skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Integrating an expanded set of reference types into engineering writing
Engineering student writers must document their reference sources in their theses, papers, proposals, reports, and related documents that they prepare. This is generally done in Microsoft Word or in a LaTeX software package and typically done in the IEEE citation style which is widely used in engineering and technology. In this work, we identify 25 primary reference types and 21 secondary reference types that are used in present-day engineering writing. Because all 46 of these engineering reference types are typically not available in commercial reference management software, we have generated customization files for the widely used EndNote reference management software package that enable referencing to be done using either Cite-While-You-Write (CWYW) for Word users or using BibTeX for LaTeX users. These customization files and instructions on how to install and use them, herein called the Georgia Tech Engineering Reference Management System (GTERMS), are made available on an open-access free-to-use basis.  more » « less
Award ID(s):
1915971
PAR ID:
10527449
Author(s) / Creator(s):
; ;
Publisher / Repository:
TEMPUS Publications
Date Published:
Journal Name:
International journal of engineering education
ISSN:
0949-149X
Subject(s) / Keyword(s):
Engineering writing reference types reference management
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Transposable elements (TEs) are mobile elements capable of introducing genetic changes rapidly. Their importance has been documented in many biological processes, such as introducing genetic instability, altering patterns of gene expression, and accelerating genome evolution. Increasing appreciation of TEs has resulted in a growing number of bioinformatics software to identify insertion events. However, the application of existing tools is limited by either narrow-focused design of the package, too many dependencies on other tools, or prior knowledge required as input files that may not be readily available to all users. Here, we reported a simple pipeline, TEfinder, developed for the detection of new TE insertions with minimal software and input file dependencies. The external software requirements are BEDTools, SAMtools, and Picard. Necessary input files include the reference genome sequence in FASTA format, an alignment file from paired-end reads, existing TEs in GTF format, and a text file of TE names. We tested TEfinder among several evolving populations of Fusarium oxysporum generated through a short-term adaptation study. Our results demonstrate that this easy-to-use tool can effectively detect new TE insertion events, making it accessible and practical for TE analysis. 
    more » « less
  2. Personal cloud storage systems increasingly offer recommendations to help users retrieve or manage files of interest. For example, Google Drive's Quick Access predicts and surfaces files likely to be accessed. However, when multiple, related recommendations are made, interfaces typically present recommended files and any accompanying explanations individually, burdening users. To improve the usability of ML-driven personal information management systems, we propose a new method for summarizing related file-management recommendations. We generate succinct summaries of groups of related files being recommended. Summaries reference the files' shared characteristics. Through a within-subjects online study in which participants received recommendations for groups of files in their own Google Drive, we compare our summaries to baselines like visualizing a decision tree model or simply listing the files in a group. Compared to the baselines, participants expressed greater understanding and confidence in accepting recommendations when shown our novel recommendation summaries. 
    more » « less
  3. Sun, Xiaoyong (Ed.)
    Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer’s accuracies, class-level precisions or user’s accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development. 
    more » « less
  4. Abstract SummaryMolecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed “BioModME,” a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. Availability and implementationAll relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME. 
    more » « less
  5. Molecular docking is a computational technique used to predict ligand binding potential, conformation, and location for a given receptor, and is regarded as an attractive method to use in drug design due to its relatively low computational and monetary cost. However, molecular docking programs tend not to be accessible to novice users. Most docking programs require at least a basic knowledge of command line and computer programming to install and configure the program. Additionally, tutorials for the most commonly used programs tend to be inflexible, requiring a specific molecule or set of molecules to be bound to a specific receptor, and need the installation and usage of other programs or websites to download and prepare structures. To increase general access to molecular docking, basil_dock utilizes a series of easy-to-use Jupyter notebooks that do not assume user familiarity with molecular docking procedures and concepts, requiring little command line usage and software installation. The series includes four notebooks that were created to reflect the different steps in the molecular docking process: (1) the preparation of ligand and protein files prior to docking, (2) the docking of ligands to a protein receptor, (3) analyzing the resulting data and determining how different functional groups in the ligand can affect protein-ligand binding, and (4) identifying essential locations for binding within the ligand and protein. The notebooks enable novice users flexibility and customization in exploring docking procedures and systems, as well as teaching users the basis behind molecular docking without having to leave the environment to obtain information and materials from other applications. The first version of basil_dock allows users to choose from receptors uploaded to the Protein Data Bank and to add additional ligands as desired. Users can then select between the Vina and Smina docking engines and change ligand functional groups to see how the substitution of atom groups affects binding affinity and ligand conformation. The data can then be analyzed to determine residues in the receptor and atom groups in the ligand that are likely to be integral to forming the ligand-protein complex and to discern which ligands are likely to be orally bioactive based on Lipinski’s Rule of Five. From this work, a package of python scripts has been created to streamline the generating, splitting, and writing of ligand files, greatly reducing the number of errors arising from attempting to split a comprehensive ligand file manually. Libraries used in basil_dock include Vina, Smina, RDKit, openbabel, and MDAnalysis. While the package has been designed based off the needs of basil_dock, it has been created to be extensible. Support for this project was provided by NSF 2142033 
    more » « less