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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Integration by Parts: Indefinite Integrals01:26

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Integration by parts is a fundamental technique in calculus for evaluating integrals involving the product of two functions. It is particularly useful when direct integration is not feasible. The method is based on the product rule for differentiation, which states that the derivative of a product equals the derivative of the first function times the second, plus the first function times the derivative of the second. By integrating this identity and rearranging terms, the integration by parts...
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Integration by Parts: Definite Integrals01:23

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Definite integrals involving the product of two functions over a fixed interval can be evaluated using integration by parts. This method rewrites the integral as the difference of a product evaluated at the endpoints and a remaining definite integral that is often simpler to compute.A representative example is the definite integral of the inverse tangent function. Since there is no direct integration formula for arctan ⁡x, the integrand is rewritten as a product of arctan⁡ x and the...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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A Data-Driven Approach to Quantifying Immune States in Sepsis
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Mejorar la integración de los refugiados mediante la asignación algorítmica basada en datos

Kirk Bansak1,2, Jeremy Ferwerda2,3, Jens Hainmueller1,2,4

  • 1Department of Political Science, Stanford University, Stanford, CA 94305, USA.

Science (New York, N.Y.)
|January 20, 2018
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo algoritmo mejora la integración de los refugiados al hacer coincidir a las personas con los lugares de reasentamiento utilizando el aprendizaje automático. Este enfoque basado en datos mejora significativamente los resultados del empleo, ofreciendo una herramienta política práctica para los gobiernos.

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Área de la Ciencia:

  • Ciencias sociales computacionales
  • Aplicaciones de aprendizaje automático
  • Sociología de la migración

Sus antecedentes:

  • Las democracias desarrolladas están reasentando a un número creciente de refugiados.
  • La integración de los refugiados en las sociedades de acogida presenta desafíos significativos.
  • Las prácticas actuales de asignación de reasentamiento pueden no optimizar los resultados de la integración.

Objetivo del estudio:

  • Desarrollar y evaluar un algoritmo basado en datos para asignar refugiados a lugares de reasentamiento.
  • Mejorar los resultados de la integración de los refugiados, en particular el empleo.
  • Proporcionar una herramienta política práctica y rentable para los gobiernos.

Principales métodos:

  • Desarrolló un algoritmo flexible basado en datos que combina el aprendizaje automático supervisado y la correspondencia óptima.
  • Aprovechar las características de los refugiados y las sinergias de los lugares de reasentamiento.
  • Probó el algoritmo con datos de registros históricos de los Estados Unidos y Suiza.

Principales resultados:

  • El algoritmo demostró mejoras significativas en los resultados de empleo de los refugiados, que oscilan entre el 40% y el 70% en promedio.
  • Estas mejoras se observaron en relación con las prácticas de asignación existentes en los países estudiados.
  • El enfoque resultó eficaz en diferentes regímenes de asignación y poblaciones de refugiados.

Conclusiones:

  • El algoritmo desarrollado ofrece un método práctico y rentable para mejorar la integración de los refugiados.
  • El enfoque basado en datos puede implementarse fácilmente dentro de las estructuras gubernamentales existentes.
  • Esta herramienta tiene el potencial de mejorar sustancialmente las perspectivas de empleo de los refugiados reasentados.