We report a multiscale modeling approach to study static and dynamical properties of polymer melts at large time and length scales. We use a bottom-up approach consisting of deriving coarse-grained models from an atomistic description of the polymer melt. We use the iterative Boltzmann inversion (IBI) procedure and a pressure-correction function to map the thermodynamic conditions of the atomistic configurations. The coarse-grained models are incorporated in the dissipative particle dynamics (DPD) method. The thermodynamic, structural, and dynamical properties of the cis-1,4-polybutadiene melt are very well reproduced by the coarse-grained DPD models with a significative computational gain. We complete this study by addressing the challenging question of the investigation of the shear modulus evolution. As expected from experiments, the stress correlation functions show behaviors that are dependent on the molecular weights defining unentangled and weakly entangled polymer melts.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1021/ct300582y | DOI Listing |
Acta Bioeng Biomech
June 2024
1Department of Biomedical Engineering, Hefei University of Technology, Hefei, People's Republic of China.
: The utilization of intra-aortic balloon pump (IABP) and Impella has been suggested as means of left ventricular unloading in veno-arterial extracorporeal membrane oxygenation (VA-ECMO) patients. This study aimed to assess the local hemodynamic alterations in VA-ECMO patients through simulation analyses. : In this study, a 0D-3D multiscale model was developed, wherein resistance conditions were employed to define the flow-pressure relationship.
View Article and Find Full Text PDFDigit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
Heliyon
January 2025
School of Information Technology, Luoyang Normal University, Luoyang 471934, China.
Carbon emissions have increasingly been the focus of all governments as countries throughout the world choose carbon neutrality as a national development strategy. The analysis of the spatiotemporal dynamics of CO emission has emerged as a significant research topic considering the dual-carbon goal. In this research, we explore the spatiotemporal changes of CO emission at different scales based on nighttime light data.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!