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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

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Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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The Representativeness Heuristic02:13

The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Mesh Analysis with Current Sources01:10

Mesh Analysis with Current Sources

1.8K
Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law...
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State Space Representation01:27

State Space Representation

426
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Related Experiment Video

Updated: Dec 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text.

Ronghui You1, Yuxuan Liu1, Hiroshi Mamitsuka2,3

  • 1School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)
|September 25, 2020
PubMed
Summary

BERTMeSH offers efficient and accurate automatic Medical Subject Headings (MeSH) indexing using deep learning. This novel method significantly improves MeSH indexing performance and speed compared to existing approaches.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • The increasing volume of biomedical literature necessitates efficient automatic Medical Subject Headings (MeSH) indexing.
  • Existing methods like FullMeSH face challenges including computational time, limited section analysis, and underutilization of the MEDLINE database.

Purpose of the Study:

  • To develop a computationally efficient, deep-learning-based MeSH indexing method using full text.
  • To enhance MeSH indexing by leveraging Bidirectional Encoder Representations from Transformers (BERT) and a novel transfer learning strategy.

Main Methods:

  • Implemented BERTMeSH, a deep learning model utilizing BERT for capturing text semantics.
  • Employed a transfer learning strategy combining full-text data from PubMed Central (PMC) and title/abstract data from MEDLINE.
  • Pre-trained BERTMeSH on 3 million MEDLINE citations and trained on approximately 1.5 million PMC full texts.

Main Results:

  • BERTMeSH achieved a Micro F-measure of 69.2% on 20,000 PMC test articles, outperforming FullMeSH by 6.3%.
  • The computational efficiency of BERTMeSH was demonstrated, with 20,000 articles indexed in 5 minutes, compared to over 10 hours for FullMeSH.
  • The method proved flexible for different section organizations within full-text articles.

Conclusions:

  • BERTMeSH represents a significant advancement in automatic MeSH indexing, offering superior accuracy and computational efficiency.
  • The proposed method effectively utilizes both full-text and abstract data, overcoming limitations of previous approaches.
  • BERTMeSH is a promising tool for large-scale biomedical literature indexing and knowledge organization.