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相关概念视频

Motor Units00:46

Motor Units

53.8K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
53.8K
Power Expended by a Constant Force00:57

Power Expended by a Constant Force

7.8K
The relationship between work done and the time taken to do it can be explained using the concept of power. For example, several sprinters in a race may have the same velocity when they reach the finish line, therefore doing the same amount of work, but the winner does it in the least amount of time. Thus, power is defined as the rate of doing work. Since work can vary as a function of time, the average power is defined as the work done during a time interval, divided by the time interval.
7.8K
Two-Dimensional Force System01:20

Two-Dimensional Force System

1.9K
A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:
1.9K
Three-Dimensional Force System01:30

Three-Dimensional Force System

3.2K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
3.2K
Motor Units01:13

Motor Units

14.2K
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
14.2K
Motor Unit Stimulation01:20

Motor Unit Stimulation

4.7K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
4.7K

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相关实验视频

Updated: May 2, 2026

Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators
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Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators

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由图形处理单元驱动的高性能半经验激发状态分子动力学.

Vishikh Athavale1, Maksim Kulichenko1, Sebastian Fernandez-Alberti2

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

The journal of physical chemistry letters
|February 19, 2026
PubMed
概括
此摘要是机器生成的。

这项研究介绍了PYSEQM,这是一个用于激发状态分子动力学 (ESMD) 模拟的GPU加速引擎. 它使用机器学习集成实现了分子系统和光谱计算的高效,长时间模拟.

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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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科学领域:

  • 量子化学 是一个量子化学.
  • 计算化学计算化学
  • 频谱学是一种光谱学.

背景情况:

  • 激发状态分子动力学 (ESMD) 对于理解光物理过程至关重要.
  • 模拟长轨迹和激发状态的大集合是计算要求很高的.
  • 现有的方法经常在复杂系统的效率和可扩展性方面扎.

研究的目的:

  • 介绍PYSEQM,这是一个GPU加速引擎,用于高效的激发状态分子动力学.
  • 实施和验证一个扩展的拉格朗日激发状态的波恩-奥本海默分子动力学 (XL-ESMD) 方案.
  • 展示平台计算光谱属性的能力及其机器学习集成的潜力.

主要方法:

  • 在PYSEQM引擎中使用PyTorch.ch开发了一个激发状态的Born-Oppenheimer分子动力学 (BOMD) 模块.
  • 实施了一个扩展的拉格朗激发状态BOMD (XL-ESMD) 方案,以提高效率和趋同.
  • 利用GPU加速和批量执行用于高吞吐量模拟.
  • 在地面和激发状态上传播轨迹,以计算吸收,发射和红外光谱.

主要成果:

  • 在单个GPU上实现了长轨迹和大型统计合集的高效模拟.
  • 从小分子到900个原子的树枝状分子的平滑缩放.
  • XL-ESMD方案以显著降低的计算成本提供了准确的光谱.
  • PYSEQM的PyTorch基础可以实现自动区分,GPU分批和ML模型集成.

结论:

  • PYSEQM为激发状态分子动力学模拟提供了一个实用和高效的平台.
  • 对于成本效益高的激发状态BOMD,XL-ESMD方案是有效的.
  • 该平台促进机器学习增强动态和未来数据驱动的非adiabatic建模.