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Verlag GmbH polymer processing: modeling and simulation
Polymer Processing: Modeling And Simulation, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/modeling+process/polymer+processing++modeling+and+simulation/pm26233107-445-3-12
Average 90 stars, based on 1 article reviews
polymer processing: modeling and simulation - by Bioz Stars, 2026-09
90/100 stars

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Polymer:

Article Title: Monte Carlo Approach to the Simulation of Ethylene Oxide and Propylene Oxide Polymerization and Copolymerization
Article Snippet: The simulation of block or random ethylene oxide (EO) and propylene oxide (PO) copolymerization with a deterministic model requires the solution of many thousands of differential and algebraic equations.. This approach, therefore, is not practicable, requiring too long computer calculation time.. The use of a stochastic model allows overcoming this drawback.

Article Title: Mechanical Microstructure Characterization of Discontinuous‐Fiber Reinforced Composites by means of Experimental‐Numerical Micro Tensile Tests
Article Snippet: .. 2019;19:e201900120. www.gamm-proceedings.com 1 of 2 https://doi.org/10.1002/pamm.201900120 c© 2019 The Authors Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH Verlag GmbH & Co. KGaA Weinheim Two reasons for the discrepancy of the simulations are possible: (i) the accuracy of the model geometry and (ii) the quality of the polymer model. On the one hand, fiber tracking based on μCT data can be inaccurate, since distinguishing single fibers might not be possible within densely packed fiber bundles. ..

Article Title: Flow Activation Energy Estimation by Thermo‐Rheological Method
Article Snippet: .. Keywords annular flow, polymers, temperature, thermal dependence, viscosity Received: March 2, 2023 Revised: May 22, 2023 Published online: June 4, 2023 [1] J. F. Agassant, P. Avenas, P. J. Carreau, B. Vergnes, M. Vincent, Polymer Processing: Principles and Modeling, Carl Hanser Verlag GmbH Co KG, Munich, Germany 2017. ..

Article Title: Modeling of Diffusive Transport of Polymers Moments Using Limiting Cases of the Maxwell–Stefan Model
Article Snippet: .. [8] K.-D. Hungenberg, M. Wulkow, Modeling and Simulation in Polymer Reaction Engineering, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany: 2018. https://doi.org/10.1002/9783527685738 [9] R. Taylor, R. Krishna, Multicomponent mass transfer. ..

Article Title: Functional Macromolecular Systems: Kinetic Pathways to Obtain Tailored Structures
Article Snippet: .. [212] J.-F. Agassant, P. Avenas, P. J. Carreau, B. Vergnes, M. Vincent, Polymer Processing: Principles and Modeling, 2nd ed., Carl Hanser Verlag, Munich 2017, p. 888. ..

Article Title: In-line viscosity identification via thermal-rheological measurements in an annular duct for polymer processing
Article Snippet: .. Carreau, B. Vergnes, M. Vincent, Polymer processing: principles and modeling, Carl Hanser Verlag GmbH Co KG, 2017. ..

Article Title: Thermo-rheological reduced order models for non-Newtonian fluid flows with power-law viscosity via the modal identification method
Article Snippet: In the framework of melted polymer flows characterization, this study deals with the formulation, construction and validation of thermo-rheological Reduced Order Models (ROMs) for incompressible flows of pseudoplastic fluids.. The dynamic viscosity is described by a shear rate power law defined by consistency index K and pseudoplastic index n . The flow dynamics are assumed to be quasi-static whereas the thermal state is unsteady.. Viscous dissipation acts as a heat source term in the energy equation.

Article Title: Modeling Strategies for the Propagation of Terminal Double Bonds During the Polymerization of N‐Vinylpyrrolidone and Experimental Validation
Article Snippet: .. [14] K.-D. Hungenberg, M. Wulkow, Modeling and Simulation in Polymer Reaction Engineering, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany 2018. .. [15] T. Meyer, J. T. F. Keurentjes, Handbook of Polymer Reaction Engineering, Wiley-VCH Verlag GmbH, Weinheim, Germany 2008.

Molecular Weight:

Article Title: Monte Carlo Approach to the Simulation of Ethylene Oxide and Propylene Oxide Polymerization and Copolymerization
Article Snippet: The simulation of block or random ethylene oxide (EO) and propylene oxide (PO) copolymerization with a deterministic model requires the solution of many thousands of differential and algebraic equations.. This approach, therefore, is not practicable, requiring too long computer calculation time.. The use of a stochastic model allows overcoming this drawback.

Viscosity:

Article Title: Flow Activation Energy Estimation by Thermo‐Rheological Method
Article Snippet: .. Keywords annular flow, polymers, temperature, thermal dependence, viscosity Received: March 2, 2023 Revised: May 22, 2023 Published online: June 4, 2023 [1] J. F. Agassant, P. Avenas, P. J. Carreau, B. Vergnes, M. Vincent, Polymer Processing: Principles and Modeling, Carl Hanser Verlag GmbH Co KG, Munich, Germany 2017. ..



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Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration. This enables accurate predictions for gene regulatory networks (GRN) and drug response analysis . b LocalCLiP for Spatial Transcriptomics: LocalCLiP utilizes a local transformer model to integrate spatial transcriptomics data, using KNN for image patch analysis and gene expression prediction, providing insights into tissue-specific molecular patterns . c BioTask Executor for Task-Specific Analysis: The BioTask Executor handles various biological tasks, from zero-shot learning to GRN inference and drug response prediction, by preprocessing data, initializing pretrained models (e.g., SCGPT, Geneformer), and fine-tuning them for task-specific applications . d Human-8CATAC-CorpuS for Multi-Tissue Analysis: The Human-8CATAC-CorpuS dataset, with 5 million cells from 31 tissues, is used to train models for gene expression prediction and cCRE signal reconstruction, enabling comprehensive analysis of tissue-specific regulatory elements . The schematics were adapted from [ , , ] and

Journal: Journal of Translational Medicine

Article Title: Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems

doi: 10.1186/s12967-025-07091-0

Figure Lengend Snippet: Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration. This enables accurate predictions for gene regulatory networks (GRN) and drug response analysis . b LocalCLiP for Spatial Transcriptomics: LocalCLiP utilizes a local transformer model to integrate spatial transcriptomics data, using KNN for image patch analysis and gene expression prediction, providing insights into tissue-specific molecular patterns . c BioTask Executor for Task-Specific Analysis: The BioTask Executor handles various biological tasks, from zero-shot learning to GRN inference and drug response prediction, by preprocessing data, initializing pretrained models (e.g., SCGPT, Geneformer), and fine-tuning them for task-specific applications . d Human-8CATAC-CorpuS for Multi-Tissue Analysis: The Human-8CATAC-CorpuS dataset, with 5 million cells from 31 tissues, is used to train models for gene expression prediction and cCRE signal reconstruction, enabling comprehensive analysis of tissue-specific regulatory elements . The schematics were adapted from [ , , ] and

Article Snippet: The schematic were adapted from [ ] and [ ] with permission Fig. 3 Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration.

Techniques: Biomarker Discovery, Gene Expression