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Related Concept Videos

¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
UV–Vis Spectrum01:30

UV–Vis Spectrum

When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
The UV–Vis spectrum of a molecule is the plot of its absorbance versus wavelength. The plot is drawn by taking molar absorptivity (ε) or log ε on the y-axis (ordinate)...
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...
UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent of conjugation in the...
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Related Experiment Video

Updated: Jun 18, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Eigenvalue spectra of spatial-dependent networks.

Joris Billen1, Mark Wilson, Arlette Baljon

  • 1Department of Physics, San Diego State University, San Diego, California 92128, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

Spatial dependence in networks influences spectral density, increasing clustering and asymmetry. Network spectrum analysis can detect and quantify spatial dependence and clustering.

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Area of Science:

  • Network science
  • Graph theory
  • Data analysis

Background:

  • Real-world networks often display spatial dependence, where connection probability is distance-related.
  • Understanding network structure is crucial for analyzing complex systems.

Purpose of the Study:

  • To investigate how spatial dependence affects network spectral density.
  • To explore the impact of spatial dependence on network properties like clustering and asymmetry.

Main Methods:

  • Analysis of Erdös-Rényi, scale-free, and small-world network models.
  • Examination of spectral density changes with increasing spatial dependence.
  • Quantification of clustering and asymmetry (skewness) in network structures.

Main Results:

  • Increasing spatial dependence alters the network spectrum.
  • Spatial dependence leads to higher degrees of clustering and more triangles.
  • Network asymmetry (skewness) increases with spatial dependence.

Conclusions:

  • Network spectral density is sensitive to spatial dependence.
  • Spectral analysis offers a method to detect and quantify clustering and spatial dependence in networks.