Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799387
A.F. Chesser, K. Bramlett, A. Atchley, C. Gray, N. Tenhundfeld
Virtual personal assistants (VPAs) like Siri and Alexa have become common objects in households. Users frequently rely on these systems to search the internet or help retrieve information. As such, it is important to know how using these products affect cognitive processes like memory. Previous research suggests that visual speech perception influences auditory perception in human-human interactions. However, many of these VPAs are designed as a box or sphere that does not interact with the user visually. This lack of visual speech perception when interacting with a VPA could affect the human interaction with a system and their retention of information such as determining how many ounces are in a cup or how to greet someone in another language. This poses the question of whether the design of these VPAs is preventing the ability of users to retain the information they get from these systems. To test this, we designed an experiment that will explore interactions between user memory and either a traditional audio presentation (as is found with Siri or Alexa, for example) or one that allows for visual speech perception. Participants were asked to listen to an audio clip of a nonsensical story. In one condition, participants were asked to listen while looking at a blank screen (analogous to the lack of visual feedback inherent when working with current VPA designs). After a block of 25 audio clips, the participants took a test on the information heard. This process was repeated with an animated face with synchronized mouth movements instead of a black screen. Other participants will experience the same two presentations, but in reverse order as to counterbalance condition presentation. Data collection is currently underway. We predicted that VPA paired with synchronized lip movement would promote visual speech perception and thus help participants retain information. While we are still collecting data, the trend currently does not show a significant difference between audio and lip movement conditions. This could be an indication of differing abilities in lipreading.
{"title":"Virtual Personal Assistant Design Effects on Memory Encoding","authors":"A.F. Chesser, K. Bramlett, A. Atchley, C. Gray, N. Tenhundfeld","doi":"10.1109/sieds55548.2022.9799387","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799387","url":null,"abstract":"Virtual personal assistants (VPAs) like Siri and Alexa have become common objects in households. Users frequently rely on these systems to search the internet or help retrieve information. As such, it is important to know how using these products affect cognitive processes like memory. Previous research suggests that visual speech perception influences auditory perception in human-human interactions. However, many of these VPAs are designed as a box or sphere that does not interact with the user visually. This lack of visual speech perception when interacting with a VPA could affect the human interaction with a system and their retention of information such as determining how many ounces are in a cup or how to greet someone in another language. This poses the question of whether the design of these VPAs is preventing the ability of users to retain the information they get from these systems. To test this, we designed an experiment that will explore interactions between user memory and either a traditional audio presentation (as is found with Siri or Alexa, for example) or one that allows for visual speech perception. Participants were asked to listen to an audio clip of a nonsensical story. In one condition, participants were asked to listen while looking at a blank screen (analogous to the lack of visual feedback inherent when working with current VPA designs). After a block of 25 audio clips, the participants took a test on the information heard. This process was repeated with an animated face with synchronized mouth movements instead of a black screen. Other participants will experience the same two presentations, but in reverse order as to counterbalance condition presentation. Data collection is currently underway. We predicted that VPA paired with synchronized lip movement would promote visual speech perception and thus help participants retain information. While we are still collecting data, the trend currently does not show a significant difference between audio and lip movement conditions. This could be an indication of differing abilities in lipreading.","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":"130611593","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/sieds55548.2022.9799344
A. T. Boland, Claire K. DeViney, Jeffrey R. Justice, Estefania D. Pages Arce, Emily C. Wiele, Nathan J. Wiens, G. Louis
Globally, coastal communities and Small Island Developing States (SIDS) are most at risk of food insecurity due to a variety of natural and economic factors [1]. Agricultural systems in these areas have a high level of exposure to climate risks including extreme weather and sea level rise [2]. The populations that are most vulnerable to the risk of food insecurity are lower-income, indigenous, rural, ethnic, and religious minority groups, as well as women and children [3]. Hydroponic Crop Cultivation (HCC) is a method of farming in which crops are grown in a nutrient rich solution in order to decrease the amount of resources, time, and space needed to grow. The project seeks to understand the role that HCC can play in mitigating risks to global food security and nutrition (GFSN) through three facets: 1) evaluation of the potential applications for HCC, including: SIDS, refugee camps, food deserts, rooftop gardens and apartment units, 2) ranking HCC against other technologies for GFSN risk mitigation, 3) build and test a floating, storm-resilient HCC system for the special case of GFSN in SIDS. The first two objectives will be ranked by a multi-criteria decision making (MCDM) method to determine the optimal use case while the last objective will be measured by the construction of a physical prototype. The system will use the Dutch bucket method of HCC to grow larger root crops, as well as enabling the functionality to grow multiple varieties of crops within the same system. The system will float in standing water and be able to withstand a reasonable amount of wind load, to allow the system to survive hurricanes. The HCC system relies on solar photovoltaic power to operate the HCC system, and will be designed to provide up to 72 hours of emergency power for communications and lighting. The functionality of the system will be assessed by testing in a calm water environment as well as simulations of wind loading.
{"title":"Hydroponic Crop Cultivation as a Strategy for Reducing Food Insecurity","authors":"A. T. Boland, Claire K. DeViney, Jeffrey R. Justice, Estefania D. Pages Arce, Emily C. Wiele, Nathan J. Wiens, G. Louis","doi":"10.1109/sieds55548.2022.9799344","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799344","url":null,"abstract":"Globally, coastal communities and Small Island Developing States (SIDS) are most at risk of food insecurity due to a variety of natural and economic factors [1]. Agricultural systems in these areas have a high level of exposure to climate risks including extreme weather and sea level rise [2]. The populations that are most vulnerable to the risk of food insecurity are lower-income, indigenous, rural, ethnic, and religious minority groups, as well as women and children [3]. Hydroponic Crop Cultivation (HCC) is a method of farming in which crops are grown in a nutrient rich solution in order to decrease the amount of resources, time, and space needed to grow. The project seeks to understand the role that HCC can play in mitigating risks to global food security and nutrition (GFSN) through three facets: 1) evaluation of the potential applications for HCC, including: SIDS, refugee camps, food deserts, rooftop gardens and apartment units, 2) ranking HCC against other technologies for GFSN risk mitigation, 3) build and test a floating, storm-resilient HCC system for the special case of GFSN in SIDS. The first two objectives will be ranked by a multi-criteria decision making (MCDM) method to determine the optimal use case while the last objective will be measured by the construction of a physical prototype. The system will use the Dutch bucket method of HCC to grow larger root crops, as well as enabling the functionality to grow multiple varieties of crops within the same system. The system will float in standing water and be able to withstand a reasonable amount of wind load, to allow the system to survive hurricanes. The HCC system relies on solar photovoltaic power to operate the HCC system, and will be designed to provide up to 72 hours of emergency power for communications and lighting. The functionality of the system will be assessed by testing in a calm water environment as well as simulations of wind loading.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"68 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":"126927805","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/sieds55548.2022.9799365
Clarissa R. Jolley, Hannah J. Lee, Kristen A. Lucas, William P. McDevitt
Assessing DNA to determine the biogeographic ancestry of an individual continues to be a major task in forensic laboratories across the world. Due to the costly nature associated with full-scale genomic data acquisition and processing, many forensic laboratories lack the ability to conduct comprehensive genetic testing involving analyzing ancestry-informative single nucleotide polymorphisms (aiSNP), therefore, creating the need for more cost effective sources of information. In the present study, we assessed the use of machine learning (ML) approaches in the analysis of short tandem repeats (STRs), non-coding repeats of a short sequence of DNA, in order to determine biogeographic ancestry. STRs are repeat sequences in which a unit of 1-to-25 nucleotides in length exists at various locations across the genome. Because of the high variability of STRs, STRs are widely used for creating unique genetic profiles of different individuals. We analyzed the performance of selected loci in random forest classification models using anonymized STR data, provided by the US Department of Defense (DoD), collected from $mathrm{N}=1747$ subjects across $mathrm{K}=5$ continents in order to predict the continental origins of each individual given their genome. Supervised classification test accuracy of subjects varied from $sim45%$ to $> 60%$ while 10-fold training accuracy varied from 60% to $sim80%$ across the profiles surveyed. Unsupervised clustering test accuracy was reported to be $sim35%$. Our findings indicate that there is a significant possibility in using STR data as a novel method for continental ancestry prediction, and with further research, high accuracy may be reached. We conclude this article with comments on future strategies for parameter optimization to maximize utility of STR analysis which may be beneficial to smaller laboratories as well as expedite biogeographic ancestry for forensic professionals and law enforcement officials.
{"title":"Short Tandem Repeat Analysis as a Novel Method for Biogeographic Ancestry Prediction","authors":"Clarissa R. Jolley, Hannah J. Lee, Kristen A. Lucas, William P. McDevitt","doi":"10.1109/sieds55548.2022.9799365","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799365","url":null,"abstract":"Assessing DNA to determine the biogeographic ancestry of an individual continues to be a major task in forensic laboratories across the world. Due to the costly nature associated with full-scale genomic data acquisition and processing, many forensic laboratories lack the ability to conduct comprehensive genetic testing involving analyzing ancestry-informative single nucleotide polymorphisms (aiSNP), therefore, creating the need for more cost effective sources of information. In the present study, we assessed the use of machine learning (ML) approaches in the analysis of short tandem repeats (STRs), non-coding repeats of a short sequence of DNA, in order to determine biogeographic ancestry. STRs are repeat sequences in which a unit of 1-to-25 nucleotides in length exists at various locations across the genome. Because of the high variability of STRs, STRs are widely used for creating unique genetic profiles of different individuals. We analyzed the performance of selected loci in random forest classification models using anonymized STR data, provided by the US Department of Defense (DoD), collected from $mathrm{N}=1747$ subjects across $mathrm{K}=5$ continents in order to predict the continental origins of each individual given their genome. Supervised classification test accuracy of subjects varied from $sim45%$ to $> 60%$ while 10-fold training accuracy varied from 60% to $sim80%$ across the profiles surveyed. Unsupervised clustering test accuracy was reported to be $sim35%$. Our findings indicate that there is a significant possibility in using STR data as a novel method for continental ancestry prediction, and with further research, high accuracy may be reached. We conclude this article with comments on future strategies for parameter optimization to maximize utility of STR analysis which may be beneficial to smaller laboratories as well as expedite biogeographic ancestry for forensic professionals and law enforcement officials.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"78 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":"121658391","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/sieds55548.2022.9799323
Christos Chen, M. Guirguis, D. Klein, Donald Brown, Marcel Durietix, Bhiken L. Naik, Christian Ndaribitse
In Rwanda and many low-and-middle-income countries (LMIC), surgical, critical care, and anesthesia flowsheets are handwritten by medical professionals due to the lack of digital infrastructure necessary to support digitization systems. Therefore, many LMIC lack macro-level health data that can be utilized to quantify and improve existing healthcare outcomes. Literature has championed post operative mortality rate (POMR) as a key indicator for institutional and national surgical safety [1]. Many surgical operations deemed as “low-risk” in high income countries (HIC) have a surgical mortality rate in LMIC more than ten times that of HIC[2]. Striving to lower POMR in LMIC, the University of Virginia (UVA) is partnering with the University Teaching Hospital of Kigali in Rwanda (CHUK) to digitize anesthesia and intraoperative paper health records. Over the past two years, UVA student capstone teams have contributed in establishing a consistent and reliable system to scan and obtain the surgical flowsheets. The focus of 2021–2022 is to design and implement a data pipeline system that enables Rwandan medical professionals at CHUK to digitize paper surgical flowsheets via a mobile application and receive rapid risk-based notifications. The application enables medical professionals to quickly engage with pertinent perioperative data relevant for improving patient outcomes while also ensuring secure storage of the data, which in turn enables macro-level research for Rwanda's healthcare system. The design presented in this paper enables the user to rapidly upload anesthesia records and receive an email notification regarding hypotension risk data in, on average, 37 seconds. Leveraging AWS storage enables 1000 GB per month and demand-based scaling, dwarfing previous storage capabilities. Compared to the previous system, the average upload time decreased 81.7% from 40 seconds to 7.34 seconds with the usage of the newly designed system. In addition, the new system does not lead to an increase in system failures, where the user is unable to proceed with the usage of the application, which remains at 0% in the newly designed version.
{"title":"Data Pipeline for Digitizing Perioperative Flowsheets from Low Middle Income Countries","authors":"Christos Chen, M. Guirguis, D. Klein, Donald Brown, Marcel Durietix, Bhiken L. Naik, Christian Ndaribitse","doi":"10.1109/sieds55548.2022.9799323","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799323","url":null,"abstract":"In Rwanda and many low-and-middle-income countries (LMIC), surgical, critical care, and anesthesia flowsheets are handwritten by medical professionals due to the lack of digital infrastructure necessary to support digitization systems. Therefore, many LMIC lack macro-level health data that can be utilized to quantify and improve existing healthcare outcomes. Literature has championed post operative mortality rate (POMR) as a key indicator for institutional and national surgical safety [1]. Many surgical operations deemed as “low-risk” in high income countries (HIC) have a surgical mortality rate in LMIC more than ten times that of HIC[2]. Striving to lower POMR in LMIC, the University of Virginia (UVA) is partnering with the University Teaching Hospital of Kigali in Rwanda (CHUK) to digitize anesthesia and intraoperative paper health records. Over the past two years, UVA student capstone teams have contributed in establishing a consistent and reliable system to scan and obtain the surgical flowsheets. The focus of 2021–2022 is to design and implement a data pipeline system that enables Rwandan medical professionals at CHUK to digitize paper surgical flowsheets via a mobile application and receive rapid risk-based notifications. The application enables medical professionals to quickly engage with pertinent perioperative data relevant for improving patient outcomes while also ensuring secure storage of the data, which in turn enables macro-level research for Rwanda's healthcare system. The design presented in this paper enables the user to rapidly upload anesthesia records and receive an email notification regarding hypotension risk data in, on average, 37 seconds. Leveraging AWS storage enables 1000 GB per month and demand-based scaling, dwarfing previous storage capabilities. Compared to the previous system, the average upload time decreased 81.7% from 40 seconds to 7.34 seconds with the usage of the newly designed system. In addition, the new system does not lead to an increase in system failures, where the user is unable to proceed with the usage of the application, which remains at 0% in the newly designed version.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"80 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":"131251192","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/sieds55548.2022.9799341
Yulian Arencibia, Nathaniel Garrido, Charles Kelly, Sasha Omadally, D. Rodriguez, Alexandra Strong, E. Barrella
With today's automotive industry shifting towards Connected and Autonomous Vehicles (CAVs), infrastructure within the urban transportation systems need to be addressed to ensure optimal operations of CAVs in U.S. cities. This project sought to provide technology investment recommendations for new and modified infrastructure to city officials and staff to support U.S. cities as they prepare for the deployment of CAVs. Analysis and recommendations were based on two case studies of major intersections in the cities of Miami, Florida, and Winston-Salem, North Carolina. These case studies showcased different types of roadways and intersection users. Using peer-reviewed research on city infrastructure, policies and regulations, available sensor technologies, and vehicle operations, alternative roadway and IT system specifications were designed with the intention of reducing vehicular accidents, improving traffic flow, improving sensor functionality, and ensuring accessibility to intersection users. Possible design elements included Modified Lanes, Mobility Hubs, Digital Transportation Management System (DTMS), and Improved Lighting System. Prototyping, subject matter expert interviews, and peer-reviewed research were used to determine the validity of the components in the designs. The feedback from the testing phase was analyzed to guide iterations of the analytical prototypes to ensure that the design follows regulations and policies, while also focusing on safety, traffic flow, and accessibility.
{"title":"On the Road to Smart Cities: Preparing U.S. Cities for the Deployment of Connected and Autonomous Vehicles (CAVs)","authors":"Yulian Arencibia, Nathaniel Garrido, Charles Kelly, Sasha Omadally, D. Rodriguez, Alexandra Strong, E. Barrella","doi":"10.1109/sieds55548.2022.9799341","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799341","url":null,"abstract":"With today's automotive industry shifting towards Connected and Autonomous Vehicles (CAVs), infrastructure within the urban transportation systems need to be addressed to ensure optimal operations of CAVs in U.S. cities. This project sought to provide technology investment recommendations for new and modified infrastructure to city officials and staff to support U.S. cities as they prepare for the deployment of CAVs. Analysis and recommendations were based on two case studies of major intersections in the cities of Miami, Florida, and Winston-Salem, North Carolina. These case studies showcased different types of roadways and intersection users. Using peer-reviewed research on city infrastructure, policies and regulations, available sensor technologies, and vehicle operations, alternative roadway and IT system specifications were designed with the intention of reducing vehicular accidents, improving traffic flow, improving sensor functionality, and ensuring accessibility to intersection users. Possible design elements included Modified Lanes, Mobility Hubs, Digital Transportation Management System (DTMS), and Improved Lighting System. Prototyping, subject matter expert interviews, and peer-reviewed research were used to determine the validity of the components in the designs. The feedback from the testing phase was analyzed to guide iterations of the analytical prototypes to ensure that the design follows regulations and policies, while also focusing on safety, traffic flow, and accessibility.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"158 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":"121268923","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/sieds55548.2022.9799371
Harish Karumuri, Livia Kimche, O. Toker, Afsaneh Doryab
In the era of information overload, the ability to access key information instantaneously is extremely important. While technological advances such as keyword search, dashboards, customizable data reports, and notifications have made information access more flexible, the underlying assumption is that the user knows what to look for. However, this assumption may not hold in many situations. For example, identifying needed information and key metrics affecting a business in Human Resource Management Systems (HRMS) can prove to be difficult. Voice assistance and recommendation systems can help improve these issues by allowing users to efficiently reach key insights which are relevant to their needs and their context. This research presents the design and evaluation of a conversational context-aware information recommendation system for business analytics where a conversational voice assistant helps the user specify the information needed for different analytics by suggesting reports and metrics often used by similar users and companies in their industry. Our prototype evaluation results show the potential of such a system to improve the user experience of searching for efficient and meaningful information in an organization using the data available within their HRMS.
{"title":"Context-Aware Recommendation Via Interactive Conversational Agents: A Case in Business Analytics","authors":"Harish Karumuri, Livia Kimche, O. Toker, Afsaneh Doryab","doi":"10.1109/sieds55548.2022.9799371","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799371","url":null,"abstract":"In the era of information overload, the ability to access key information instantaneously is extremely important. While technological advances such as keyword search, dashboards, customizable data reports, and notifications have made information access more flexible, the underlying assumption is that the user knows what to look for. However, this assumption may not hold in many situations. For example, identifying needed information and key metrics affecting a business in Human Resource Management Systems (HRMS) can prove to be difficult. Voice assistance and recommendation systems can help improve these issues by allowing users to efficiently reach key insights which are relevant to their needs and their context. This research presents the design and evaluation of a conversational context-aware information recommendation system for business analytics where a conversational voice assistant helps the user specify the information needed for different analytics by suggesting reports and metrics often used by similar users and companies in their industry. Our prototype evaluation results show the potential of such a system to improve the user experience of searching for efficient and meaningful information in an organization using the data available within their HRMS.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"20 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":"134257508","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/sieds55548.2022.9799331
Gabriel Bahia De Sousa, Bruna Stamer Janikian, Olivia O'Hearn, Saachi Mehrotra
In efforts to find alternative uses for coal, in line with environmental issues, a new coal production plant was designed and will be built in West Virginia to reconstitute coal into environmentally friendly outputs: (i) absorbents, (ii) fertilizers, and (iii) pellets. The coal will be put through a batch production line containing continuous and non-continuous machines. It needs to operate as efficiently as possible to optimize output and reduce costs. The goal of this project is to create a tool in Microsoft Excel that models the operation of the coal plant and simulates its functionality. The platform assists in the optimization and operation of the coal plant by displaying the three interconnected processes performed within the plant. The final deliverable is a user-friendly tool intended for client use that can assist in the coal plant facility design. The design alternatives were identified by modeling the full plant process for all three products, where rows in Excel are utilized to model the time increments. This dynamic model takes the user-specified facility design factors as inputs and displays the entire process in detailed steps to easily visualize a 2D model of the plant and make decisions based on the information displayed. The model can display how the different design specifications of the coal plant, such as conveyor belt length, would affect the output over time. Furthermore, the tool also informs how the tradeoff between capacity and cook time for the Solar Kiln and the Furnace can influence productivity. Although the scope of this project is one coal plant in West Virginia, the tool developed in this project can be used as a template for the optimization of other similar manufacturing systems.
{"title":"Dynamic Coal Production Line: Plant Design and Analysis Tool","authors":"Gabriel Bahia De Sousa, Bruna Stamer Janikian, Olivia O'Hearn, Saachi Mehrotra","doi":"10.1109/sieds55548.2022.9799331","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799331","url":null,"abstract":"In efforts to find alternative uses for coal, in line with environmental issues, a new coal production plant was designed and will be built in West Virginia to reconstitute coal into environmentally friendly outputs: (i) absorbents, (ii) fertilizers, and (iii) pellets. The coal will be put through a batch production line containing continuous and non-continuous machines. It needs to operate as efficiently as possible to optimize output and reduce costs. The goal of this project is to create a tool in Microsoft Excel that models the operation of the coal plant and simulates its functionality. The platform assists in the optimization and operation of the coal plant by displaying the three interconnected processes performed within the plant. The final deliverable is a user-friendly tool intended for client use that can assist in the coal plant facility design. The design alternatives were identified by modeling the full plant process for all three products, where rows in Excel are utilized to model the time increments. This dynamic model takes the user-specified facility design factors as inputs and displays the entire process in detailed steps to easily visualize a 2D model of the plant and make decisions based on the information displayed. The model can display how the different design specifications of the coal plant, such as conveyor belt length, would affect the output over time. Furthermore, the tool also informs how the tradeoff between capacity and cook time for the Solar Kiln and the Furnace can influence productivity. Although the scope of this project is one coal plant in West Virginia, the tool developed in this project can be used as a template for the optimization of other similar manufacturing systems.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"24 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":"114520012","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/sieds55548.2022.9799294
Betsy Guzmán, Sarah Deresky, Sabrina Taylor, Hawk Wimmer, Ali Momen, Chad C. Tossell, Michael Boyce, Joel Cartwright, Charles R. Amburn, Ben Sawyer
Increasingly advanced technologies are penetrating military domains (e.g., air, land, sea, cyber, space) requiring more complex decision-making to support activities that apply across these domains (multi-domain planning & operations). These decisions often require humans to perceive, comprehend, project, and then communicate information in a timely and accurate manner, oftentimes with life-or-death consequences. To support these decisions, Department of Defense leaders are calling for more effective representations and displays of joint warfighting environments. This project addresses this requirement by examining novel technologies for integrating and displaying complex MDO plans for human decision-making. Using a mission planning scenario, we assessed situation awareness (SA), usability, cost, and overall effectiveness of a two-dimensional (2D) representation of a common joint warfighting display on a Samsung tablet against a three-dimensional (3D) display of the same information designed for use in a Microsoft HoloLens mixed reality system. A total of 22 U.S. Air Force Academy cadets were randomly assigned to either use the tablet or the HoloLens to develop and analyze a mission plan and assessed for situation awareness across two scenarios. Interestingly, the HoloLens did not provide any additional SA relative to the tablet. The tablet was also perceived as more usable and effective in terms of cost and overall performance. These results suggest more traditional technologies, such as a tablet, can provide SA at similar levels as more advanced technology with increased usability and affordability.
{"title":"Evaluating Mixed Reality and Tablet Technologies in Military Planning","authors":"Betsy Guzmán, Sarah Deresky, Sabrina Taylor, Hawk Wimmer, Ali Momen, Chad C. Tossell, Michael Boyce, Joel Cartwright, Charles R. Amburn, Ben Sawyer","doi":"10.1109/sieds55548.2022.9799294","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799294","url":null,"abstract":"Increasingly advanced technologies are penetrating military domains (e.g., air, land, sea, cyber, space) requiring more complex decision-making to support activities that apply across these domains (multi-domain planning & operations). These decisions often require humans to perceive, comprehend, project, and then communicate information in a timely and accurate manner, oftentimes with life-or-death consequences. To support these decisions, Department of Defense leaders are calling for more effective representations and displays of joint warfighting environments. This project addresses this requirement by examining novel technologies for integrating and displaying complex MDO plans for human decision-making. Using a mission planning scenario, we assessed situation awareness (SA), usability, cost, and overall effectiveness of a two-dimensional (2D) representation of a common joint warfighting display on a Samsung tablet against a three-dimensional (3D) display of the same information designed for use in a Microsoft HoloLens mixed reality system. A total of 22 U.S. Air Force Academy cadets were randomly assigned to either use the tablet or the HoloLens to develop and analyze a mission plan and assessed for situation awareness across two scenarios. Interestingly, the HoloLens did not provide any additional SA relative to the tablet. The tablet was also perceived as more usable and effective in terms of cost and overall performance. These results suggest more traditional technologies, such as a tablet, can provide SA at similar levels as more advanced technology with increased usability and affordability.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"65 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":"129583122","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/sieds55548.2022.9799388
Melanie Sattler, Khoi H. Tran, Haley A. Blair, Bryce Runey
Missing Persons cases are a race against time, where every minute is critical to save a life. The more information a Search and Rescue (SAR) team has to work with, the more likely the success of the search. dbS Productions created a Search and Rescue database with over 20,000 search and rescue cases across the world to assist rescuers in their SAR efforts. The database includes search-specific information such as location, eco-division, and limited weather information. It also includes personal data, including sex, age, clothing, and equipment, as well as various characterizations of the missing person, such as whether they are a hunter, a hiker, or have various medical conditions, such as dementia. All of these factors can be used to determine where a missing person may have headed while they were lost and try to locate them more efficiently. The primary goal of this research is to create a predictive model by augmenting existing spatial models implemented by dbS Productions with additional weather features, determining how weather conditions impact the distance traveled by lost persons, thus improving the efficiency of search and rescue operations. This process was established through regression modeling and other machine learning methods. Several models included in order to determine the effect of weather on the distance traveled, including regression models, models using support vector machines (SVM), and the most successful model using XGBoost. The results showed that there was a relationship between the distance traveled and the maximum temperature and the minimum temperature. Overall showing that the weather extremes have a significant impact on the distance traveled by lost persons.
{"title":"Modeling the impact of Weather on Distance Traveled by Lost Persons","authors":"Melanie Sattler, Khoi H. Tran, Haley A. Blair, Bryce Runey","doi":"10.1109/sieds55548.2022.9799388","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799388","url":null,"abstract":"Missing Persons cases are a race against time, where every minute is critical to save a life. The more information a Search and Rescue (SAR) team has to work with, the more likely the success of the search. dbS Productions created a Search and Rescue database with over 20,000 search and rescue cases across the world to assist rescuers in their SAR efforts. The database includes search-specific information such as location, eco-division, and limited weather information. It also includes personal data, including sex, age, clothing, and equipment, as well as various characterizations of the missing person, such as whether they are a hunter, a hiker, or have various medical conditions, such as dementia. All of these factors can be used to determine where a missing person may have headed while they were lost and try to locate them more efficiently. The primary goal of this research is to create a predictive model by augmenting existing spatial models implemented by dbS Productions with additional weather features, determining how weather conditions impact the distance traveled by lost persons, thus improving the efficiency of search and rescue operations. This process was established through regression modeling and other machine learning methods. Several models included in order to determine the effect of weather on the distance traveled, including regression models, models using support vector machines (SVM), and the most successful model using XGBoost. The results showed that there was a relationship between the distance traveled and the maximum temperature and the minimum temperature. Overall showing that the weather extremes have a significant impact on the distance traveled by lost persons.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"R-27 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":"126627383","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/sieds55548.2022.9799307
Zachery Key, Andrea Parrish, Conner Snavely, M. Shafiee-Jood
In anticipation of high impact weather events such as hurricanes, wildfires, and flash floods, public officials need to make life saving and time sensitive decisions under uncertainty. For example, when a hurricane is forming in the Atlantic, public officials need to decide whether and when to issue an evacuation order. However, there is always a large risk in issuing an order early because of the uncertain nature of weather forecasting. Besides the preparation costs, the public could lose trust in officials and forecast information. Previous studies have identified a number of sociodemographic factors contributing to individuals’ likelihood to evacuate. These research efforts have proven that the probability of evacuation shares a strong positive correlation with both economic and physical mobility, meaning older populations, low-income populations or those with larger families are less likely to evacuate. While these efforts have provided policy makers with valuable insight to provide for these low evacuation populations, there has been very little analysis of the impact of evacuation orders on constituents’ evacuation mobility patterns. To bridge the gap in literature, we investigate the relationship between evacuation policy and observed evacuation patterns during Hurricane Florence (2018). Specifically, we evaluate the evacuation index at the census block group level of communities in Virginia encountering a false positive compared to those in South Carolina experiencing a true positive. By overlaying evacuation order data with cellular mobility data and forecast information from the National Hurricane Center, we aim to capture interactions between policy measures and socioeconomic factors to assess their relationship with evacuation behavior.
{"title":"Emergency Management and Underserved Communities: Using Big Data to Improve Emergency Management Preparedness, Response and Resilience","authors":"Zachery Key, Andrea Parrish, Conner Snavely, M. Shafiee-Jood","doi":"10.1109/sieds55548.2022.9799307","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799307","url":null,"abstract":"In anticipation of high impact weather events such as hurricanes, wildfires, and flash floods, public officials need to make life saving and time sensitive decisions under uncertainty. For example, when a hurricane is forming in the Atlantic, public officials need to decide whether and when to issue an evacuation order. However, there is always a large risk in issuing an order early because of the uncertain nature of weather forecasting. Besides the preparation costs, the public could lose trust in officials and forecast information. Previous studies have identified a number of sociodemographic factors contributing to individuals’ likelihood to evacuate. These research efforts have proven that the probability of evacuation shares a strong positive correlation with both economic and physical mobility, meaning older populations, low-income populations or those with larger families are less likely to evacuate. While these efforts have provided policy makers with valuable insight to provide for these low evacuation populations, there has been very little analysis of the impact of evacuation orders on constituents’ evacuation mobility patterns. To bridge the gap in literature, we investigate the relationship between evacuation policy and observed evacuation patterns during Hurricane Florence (2018). Specifically, we evaluate the evacuation index at the census block group level of communities in Virginia encountering a false positive compared to those in South Carolina experiencing a true positive. By overlaying evacuation order data with cellular mobility data and forecast information from the National Hurricane Center, we aim to capture interactions between policy measures and socioeconomic factors to assess their relationship with evacuation behavior.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"36 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":"116650760","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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