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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
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Updated: Aug 29, 2025

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Bottom-up Coarse-Graining: Principles and Perspectives.

Jaehyeok Jin1, Alexander J Pak1, Aleksander E P Durumeric1

  • 1Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, United States.

Journal of Chemical Theory and Computation
|September 7, 2022
PubMed
Summary
This summary is machine-generated.

Systematic coarse-grained (CG) modeling connects microscopic details to macroscopic properties. Machine learning can approximate potentials of mean force, enabling accurate CG simulations for complex systems.

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Area of Science:

  • Computational chemistry and materials science
  • Multiscale modeling and simulation
  • Statistical mechanics

Background:

  • Large-scale molecular models investigate microscopic effects on mesoscopic behavior.
  • Understanding molecular interactions driving material properties is crucial.
  • Simulations are often computationally expensive, limiting scope.

Purpose of the Study:

  • To establish a systematic connection between microscopic details and emergent mesoscopic behavior.
  • To address the computational cost and scale limitations of molecular simulations.
  • To develop effective coarse-grained (CG) modeling strategies.

Main Methods:

  • Employing systematic bottom-up CG techniques to transfer hypotheses from small to large systems.
  • Defining CG systems via representation (mapping) and energetics (potentials of mean force, PMFs).
  • Exploring machine learning for approximating PMFs and analyzing CG models.

Main Results:

  • Demonstrated a method to bridge simulation scales using CG models.
  • Identified CG representation and energetics as key to connecting different system scales.
  • Showcased the potential of machine learning in enhancing CG method accuracy.

Conclusions:

  • Systematic CG modeling provides a viable approach to study complex systems.
  • The proposed connection between parametrization and target systems is independent of PMF optimization.
  • Machine learning offers promising avenues for advancing CG methodology and analysis.