Pub Date : 2013-04-26DOI: 10.1109/SIEDS.2013.6549500
Stephanie J. Wangeman
Improvised Explosive Devices are the most dangerous threat to United States troops in Iraq and Afghanistan. Regardless of where troops deploy next, there is a strong likelihood of IED use in future conflicts. While great progress in analysis and exploitation of IED data has been made, the data is generally limited to historical attacks in current theaters of operation. The purpose of this analysis is to develop a tool that generates IED placement data based on statistically valid assumptions and distributions in any theater. These distributions can be used to train analysts prior to deployment to new environments. The research will generate data representing hypothetical IED attacks in a table structured like the CIDNE database. IED data from the CIDNE database will be used to develop distributions for each attack detailing IED location and type (type includes: command detonated, victim detonated, timer operated and radio controlled). Hypotheses will be tested using CIDNE data to determine some trends, including: seasonality, distance from road and location relative to terrain features. Results of these tests will shape the distributions used in the generation of synthetic datasets. The datasets will be generated based on road networks, terrain and population centers in Colorado, but shape files could be used from any region of the world. The results will generate various datasets that can be used for training analysts on IED attacks specific to any Operating Environment. This simulation will expose analysts to realistic data better preparing them for combat operations in their theater.
{"title":"IED dataset generation: Analysis across theaters","authors":"Stephanie J. Wangeman","doi":"10.1109/SIEDS.2013.6549500","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549500","url":null,"abstract":"Improvised Explosive Devices are the most dangerous threat to United States troops in Iraq and Afghanistan. Regardless of where troops deploy next, there is a strong likelihood of IED use in future conflicts. While great progress in analysis and exploitation of IED data has been made, the data is generally limited to historical attacks in current theaters of operation. The purpose of this analysis is to develop a tool that generates IED placement data based on statistically valid assumptions and distributions in any theater. These distributions can be used to train analysts prior to deployment to new environments. The research will generate data representing hypothetical IED attacks in a table structured like the CIDNE database. IED data from the CIDNE database will be used to develop distributions for each attack detailing IED location and type (type includes: command detonated, victim detonated, timer operated and radio controlled). Hypotheses will be tested using CIDNE data to determine some trends, including: seasonality, distance from road and location relative to terrain features. Results of these tests will shape the distributions used in the generation of synthetic datasets. The datasets will be generated based on road networks, terrain and population centers in Colorado, but shape files could be used from any region of the world. The results will generate various datasets that can be used for training analysts on IED attacks specific to any Operating Environment. This simulation will expose analysts to realistic data better preparing them for combat operations in their theater.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053917","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549490
C. Ezekannagha, T. Jasien, Sung-Hoon Kim, J. Springer
As part of a year-long study for the Department of Systems Engineering at West Point, our Capstone project seeks to provide Tobyhanna Army Depot with a decision support tool that will quantify how changes in Depot Maintenance programs, to include the exploration of reset and recap maintenance for the AN/TPQ 37 Firefinder will affect the readiness, time, cost, reliability, and support costs of the Firefinder. The AN/TPQ 37 Firefinder is a mobile radar system that provides detection of incoming artillery and rocket fire and tracks its movement in order to provide a location for counter-battery fire. This study is important because it incorporates an interdisciplinary approach to solving a complex problem using elements of industrial engineering, systems thinking, as well as modeling in producing a decision support tool that will allow Tobyhanna to make changes to their resources and service times in order to analyze these effects on turnaround times. The problem will be solved in two phases. The first phase will be to model the current operations at Tobyhanna Army Depot by using the AnyLogic simulation tool. This will be done by analyzing maintenance data and using the AnyLogic simulation tool in order to model their current system turnaround of 180 days. Once this is complete and approved by Tobyhanna Army Depot, inputs in AnyLogic will be altered in order to show how these changes will impact service turnaround times. So far, we have achieved an average turnaround time of 6 months with our current model. We have also succeeded in providing a decision support tool to Tobyhanna Army Depot that will allow them to alter their resource and labor constraints in order to accurately model what its impact on turnaround time will be.
{"title":"Analysis of changes to Tobyhanna Army Depot maintenance cycle","authors":"C. Ezekannagha, T. Jasien, Sung-Hoon Kim, J. Springer","doi":"10.1109/SIEDS.2013.6549490","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549490","url":null,"abstract":"As part of a year-long study for the Department of Systems Engineering at West Point, our Capstone project seeks to provide Tobyhanna Army Depot with a decision support tool that will quantify how changes in Depot Maintenance programs, to include the exploration of reset and recap maintenance for the AN/TPQ 37 Firefinder will affect the readiness, time, cost, reliability, and support costs of the Firefinder. The AN/TPQ 37 Firefinder is a mobile radar system that provides detection of incoming artillery and rocket fire and tracks its movement in order to provide a location for counter-battery fire. This study is important because it incorporates an interdisciplinary approach to solving a complex problem using elements of industrial engineering, systems thinking, as well as modeling in producing a decision support tool that will allow Tobyhanna to make changes to their resources and service times in order to analyze these effects on turnaround times. The problem will be solved in two phases. The first phase will be to model the current operations at Tobyhanna Army Depot by using the AnyLogic simulation tool. This will be done by analyzing maintenance data and using the AnyLogic simulation tool in order to model their current system turnaround of 180 days. Once this is complete and approved by Tobyhanna Army Depot, inputs in AnyLogic will be altered in order to show how these changes will impact service turnaround times. So far, we have achieved an average turnaround time of 6 months with our current model. We have also succeeded in providing a decision support tool to Tobyhanna Army Depot that will allow them to alter their resource and labor constraints in order to accurately model what its impact on turnaround time will be.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115172642","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549504
Kyle Brew, P. Brown, S. Cao, B. McElhinny, B. Patterson, W. Scherer
The number of mobile-connected devices has been growing at a tremendous rate in recent years. These increasingly powerful tablets and smartphones are portable and personal, giving advertisers the potential to reach consumers on a one-on-one basis with personalized advertisements based on location, recent behaviors, and much more. A substantial difference between mobile media usage and mobile advertising spending suggests a significant growth opportunity in the mobile advertising market. Our work involves improving the decision-making technology used by Advertising.com, a large online advertising network, as it attempts to increase its presence in the mobile advertising market. We examined the factors that differentiate mobile consumers in order to target them more effectively, and drive the direction of Advertising.com's future mobile optimization technology development. Data was available to us from Advertising.com's back-end database, as well as in its front-end campaign reporting system. To investigate this data and determine the most valuable mobile variables, we performed data analysis utilizing tools including Microsoft Excel, R, SQL, and Minitab. We also leveraged Advertising.com's existing decision algorithm, AdLearn, as well as looked to existing mobile advertising studies. Our analyses indicate several factors are influential in the effectiveness of mobile advertisements including hour of day, day of week and device type. We found that mobile campaigns perform best during the morning hours and late at night in terms of both impressions and conversions. Also, we found that weekends have statistically superior conversion rates.
{"title":"Advertising.com mobile optimization","authors":"Kyle Brew, P. Brown, S. Cao, B. McElhinny, B. Patterson, W. Scherer","doi":"10.1109/SIEDS.2013.6549504","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549504","url":null,"abstract":"The number of mobile-connected devices has been growing at a tremendous rate in recent years. These increasingly powerful tablets and smartphones are portable and personal, giving advertisers the potential to reach consumers on a one-on-one basis with personalized advertisements based on location, recent behaviors, and much more. A substantial difference between mobile media usage and mobile advertising spending suggests a significant growth opportunity in the mobile advertising market. Our work involves improving the decision-making technology used by Advertising.com, a large online advertising network, as it attempts to increase its presence in the mobile advertising market. We examined the factors that differentiate mobile consumers in order to target them more effectively, and drive the direction of Advertising.com's future mobile optimization technology development. Data was available to us from Advertising.com's back-end database, as well as in its front-end campaign reporting system. To investigate this data and determine the most valuable mobile variables, we performed data analysis utilizing tools including Microsoft Excel, R, SQL, and Minitab. We also leveraged Advertising.com's existing decision algorithm, AdLearn, as well as looked to existing mobile advertising studies. Our analyses indicate several factors are influential in the effectiveness of mobile advertisements including hour of day, day of week and device type. We found that mobile campaigns perform best during the morning hours and late at night in terms of both impressions and conversions. Also, we found that weekends have statistically superior conversion rates.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674908","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549522
I. Motivation, M. E. Baltrusaitis, A. M. Lora, L. M. Hickman, A. C. Rodrigue, M. E. Baltrusaitis, Lingtian Wan, A. M. Lora, A. C. Rodrigue, Irene Y Kwon, L. M. Hickman, Daniel Fischer, Mark R Sochor, Gregory J Gerling
Hip dislocations are rare events. As a consequence, medical residents have little opportunity to gain experience through repeated practice. In fact, little is known about the forces and displacements that experienced physicians employ during the procedure. This study seeks to quantify the strategic maneuvering and substantial force required to reposition the femoral head into the pelvis during a hip reduction, toward a long-term goal of building a high fidelity training simulator. In particular, the work herein describes the design, construction and evaluation of a custom-built, force and motion measurement system (FMMS). As a physician attempts to relocate the hip, the FMMS measures force about the patient's waist using a seatbelt and inline load cell (4448 N range) and displacement of the dislocated leg with four magnetic displacement sensors (each 6 DOF). Iterations of the system have been tested on cadavers and able-bodied participants, with the current system deployed for collection on hip reduction patients. The results preliminarily indicate that forces at the hip range from 56.8 to 110.8 N, the forces at the leg range from 254.5 to 496.6 N, and that maximum angular movements from the pelvis to the thigh, from the thigh to the knee, and from the knee to the ankle are 80.1, 31.5 and 20.7 degrees, respectively.
{"title":"Quantitative characterization of human performance during hip reduction","authors":"I. Motivation, M. E. Baltrusaitis, A. M. Lora, L. M. Hickman, A. C. Rodrigue, M. E. Baltrusaitis, Lingtian Wan, A. M. Lora, A. C. Rodrigue, Irene Y Kwon, L. M. Hickman, Daniel Fischer, Mark R Sochor, Gregory J Gerling","doi":"10.1109/SIEDS.2013.6549522","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549522","url":null,"abstract":"Hip dislocations are rare events. As a consequence, medical residents have little opportunity to gain experience through repeated practice. In fact, little is known about the forces and displacements that experienced physicians employ during the procedure. This study seeks to quantify the strategic maneuvering and substantial force required to reposition the femoral head into the pelvis during a hip reduction, toward a long-term goal of building a high fidelity training simulator. In particular, the work herein describes the design, construction and evaluation of a custom-built, force and motion measurement system (FMMS). As a physician attempts to relocate the hip, the FMMS measures force about the patient's waist using a seatbelt and inline load cell (4448 N range) and displacement of the dislocated leg with four magnetic displacement sensors (each 6 DOF). Iterations of the system have been tested on cadavers and able-bodied participants, with the current system deployed for collection on hip reduction patients. The results preliminarily indicate that forces at the hip range from 56.8 to 110.8 N, the forces at the leg range from 254.5 to 496.6 N, and that maximum angular movements from the pelvis to the thigh, from the thigh to the knee, and from the knee to the ankle are 80.1, 31.5 and 20.7 degrees, respectively.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121400394","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549513
Anne E. Hovland, Amanda M. Wagner, Katherine M. Pierce, John E. Drahos, Donald E. Brown
Chronic Obstructive Pulmonary Disease (COPD) is a serious respiratory ailment that affects millions of Americans. Several studies have shown that weather conditions and pollution can increase the occurrence of respiratory distress. The goal of the work described in this paper was to determine if the relationships between environmental variables and admissions rates for COPD were strong enough to enable the development of a surveillance system that could alert the population of potentially hazardous conditions. To conduct this study we obtained data on COPD admissions in the Shenandoah Valley of Virginia, an area of approximately 33,705 km<;sup>2<;/sup>. The data were coded at the zip code level (approximately 250 km<;sup>2<;/sup>). We obtained data for weather variables from 6 monitoring stations and used Kriging to estimate their values at the zip code level. We controlled for the effects of influenza in admission rates, although this required smoothing methods to impute missing values. We also controlled for different types of land use. To predict COPD admissions we developed three types of models: generalized linear models (GLM), multivariate adaptive regression splines (MARS), and random forests. All models showed that temperature or season was a significant (p <; 0.05) predictor for COPD admissions. In terms of predictive accuracy the random forest model provided the best performance measured by the receiver operations characteristic (ROC) and can provide the basis for strategic planning rather than tactical alerting.
{"title":"Environmental surveillance modeling: A predictive respiratory alert model for the Shenandoah Valley, Virginia","authors":"Anne E. Hovland, Amanda M. Wagner, Katherine M. Pierce, John E. Drahos, Donald E. Brown","doi":"10.1109/SIEDS.2013.6549513","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549513","url":null,"abstract":"Chronic Obstructive Pulmonary Disease (COPD) is a serious respiratory ailment that affects millions of Americans. Several studies have shown that weather conditions and pollution can increase the occurrence of respiratory distress. The goal of the work described in this paper was to determine if the relationships between environmental variables and admissions rates for COPD were strong enough to enable the development of a surveillance system that could alert the population of potentially hazardous conditions. To conduct this study we obtained data on COPD admissions in the Shenandoah Valley of Virginia, an area of approximately 33,705 km<;sup>2<;/sup>. The data were coded at the zip code level (approximately 250 km<;sup>2<;/sup>). We obtained data for weather variables from 6 monitoring stations and used Kriging to estimate their values at the zip code level. We controlled for the effects of influenza in admission rates, although this required smoothing methods to impute missing values. We also controlled for different types of land use. To predict COPD admissions we developed three types of models: generalized linear models (GLM), multivariate adaptive regression splines (MARS), and random forests. All models showed that temperature or season was a significant (p <; 0.05) predictor for COPD admissions. In terms of predictive accuracy the random forest model provided the best performance measured by the receiver operations characteristic (ROC) and can provide the basis for strategic planning rather than tactical alerting.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122318118","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549485
K. Edwards
Simulation model reuse has the potential to consume vast amounts of time, resources & money, and, result in poorly-tuned instruments completely inadequate for their intended applications. To avoid major pitfalls, it is important to conduct analyses of feasibility and alternative solutions to simulation model reuse and, optimally, involve a team of experts possessing broad skill sets. Consequent investigative activities encompass the following: Gaining familiarity with model parameters and construction; Identifying difficulties in the validation of development methods and model inputs; and, Justifying the time and cost of modifying existing models to new applications. Competencies to address these challenges include the application of project management techniques; the comprehension of the intricacies inherent in simulation modeling, programming and scripting language; the ability to transform data and design appropriate statistical experiments; and when appropriate, the capacity to conduct literature research and communicate findings in the form of written technical reports and in-person presentations. Defining the elements of these wide-ranging proficiencies forms the basis of this paper which chronicles the possible reuse of four discrete-event simulation models designed to compare internal patient queueing methods in a Veterans Administration Health System specialty cardiac clinic. Replete with generalizable examples, this six month case study illustrates a number of challenges, issues of feasibility, and practicalities involved in exploring reusability of existing simulation models.
{"title":"Exploring the reusability of discrete-event simulation models: A case study of project challenges and issues of feasibility","authors":"K. Edwards","doi":"10.1109/SIEDS.2013.6549485","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549485","url":null,"abstract":"Simulation model reuse has the potential to consume vast amounts of time, resources & money, and, result in poorly-tuned instruments completely inadequate for their intended applications. To avoid major pitfalls, it is important to conduct analyses of feasibility and alternative solutions to simulation model reuse and, optimally, involve a team of experts possessing broad skill sets. Consequent investigative activities encompass the following: Gaining familiarity with model parameters and construction; Identifying difficulties in the validation of development methods and model inputs; and, Justifying the time and cost of modifying existing models to new applications. Competencies to address these challenges include the application of project management techniques; the comprehension of the intricacies inherent in simulation modeling, programming and scripting language; the ability to transform data and design appropriate statistical experiments; and when appropriate, the capacity to conduct literature research and communicate findings in the form of written technical reports and in-person presentations. Defining the elements of these wide-ranging proficiencies forms the basis of this paper which chronicles the possible reuse of four discrete-event simulation models designed to compare internal patient queueing methods in a Veterans Administration Health System specialty cardiac clinic. Replete with generalizable examples, this six month case study illustrates a number of challenges, issues of feasibility, and practicalities involved in exploring reusability of existing simulation models.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122839839","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549484
M. Baker, D. Megersa, A. Panlilio
Aviation is one of the most important industries in the United States and around the world, as it is a major driving force in maintaining a good economy. Every year it becomes an increasingly essential mode of transportation for people and various high-value, lightweight goods, and that increase is expected to continue. Runways are the “bottleneck” in the air transportation process and are a major source of flight delays. To meet the demand for more air traffic, especially for major airports, the capacity of runways needs to be increased while maintaining Target Levels of Safety. The focus of this work is the arrival and landing process of aircrafts onto runways because this is where aircrafts are closest and collision risk is highest. Since this process is inherently stochastic, proposed changes to flight separation standards and runway occupancy times to increase capacity, must be accompanied by a system that monitors the throughput and safety of runways for the approach and landing process. Analysis described in this paper shows that reducing the standard deviation of the runway occupancy time and the air-traffic control buffer both improved safety. These improvements in safety then allowed the reduction in the mean to improve capacity.
{"title":"Runway operational quality assurance","authors":"M. Baker, D. Megersa, A. Panlilio","doi":"10.1109/SIEDS.2013.6549484","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549484","url":null,"abstract":"Aviation is one of the most important industries in the United States and around the world, as it is a major driving force in maintaining a good economy. Every year it becomes an increasingly essential mode of transportation for people and various high-value, lightweight goods, and that increase is expected to continue. Runways are the “bottleneck” in the air transportation process and are a major source of flight delays. To meet the demand for more air traffic, especially for major airports, the capacity of runways needs to be increased while maintaining Target Levels of Safety. The focus of this work is the arrival and landing process of aircrafts onto runways because this is where aircrafts are closest and collision risk is highest. Since this process is inherently stochastic, proposed changes to flight separation standards and runway occupancy times to increase capacity, must be accompanied by a system that monitors the throughput and safety of runways for the approach and landing process. Analysis described in this paper shows that reducing the standard deviation of the runway occupancy time and the air-traffic control buffer both improved safety. These improvements in safety then allowed the reduction in the mean to improve capacity.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127584191","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549518
Siddhartha Pailla, C. Pruitt
Several decision-aid frameworks attempt to provide an optimal solution to the complex, challenging problem of delivering water supply and sanitation services to communities in need. However, a failure in proper needs assessment or handoff causes systemic failure of an installed system. Capacity factors analysis (CFA) is one such framework that focuses on personalized technology-specific alternative recommendations; and it faces similar challenges. While helpful in many ways, designers using CFA still make critical assumptions with regards to community's expressed needs and only passively include the community members in the design process. Appreciative inquiry (AI) is introduced as a means to bridge this gap and increase community empowerment. The AI approach is a four-phase process: discovery, dream, design, and destiny. The process starts with asking community members about their strengths and capabilities, follows with their vision of the community, creates a space for collaborative design, and ends with implementation. A service-learning experience in Tshapasha is provided to demonstrate AI's benefits. The results are compared to a CFA-focused study of Tshapasha from 2011.
{"title":"Start with the discovery: Improving capacity factors analysis with the appreciative inquiry approach","authors":"Siddhartha Pailla, C. Pruitt","doi":"10.1109/SIEDS.2013.6549518","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549518","url":null,"abstract":"Several decision-aid frameworks attempt to provide an optimal solution to the complex, challenging problem of delivering water supply and sanitation services to communities in need. However, a failure in proper needs assessment or handoff causes systemic failure of an installed system. Capacity factors analysis (CFA) is one such framework that focuses on personalized technology-specific alternative recommendations; and it faces similar challenges. While helpful in many ways, designers using CFA still make critical assumptions with regards to community's expressed needs and only passively include the community members in the design process. Appreciative inquiry (AI) is introduced as a means to bridge this gap and increase community empowerment. The AI approach is a four-phase process: discovery, dream, design, and destiny. The process starts with asking community members about their strengths and capabilities, follows with their vision of the community, creates a space for collaborative design, and ends with implementation. A service-learning experience in Tshapasha is provided to demonstrate AI's benefits. The results are compared to a CFA-focused study of Tshapasha from 2011.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125204571","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549505
I. A. Khan
The Personalized Electronic Health Record System for Monitoring Patients with Chronic Disease (PEHRS-MPCD) is designed to permit tracking and monitoring of the symptoms of patients with chronic disease and provide healthcare professionals with data on patients' lifestyle changes, medication (drug) changes, diet changes and symptom changes. The current method of assessing the symptoms of patients with chronic disease uses an episodic approach that includes phone calls to the patient, paper surveys of health status and on-site examinations. A preventative approach that can actively involve the patient, monitor multiple conditions and provide real time information about a patient's health condition is proven to be effective in chronic disease care. PEHRS-MPCD is designed to continually monitor patients. The goal of the application is to continuously gain and provide patients' information to themselves and healthcare professionals in-order to improve the efficiency of the diagnosis and timely intervention which would yield better quality of care and quality of life for the patient. This personalized electronic health record system (PEHRS-MPCD) will be objective in providing feedback about patient lifestyle changes and choices, and in channeling this information to healthcare providers. PEHRS-MPCD would (a) allow for relevant data to be entered by the patient, (b) make relevant data available to patient's care provider, at real-time and at doctor's visit, (c) generate reports and graphs for the data and (d) provide secure storage of the data. PEHRS-MPCD is a work in progress as a lot of its functionalities and user interface design are still being amended. This paper describes the purpose, need and design of the application.
{"title":"Personalized Electronic Health Record System for Monitoring Patients with Chronic Disease","authors":"I. A. Khan","doi":"10.1109/SIEDS.2013.6549505","DOIUrl":"https://doi.org/10.1109/SIEDS.2013.6549505","url":null,"abstract":"The Personalized Electronic Health Record System for Monitoring Patients with Chronic Disease (PEHRS-MPCD) is designed to permit tracking and monitoring of the symptoms of patients with chronic disease and provide healthcare professionals with data on patients' lifestyle changes, medication (drug) changes, diet changes and symptom changes. The current method of assessing the symptoms of patients with chronic disease uses an episodic approach that includes phone calls to the patient, paper surveys of health status and on-site examinations. A preventative approach that can actively involve the patient, monitor multiple conditions and provide real time information about a patient's health condition is proven to be effective in chronic disease care. PEHRS-MPCD is designed to continually monitor patients. The goal of the application is to continuously gain and provide patients' information to themselves and healthcare professionals in-order to improve the efficiency of the diagnosis and timely intervention which would yield better quality of care and quality of life for the patient. This personalized electronic health record system (PEHRS-MPCD) will be objective in providing feedback about patient lifestyle changes and choices, and in channeling this information to healthcare providers. PEHRS-MPCD would (a) allow for relevant data to be entered by the patient, (b) make relevant data available to patient's care provider, at real-time and at doctor's visit, (c) generate reports and graphs for the data and (d) provide secure storage of the data. PEHRS-MPCD is a work in progress as a lot of its functionalities and user interface design are still being amended. This paper describes the purpose, need and design of the application.","PeriodicalId":145808,"journal":{"name":"2013 IEEE Systems and Information Engineering Design Symposium","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121947069","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 : 2013-04-26DOI: 10.1109/SIEDS.2013.6549502
J. Elliott, H. Jayachandran, P. Kumar, K. Metzer
George Mason University is a commuter campus in Fairfax, VA. To meet forecast growth, parking lots are being removed to make space for classroom buildings. The loss of parking slots requires either additional parking garages or mass-transit shuttles to make up for the loss in parking spaces. There are also concerns about traffic congestion and emissions caused by the college's commuters. The Fairfax campus population is predominantly made up of commuters, with 77% of the population living off campus, a vast majority of which drive single occupant vehicles (SOV) to campus due to poor access to mass transit. At present, the GMU Fairfax campus has a parking utilization of 86%, leaving a surplus of approximately 900 parking spaces. Fairfax campus is predicted to start having a parking deficit sometime between 2014 and 2015. Projections out to 2020 show a deficit of 3,800 spaces. To counter that deficit, more than 10,600 current commuters would need to switch from SOV transportation to alternate forms of transit. A reduction of SOV commuters would also lead to lower CO2 emissions from the Fairfax campus. This paper describes the design of a system for addressing transportation and parking demand. This has 3 major parts: (1) identifying surrounding areas of significant Fairfax commuter populations; (2) the ability to predict ridership if a shuttle stop were to be placed in any of those areas by using a decision support tool (DST); and (3) a utility analysis (CO2, parking, and cost) of creating shuttle routes or garages to meet parking needs. In order for GMU to continue growing, both commuter shuttles and parking garages will be needed. If four additional routes are added, GMU can continue to grow through 2016 with a parking utilization of 96%. To continue growth beyond that point will require additional parking garages or bus routes.
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