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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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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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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Updated: Sep 26, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Sparse Damage Detection with Complex Group Lasso and Adaptive Complex Group Lasso.

Vasileios Dimopoulos1,2, Wim Desmet1,2, Elke Deckers2,3

  • 1Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparsity-based method using complex Group Lasso for structural defect localization. The technique offers a robust and efficient alternative for damage detection in composite materials.

Keywords:
adaptive complex Group Lassocomplex Group Lassolow-frequency inspectionsparse damage detection

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Area of Science:

  • Materials Science
  • Structural Health Monitoring
  • Signal Processing

Background:

  • Sparsity-based methods offer robust and efficient alternatives for traditional damage detection.
  • Low-frequency inspection enables global monitoring through long-wave propagation.

Purpose of the Study:

  • To present a single-sensor complex Group Lasso methodology for structural defect localization.
  • To evaluate the methodology using compressive sensing and complex low-frequency response functions.

Main Methods:

  • A complex Group Lasso methodology was developed for defect localization.
  • The method was applied to composite plates with induced scatterers.
  • An adaptive setting was proposed to enhance resolution.

Main Results:

  • The complex Group Lasso methodology demonstrated high and competitive performance in defect localization.
  • Results were compared against a super-resolution MUSIC technique.
  • The adaptive setting further improved resolution.

Conclusions:

  • The proposed single-sensor complex Group Lasso method is effective for structural defect localization.
  • Sparsity-based approaches with low-frequency data provide a viable alternative for damage detection.