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.
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 abilities81,82. 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 MPC89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability91,92. 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.
In a major step for the international Deep Underground Neutrino Experiment (DUNE), scientists have detected the first neutrinos using a DUNE prototype particle detector at the U.S. Department of Energy’s Fermi National Accelerator Laboratory (Fermilab).
In the famous double-slit experiment, an interference pattern consisting of dark and bright bands emerges when a beam of light hits two narrow slits. The same effect has also been seen with particles such as electrons and protons, demonstrating the wave nature of propagating particles in quantum mechanics.
Microplastics are a global problem: they end up in rivers and oceans, they accumulate in living organisms and disrupt entire ecosystems. How tiny particles behave in a current is difficult to describe scientifically, especially in the case of thin fibers, which make up more than half of microplastic contamination in marine life-forms. In turbulent currents, it is almost impossible to predict their movement.
Since the first demonstration of the laser in the 1960s, laser spectroscopy has become an essential tool for studying the detailed structures and dynamics of atoms and molecules. Advances in laser technology have further enhanced its capabilities. There are two main types of laser spectroscopy: frequency comb-based laser spectroscopy and tunable continuous-wave (CW) laser spectroscopy.
In a major step for the international Deep Underground Neutrino Experiment, scientists have detected the first neutrinos using a DUNE prototype particle detector at the US Department of Energy’s Fermi National Accelerator Laboratory.
The prototype of a novel particle detection system for the international Deep Underground Neutrino Experiment successfully recorded its first accelerator neutrinos.
