Clarkson University

About: Clarkson University is a education organization based out in Potsdam, New York, United States. It is known for research contribution in the topics: Particle & Turbulence. The organization has 4414 authors who have published 10009 publications receiving 305356 citations. The organization is also known as: Thomas S. Clarkson Memorial School of Technology & Thomas S. Clarkson Memorial College of Technology.

A comparison of methods to assess cell mechanical properties.

Abstract: The mechanical properties of cells influence their cellular and subcellular functions, including cell adhesion, migration, polarization, and differentiation, as well as organelle organization and trafficking inside the cytoplasm. Yet reported values of cell stiffness and viscosity vary substantially, which suggests differences in how the results of different methods are obtained or analyzed by different groups. To address this issue and illustrate the complementarity of certain approaches, here we present, analyze, and critically compare measurements obtained by means of some of the most widely used methods for cell mechanics: atomic force microscopy, magnetic twisting cytometry, particle-tracking microrheology, parallel-plate rheometry, cell monolayer rheology, and optical stretching. These measurements highlight how elastic and viscous moduli of MCF-7 breast cancer cells can vary 1,000-fold and 100-fold, respectively. We discuss the sources of these variations, including the level of applied mechanical stress, the rate of deformation, the geometry of the probe, the location probed in the cell, and the extracellular microenvironment.

Inductive Learning Algorithms for Complex Systems Modeling

Abstract: Introduction: Systems and Cybernetics. Inductive Learning Algorithms: Self-Organization Method. Network Structures. Long Term Quantitative Predictions. Dialogue Language Generalization. Noise Immunity and Convergence: Analogy with Information Theory. Classification and Analysis of Criteria. Improvement of Noise Immunity. Asymptotic Properties of Criteria. Balance Criterion of Predictions. Convergence of Algorithms. Physical Fields and Modeling: Finite-Difference Pattern Schemes. Comparative Studies. Cyclic Processes. Clusterization and Recognition: Self-Organization Modeling and Clustering. Methods of Self-Organization Clustering. Objective Computer Clustering Algorithm. Levels of Discretization and Balance Criterion. Forecasting Methods of Analogues. Applications: Fields of Application. Weather Modeling. Ecological System Studies. Modeling of Economical Systems. Agricultural System Studies. Modeling of Solar Activity. Inductive and Deductive Networks: Self-Organization Mechanism in the Networks. Network Techniques. Generalization. Comparison and Simulation Results. Basic Algorithms and Program Listings: Computational Aspects of Multilayered Algorithm. Computational Aspects of Combinatorial Algorithm. Computational Aspects of Harmonical Algorithm.

Inertial migration of a small sphere in linear shear flows

Abstract: The motion of a small, rigid sphere in a linear shear flow is considered. Saffman's analysis is extended to other asymptotic cases in which the particle Reynolds number based on its slip velocity is comparable with or larger than the square root of the particle Reynolds number based on the velocity gradient. In all cases, both particle Reynolds numbers are assumed to be small compared to unity. It is shown that, as the Reynolds number based on particle slip velocity becomes larger than the square root of the Reynolds number based on particle shear rate, the magnitude of the inertial migration velocity rapidly decreases to very small values. The latter behaviour suggests that contributions that are higher order in the particle radius may become important in some situations of interest.