Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137893
Casey Calixto, J. Chavez, Arsalan Heydarian, Abid Hussain, Kathryn E. Owens, Alex Repak
Construction sites are well known for being significant sources of air and noise pollution, impacting both individuals who work on those sites and surrounding communities. Construction projects on the Grounds of the University of Virginia are no exception. On-Grounds projects are located within one mile of UVA Health, meaning any pollutants, construction waste or noise from the project may impact a large number of people and individuals in educational, workplace, residential, and healthcare settings. While the presence of dust and other sources of pollution has been observed across jobsites, existing site management techniques do not provide opportunities to understand the causes or extent of various pollution events. The purpose of this project is to develop a prototype environmental monitoring dashboard which incorporates real-time data from air and noise quality sensors installed on-site, and link the data to specific construction activities on a detailed as-built schedule. The development of this type of monitoring system has become much more feasible in recent years due to the increased availability of affordable and reliable sensors and this project shows this type of technology can be utilized in a construction context. Sensors are installed in high traffic locations on-site including on the first two floors the building under construction and in the jobsite trailer to specifically track noise, CO2, VOC, PM2.5, temperature and humidity levels at 5 minute frequency. Information related to on-site activities is collected through an analysis of construction documents, like a detailed schedule and plan sheets. Spatial trends found included the first floor of the site having higher PM2.5 levels, PM2.5 levels decreasing from the roadside to trailer side, and the second floor having higher noise levels. Time trends include lower noise and PM2.5 levels at noon and higher levels between 8AM-11AM and 1PM-3PM. Lastly, there the middle first floor sensor PM2.5 levels was found to be significantly correlated with a masonry subcontractor’s daily hour with an R squared value of .6125.
{"title":"Developing An Environmental Monitoring Dashboard to Identify Construction Activities That Affect On-Site Air Quality and Noise","authors":"Casey Calixto, J. Chavez, Arsalan Heydarian, Abid Hussain, Kathryn E. Owens, Alex Repak","doi":"10.1109/SIEDS58326.2023.10137893","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137893","url":null,"abstract":"Construction sites are well known for being significant sources of air and noise pollution, impacting both individuals who work on those sites and surrounding communities. Construction projects on the Grounds of the University of Virginia are no exception. On-Grounds projects are located within one mile of UVA Health, meaning any pollutants, construction waste or noise from the project may impact a large number of people and individuals in educational, workplace, residential, and healthcare settings. While the presence of dust and other sources of pollution has been observed across jobsites, existing site management techniques do not provide opportunities to understand the causes or extent of various pollution events. The purpose of this project is to develop a prototype environmental monitoring dashboard which incorporates real-time data from air and noise quality sensors installed on-site, and link the data to specific construction activities on a detailed as-built schedule. The development of this type of monitoring system has become much more feasible in recent years due to the increased availability of affordable and reliable sensors and this project shows this type of technology can be utilized in a construction context. Sensors are installed in high traffic locations on-site including on the first two floors the building under construction and in the jobsite trailer to specifically track noise, CO2, VOC, PM2.5, temperature and humidity levels at 5 minute frequency. Information related to on-site activities is collected through an analysis of construction documents, like a detailed schedule and plan sheets. Spatial trends found included the first floor of the site having higher PM2.5 levels, PM2.5 levels decreasing from the roadside to trailer side, and the second floor having higher noise levels. Time trends include lower noise and PM2.5 levels at noon and higher levels between 8AM-11AM and 1PM-3PM. Lastly, there the middle first floor sensor PM2.5 levels was found to be significantly correlated with a masonry subcontractor’s daily hour with an R squared value of .6125.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125396965","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 : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137848
Aditi Jain, Aram Bahrini, Eric Nour, Harshal Patel, Emily Riggleman, Tyson Wittmann, Karen Measells, Kimberly Dowdell, Sara Riggs, R. Riggs
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.
{"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":"4294 1","pages":"0"},"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}
Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137906
Atefe Sedaghat, A. Taleizadeh
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.
{"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":"376 1","pages":"0"},"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}
Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137912
Elizabeth Dula, Andres Rosero, Elizabeth Phillips
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.
{"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":"15 10 1","pages":"0"},"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}
Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137794
V. Krause, M. Hermes, Jordan Wels, Lauren Hanchar, Jonathan T. Su
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.
{"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":"30 1","pages":"0"},"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}
Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137869
Jordan Huff, Jeremy Watts, Anahita Khojandi, J. Hathaway
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.
{"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":"23 1","pages":"0"},"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}
Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137831
Ethan A. Gerlach, Arthur Hoang, Saffiata Kamara, Anwar Longi, Derek A. Sprincis, Ethan W. Thurmond, Boyang Lu, G. Louis
This capstone project aims to modify and finalize an existing hydroponic crop cultivation (HCC) system, called the "Fold-out-Farm," to operate on a floating platform in Small Island Developing States (SIDS) that are susceptible to food insecurity due to natural and economic factors. Specifically, when SIDS are hit by natural disasters, crops and agricultural infrastructure can be severely damaged, causing many people to suffer from a lack of both food access and job opportunity. The Fold-out-Farm is completely self-sufficient – it has its own water collection system, solar-based power generation, and on-board growing pods. The unit can float to combat disaster consequences from incidents such as hurricanes. Specifically, the project is working to add a rainwater harvesting system and validate the structural integrity of the unit during a flood. The farm is designed to use off-the-shelf nutrient solutions to grow a variety of crops and the team will find the most suitable option. The team will also expand the market niche for the HCC system by determining the optimal use for the product in urban food deserts, refugee camps, and rooftop gardens. The approach taken has involved communication and research to understand the needs of those who could benefit from a Fold-out-Farm, as well as various testing methods for crops and structure of the unit. Testing has been done through expert surveys, estimation of structural performance, simulation software analysis, and evaluation of crop yield from the unit relative to a control crop grown in soil. Results will be continuously measured, first in testing the system’s ability to deliver water, sun and nutrients to growing modules, its crop yield, and stability in an open water test in the Rivanna river, and finally when presenting the design to sponsors and potential users. Future researchers may build upon these findings to further improve the unit and its potential use to ensure that it is understandable and acceptable to the communities who will be using it. The project will have a market-ready product capable of reducing food insecurity in SIDS and potentially in urban food deserts, refugee camps and rooftop gardens in land scarce areas.
{"title":"A Floating Farm for Hydroponic Crop Cultivation in Small Island Developing States1","authors":"Ethan A. Gerlach, Arthur Hoang, Saffiata Kamara, Anwar Longi, Derek A. Sprincis, Ethan W. Thurmond, Boyang Lu, G. Louis","doi":"10.1109/SIEDS58326.2023.10137831","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137831","url":null,"abstract":"This capstone project aims to modify and finalize an existing hydroponic crop cultivation (HCC) system, called the \"Fold-out-Farm,\" to operate on a floating platform in Small Island Developing States (SIDS) that are susceptible to food insecurity due to natural and economic factors. Specifically, when SIDS are hit by natural disasters, crops and agricultural infrastructure can be severely damaged, causing many people to suffer from a lack of both food access and job opportunity. The Fold-out-Farm is completely self-sufficient – it has its own water collection system, solar-based power generation, and on-board growing pods. The unit can float to combat disaster consequences from incidents such as hurricanes. Specifically, the project is working to add a rainwater harvesting system and validate the structural integrity of the unit during a flood. The farm is designed to use off-the-shelf nutrient solutions to grow a variety of crops and the team will find the most suitable option. The team will also expand the market niche for the HCC system by determining the optimal use for the product in urban food deserts, refugee camps, and rooftop gardens. The approach taken has involved communication and research to understand the needs of those who could benefit from a Fold-out-Farm, as well as various testing methods for crops and structure of the unit. Testing has been done through expert surveys, estimation of structural performance, simulation software analysis, and evaluation of crop yield from the unit relative to a control crop grown in soil. Results will be continuously measured, first in testing the system’s ability to deliver water, sun and nutrients to growing modules, its crop yield, and stability in an open water test in the Rivanna river, and finally when presenting the design to sponsors and potential users. Future researchers may build upon these findings to further improve the unit and its potential use to ensure that it is understandable and acceptable to the communities who will be using it. The project will have a market-ready product capable of reducing food insecurity in SIDS and potentially in urban food deserts, refugee camps and rooftop gardens in land scarce areas.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134569863","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 : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137797
Jared Macshane, A. Ahmadinia
Hiking trail maps are typically created manually by survey, a time-consuming process. This process is expensive and must be repeated over time to improve accuracy. This paper proposed an inexpensive, automatic, and accurate trail network generation method from anonymous public GPS data utilizing a growing self-organizing map (GSOM). This technique does not rely on sequential GPS traces to learn network topology, unlike other approaches. Tuning several hyper-parameters can adjust this process for datasets and networks with unique characteristics. Reconstruction and adaption are also possible based on newly acquired data sources. Constructed trail maps, trained on publicly available GPS data, are compared against a ground truth map from Open Street Map (OSM). Performance is evaluated based on completeness, accuracy, and topological correctness. Testing on sparse networks with minimal GPS noise suggests favorable performance.
{"title":"AI Assisted Trail Map Generation based on Public GPS Data","authors":"Jared Macshane, A. Ahmadinia","doi":"10.1109/SIEDS58326.2023.10137797","DOIUrl":"https://doi.org/10.1109/SIEDS58326.2023.10137797","url":null,"abstract":"Hiking trail maps are typically created manually by survey, a time-consuming process. This process is expensive and must be repeated over time to improve accuracy. This paper proposed an inexpensive, automatic, and accurate trail network generation method from anonymous public GPS data utilizing a growing self-organizing map (GSOM). This technique does not rely on sequential GPS traces to learn network topology, unlike other approaches. Tuning several hyper-parameters can adjust this process for datasets and networks with unique characteristics. Reconstruction and adaption are also possible based on newly acquired data sources. Constructed trail maps, trained on publicly available GPS data, are compared against a ground truth map from Open Street Map (OSM). Performance is evaluated based on completeness, accuracy, and topological correctness. Testing on sparse networks with minimal GPS noise suggests favorable performance.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133783116","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 : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137888
Chase Coleman, Matthew Jenkins, William Roberts, Charlie Thomas, William Westerkamp, Rod MacDonald, A. Salman
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.
{"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":"114 1","pages":"0"},"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}
Pub Date : 2023-04-27DOI: 10.1109/SIEDS58326.2023.10137767
Hosam Alamleh, A. A. AlQahtani, A. ElSaid
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.
{"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":"10 1","pages":"0"},"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}
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