Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge-bundling through splines; (b) visualization of "branched " clusters to reveal the distribution of the data; (c) opacity-based hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes;and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non-experts.
Cerium oxide nanoparticles have found numerous applications in the biomedical industry due to their strong antioxidant properties. In the current study, we report the influence of nine different physical and chemical parameters: pH, aeration and, concentrations of MgSO4, CaCl2, KCl, natural organic matter, fructose, nanoparticles and Escherichia coli, on the antibacterial activity of dextran coated cerium oxide nanoparticles. A least-squares quadratic regression model was developed to understand the collective influence of the tested parameters on the anti-bacterial activity and subsequently a computer-based, interactive visualization tool was developed. The visualization allows us to elucidate the effect of each of the parameters in combination with other parameters, on the antibacterial activity of nanoparticles. The results indicate that the toxicity of CeO2 NPs depend on the physical and chemical environment; and in a majority of the possible combinations of the nine parameters, non-lethal to the bacteria. In fact, the cerium oxide nanoparticles can decrease the anti-bacterial activity exerted by magnesium and potassium salts.
We present a novel surface reconstruction algorithm that can recover high-quality surfaces from noisy and defective data sets without any normal or orientation information. A set of new techniques are introduced to afford extra noise tolerability, robust orientation alignment, reliable outlier removal, and satisfactory feature recovery. In our algorithm, sample points are first organized by an octree. The points are then clustered into a set of monolithically singlyoriented groups. The inside/outside orientation of each group is determined through a robust voting algorithm. We locally fit an implicit quadric surface in each octree cell. The locally fitted implicit surfaces are then blended to produce a signed distance field using the modified Shepard's method. We develop sophisticated iterative fitting algorithms to afford improved noise tolerance both in topology recognition and geometry accuracy. Furthermore, this iterative fitting algorithm, coupled with a local model selection scheme, provides a reliable sharp feature recovery mechanism even in the presence of bad input.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.