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Related Concept Videos

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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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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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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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.
Synarthrosis
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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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The sympathetic division can influence tissues and organs by releasing norepinephrine at peripheral synapses and distributing epinephrine and norepinephrine through the bloodstream. In times of crisis or stress, sympathetic activation occurs, which is regulated by sympathetic centers in the hypothalamus. As a result, sympathetic activation prepares the body for physical exertion, rapid ATP production, and heightened alertness, allowing individuals to respond effectively to challenging or...
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Temporal Relation Extraction with Joint Semantic and Syntactic Attention.

Panpan Jin1,2,3,4,5, Feng Li1,2,5, Xiaoyu Li1,2

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.

Computational Intelligence and Neuroscience
|May 9, 2022
PubMed
Summary

This study introduces the Joint Semantic and Syntactic Attention (JSSA) model to improve event temporal relationship extraction in natural language understanding. The JSSA model enhances accuracy in complex contexts by integrating semantic and syntactic information.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Determining temporal relationships between events is a complex Natural Language Understanding (NLU) challenge.
  • Existing methods using neural networks or artificial features struggle with extensive or complex event contexts.

Purpose of the Study:

  • To propose a novel model, Joint Semantic and Syntactic Attention (JSSA), for more robust temporal relationship extraction.
  • To enhance the analysis of contextual information by combining semantic and syntactic data.

Main Methods:

  • Developed the JSSA model integrating coarse-grained semantic and fine-grained syntactic information.
  • Utilized neighbor triples from syntactic dependency trees and event triples.
  • Constructed syntactic attention mechanisms to guide context analysis.

Main Results:

  • The JSSA model demonstrated effectiveness on the TB-Dense and MATRES datasets.
  • The integration of semantic and syntactic levels improved the extraction of temporal event relationships.

Conclusions:

  • The proposed JSSA model offers a significant advancement in accurately determining event temporal relationships.
  • Combining semantic and syntactic information is crucial for handling complex contextual nuances in NLU tasks.