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相关概念视频

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The setting time of cement refers to the process of cement paste transitioning from a plastic state to a solid state. This process is crucial in construction as it dictates the timeframe for concrete placement, compaction, and finishing. The onset of this solidification is termed the initial set, indicating when the paste becomes unworkable. The final set is when the paste has solidified completely, and further handling or manipulation can no longer affect its shape. The cement strength is...
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Pulse assessment sites are crucial in evaluating a patient's cardiovascular health. By assessing the pulsations of arteries at specific anatomical locations, healthcare professionals can gather valuable information about blood flow, heart rate, and peripheral circulation. Understanding these pulse assessment sites is essential for conducting comprehensive cardiovascular evaluations and monitoring patients' overall health. These sites are strategically chosen due to the accessibility and...
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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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When a rigid body is hanging freely from a fixed pivot point and is displaced, it oscillates similar to a simple pendulum and is known as a physical pendulum. The period and angular frequency of a physical pendulum are obtained by using the small-angle approximation and drawing parallels with a spring-mass system. The small-angle approximation (sinθ=θ) is valid up to about 14°.
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相关实验视频

Updated: May 24, 2025

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
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基于鱼优化算法的混合深度学习模型的PM2.5度预测在中国北京.

Qing Wei1, Huijin Zhang1, Ju Yang2

  • 1College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, 200092, China.

Environmental pollution (Barking, Essex : 1987)
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概括

一个新的混合模型准确地预测每日细颗粒物 (PM2.5) 度,改善空气质量预测和公共卫生警告. 这种先进的方法为有效的污染控制提供了可靠的短期和中期预测.

关键词:
卷积神经网络是一种卷积神经网络.长期短期记忆 长期短期记忆在PM(2.5) 预测预测.沙普利的添加剂解释解释鱼优化算法 鱼优化算法

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科学领域:

  • 环境科学 环境科学
  • 大气化学 大气化学
  • 数据科学数据科学数据科学

背景情况:

  • 颗粒物 (PM2.5) 是全球主要的空气污染物,影响可见性,气候和公共健康.
  • 准确的PM2.5度预测对于风险评估和预警系统至关重要.

研究的目的:

  • 开发和评估一种新的混合机器学习模型,用于预测每日PM2.5度.
  • 提高短期和中期PM2.5预测的准确性和可靠性.

主要方法:

  • 开发了一个混合模型,结合了鱼优化算法 (WOA),卷积神经网络 (CNN),长短期记忆 (LSTM) 和注意力机制 (AM).
  • 该模型是使用2014年至2018年的每日气象和空气污染数据进行训练和测试的.
  • 使用SHAP分析确定PM2.5度的关键预测因素.

主要成果:

  • 与独立的CNN和LSTM模型相比,WOA-CNN-LSTM-AM模型显著减少了预测错误.
  • 取得的MAE为14.29,RMSE为21.96,MBE为-0.23,R2为0.93. 这两种情况均为1.
  • 与WOA-CNN-LSTM相比,中期预测的准确性高出30%-54%,与CNN-LSTM-AM相比,准确性高出26%-39%.

结论:

  • 拟议的混合模型为日常PM2.5预测提供了可靠和准确的工具.
  • NO2和CO被确定为影响PM2.5度的主要驱动因素.
  • 该模型通过改进的预测支持有效的空气污染控制策略.