2021 Systems and Information Engineering Design Symposium (SIEDS)最新文献

{"title":"Identifying Pediatric Crohn’s Disease Using Deep Learning to Classify Magnetic Resonance Enterography (MRE) Images","authors":"Marissa Shand, Joseph T. Manderfield, Surbhi Singh, Clair McLafferty, Y. Sharma, S. Sengupta, P. Fernandes, D. Koroulakis, S. Syed, Donald E. Brown","doi":"10.1109/SIEDS52267.2021.9483720","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483720","url":null,"abstract":"Crohn’s Disease (CD) diagnosis is a constant challenge for clinicians. Even with extensive magnetic resonance enterography (MRE) scans, identifying tissue damaged by CD can still be difficult, even for experts. Deep learning approaches for medical applications have recently gained traction as tools to complement radiologist consultation. Computer aided diagnosis can potentially save time and labor resources spent on routine manual diagnosis. For imaging of the gastrointestinal tract, these cutting-edge techniques could help distinguish subtle structures indicative of Crohn’s Disease (CD) that are not visible to the human eye. In this paper, we explore existing segmentation and neural network approaches more traditionally used for non-medical imaging and compare their diagnostic potential for identifying CD from MRE images.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132096593","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}

{"title":"Managing Operational and Environmental Risks in the Strategic Plan of a Maritime Container Port","authors":"Christopher G Gacek, Derek J Gimbel, Samuel J Longo, Benjamin I Mendel, Gabriel N Sampaio, Thomas L. Polmateer, Mark C. Manasco, Daniel C. Hendrickson, Timothy L. Eddy, J. Lambert","doi":"10.1109/SIEDS52267.2021.9483787","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483787","url":null,"abstract":"Shipping trends in technology, regulation, energy and environment require maritime container ports to adapt their operations to better suit current and future conditions. This paper focuses on innovative solutions in three main areas of interest for ports: (1) clean energy technologies, (2) alternative financing and (3) automated process technologies. In this analysis, these areas of interest are explored using the Port of Virginia as a case study. Results are derived using scenario analysis methodology drawn from systems, risk and resilience analysis. Investment strategies in renewable energy sources are evaluated and project funding approaches, including the use of green bonds, are explored. AI systems relevant to port operations integration and container security are also described. The key results of this paper are twofold: (1) a demonstration ranking of initiatives for a port strategic plan and (2) a ranking of scenarios by their disruption on initiative impact. The results of the case study are of interest to the strategic planners at industrial ports and the maritime industry.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"688 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957270","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}

{"title":"Water Out of Thin Air: Designing an Atmospheric Water Generator to Address Water Scarcity","authors":"Devin P. Simons, Declan R. Tyranski, Zachary D. High, K. Altaii","doi":"10.1109/SIEDS52267.2021.9483753","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483753","url":null,"abstract":"Water scarcity is a significant and escalating issue that is currently affecting every continent on the globe. According to the United Nations, water usage has been increasing at more than twice the rate of population growth, and it is estimated that 4 billion people experience severe water scarcity during at least one month of the year [1]. Often, there is an abundance of water in the atmosphere, with nearly 12,900 cubic kilometers of water present at any time [2]. To take advantage of this water source, our solution involves the design of an Atmospheric Water Generator (AWG) that can provide water to areas with medium to high humidity. The design converts water vapor into a liquid while minimizing the amount of input energy needed. The Earth is known to have a relatively cool and constant temperature of around 55°F at shallow underground depths [3]. The design uses the ground as a thermal sink by incorporating an optimized underground heat exchanger. This is accomplished with a closed-loop system of geothermal piping configured in a helical arrangement and a circulating pump powered by a photovoltaic (PV) panel. This allows for a liquid coolant (water) to reduce in temperature and be utilized above ground. The above-ground portion of the system consists of a crossflow finned heat exchanger, which allows for the cooled water to enter and condense humidity in the air. Air at 26.7°C and 85% relative humidity is directed at a rate of 0.23 kg per second across the heat exchanger by a fan which is powered by the PV panel. The air cools as it passes the heat exchanger and allows for water to condensate, which is collected, measured, and recorded via a remote data collection system. The system generates 200 milliliters of water every hour. Parameters such as temperatures, relative humidity, flow rate, and atmospheric conditions are also collected to verify the design and to model data for other regions around the world. It is an innovative design that offers a unique solution to help alleviate water scarcity.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122822726","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}

{"title":"Leveraging Google BERT to Detect and Measure Innovation Discussed in News Articles","authors":"Keyu Chen, Benjamin Cosgro, Oretha Domfeh, Alex Stern, Gizem Korkmaz, Neil Alexander Kattampallil","doi":"10.1109/SIEDS52267.2021.9483744","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483744","url":null,"abstract":"In this paper, we leverage non-survey data (i.e., news articles), natural language processing (NLP), and deep learning methods to detect and measure innovation, ultimately enriching innovation surveys. Our dataset is composed of 1.9M news articles published between 2013 and 2018 acquired from Dow Jones Data, News, and Analytics. We use Bidirectional Encoder Representation from Transformers (BERT), a neural network-based technique for NLP pre-training developed by Google. Our methods involve: (i) utilizing Google’s BERT as a binary classifier to identify articles that mention innovation, (ii) developing BERT’s named-entity recognition algorithm to extract company names from these articles, (iii) leveraging BERT’s question and answering capabilities to extract company and product names. As a result, we obtain innovation indicators, i.e., company innovations in the pharmaceutical sector.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124714401","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}

{"title":"Employing Predictive Trend Analysis to Decrease Construction Schedule Delay","authors":"V. Austin, Zachary McLane, C. O'Keeffe, D. Rashid, Ariana Zimmerman, Diana Franco Duran, Arsalan Heydarian, Todd Bagwell","doi":"10.1109/SIEDS52267.2021.9483796","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483796","url":null,"abstract":"Construction projects of all kinds are plagued by inefficiencies, creating excess risk, and leading to delays and cost overruns. Existing research has focused on analyzing delays in completed construction projects for forensic claims disputes. However, this data could also be used to decrease the risk of future schedule delays through the use of predictive trend modeling and data analysis. This form of data analytics is becoming increasingly prevalent and valuable in the construction industry as a means of identifying and allowing for the prevention of potential delays. An interdisciplinary team at the University of Virginia (referred to as the capstone team) seeks to provide insight into delay causation and prevention for Hourigan, a general contracting and construction firm. This work focuses on the analysis of scheduling data and project teams’ input from three medium-sized construction projects recently completed by Hourigan, referred to by the placeholder names projects A, B, and C. These data sets were interpreted using statistical analyses to assess correlations between owner, designer, or contractor-related delays and frequent delays. Interviews with the project team for each Hourigan project were conducted to obtain qualitative data regarding specific delay events. The main causes of delay for Project A were found to be the owner and designer; for Project B the designer and subcontractors; and for Project C the subcontractors, materials, and external factors. The capstone team also identified that Hourigan would benefit from recording more data related to project schedules as well as costs incurred due to specific delays. These findings will allow Hourigan to better manage, avoid, and overcome future challenges due to project delays.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124721176","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}

{"title":"Safe and Sustainable Fleet Management with Data Analytics and Training","authors":"Tom Gresham, Joshua Kim, James McDonald, Nick Scoggins, Moeen Mostafavi, B. Park, Michael D. Porter, Michael E. Duffy, Sandra A. Smith","doi":"10.1109/SIEDS52267.2021.9483746","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483746","url":null,"abstract":"In scores of vehicle fleets, telematic tracking systems provide fleet managers with information regarding energy consumption, the obedience of safety regulations and driver performance. For a University’s Facilities Management (FM) Fleet to take the next steps towards an elevated Sustainable Fleet accreditation and overall team performance, the management has recognized the importance of effective energy and safety tracking methods combined with data analytics and a comprehensive systems analysis in order to aid the reinforcement, training and maintenance of safe and sustainable driving practices by fleet drivers. This paper outlines the design of a unique safety and eco-driving training program that will prompt University FM drivers to reflect on, educate and develop mindful driving habits which reduce environmental impact, cost and risk. We analyzed historical driver behavior data, including idling time, harsh acceleration, crash incident details, resulting in the identification of risk factors and areas for significant improvement. Educational aspects of the training program were influenced by focus groups, interviews administered with industry experts, and professional fleet training modules. The efficacy of the customized training program has been assessed through a statistical evaluation of telematic data collected before and after the training program was delivered. The results indicate that agency-specific mindful driving training was statistically significant in improving five of the six behavioral metrics measured - idling time, seat belt usage, speeding, hard acceleration, and hard braking - compared to the control group.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124818686","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}

{"title":"Mutations in STAT3 and TTN Associated with Clinical Outcomes in Large Granular Lymphocyte Leukemia","authors":"Rachel Filderman, Buckley Dowdle, Youssef Abubaker, David Vann","doi":"10.1109/SIEDS52267.2021.9483763","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483763","url":null,"abstract":"Large granular lymphocyte (LGL) leukemia is a rare, chronic leukemia associated with clinical manifestations of anemia (RBC < 4.5 million/mcL in males, < 4 million/mcL in females), neutropenia (ANC < 1500/mm3), and autoimmune disease. Progress has been made in identifying a significant and frequent mutation in the STAT3 gene among LGL patients; however, STAT signaling is still largely unexplained in about 60% of those LGL leukemia patients lacking STAT3 mutations. This paper sought to confirm previous studies regarding the association of a STAT3 mutation with clinical manifestations, as well as search for other significant mutations across the rest of the genome in order to determine whether the specific clinical features of autoimmune disease, anemia, and neutropenia present in LGL leukemia patients are associated with additional genomic mutations in LGL cells. As LGL leukemia is rare, presents heterogeneous conditions, and does not have a high mutation burden, our approach is distinct from standard approaches in cancer research where mutation rates are much higher. Methods of dimension reduction are employed in tandem with association analysis and decision trees to search for signals between significant genetic mutations and clinical manifestations of anemia, neutropenia, and autoimmune disease within the LGL patient sample. Results indicate an association exists between anemia and concurrent mutations in STAT3 and TTN (p = 0.03) in T-LGLL patients. Additionally, an association was identified between neutropenia and a mutation in either TTN (p = 0.049) or STAT3 (p = 0.03) in T-LGLL patients as well. These findings imply that TTN may be responsible for STAT activation in combination with a STAT3 mutation or independently in T-LGLL patients. Through XGBoost, 66% accuracy was achieved in predicting neutropenia and 55% accuracy in predicting anemia using gene mutations as the predictor variables. However, the relatively small sample size (N=116 patients), presents concerns of limited statistical power, and the expectation on the number of times these findings might be repeated in independent samples. The ideal sample size needed for an association test to have adequate statistical power was examined. Additionally, a review of past LGL leukemia publications was undertaken to compare the statistical power of their reported analyses. To obtain satisfactory statistical power in analyzing the association between a STAT3 gene mutation and neutropenia in the T-LGLL population (e.g. p ≤ 0.01, power = 0.9), the T-LGLL sample size must be at least 312 patients. This sample size exceeds not only that of the present study but also that in the majority of sample sizes in the LGLL literature. The pooling of extant LGLL datasets as well as the undertaking of new, major multi-site trials is, therefore, warranted.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124388833","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}

{"title":"Guiding the Design of Inclusive Playgrounds through Needs Assessment and Materials Selection","authors":"Reid Auchterlonie, Chloe Brannock, Victoria Jackson, Anastasia Luong, Kiley Weeks, R. Valdez","doi":"10.1109/SIEDS52267.2021.9483727","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483727","url":null,"abstract":"Playgrounds can serve as an influential site in children’s lives, but their designs and features often exclude those with disabilities and their social, emotional, and physical needs. This study was conducted in collaboration with Bennett’s Village, a Charlottesville-based nonprofit seeking to build an inclusive playground. The purpose of the study was to investigate the needs of adolescents and young adults in the disability space and to create a materials recommendation for playground surfacing. The parameters of these analyses were established and prioritized alongside Bennett’s Village. For the qualitative needs assessment, the team recruited members from organizations focused on the disability community and had 6 participants in the semi-structured interviews and 77 participants in the survey. Through qualitative content analysis of interview and open-ended survey responses and descriptive statistics analysis of close-ended survey responses, we found that, among other trends, participants viewed playgrounds as a site for community and socialization, wanted open spaces that could serve a variety of purposes, and emphasized the importance of nature. For the materials recommendation, the team created a life cycle assessment and cost-benefit analysis and found that poured-in-place (PIP) rubber was the optimal surfacing material with regard to factors such as permeability, local weather factors, and traffic/usage. These findings will be passed on to Bennett’s Village to use in their design of their playground and will also contribute to future inclusive playground design broadly.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126909634","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}

{"title":"Autonomous Inspection System for Railroads","authors":"C. Anderson, W. J. Hinkle, Lachlan Hudson, Ethan Keck, Trevor Kraeutler, Zach Wenzler, A. Zahorchak, Jacquelyn K. S. Nagel","doi":"10.1109/SIEDS52267.2021.9483733","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483733","url":null,"abstract":"From January 2010 to November 2019, derailments in the United States have cost railroad companies over $350,000,000 in damages in addition to the costly environmental remediation cleanups and injuries. The State of Virginia is home to many small scale railroads that have less than a total of 100 miles of track between destinations. Manual inspection is common, but inspectors can miss small details, leading many railroads to supplement with autonomous inspection. Many small scale railroads do not have the resources to autonomously inspect their tracks and cannot afford the cost of large scale railroad inspection equipment in addition to the resources needed to run these inspection systems. Surface level defects are the most common reason for derailment and can lead to serious damage to the train and track if left untreated. The ultimate goal of the project is to prevent or greatly decrease the likelihood of train derailment by focusing on the detection of surface level defects on rails, which will have an impact on all railroads while especially helping local short line railroads.By creating an inspection system that works for all railroad companies, anyone can more accurately and precisely find surface level defects than current manual inspections. Working with industry experts, the most common and dangerous surface level defects and the appropriate methods of detection were determined. The resulting solution is the design of a small autonomous rail cart that takes images of the track from a video feed and sorts them into \"good\" or \"bad\" photos using machine learning. Locations with \"bad\" rail can then be re-inspected by manual inspectors to determine the severity of the problem and work towards fixing it. This system will identify and alert users to dangerous levels of rail damage while providing a cheaper inspection alternative for smaller railroads. It also acknowledges and takes advantage of the expertise and abilities of trained manual inspectors to make the final decision.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131685669","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}

{"title":"Mutational Hotspot Detection in LGL Leukemia","authors":"Nikki Aaron, Prabhjot Singh, Siddharth Surapaneni, Joseph Wysocki","doi":"10.1109/SIEDS52267.2021.9483797","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483797","url":null,"abstract":"Cancer genomics has been focused primarily on identifying and studying mutations that are over-represented in known genes. This project applied methods to scan through entire chromosomes and label these loci as \"genomic probabilistic hotspots\" (GPHs). A GPH is defined as any area on a patient’s chromosome where the observed rate of mutations over positions of a given chromosome window far exceeds what would be expected from random variation. The approach is then applied to 39 patients diagnosed with large granular lymphocyte (LGL) leukemia - a rare form of blood cancer. In order to calculate expected mutation rates in non-LGL patients, data were obtained from the 1000 Genome Project. A negative binomial test was employed to isolate specific GPHs where the distribution of mutations within the LGL patient sample was significantly high. The Negative Binomial approach identified a median of 1 to 2 patient hotspots per chromosome with a mean Jaccard’s distance between patients being 0.90. The KDE method found a median of 40 hotspots with wider span resulting in a mean Jaccard’s distance of 0.43. The results from the Negative Binomial approach indicated heterogeneity between hotspot locations, whereas KDE results were more homogeneous. Negative binomial is best for pinpointing the most significantly dense regions, whereas KDE is best for identifying all broad regions that are more mutated than a reference. These new, gene-agnostic approaches provide novel methods to search chromosomes for mutational abnormalities and can be generalized and scaled to any clinical syndrome. Future directions include extension of the GPH method across genomes, developing a robust library of disease- and/or model species-specific hotspot profiles. These may serve as reference guides in studies seeking to understand the exact biochemical processes driving the onset and progression of rare cancers.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729480","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}