Quantum resilience is a pragmatic theory that allows systems engineers to formally characterize the resilience of systems. As a generalized theory, it not only clarifies resilience in the literature, but also can be applied to all disciplines and domains of discourse. Operationalizing resilience in this manner permits decision-makers to compare and contrast system deployment options for suitability in a variety of environments and allows for consistent treatment of resilience across domains. Systems engineers, whether planning future infrastructures or managing ecosystems, are increasingly asked to deliver resilient systems. Quantum resilience provides a way forward that allows specific resilience requirements to be specified, validated, and verified.

Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.

Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.

Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful, are not the most suitable vehicle for understanding the dynamics of dosage changes to patient response. A secondary analysis of intensive longitudinal data from a naltrexone intervention for fibromyalgia examined in this dissertation shows the promise of system identification and control. This includes datacentric identification methods such as Model-on-Demand, which are attractive techniques for estimating nonlinear dynamical systems from noisy data. These methods rely on generating a local function approximation using a database of regressors at the current operating point, with this process repeated at every new operating condition. This dissertation examines generating input signals for data-centric system identification by developing a novel framework of geometric distribution of regressors and time-indexed output points, in the finite dimensional space, to generate sufficient support for the estimator. The input signals are generated while imposing “patient-friendly” constraints on the design as a means to operationalize single-subject clinical trials. These optimization-based problem formulations are examined for linear time-invariant systems and block-structured Hammerstein systems, and the results are contrasted with alternative designs based on Weyl's criterion. Numerical solution to the resulting nonconvex optimization problems is proposed through semidefinite programming approaches for polynomial optimization and nonlinear programming methods. It is shown that useful bounds on the objective function can be calculated through relaxation procedures, and that the data-centric formulations are amenable to sparse polynomial optimization. In addition, input design formulations are formulated for achieving a desired output and specified input spectrum. Numerical examples illustrate the benefits of the input signal design formulations including an example of a hypothetical clinical trial using the drug gabapentin. In the final part of the dissertation, the mixed logical dynamical framework for hybrid model predictive control is extended to incorporate a switching time strategy, where decisions are made at some integer multiple of the sample time, and manipulation of only one input at a given sample time among multiple inputs. These are considerations important for clinical use of the algorithm.

This dissertation focused on the development and application of state-of-the-art monitoring tools and analysis methods for tracking the fate of trace level contaminants in the natural and built water environments, using fipronil as a model; fipronil and its primary degradates (known collectively as fiproles) are among a group of trace level emerging environmental contaminants that are extremely potent arthropodic neurotoxins. The work further aimed to fill in data gaps regarding the presence and fate of fipronil in engineered water systems, specifically in a wastewater treatment plant (WWTP), and in an engineered wetland. A review of manual and automated “active” water sampling technologies motivated the development of two new automated samplers capable of in situ biphasic extraction of water samples across the bulk water/sediment interface of surface water systems. Combined with an optimized method for the quantification of fiproles, the newly developed In Situ Sampler for Biphasic water monitoring (IS2B) was deployed along with conventional automated water samplers, to study the fate and occurrence of fiproles in engineered water environments; continuous sampling over two days and subsequent analysis yielded average total fiprole concentrations in wetland surface water (9.9 ± 4.6 to 18.1 ± 4.6 ng/L) and wetland sediment pore water (9.1 ± 3.0 to 12.6 ± 2.1 ng/L). A mass balance of the WWTP located immediately upstream demonstrated unattenuated breakthrough of total fiproles through the WWTP with 25 ± 3 % of fipronil conversion to degradates, and only limited removal of total fiproles in the wetland (47 ± 13%). Extrapolation of local emissions (5–7 g/d) suggests nationwide annual fiprole loadings from WWTPs to U.S. surface waters on the order of about one half to three quarters of a metric tonne. The qualitative and quantitative data collected in this work have regulatory implications, and the sampling tools and analysis strategies described in this thesis have broad applicability in the assessment of risks posed by trace level environmental contaminants.

Engineering education can provide students with the tools to address complex, multidisciplinary grand challenge problems in sustainable and global contexts. However, engineering education faces several challenges, including low diversity percentages, high attrition rates, and the need to better engage and prepare students for the role of a modern engineer. These challenges can be addressed by integrating sustainability grand challenges into engineering curriculum.

Two main strategies have emerged for integrating sustainability grand challenges. In the stand-alone course method, engineering programs establish one or two distinct courses that address sustainability grand challenges in depth. In the module method, engineering programs integrate sustainability grand challenges throughout existing courses. Neither method has been assessed in the literature.

This thesis aimed to develop sustainability modules, to create methods for evaluating the modules’ effectiveness on student cognitive and affective outcomes, to create methods for evaluating students’ cumulative sustainability knowledge, and to evaluate the stand-alone course method to integrate sustainability grand challenges into engineering curricula via active and experiential learning.

The Sustainable Metrics Module for teaching sustainability concepts and engaging and motivating diverse sets of students revealed that the activity portion of the module had the greatest impact on learning outcome retention.

The Game Design Module addressed methods for assessing student mastery of course content with student-developed games indicated that using board game design improved student performance and increased student satisfaction.

Evaluation of senior design capstone projects via novel comprehensive rubric to assess sustainability learned over students’ curriculum revealed that students’ performance is primarily driven by their instructor’s expectations. The rubric provided a universal tool for assessing students’ sustainability knowledge and could also be applied to sustainability-focused projects.

With this in mind, engineering educators should pursue modules that connect sustainability grand challenges to engineering concepts, because student performance improves and students report higher satisfaction. Instructors should utilize pedagogies that engage diverse students and impact concept retention, such as active and experiential learning. When evaluating the impact of sustainability in the curriculum, innovative assessment methods should be employed to understand student mastery and application of course concepts and the impacts that topics and experiences have on student satisfaction.

Node-link diagrams are widely used to visualize the relational structure of real world datasets. As identical data can be visualized in infinite ways by simply changing the spatial arrangement of the nodes, one of the important research topics of the graph drawing community is to visualize the data in the way that can facilitate people's comprehension. The last three decades have witnessed the growth of algorithms for automatic visualization. However, despite the popularity of node-link diagrams and the enthusiasm in improving computational efficiency, little is known about how people read these graphs and what factors (layout, size, density, etc.) have impact on their effectiveness (the usability aspect of the graph, e.g., are they easy to understand?). This thesis is comprehensive research to investigate the factors that affect people's understanding of node-link diagrams using eye-tracking methods. Three experiments were conducted, including 1) a pilot study with 22 participants to explore the layout and size effect; 2) an eye tracking experiment with 43 participants to investigate the layout, size and density effect on people's graph comprehension using abstract node-link diagram and generic tasks; and 3) an eye tracking experiment with the same participants to investigate the same effects using a real visualization analytic application. Results showed that participants' spatial reasoning ability had significant impact on people's graph reading performance. Layout, size, and density were all found to be significant effects under different task circumstances. The applicability of the eye tracking methods on visualization evaluation has been confirmed by providing detailed evidence that demonstrates the cognitive process of participants' graph reading behavior.

A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.

Surface plasmon resonance (SPR) has emerged as a popular technique for elucidating subtle signals from biological events in a label-free, high throughput environment. The efficacy of conventional SPR sensors, whose signals are mass-sensitive, diminishes rapidly with the size of the observed target molecules. The following work advances the current SPR sensor paradigm for the purpose of small molecule detection. The detection limits of two orthogonal components of SPR measurement are targeted: speed and sensitivity. In the context of this report, speed refers to the dynamic range of measured kinetic rate constants, while sensitivity refers to the target molecule mass limitation of conventional SPR measurement. A simple device for high-speed microfluidic delivery of liquid samples to a sensor surface is presented to address the temporal limitations of conventional SPR measurement. The time scale of buffer/sample switching is on the order of milliseconds, thereby minimizing the opportunity for sample plug dispersion. The high rates of mass transport to and from the central microfluidic sensing region allow for SPR-based kinetic analysis of binding events with dissociation rate constants (kd) up to 130 s-1. The required sample volume is only 1 μL, allowing for minimal sample consumption during high-speed kinetic binding measurement. Charge-based detection of small molecules is demonstrated by plasmonic-based electrochemical impedance microscopy (P-EIM). The dependence of surface plasmon resonance (SPR) on surface charge density is used to detect small molecules (60-120 Da) printed on a dextran-modified sensor surface. The SPR response to an applied ac potential is a function of the surface charge density. This optical signal is comprised of a dc and an ac component, and is measured with high spatial resolution. The amplitude and phase of local surface impedance is provided by the ac component. The phase signal of the small molecules is a function of their charge status, which is manipulated by the pH of a solution. This technique is used to detect and distinguish small molecules based on their charge status, thereby circumventing the mass limitation (~100 Da) of conventional SPR measurement.

Females and underrepresented ethnic minorities earn a small percentage of engineering and computer science bachelor's degrees awarded in the United States, earn an even smaller proportion of master's and doctoral degrees, and are underrepresented in the engineering workforce (Engineering Workforce Commission, [2006], as cited in National Science Foundation, 2012; United States Department of Education, [2006], as cited in National Science Foundation, 2009a; United States Department of Education, [2006], as cited in National Science Foundation, 2009b). Considerable research has examined the perceptions, culture, curriculum, and pedagogy in engineering that inhibits the achievement of women and underrepresented ethnic minorities. This action research study used a qualitative approach to examine the characteristics and experiences of Latina students who pursued a bachelor's degree in the Ira A. Fulton Schools of Engineering at Arizona State University (ASU) as part of the 2008 first-time full-time freshman cohort. The researcher conducted two semi-structured individual interviews with seven undergraduate Latina students who successfully persisted to their fourth (senior) year in engineering. The researcher aimed to understand what characteristics made these students successful and how their experiences affected their persistence in an engineering major. The data collected showed that the Latina participants were motivated to persist in their engineering degree program due to their parents' expectations for success and high academic achievement; their desire to overcome the discrimination, stereotyping, and naysayers that they encountered; and their aspiration to become a role model for their family and other students interested in pursuing engineering. From the data collected, the researcher provided suggestions to implement and adapt educational activities and support systems within the Ira A. Fulton Schools of Engineering to improve the retention and graduation rates of Latinas in engineering at ASU.

This report will review the mechanical and microstructural properties of the refractory element rhenium (Re) deposited using Laser Additive Manufacturing (LAM). With useable structural strength over 2200 °C, existing applications up to 2760 °C, very high strength, ductility and chemical resistance, interest in Re is understandable. This study includes data about tensile properties including tensile data up to 1925 °C, fracture modes, fatigue and microstructure including deformation systems and potential applications of that information. The bulk mechanical test data will be correlated with nanoindentation and crystallographic examination. LAM properties are compared to the existing properties found in the literature for other manufacturing processes. The literature indicates that Re has three significant slip systems but also twins as part of its deformation mechanisms. While it follows the hcp metal characteristics for deformation, it has interesting and valuable extremes such as high work hardening, potentially high strength, excellent wear resistance and superior elevated temperature strength. These characteristics are discussed in detail.

Soft magnetic alloys play a significant role for magnetic recording applications and highly sensitivity magnetic field sensors. In order to sustain the magnetic areal density growth, development of new synthesis techniques and materials is necessary. In this work, the effect of oxygen incorporation during electrodeposition of CoFe alloys on magnetic properties, magnetoresistance and structural properties has been studied. Understanding the magnetic properties often required knowledge of oxygen distribution and structural properties of the grown films. Transmission electron microscopy (TEM) was a powerful tool in this study to correlate the oxygen-distribution nanostructure to the magnetic properties of deposited films. Off-axis electron holography in TEM was used to measure magnetic domain wall width in the deposited films. Elemental depth profiles of Fe, Co, O were investigated by secondary ion mass spectroscopy (SIMS). Magnetic properties have been determined by superconducting quantum interference device (SQUID) measurements. Oxygen content in the CoFe deposited films was controlled by electrolyte composition. Films were deposited on Si 100 substrates and on other substrates such as Cu and Al. However, a good film quality was achieved on Si substrate. Electron energy loss and x-ray spectroscopies showed that the low oxygen films contained intragranular Fe2+ oxide (FeO) particles and that the high oxygen films contained intergranular Fe3+ (Fe2O3) along grain boundaries. The films with oxide present at the grain boundary had significantly increased coercivity, magnetoresistance and reduced saturation magnetization relative to the lower oxygen content films with intragranular oxide. The differences in magnetic properties between low oxygen and high oxygen concentration films were attributed to stronger mobile domain wall interactions with the grain boundary oxide layers. The very high magnetoresistance values were achieved for magnetic devices with nanocontact dimension < 100 nm and oxide incorporation in this nanoconfined geometry. The content of oxide phase in nanocontact was controlled by concentration of the Fe3+ ions in the electrodeposition solution. Magnetic device integrity was improved by varying amount of additive into plating solution. These results indicated that electrodeposited CoFe nanocontact is a novel class of materials with large application for magnetic field sensors.