Higgs predicted the existence of the Higgs boson particle, helping explain how matter formed after the Big Bang. His death at 94 was announced by the University of Edinburgh, where he was a professor.

Particle Swarm Optimization Based Support Vector Machine (P-SVM) for the Segmentation and Classification of Plants Abstract: With the rapid growth in urbanization and population, it has become an earnest task to nurture and grow plants that are both important in sustaining the nature and the living beings needs. In addition, there is a …

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European particle-physics lab will pursue a 100-kilometre machine to uncover the Higgs boson's secrets — but it doesn't yet have the funds.

Particle accelerators are complex systems composed of multiple subsystems that must work together to produce high quality beams employed for physics experiments. A fault or an anomalous behaviour in one of such subsystems can lead to expensive downtime for the whole facility. Thus, it is of paramount importance to be able to promptly detect …

Research highlights The novel model which combines particle swarm optimization and support vector machine (PSO–SVM) is presented to forecast traffic safety. The decision-making attributes of traffic safety forecasting include the number of incidents, death toll and number of injury. Traffic safety forecasting by PSO–SVM is …

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The Large Hadron Collider (LHC) is the world's largest and most powerful particle accelerator. It consists of a 27-kilometre ring of superconducting magnets with a number …

Particle was founded by Sara Beykpour, a former Senior Director of Product Management at Twitter, and Marcel Molina, a former senior engineer at both Twitter and Tesla. Beykpour expressed the vision behind Particle, emphasizing the need to use AI to transform the way people interact with news, offering quick, bulleted …

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Accelerators use electromagnetic fields to accelerate and steer particles. Radiofrequency cavities boost the particle beams, while magnets focus the beams and bend their trajectory. In a circular accelerator, the particles repeat the same circuit for as long as necessary, getting an energy boost at each turn.

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In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems.

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Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, …

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A Living Review of Machine Learning for Particle Physics¶ Modern machine learning techniques, including deep learning, is rapidly being applied, adapted, and developed for high energy physics. The goal of this document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental ...

Comparison of accuracy parameters representing particle size by machine vision indicated the minor axis of equivalent ellipse and breadth of the best-fit rectangle were the most suitable for representing particles size. Many methods based on machine vision were used to estimate coarse particles size distribution in recent years, but comparison of …

Lap Former Machine: To produce better quality of cotton yarn means combed yarn, we required two more extra machines in our spinning section comparing to conventional carded yarn production process. Lap former are to produce regular laps, after that comber machine to transfer those laps into uniform slivers. The Lap former …

The Higgs boson is a fundamental particle discovered on July 4, 2012, by researchers at the Large Hadron Collider (LHC) located at CERN, Switzerland.

A new physics-informed Galerkin neural network is presented that allows for learning the particle stresses when direct measurements are not possible and it is shown that when a training set of stress measurements is available, the MOR-physics operator learning method can also capture the particles stresses accurately. Numerical simulations are used to …

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Particle-based systems provide a flexible and unified way to simulate physics systems with complex dynamics. Most existing data-driven simulators for particle-based systems adopt graph neural networks (GNNs) as their network backbones, as particles and their interactions can be naturally represented by graph nodes and graph edges. …

Particleboard, also called chipboard (short: PB, sometimes also MDP), is one of the oldest wood-based materials and perhaps the best known. It consists of glued flakes of …

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From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were ...

Particleboard offers you maximum flexibility when purchasing wood. From logs to sawdust, almost any type of wood can be used, effectively reducing your raw material costs. …