2023 Systems and Information Engineering Design Symposium (SIEDS)最新文献

{"title":"Improving Patient Flow in a Healthcare Clinic Post COVID-19: A Data Validation and Exploratory Analysis Approach","authors":"Aditi Jain, Aram Bahrini, Eric Nour, Harshal Patel, Emily Riggleman, Tyson Wittmann, Karen Measells, Kimberly Dowdell, Sara Riggs, R. Riggs","doi":"10.1109/SIEDS58326.2023.10137848","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137848","url":null,"abstract":"Since the beginning of the COVID-19 pandemic, healthcare clinics have faced increased inefficiencies due to an influx of patients returning to clinical care. The strain on nursing resources leads to long patient waiting times, which can lead to provider burnout and more stressful patient care. Here we compare the electronic medical record (EMR) timestamp data with observational data to understand better the current patient flow at the University Physicians of Charlottesville (UPC) clinic, a primary care clinic within the UVA Health System. Our overarching goal for this study is to propose data-driven solutions to improve clinic efficiency and reduce stress for providers, nurses, and staff. We implemented a two-phased analysis approach. The first phase involved cross-checking the EMR timestamp data with observed data to validate the consistency and reliability of the EMR timestamp data and thus allow us to confidently identify areas of improvement within the clinic, such as peak waiting periods. In the second phase, we used the validated data to analyze the distribution of delays during different appointment stages. Using a discrete event simulation, we recommend solutions that could improve the patient experience and reduce stress on medical personnel. The findings are further supported by graphical analyses of the delays in patient rooming depending on the time of day, length of the appointment, and provider. Overall, the two-phased approach will provide the clinic with a holistic understanding of the causes behind delays in patient care.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121442790","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":"Pricing and Carbon Emission Reduction Decisions in a Dual-Channel Supply Chain","authors":"Atefe Sedaghat, A. Taleizadeh","doi":"10.1109/SIEDS58326.2023.10137906","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137906","url":null,"abstract":"Due to the importance of environmental issues, customers prefer to buy low-carbon products and have an Ecofriendly behavior. Manufacturers produce the substitutable product under cap-and-trade regulations. Two chains are incompete on a product's green level, which is determined by the manufacturer. In some cases, firms have difficulties in providing sufficient capital to buy extra carbon emission quotas which force them to take loans from the banks. In this study, a two-echelon dual-channel supply chain consisting of one manufacturer and one retailer have been studied and a Stackelberg game is implemented on vertical and horizontal approaches. Various metaheuristic and hybrid metaheuristic methods are applied to optimize the revenue based on optimal decision variables such as retailer prices, carbon emission reduction rate, bank interest rate, and wholesale price. Performance of the applied methods are compared which determines the best algorithm in each case.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122452687","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":"Identifying Dark Patterns in Social Robot Behavior","authors":"Elizabeth Dula, Andres Rosero, Elizabeth Phillips","doi":"10.1109/SIEDS58326.2023.10137912","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137912","url":null,"abstract":"Social robots have become increasingly utilized in intimate environments where their roles can include caretakers for the elderly, general physical or emotional support, entertainment, and educators for children. To accommodate for these increasingly intimate relationships, robotics companies have begun employing robotics with the ability to identity emotions and respond with emotionality in return. This faux emotional relationship opens the door for potential user manipulation and exploitation through deceptive robot design. Dark patterns are deceptive design patterns used by websites or apps to manipulate users into actions the user did not intend. We argue that dark patterns can be programmed into social robotics to leverage these unidirectional human - robot emotional bonds to manipulate users, which could result in the exploitation of vulnerable populations like children and the elderly. Drawing from the dark pattern and social robotics literature, we suggest ways that dark patterns can manifest themselves in these relationships. We also provide recommendations for ethical practices when designing emotional social robots.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126044771","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":"Investigating the Stability of Organic Materials for Commercial Dyeing","authors":"V. Krause, M. Hermes, Jordan Wels, Lauren Hanchar, Jonathan T. Su","doi":"10.1109/SIEDS58326.2023.10137794","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137794","url":null,"abstract":"Sustainability and environmental ethics are major focuses of future developments in many fields of infrastructure and industry. One of these fields is the industry of garment dyeing. With only a handful of garment dyeing facilities in the country, TS Designs, located in Burlington, North Carolina, has developed a niche clientele and craft of the use of natural materials for use in commercial dyeing. Organic materials have been used in textile dyeing since the very beginning of documented history, but limited research has been done in the translation of these practices to industrial contexts. Natural dye can be derived from organic waste products and is a great way to incorporate eco-friendly methods in the industrial production of clothing. Unfortunately, due to the dyes being made from organic materials, the resulting color of the product may change over time as the material degrades, which is not preferable for the sale of a consistent product. It is important to extract dye from materials as they are available before they degrade in order to reduce waste. The goal of our research is to be able to test the dye stability of organic materials and determine proper practices for preserving each dye extract.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115268192","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":"Deep Temporal Neural Networks for Water Level Predictions of Watershed Systems","authors":"Jordan Huff, Jeremy Watts, Anahita Khojandi, J. Hathaway","doi":"10.1109/SIEDS58326.2023.10137869","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137869","url":null,"abstract":"Rainfall-runoff systems are complex hydrological environments that play a critical role in flood prevention. Currently, physics-based, process-driven computational models are often used to forecast future flooding events. However, these physics-based models are computationally expensive and require intensive physical measurements of hydrological environments beyond remote data collection. There is a growing body of literature that applies deep neural networks to time-series data for computationally efficient, real-time flooding predictions without the need for the complete virtual modeling of the hydrological system. However, these deep-learning networks’ robustness at forecasting far into the future remains an open question. In this study, we examine the capabilities of Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCN), state-of-the-art temporal deep neural networks, to forecast rainfall-runoff system depths. Specifically, this study leverages primary, multi-modal, time-series data collected by remote sensors in the watershed system of Conner Creek, a tributary of the Clinch River in eastern Tennessee. These data were collected in 5-minute intervals over a course of 5 months. Notably, the Conner Creek watershed system consists of four interconnected reservoir basins. We forecast the water level of each reservoir basin independently for times ranging from five minutes to two hours into the future. Our results show that both the LSTM and TCN can effectively model and forecast future reservoir basin water levels. Specifically, when averaged across the four reservoir basins, the LSTM has an mean absolute error (MAE), with a 95% confidence interval, of 0.158 ± 0.049 ft and 0.490 ± 0.260 ft at 5 minutes and 120 minutes into the future, respectively. In comparison, the TCN has an MAE of 0.258 ± 0.160 ft and 0.375 ± 0.245 ft at 5 minutes and 120 minutes into the future, respectively. Our results show that the LSTM model outperforms the TCN for near lead time forecasting; however, the TCN retains a greater relative accuracy at larger lead time forecasting periods (two hours). Nevertheless, both models can be considered effective at capturing future trends of watershed systems, demonstrating them to be powerful tools for use in flood risk management systems.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131489318","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":"Analysis of the Machine Learning Classification of Cardiac Disease on Embedded Systems","authors":"Sanda Thura, Jason Forsyth, Kevin Molloy, Jacob Couch","doi":"10.1109/SIEDS58326.2023.10137784","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137784","url":null,"abstract":"Heart disease has become a major global health concern that is affecting millions of people worldwide. The situation is particularly critical in developing countries where the access to medical facilities is limited. This barrier to health care leads to increased fatalities from heart disease. Early diagnosis of cardiovascular conditions can be lifesaving. However, personal medical-grade equipment can be expensive and not easily accessible for people living in these areas. It is important to expand the same level of medical care to these communities at an affordable price. Our research aims to investigate the performance of a machine learning model on a low-cost embedded system. This study will evaluate the accuracy, run time, and overall performance of the model in diagnosing cardiovascular diseases. The results will help us determine the feasibility of using machine learning models for classifying cardiovascular disease in low-cost embedded systems. A selected machine learning model has been trained, modified, and compiled into the embedded system. The model returns the classification results based on preprocessed input data. Multiple metrics are collected to measure the performance of the model and the embedded system. The preliminary results are promising with accuracy levels similar to the original model. If these results hold up in multiple trials, it is expected that the machine learning model for classifying cardiovascular diseases on the embedded system will be practical and useful in extending affordable medical care to developing countries.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528705","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":"Horizontal Gaze Nystagmus Transmission Interlock System","authors":"Chase Coleman, Matthew Jenkins, William Roberts, Charlie Thomas, William Westerkamp, Rod MacDonald, A. Salman","doi":"10.1109/SIEDS58326.2023.10137888","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137888","url":null,"abstract":"Driving while intoxicated continues to be a morbid issue in the United States, responsible for causing approximately one-third of all fatal car crashes, claiming 11,000 victims each year. Psychological studies have shown that those who drive under the influence are likely to be repeat-offenders. The objective of this project is to remove human error from the equation by building a technological solution to address the needs specified by the Department of Transportation. While incorporating physiological analysis to determine sobriety based upon a passive HGN test, if an individual is attempting to drive while intoxicated, a personalized machine-learning algorithm will be calibrated to said individual to test their sobriety while protecting their privacy. The result of the sobriety test will determine if the individual is able to operate the vehicle, immobilizing the vehicle temporarily, if the driver is intoxicated. We show through our results that our system can identify whether or not a driver is impaired with a clear distinction in a very short amount of time without compromising on the user’s privacy.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124122078","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":"Distinguishing Human-Written and ChatGPT-Generated Text Using Machine Learning","authors":"Hosam Alamleh, A. A. AlQahtani, A. ElSaid","doi":"10.1109/SIEDS58326.2023.10137767","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137767","url":null,"abstract":"The use of sophisticated Artificial Intelligence (AI) language models, including ChatGPT, has led to growing concerns regarding the ability to distinguish between human-written and AI-generated text in academic and scholarly settings. This study seeks to evaluate the effectiveness of machine learning algorithms in differentiating between human-written and AI-generated text. To accomplish this, we collected responses from Computer Science students for both essay and programming assignments. We then trained and evaluated several machine learning models, including Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM), Neural Networks (NN), and Random Forests (RF), based on accuracy, computational efficiency, and confusion matrices. By comparing the performance of these models, we identified the most suitable one for the task at hand. The use of machine learning algorithms for detecting text generated by AI has significant potential for applications in content moderation, plagiarism detection, and quality control for text generation systems, thereby contributing to the preservation of academic integrity in the face of rapidly advancing AI-driven content generation.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125288800","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":"Analyzing Efficacy of Home Electronic Incarceration on Return-to-Custody Rates for Inmates During the COVID-19 Pandemic","authors":"Josh Dornfeld, Imani Hankinson, Livia Hughes, Sarah Murphy, Ronica Peraka, McBride Rawson, L. Alonzi, Michael Smith, K. P. White, Neal Goodloe","doi":"10.1109/SIEDS58326.2023.10137861","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137861","url":null,"abstract":"Home Electronic Incarceration (HEI) is a tech-enabled alternative allowing the Albemarle-Charlottesville Regional Jail (ACRJ) to monitor individuals outside the correctional facility. Carefully selected individuals are allowed to serve their sentences within the boundaries of an approved location. In response to the COVID-19 pandemic, local courts and ACRJ adjusted sentencing and incarceration practices to reduce jail occupancy and limit the spread of coronavirus (N. Goodloe, personal communication, September 12, 2022). We wish to explore whether the increased use of HEI affected the return to custody (RTC) at ACRJ.These methods consist of comprehensive, quantitative analysis of booking data provided by ACRJ, in conjunction with continued insight and guidance from Region Ten Community Services (locally known as \"R10\", a provider of mental health resources), Offender and Aid Restoration- Jefferson Area Community Corrections (OAR-JACC) and the Blue Ridge Area Coalition for the Homeless (BRACH). This paper presents results of ACRJ inmate outcomes within two areas of focus: HEI sentences pre- vs. post-COVID and HEI vs. non-HEI individuals during and since the onset of COVID. In addition to this analysis, we have collaborated with key community stakeholders to better understand the state of the Albemarle-Charlottesville criminal justice system as it recovers from the pandemic.We found that prior to the onset of the pandemic, HEI was reserved for frequent offenders who typically were serving felony charges. After the beginning of the pandemic, ACRJ began placing individuals on HEI who were more representative of the jail population as a whole in terms of prior criminal history and the mix of misdemeanor and felony offenders. We also demonstrated that individuals on HEI are incarcerated for significantly extended periods for comparable offenses than those who serve their sentence in ACRJ, as individuals in jail can get days off of their sentence for good behavior, while HEI participants are ineligible for such time credits. Finally, our analysis of RTC rates at ACRJ shows that HEI results in lower RTC rates than traditional jail sentences, pre- and post-COVID, and when split between misdemeanor and felony offenses. This analysis provides strong evidence for the efficacy of HEI as an alternative to incarceration in our local community, which may give an example for other jurisdictions to adopt or expand HEI usage in the future.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020309","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":"A Systems Approach to Improving the Spectator Experience at Collegiate Football Games","authors":"Haley Austin, Abigail Freed, Alexandra Labus, Brendan Lynch, Jo-Anne Mastrullo, Julia Sharff, R. Riggs","doi":"10.1109/SIEDS58326.2023.10137886","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137886","url":null,"abstract":"As ticket sales and student attendance for University of Virginia (UVA) home football games decline, the university must find ways to engage fans with the football program. The following technical evaluation used a systems methodology to improve the customer experience for Scott Stadium spectators, with the additional hope of paralleling an improvement in the school’s football community. Taking a three-pronged approach, the analysis focused on traffic, in-game experience, and website design. A ride-along and interviews with the University Police Department (UPD) yielded observational data regarding game day pedestrian and vehicular traffic. The UVA Athletics Department provided ticketing data. Concessions numbers supplied by Aramark, a student survey, and the team’s observations from game days offered information regarding in-game experience. The research team’s examination of the department’s digital presence gave an analysis of the website design. The interview data and analysis of patron and vehicular traffic patterns indicated that a paucity of signage, GPS directions that only route drivers to prepaid parking, and a dated traffic plan contribute to pregame traffic backups. Investigating ticketing statistics showed that tardy students and inefficient distribution of stadium staff create sparsely attended kickoffs and entrance bottlenecks. An assessment of the game day website revealed a User Experience (UX) design that hinders fans from finding parking, concessions, and general information efficiently. Analysis of concessions data revealed that stadium staff fail to make student-preferred food items available in multiple convenient locations. Finally, the survey data revealed that many students leave before halftime, find the in-game entertainment in need of improvement, and attend games to fraternize with friends rather than watch football. Due to these results, the primary traffic recommendations involve increasing parking signage during game days and an updated traffic plan. To improve the in-game experience, suggestions include prioritizing student-preferred food items, rearranging event staff at entrance gates, incorporating incentives that encourage students and fans to arrive early and stay late at games, and updating in-game entertainment to shift student focus to on-field activities. Finally, recommendations to restructure the game day website include reducing text by utilizing images and bullet points, highlighting critical content through bolding and underlining, and grouping similar information with panels and icons.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125728921","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}