In his essay Evolution and Tinkering, published in
Science in 1977, Francois Jacob argued that a common analogy
between the process of evolution by natural selection and the
methods of engineering is problematic. Instead, he proposed to
describe the process of evolution with the concept of
bricolage (tinkering). In this essay, Jacob did not deny the
importance of the mechanism of natural selection in shaping complex
adaptations. Instead, he maintained that the cumulative effects of
history on the evolution of life, made evident by molecular data,
provides an alternative account of the patterns depicting the
history of life on earth. Jacob's essay contributed to
genetic research in the late twentieth century that emphasized
certain types of topics in evolutionary and developmental biology,
such as genetic regulation, gene duplication events, and the genetic
program of embryonic development. It also proposed why, in future
research, biologists should expect to discover an underlying
similarity in the molecular structure of genomes, and that they
should expect to find many imperfections in evolutionary history
despite the influence of natural selection.

Adaptability has emerged as an essential skill in the engineering workforce due to constant technological and social change, engineering grand challenges, and the recent global pandemic. Although engineering employers and national reports have called for increased adaptability among engineers, what adaptability means in the engineering workplace has not been investigated. This dissertation uses qualitative semi-structured critical incident interviews with engineering managers from four corporations to better understand their perceptions of adaptability and then incorporates these findings into a scenario-based intervention for the engineering classroom. Thematic analysis of the interviews with engineering managers expanded existing frameworks for workplace adaptability to provide an engineering-specific understanding of adaptability as a construct. Managers’ perceptions of adaptability span six dimensions, each important when teaching this competency to engineering students: Creative Problem Solving; Interpersonal Adaptability; Handling Work Stress; Dealing with Uncertain and Unpredictable Situations; Learning New Technologies, Tasks, and Procedures; and Cultural Adaptability. Managers’ beliefs about the importance of a balanced approach to being adaptable in different work contexts, and the influence of personal characteristics such as self-awareness and having had specific experiences related to being adaptable, emerged from the findings as well. Composite narratives reflecting real-life situations encountered by engineers in the workplace were developed based on findings from the engineering manager interviews to provide greater texture to the data. Six of the narratives mapped to the six dimensions of adaptability identified in the thematic analysis, while the seventh narrative illustrated the importance of balance and context when deciding whether and how to be adaptable. They revealed how multiple dimensions of adaptability work together and that contextual factors like support from managers and coworkers are integral to an engineer’s adaptability. The narratives were condensed into two scenarios for use in a classroom-based intervention with first-year engineering students at a large public university. After the intervention, many students’ definitions of adaptability became more multi-dimensional and reflective of adaptability context and balance. Students also reported a better understanding of engineering work, an expanded definition of adaptability, greater delineation of adaptability, increased self-awareness, greater appreciation for the importance of adaptability balance, and enhanced feelings of job preparedness.

Engineering higher education is growing rapidly across the world, especially in the Global South. For many of these countries, the dominant engineering university models were imported and established by colonial European empires. These imported systems of higher education and engineering evolved to meet the local contexts of Europe and the United States in response to political and technological change. Today, engineers are being seen by national and international policymakers as key for innovation and technological development. Given that these models are exogenous to these countries and may carry embedded design values that correspond to the needs of the Global North, this study explores how engineering universities are aligned with societal values in Cameroon, a country with three colonial legacies, a highly diverse institutional landscape, and an engineering university system that is rapidly expanding.To assess the alignment of the Cameroonian engineering education system with Cameroonian perceptions of the common good, this dissertation employs a modified public value mapping method, comparing exogenous public values with endogenous perceptions of public value success or failure. Exogenous values embedded in global engineering education are determined using historical analysis of the evolution of engineering and higher education models in Europe and the United States. Endogenous perceptions of public value success or failure associated with Cameroonian engineering education are determined using a grounded analysis of 49 semi-structured interviews and focus groups. These two sets of values are mapped using historical narrative analysis to illuminate the social impacts of exogenous educational models. This study finds that the engineering curriculum, institutional models of innovation, and methods of academic advancement are all perceived by Cameroonians to be misaligned with the public good. While a grassroots technology start-up culture, inspired by Silicon Valley, has been modified to meet the perceived common good. Furthermore, there is evidence that private grassroots engineering universities may hold stronger ties with their surrounding community than state supported institutions, thus addressing a societal value that would otherwise be neglected. This study suggests that both endogenous and modified exogenous models are more likely to meet perceptions of the common good, while models which are developed outside of a culture are more likely to be perceived as misaligned with societal goals.

Over the past few decades, medical imaging is becoming important in medicine for disease diagnosis, prognosis, treatment assessment and health monitoring. As medical imaging has progressed, imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. While large objects can often be automatically or semi-automatically delineated, segmenting small objects (blobs) is challenging. The small object of particular interest in this dissertation are glomeruli from kidney magnetic resonance (MR) images. This problem has its unique challenges. First of all, the size of glomeruli is extremely small and very similar with noises from images. Second, there are massive of glomeruli in kidney, e.g. over 1 million glomeruli in human kidney, and the intensity distribution is heterogenous. A third recognized issue is that a large portion of glomeruli are overlapping and touched in images. The goal of this dissertation is to develop computational algorithms to identify and discover glomeruli related imaging biomarkers. The first phase is to develop a U-net joint with Hessian based Difference of Gaussians (UH-DoG) blob detector. Joining effort from deep learning alleviates the over-detection issue from Hessian analysis. Next, as extension of UH-DoG, a small blob detector using Bi-Threshold Constrained Adaptive Scales (BTCAS) is proposed. Deep learning is treated as prior of Difference of Gaussian (DoG) to improve its efficiency. By adopting BTCAS, under-segmentation issue of deep learning is addressed. The second phase is to develop a denoising convexity-consistent Blob Generative Adversarial Network (BlobGAN). BlobGAN could achieve high denoising performance and selectively denoise the image without affecting the blobs. These detectors are validated on datasets of 2D fluorescent images, 3D synthetic images, 3D MR (18 mice, 3 humans) images and proved to be outperforming the competing detectors. In the last phase, a Fréchet Descriptors Distance based Coreset approach (FDD-Coreset) is proposed for accelerating BlobGAN’s training. Experiments have shown that BlobGAN trained on FDD-Coreset not only significantly reduces the training time, but also achieves higher denoising performance and maintains approximate performance of blob identification compared with training on entire dataset.

The technology and science capabilities of SmallSats continue to grow with the increase of capabilities in commercial off the shelf components. However, the maturation of SmallSat hardware has also led to an increase in component power consumption, this poses an issue with using traditional passive thermal management systems (radiators, thermal straps, etc.) to regulate high-power components. High power output becomes limited in order to maintain components within their allowable temperature ranges. The aim of this study is to explore new methods of using additive manufacturing to enable the usage of heat pipe structures on SmallSat platforms up to 3U’s in size. This analysis shows that these novel structures can increase the capabilities of SmallSat platforms by allowing for larger in-use heat loads from a nominal power density of 4.7 x 10^3 W/m3 to a higher 1.0 x 10^4 W/m3 , an order of magnitude increase. In addition, the mechanical properties of the SmallSat structure are also explored to characterize effects to the mechanical integrity of the spacecraft. The results show that the advent of heat pipe integration to the structures of SmallSats will lead to an increase in thermal management capabilities compared to the current state-of-the-art systems, while not reducing the structural integrity of the spacecraft. In turn, this will lead to larger science and technology capabilities for a field that is growing in both the education and private sectors.

Microstructure refinement and alloy additions are considered potential routes to increase high temperature performance of existing metallic superalloys used under extreme conditions. Nanocrystalline (NC) Cu-10at%Ta exhibits such improvements over microstructurally unstable NC metals, leading to enhanced creep behavior compared to its coarse-grained (CG) counterparts. However, the low melting point of Cu compared to other FCC metals, e.g., Ni, might lead to an early onset of diffusional creep mechanisms. Thus, this research seeks to study the thermo-mechanical behavior and stability of hierarchical (prepared using arc-melting) and NC (prepared by collaborators through powder pressing and annealing) Ni-Y-Zr alloys where Zr is expected to provide solid solution and grain boundary strengthening in hierarchical and NC alloys, respectively, while Ni-Y and Ni-Zr intermetallic precipitates (IMCs) would provide kinetic stability. Hierarchical alloys had microstructures stable up to 1100 °C with ultrafine eutectic of ~300 nm, dendritic arm spacing of ~10 μm, and grain size ~1-2 mm. Room temperature hardness tests along with uniaxial compression performed at 25 and 600 °C revealed that microhardness and yield strength of hierarchical alloys with small amounts of Y (0.5-1wt%) and Zr (1.5-3 wt%) were comparable to Ni-superalloys, due to the hierarchical microstructure and potential presence of nanoscale IMCs. In contrast, NC alloys of the same composition were found to be twice as hard as the hierarchical alloys. Creep tests at 0.5 homologous temperature showed active Coble creep mechanisms in hierarchical alloys at low stresses with creep rates slower than Fe-based superalloys and dislocation creep mechanisms at higher stresses. Creep in NC alloys at lower stresses was only 20 times faster than hierarchical alloys, with the difference in grain size ranging from 10^3 to 10^6 times at the same temperature. These NC alloys showed enhanced creep properties over other NC metals and are expected to have rates equal to or improved over the CG hierarchical alloys with ECAP processing techniques. Lastly, the in-situ wide-angle x-ray scattering (WAXS) measurements during quasi-static and creep tests implied stresses being carried mostly by the matrix before yielding and in the primary creep stage, respectively, while relaxation was observed in Ni5Zr for both hierarchical and NC alloys. Beyond yielding and in the secondary creep stage, lattice strains reached a steady state, thereby, an equilibrium between plastic strain rates was achieved across different phases, so that deformation reaches a saturation state where strain hardening effects are compensated by recovery mechanisms.

The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent can have an impact on learning outcomes and perceptions of the instructor. However, these outcomes and perceptions have yet to be fully understood in the context of a virtual human instructor. Outcome measures collected included: knowledge retention, knowledge transfer, and cognitive load. Perception measures were collected using the Agent Persona Instrument-Revised, API-R, and a speaker-rating survey. Overall, there were no significant differences between the accented conditions. However, the synthetic condition had significantly lower knowledge retention, knowledge transfer, and mental effort efficiency than the professional voices in the human condition. Participants rated the human recordings higher on speaker-rating and API-R measures. These findings demonstrate the importance of considering the quality of the voice when designing multimedia learning environments.

Fine control of standing postural balance is essential for completing various tasks in daily activities, which might be compromised when interacting with dynamically challenging environments (e.g., moving ground). Among various biofeedback to improve postural balance control, vibrotactile feedback has an advantage of providing supplementary information about balance control without disturbing other core functions (e.g., seeing and hearing). This paper investigated the effectiveness of a waist vibrotactile feedback device to improve postural control during standing balance on a dynamically moving ground simulated by a robotic balance platform. Four vibration motors of the waist device applied vibration feedback in the anterior-posterior and medio-lateral direction based on the 2-dimensional sway angle, measured by an inertia measurement unit. Experimental results with 15 healthy participants demonstrated that the waist vibrotactile feedback is effective in improving postural control, evidenced by improvements in center-of-mass and center-of-pressure stability measures. In addition, this study confirmed the effectiveness of the waist vibrotactile feedback in improving standing balance control even under muscle fatigue induced by lower body exercise. The study further confirmed that the waist feedback is more effective in people with lower baseline balance performance in both normal and fatigue conditions.

Severe forms of mental illness, such as schizophrenia and bipolar disorder, are debilitating conditions that negatively impact an individual's quality of life. Additionally, they are often difficult and expensive to diagnose and manage, placing a large burden on society. Mental illness is typically diagnosed by the use of clinical interviews and a set of neuropsychiatric batteries; a key component of nearly all of these evaluations is some spoken language task. Clinicians have long used speech and language production as a proxy for neurological health, but most of these assessments are subjective in nature. Meanwhile, technological advancements in speech and natural language processing have grown exponentially over the past decade, increasing the capacity of computer models to assess particular aspects of speech and language. For this reason, many have seen an opportunity to leverage signal processing and machine learning applications to objectively assess clinical speech samples in order to automatically compute objective measures of neurological health. This document summarizes several contributions to expand upon this body of research. Mainly, there is still a large gap between the theoretical power of computational language models and their actual use in clinical applications. One of the largest concerns is the limited and inconsistent reliability of speech and language features used in models for assessing specific aspects of mental health; numerous methods may exist to measure the same or similar constructs and lead researchers to different conclusions in different studies. To address this, a novel measurement model based on a theoretical framework of speech production is used to motivate feature selection, while also performing a smoothing operation on features across several domains of interest. Then, these composite features are used to perform a much wider range of analyses than is typical of previous studies, looking at everything from diagnosis to functional competency assessments. Lastly, potential improvements to address practical implementation challenges associated with the use of speech and language technology in a real-world environment are investigated. The goal of this work is to demonstrate the ability of speech and language technology to aid clinical practitioners toward improvements in quality of life outcomes for their patients.