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Damage detection, damage localization, and fatigue life prediction for large-area FRP composites
Get PDFIn this work, a novel self-sensing technology is introduced for fiber-reinforced polymer (FRP) composites, offering an accurate and cost-effective solution for damage detection, localization, and fatigue life prediction. This dissertation implements an innovative approach, transforming structural carbon fiber tows into piezoresistive sensors that enable real-time structural health monitoring (SHM) without the need for additional sensor devices. The self-damage detection and memory (SDDM) hybrid composite material leverages the carbon fiber as a sensor network, with glass fiber providing electrical insulation. Damage detection capabilities are first introduced, demonstrated by tensile testing that revealed two distinct loading peaks and a sharp nonlinear increase in resistance at the point of carbon fiber failure, highlighting its capabilities for damage early warning. Progressive impact tests further confirmed the material's ability to permanently record microdamage, showcasing a self-memory function that could inform life-cycle predictions.
Next, a practical sensor layout was developed, utilizing carbon fiber sensor tow branches connected in parallel each with varying resistances. This novel design can monitor large areas while minimizing the number of connections required to the DAQ circuit, significantly reducing manufacturing costs and complexities. Impact tests on carbon and glass fiber-reinforced composites validated the system’s ability to detect and precisely locate damage, with less than three percent error between the measured resistance and predicted damage location. These results highlight the effectiveness of the proposed damage localization framework, offering an efficient SHM solution for large-area composite structures.
Lastly, this dissertation introduces a low-cost, real-time fatigue life prediction system that leverages the piezoresistive cumulative damage behavior of carbon fiber sensor tows. A Bidirectional Long Short-Term Memory (LSTM) neural network was implemented to predict fatigue life based solely on resistance time-history, with no need for explicit stress inputs. Fatigue tests conducted across various stress amplitudes were used to train and evaluate a LSTM model, with results indicating the model’s ability to accurately predict remaining life. Moreover, testing results showed a sharp increase in resistance before failure, demonstrating the carbon fiber sensor tow's damage early warning capabilities for both cyclic and quasistatic monotonic loading. This system presents a promising, cost-effective SHM method that not only ensures structural safety but also extends the service life of FRP composites through accurate fatigue life prediction
Statistical Methods for High-dimensional Neuroimaging Data Analysis
No full textNeuroimaging data, often high-dimensional and collected across multiple imaging modalities, is a valuable tool for studying the underlying mechanisms of how the human brain structures, functions, and thus impacts cognition. This dissertation aims to address the challenges of analyzing high-dimensional neuroimaging data, such as the missing data issue in multimodal fusion, the preservation of underlying hierarchical structure between mediators and exposure-by-mediator interactions in model selection with high-dimensional potential mediators, and the false discovery rate control for mediator selection from a high-dimensional candidate set.
The first part of this dissertation aims to address the commonly occurring missing data issue during multimodal fusion. Recent advances in multimodal imaging acquisition techniques have allowed us to measure different aspects of brain structure and function. Multimodal fusion, such as linked independent component analysis (LICA), is a popular approach to integrate complementary information. However, these methods are severely limited by the common occurrence of missing data in brain imaging. In the first chapter, we propose a Full Information LICA algorithm (FI-LICA) to handle the missing data problem during multimodal fusion under the LICA framework. Built upon the principle of full information from complete cases, our method utilizes all available information to recover the missing latent information. Our simulation experiments show the ideal performance of FI-LICA compared to current practices. Further, applying to multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, FI-LICA demonstrates better performance in classifying current diagnosis and in predicting the transition of participants with mild cognitive impairment (MCI) to AD, thereby highlighting the practical utility of our proposed method.
The second part of this dissertation aims to preserve the underlying hierarchical structure between mediators and exposure-by-mediator interactions during model selection in the high-dimensional mediator settings. In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators (M) and exposure-by-mediator (X-by-M) interactions. Although several high-dimensional mediation methods can naturally handle X-by-M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, in the second chapter, we develop the XMInt procedure to select M and X-by-M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments show promising selection results. Furthermore, we apply our method to ADNI morphological data and examine the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.
The third part of this dissertation aims to control the false discovery rate (FDR) when selecting mediators from a high-dimensional candidate set. Specifically, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set and propose a method, which extends the recent development in FDR-controlled variable selection with knockoff, to select mediators with FDR control. We show that the proposed method and algorithm achieve finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method.
Lastly, we demonstrate the method by analyzing data from the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox
Human brain optimized light sheet microscopy (HOLiS) for high-throughput cell type atlasing of whole human brains
No full textIn a human brain around 200 billion cells are working together shaping who we are. Grasping their complex organization, even in a single brain remains a major challenge. Advances in tissue clearing provide a window onto cells inside of intact brains, and have recently been optimized for clearing and immunostaining human brain tissue. However, imaging cleared tissues on the scale of the whole human brain presents many challenges, with conventional methods likely to require many months or years of acquisition time per brain.
The work of this thesis presents a complete imaging and analysis pipeline developed specifically to achieve high-throughput, multispectral imaging of entire, cleared and immunolabelled human brains at cellular resolution for cell type atlasing with acquisition times of less than 2 weeks. Our human brain optimized light-sheet (HOLiS) microscopy system is a form of oblique-plane single objective light-sheet, capable of imaging 5 mm thick, optically cleared, complete coronal sections of human brain.
By imaging thick sections, we can reduce tissue deformations and cut-edge effects, although this necessitated novel optical solutions to enable implementation of a long working distance, multi-immersion primary objective lens. Another feature of HOLiS is its ability to image multiple spectral channels in parallel to enable multiplexed antibody labeling of the different cell types. Combining multiple laser lines for simultaneous excitation, emitted fluorescence is spectrally divided by our novel 4-way image splitter for simultaneous detection of 5 spectral channels with the capacity for 9 channels imaged in parallel. The 5th channel images a nuclear dye, providing fiducials for every cell. Although spectral multiplexing adds complexity, it greatly accelerates HOLiS acquisition time and reduces data processing burden. Using ultra-fast cameras, we acquire multi-spectral images up to at 0.75 mm³/sec with micron sampling translating to image an entire human brain within 2 weeks.
At modest sampling density and 9 spectral channels, the data of a single human brain scan is expected to exceed 3 PB, imposing constraints on transfer, storage, data pre-processing, and accessibility. Using a parallelizable analysis pipeline which locates every nucleus, and extracts cell type information from the spectral channels to provide compressed point-cloud representations of the data that can be quantitatively analyzed, shared and compared between brains. These rich datasets can be clustered to explore cell type distributions and used to guide more complex feature-based analysis, neuroanatomical segmentation and efficient visualization of raw data
Foreword: Annual Meeting of Postgraduates in the Reception of the Ancient World
No full textBuilt in Neoclassical style in 1934, Columbia University’s Butler Library is emblematic of a common association of Greco-Roman antiquity with elite culture and privileged knowledge: engraved on the frieze of the main façade are the names “Homer, Herodotus, Sophocles, Plato, Aristotle, Demosthenes, Cicero, Vergil.” In October 2019, Columbia students (supported by the university Libraries) unfurled a new banner bearing the names of eight female and transgender writers above the original inscriptions.
We begin the introduction to this volume with this anecdote because we see a profound affinity of intent between the 2019 “Butler Banner Project” and the papers collected here: to decenter Greco-Roman antiquity, in an effort to reflect on the role it has played in creating harmful and exclusive ideology. Recent work in the field has focused on acknowledging and dismantling elitist and exclusionary legacies associated with the Greco-Roman world , as well as renegotiating the role of the classicist vis-à-vis the power structures which classical antiquity contributed to creating . We thus picked the theme “Center & Periphery” to reflect the tremendous paradigm shift which our discipline is undergoing
Subsidy wars and modern industrial policy
No full textTraditional economic nationalism, techno-nationalism and national security concerns have converged into a modern industrial policy, which is being pursued by multiple actors, including the US, China, India, Republic of Korea, and the EU. Driven by new subsidy wars, these policies pose a tremendous challenge for global economic relations
东盟在制定 FDI 规则方面不断扩大的作用
No full textThis Perspective argues that the ASEAN-led RCEP agreement and the ASEAN Outlook on the Indo-Pacific have created significant momentum for ASEAN to further reinforce its central role in advancing Asia-Pacific economic integration
OT for OTs. An In-Depth Exploration of Factors Affecting Job Satisfaction and Well-being Among Occupational Therapists
No full textThis paper addresses the understudied issue of job satisfaction and well-being among occupational therapists (OTs) in the United States and the potential relationship between these concepts and those of practitioner efficacy and patient outcomes. To explore the multifaceted factors influencing their work experiences, 22 currently practicing occupational therapists with at least three years of experience were recruited for a descriptive exploratory qualitative study.
Data were collected through semi-structured in-depth interviews focused on personal and professional histories, factors contributing to job satisfaction (person, interpersonal, work itself, environment, time), the relationship between job satisfaction and well-being, and recommendations for improvement. The most important findings revealed that job satisfaction is significantly influenced by supportive relationships with supervisors and colleagues, autonomy and flexibility in practice, clarity of values and meaningful work aligned with professional values, and a supportive work environment. Conversely, unrealistic productivity standards, administrative burdens, perceived lack of professional recognition, and constraints of the medical model significantly detract from satisfaction and contribute to burnout. In extreme cases, these factors can lead to moral injury, which refers to the lasting emotional, psychological, social, behavioral, and spiritual impacts of actions that violate a person’s core moral values and behavioral expectations of self or others (Litz et al., 2009). Participants perceived a strong bidirectional relationship between job satisfaction and overall well-being, with each influencing the other and impacting their effectiveness as therapists.
In conclusion, the study highlights the complex interplay of individual, organizational, and systemic factors shaping OTs' job satisfaction and well-being, underscoring the need for holistic approaches to support these professionals. Implications include the need for enhanced self-advocacy among OTs, supportive organizational cultures, and advocacy for systemic changes to improve working conditions, ultimately fostering a more sustainable and effective occupational therapy workforce and enhancing patient care. Future research should further explore the discrepancy between expectations and reality for OTs, the impact of specific organizational and environmental factors, the potential of non-traditional career paths for OTs, and the potential for this knowledge to generalize to and inform advances for other allied healthcare professionals
Dudley Weldon Woodard and the Graduate Program in Mathematics at Howard University
No full textHistorically Black Colleges and Universities (HBCUs) graduate a large number of Black students who receive graduate degrees in science, engineering, technology, and mathematics (STEM) (Einaudi et al., 2022; Cooper, 2004; National Science Foundation, 2008; Shuler et al., 2022; Upton & Tanenbaum, 2014). Researchers have examined this phenomenon to better understand how these mathematics departments prepare their student’s for PhDs in mathematics.
Howard University has a long history of supporting Black students and Faculty. Howard is among the top HBCUs to produce Black students who go on to receive PhDs in mathematics (Einaudi et al., 2022) and the mathematics department at Howard was well known for doing advanced mathematics during the early parts of the twentieth century (Donaldson & Fleming, 2000; Parshall, 2016; Walker, 2014). Mathematician and mathematics educator Dudley Weldon Woodard (1881-1965), the second African American to earn a PhD in mathematics spent the second half of his career at Howard University and was instrumental in the development of graduate studies at Howard, though not much is known about his life and career. By exploring both Woodard’s life and career we understand better the foundational impact he had on Howard University’s mathematics department and support of Black students in mathematics.
This dissertation is a historical case study which examines the life and career of mathematician Dudley Weldon Woodard and his involvement in the establishment of graduate work at Howard University. With the use of archival documents like governmental records, course catalogs, commencement programs, yearbooks, and newspapers, this study weaves together the details of Woodard’s early life, education and early career all of which led him to Howard University. This study also examines Woodard’s contributions to the establishment of the masters program in mathematics at Howard University and his mentorship of students in the program. Woodard was instrumental in formalizing graduate studies throughout Howard University. His commitment and leadership laid a strong foundation for the mathematical sciences at Howard University and beyond, by seeding teachers, faculty, and researchers in the mathematical sciences whose contributions are still felt today
Haunting Whiteness in Teaching
No full textTeaching is haunted by whiteness, and it also bears the capacity to interrupt it. This study makes a cut into the ghostly resonances of teaching and its normalizing practices by following the residue of the material-affective-embodied ways of whiteness in teaching and imagining it otherwise. Dwelling with these ghosts can etch the ways whiteness is baked into the everyday spaces, times, and practices of teaching through its atmospheric and ordinary violences.
Drawing on affect studies, critical posthumanism, Black studies, and hauntology, I ask: What aspects of teaching are haunted by atmospheres of whiteness? How do racialized hauntings materialize in the after hours of school? Over the course of nine months, I conducted case studies at two urban public high schools with six teacher participants. I pursued the racialized hauntings by spending time with teachers in the after hours of the school day, when classes had ended, and the sticky events lingered.
Through practices of interviewing, shadowing, arts-based artifact creation, and poetic inquiry, I rendered haunting atmospheres of whiteness in three sites: the palimpsest school space that held the leftovers of white flight, materialized in the carceral and ill-fitting spatialization; windowless classrooms that flickered inter(n)ment in the holding spaces; and the after hours that surfaced the racialized remains of the day alongside the attenuation of the teacher body. White teachers in the study were agentive and injurious in atmospheric whiteness, but also worn down by its frictions. Whiteness worked to rub smooth its violence and fix the past as fully past, but the racial hauntings belied this erasure. Attending to these ghosts invites the spectral speculative to imagine and sustain affirmative possibilities for teaching, working against the slow death of a haunted profession.
This dissertation study contributes to research on teaching and research on whiteness and teaching that inquires beyond the bounds of the sovereign subject, as well as non-representational and speculative research methodologies
T cell fate in the solid tumor microenvironment
No full textT cell-based immunotherapies have transformed treatment for hematological cancers, yet their efficacy in solid tumors remains limited. Mechanical cues within the tumor microenvironment influence T cell function, but their role in shaping T cell fate is poorly understood.
This thesis investigates how substrate and tissue stiffness affect T cell activation, proliferation, and phenotype, with a focus on mechanosensitive expansion ex vivo and tumor stiffness in vivo. To address variability in mechanosensitive responses, we analyzed primary human T cells from healthy donors, assessing their proliferation on substrates of different stiffnesses. Flow cytometry and transcriptomic profiling revealed that effector T cells (TEff) mediate mechanosensitive expansion. Depleting TEff cells abolished the stiffness-dependent response, highlighting their central role in promoting cell proliferation of other subtypes via cytokine-mediated signaling in a mechanosensitive manner.
Further, single-cell RNA sequencing identified subset-specific transcriptional changes induced by mechanical signals. Extending these findings to the in vivo setting, we mapped tumor stiffness in murine and human tumor samples and developed a framework for integrating spatial stiffness maps with spatial transcriptomics to analyze the impact of tissue stiffness on T cell gene expression in vivo. Herein we present the first in vivo generated mechanosensitive signature of T cells in tumors. These results establish a mechanobiological framework for optimizing T cell expansion and improving immunotherapy for solid tumors