Lifeboat Foundation

A team of researchers has made strides in understanding the formation of massif-type anorthosites, enigmatic rocks that only formed during the middle part of Earth’s history. These plagioclase-rich igneous rock formations, which can cover areas as large as 42,000 square kilometers and host titanium ore deposits, have puzzled scientists for decades due to conflicting theories about their origins.

Due to its excellent material properties and its adaptability to gallium nitride (GaN), AlYN has enormous potential for use in energy-efficient high-frequency and high-performance electronics for information and communications technology.

Aluminum yttrium nitride (AlYN) has attracted the interest of many research groups around the world due to its outstanding material properties. However, the growth of the material has been a major challenge. Until now, AlYN could only be deposited by magnetron sputtering.

Researchers at the Fraunhofer Institute for Applied Solid State Physics IAF have now succeeded in fabricating the new material using metal-organic chemical vapor deposition (MOCVD) technology, thus enabling the development of new, diverse applications.

J0524-0336 contains 100,000 times more lithium than the sun does at its current age. This amount challenges the prevailing models of how stars evolve.

“These are the oldest rocks that may have been deposited by water, that we’ve ever laid hands or rover arms on,” said Dr. Benjamin Weiss. “That’s exciting, because it means these are the most promising rocks that may have preserved fossils, and signatures of life.”

Did life once exist on Mars, and if so, where will we find it? This is what a recent study published in AGU Advances hopes to address as a team of several dozen international researchers led by the Massachusetts Institute of Technology (MIT) investigated rocks samples obtained by NASA’s Perseverance (Percy) rover obtained in Jezero Crater on Mars, and which allegedly contain minerals only found in water. This study holds the potential to help scientists better understand the conditions for life to have emerged on the Red Planet long ago, along with identifying what evidence could be used to find life elsewhere in the solar system.

For the study, the researchers analyzed data obtained from seven rock samples collected by Percy along Jezero’s western slope, which scientists have hypothesized was an ancient lake long ago. After examining Percy’s images of the surrounding area and the chemical analyses from the rock samples, the team determined that the rocks contain evidence of water, meaning this location likely contained a lake long ago. However, the potential for this lake having life is still unknown since the team did not identify evidence of organic matter within the samples. Despite this, the team determined that the rocks were created more than 3.5 billion years ago, long before life emerged on the Earth.

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A silent symphony is playing inside your brain right now as neurological pathways synchronize in an electromagnetic chorus that’s thought to give rise to consciousness.

Yet how various circuits throughout the brain align their firing is an enduring mystery, one some theorists suggest might have a solution that involves quantum entanglement.

The proposal is a bold one, not least because quantum effects tend to blur into irrelevance on scales larger than atoms and molecules. Several recent findings are forcing researchers to put their doubts on hold and reconsider whether quantum chemistry might be at work inside our minds after all.

A new publication has discovered ways to reduce the toxicity of graphene oxide (GO), an ultra-thin sheet of nanomaterial derived from graphite, laying the groundwork to use it as a drug delivery system.

Professor Khuloud Al-Jamal, who led the study, said: “Researchers have been incredibly excited in the potential medical applications of graphene since experiments into the nanomaterial were recognised with the Nobel Prize in Physics in 2010. However, concerns around toxicity have remained a consistent obstacle.”

Graphene oxide (GO) is an ultra-thin sheet derived from graphite. It is similar to pencil lead but includes attached oxygen atoms, making it compatible with water. Its unique physical and chemical properties mean it has a high capacity for carrying antibiotics and anticancer drugs, among others, as well as targeting specific cells, making it a potentially effective drug delivery system.

Nanomaterials, with their distinctive physical and chemical properties, hold significant promise for revolutionizing the housing construction industry. By enabling the development of stronger, more durable, efficient, and sustainable structures, nanotechnology offers solutions to challenges such as climate change and global urbanization.

The use of nanomaterials in construction began in the mid-1980s with the advent of carbon-based structures. Since then, their application has become more widespread, driving innovations in the sector. Today, advances in nanotechnology are leading to the creation of increasingly sophisticated, selective, and efficient nanomaterials, broadening the scope of construction capabilities.

This study explored the application of various nanomaterials—titanium dioxide, carbon nanotubes (CNTs), nanosilica, nanocellulose, nanoalumina, and nanoclay—in residential construction. These materials were chosen for their potential to enhance the structural integrity, thermal performance, and overall functionality of building materials used in housing.

The Driving Training Based Optimization (DTBO) algorithm, proposed by Mohammad Dehghani, is one of the novel metaheuristic algorithms which appeared in 2022⁸⁰. This algorithm is founded on the principle of learning to drive, which unfolds in three phases: selecting an instructor from the learners, receiving instructions from the instructor on driving techniques, and practicing newly learned techniques from the learner to enhance one’s driving abilities^81,82. In this work, DTBO algorithm is used, due to its effectiveness, which was confirmed by a comparative study⁸³ with other algorithms, including particle swarm optimization⁸⁴, Gravitational Search Algorithm (GSA)⁸⁵, teaching learning-based optimization, Gray Wolf Optimization (GWO)⁸⁶, Whale Optimization Algorithm (WOA)⁸⁷, and Reptile Search Algorithm (RSA)⁸⁸. The comparative study has been done using various kinds of benchmark functions, such as constrained, nonlinear and non-convex functions.

Lyapunov-based Model Predictive Control (LMPC) is a control approach integrating Lyapunov function as constraint in the optimization problem of MPC^89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability^91,92. Since its appearance, the LMPC method has been utilized extensively for controlling a various nonlinear systems, such as robotic systems⁹³, electrical systems⁹⁴, chemical processes⁹⁵, and wind power generation systems⁹⁰. In contrast to the LMPC, both the regular MPC and the NMPC lack explicit stability restrictions and can’t combine stability guarantees with interpretability, even with their increased flexibility.

The proposed method, named Lyapunov-based neural network model predictive control using metaheuristic optimization approach (LNNMPC-MOA), includes Lyapunov-based constraint in the optimization problem of the neural network model predictive control (NNMPC), which is solved by the DTBO algorithm. The suggested controller consists of two parts: the first is responsible for calculating predictions using a neural network model of the feedforward type, and the second is responsible to resolve the constrained nonlinear optimization problem using the DTBO algorithm. This technique is suggested to solve the nonlinear and non-convex optimization problem of the conventional NMPC, ensure on-line optimization in reasonable time thanks to their easy implementation and guaranty the stability using the Lyapunov function-based constraint. The efficiency of the proposed controller regarding to the accuracy, quickness and robustness is assessed by taking into account the speed control of a three-phase induction motor, and its stability is mathematically ensured using the Lyapunov function-based constraint. The acquired results are compared to those of NNMPC based on DTBO algorithm (NNMPC-DTBO), NNMPC using PSO algorithm (NNMPC-PSO), Fuzzy Logic controller optimized by TLBO (FLC-TLBO) and optimized PID controller using PSO algorithm (PID-PSO)⁹⁵.

Jim Clarke, Director of Quantum Hardware at Intel Labs, discusses how chemistry and physics drive the development of qubits in these unique systems. These systems will bring mind-blowing computing power to the world in the next decade and beyond.

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