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相关概念视频

Intracellular Signaling Cascades01:24

Intracellular Signaling Cascades

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Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...
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Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
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Rab Cascades01:25

Rab Cascades

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Rab GTPases act in a regulated cascade during membrane fusion, helping the lipid bilayers mix. The Rab family of proteins are active when bound to GTP, and inactive when bound to GDP. Hence, they act as guanine nucleotide-dependent molecular switches. Rab-GTP recognizes and binds to long or short-range tethering proteins to capture the target vesicle. These tethers coordinate with SNAREs on the vesicle and the target membrane to assemble the trans SNARE complex that locks the mixing bilayers.
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Classifying Matter by State02:49

Classifying Matter by State

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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Cascaded Op Amps01:16

Cascaded Op Amps

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Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
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MAPK Signaling Cascades01:07

MAPK Signaling Cascades

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Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
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用图像分割级联分类器进行自主导航绘图的水花检测.

D Saranyaraj1, N Govardanan2, Sri Nirmal Kumar3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India. saranyaraj.d@vit.ac.in.

Scientific reports
|January 21, 2026
PubMed
概括

一个自主船系统有效地使用AI检测和去除侵入性水. 这种低成本,开放式设计的解决方案为小型水体提供可扩展,环保的水生杂草管理.

关键词:
自主导航自主导航自主导航自主导航自主导航自主导航图像细分 图像细分 图像细分模式识别 模式识别 模式识别联合国网络 联合国网络 联合国网络在VGG19分类器.水花水花水花

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科学领域:

  • 机器人和自动化 机器人和自动化
  • 环境科学 环境科学
  • 人工智能的人工智能

背景情况:

  • 水 (Eichhornia crassipes) 是一种高度入侵的水生杂草,在全球范围内造成了重大的生态和经济损害.
  • 现有的清除方法往往是昂贵的,劳动密集型的或破坏环境的.
  • 需要有效,有针对性和可持续的解决方案来管理中小水体的水生植被.

研究的目的:

  • 开发和评估一个低成本的,自主式的船系统,用于检测和机械去除水.
  • 实施强大的AI驱动感知管道,以准确识别和定位植被.
  • 为了证明系统在现实环境中的有效性,用于实际的水生杂草管理.

主要方法:

  • 使用3D打印的PETG建造了一艘75厘米双体船体,配备了双个无刷直流电机和输送带收集器.
  • 使用UNet进行图像细分和VGG19进行分类的感知管道部署在NVIDIA Jetson Nano上进行实时检测.
  • 通过将检测结果映射到图像象限,并向中心目标发出方向盘命令来实现自主导航.

主要成果:

  • 在UNet模型中,水生植被细分的平均Dice系数为0.906,平均IOU为0.831.
  • VGG19分类器准确地识别了水,准确率为96%,精度为0.97,回忆率为0.95,F1分数为0.96.
  • 印度奈的实地试验证明了稳定的自主操作,有效的导航和在各种条件下成功收集高达25公斤的生物质.

结论:

  • 开发的自主船系统提供了一个有效的,低成本的解决方案,用于有针对性的清除水.
  • 由人工智能驱动的感知系统确保准确检测和歧视入侵杂草.
  • 这种可扩展,开放式设计的技术支持可持续的水生生态系统管理,并与联合国可持续发展目标6和9保持一致.