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Related Concept Videos

Shrinkage in Concrete01:27

Shrinkage in Concrete

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Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
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Drying Shrinkage01:21

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When hardened concrete is exposed to air with a relative humidity of less than 100 percent, it begins to lose the free water within its capillaries. As this water evaporates, the water initially adsorbed onto the calcium silicate hydrates migrates towards these now empty spaces and eventually evaporates as well. Over time, as more water leaves, the volume of the concrete decreases, a phenomenon known as drying shrinkage.
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Carbonation Shrinkage01:24

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Atmospheric CO2 penetrates the concrete's pores and, in the presence of moisture, forms carbonic acid, which then reacts with calcium hydroxide in the hydrated cement, forming calcium carbonate. This process reduces the concrete's volume and is termed carbonation shrinkage.
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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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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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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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Adaptive fixed-point iterative shrinkage/thresholding algorithm for MR imaging reconstruction using compressed

Geming Wu1, Shuqian Luo1

  • 1School of Biomedical Engineering, Capital Medical University, Beijing, China.

Magnetic Resonance Imaging
|February 12, 2014
PubMed
Summary
This summary is machine-generated.

Compressed sensing (CS) MRI reconstruction requires careful parameter tuning. This study introduces a minimax threshold selection method for adaptive regularization parameter choice in FPIST, improving MRI reconstruction quality without extra parameters.

Keywords:
Compressed sensingMagnetic resonance imagingNon-linear reconstructionRegularization parameter

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Compressed sensing (CS) significantly reduces MRI signal acquisition time by enabling undersampled image reconstruction.
  • Accurate and efficient CS-based MRI reconstruction depends critically on proper selection of regularization and algorithm parameters.
  • The regularization parameter balances image sparsity against k-space data fidelity, directly impacting reconstructed image quality.

Purpose of the Study:

  • To propose a novel method for adaptive selection of the regularization parameter in CS-based MRI reconstruction.
  • To address the complexity of parameter tuning in practical CS MRI implementations.
  • To enhance the accuracy and efficiency of MR image reconstruction using compressed sensing.

Main Methods:

  • The proposed approach treats the regularization parameter as a threshold within a fixed-point iterative shrinkage/thresholding algorithm (FPIST).
  • A minimax threshold selection method is employed to determine the optimal regularization parameter.
  • The FPIST algorithm is utilized without introducing additional parameters beyond the selected threshold.

Main Results:

  • The proposed method adaptively selects the regularization parameter, eliminating manual tuning.
  • High-quality MR image reconstruction was achieved on both synthetic and real complex-valued MRI data.
  • The method demonstrated effectiveness in practical CS-based MRI applications.

Conclusions:

  • The minimax threshold selection for the regularization parameter in FPIST offers an effective solution for CS-based MRI.
  • This approach simplifies the implementation of CS MRI by automating parameter selection.
  • The method holds significant potential for improving the clinical utility of accelerated MRI scans.