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Related Concept Videos

Extended Versions of Green’s Theorem01:27

Extended Versions of Green’s Theorem

Green’s Theorem connects the circulation of a vector field around a closed curve with the behavior of the field across the region enclosed by that curve. It provides a way to replace a line integral around a boundary with a double integral over the interior region, making it especially useful in plane geometry, fluid flow, and vector calculus.Although Green’s Theorem is often introduced using simple regions without gaps, it can also be applied to regions made from several simple parts. This...
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The study of fluid motion often involves understanding how local rotational behavior relates to global circulation. In the context of a pond with pollutants, direct measurement of water movement along an irregular shoreline can be impractical. Green’s Theorem in vector form provides an alternative by relating the circulation around a closed boundary to properties of the flow within the enclosed region.Measurements of water velocity at different points define a continuous vector field that...
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Green’s Theorem establishes a relationship between a line integral around a closed plane curve and a double integral over the region enclosed by that curve. It applies to a vector field F(x, y) = 〈P(x, y), Q(x, y)〉, where P and Q have continuous first partial derivatives on an open set containing the region.Let C be a positively oriented, simple, closed, piecewise smooth curve, and let R be the plane region bounded by C. Green’s Theorem states that\begin{equation*}\oint_C P\,dx+Q\,dy =\iint_R...
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

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Robotic Sensing and Stimuli Provision for Guided Plant Growth
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Experimental study of modified voting algorithm for planted (l,d)-motif problem.

Hazem M Bahig1, Mostafa M Abbas, Ashraf Bhery

  • 1Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo 11566, Egypt. hbahig@asunet.shams.edu.eg

Advances in Experimental Medicine and Biology
|September 25, 2010
PubMed
Summary
This summary is machine-generated.

The modified voting algorithm, incorporating a new strategy, was tested against the original voting algorithm for planted (l,d)-motif search. The original algorithm was generally faster, except in specific challenging instances.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Analysis

Background:

  • The planted (l,d)-motif search problem is crucial for identifying conserved DNA sequences.
  • Existing voting algorithms face challenges with efficiency and accuracy in motif discovery.

Purpose of the Study:

  • To experimentally evaluate the performance of a modified voting algorithm using the Balla, Davila, and Rajasekaran strategy.
  • To compare the computational efficiency of the original and modified voting algorithms on simulated datasets.

Main Methods:

  • Implementation and testing of the modified voting algorithm on simulated data for planted (l,d)-motif search.
  • Systematic comparison of running times between the original and modified algorithms across various (l,d) parameters.
  • Analysis of the impact of parameter 'h' on the modified algorithm's performance.

Main Results:

  • The original voting algorithm demonstrated superior speed in most tested instances, with the modified version only outperforming it at (15,3).
  • The study identified specific thresholds for the number of sequences where the modified algorithm becomes more efficient.
  • Performance variations were observed based on the interplay between motif length (l) and substitution rate (d).

Conclusions:

  • The modified voting algorithm offers a potential speed advantage in specific, challenging (l,d)-motif search scenarios.
  • Further research is needed to optimize the modified algorithm for broader applicability and efficiency gains.
  • The experimental findings provide valuable insights into the practical performance of voting-based motif search strategies.