February 23 detailing the breakthrough in Physical Review Letters.

Researchers at EPFL have developed a new, uniquely modular machine learning model for flexible decision-making. It is able to input any mode of text, video, image, sound, and time-series and then output any number, or combination, of predictions.

We’ve all heard of large language models, or LLMs—massive scale deep learning models trained on huge amounts of text that form the basis for chatbots like OpenAI’s ChatGPT. Next-generation multimodal models (MMs) can learn from inputs beyond text, including video, images, and sound.

Creating MM models at a smaller scale poses significant challenges, including the problem of being robust to non-random missing information. This is information that a model doesn’t have, often due to some biased availability in resources. It is thus critical to ensure the model does not learn the patterns of biased missingness in making its predictions.