We study the scale dependence of effective diffusion of fluid tracers, specifically, its dependence on the Péclet number, a dimensionless parameter of the ratio between advection and molecular diffusion. Here, we address the case that length and time scales on which the effective diffusion can be described are not separated from those of advection and molecular diffusion. For this, we propose an alternate method for characterizing the effective diffusivity without relying on the scale separation. For a given spatial domain inside which the effective diffusion can emerge, a time constant related to the diffusion is identified by considering the spatiotemporal evolution of a test advection-diffusion equation, where its initial condition is set at a pulse function. Then, the value of effective diffusivity is identified by minimizing the L_{∞} distance between solutions of the above test equation and the diffusion one with mean drift. With this method, for time-independent gyre and time-periodic shear flows, we numerically show the scale dependence of the effective diffusivity and its discrepancy from the classical limits that were derived on the assumption of the scale separation. The kinematic origins of the discrepancy are revealed as the development of the molecular diffusion across flow cells of the gyre and as the suppression of the drift motion due to a temporal oscillation in the shear.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1103/PhysRevE.105.045103 | DOI Listing |
Pathology
December 2024
Department of Pathology, Amsterdam University Medical Centers/VUmc, Amsterdam, The Netherlands; Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
In the course of the last decade, the pathological diagnosis of many tumours of the central nervous system (CNS) has transitioned from a purely histological to a combined histological and molecular approach, resulting in a more precise 'histomolecular diagnosis'. Unfortunately, translation of this refinement in CNS tumour diagnostics into more effective treatment strategies is lagging behind. There is hope though that incorporating the assessment of predictive markers in the pathological evaluation of CNS tumours will help to improve this situation.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, China. Electronic address:
In the past few years, three protein molecules-USP53, NPY2R, and DCTN1-AS1-have garnered significant attention in scientific research due to their potential implications in tumor development. Mass spectrometry and proteomics techniques were used to analyze the three-dimensional structure of these protein molecules and predict their active sites and functional domains. The effects of USP53, NPY2R and DCTN1-AS1 on biological behavior of tumor cells were studied by constructing gene knockout and overexpression cell models.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
Department of Hematology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian 223300, Jiangsu Province, PR China; Key Laboratory of Hematology of Nanjing Medical University, Nanjing 210029, Jiangsu Province, PR China. Electronic address:
Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma in adults, which characterized by a high degree of heterogeneity in terms of clinical presentation, molecular phenotype, and genetic features. However, approximately 30 %-40 % of patients are refractory to standard chemotherapy, and their prognosis is poor. The emergence of small-molecule inhibitors, such as Bruton's tyrosine kinase inhibitors (BTKi), has greatly improved the treatment of DLBCL; however, drug resistance associated with small-molecule inhibitors has greatly limited their clinical application.
View Article and Find Full Text PDFNeuroimage
January 2025
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China. Electronic address:
Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis.
View Article and Find Full Text PDFJ Colloid Interface Sci
January 2025
Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8904, Tokyo, Japan; Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan. Electronic address:
Phase separation, a fundamental phenomenon in both natural and industrial settings, involves the coarsening of domains over time t to reduce interfacial energy. While well-understood for simple viscous liquid mixtures, the physical laws governing coarsening dynamics in complex fluids, such as colloidal suspensions, remain unclear. Here, we investigate colloidal phase separation through particle-based simulations with and without hydrodynamic interactions (HIs).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!