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Related Concept Videos

Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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Residual Stresses01:26

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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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In materials that exhibit elastic and plastic behavior, known as elastoplastic materials, residual stresses can accumulate when these materials experience plastic deformation. This deformation arises from either high levels of shearing stress or significant strains. Residual stresses are internal stresses that persist within a material after removing the external force causing deformation. This phenomenon is demonstrated when observing the behavior of a shaft under torque; notably, the...
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Residual Stresses in Bending01:18

Residual Stresses in Bending

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In the study of elastoplastic members subjected to bending moments, understanding the loading and unloading phases is crucial for assessing material behavior and structural integrity. During the loading phase, as the bending moment increases, the material initially responds elastically, adhering to Hooke's Law, where stress is directly proportional to strain. When the load exceeds the yield strength, plastic deformation occurs, resulting in permanent strain and deformation that remains even...
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Residuals and Least-Squares Property01:11

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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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Classification Improvements in Automated Gunshot Residue (GSR) Scans.

Micha Mandel1, Osnat Israelsohn Azulay2, Yigal Zidon2

  • 1Department of Statistics, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem, 9190501, Israel.

Journal of Forensic Sciences
|July 3, 2018
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Summary
This summary is machine-generated.

A new algorithm improves gunshot residue (GSR) particle classification, significantly reducing forensic analysis time. This method enhances accuracy by refining the initial automated screening of potential GSR particles.

Keywords:
SEM-EDXelemental analysisforensic sciencegunshot residue conformationregression treesupervised learning

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Computer Science

Background:

  • Current methods for classifying gunshot residue (GSR) particles involve a time-consuming semi-automatic approach.
  • Manual analysis of potentially GSR particles by forensic examiners leads to significant delays and inefficiencies.

Purpose of the Study:

  • To develop and validate a novel algorithm for improving the initial classification of GSR particles.
  • To reduce the number of particles requiring manual examination, thereby decreasing overall analysis time.

Main Methods:

  • A binary tree-based classification algorithm was developed and trained on a dataset of nearly 16,000 particles from 43 sample stubs.
  • The algorithm's performance was evaluated using 5,900 particles from 23 independent stubs, assessing false positive and false negative rates.

Main Results:

  • The new algorithm demonstrated high performance in classifying GSR particles, with low false positive and false negative rates.
  • The developed algorithm significantly improves the accuracy of the initial automated classification step.

Conclusions:

  • Routine implementation of the new algorithm can substantially decrease the time required for GSR particle analysis.
  • This advancement offers a more efficient and accurate approach to GSR identification in forensic investigations.