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Classification of Systems-I01:26

Classification of Systems-I

296
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Classification of Systems-II01:31

Classification of Systems-II

240
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

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According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
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Video Experimental Relacionado

Updated: Sep 10, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Desarrollo de modelos de temas etiquetados automáticamente basados en la estructura de SAO para el análisis

Minyoung Park1, Sunhye Kim2, Byungun Yoon3

  • 1Master student, Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, Seoul, Korea.

PloS one
|August 26, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo enfoque sujeto-acción-objeto (SAO) para el modelado de temas en patentes, mejorando el análisis de tendencias tecnológicas. El método mejora la precisión de etiquetado automático y proporciona información semántica más profunda para la gestión de la tecnología.

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Área de la Ciencia:

  • Ciencias de la información
  • Gestión de la tecnología
  • Lingüística computacional

Sus antecedentes:

  • El modelado de temas es crucial para identificar tendencias tecnológicas y pronosticar avances.
  • Los métodos de etiquetado automático existentes en los documentos de patentes tienen limitaciones en la evaluación cuantitativa y la captura de la jerarquía tecnológica.
  • Los enfoques tradicionales a menudo pasan por alto el significado funcional de las tecnologías.

Objetivo del estudio:

  • Introducir un enfoque basado en sujeto-acción-objeto (SAO) para el modelado de temas en documentos de patentes.
  • Superar las limitaciones de las metodologías de etiquetado automático existentes mediante la utilización del concepto de "bolsa de SAO".
  • Mejorar la precisión y la interpretabilidad del análisis de tendencias tecnológicas.

Principales métodos:

  • Desarrolló una metodología de etiquetado automático que combina el modelado y la puntuación de temas basados en SAO.
  • Resumen integrado del texto y análisis de la red para una evaluación integral.
  • Se ha incorporado una estructura jerárquica basada en las subclases de la Clasificación Cooperativa de Patentes (CPC) para mejorar la interpretabilidad.

Principales resultados:

  • El modelo propuesto demostró su eficacia en la captura de significados funcionales dentro del contexto tecnológico, evaluado utilizando puntajes de ROUGE, relevancia, cobertura y discriminación.
  • El enfoque basado en SAO mejoró la precisión de las etiquetas de temas y proporcionó ideas semánticas más profundas.
  • La estructura jerárquica mejoró la visión del desarrollo y las tendencias tecnológicas.

Conclusiones:

  • La metodología de etiquetado automático basada en SAO ofrece un enfoque más preciso e interpretable para el modelado de temas en documentos de patentes.
  • Este enfoque contribuye a una gestión de la tecnología, la innovación y la formulación de políticas más eficientes.
  • El estudio sienta las bases para el perfeccionamiento de las metodologías de previsión y diagnóstico tecnológico.