2D Canvas Research Articles

CineMol: a programmatically accessible direct-to-SVG 3D small molecule drawer

Effective visualization of small molecules is paramount in conveying concepts and results in cheminformatics. Scalable vector graphics (SVG) are preferred for creating such visualizations, as SVGs can be easily altered in post-production and exported to other formats. A wide spectrum of software applications already exist that can visualize molecules, and customize these visualizations, in many ways. However, software packages that can output projected 3D models onto a 2D canvas directly as SVG, while being programmatically accessible from Python, are lacking. Here, we introduce CineMol, which can draw vectorized approximations of three-dimensional small molecule models in seconds, without triangulation or ray tracing, resulting in files of around 50–300 kilobytes per molecule model for compounds with up to 45 heavy atoms. The SVGs outputted by CineMol can be readily modified in popular vector graphics editing software applications. CineMol is written in Python and can be incorporated into any existing Python cheminformatics workflow, as it only depends on native Python libraries. CineMol also provides programmatic access to all its internal states, allowing for per-atom and per-bond-based customization. CineMol’s capacity to programmatically create molecular visualizations suitable for post-production offers researchers and scientists a powerful tool for enhancing the clarity and visual impact of their scientific presentations and publications in cheminformatics, metabolomics, and related scientific disciplines.Scientific contributionWe introduce CineMol, a Python-based tool that provides a valuable solution for cheminformatics researchers by enabling the direct generation of high-quality approximations of two-dimensional SVG visualizations from three-dimensional small molecule models, all within a programmable Python framework. CineMol offers a unique combination of speed, efficiency, and accessibility, making it an indispensable tool for researchers in cheminformatics, especially when working with SVG visualizations.

An Innovative Approach to Design and Optimize Flow Field Pattern of Polymer Electrolyte Membrane Fuel Cell

To increase power density of polymer electrolyte membrane fuel cells (PEMFCs), further optimization and downsizing of cell and stack design are necessary.To support cell and stack design, we have been developing a three-dimensional (3D) PEMFC simulator P-Stack [1]. P-Stack can estimate power generation performance in good agreement with experimental results, and can clarify relationships between the performance and internal states depending on cell designs, operation conditions, and material properties [1]. Although 3D PEMFC simulations are powerful means, preparing 3D model is very time consuming, thus it was difficult to explore various cell designs.In the present study, we have developed a new modeling technology for PEMFC flow fields. In this technology (Figure (a)), flow fields of anode and cathode gases are drawn by lines in 2D canvas. Properties of the lines such as width, depth and wall angle are given, then it automatically generate 3D model for anode/cathode flow fields and bipolar plates, coolant flow field, seal, gasket and MEA. Power generation performance of the 3D model can be estimated by P-Stack continuously. In addition, the line properties, vertex positions, and the number of repeat can be parametrized, enabling the parametric study of cell design.In this technology, pressure and flow rate distributions can be calculated by a network model based on Darcy’s law [1], without generating 3D model. As shown in Figure (b), the result is in good agreement with computational fluid dynamics (CFD) calculation of 3D model. Although the CFD calculation takes several hours using 32 parallel processes, the calculation of the network model is finished within several seconds using single process. In similar manner, it can calculate flow rate distribution of cells in the stacking axis. Since the execution time per one case is very short, it can be combined with optimization by genetic algorithm (GA).In the presentation, we will talk about a result of the parametric study and optimization for more practical cell design. Influence of flow field design on power generation performance and internal states will be discussed.

ENTROPY REVERSIBILITY SCENARIO – OVER PROJECTIONS AND SIMULATIONS

Combining the Holographic Principles of Leonard Susskind and Simulation Theory of Nick Bostrom, a theory has been reproduced in this paper dictating the negation of the relative entropy persistent in the present, thereby forbidding their collapse as subject to increment of entropy while moving forward in time and decrement of entropy while moving backward in time, preventing a phase of state before the Big Bang or the collapse of the convergence. The universe in its own way diverges taking the simultaneous array of Past, Present and Future with the Bread-Slice concept of time, the reality being augmented by some future advanced civilizations to create a Spatio-Temporal 3D Hologram on a 2D Canvas, projecting through a simulation, thus creating exponential channels of realistic layers with a certain percentile of errors, which are so minimal in present stage, that, the universal constants of nature, G,c,h remains unaltered, which may alter in future if the error fragmentation over simulation takes growth, censoring the future reality in a state of complete superposition excluding us, who are residing in the exponential shadows of simulations.

Human spatial learning strategies in wormhole virtual environments

ABSTRACT Humans can learn spatial information through navigation in the environment. The nature of these spatial representations is constantly debated, including whether they conform to Euclidean geometry. The present study examined the types of Euclidean representations people may form while learning virtual wormhole mazes. Participants explored Euclidean or non-Euclidean tunnel mazes and drew maps of the landmark layout on a 2D canvas. The results showed that people have different, consistent strategies, some mainly preserving distance information while others mainly preserving turning angles. The straightness of the segments was mostly preserved. These results suggest that representations of non-Euclidean space may be highly variable across individuals, and possible Euclidean solutions need to be carefully examined before testing Euclidean vs alternative models.

Temporal scatterplots

Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.

Dynamic Deep Octree for High‐resolution Volumetric Painting in Virtual Reality

AbstractWith virtual reality, digital painting on 2D canvas is now being extended to 3D space. In this paper, we generalize the 2D pixel canvas to a 3D voxel canvas to allow artists to synthesize volumetric color fields. We develop a deep and dynamic octree‐based painting and rendering system using both CPU and GPU to take advantage of the characteristics of both processors (CPU for octree modeling and GPU for volume rendering). On the CPU‐side, we dynamically adjust an octree and incrementally update the octree to GPU with low latency without compromising the frame rates of the rendering. Our octree is balanced and uses a novel 3‐neighbor connectivity for format simplicity and efficient storage, while allowing constant neighbor access time in ray casting. To further reduce the GPU‐side 3‐neighbor computations, we precompute a culling mask in CPU and upload it to GPU. Finally, we analyze the numerical error‐propagation in ray casting through high resolution octree and present a theoretical error bound.

Similarity-based image organization and browsing using multi-resolution self-organizing map

An algorithm is presented in this paper to facilitate the exploration of large image collections based on visual similarities. Starting with an unordered and unannotated set of images, the algorithm first extracts the salient details into feature vectors using both color and gradient information. The feature vectors are then used to train a self-organizing map which maps high-dimensional feature vectors onto a 2D canvas so that images with similar feature vectors are grouped together. When users browse the image collection, an image collage is generated that selects and displays the most pertinent set of images based on which portion of the 2D canvas is currently in view. Flowing from an overview to details is a seamless operation controlled simply by pan and zoom, with representative images selected in a consistent and predictable way. To make organizing larger image collections practical in interactive time, the organization algorithm is designed to run in parallel on graphics processing units. Overall this paper presents an end-to-end solution that facilitates the surfing of image collections in a fresh way.