Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799358
Timur Guler, MacKenzye Leroy, C. O'Brien, Ryan Pindale
Large-scale instruments are vital to the progression of scientific discovery. Instrument downtime often stalls research; by reducing downtime, experimenters can increase research productivity and attain higher returns on investment. Our team focused on instruments of high complexity, where electrical issues in various subcomponents have the potential to cause problems ranging from simple experimental failure to catastrophic system damage. We propose a novel approach for preemptive detection of electrical faults using a variety of machine learning methods on signal data from Oak Ridge Laboratory's Spallation Neutron Source (SNS) particle accelerator. We compared four methods: a prototypical network that uses Symbolic Fourier Approximation for feature engineering and few shot learning for training, a Gaussian Process Classifier, an Approximated Bayesian Neural Network using Monte Carlo Dropout, and an LSTM Autoencoder. We evaluate these methods based on their ROC curves and provide a general commentary on the advantages and disadvantages of each method. Our results demonstrate capacity for identifying the imminence of certain failure states and provide avenues for future enhancement.
{"title":"Preemptive Detection of Electrical System Anomalies in Particle Accelerators","authors":"Timur Guler, MacKenzye Leroy, C. O'Brien, Ryan Pindale","doi":"10.1109/sieds55548.2022.9799358","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799358","url":null,"abstract":"Large-scale instruments are vital to the progression of scientific discovery. Instrument downtime often stalls research; by reducing downtime, experimenters can increase research productivity and attain higher returns on investment. Our team focused on instruments of high complexity, where electrical issues in various subcomponents have the potential to cause problems ranging from simple experimental failure to catastrophic system damage. We propose a novel approach for preemptive detection of electrical faults using a variety of machine learning methods on signal data from Oak Ridge Laboratory's Spallation Neutron Source (SNS) particle accelerator. We compared four methods: a prototypical network that uses Symbolic Fourier Approximation for feature engineering and few shot learning for training, a Gaussian Process Classifier, an Approximated Bayesian Neural Network using Monte Carlo Dropout, and an LSTM Autoencoder. We evaluate these methods based on their ROC curves and provide a general commentary on the advantages and disadvantages of each method. Our results demonstrate capacity for identifying the imminence of certain failure states and provide avenues for future enhancement.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Student well-being has been affected by the COVID-19 pandemic. Albemarle County Public Schools (ACPS) has collected a significant and varied amount of K-12 student data throughout COVID-19. Researchers seek to utilize the student data to drive evidence-based policy changes with regard to ACPS student well-being. A structured data system for performing school-related research associated with the well-being of students throughout the pandemic does not exist. We have designed a sustainable, relational data structure for data consolidation and to advance the ongoing research initiatives related to COVID-19 student well-being in collaboration with ACPS. The data structure aims to play an important role in promoting student well-being policies through simplifying data collection, enhancing analysis, and acting as an ongoing tool that can support future phases of research. The design architecture includes a relational database populated with de-identified student data to be hosted in the cloud. Design implementation includes data cleaning, data preprocessing, populating the database, and querying data for validation. Specialized queries are utilized to answer the early questions posed to the data. Validation testing is performed to confirm the database is working as expected. Details of the data pipeline, validation, best data practices, and database design are discussed in the paper.
{"title":"Designing a Sustainable Data Structure for Exploring Student Well-Being Patterns in Albemarle County Public Schools Throughout COVID-19","authors":"Katherine Grace Brickley, Jeannette Jiang, Maureen O'Shea","doi":"10.1109/sieds55548.2022.9799432","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799432","url":null,"abstract":"Student well-being has been affected by the COVID-19 pandemic. Albemarle County Public Schools (ACPS) has collected a significant and varied amount of K-12 student data throughout COVID-19. Researchers seek to utilize the student data to drive evidence-based policy changes with regard to ACPS student well-being. A structured data system for performing school-related research associated with the well-being of students throughout the pandemic does not exist. We have designed a sustainable, relational data structure for data consolidation and to advance the ongoing research initiatives related to COVID-19 student well-being in collaboration with ACPS. The data structure aims to play an important role in promoting student well-being policies through simplifying data collection, enhancing analysis, and acting as an ongoing tool that can support future phases of research. The design architecture includes a relational database populated with de-identified student data to be hosted in the cloud. Design implementation includes data cleaning, data preprocessing, populating the database, and querying data for validation. Specialized queries are utilized to answer the early questions posed to the data. Validation testing is performed to confirm the database is working as expected. Details of the data pipeline, validation, best data practices, and database design are discussed in the paper.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129639784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799298
Andrew J. Sklavounos, Daniel I. Castaneda
The Industrial Revolution in the 19th Century transformed the United States from a largely agrarian country to an industrialized nation. Many technical solutions met technical needs, which contributed to changing qualities of life for the better and worse. As innovative technologies were adopted to enhance human comforts, typical technology practices of the time overlooked impacts to the environment and to human health. Widespread areas of air and water became polluted, and dangerous working conditions were commonplace. Engineers participated in this revolution, as well, by creating new infrastructure to move manufactured goods. While engineering solutions were similarly technical in nature, there exist rare examples in the 19th Century where civil and structural engineering followed an atypical, empathetic approach to meet the needs of people. The design and construction of the Linville Creek Bridge in 1898 at Broadway, Virginia, for example, is a unique case study that structural engineers and engineers-in-training will be interested to learn about due to the project's incorporation of community input in its technical design and construction. This project is an early example of a bridge's design and siting being informed by the community in which it is located. This paper presents an argument that the design and construction of this 19th Century bridge followed steps that are similar to the modern-era human-centered Design Thinking (DT) process now used in the 21st Century. We highlight how civic leaders and engineers in the town of Broadway used the community's input to empathize, define, ideate, prototype, and test a bridge that would meet the needs of the community. Thus, even though the DT process did not exist at the time, the specific steps undertaken helped to create and design a unique truss structure that fit the desires and needs of the community and contributed toward the truss bridge gaining sociocultural value over time, now recognized as a historic landmark. This case study exploration of the DT steps being used by structural engineers in the 19th Century is a valuable lesson for structural engineers of the 21st Century to gain insight into how structural engineering has been conducted in the past and at present to see how the incorporation of human dimensions of engineering problem solving has grown tremendously. The sociotechnical success of the Linville Creek Bridge shows how it is valuable to meet the needs of the people in structural engineering design and construction to create successful structures that satisfy the needs of communities for decades to come.
{"title":"The Linville Creek Bridge: A Case Study of Design Thinking in Structural Engineering","authors":"Andrew J. Sklavounos, Daniel I. Castaneda","doi":"10.1109/sieds55548.2022.9799298","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799298","url":null,"abstract":"The Industrial Revolution in the 19th Century transformed the United States from a largely agrarian country to an industrialized nation. Many technical solutions met technical needs, which contributed to changing qualities of life for the better and worse. As innovative technologies were adopted to enhance human comforts, typical technology practices of the time overlooked impacts to the environment and to human health. Widespread areas of air and water became polluted, and dangerous working conditions were commonplace. Engineers participated in this revolution, as well, by creating new infrastructure to move manufactured goods. While engineering solutions were similarly technical in nature, there exist rare examples in the 19th Century where civil and structural engineering followed an atypical, empathetic approach to meet the needs of people. The design and construction of the Linville Creek Bridge in 1898 at Broadway, Virginia, for example, is a unique case study that structural engineers and engineers-in-training will be interested to learn about due to the project's incorporation of community input in its technical design and construction. This project is an early example of a bridge's design and siting being informed by the community in which it is located. This paper presents an argument that the design and construction of this 19th Century bridge followed steps that are similar to the modern-era human-centered Design Thinking (DT) process now used in the 21st Century. We highlight how civic leaders and engineers in the town of Broadway used the community's input to empathize, define, ideate, prototype, and test a bridge that would meet the needs of the community. Thus, even though the DT process did not exist at the time, the specific steps undertaken helped to create and design a unique truss structure that fit the desires and needs of the community and contributed toward the truss bridge gaining sociocultural value over time, now recognized as a historic landmark. This case study exploration of the DT steps being used by structural engineers in the 19th Century is a valuable lesson for structural engineers of the 21st Century to gain insight into how structural engineering has been conducted in the past and at present to see how the incorporation of human dimensions of engineering problem solving has grown tremendously. The sociotechnical success of the Linville Creek Bridge shows how it is valuable to meet the needs of the people in structural engineering design and construction to create successful structures that satisfy the needs of communities for decades to come.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114079902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799293
Elizabeth R. Murphy, I. Williams, Elizabeth Martin
Sweet Briar College boasts a sustainability program that maintains the on-campus greenhouse, among other activities. The program requested a solar powered golf cart to aid in the transport of people and produce around campus. Currently, a pickup truck is used to transport produce from the greenhouse to the dining hall. The sustainability program wanted a more environmentally friendly option. The goal of the project is to modify a golf cart to run primarily on solar power. The cart will be used to transport produce from the campus greenhouse to the dining hall as well as to give campus tours. Without any modifications, the golf cart was able to navigate all the areas on campus that it would be required to in order to transport produce and give tours. Based on the runtime and charging time of the unmodified golf cart, both the solar powered runtime and charging time were determined to be between two and three hours. The panel was mounted on the existing roof of the cart and the charge controller was placed below the back seat. Several designs for produce transportation were considered before settling on a removable storage system that fits onto the back seat of the golf cart. This was determined to meet sustain ability, cost, and capacity needs. A system was designed and installed on the rear of the cart. The next steps of the project are to test the system to ensure it can remain structurally sound with the additional weight, the charging rate, and the maximum runtime of the golf cart. By the project timeline the golf cart should be functional, running on solar power, and carrying produce by May.
{"title":"Designing a Solar Powered Golf Cart for Sweet Briar College Sustainability","authors":"Elizabeth R. Murphy, I. Williams, Elizabeth Martin","doi":"10.1109/sieds55548.2022.9799293","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799293","url":null,"abstract":"Sweet Briar College boasts a sustainability program that maintains the on-campus greenhouse, among other activities. The program requested a solar powered golf cart to aid in the transport of people and produce around campus. Currently, a pickup truck is used to transport produce from the greenhouse to the dining hall. The sustainability program wanted a more environmentally friendly option. The goal of the project is to modify a golf cart to run primarily on solar power. The cart will be used to transport produce from the campus greenhouse to the dining hall as well as to give campus tours. Without any modifications, the golf cart was able to navigate all the areas on campus that it would be required to in order to transport produce and give tours. Based on the runtime and charging time of the unmodified golf cart, both the solar powered runtime and charging time were determined to be between two and three hours. The panel was mounted on the existing roof of the cart and the charge controller was placed below the back seat. Several designs for produce transportation were considered before settling on a removable storage system that fits onto the back seat of the golf cart. This was determined to meet sustain ability, cost, and capacity needs. A system was designed and installed on the rear of the cart. The next steps of the project are to test the system to ensure it can remain structurally sound with the additional weight, the charging rate, and the maximum runtime of the golf cart. By the project timeline the golf cart should be functional, running on solar power, and carrying produce by May.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"36 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133314104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799290
Derek Banks, Camille Leonard, Shilpa Narayan, Nicholas Thompson, Brandon L. Kramer, Gizem Korkmaz
Open Source Software (OSS) is computer software that has its source code publicly available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Despite its extensive use, reliable measures of the scope and impact of OSS are scarce. In this paper, we focus on packages developed for Python programming language as it is one of the most widely-used languages mainly due to its flexibility and simple syntax that makes its framework easy to learn and share. We aim to develop a framework to measure the impact of Python packages listed on Package Index (PyPI.org). We use data from GitHub repositories (where these packages are developed) to obtain information about their development activity e.g., lines of code. Our goal is to identify influential actors, e.g., packages, developers, countries by using the impact measures. We use network-based and OSS-based measures such as number of downloads. Network-based statistics include centrality measures such as degree, and eigenvector centrality. Moreover, we calcu-late the cost of OSS as intangible capital using the COCOMO II model [1] to determine the cost of development and study the relationship between development cost and impact of Python projects. The findings show that the number of downloads for a package are correlated with the centrality statistics, supporting the hypothesis that the most influential are the most downloaded as well. We show which packages are saving on development cost by leveraging dependencies. This framework and measures can be applied more broadly to the OSS ecosystem and contribute to the National Science Foundation (NSF) policy indicators for measurement of innovation.
开放源码软件(OSS)是一种计算机软件，它的源代码是公开的，并带有许可，其中版权所有者提供了研究、更改和将软件分发给任何人和任何目的的权利。尽管它被广泛使用，但是对OSS的范围和影响的可靠度量是稀缺的。在本文中，我们主要关注Python编程语言开发的包，因为它是最广泛使用的语言之一，主要是由于它的灵活性和简单的语法，使其框架易于学习和共享。我们的目标是开发一个框架来衡量Package Index (PyPI.org)上列出的Python包的影响。我们使用来自GitHub存储库(这些包开发的地方)的数据来获取有关其开发活动的信息，例如代码行。我们的目标是通过使用影响措施确定有影响的行为者，例如，一揽子计划、开发人员和国家。我们使用基于网络和基于oss的指标，如下载量。基于网络的统计包括中心性度量，如度和特征向量中心性。此外，我们使用COCOMO II模型[1]计算OSS作为无形资本的成本，以确定开发成本，并研究开发成本与Python项目影响之间的关系。研究结果表明，一个软件包的下载次数与中心性统计相关，这支持了最具影响力的软件包也是下载次数最多的假设。我们展示了哪些包通过利用依赖关系节省了开发成本。这个框架和措施可以更广泛地应用于OSS生态系统，并有助于国家科学基金会(NSF)衡量创新的政策指标。
{"title":"Measuring the Impact of Open Source Software Innovation Using Network Analysis on GitHub Hosted Python Packages","authors":"Derek Banks, Camille Leonard, Shilpa Narayan, Nicholas Thompson, Brandon L. Kramer, Gizem Korkmaz","doi":"10.1109/sieds55548.2022.9799290","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799290","url":null,"abstract":"Open Source Software (OSS) is computer software that has its source code publicly available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Despite its extensive use, reliable measures of the scope and impact of OSS are scarce. In this paper, we focus on packages developed for Python programming language as it is one of the most widely-used languages mainly due to its flexibility and simple syntax that makes its framework easy to learn and share. We aim to develop a framework to measure the impact of Python packages listed on Package Index (PyPI.org). We use data from GitHub repositories (where these packages are developed) to obtain information about their development activity e.g., lines of code. Our goal is to identify influential actors, e.g., packages, developers, countries by using the impact measures. We use network-based and OSS-based measures such as number of downloads. Network-based statistics include centrality measures such as degree, and eigenvector centrality. Moreover, we calcu-late the cost of OSS as intangible capital using the COCOMO II model [1] to determine the cost of development and study the relationship between development cost and impact of Python projects. The findings show that the number of downloads for a package are correlated with the centrality statistics, supporting the hypothesis that the most influential are the most downloaded as well. We show which packages are saving on development cost by leveraging dependencies. This framework and measures can be applied more broadly to the OSS ecosystem and contribute to the National Science Foundation (NSF) policy indicators for measurement of innovation.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131657259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799321
C. Wolfe, Yayi Feng, David Chen, E. Purcell, Anne M. Talkington, Sepideh Dolatshahi, Heman Shakeri
The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natural defense mechanism, in the vicinity of cancerous cells is essential for developing beneficial treatments. Single cell RNA sequencing (scRNA-seq) enables the examination of gene expression on an individual cell basis, providing crucial information regarding both the disturbances in cell functioning caused by cancer and cell-cell communication in the tumor microenvironment. This novel technique generates large amounts of data, which require proper processing. Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them. We aimed to develop a standardized pipeline (GeoTyper, https://github.com/celineyayifeng/GeoTyper) that integrates multiple scRNA-seq tools for processing raw sequence data extracted from NCBI GEO, visualization of results, statistical analysis, and cell type identification. This pipeline leverages existing tools, such as Cellranger from 10X Genomics, Alevin, and Seurat, to cluster cells and identify cell types based on gene expression profiles. We successfully tested and validated the pipeline on several publicly available scRNA-seq datasets, resulting in clusters corresponding to distinct cell types. By determining the cell types and their respective frequencies in the tumor microenvironment across multiple cancers, this workflow will help quantify changes in gene expression related to cell-cell communication and identify possible therapeutic targets.
{"title":"GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification","authors":"C. Wolfe, Yayi Feng, David Chen, E. Purcell, Anne M. Talkington, Sepideh Dolatshahi, Heman Shakeri","doi":"10.1109/sieds55548.2022.9799321","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799321","url":null,"abstract":"The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natural defense mechanism, in the vicinity of cancerous cells is essential for developing beneficial treatments. Single cell RNA sequencing (scRNA-seq) enables the examination of gene expression on an individual cell basis, providing crucial information regarding both the disturbances in cell functioning caused by cancer and cell-cell communication in the tumor microenvironment. This novel technique generates large amounts of data, which require proper processing. Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them. We aimed to develop a standardized pipeline (GeoTyper, https://github.com/celineyayifeng/GeoTyper) that integrates multiple scRNA-seq tools for processing raw sequence data extracted from NCBI GEO, visualization of results, statistical analysis, and cell type identification. This pipeline leverages existing tools, such as Cellranger from 10X Genomics, Alevin, and Seurat, to cluster cells and identify cell types based on gene expression profiles. We successfully tested and validated the pipeline on several publicly available scRNA-seq datasets, resulting in clusters corresponding to distinct cell types. By determining the cell types and their respective frequencies in the tumor microenvironment across multiple cancers, this workflow will help quantify changes in gene expression related to cell-cell communication and identify possible therapeutic targets.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130899410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799400
Gabe Salinger, Luca Pisani, Hannah Silvestro, Nathan Herdener, Ciara M. Sibley, Joseph T. Coyne, Sabrina Drollinger, Kenneth M. King, Chad C. Tossell
The United States military is suffering from a pilot shortage and lack of diversity in its corps. Thus, selecting capable pilots from the widest range of talent is a priority for the United States Air Force (USAF). As part of this goal, our project aimed to modernize the Test of Basic Aviation Skills (TBAS) - one of the USAF's three primary methods of assessing an individual's potential for successfully completing pilot training - in order to better predict pilot performance and reduce unnecessary demographic subgroup differences. A prototype system and initial experiment was developed to this end. After obtaining requirements, conducting a system analysis, and a review of previous research, we designed the Pilot Selection and Assessment for Future Flight (PSAFF) system as a capability to conduct experiments on novel tests, methods, and technologies that have potential in a modernized version of the TBAS. Our proof-of-concept experiment examined two types of controls to perform spatial tasks in PSAFF taking into account gender, gaming, usability, and face validity. We found no performance differences in these controls. However, variance between individuals was elicited based on performance and results show the two devices hold promise for replacing outdated equipment and encourages future work to explore and compare input devices with larger sample sizes. The analysis will enable future design iterations and studies.
{"title":"Modernizing Pilot Selection Methods & Technologies at the United States Air Force Academy","authors":"Gabe Salinger, Luca Pisani, Hannah Silvestro, Nathan Herdener, Ciara M. Sibley, Joseph T. Coyne, Sabrina Drollinger, Kenneth M. King, Chad C. Tossell","doi":"10.1109/sieds55548.2022.9799400","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799400","url":null,"abstract":"The United States military is suffering from a pilot shortage and lack of diversity in its corps. Thus, selecting capable pilots from the widest range of talent is a priority for the United States Air Force (USAF). As part of this goal, our project aimed to modernize the Test of Basic Aviation Skills (TBAS) - one of the USAF's three primary methods of assessing an individual's potential for successfully completing pilot training - in order to better predict pilot performance and reduce unnecessary demographic subgroup differences. A prototype system and initial experiment was developed to this end. After obtaining requirements, conducting a system analysis, and a review of previous research, we designed the Pilot Selection and Assessment for Future Flight (PSAFF) system as a capability to conduct experiments on novel tests, methods, and technologies that have potential in a modernized version of the TBAS. Our proof-of-concept experiment examined two types of controls to perform spatial tasks in PSAFF taking into account gender, gaming, usability, and face validity. We found no performance differences in these controls. However, variance between individuals was elicited based on performance and results show the two devices hold promise for replacing outdated equipment and encourages future work to explore and compare input devices with larger sample sizes. The analysis will enable future design iterations and studies.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128220933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799353
Ana Daley, Arjun Ganesh, Juliet Holmes, Aparna Marathe
Groundwater, water flowing beneath the Earth's surface, provides the largest and most accessed source of freshwater. When groundwater is contaminated, the pollutant will disperse and travel in the same direction as the flow of groundwater, which directly threatens the integrity of drinking water and irrigation. All instances of groundwater contamination incur environmental, health, and monetary costs, but when not mitigated promptly, these costs can increase drastically. Currently, the method for determining the direction a contaminant plume will travel requires physically visiting the site and surveying the groundwater. This project addresses this issue by leveraging geospatial data and statistical learning methods. The aims of this project were two-fold. First, we aggregated known features, relevant to the direction of groundwater flow, at sites across the United States into a database. Having a centralized source of data regarding these properties is an improvement on the current system of sparse, disjoint, and at times inaccessible data sets. Second, we utilized that data in conjunction with machine learning techniques to develop a model that receives latitude and longitude as inputs and generates a prediction of the direction of groundwater flow at any location within the United States. Having accurate predictions directly improves efficiency by reducing response times and overall mitigation costs. We validated our model predictions against the known direction of groundwater flow using the smallest angle differences between the two.
{"title":"Predicting the Direction of Groundwater Flow Using Geospatial Data Analysis","authors":"Ana Daley, Arjun Ganesh, Juliet Holmes, Aparna Marathe","doi":"10.1109/sieds55548.2022.9799353","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799353","url":null,"abstract":"Groundwater, water flowing beneath the Earth's surface, provides the largest and most accessed source of freshwater. When groundwater is contaminated, the pollutant will disperse and travel in the same direction as the flow of groundwater, which directly threatens the integrity of drinking water and irrigation. All instances of groundwater contamination incur environmental, health, and monetary costs, but when not mitigated promptly, these costs can increase drastically. Currently, the method for determining the direction a contaminant plume will travel requires physically visiting the site and surveying the groundwater. This project addresses this issue by leveraging geospatial data and statistical learning methods. The aims of this project were two-fold. First, we aggregated known features, relevant to the direction of groundwater flow, at sites across the United States into a database. Having a centralized source of data regarding these properties is an improvement on the current system of sparse, disjoint, and at times inaccessible data sets. Second, we utilized that data in conjunction with machine learning techniques to develop a model that receives latitude and longitude as inputs and generates a prediction of the direction of groundwater flow at any location within the United States. Having accurate predictions directly improves efficiency by reducing response times and overall mitigation costs. We validated our model predictions against the known direction of groundwater flow using the smallest angle differences between the two.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133898673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799334
Gowri Prathap, Ekrem Kaya, Luke Palmieri, A. Korb, Saltuk Karahan, H. Kavak
Misinformation and disinformation are two significant challenges of our century with societal, political, and economic implications. This study focuses on building a software system to investigate the role of social media in instilling anti-American sentiment among US allies through misinformation and disinformation efforts. Our system has four major components, which are executed stepwise: (1) Data collection, (2) Data handling, (3) Machine learning, and (4) Analysis. We designed and implemented this system for Twitter using the Python ecosystem. As a use case, we selected Turkey - a US ally and NATO-member country with notable support of anti-American views. We automatically translated the tweets into English and used sentiment and emotion analysis to determine support for or opposition to the USA. Then, we categorized people into bots and non-bots. From Jan 2019-Dec 2021, there were 11,988,406 Turkish tweets related to the USA. Our data showed several peaks, such as President Biden's inauguration day on January 20, 2021 and Biden's recognition of the Armenian Genocide on April 24, 2021. Turkish tweets against the United States are dominated by disgust, followed by anger and fear.
{"title":"A System to Study Anti-American Misinformation and Disinformation Efforts on Social Media","authors":"Gowri Prathap, Ekrem Kaya, Luke Palmieri, A. Korb, Saltuk Karahan, H. Kavak","doi":"10.1109/sieds55548.2022.9799334","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799334","url":null,"abstract":"Misinformation and disinformation are two significant challenges of our century with societal, political, and economic implications. This study focuses on building a software system to investigate the role of social media in instilling anti-American sentiment among US allies through misinformation and disinformation efforts. Our system has four major components, which are executed stepwise: (1) Data collection, (2) Data handling, (3) Machine learning, and (4) Analysis. We designed and implemented this system for Twitter using the Python ecosystem. As a use case, we selected Turkey - a US ally and NATO-member country with notable support of anti-American views. We automatically translated the tweets into English and used sentiment and emotion analysis to determine support for or opposition to the USA. Then, we categorized people into bots and non-bots. From Jan 2019-Dec 2021, there were 11,988,406 Turkish tweets related to the USA. Our data showed several peaks, such as President Biden's inauguration day on January 20, 2021 and Biden's recognition of the Armenian Genocide on April 24, 2021. Turkish tweets against the United States are dominated by disgust, followed by anger and fear.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131668342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799309
Hania Abboud, Erin Baker, Teagan Baiotto, Christopher Weigand, J. Quinn
The Colorado River Basin is experiencing water shortages of increasing severity and frequency. Due to the scale of the Colorado River Basin, its multitude of end users, and ecosystems dependent on its consistent water supply, these water shortages present a dire problem. If the Colorado River ran dry it would lead to a loss of $1.4 trillion in economic activity [1] not to mention the fatal impact on wildlife, Western populations, and environmental systems within its watershed. Our work focuses on optimizing Lake Mead reservoir operations to support the Lower Colorado River Basin. We produce alternative reservoir operating rules to address historic and present environmental justice issues. Our final results will be fundamentally driven by the hydrological budget of the river system and by human and environmental needs both now and in the future. We couple the Borg multi-objective optimization algorithm with the RiverWare river model, a decision support system, to design operating rules that prioritize flows to Native American reservations and tribal groups and Mexico. Our work distinguishes itself from past optimization applications by explicitly considering climate change scenarios and potential impacts on water justice issues faced by Native American tribal communities. Our results aid in identifying promising reservoir management alternatives at Lake Mead for controlling droughts both now and under future possible climate conditions. This work can inform the redesign of the Basin's operating policies after the current Interim Guidelines expire at the end of 2025.
{"title":"Optimizing for Water Equity in the Colorado River Basin","authors":"Hania Abboud, Erin Baker, Teagan Baiotto, Christopher Weigand, J. Quinn","doi":"10.1109/sieds55548.2022.9799309","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799309","url":null,"abstract":"The Colorado River Basin is experiencing water shortages of increasing severity and frequency. Due to the scale of the Colorado River Basin, its multitude of end users, and ecosystems dependent on its consistent water supply, these water shortages present a dire problem. If the Colorado River ran dry it would lead to a loss of $1.4 trillion in economic activity [1] not to mention the fatal impact on wildlife, Western populations, and environmental systems within its watershed. Our work focuses on optimizing Lake Mead reservoir operations to support the Lower Colorado River Basin. We produce alternative reservoir operating rules to address historic and present environmental justice issues. Our final results will be fundamentally driven by the hydrological budget of the river system and by human and environmental needs both now and in the future. We couple the Borg multi-objective optimization algorithm with the RiverWare river model, a decision support system, to design operating rules that prioritize flows to Native American reservations and tribal groups and Mexico. Our work distinguishes itself from past optimization applications by explicitly considering climate change scenarios and potential impacts on water justice issues faced by Native American tribal communities. Our results aid in identifying promising reservoir management alternatives at Lake Mead for controlling droughts both now and under future possible climate conditions. This work can inform the redesign of the Basin's operating policies after the current Interim Guidelines expire at the end of 2025.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130194484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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