Multi-dimensional Modeling
Multi-dimensional Modeling - Related Videos
08:46
Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages
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Cited by 5 •
2016
For this study synchrotron radiation micro-tomography, a non-destructive three-dimensional imaging technique, is employed to investigate an entire microelectronic package with a cross-sectional area of 16 x 16 mm. Due to the synchrotron's high flux and brightness the sample was imaged in just 3 min with an 8.7 µm spatial resolution.
07:39
Basic Three-Dimensional (3D) Intestinal Model System with an Immune Component
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Cited by 5 •
2023
Here we describe constructing a basic three-dimensional (3D) intestinal cell line model system and a paraffin embedding protocol for light microscopic evaluation of fixed intestinal equivalents. Staining of selected proteins permits the analysis of multiple visual parameters from a single experiment for potential use in preclinical drug screening studies.
10:16
Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
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Cited by 49 •
2011
The methodology for fabricating synthetic vocal fold models is described. The models are life-sized and mimic the multi-layer structure of the human vocal folds. Results show the models to self-oscillate at pressures comparable to lung pressure and demonstrate flow-induced vibratory responses that are similar to those of human vocal folds.
02:02
A Three-Dimensional Co-culture Model of Infiltrating T Cells in Tumor Spheroids
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2025
This video demonstrates a method to establish a three-dimensional tumor spheroid and T cell co-culture. An agarose cast with microwells containing tumor spheroids is prepared, followed by the addition of T cells. After incubation, this setup allows studying the interaction of T cells with tumor spheroids in a controlled three-dimensional environment.
04:22
Pipeline for Multi-Scale Three-Dimensional Anatomic Study of the Human Heart
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Cited by 4 •
2024
This protocol presents a comprehensive pipeline to analyze samples obtained from human hearts that span the microscopic and macroscopic scales.
FAQs
It links dimensions such as geometry, time, material properties, loads, and operating conditions through mathematical relationships, computational algorithms, or simulations. Changing one variable can therefore alter the modeled response across other dimensions. This interconnected structure helps engineers examine coupled effects instead of evaluating each factor independently, providing a more informative basis for analyzing system behavior.
The influential variables depend on the system being studied, but the overview identifies geometry, time, material properties, loads, and operating conditions as important dimensions. Their values and relationships determine how the model responds to changes. Examining these dimensions together allows engineers to identify constraints and assess performance under varied conditions rather than relying on one fixed scenario.
A single simplified description can omit interactions among important system factors. Multi-dimensional Modeling preserves several perspectives, allowing engineers to compare how changes in geometry, materials, loads, timing, or operating conditions affect one another. This broader representation supports more reliable evaluation of alternatives and improves the ability to study system behavior across different conditions.
Engineers first identify the relevant dimensions, such as geometry, time, material properties, loads, and operating conditions. They then combine those factors using mathematical relationships, computational algorithms, or simulations. The resulting model can be evaluated under varied conditions, compared across alternatives, and examined for constraints, performance differences, or predicted changes in system behavior.
This approach is useful when design performance depends on several interacting factors or when engineers must evaluate alternatives under varied conditions. It can support design optimization, performance analysis, process control, and prediction of system behavior. By comparing modeled outcomes, teams can assess trade-offs, identify constraints, and select designs or operating conditions that better meet engineering requirements.
The models can show how changes in one dimension affect others and can support comparisons among design or operating alternatives. They also help identify constraints, evaluate performance, optimize designs, guide process control, and predict behavior under varied conditions. These outcomes give engineers a structured way to use integrated data and physical relationships when developing more reliable and efficient technologies.