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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Viruses are extraordinarily diverse in shape and size, but they all have several structural features in common. All viruses have a core that contains a DNA- or RNA-based genome. The core is surrounded by a protective coat of proteins called the capsid. The capsid is composed of subunits called capsomeres. The capsid and genome-containing core are together known as the nucleocapsid.
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
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Functional Classification of Joints01:09

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Related Experiment Video

Updated: Dec 29, 2025

Decoding Natural Behavior from Neuroethological Embedding
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SAO2Vec: Development of an algorithm for embedding the subject-action-object (SAO) structure using Doc2Vec.

Sunhye Kim1, Inchae Park1,2, Byungun Yoon1

  • 1Department of Industrial & Systems Engineering, College of Engineering, Dongguk University, Seoul, SOUTH KOREA.

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|February 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces SAO2Vec, a novel method for analyzing technical documents by embedding subject-action-object (SAO) structures into vectors. SAO2Vec enhances text mining by capturing both meaning and context, improving accuracy in natural-language processing tasks.

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Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Computational Linguistics

Background:

  • Subject-action-object (SAO) structures aid in converting unstructured text to structured data for technical analysis.
  • Current SAO analysis lacks contextual representation, necessitating extensive manual processing.
  • Existing methods struggle to capture the nuanced relationships within technical documents.

Purpose of the Study:

  • To introduce SAO2Vec, a novel vector embedding technique for analyzing technical documents.
  • To improve text mining and analysis of technical documents by incorporating sentence and document context.
  • To enhance the accuracy of grouping and similarity analysis in technical text data.

Main Methods:

  • Extraction of SAO structures from technical documents.
  • Utilizing the Doc2Vec algorithm to generate initial sentence vectors.
  • Updating sentence vectors with SAO word vectors to create SAO vectors for sentences and documents.

Main Results:

  • SAO2Vec demonstrated a 3.1% improvement in accuracy over the Doc2Vec method.
  • SAO2Vec showed a 115.0% improvement in accuracy compared to SAO frequency alone.
  • Experimental results in the Internet of Things field validate the effectiveness of SAO2Vec.

Conclusions:

  • SAO2Vec effectively integrates semantic meaning and contextual information for technical document analysis.
  • The proposed algorithm significantly enhances grouping and similarity analysis capabilities.
  • SAO2Vec offers a more efficient and accurate approach to text mining in specialized domains.