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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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相关实验视频

Updated: Jan 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

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基于联合学习的跨境物流风险预警系统.

Xinwen Liang1

  • 1Logistics Management, School of Business Administration Group, Shanxi Finance and Economics University, Taiyuan, 030006, Shanxi, China. 17200677598@163.com.

Scientific reports
|November 7, 2025
PubMed
概括

本研究介绍了全球贸易的安全和联合物流风险预警系统 (SafeLogFL). 它使物流合作伙伴之间能够进行安全的私人协作,提高风险预测和合规性.

科学领域:

  • 物流和供应链管理的物流和供应链管理.
  • 信息安全 信息安全
  • 人工智能的人工智能

背景情况:

  • 跨境物流面临越来越多的复杂性和风险.
  • 传统的集中式风险预测系统引发了隐私问题,并阻碍了区域间的合作.
  • 需要一个安全的,分散的全球物流风险管理系统.

研究的目的:

  • 为跨境物流开发一个保护隐私的风险预警系统.
  • 实现物流合作伙伴之间的安全,分散的协作,而无需共享敏感数据.
  • 加强国际贸易中延迟,中断和合规问题的预测.

主要方法:

  • 提出了一个名为安全和联合后勤风险预警系统使用联合学习 (SafeLogFL) 的框架.
  • 利用联合学习用于对本地数据进行去中心化模型培训.
  • 采用联邦平均化 (Fed Avg) 算法来安全地汇总模型更新.

主要成果:

  • 在预测物流风险方面达到91.3%的平均准确率.
  • 证明遵守隐私法规,如一般数据保护条例 (GDPR).
  • 验证了系统在识别潜在延误,中断和合规失败方面的有效性.
关键词:
跨境物流 跨境物流数据隐私 数据隐私联合学习是联合学习.多层感知器 多层感知器风险预测风险预测

相关实验视频

Last Updated: Jan 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

结论:

  • SafeLogFL为全球物流风险管理提供了一个可扩展的,保护隐私的解决方案.
  • 该系统促进多个物流实体之间的安全协作.
  • 分散的方法提高数据隐私,同时保持高预测准确度.