Automated Solving Algorithm and Application for Oriented Bounding Box Based on Principal Axis of Inertia

In mechanical design and manufacture, the minimum size of raw material of each part should be measured before processing. Generally, this size could be equal to the size of minimum bounding box. When the assembled parts of a product reach an enormous quantity, the measurement would be a rough work. This paper introduced an automated solving algorithm for the oriented bounding box based on principal axis of inertia, combined with CATIA structural tree recursive traversal algorithm according to CAA V5 Automation API, to implement an automated solution of the engineering acceptable minimum oriented bounding box of CATIA model.

Exploring graph traversal algorithms in graph-based molecular generation

Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical

A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.

RNAMotifContrast: a method to discover and visualize RNA structural motif subfamilies

Understanding the 3D structural properties of RNAs will play a critical role in identifying their functional characteristics and designing new RNAs for RNA-based therapeutics and nanotechnology. While several existing computational methods can help in the analysis of RNA properties by recognizing structural motifs, they do not provide the means to compare and contrast those motifs extensively. We have developed a new method, RNAMotifContrast, which focuses on analyzing the similarities and variations of RNA structural motif characteristics. In this method, a graph is formed to represent the similarities among motifs, and a new traversal algorithm is applied to generate visualizations of their structural properties. Analyzing the structural features among motifs, we have recognized and generalized the concept of motif subfamilies. To asses its effectiveness, we have applied RNAMotifContrast on a dataset of known RNA structural motif families. From the results, we observed that the derived subfamilies possess unique structural variations while holding standard features of the families. Overall, the visualization approach of this method presents a new perspective to observe the relation among motifs more closely, and the discovered subfamilies provide opportunities to achieve valuable insights into RNA’s diverse roles.

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

On the complexity of haplotyping a microbial community

Motivation Population-level genetic variation enables competitiveness and niche specialization in microbial communities. Despite the difficulty in culturing many microbes from an environment, we can still study these communities by isolating and sequencing DNA directly from an environment (metagenomics). Recovering the genomic sequences of all isoforms of a given gene across all organisms in a metagenomic sample would aid evolutionary and ecological insights into microbial ecosystems with potential benefits for medicine and biotechnology. A significant obstacle to this goal arises from the lack of a computationally tractable solution that can recover these sequences from sequenced read fragments. This poses a problem analogous to reconstructing the two sequences that make up the genome of a diploid organism (i.e. haplotypes), but for an unknown number of individuals and haplotypes. Results The problem of single individual haplotyping (SIH) was first formalised by Lancia et al. in 2001. Now, nearly two decades later, we discuss the complexity of “haplotyping” metagenomic samples, with a new formalisation of Lancia et al’s data structure that allows us to effectively extend the single individual haplotype problem to microbial communities. This work describes and formalizes the problem of recovering genes (and other genomic subsequences) from all individuals within a complex community sample, which we term the metagenomic individual haplotyping (MIH) problem. We also provide software implementations for a pairwise single nucleotide variant (SNV) co-occurrence matrix and greedy graph traversal algorithm. Availability and implementation Our reference implementation of the described pairwise SNV matrix (Hansel) and greedy haplotype path traversal algorithm (Gretel) are open source, MIT licensed and freely available online at github.com/samstudio8/hansel and github.com/samstudio8/gretel, respectively.