Lifeboat Foundation

Scientists Can Now Test for Extra Dimensions and Unveil New Realities with the LHC

TL;DR

The Large Hadron Collider (LHC) is pushing the boundaries of physics by enabling scientists to search for the Higgs Boson, explore the mysteries of dark matter, and potentially detect evidence of extra dimensions. Despite wild conspiracy theories claiming the LHC could open portals to parallel dimensions or create black holes, the reality is grounded in groundbreaking scientific exploration. The LHC may even briefly produce microscopic black holes, offering insights into the existence of extra dimensions without any danger to our planet. These discoveries could revolutionise our understanding of the universe.

Avshalom Elitzur, Claudia de Rham and Harry Cliff debate the relationship between mystery and scientific discovery.

Does science eradicate mystery or expand it?

Continue reading “Claudia de Rham” »

Construction workers have finished the excavation of the huge caverns that will house the international Deep Underground Neutrino Experiment. While engineers and technicians are preparing for the installation of the gigantic neutrino detectors into these caverns a mile underground, scientists around the world are working to optimize DUNE’s particle detector technology.

Adding or removing neutrons from an atomic nucleus leads to changes in the size of the nucleus. This in turn causes tiny changes in the energy levels of the atom’s electrons, known as isotope shifts. Scientists can use precision measurements of these energy shifts to measure the radius of the nucleus of an isotope.

In the quantum realm, a particle’s properties can be separate from the particle itself, including its angular momentum – which could require a rethinking of fundamental laws.

By Karmela Padavic-Callaghan

Researchers at the University of Colorado, Boulder; KU Leuven; the Flatiron Institute and the University of Wisconsin–Madison recently set out to answer a long-standing research question, specifically whether charged particles in the turbulent flows commonly surrounding black holes and other compact objects can be accelerated to very high energies.

Scientists have discovered a way to simulate gravitational waves using quantum particles and Bose-Einstein Condensate (BEC).

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.

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.