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The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable…

Image and Video Processing · Electrical Eng. & Systems 2024-12-03 Maximilian E. Tschuchnig , Michael Gadermayr

This study uses advanced numerical methods to estimate the mean force potential (PMF) between charged, polarizable colloidal particles in dense electrolytes. We observe that when the Debye screening length, $\lambda_{\mathrm{D}}$, is below…

Soft Condensed Matter · Physics 2025-02-10 Emily Krucker-Velasquez , Martin Z. Bazant , Alfredo Alexander-Katz , James W. Swan

Because micro-ions accumulate around highly charged colloidal particles in electrolyte solutions, the relevant parameter to compute their interactions is not the bare charge, but an effective (or renormalized) quantity, whose value is…

Soft Condensed Matter · Physics 2009-11-07 Lyderic Bocquet , Emmanuel Trizac , Miguel Aubouy

Anomalies are strange data points; they usually represent an unusual occurrence. Anomaly detection is presented from the perspective of Wireless sensor networks. Different approaches have been taken in the past, as we will see, not only to…

Machine Learning · Computer Science 2017-08-30 Pelumi Oluwasanya

Charge-stabilized colloidal spheres dispersed in weak 1:1 electrolytes are supposed to repel each other. Consequently, experimental evidence for anomalous long-ranged like-charged attractions induced by geometric confinement inspired a…

Soft Condensed Matter · Physics 2009-11-10 David G. Grier , Yilong Han

An exact, analytic solution for a simple electrostatic model applicable to biomolecular recognition is presented. In the model, a layer of high dielectric constant material (representative of the solvent, water) whose thickness may vary…

Classical Physics · Physics 2013-05-29 T. P. Doerr , Yi-Kuo Yu

Processes based on electrostatic projection are used extensively in industry, e.g. for mineral separations, electrophotography or manufacturing of coated abrasives, such as sandpaper. Despite decades of engineering practice, there are still…

Soft Condensed Matter · Physics 2020-04-08 Arash Sayyah , Mohammad Mirzadeh , Yi Jiang , Warren V. Gleason , William C. Rice , Martin Z. Bazant

The interaction between an electric field and the electric charges in a material is described by electrostatic screening, which in metallic systems is commonly thought to be confined within a distance of the order of the Thomas-Fermi…

Mesoscale and Nanoscale Physics · Physics 2018-10-29 Erik Piatti , Davide Romanin , Renato S. Gonnelli , Dario Daghero

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous…

Efficient and reliable operation of Concentrated Solar Power (CSP) plants is essential for meeting the growing demand for sustainable energy. However, high-temperature solar receivers face severe operational risks, such as freezing,…

Machine Learning · Computer Science 2025-08-07 Yorick Estievenart , Sukanya Patra , Souhaib Ben Taieb

Unscreened electrostatic interactions exert a profound effect on the onset of the buckling instability of a charged rod. When this interaction is unscreened, the threshold value of the compressional force needed to induce buckling is…

Soft Condensed Matter · Physics 2007-05-23 Roya Zandi , Ramin Golestanian , Joseph Rudnick

Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex…

Computer Vision and Pattern Recognition · Computer Science 2021-09-14 Nina Shvetsova , Bart Bakker , Irina Fedulova , Heinrich Schulz , Dmitry V. Dylov

Sub-diffusion in biological systems is conventionally treated as anomalous, requiring fractional derivatives, heavy-tailed waiting times, or fitted memory kernels. We argue that this anomaly is an artifact of an incomplete phase space.…

Statistical Mechanics · Physics 2026-05-19 Patrick BarAvi

Experiments using the Surface Force Apparatus (SFA) have found anomalously long ranged charge-charge underscreening in concentrated salt solutions. Meanwhile, theory and simulations have suggested ion clustering to be the possible origin of…

Soft Condensed Matter · Physics 2024-08-13 David Ribar , Clifford E. Woodward , Sture Nordholm , Jan Forsman

Charge correlations in dense ionic fluids give rise to novel effects such as long-range screening and colloidal stabilization which are not predicted by the classic Debye-Huckel theory. We show that a Coulomb or charge-frustrated Ising…

Soft Condensed Matter · Physics 2018-11-01 Nicholas B. Ludwig , Kinjal Dasbiswas , Dmitri V. Talapin , Suriyanarayanan Vaikuntanathan

A density estimation method in a Bayesian nonparametric framework is presented when recorded data are not coming directly from the distribution of interest, but from a length biased version. From a Bayesian perspective, efforts to…

Statistics Theory · Mathematics 2015-10-23 Spyridon J. Hatjispyros , Theodoros Nicoleris , Stephen G. Walker

The aim of the paper is to study the renormalizations of the charge and of the screening length that appear in the large-distance behavior of the effective pairwise interaction between two charges in a dilute electrolyte solution, both…

Statistical Mechanics · Physics 2007-05-23 J. -N. Aqua , F. Cornu

We have measured equilibrium sedimentation profiles in a colloidal model system with confocal microscopy. By tuning the interactions, we have determined the gravitational length in the limit of hard-sphere-like interactions, and using the…

Soft Condensed Matter · Physics 2009-11-10 C. P. Royall , R. van Roij , A. van Blaaderen

Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, such as conceptual…

Machine Learning · Computer Science 2022-10-12 Joseph Gallego-Mejia , Oscar Bustos-Brinez , Fabio Gonzalez

Wireless sensor networks usually comprise a large number of sensors monitoring changes in variables. These changes in variables represent changes in physical quantities. The changes can occur for various reasons; these reasons are…

Machine Learning · Computer Science 2017-08-29 Pelumi Oluwasanya