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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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相关实验视频

Updated: Sep 13, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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引用网络中的知识转移,知识缺口和知识孤岛.

Eoghan Cunningham1,2, Derek Greene1,2

  • 1School of Computer Science, University College Dublin, Dublin, Ireland.

PloS one
|August 1, 2025
PubMed
概括

追踪科学知识的流动是具有挑战性的. 本研究介绍了一个网络分析框架,用于绘制跨学科整合的地图,并识别诸如可解释AI (XAI) 等领域的知识差距,帮助创新.

科学领域:

  • 图书计量和网络科学应用于科学知识的演变.

背景情况:

  • 科学的进步需要跨学科的知识交流,但在不断发展的领域中追踪这一点是困难的.
  • 现有的方法难以捕捉知识流动的动态和多学科研究领域的整合.

研究的目的:

  • 开发和演示一种新的网络分析框架,用于研究知识转移动态.
  • 通过引用数据识别研究社区,他们的生命周期和知识转移模式.
  • 在可解释的人工智能 (XAI) 的新兴领域中分析跨学科的整合.

主要方法:

  • 在累积的,随时间变化的引用网络上使用动态社区检测.
  • 确定研究领域作为与共享知识来源和成果的论文组.
  • 将框架应用于可解释的人工智能 (XAI) 研究的案例研究.

主要成果:

  • 在基础主题 (机器学习,统计学,心理学) 和当代XAI研究之间观察到有限的知识转移.
  • 在XAI的某些应用领域内识别孤立的"知识孤岛".
  • 在相关的XAI研究领域之间发现了显著的"知识差距",表明了交叉授粉的潜力.

结论:

  • 拟议的框架有效地绘制了跨学科整合的地图,并确定了不断发展的科学领域的知识差距.

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  • 调查结果突出了在XAI和其他多学科领域改善知识综合和创新的机会.
  • 该框架通过从引用数据分析知识生态系统,为文献审查,研究规划和科学政策提供了有价值的见解.