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Control Volume and System Representations01:16

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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
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Physical Methods for Controlling Microbial Growth: Temperature01:23

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Heat is a widely used method to control microbial growth by targeting and denaturing cellular proteins, thereby killing or inactivating microbes. This method's effectiveness is quantified using parameters such as the thermal death point (TDP), thermal death time (TDT), and decimal reduction time (D value). TDP represents the lowest temperature at which all microorganisms in a liquid suspension are eliminated within 10 minutes, whereas TDT is the time necessary to achieve sterilization at a...
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Physical Methods for Controlling Microbial Growth: Radiation and Filtration01:26

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Radiation and filtration are essential tools for microbial control, targeting microorganisms through distinct mechanisms. Radiation eliminates microbes by damaging their DNA, either killing them or inhibiting their growth. Based on wavelength, radiation is classified into two types: nonionizing and ionizing radiation.Non-ionizing radiation, such as UV radiation (200–400 nm), is absorbed by DNA, causing defects that effectively disinfect surfaces, air, and water, including safety cabinets.
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State Space Representation01:27

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

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Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
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Vector Representation of Complex Numbers01:16

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Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
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High Resolution Physical Characterization of Single Metallic Nanoparticles
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MolCluster: Un marco de trabajo no supervisado para representaciones moleculares multiescala con control de

Zhixuan Zhong1,2, Linbo Ma3, Jian Jiang1,2

  • 1Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Polymer Physics and Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, P. R. China.

Journal of chemical theory and computation
|January 21, 2026
PubMed
Resumen
Este resumen es generado por máquina.

MolCluster, un modelo no supervisado, crea modelos moleculares precisos de grano grueso (CG) utilizando redes neuronales de grafos. Este enfoque novedoso supera las limitaciones de los métodos tradicionales, permitiendo una resolución personalizable para sistemas complejos.

Palabras clave:
modelado de grano gruesoredes neuronales de grafossimulaciones molecularesresolución personalizablequímica computacionalciencia de materialesbiofísica

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Área de la Ciencia:

  • Química Computacional
  • Ciencia de Materiales
  • Biofísica

Sus antecedentes:

  • El modelado tradicional de grano grueso (CG) enfrenta desafíos con diversas estructuras químicas y reglas de mapeo fijas.
  • Los métodos CG supervisados están limitados por pequeños conjuntos de datos etiquetados y la falta de control de resolución para el modelado multiescala.

Objetivo del estudio:

  • Desarrollar un modelo no supervisado, MolCluster, para extraer representaciones personalizadas de grano grueso (CG).
  • Permitir un control preciso sobre la resolución del mapeo para diversos sistemas moleculares.

Principales métodos:

  • Integración de redes neuronales de grafos y detección de comunidades para la extracción de representaciones CG no supervisadas.
  • Implementación de una pérdida de pares de grupos predefinida para la preservación del grupo objetivo.
  • Utilización de una estrategia de bisección para el control de resolución personalizable.

Principales resultados:

  • MolCluster demuestra un rendimiento superior en el mapeo CG y la predicción de tipos de cuentas en comparación con modelos tradicionales y supervisados en el conjunto de datos MARTINI2.
  • La estrategia de preentrenamiento sin etiquetas mejora significativamente la efectividad de MolCluster.

Conclusiones:

  • MolCluster ofrece un enfoque novedoso y no supervisado para el mapeo CG químicamente consistente y personalizable.
  • Este modelo muestra un potencial significativo para aplicaciones en polímeros, proteínas y sistemas multiescala complejos.