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Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Calculation of First-Law Quantities II

The first law of thermodynamics establishes that the change in internal energy of a system is given by ΔU = q + w, where q is the heat exchanged, and w is the work performed. For a perfect gas, both internal energy (U) and enthalpy (H) depend solely on temperature. Consequently, for any change of state, whether reversible or irreversible, the internal energy change is determined by integrating the heat capacity at constant volume, and the enthalpy change by integrating the heat capacity at...
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In any system of units, the units for some physical quantities must be specified through a measurement process. These measurements are the base quantities of the system, and their units are the base units of the system. The algebraic combinations of the base values can then be used to express all other physical quantities. Each of these physical quantities is then referred to as a derived quantity, with each unit being referred to as a derived unit.
The International Organization for...
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The average value of a function over a closed interval can be interpreted geometrically as the height of a rectangle whose area equals the net area under the curve across that interval. This net area accounts for both positive and negative contributions of the function, providing a single representative value that reflects the function’s overall behaviorA practical illustration of this idea arises when monitoring the temperature inside a greenhouse over a twenty-four-hour period. Although the...
Agonism and Antagonism: Quantification01:14

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Thermodynamic systems undergoing phase transitions or temperature changes experience energy transfer in the form of heat (q) and work (w). For a reversible phase change at constant temperature (T) and pressure (p), the process involves no chemical reaction but results in energy exchange between distinct phases.The heat transferred during this process corresponds to the latent heat of transition, which is the amount of heat energy absorbed or released by a substance when it changes from one...

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Updated: Jul 6, 2026

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Some quantifier functions from weighting functions with constant value of orness.

Byeong Seok Ahn1

  • 1College of Business Administration, Chung-Ang University, Seoul, Korea. bsahn@cau.ac.kr

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|March 20, 2008
PubMed
Summary

New quantifier functions enhance multicriteria aggregation by providing consistent orness values, leading to stable ordered weighted averaging (OWA) results, especially with a sufficient number of criteria.

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Last Updated: Jul 6, 2026

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Area of Science:

  • Decision Sciences
  • Operations Research
  • Artificial Intelligence

Background:

  • Multicriteria aggregation relies on quantifier-guided methods where quantifier selection is critical for determining aggregation weights.
  • Existing research on quantifiers for aggregation is limited, with Yager's work on relative quantifiers being a notable exception.

Purpose of the Study:

  • To introduce novel quantifier functions for multicriteria aggregation.
  • To develop quantifiers that maintain a constant orness value regardless of the number of aggregated criteria.

Main Methods:

  • Proposed new quantifier functions based on weighting functions with constant orness.
  • Introduced regular increasing monotone and regular decreasing monotone quantifiers.
  • Analyzed the convergence of quantifier orness towards the orness of the underlying weighting functions.

Main Results:

  • The proposed regular increasing and decreasing monotone quantifiers yield the same orness as their originating weighting functions.
  • The orness of the proposed quantifiers rapidly converges to the constant orness value of the weighting functions.
  • This convergence suggests stable ordered weighted averaging (OWA) aggregation outcomes when the number of criteria is not minimal.

Conclusions:

  • The newly proposed quantifiers offer a valuable addition to the field of multicriteria aggregation.
  • These quantifiers ensure consistent aggregation behavior, particularly in ordered weighted averaging (OWA) contexts.
  • The findings are significant for applications requiring robust aggregation across multiple criteria.