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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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相关实验视频

Updated: Jul 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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基于混合量子算法的多技能资源受限制的多模式项目调度问题.

Jun Long Peng1, Xiao Liu2, Chao Peng3

  • 1Changsha University of Science & Technology, Changsha, People's Republic of China.

Scientific reports
|October 29, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于多技能资源的多模式项目调度问题 (MRCMPSP) 和一种新的混合量子粒子群算法 (HQPSO),以尽量减少项目的持续时间. 该HQPSO表现出卓越的性能,用于有效的现实世界的项目调度.

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科学领域:

  • 运营研究 运营研究
  • 管理科学 管理科学
  • 计算机科学 计算机科学

背景情况:

  • 传统的项目调度模型往往通过考虑单个因素而过度简化,而不是反映现实世界的复杂性.
  • 现有的算法可能缺乏效率或适用于多方面的项目环境.

研究的目的:

  • 提出和建模基于多技能资源的多模式项目调度问题 (MRCMPSP).
  • 开发和验证一种新的混合量子粒子群集优化 (HQPSO) 算法,用于解决MRCMPSP.
  • 增强项目调度算法,并提供实际的管理见解.

主要方法:

  • 使用资源能力矩阵和约束来开发MRCMPSP.
  • 选择和增强量子粒子群算法 (QPSO) 成为混合量子算法 (HQPSO) 具有JAYA优化.
  • 设计比较实验 (独立和实用) 以评估各种规模的算法性能.

主要成果:

  • 拟议的HQPSO算法表现出卓越的融合性能和解决方案准确性.
  • 实验结果验证了MRCMPSP的算法的有效性和通用性.
  • 该研究成功地为现实世界的场景提供了有效的调度解决方案.

结论:

  • 开发的MRCMPSP为项目调度提供了更现实的框架.
  • HQPSO算法是解决复杂调度问题的强大而高效的方法.
  • 该研究提供了有价值的管理见解,并推进了项目管理方法.