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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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As defined by regulatory standards, pharmaceutical equivalents require generic drug products to have identical dosage forms and chemically identical active pharmaceutical ingredients (APIs). They must adhere to compendial or applicable standards for potency, content uniformity, disintegration times, and dissolution rates. In the case of modified-release dosage forms, variations in drug content are permissible as long as the delivered amount remains consistent with the innovator drug product.
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Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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  • 1Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela 15782 Santiago de Compostela, Spain.

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Resumen
Este resumen es generado por máquina.

Un nuevo marco GPT-4 automatiza la extracción de datos farmacéuticos de la literatura, creando conjuntos de datos de alta calidad. Esta ingeniería de indicaciones profunda reduce significativamente el esfuerzo manual y acelera el desarrollo de modelos de aprendizaje automático en dominios especializados.

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fabricación aditivaGPT-4Creación de grandes conjuntos de datosModelos de lenguaje grandesAprendizaje automáticoProcesamiento del lenguaje natural

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Área de la Ciencia:

  • Investigación farmacéutica
  • Aprendizaje automático
  • Ciencia de datos

Sus antecedentes:

  • Los conjuntos de datos de alta calidad son cruciales para el aprendizaje automático (ML) en campos especializados como la investigación farmacéutica.
  • La extracción manual de datos de la literatura científica consume mucho tiempo y mano de obra.
  • Los métodos existentes tienen dificultades con las fuentes de datos heterogéneas en el desarrollo de fármacos.

Objetivo del estudio:

  • Desarrollar un nuevo marco de ingeniería de indicaciones profunda para automatizar la generación de conjuntos de datos para la investigación farmacéutica.
  • Transformar GPT-4 en una herramienta para la extracción acelerada y precisa de parámetros críticos de la literatura.
  • Reducir el esfuerzo manual y el tiempo requerido para crear conjuntos de datos estructurados para modelos de ML.

Principales métodos:

  • Se empleó una estrategia de indicaciones de conjuntos múltiples utilizando GPT-4 para analizar 70 artículos de texto completo sobre impresión de inyección de tinta farmacéutica.
  • Se extrajeron y calcularon 22 variables relevantes para el dominio, categorizadas en parámetros de impresión, reología y dosis de fármacos.
  • Los resultados se compararon rigurosamente con un conjunto de datos curado por humanos compilado por expertos en el campo.

Principales resultados:

  • El marco GPT-4 logró una precisión general de 0.942 en 4.217 puntos de datos.
  • Las variables calculadas demostraron una alta precisión (0.983), incluso con cálculos complejos y conversiones de unidades.
  • El tiempo de procesamiento por artículo se redujo de horas de esfuerzo humano a menos de 3,5 minutos.

Conclusiones:

  • El nuevo enfoque de ingeniería de indicaciones permite a GPT-4 generar conjuntos de datos confiables y de alta calidad derivados de la literatura.
  • Este método reduce significativamente el esfuerzo manual y al mismo tiempo mantiene una precisión a nivel de experto para el entrenamiento de modelos de ML.
  • La estrategia facilita la escalabilidad del aprendizaje automático en dominios farmacéuticos y otros dominios intensivos en datos.