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Related Concept Videos

Method of Joints01:30

Method of Joints

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The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint.
Since plane truss members are in the same plane, each joint is subjected to a coplanar and concurrent force system. To apply the method of joints, the first step is to...
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Method of Joints: Problem Solving I01:30

Method of Joints: Problem Solving I

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The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint. Consider a truss structure with two forces of 20 N and 10 N acting at joints C and D, respectively. The method of joints can be used to determine the forces FCB, FDC,...
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Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

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Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
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Introduction and Methods of Leveling01:26

Introduction and Methods of Leveling

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Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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An integrative U method for joint analysis of multi-level omic data.

Pei Geng1, Xiaoran Tong2, Qing Lu3

  • 1Department of Mathematics, Illinois State University, Normal, IL, 61761, USA.

BMC Genetics
|April 11, 2019
PubMed
Summary

A new integrative U (IU) method effectively analyzes complex multi-level omic data. This non-parametric approach offers robust performance and higher power for genetic research compared to traditional methods.

Keywords:
Functional data analysisIntegrative analysisNon-parametric method

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • High-throughput technologies enable diverse omic data collection in large studies.
  • High dimensionality and complex relationships in multi-level omic data pose analytic challenges.
  • Existing methods for omic data association analysis are limited.

Purpose of the Study:

  • To develop an integrative U (IU) method for multi-level omic data analysis.
  • To address challenges posed by high dimensionality and complex interactions in omic data.
  • To provide a flexible, non-parametric approach for diverse data types.

Main Methods:

  • Developed a non-parametric integrative U (IU) method.
  • The IU method accommodates various omic and phenotype data types.
  • The method considers interactive relationships among different omic data levels.

Main Results:

  • The IU test demonstrated robust type I error performance.
  • The IU test achieved higher empirical power than variance component tests.
  • Performance was validated across various phenotypes and interaction effects.

Conclusions:

  • The proposed IU method is a powerful tool for multi-level omic data analysis.
  • The IU method offers advantages over traditional variance component tests.
  • This approach enhances genetic research by overcoming analytic challenges.