Matching Items (8)

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Complex Systems Approach for Simulation & Analysis of Socio-Technical Infrastructure Systems - An Empirical Demonstration

Description

Over the past century, the world has become increasingly more complex. Modern systems (i.e blockchain, internet of things (IoT), and global supply chains) are inherently difficult to comprehend due to

Over the past century, the world has become increasingly more complex. Modern systems (i.e blockchain, internet of things (IoT), and global supply chains) are inherently difficult to comprehend due to their high degree of connectivity. Understanding the nature of complex systems becomes an acutely more critical skill set for managing socio-technical infrastructure systems. As existing education programs and technical analysis approaches fail to teach and describe modern complexities, resulting consequences have direct impacts on real-world systems. Complex systems are characterized by exhibiting nonlinearity, interdependencies, feedback loops, and stochasticity. Since these four traits are counterintuitive, those responsible for managing complex systems may struggle in identifying these underlying relationships and decision-makers may fail to account for their implications or consequences when deliberating systematic policies or interventions.

This dissertation details the findings of a three-part study on applying complex systems modeling techniques to exemplar socio-technical infrastructure systems. In the research articles discussed hereafter, various modeling techniques are contrasted in their capacity for simulating and analyzing complex, adaptive systems. This research demonstrates the empirical value of a complex system approach as twofold: (i) the technique explains systems interactions which are often neglected or ignored and (ii) its application has the capacity for teaching systems thinking principles. These outcomes serve decision-makers by providing them with further empirical analysis and granting them a more complete understanding on which to base their decisions.

The first article examines modeling techniques, and their unique aptitudes are compared against the characteristics of complex systems to establish which methods are most qualified for complex systems analysis. Outlined in the second article is a proof of concept piece on using an interactive simulation of the Los Angeles water distribution system to teach complex systems thinking skills for the improved management of socio-technical infrastructure systems. Lastly, the third article demonstrates the empirical value of this complex systems approach for analyzing infrastructure systems through the construction of a systems dynamics model of the Arizona educational-workforce system, across years 1990 to 2040. The model explores a series of dynamic hypotheses and allows stakeholders to compare policy interventions for improving educational and economic outcome measures.

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Created

Date Created
  • 2020

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Improving proctoring by using non-verbal cues during remotely administrated exams

Description

This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head

This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head pose and affective state for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s non-verbal cues indicated that time delay, the variation of a student’s head pose relative to the computer screen and confusion had significantly statistical relation to cheating behaviors. Additionally, time delay, head pose relative to the computer screen, confusion, and the interaction term of confusion and time delay were predictors in a support vector machine of cheating prediction with an average accuracy of 70.7%. The current algorithm could automatically flag suspicious student behavior for proctors in large scale online courses during remotely administered exams.

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Created

Date Created
  • 2015

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The impact of coordination quality on coordination dynamics and team performance: when humans team with autonomy

Description

This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to

This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions.

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Created

Date Created
  • 2017

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Re-thinking Engineering Doctoral Students’ Sense of Belonging: In Consideration of Diversity in Citizenship and Interpersonal Interactions

Description

A defining feature of many United States (U.S.) doctoral engineering programs is their large proportion of international students. Despite the large student body and the significant impacts that they bring

A defining feature of many United States (U.S.) doctoral engineering programs is their large proportion of international students. Despite the large student body and the significant impacts that they bring to the U.S. education and economy, a scarcity of research on engineering doctoral students has taken into consideration the existence of international students and the consequential diversity in citizenship among all students. This study was designed to bridge the research gap to improve the understanding of sense of belonging from the perspective of international engineering doctoral students.

A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.

The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.

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Created

Date Created
  • 2020

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Holistic learning for multi-target and network monitoring problems

Description

Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic

Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.

The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.

The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.

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Created

Date Created
  • 2014

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Growth mindset training to increase women's self-efficacy in science and engineering: a randomized-controlled trial

Description

Undeclared undergraduates participated in an experimental study designed to explore the impact of an Internet-delivered "growth mindset" training on indicators of women's engagement in science, engineering, technology, and mathematics ("STEM")

Undeclared undergraduates participated in an experimental study designed to explore the impact of an Internet-delivered "growth mindset" training on indicators of women's engagement in science, engineering, technology, and mathematics ("STEM") disciplines. This intervention was hypothesized to increase STEM self-efficacy and intentions to pursue STEM by strengthening beliefs in intelligence as malleable ("IQ attitude") and discrediting gender-math stereotypes (strengthening "stereotype disbelief"). Hypothesized relationships between these outcome variables were specified in a path model. The intervention was also hypothesized to bolster academic achievement. Participants consisted of 298 women and 191 men, the majority of whom were self-identified as White (62%) and 18 years old (85%) at the time of the study. Comparison group participants received training on persuasive writing styles and control group participants received no training. Participants were randomly assigned to treatment, comparison, or control groups. At posttest, treatment group scores on measures of IQ attitude, stereotype disbelief, and academic achievement were highest; the effects of group condition on these three outcomes were statistically significant as assessed by analysis of variance. Results of pairwise comparisons indicated that treatment group IQ attitude scores were significantly higher than the average IQ attitude scores of both comparison and control groups. Treatment group scores on stereotype disbelief were significantly higher than those of the comparison group but not those of the control group. GPAs of treatment group participants were significantly higher than those of control group participants but not those of comparison group participants. The effects of group condition on STEM self-efficacy or intentions to pursue STEM were not significant. Results of path analysis indicated that the hypothesized model of the relationships between variables fit to an acceptable degree. However, a model with gender-specific paths from IQ attitude and stereotype disbelief to STEM self-efficacy was found to be superior to the hypothesized model. IQ attitude and stereotype disbelief were positively related; IQ attitude was positively related to men's STEM self-efficacy; stereotype disbelief was positively related to women's STEM self-efficacy, and STEM self-efficacy was positively related to intentions to pursue STEM. Implications and study limitations are discussed, and directions for future research are proposed.

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Created

Date Created
  • 2014

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The Optimal Control of Child Delivery for Women with Hypertensive Disorders of Pregnancy

Description

Hypertensive disorders of pregnancy (HDP) affect up to 5%-15% of pregnancies around the globe, and form a leading cause of maternal and neonatal morbidity and mortality. HDP are progressive disorders

Hypertensive disorders of pregnancy (HDP) affect up to 5%-15% of pregnancies around the globe, and form a leading cause of maternal and neonatal morbidity and mortality. HDP are progressive disorders for which the only cure is to deliver the baby. An increasing trend in the prevalence of HDP has been observed in the recent years. This trend is anticipated to continue due to the rise in the prevalence of diseases that strongly influence hypertension such as obesity and metabolic syndrome. In order to lessen the adverse outcomes due to HDP, we need to study (1) the natural progression of HDP, (2) the risks of adverse outcomes associated with these disorders, and (3) the optimal timing of delivery for women with HDP.

In the first study, the natural progression of HDP in the third trimester of pregnancy is modeled with a discrete-time Markov chain (DTMC). The transition probabilities of the DTMC are estimated using clinical data with an order restricted inference model that maximizes the likelihood function subject to a set of order restrictions between the transition probabilities. The results provide useful insights on the progression of HDP, and the estimated transition probabilities are used to parametrize the decision models in the third study.

In the second study, the risks of maternal and neonatal adverse outcomes for women with HDP are quantified with a composite measure of childbirth morbidity, and the estimated risks are compared with respect to type of HDP at delivery, gestational age at delivery, and type of delivery in a retrospective cohort study. Furthermore, the safety of child delivery with respect to the same variables is assessed with a provider survey and technique for order performance by similarity to ideal solution (TOPSIS). The methods and results of this study are used to parametrize the decision models in the third study.

In the third study, the decision problem of timing of delivery for women with HDP is formulated as a discrete-time Markov decision process (MDP) model that minimizes the risks of maternal and neonatal adverse outcomes. We additionally formulate a robust MDP model that gives the worst-case optimal policy when transition probabilities are allowed to vary within their confidence intervals. The results of the decision models are assessed within a probabilistic sensitivity analysis (PSA) that considers the uncertainty in the estimated risk values. In our PSA, the performance of candidate delivery policies is evaluated using a large number of problem instances that are constructed according to the orders between model parameters to incorporate physicians' intuition.

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Created

Date Created
  • 2018

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Training the Code Team Leader as a Forcing Function to Improve Overall Team Performance During Simulated Code Blue Events

Description

The American Heart Association (AHA) estimates that there are approximately 200,000 in-hospital cardiac arrests (IHCA) annually with low rates of survival to discharge at about 22%. Training programs for cardiac

The American Heart Association (AHA) estimates that there are approximately 200,000 in-hospital cardiac arrests (IHCA) annually with low rates of survival to discharge at about 22%. Training programs for cardiac arrest teams, also termed code teams, have been recommended by the Institute of Medicine (IOM) and in the AHA's consensus statement to help improve these dismal survival rates. Historically, training programs in the medical field are procedural in nature and done at the individual level, despite the fact that healthcare providers frequently work in teams. The rigidity of procedural training can cause habituation and lead to poor team performance if the situation does not match the original training circumstances. Despite the need for team training, factors such as logistics, time, personnel coordination, and financial constraints often hinder resuscitation team training. This research was a three-step process of: 1) development of a metric specific for the evaluation of code team performance, 2) development of a communication model that targeted communication and leadership during a code blue resuscitation, and 3) training and evaluation of the code team leader using the communication model. This research forms a basis to accomplish a broad vision of improving outcomes of IHCA events by applying conceptual and methodological strategies learned from collaborative and inter-disciplinary science of teams.

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Agent

Created

Date Created
  • 2017