Multi-scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data ACM Computing Surveys

multi-scale analysis

Solving each scale individually and linking their results is much faster than trying to solve a single high-resolution model containing all relevant details. While heterogeneity offers huge advantages in performance (making airplanes, space shuttles and lightweight cars possible), it also introduces difficulties in the engineering design. Presently, there is not enough computational power to include all the important details within a single Finite Element (FE) model, as is customary in industry. This is because that would require a high-resolution model too complex to be feasibly solved.

  • (3.28)], are performed, supplying results that are compared and discussed.
  • Can we leverage our knowledge of machine learning and multiscale modeling in the biological, biomedical, and behavioral sciences to accelerate developments towards a Digital Twin?
  • At smaller scale, built-up land plays a significant role in influencing water quality, which may results from the urban domestic sewage and industrial waste water.
  • This study showed how post-surgical cognition-related neuroactive microbes and metabolites participate in these gut-brain communication pathways, providing a significant addition to the existing body of scientific evidence.
  • F, Macro-averaged F1 score and the overall integration score of the models capable of both data integration and label transfer.

From multiscale modeling to meso-science: A chemical engineering perspective

For further research, we will study the water quality of the whole basin, and the impacts of Huzhou City water quality on the whole basin. In addition, the temporal scale would be wider, which is better to consider the water quality of 1990s. Water quality including BOD, CODMn, NH3-N, petroleum and DTP concentrations was slightly better in flood than in low water period, especially after the year of 2004 (Fig. 3). It was observed that TN has the biggest value in normal water period, followed with low water period. The mean of DO showed the lowest value in flood period, implying that the water quality was worse than other periods. The minimum distance between the latent representation and any reference prototype is used as a proxy for uncertainty for unknown cell type detection.

multi-scale analysis

Regional relationships between land uses and water quality

Once a region of interest is identified, DualBeam (focused ion beam and scanning electron microscopy, FIB-SEM) instrumentation is used for closer surface analysis and sample extraction. multi-scale analysis The addition of a femtosecond laser to the PFIB-SEM allows for even more rapid sample preparation, cross-sectioning or serial sectioning. Subsequent TEM analysis provides atomic-scale materials characterization for complete insight into a sample’s elemental and structural composition.

  • However, the relationship between the radial dynamic stiffness and the rotor speed was not completely linear.
  • We first discuss projecting a spline pSj ∈ pVj onto the subspace pVj − 1.
  • Unlike conventional analysis, the macroscale FE analysis does not require homogenized constitutive properties because these are derived from the microscale FE simulations at the representative volume element (RVE) level.
  • Realizing that the crest lines of the original image fit with the narrow grain boundaries, the watershed transform, denoted W, is directly applied to smoothed images, processed with usual and adaptive closing-opening filters, to avoid oversegmentation.
  • It has poor water quality with non-point pollution as the primary pollution source, which is from rural areas and sewage waste water12.
  • Taihu Lake and the rivers surrounding it support the local industrial, agricultural and domestic water demands.

Filling gaps in simulation of complex systems: the background and motivation for CoSMoS

These data contain two cell identities not present in the reference (cancer and erythrocytes). We observed that scPoli successfully mapped the query dataset (Supplementary Fig. 6a). Since this query has a much coarser cell type annotation, we mapped the labels obtained with scPoli to the cell types present in the query via a mapping obtained from the authors of the study. We observed that almost all cancer cells mapped to a cluster whose label prediction had high uncertainty and was classified as unknown (Supplementary Fig. 6b,c). We observed that 85% of cancer cells and 98% of erythrocytes were identified as unknown (Supplementary Fig. 6d).

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Additionally, we trained a model with a dummy batch covariate, which was equal for all cells. In this case, scPoli will leverage exclusively cell type annotations and the prototype loss to perform integration. All models conditioned on an actual batch variable outperformed the one trained on a dummy covariate.

multi-scale analysis

Multiscale Modeling of Diseases: Overview

In order to verify the accuracy of the analysis results presented above, stiffness tests of C/C composite finger beams were conducted. Finger seal specimens were fabricated with the same plain woven C/C composite plates as above, and the stiffness of each finger beam was tested. The actual C/C composite for finger seals was composed of carbon fiber preform and carbon matrix by chemical vapor deposition (CVD).

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Additionally, LSG increased rsFC between the inferior temporal gyrus and language network, fusiform gyrus (temporal part) and dorsal attention network, and sensory-motor integration network, explaining the improvements in memory and executive function post-LSG. Scale is an important element affecting relationships between land use types and water quality parameters. At smaller scale, built-up land plays a significant role in influencing water quality, which may results from the urban domestic sewage and industrial waste water. And at local scale, all other land use types influence the water quality. It is strange that landscape index of built-up land has negative correlation with TN and landscape index of forest has positive correlation with TN. It may results from that TN has decreased during these seven years, and with built-up land increase and forest decrease, these two land use types play different influences on TN.

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