Dissertations / Theses on the topic '091599 Interdisciplinary Engineering not elsewhere classified'

In the Australian sugar industry, sugar cane is smashed into a straw like material by hammers before being squeezed between large rollers to extract the sugar juice. The straw like material is initially called prepared cane and then bagasse as it passes through successive roller milling units. The sugar cane materials are highly compressible, have high moisture content, are fibrous, and they resemble some peat soils in both appearance and mechanical behaviour. A promising avenue to improve the performance of milling units for increased throughput and juice extraction, and to reduce costs is by modelling of the crushing process. To achieve this, it is believed necessary that milling models should be able to reproduce measured bagasse behaviour. This investigation sought to measure the mechanical (compression, shear, and volume) behaviour of prepared cane and bagasse, to identify limitations in currently used material models, and to progress towards a material model that can predict bagasse behaviour adequately. Tests were carried out using a modified direct shear test equipment and procedure at most of the large range of pressures occurring in the crushing process. The investigation included an assessment of the performance of the direct shear test for measuring bagasse behaviour. The assessment was carried out using finite element modelling. It was shown that prepared cane and bagasse exhibited critical state behavior similar to that of soils and the magnitudes of material parameters were determined. The measurements were used to identify desirable features for a bagasse material model. It was shown that currently used material models had major limitations for reproducing bagasse behaviour. A model from the soil mechanics literature was modified and shown to achieve improved reproduction while using magnitudes of material parameters that better reflected the measured values. Finally, a typical three roller mill pressure feeder configuration was modelled. The predictions and limitations were assessed by comparison to measured data from a sugar factory.

Effective collaboration in a team is a crucial skill. When people interact together to perform physical tasks, they rely on gestures to convey instructions. This thesis explores gestures as means to assess physical collaborative task understanding. This research proposes a framework to represent, compare, and assess gestures’ morphology, semantics, and pragmatics, as opposed to traditional approaches that rely mostly on the gestures’ physical appearance. By leveraging this framework, functionally equivalent gestures can be identified and compared. In addition, a metric to assess the quality of assimilation of physical instructions is computed from gesture matchings, which acts as a proxy metric for task understanding based on gestural analysis. The correlations between this proposed metric and three other task understanding proxy metrics were obtained. Our framework was evaluated through three user studies in which participants completed shared tasks remotely: block assembly, origami, and ultrasound training. The results indicate that the proposed metric acts as a good estimator for task understanding. Moreover, this metric provides task understanding insights in scenarios where other proxy metrics show inconsistencies. Thereby, the approach presented in this research acts as a first step towards assessing task understanding in physical collaborative scenarios through the analysis of gestures.

In 2011, the Deep River Portage-Burns Waterway Watershed was identified as a priority in the Northwest Indiana watershed management framework by the Northwester Indiana Regional Planning Committee. 319 grant cost-share programs were initiated in effort of maintaining and restoring the health of Deep River Portage-Burns Waterway Watershed. A watershed management plans have been developed for this watershed which proposed the implementation of vegetated filter strips (VFS) as an option. In this thesis work, the effectiveness of VFS as a best management practice (BMP) for the Deep River system was evaluated using a hydrological model scheme.

Engineering students are expected to develop professional skills in addition to their technical knowledge as outcomes of accredited engineering programs. Among the most critical professional skills is the ability to work effectively in a team. Working effectively in teams has learning benefits and also provides an environment for developing other professional skills such as communication, leadership skills, and time management. However, students will develop those skills only if their teams function effectively.

This dissertation includes three studies that together inform team formation and management practices to improve team dynamics. The first study investigates mixed-gender team dynamics to determine whether those teams are realizing their potential. The second study explores the relationship of individual psychological safety and students’ team member effectiveness and the moderating effects of team-level psychological safety. The third study explores self-rating bias among first-year engineering students and its relationship to student characteristics and dimensions of team-member effectiveness.

Although mixed-gender teams had equal team dynamics with all-male teams, more team facilitation and training are needed to improve the experience of mixed-gender teams. Asian, Black, and Hispanic/Latino students, as well as students with lower GPA, report lower psychological safety, which is associated with lower team-member effectiveness. Team-level psychological safety moderated this effect for Asian and Hispanic/Latino students. Students’ effort in teams was associated with lower self-rating bias, likely an indication of greater self-awareness. Together, these studies and their findings contribute to a broader understanding that there are interrelationships among team composition, team dynamics, and team-member effectiveness, and that these relationships differ based on student characteristics such as race/ethnicity, gender, and prior knowledge. This work adds to the body of research demonstrating the importance of teaching students about effective teamwork, conducting regular peer evaluations of team functioning, and interpreting those peer evaluations carefully to avoid perpetuating any biases. This work also demonstrates the usefulness of psychological safety as an important indicator of marginalization.

In urbanized river basins, sanitary wastewater and urban runoff (non-sanitary water) from urban agglomerations drain to complex engineered networks, are treated at centralized wastewater treatment plants (WWTPs) and discharged to river networks. Discharge from multiple WWTPs distributed in urbanized river basins contributes to impairments of river water-quality and aquatic ecosystem integrity. The size and location of WWTPs are determined by spatial patterns of population in urban agglomerations within a river basin. Economic and engineering constraints determine the combination of wastewater treatment technologies used to meet required environmental regulatory standards for treated wastewater discharged to river networks. Thus, it is necessary to understand the natural-human-engineered networks as coupled systems, to characterize their interrelations, and to understand emergent spatiotemporal patterns and scaling of geochemical and ecological responses.

My PhD research involved data-model synthesis, using publicly available data and application of well-established network analysis/modeling synthesis approaches. I present the scope and specific subjects of my PhD project by employing the Drivers-Pressures-Status-Impacts-Responses (DPSIR) framework. The defined research scope is organized as three main themes: (1) River network and urban drainage networks (Foundation-Pathway of Pressures); (2) River network, human population, and WWTPs (Foundation-Drivers-Pathway of Pressures); and (3) Nutrient loads and their impacts at reach- and basin-scales (Pressures-Impacts).

Three inter-related research topics are: (1) the similarities and differences in scaling and topology of engineered urban drainage networks (UDNs) in two cities, and UDN evolution over decades; (2) the scaling and spatial organization of three attributes: human population (POP), population equivalents (PE; the aggregated population served by each WWTP), and the number/sizes of WWTPs using geo-referenced data for WWTPs in three large urbanized basins in Germany; and (3) the scaling of nutrient loads (P and N) discharged from ~845 WWTPs (five class-sizes) in urbanized Weser River basin in Germany, and likely water-quality impacts from point- and diffuse- nutrient sources.

I investigate the UDN scaling using two power-law scaling characteristics widely employed for river networks: (1) Hack’s law (length-area power-law relationship), and (2) exceedance probability distribution of upstream contributing area. For the smallest UDNs, length-area scales linearly, but power-law scaling emerges as the UDNs grow. While area-exceedance plots for river networks are abruptly truncated, those for UDNs display exponential tempering. The tempering parameter decreases as the UDNs grow, implying that the distribution evolves in time to resemble those for river networks. However, the power-law exponent for mature UDNs tends to be larger than the range reported for river networks. Differences in generative processes and engineering design constraints contribute to observed differences in the evolution of UDNs and river networks, including subnet heterogeneity and non-random branching.

In this study, I also examine the spatial patterns of POP, PE, and WWTPs from two perspectives by employing fractal river networks as structural platforms: spatial hierarchy (stream order) and patterns along longitudinal flow paths (width function). I propose three dimensionless scaling indices to quantify: (1) human settlement preferences by stream order, (2) non-sanitary flow contribution to total wastewater treated at WWTPs, and (3) degree of centralization in WWTPs locations. I select as case studies three large urbanized river basins (Weser, Elbe, and Rhine), home to about 70% of the population in Germany. Across the three river basins, the study shows scale-invariant distributions for each of the three attributes with stream order, quantified using extended Horton scaling ratios; a weak downstream clustering of POP in the three basins. Variations in PE clustering among different class-sizes of WWTPs reflect the size, number, and locations of urban agglomerations in these catchments.

WWTP effluents have impacts on hydrologic attributes and water quality of receiving river bodies at the reach- and basin-scales. I analyze the adverse impacts of WWTP discharges for the Weser River basin (Germany), at two steady river discharge conditions (median flow; low-flow). This study shows that significant variability in treated wastewater discharge within and among different five class-sizes WWTPs, and variability of river discharge within the stream order <3, contribute to large variations in capacity to dilute WWTP nutrient loads. For the median flow, reach-scale water quality impairment assessed by nutrient concentration is likely at 136 (~16%) locations for P and 15 locations (~2%) for N. About 90% of the impaired locations are the stream order < 3. At basin-scale analysis, considering in stream uptake resulted 225 (~27%) P-impaired streams, which was ~5% reduction from considering only dilution. This result suggests the dominant role of dilution in the Weser River basin. Under the low flow conditions, water quality impaired locations are likely double than the median flow status for the analyses. This study for the Weser River basin reveals that the role of in-stream uptake diminishes along the flow paths, while dilution in larger streams (4≤ stream order ≤7) minimizes the impact of WWTP loads.

Furthermore, I investigate eutrophication risk from spatially heterogeneous diffuse- and point-source P loads in the Weser River basin, using the basin-scale network model with in-stream losses (nutrient uptake).Considering long-term shifts in P loads for three representative periods, my analysis shows that P loads from diffuse-sources, mainly from agricultural areas, played a dominant role in contributing to eutrophication risk since 2000s, because of ~87% reduction of point-source P loads compared to 1980s through the implementation of the EU WFD. Nevertheless, point-sources discharged to smaller streams (stream order < 3) pose amplification effects on water quality impairment, consistent with the reach-scale analyses only for WWTPs effluents. Comparing to the long-term water quality monitoring data, I demonstrate that point-sources loads are the primary contributors for eutrophication in smaller streams, whereas diffuse-source loads mainly from agricultural areas address eutrophication in larger streams. The results are reflective of spatial patterns of WWTPs and land cover in the Weser River basin.

Through data-model synthesis, I identify the characteristics of the coupled natural (rivers) – humans – engineered (urban drainage infrastructure) systems (CNHES), inspired by analogy, coexistence, and causality across the coupled networks in urbanized river basins. The quantitative measures and the basin-scale network model presented in my PhD project could extend to other large urbanized basins for better understanding the spatial distribution patterns of the CNHES and the resultant impacts on river water-quality impairment.

A neural network model was developed to predict the E. Coli levels and classes in six (6) select Lake Michigan beaches. Water quality observations at the time of sampling and discharge information from two close tributaries were used as input to predict the E. coli. This research was funded by the Indiana Department of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was developed based on the best model for making future predictions of E. coli classes. This tool will facilitate beach managers to take real-time decisions.

The nowcast model was developed based on historical tributary flows and water quality measurements (physical, chemical and biological). The model uses experimentally available information such as total dissolved solids, total suspended solids, pH, electrical conductivity, and water temperature to estimate whether the E. Coli counts would exceed the acceptable standard. For setting up this model, field data collection was carried out during 2019 beachgoer’s season.

IDEM recommends posting an advisory at the beach indicating swimming and wading are not recommended when E. coli counts exceed advisory standards. Based on the advisory limit, a single water sample shall not exceed an E. Coli count of 235 colony forming units per 100 milliliters (cfu/100ml). Advisories are removed when bacterial levels fall within the acceptable standard. However, the E. coli results were available after a time lag leading to beach closures from previous day results. Nowcast models allow beach managers to make real-time beach advisory decisions instead of waiting a day or more for laboratory results to become available.

Using the historical data, an extensive experiment was carried out, to obtain the suitable input variables and optimal neural network architecture. The best feed-forward neural network model was developed using Bayesian Regularization Neural Network (BRNN) training algorithm. Developed ANN model showed an average prediction accuracy of around 87% in predicting the E. coli classes.

Infant exposure to the microbial and allergenic content of indoor floor dust has been shown to play a significant role in both the development of, and protection against, allergies and asthma later in life. Resuspension of floor dust during infant locomotion induces a vertical transport of particles to the breathing zone, leading to inhalation exposure to a concentrated cloud of coarse (> 1μm) and fine (≤ 1μm) particles. Resuspension, and subsequent exposure, during periods of active infant locomotion is likely influenced by gait parameters. This dependence has been little explored to date and may play a significant role in floor dust resuspension and exposure associated with forms of locomotion specific to infants. This study explores associations between infant locomotion dynamics and floor dust resuspension and exposure in the indoor environment. Infant gait parameters for walking and physiological characteristics expected to influence dust resuspension and exposure were identified, including: contact frequency (steps min^-1), contact area per step (m²), locomotion speed (m s^-1), breathing zone height (cm), and time-resolved locomotion profiles. Gait parameter datasets for standard gait experiments were collected for infants in three age groups: 12, 15, and 19 months-old (m/o). The gait parameters were integrated with an indoor dust resuspension model through a Monte Carlo framework to predict how age-dependent variations in locomotion affect the resuspension mass emission rate (mg h^-1) for five particle size fractions from 0.3 to 10 μm. Eddy diffusivity coefficients (m² s^-1) were estimated for each age group and used in a particle transport model to determine the vertical particle concentration profile above the floor.

Probability density functions of contact frequency, contact area, locomotion speed, breathing zone height, and size-resolved resuspension mass emission rates were determined for infants in each group. Infant standard gait contact frequencies were generally in the range of 100 to 300 steps min^-1 and increased with age, with median values of 186 steps min^-1 for 12 m/o, 207 steps min^-1 for 15 m/o, and 246.2 steps min^-1 for 19 m/o infants. Similarly, locomotion speed increased with age, from 67.3 cm s^-1 at 12 m/o to 118.83 cm s^-1 at 19 m/o, as did the breathing zone height, which varied between 60 and 85 cm. Resuspension mass emission rates increased with both infant age and particle size. A 19 m/o infant will resuspend comparably more particles from the same indoor settled dust deposit compared to a 15 m/o or 12 m/o infant. Age-dependent variations in the resuspension mass emission rate and eddy diffusivity coefficient drove changes in the vertical particle concentration profile within the resuspended particle cloud. For all particle size fractions, there is an average of a 6% increase in the resuspended particle concentration at a height of 1 m from the floor for a 19 m/o compared to a 12 m/o infant. Time-resolved locomotion profiles were obtained for infants in natural gait during free play establish the transient nature of walking-induced particle resuspension and associated exposures for infants, with variable periods of active locomotion, no motion, and impulsive falls. This study demonstrates that floor dust resuspension and exposure can be influenced by the nature of infant locomotion patterns, which vary with age and are distinctly different from those for adults.

The aerodynamics of insect flight is not well understood despite it has been extensively investigated with various techniques and methods. Its complexities mainly have two folds: complex flow behavior and intricate wing morphology. The complex flow behavior in insect flight are resulted from flow unsteadiness and three-dimensional effects. However, most of the experimental studies on insect flight were performed with 2D flow measurement techniques whereas the 3D flow measurement techniques are still under developing. Even with the most advanced 3D flow measurement techniques, it is still impossible to measure the flow field closed to the wings and body. On the other hand, the intricate wing morphology complicates the experimental studies with mechanical flapping wings and make mechanical models difficult to mimic the flapping wing motion of insects. Therefore, to understand the authentic flow phenomena and associated aerodynamics of insect flight, it is inevitable to study the actual flying insects.

In this thesis, a recently introduced technique of schlieren photography is first tested on free jet of match rockets with a physics based optical flow method to explore its potential of flow quantification of unsteady flow. Then the schlieren photography and optical flow method are adapted to tethered and feely flying houseflies to investigate the complex wake flow and structures. In the end, a particle tracking velocimetry system: Shake the Box system, is utilized to resolve the complex wake flow on a tethered house fly and to acquire some preliminary 3D flow field data

Robotic-assisted surgery (RAS) is one of the most significant advancements in surgical techniques in the past three decades. It provides benefits of reduced infection risks and shortened recovery time over open surgery as well as improved dexterity, stereoscopic vision, and ergonomic console over laparoscopic surgery. The prevalence of RAS systems has increased over years and is expected to grow even larger. However, the major concerns of RAS are the technical difficulty and the system complexity, which can result in long learning time and impose extra cognitive workload and stress on the operating room. Human Factor and Ergonomics (HFE) perspective is critical to patient safety and relevant researches have long provided methods to improve surgical outcomes. Yet, limited studies especially using objective measurements, have been done in the RAS environment.

With advances in wearable sensing technology and data analytics, the applications of physiological measures in HFE have been ever increasing. Physiological measures are objective and real-time, free of some main limitations in subjective measures. Eye tracker as a minimally-intrusive and continuous measuring device can provide both physiological and behavioral metrics. These metrics have been found sensitive to changes in workload in various domains. Meanwhile, electroencephalography (EEG) signals capture electrical activity in the cerebral cortex and can reflect cognitive processes that are difficult to assess with other objective measures. Both techniques have the potential to help address some of the challenges in RAS.

In this study, eight RAS trainees participated in a 3-month long experiment. In total, they completed 26 robotic skills simulation sessions. In each session, participants performed up to 12 simulated RAS exercises with varying levels of difficulty. For Research Question I, correlation and mixed effect analyses were conducted to explore the relationships between eye tracking metrics and workload. Machine learning classifiers were used to determine the sensitivity of differentiating low and high workload with eye tracking metrics. For Research Question II, two eye tracking metrics and one EEG metric were used to explain participants’ performance changes between consecutive sessions. Correlation and ANOVA analyses were conducted to examine whether variations in performance had significant relationships with variations in objective metrics. Classification models were built to examine the capability of objective metrics in predicting improvement during RAS training.

In Research Question I, pupil diameter and gaze entropy distinguished between different task difficulty levels, and both metrics increased as the level of difficulty increased. Yet only gaze entropy was correlated with subjective workload measurement. The classification model achieved an average accuracy of 89.3% in predicting workload levels. In Research Question II, variations in gaze entropy and engagement index were negatively correlated with variations in task performance. Both metrics tended to decrease when performance increased. The classification model achieved an average accuracy of 68.5% in predicting improvements.

Eye tracking metrics can measure both task workload and perceived workload during simulated RAS training. It can potentially be used for real-time monitoring of workload in RAS procedure to identify task contributors to high workload and provide insights for training. When combined with EEG, the objective metrics can explain the performance changes during RAS training, and help estimate room for improvements.

Socially engaged design programs, community development coalitions, and intentional and unintentional design spaces are rich with expertise and thinkers who are developing solutions to very pressing, yet complicated problems. Little research has been conducted on the expertise and sense-making of the community partners who participate in these situations. The goal of this research endeavor is to unpack the ways various community partners make meaning of their design experiences by answering the question: What evidence of system’s thinking can be seen in the way community partners describe their work or context? A qualitative research study was conducted in which three community partners were interviewed at various points during their engagement with socially engaged design programs. They demonstrated their systems thinking ability most strongly across the following domains: differentiate and qualify elements, explore multiple perspectives, consider issues appropriately, recognize systems, identify and characterize relationships. These findings imply that the community partners are not only capable of systems thinking but have the potential to be more deeply involved in developing solutions within these settings. Future studies should investigate systems thinking beyond socially engaged design in formal settings and should consider investigation protocols that more directly surface systems thinking domains. Overall, this study contributes to existing work in systems thinking by calling for a more expansive and inclusive engagement of community partners in socially engaged work.

Developing engineering talent in organizations has long been an issue for industries. Notably, with rapidly changing business models and flattened organizational structures, engineers are required to transition into managerial and leadership roles more quickly than ever before. Yet engineers and employers alike often characterize this as a difficult transition. Further, there remains a lack of empirical research on the nature of engineering managerial work practices. To address these issues, this dissertation aims to holistically uncover the experiences of recently transitioned engineering managers. Specifically, the study investigates the meaning-making and experiences of the participants’ transitional journeys and also addresses related questions such as what changes and challenges they face during the transition and how they navigate the challenges associated with the transition. The study is examined through the lens of work-role transition frameworks and models that emphasize the role of the individual in the transition.

In order to address the research objectives, an interpretive qualitative study is employed. To explore and understand the lived experiences of recently transitioned engineering managers, I conducted semi-structured interviews with 16 newly transitioned engineering managers at a Telecom firm in the United States. The interviews were then used to develop narrative accounts of participants describing their journeys of transition. The interviews were also analyzed thematically to identify: a) specific patterns in how the participants experience and make sense of their transition to engineering managerial roles; b) changes experienced by engineers during the transition; c) challenges faced by engineers as they transition to managerial roles, and d) new skills developed by participants to navigate the transition.

The findings suggest that most engineers struggled with the transition, especially during the early stages. This difficulty in part stems from the various personal changes that they experience as a result of the transition, changes related to their individual cognitive, physiological, and social aspects. Moreover, the transition experiences are also impacted by both the situational factors of the individual (e.g., demographics, career progression) as well as the organizational factors, including HR policies related to training and development, dual pathway offerings, etc. The findings in this study, in part presented as narratives, are expected to contribute to the field of engineering education and practice by providing insights into the experiences of engineering professionals taking up managerial and leadership roles. More specifically, the narratives are expected to serve as examples and provide inspiration for engineers at a variety of career stages. The thematic findings are also expected to help students, engineering educators, engineering leadership faculty, and industry affiliates understand and improve the managerial transition process and associated role expectations, which for the most part, remain largely unexplored.

A Phenomenological Analysis of Emergent Design is performed based on the foundations of General Schemas Theory. The concept of Sign Engineering is explored in terms of Hermeneutics, Dialectics, and Ontology in order to define Emergent Systems and Meta-systems Engineering based on the concept of Meta-dialectics. Phenomenology, Ontology, Hermeneutics, and Dialectics will dominate our inquiry into the nature of the Emergent Design of the System and its inverse dual, the Meta-system. This is an speculative dissertation that attempts to produce a philosophical, mathematical, and theoretical view of the nature of Systems Engineering Design. Emergent System Design, i.e., the design of yet unheard of and/or hitherto non-existent Systems and Meta-systems is the focus. This study is a frontal assault on the hard problem of explaining how Engineering produces new things, rather than a repetition or reordering of concepts that already exist. In this work the philosophies of E. Husserl, A. Gurwitsch, M. Heidegger, J. Derrida, G. Deleuze, A. Badiou, G. Hegel, I. Kant and other Continental Philosophers are brought to bear on different aspects of how new technological systems come into existence through the midwifery of Systems Engineering. Sign Engineering is singled out as the most important aspect of Systems Engineering. We will build on the work of Pieter Wisse and extend his theory of Sign Engineering to define Meta-dialectics in the form of Quadralectics and then Pentalectics . Along the way the various ontological levels of Being are explored in conjunction with the discovery that the Quadralectic is related to the possibility of design primarily at the Third Meta-level of Being, called Hyper Being. Design Process is dependent upon the emergent possibilities that appear in Hyper Being. Hyper Being, termed by Heidegger as Being (Being crossed-out) and termed by Derrida as Differance, also appears as the widest space within the Design Field at the third meta-level of Being and therefore provides the most leverage that is needed to produce emergent effects. Hyper Being is where possibilities appear within our worldview. Possibility is necessary for emergent events to occur. Hyper Being possibilities are extended by Wild Being propensities to allow the embodiment of new things. We discuss how this philosophical background relates to meta-methods such as the Gurevich Abstract State Machine and the Wisse Metapattern methods, as well as real-time architectural design methods as described in the Integral Software Engineering Methodology . One aim of this research is to find the foundation for extending the ISEM methodology to become a general purpose Systems Design Methodology. Our purpose is also to bring these philosophical considerations into the practical realm by examining P. Bourdieu?s ideas on the relationship between theoretical and practical reason and M. de Certeau?s ideas on practice. The relationship between design and implementation is seen in terms of the Set/Mass conceptual opposition. General Schemas Theory is used as a way of critiquing the dependence of Set based mathematics as a basis for Design. The dissertation delineates a new foundation for Systems Engineering as Emergent Engineering based on General Schemas Theory, and provides an advanced theory of Design based on the understanding of the meta-levels of Being, particularly focusing upon the relationship between Hyper Being and Wild Being in the context of Pure and Process Being.

This dissertation investigates the influence of spatially explicit animal behavior active subsidy distribution patterns. Active subsidies are animal-transported consumption and resources transfers from donor to recipient ecosystems. Active subsidies influence ecosystem structure, function and services in recipient ecosystems. Even though active subsidies affect ecosystem dynamics, most ecosystem models consider the influence of spatially-explicit animal behavior on active subsidy distributions, limiting the ability to predict corresponding spatial impacts across ecosystems. Spatial subsidy research documents the need for systematic models and analyses frameworks to provide generally insights into the relationship between animal space use behavior and active subsidy patterns, and advance knowledge of corresponding ecosystem impacts for a variety of taxa and ecological scenarios.

To advance spatial subsidy research, this dissertation employs a combined individual-based and movement ecology approach in abstract modeling frameworks to systematically investigate the influence of 1) animal movement behavior given mortality (chapter 2), 2) animal sociality (chapter 3) and 3) landscape heterogeneity (chapter 4) on active subsidy distribution. This dissertation shows that animal movement behavior, sociality and landscape heterogeneity influence the extent and intensity of active distribution and impacts in recipient ecosystems. Insights from this dissertation demonstrate that accounting for these factors in the development of ecosystem models will consequentially enhance their utility for predicting active subsidy spatial patterns and impacts. This dissertation advances spatial subsidy research by providing a road map for developing a comprehensive, unifying framework of the relationship between animal behavior and active subsidy distributions.

Recent advances in sensor technologies and data acquisition platforms have led to the era of Big Data. The rapid growth of artificial intelligence (AI), computing power and machine learning (ML) algorithms allow Big Data to be processed within affordable time constraints. This opens abundant opportunities to develop novel and efficient approaches to enhance the sustainability and resilience of Smart Cities. This work, by starting with a review of the state-of-the-art data fusion and ML techniques, focuses on the development of advanced solutions to structural health monitoring (SHM) and metamaterial design and discovery strategies. A deep convolutional neural network (CNN) based approach that is more robust against noisy data is proposed to perform structural response estimation and system identification. To efficiently detect surface defects using mobile devices with limited training data, an approach that incorporates network pruning into transfer learning is introduced for crack and corrosion detection. For metamaterial design, a reinforcement learning (RL) and a neural network based approach are proposed to reduce the computation efforts for the design of periodic and non-periodic metamaterials, respectively. Lastly, a physics-constrained deep auto-encoder (DAE) based approach is proposed to design the geometry of wave scatterers that satisfy user-defined downstream acoustic 2D wave fields. The robustness of the proposed approaches as well as their limitations are demonstrated and discussed through experimental data or/and numerical simulations. A roadmap for future works that may benefit the SHM and material design research communities is presented at the end of this dissertation.

Analyzing the structure of a dataset is a challenging problem in high-dimensions as the volume of the space increases at an exponential rate and typically, data becomes sparse in this high-dimensional space. This poses a significant challenge to machine learning methods which rely on exploiting structures underlying data to make meaningful inferences. This dissertation proposes the n-TARP method as a building block for high-dimensional data analysis, in both supervised and unsupervised scenarios.