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相关概念视频

End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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基于时空空间多图形卷积网络的省级一天级恐怖主义风险预测.

Lanjun Luo1, Boxiao Li2, Chao Qi2

  • 1School of Management, North Sichuan Medical College, Nanchong, China.

Risk analysis : an official publication of the Society for Risk Analysis
|October 23, 2023
PubMed
概括
此摘要是机器生成的。

由于恐怖主义的传染性,预测恐怖主义风险具有挑战性. 本研究引入了一个扩展的时空图卷积网络 (STGCN),通过分析省际恐怖主义动态来预测日常风险.

关键词:
深度学习是一种深度学习.图表 卷积网络 卷积网络多个图表表示的多个图表表示.恐怖主义风险预测和预测

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科学领域:

  • 计算社会科学 计算社会科学
  • 网络科学 网络科学
  • 人工智能的人工智能

背景情况:

  • 预测恐怖主义风险对于有效的反恐战略至关重要.
  • 恐怖主义风险表现出复杂的时空传染性特征,受到省际攻击和内部/外部因素的影响.
  • 现有的模型很难捕捉到恐怖主义传播中固有的多维,非欧几里德关系.

研究的目的:

  • 提出一种基于时空图卷积网络 (STGCN) 的新型扩展方法,用于预测日常恐怖主义风险.
  • 为了建模恐怖主义风险在各省的复杂传染性扩散.
  • 通过结合多维时空相关性来提高恐怖主义风险预测的准确性.

主要方法:

  • 开发了一个扩展的时空图卷积网络 (STGCN),包含了长期短期记忆和自我注意层,用于时间动态.
  • 构建了三个图形结构 (距离,根源相似性,自我激发) 来表示省际传染过程.
  • 利用一维卷积神经网络内核和光谱图卷积模块,分别捕捉时间和空间特征.

主要成果:

  • 与其他机器学习模型相比,提议的扩展STGCN方法在预测恐怖主义风险方面表现出更高的有效性.
  • 对阿富汗恐怖袭击数据 (2005-2020年) 的实验结果验证了该模型的预测能力.
  • 该研究强调了为准确预测风险而捕捉各省之间全面的时空相关性的至关重要.

结论:

  • 扩展的STGCN为理解和预测恐怖主义风险扩散提供了一个强大的工具.
  • 调查结果为反恐管理提供了宝贵的见解,强调长期根源原因缓解和短期局势预防.
  • 准确的恐怖主义风险预测需要采用整体方法,考虑相互关联的时空因素.