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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Related Experiment Video

Updated: Aug 1, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Research proposal content extraction using natural language processing and semi-supervised clustering: A

Benjamin M Knisely1, Holly H Pavliscsak1

  • 1Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, Fort Detrick, MD 21702 USA.

Scientometrics
|April 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a semi-supervised clustering method to automatically classify research proposals by theme. The approach enhances thematic analysis for funding institutions, improving understanding of research landscapes.

Keywords:
Cluster validationDocument clusteringMachine learningResearch portfolioText mining

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Area of Science:

  • Information Science
  • Computational Linguistics
  • Data Science

Background:

  • Funding institutions receive numerous research proposals for evaluation.
  • Analyzing these proposals manually is time-consuming and resource-intensive.
  • Understanding research supply and thematic distribution is crucial for strategic funding.

Purpose of the Study:

  • To develop and evaluate an end-to-end methodology for semi-supervised document clustering.
  • To automate the classification of research proposals into thematic areas.
  • To assess the effectiveness of different clustering techniques and vectorization methods.

Main Methods:

  • A three-stage methodology: manual annotation, semi-supervised clustering, and expert evaluation.
  • Utilized Bidirectional Encoder Representations from Transformers (BERT) for document vectorization.
  • Compared unsupervised versus semi-supervised clustering and various selection strategies.

Main Results:

  • Semi-supervised clustering achieved approximately 25% higher coherence ratings than unsupervised clustering.
  • Pretrained BERT embeddings outperformed older text embedding techniques.
  • A balanced cluster selection strategy optimized results.

Conclusions:

  • The proposed semi-supervised clustering framework effectively categorizes research proposals.
  • This methodology offers a promising tool for institutions to analyze administrative documents.
  • It facilitates unlocking insights from untapped research archives.