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Related Concept Videos

Parallel RLC Circuits01:14

Parallel RLC Circuits

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Street lamps equipped with RLC surge protectors are an excellent example of applying circuit analysis in practical scenarios. These surge protectors safeguard the lamp's components against sudden voltage spikes.
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Radicals, the highly reactive species, gain stability by undergoing three different reactions. The first reaction involves a radical-radical coupling, in which a radical combines with another radical, forming a spin‐paired molecule. The second reaction is between a radical and a spin‐paired molecule, generating a new radical and a new spin‐paired molecule. The third reaction is radical decomposition in a unimolecular reaction, forming a new radical and a spin‐paired...
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Within the field of electrical circuits, source-free RLC circuits present an intriguing domain. These circuits comprise a series arrangement of a resistor, inductor, and capacitor, operating independently of external energy sources. Their initiation hinges upon utilizing the initial energy stored within the capacitor and inductor to instigate their functionality. Their mathematical equation, a second-order differential equation, sets these circuits apart. This equation captures how the...
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Comparison between RL and RC circuits01:24

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An RC circuit consists of resistance and capacitance, while in an RL circuit, capacitance is replaced by an inductor. RL and RC circuits are first-order differential circuits that store energy. An RC circuit stores energy in the electric field, while an RL circuit stores energy in the magnetic field. When connected to a battery, an RC circuit charges the capacitor, causing the current to decrease from maximum to zero upon being fully charged. This increases the voltage across the capacitor from...
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Integrals involving non-rational functions are often difficult to evaluate using standard techniques, especially when radicals appear in the integrand. Rationalizing substitution provides a systematic method for simplifying such integrals by converting them into rational forms that are easier to handle.Consider a rod whose linear mass density depends on a constant linear density, a characteristic length, and the distance from the left end of the rod. Determining the total mass requires...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Related Experiment Video

Updated: Apr 26, 2026

Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
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Understanding LI-RADS: a primer for practical use.

Cynthia S Santillan1, An Tang2, Irene Cruite3

  • 1Department of Radiology, University of California San Diego, 200 West Arbor Drive, San Diego, CA 92130, USA.

Magnetic Resonance Imaging Clinics of North America
|August 4, 2014
PubMed
Summary
This summary is machine-generated.

The Liver Imaging-Reporting and Data System (LI-RADS) provides standardized reporting for liver imaging in patients at risk for hepatocellular carcinoma. This primer introduces radiologists to the LI-RADS diagnostic algorithm, categories, and imaging features.

Keywords:
Hepatocellular carcinomaLI-RADSLiverMagnetic resonance imaging

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Area of Science:

  • Radiology
  • Hepatology
  • Oncology

Background:

  • Hepatocellular carcinoma (HCC) is a significant health concern in at-risk populations.
  • Standardized reporting is crucial for accurate diagnosis and management of liver lesions.
  • Existing imaging interpretation methods can lack consistency.

Purpose of the Study:

  • To introduce radiologists to the Liver Imaging-Reporting and Data System (LI-RADS).
  • To explain the LI-RADS diagnostic algorithm and its components.
  • To define major imaging features used in LI-RADS categorization.

Main Methods:

  • Review of the LI-RADS diagnostic algorithm.
  • Explanation of LI-RADS categories for liver observations.
  • Definition of key imaging features for lesion characterization.
  • Inclusion of case examples for practical illustration.

Main Results:

  • LI-RADS offers a structured approach to interpreting CT and MRI in patients at risk for HCC.
  • The system standardizes the reporting of liver imaging findings.
  • Radiologists can effectively categorize liver observations using defined features and algorithms.

Conclusions:

  • LI-RADS enhances consistency and accuracy in liver imaging interpretation.
  • This primer serves as a foundational guide for radiologists utilizing LI-RADS.
  • Adoption of LI-RADS improves patient management for hepatocellular carcinoma risk.