Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Distributed Loads01:19

Distributed Loads

528
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
528
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

639
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...
639
Local Anesthetics: Differential Sensitivity of Nerve Fibers01:24

Local Anesthetics: Differential Sensitivity of Nerve Fibers

807
Local anesthetics (LAs) block the sodium channels of nerve trunks, sensory nerve endings, and neuromuscular junctions. Although LAs can block all kinds of nerves, the sensitivity of nerve fibers differs according to nerve types and structures. LAs are known to block myelinated fibers faster than unmyelinated ones. Also, they block pain or sensory neurons at low concentrations without affecting the motor neurons involved in muscle contractions. This helps relieve labor pain without affecting the...
807
Storage01:23

Storage

83
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
83
Lagging Strand Synthesis01:59

Lagging Strand Synthesis

50.9K
During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...
50.9K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

613
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?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
613

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

DeepCMS: A Feature Selection-Driven Model for Cancer Molecular Subtyping with a Case Study on Testicular Germ Cell Tumors.

Diagnostics (Basel, Switzerland)·2025
Same author

Characterization of CO<sub>2</sub> Adsorption Behavior in Pyrolyzed Shales for Enhanced Sequestration Applications.

Molecules (Basel, Switzerland)·2025
Same author

Enhancing security in financial transactions: a novel blockchain-based federated learning framework for detecting counterfeit data in fintech.

PeerJ. Computer science·2024
Same author

Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches.

Sensors (Basel, Switzerland)·2024
Same author

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

Sensors (Basel, Switzerland)·2024
Same author

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 21, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

531

在无服务器计算中,异质节点之间的延迟敏感函数配置.

Urooba Shahid1,2, Ghufran Ahmed1, Shahbaz Siddiqui1

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

功能即服务 (FaaS) 提供了可适应的智能城市解决方案. 本研究介绍了一种自适应机器学习模型,以优化 FaaS 的配置,提高资源利用率和最后期限的遵守.

关键词:
这是一个云云云.它们是分布式的.边缘的边缘 边缘的边缘这是一个层次结构.机器学习是机器学习.多层次的多层次的服务器少,服务器少.

更多相关视频

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

相关实验视频

Last Updated: Jun 21, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

531
Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 智慧城市技术 智慧城市技术

背景情况:

  • 功能即服务 (FaaS) 提供无服务器计算,简化了开发人员的基础设施管理.
  • 与物联网 (IoT) 集成的FaaS可以实现事件驱动的行动和实时计算,这对智能城市至关重要.
  • 在FaaS中优化功能配置对于满足性能要求至关重要,例如截止日期和高效的资源使用.

研究的目的:

  • 开发和评估基于概率的自适应机器学习模型,以实现最佳的 FaaS 功能配置.
  • 为了提高资源利用率,并确保在智慧城市背景下在 FaaS 部署中遵守截止日期.
  • 解决分布式 FaaS 环境中网络延迟和计算需求的挑战.

主要方法:

  • 采用了自适应机器学习模型,使用XGBoost回归器来估计执行时间,并使用决策树回归器来预测网络延迟.
  • 将网络延迟,到达计算和资源重视等因素纳入机器学习模型,用于放置决策.
  • 利用Docker容器进行复制,专注于无服务器节点类型,函数位置,截止日期和边缘云拓.

主要成果:

  • 拟议的机器学习模型有效地帮助选择FaaS函数的最佳配置.
  • 有效的资源利用被证明与加强的截止日期遵守直接相关.
  • 该研究证实了FaaS在智能城市基础设施中的好处,通过优化放置策略.

结论:

  • 适应性机器学习模型为优化智能城市中的 FaaS 功能配置提供了有效的解决方案.
  • 通过智能 FaaS 部署策略,可以实现高效的资源管理和遵守严格的截止日期.
  • 这项研究有助于无服务器计算的进步,用于可扩展和响应的智能城市应用程序.