Modelling and Characterization of Force Plate Measurements on Subacute Post-Concussion Subjects Through Machine Learning

Mild traumatic brain injuries (mTBI) are one of the leading causes of neurological disorders. Symptoms after a mTBI may include headache, dizziness, and balance issues, among others, with vestibular disorders observed in up to 80% of these patients. These symptoms generally resolve in the first few weeks after the injury, but some patients may develop persistent symptoms. Patients with Post-Concussion Vestibular Dysfunction (PCVD) may present alterations in the peripheral and central vestibular systems. These alterations may then affect postural control and stability, which coupled with visual motion sensitivity, cause the prolonged symptomatology. In this study, we evaluated postural control strategies in Healthy Controls (HC) and Subacute PCVD patients (ST) to identify underlying changes in the postural control system. Sensory Organization Test (SOT) was employed to measure Centre Of Pressure (COP) signals under different sensory conditions. Analysis of traditional linear metrics and entropy metrics of the COP signals demonstrated significant differences between groups. Complexity index was reduced for the ST group during “Eyes Closed” condition, with a median value of 7.93 vs 9.59 for the HC in the Medial-Lateral direction (p=0.002), and 5.17 vs 6.22 Anterior-Posterior direction (p=0.0009). Moreover, analysis of these metrics through machine learning, showed indications of interactions between these variables that may be predictive of the health condition of the patient. These results remark the potential of these metrics for evaluating changes in postural dynamics in patients with PCVD, and opens a new path for analysis of the COP signals with the support of machine learning models.M.S

“Subordination” In Modern Thai Architecture, 1960s-1980s: Case Studies of Crypto-Colonialism

ConCave Ph.D. Symposium 2022 Proceedings, April 7-8, 2022. Georgia Institute of Technology, Atlanta, Georgia.This paper offers an examination of crypto-colonial discourse in Modern Thai Architecture from the 1960s tothe 1980s. It argues that the transplantation of western Modern architecture in Thailand initiated a neo-colonialcultural dynamic as the architects’ creations were subtly subjected to an American Cold War agenda established in the Southeast Asia region since the 1950s. According to the recent scholarship of Thai postcolonial studies, the term Crypto-colonialism is applied to Thailand based on its unique form of political marginality. This theory characterizes Thailand’s relation to the West as being a technically independent though essentially tributary nationstate because the country was materially dependent on western economic and political power (Herzfeld 2002, 900-901). This research thus looks at the “subordination” characteristics of Modern Thai architecture from the 1960sto the 1980s, when western powers, especially the United States, imposed their culture upon that of Thailand to undermine or deny its existence. Its analysis shows that, during these Cold War years, urban infrastructure and the hospitality industry in Bangkok and its suburbs grew rapidly due to American economic aid as well as to the need to provide accommodations for western tourists and the American military presence. The architectural design of this period was dominated by spatial concerns that rejected the new and powerful infuence of the united states over traditional Thai architectural planning. By tracing the historical consolidation of Modern architectural consumerismin Thailand and the works of American architects who were working in Thailand during the 1960s and the 1980s, this research will challenge the idea that colonial discourses were only confined to countries or regions that were directly occupied by western nations

Responsive Building Performance: A Case Study of Electrochromic Building Envelopes

ConCave Ph.D. Symposium 2022 Proceedings, April 7-8, 2022. Georgia Institute of Technology, Atlanta, Georgia.Building envelopes play an important role in the building performance of energy efficiency, thermal insulation, and visual comfort. Controlling solar radiation and daylight through responsive building envelope systems is an emerging sustainable strategy to improve building performance. The effectiveness of responsive building envelopes depends on the dynamic properties of building envelope materials and control algorithms. Architects and researchers are exploring possible ways to integrate responsive electrochromic (EC) glazing materials in building envelopes and testing the dynamic impacts on building performance (DeForest et al. 2013; Hamidpour and Blouin 2018; Eleanor S. Lee et al. 2013). Up to now, the research has tended to focus on control logic, rather than on the responsiveness of the building envelope itself. The modeling of responsive behaviors of an electrochromic building envelope system is challenging due to the dynamic properties of the electrochromic materials and unpredictable behaviors. In this paper, we proposed a case study using four different electrochromic glazing materials to test the impacts of responsiveness on building performance in terms of visual comfort and energy saving for the climate conditions in Tampa, FL. We developed a novel approach, Dynamic Sequence Modeling (DSM), by which these responsive EC building envelope behaviors can be simulated. The simulation results are then used to feed our Supervised Machine Learning (SML) algorithms to enable prediction under changing weather conditions. The SML algorithms are promising avenues to solve this type of predictive learning problem (Murphy 2012). Our SML algorithms seek to optimize performance with altered responsiveness of our EC building envelopes, as a generally capable agent to predict effective responses given similar weather conditions to the learned representation of the climate model. We find that all three responsive building envelope variants demonstrate large improvements in both energy and visual comfort performance compared to the static building envelope. In three EC alternatives, where each has different tint responsiveness, the cooling and heating energy loads were reduced by 54.36% on average, and the illuminance measures had almost the same mean values close to the visual comfort threshold. The most responsive 4-mode EC had the least absolute deviations. On the other hand, the prediction accuracy of supervised machine learning models decreases as the complexity of tint responsiveness (tint mode) increases in electrochromic building envelopes. Our study demonstrates the impacts of responsive electrochromic materials on building performance. Moreover, we show that the complexity of responsiveness decreases the prediction accuracy for SML-based building control of dynamic materials

Use Of XR Technologies to Trigger Interest in High School Students in a Construction Management Career

The construction management skilled workforce in the United States is shrinking as a big number of its employees approach retirement and are not being replaced quickly enough by younger generations. According to the literature, pre-college educational programs can help address this issue by attracting a broader and more varied pool of students into Construction Management and related programs. The literature also indicates that the application of Extended Reality (XR) modalities generates student benefits such as increased engagement and self-efficacy that could be derived from bringing these modalities into educational settings. These benefits, in turn, help recruitment efforts for these domains. Georgia Tech’s School of Building Construction developed a Building Construction Summer Camp in 2022 using the Model of Domain (MDL) educational framework and its theory on triggering situational interest in students, to recruit students to the Bachelor of Science in Building Construction program. To trigger interest, memorable situational activities must be incorporated. As such, all camp activities were carefully selected to be engaging and memorable and included hands-on activities such as building a masonry wall with professional masons and use of advanced technology, such as Building Information Modeling (BIM) technology such as REVIT and Masonry iQ, infrared cameras, laser scanners, and various XR modalities. Pre- and post-surveys for the entire summer camp and shorter surveys after three specific activities using XR modalities were conducted to evaluate the effectiveness of the camp in triggering interest in the participants into pursuing a career in construction management. This thesis summarizes the evidence-based research results on the impact of these specific activities that used XR modalities as well as the overall camp on triggering situational interest in students. The post-camp survey results show a significant increase in the participants’ interest in a career in Construction Management after the camp. The findings contribute to the body of knowledge regarding the use of hands-on and XR-technology-based educational activities, specifically in the context of a summer camp for student recruitment purposes. Moreover, the findings provide an empirical foundation for developing a pre-college educational program to intrigue high school students' interests in the construction management domain. Analysis of the results also presents findings and recommendations useful to academia with respect to proper selection of XR modalities when different educational objectives and priorities are considered, such as student comfort. A limitation of the study is the small sample size, but data from future camps will be used to verify these findings.M.S

Advancing Product Development of Ultrafine Fibers: From Formulation Considerations to Thermoelectric Textiles

Ultrafine fibers are of great interest in new product development in the fiber and textile industry especially for complex, high value-add materials. Due to their unique inherent properties of fine diameter, high surface area to volume ratio, high porosity, and molecular orientation along the fiber length they are highly desired for applications in biomedical fields, filtration, and wearable electronics. Electrospinning is the most common method used for ultrafine fiber production, but it classically has many material limitations such as requiring a high molecular weight linear polymer dissolved in a conductive and volatile solvent at concentrations above the entanglement concentration. These limitations hinder the development of more functional materials. There are significant knowledge gaps in how complex formulations needed for high value-add materials influence spinning solutions and the manufacturing space. This thesis explores methods of circumventing the material limitations for ultrafine fibers in order to advance future product development. Poor fiber formation is often the result of jet breakup when the elasticity of the polymer solution is insufficient to suppress instabilities. The primary instability driving jet breakup is capillary force driven instabilities, where low elasticity in solutions is insufficient to suppress Rayleigh instabilities. A decrease in capillary force should allow for less elastic solutions to form smooth fibers. This was studied through the effect of surface tension on the electrospinnability with the use of extensional rheology. PVP was tested with methanol, and water, and water with surfactant to directly study the impact of surface tension on electrospinnability. Low surface tension solutions readily formed fibers at lower concentrations than high surface tension solutions, supporting the theory that less elasticity is needed to stabilize the jet from breakup. Through this study I provide a deeper understanding of the connection between solvent characteristics, viscoelasticity, and electrospinnability, which enables the rational preparation for more complex spinning solutions being explored. Electrospun fibers are typically composed of solely high molecular weight polymers, but including hard particles is also of interest in ultrafine fibers for drug delivery, active filtration, wearable electronics, etc. In this thesis, I show that large particles (10x the fiber diameter) of varying shapes, densities, and chemistries can be incorporated in fibers at loadings exceeding the polymer concertation without disrupting fiber formation. Additionally, the large particles increase the mechanical strength of the fibers in the same manner as fiber reinforced composites. Fiber mats were found not to fracture when particles are included, indicating they are not creating weak points in the fibers. Hence, large functional particles such as active pharmaceutical ingredients can be encapsulated in fibers at high quantities without damaging the fiber integrity. Conducting polymers are typically low molecular weight or are particle dispersions which fall outside the realm of electrospinnable materials, yet they are of great interest in ultrafine fiber applications. Developing methods of creating conducting ultrafine fibers enables their use in high performing thermoelectric textiles for wearable electronics. Ultrafine fibers with their inherent porosity and high surface area are of ideal use in wearable electronics, as they are breathable and provide a good platform for charge transport. With the knowledge gained in the early work in this thesis, particle electrospinning and surface tension effects on fiber formation, fibers with a high concentration of PEDOT:PSS were created that exhibit thermoelectric properties similar to equivalent thin films while maintaining porosity and flexibility. Additionally ultrafine poly(NiETT) fibers were synthesized for the first time, providing a novel n-type textile with conductivities rivaling other n-type textiles. Through the deeper understanding of the connection between solvents, polymers, and additives in formulations to viscoelasticity and electrospinnability contributes to greater knowledge of ultrafine fiber formation. The results of this thesis show how better understanding of fiber formation and proper formulations expand the realm of electrospinnable materials and give insights into future product development of novel and advanced textile based materials.Ph.D

Enhancement of Cavitation Intensity in Co-Flow and Ultrasonic Cavitation Peening

Water cavitation peening is a surface treatment process used to generate beneficial compressive residual stresses while being environmentally sustainable. Compressive residual stresses generated by the collapse of the cavitation cloud at the workpiece surface result in enhanced high cycle fatigue and wear performance. Co-flow water cavitation peening, a variant of cavitation peening involves injection of a high-speed jet into a low-speed jet of water, which makes the process amenable to automation and imparts the variant with the ability to process large structural components. Ultrasonic cavitation peening, another variant of cavitation peening, is used for peening small areas. However, an increase in cavitation intensity is needed to reduce the processing time for practical applications and to enhance process capabilities for a wide range of materials in both these variants. An experimental investigation along with numerical modelling is presented to demonstrate cavitation intensity enhancement through suitable modifications to the inner jet nozzle design in co-flow water cavitation peening. Particularly, the effects of upstream inner jet organ pipe nozzle geometry, inner jet nozzle orifice taper, and inner jet nozzle orifice length are studied to show enhanced cavitation intensity, measured via extended mass loss tests, strip curvature and residual stress measurements, high-speed videography, and impulse pressure measurements. It is found that the optimum inner jet organ pipe nozzle design, which generates enhanced pressure fluctuations through the introduction of a resonating chamber in the upstream section of the inner jet nozzle, generates 61% greater mass loss compared to the unexcited inner jet nozzle. Strip curvature, high speed imaging, and impulse pressure measurements support the mass loss results. Finally, residual stresses generated with the optimum organ pipe nozzle are shown to be deeper and more compressive than those generated with the unexcited nozzle design. The inner jet nozzle variants with diverging, zero and converging tapers are investigated experimentally and numerically to understand their influence on cavitation intensity. It is shown that the converging taper nozzle generates greater cavitation intensity, measured via mass loss and strip curvature measurements, than the zero and diverging taper nozzles. Impulse pressure measurements show the greater frequency of high-intensity events generated by the converging taper nozzle compared to the zero and diverging taper nozzles. Computational fluid dynamics (CFD) simulations help explain the experimental findings. Four nozzle variants with varying inner jet nozzle orifice length to orifice diameter ratios of 1,2,5 and 10 are investigated experimentally and numerically. The inner jet nozzle with an orifice length to orifice diameter ratio of 2 is shown to generate greater cavitation intensity than the other inner jet nozzles. A PEO aqueous solution (cavitation media) with 1000 parts per million by weight (wppm) polymer concentration is shown to enhance cavitation intensity by 69% over cavitation media with only water. High speed videography, impulse force, and surface roughness measurements confirm the greater cavitation activity in the 1000 wppm PEO aqueous solution. This demonstrates that suitable modifications can be engineered in the cavitation media to enhance cavitation intensity in ultrasonic cavitation peening. Thus, this thesis presents experimental and numerical investigations leading to superior inner jet nozzle design in co-flow cavitation peening and an experimental investigation of the role of polymer additives for suitable modification of cavitation media to enhance cavitation intensity in ultrasonic cavitation peening.Ph.D

Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, And Resilience

Hybrid sensor networks (HSN) consist of both static and mobile sensors deployed to fulfill a common monitoring task. The hybrid structure generalizes the network’s design problem and offers a rich set of possibilities for a host of environmental monitoring and anomaly detection applications. HSN also raise a new set of research questions. Their deployment and optimization provide unique opportunities to improve the network’s monitoring performance and resilience. This thesis addresses three challenges associated with HSN related to the collaboration, optimization, and resilience aspects of the network. Broadly speaking, these challenges revolve around the following questions: (1) how to collaboratively allocate the static sensors and devise the path planning of the mobile sensors to improve the monitoring performance? (2) how to select and optimize the sensor portfolio (the mix of each type of sensors) under given cost constraints? And (3) how to embed resilience in a HSN to sustain the monitoring performance in the face of sensor failures and disruptions? In part I, collaboration, this thesis develops a novel deployment strategy for HSN. The strategy solves the static sensor allocation problem, the mobile sensor path planning problem, and most importantly, the collaboration between these two types of sensors. Previous research in this area has addressed these problems separately in simplified environments. In this thesis, a collaborative deployment strategy of HSN is developed to improve the ultimate monitoring performance in complex environments with obstacles and non-uniform risk distribution. In part II, optimization, this thesis addresses the HSN sensor portfolio selection problem. It investigates the tradeoff between the static and mobile sensors to achieve the optimal monitoring performance under different cost constraints. Previous research in this area has studied the optimization problem for networks with a single type of sensor. In this thesis, a general optimization problem is formulated for HSN with static and mobile sensors and solved to identify the optimal portfolio mix and its main drivers. In part III, resilience, this thesis identifies monitoring resilience as a key feature enabled by HSN. This part focuses on the performance degradation of HSN in the presence of sensor failures and disruptions, and it identifies the means to embed resilience in a HSN to mitigate this performance degradation. Monitoring resilience is achieved by accounting for potential sensor failures in the deployment strategy of both static and mobile sensors through a novel, carefully designed probability sum technique. Previous research in this area has examined the reliability problem from a coverage point of view. This thesis extends the scope of investigation of HSN from reliability to resilience, and it shifts the focus from coverage considerations to the actual monitoring performance (e.g., detection time lag) and its resilience in the face of disruptions. To demonstrate and validate this novel perspective on HSN and the associated technical developments, this thesis focused on two examples of fire detection in a multi-room apartment using temperature sensors and CO leak detection in a 3D space station module with ventilation system. Three metrics are adopted as the ultimate monitoring performance, namely the detection time lag, the anomaly source localization uncertainty, and the state estimation error. A simulation environment based on the advection-conduction heat propagation model is developed for the computational experiments. The results (1) demonstrate that the optimal collaborative deployment strategy allocates the static sensors at high-risk locations and directs the mobile sensors to patrol the rest of the low-risk areas; (2) identify a set of conditions under which HSN significantly outperform purely static and purely mobile sensor networks across the three performance metrics here considered; and (3) establish that while sensor failures can considerably degrade the monitoring performance of traditional static sensor networks, the resilient deployment of HSN drastically reduces the performance degradation.Ph.D

Understanding and Mitigating Privacy Vulnerabilities in Deep Learning

Advancements in Deep Learning (DL) have enabled leveraging large-scale datasets to train models that perform challenging tasks at a level that mimics human intelligence. In several real-world scenarios, the data used for training, the trained model, and the data used for inference can be private and distributed across multiple distrusting parties, posing a challenge for training and inference. Several privacy-preserving training and inference frameworks have been developed to address this challenge. For instance, frameworks like federated learning and split learning have been proposed to train a model collaboratively on distributed data without explicitly sharing the private data to protect training data privacy. To protect model privacy during inference, the model owners have adopted a client-server architecture to provide inference services, wherein the end-users are only allowed black-box access to the model’s predictions for their input queries. The goal of this thesis is to provide a better understanding of the privacy properties of the DL frameworks used for privacy-preserving training and inference. While these frameworks have the appearance of keeping the data and model private, the information exchanged during training/inference has the potential to compromise the privacy of the parties involved by leaking sensitive data. We aim to understand if these frameworks are truly capable of preventing the leakage of model and training data in realistic settings. In this pursuit, we discover new vulnerabilities that can be exploited to design powerful attacks that can overcome the limitations of prior works and break the illusion of privacy. Our findings highlight the limitations of these frameworks and underscore the importance of principled techniques to protect privacy. Furthermore, we leverage our improved understanding to design better defenses that can significantly deter the efficacy of an attack.Ph.D

Evolution and Architecture of Epigenetic Regulation in the Genome

Epigenetic modifications are genomic alterations which regulate the expression and activity of genes by changing the structure of chromatin. These mechanisms of regulation expand the proportion of the genome that is functional well beyond the comparably rare instances of protein coding genes, which, in humans, only correspond to ~2% of the genome. The aim of this dissertation is to leverage advances in the genomic identification and annotation of epigenetic modifications to explore questions regarding the (1) role of DNA methylation in X chromosome regulation through comparative genomic analyses, (2) the organization and (3) evolution of enhancers identified from histone modifications. In the second chapter of this thesis, we consider the role of DNA methylation in an iconic example of epigenetic regulation, namely the X chromosome inactivation (XCI). XCI is the process by which one of the two female X chromosomes is silenced to balance the expression of X-linked genes in male and female genomes and is functionally conserved in two branches of mammals (eutherians and marsupials). In eutherians, it is well established that DNA methylation plays a role in establishing XCI through the silencing of the lncRNA Xist on the active X chromosome as well as in the long-term maintenance of inactive X-linked genes. However, the role of DNA methylation in marsupials remains controversial. We utilize novel multi-tissue, sex-inclusive Whole Genome Bisulfite Sequencing (WGBS) coupled with improved genomic annotations to elucidate the role of DNA methylation in X chromosome regulation in a representative marsupial, the modern koala (Phascolarctos cinereus). Consequently, we clarify conserved and divergent roles of DNA methylation on the regulation of XCI in marsupials and eutherians. In the following two chapters, we integrate multi “-omics” datasets including whole genome chromatin state maps and gene expression data from a diverse set of tissues to elucidate the organization and evolution of human enhancers, a hallmark of the (epi)genomic regulatory landscape. Enhancers are short, mostly non-coding DNA sequences that orchestrate the context- and developmental time-specific expression of associated genes. Enhancers are often studied as highly tissue-specific regulatory elements in what has been deemed a “paradigm of modularity.” However, contrary evidence, indicating that a subset of enhancers may be repurposed in multiple tissue and/or developmental contexts, is mounting. In this study, we characterize the previously unknown frequency and genomic characteristics of these highly “pleiotropic” enhancers. We further evaluate the organization of the larger gene-enhancer interaction network considering (1) the distribution of enhancer pleiotropy, (2) the variations in the number of enhancer-target gene links, and (3) the expression breadth of target genes. Furthermore, we explore the evolution of human enhancer through genomic duplication events. Duplications are a canonical reservoir of the raw material needed for the evolution of novel functional elements in the genome and have been studied extensively with respect to genes. The selective processes governing the maintenance of duplicate genes are well characterized, and similar evolutionary mechanisms have been proposed for non-coding regulatory elements. However, whether duplication events affect enhancer evolution and maintenance is currently unknown. Through sequence homology analyses, we identify likely candidate duplicate enhancers in our large dataset to determine the frequency of duplicate enhancer retention in the human genome. Additionally, we determine the characteristics of duplicate enhancers contributing to their evolutionary maintenance. We demonstrate that duplication of enhancers has significant footprint on pleiotropic enhancers and that recently duplicated human enhancers exhibit signatures of accelerated evolution and specialized for immune related functions. Together, these studies reveal previously unknown patterns of conservation and divergence of epigenetic regulatory mechanisms along two deep branches of mammals, as well as elucidate the molecular architecture and the impact of duplication on the genomic landscape of enhancer-gene regulation.Ph.D