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Diffusion

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Consensus analysis of networks with time-varying topology and event-triggered diffusions.

Neural networks : the official journal of the International Neural Network Society
This paper studies the consensus problem of networks with time-varying topology. Event-triggered rules are employed in diffusion coupling terms to reduce the updating load of the coupled system. Two strategies are considered: event-triggered strategy...

Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow.

International journal of neural systems
In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as che...

Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusion models as a strategy for enhancing the robustness of machine learning model training. Synthetic data plays a pivotal role in mitigating challenges ...

GANs-guided Conditional Diffusion Model for Synthesizing Contrast-enhanced Computed Tomography Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In contrast to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans can highlight discrepancies between abnormal and normal areas, commonly used in clinical diagnosis of focal liver lesions. However, the use of contrast age...

Innovation Diffusion Across 13 Specialties and Associated Clinician Characteristics.

Advances in health care management
Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new t...

Moringa oleifera-mediated iron oxide nanoparticles, characterization and their anti-proliferative potential on MDA-MB 231 human breast cancer cells.

Pakistan journal of pharmaceutical sciences
Iron oxide nanoparticles (FeO NPs) stabilized with Moringa oleifera (M.O.) were successfully synthesized. The study aimed to explore the cytotoxic, anti-proliferative and anti-microbial potential of FeO NPs through various assays, including trypan bl...

Global Mittag-Leffler synchronization of coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks via sliding mode control.

Chaos (Woodbury, N.Y.)
This paper studies the sliding mode control method for coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks on a directed non-strongly connected topology. A novel fractional integral sliding mode surface and the corresponding...

Prespecified-time bipartite synchronization of coupled reaction-diffusion memristive neural networks with competitive interactions.

Mathematical biosciences and engineering : MBE
In this paper, we investigate the prespecified-time bipartite synchronization (PTBS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with both competitive and cooperative interactions. Two types of bipartite synchronization are con...

A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor.

Briefings in bioinformatics
We present a concatenated deep-learning multiple neural network system for the analysis of single-molecule trajectories. We apply this machine learning-based analysis to characterize the translational diffusion of the nicotinic acetylcholine receptor...

Learning representation for multiple biological networks via a robust graph regularized integration approach.

Briefings in bioinformatics
Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classifi...