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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Design Example: Traverse Angle Computations01:25

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Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
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Area Computation by the Alternative Coordinate Method01:24

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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The Role of Ion Channels in Neuronal Computation01:19

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
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Molecular Models02:00

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Related Experiment Video

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Multiscale modelling, simulation and computing: from the desktop to the exascale.

Alfons G Hoekstra1,2, Simon Portegies Zwart3, Peter V Coveney4

  • 11 Computational Science Laboratory , Institute for Informatics , Faculty of Science , University of Amsterdam , Amsterdam , The Netherlands.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 11, 2019
PubMed
Summary
This summary is machine-generated.

This theme issue explores multiscale modeling and computing, focusing on high-performance computing and exascale challenges. It covers applications across diverse scientific fields, offering a state-of-the-art overview.

Keywords:
high performance computingmodelling and simulationmultiscale

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Area of Science:

  • Computational Science
  • Scientific Computing
  • Multiscale Modeling

Background:

  • The increasing complexity of scientific problems necessitates advanced computational approaches.
  • The advent of exascale computing presents new opportunities and challenges for simulation and modeling.
  • Existing multiscale computing environments require critical assessment and development.

Purpose of the Study:

  • To introduce a theme issue on multiscale modeling, simulation, and computing.
  • To present cutting-edge research in generic multiscale modeling and computing.
  • To explore the application of these concepts on high-performance computing systems, including exascale.

Main Methods:

  • A collection of articles presenting research findings.
  • A position paper discussing multiscale computing in the exascale era.
  • A review and critical assessment of current multiscale computing environments.

Main Results:

  • The theme issue provides a state-of-the-art account of generic multiscale computing.
  • Diverse applications of multiscale computing are showcased, from astrophysics to biomedical sciences.
  • The potential and challenges of exascale computing for multiscale simulations are discussed.

Conclusions:

  • Multiscale modeling and computing are crucial for tackling complex scientific challenges.
  • The integration of multiscale approaches with high-performance computing, especially exascale, is vital for future scientific discovery.
  • This theme issue serves as a comprehensive resource on the current landscape and future directions of multiscale computing.