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Title: A NOVEL METHOD FOR THE IMPLEMENTATION OF STRUCTURAL CONTACT IN FINITE ELEMENT METHODS OFFERING SIMPLIFIED TREATMENT OF ENERGY DISSIPATION
Creator: Grudzinski, James John
Date: 2012-04-24, 2012-05
Description: A novel method for implementing contact/impact in an implicit nite element formulation is presented. The method uses the ideas of buoyancy to...
Show moreA novel method for implementing contact/impact in an implicit nite element formulation is presented. The method uses the ideas of buoyancy to enforce the normal contact constraint and a velocity dependent force to model energy dissipation. Upon contact (penetration) a normal force equal to the depth of penetration times a target weight density (di erent and much larger than the actual material weight density) creates a normal pressure on the contacting body. In addition to the buoyancy force, the penetrating surface area is subjected to a drag-like force that acts in a direction opposite the velocity vector of the penetrating node of the contacting body . This rate dependence is broken up into components tangential and normal to the target surface. The normal component of the drag performs two functions. First it provides for an energy absorbing mechanism similar to a coe cient of restitution for modeling non-conservative systems. Secondly, it can provide damping (analogous to mathematical damping) which can aid in solution convergence. The tangential component of the damping force serves the function of modeling friction in a simpli ed manner. The method applies contact forces in the manner of external forces and as such lends itself well to simpli ed contact detection schemes which rely on functional representation of bodies. The method is described and demonstrated through several examples including a comparison to experimental data.
Ph.D. in Mechanical and Aerospace Engineering, May 2012
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Title: TEMPERATURE PROFILES THROUGH THE SHELL IN EGGS HEATED BY INFRARED ENERGY
Creator: Guo, Jingxin
Date: 2012-07-30, 2012-07
Description: Infrared heating is an effective method for surface pasteurization of shell eggs. As the external temperature reaches the inactivation...
Show moreInfrared heating is an effective method for surface pasteurization of shell eggs. As the external temperature reaches the inactivation temperature, internal temperatures could become great enough to denature albumen. However, the internal temperature of albumen at the shell cannot be measured directly. The purpose of this study is to use experimental data to understand the response of the albumen temperature at the shell to infrared radiation impinging at its outer surface. Fresh eggs were obtained from a local producer and refrigerated until needed. They were prepared for each experiment by overnight equilibration with ambient conditions. Each egg was placed on a metal stage over which an infrared lamp was positioned. The stage allowed a K-type thermocouple to be inserted through a small hole in the shell opposite of the shell area facing the lamp. The thermocouple was pushed into the egg such that the tip was against the inner surface of the shell just underneath the exposed area. The external temperature of this area was measured by an infrared pyrometer. Variables for these experiments were temperature of the IR lamp (277°C, 329°C, 391°C and 452°C), distance between shell egg and IR lamp (3.13 cm – 15.83 cm), and treatment time. All experiments share the same initial lag around 30s, suggested that the initial lag did not vary with changing of lamp-egg distances and lamp temperatures. After the initial lag, internal and external temperatures were found to increase at the same rate regardless of experimental parameters, indicating and equilibrium between the infrared energy impinging on the surface and its dissipation in the interior of the egg. The difference of temperature distributes between 10°C to 20°C. However, lowering lamp temperatures or increasing distances did not change this difference significantly. Moreover, the temperature gap sustains the same temperature after initial lag. The results showed that the internal temperature could be inferred via measuring external temperature. This is valuable in processing to maximize external surface temperature while protecting heat sensitive albumen. Future work will involve modeling the heating phenomenon to determine if knowing only the lamp temperature and distance is sufficient for predicting internal temperature.
M.S. in Food Processing Engineering, July 2012
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Title: CONSTRUCTIONS IN NON-ADAPTIVE GROUP TESTING STEINER SYSTEMS AND LATIN SQUARES
Creator: Balint, Gergely `greg' T.
Date: 2014, 2014-05
Description: This thesis explores and introduces new constructions for non-adaptive group testing which are particulary important for the parameter range...
Show moreThis thesis explores and introduces new constructions for non-adaptive group testing which are particulary important for the parameter range we encounter in real life problems. After a summary of existing results, the rst part of this thesis introduces our own constructions, the Latin Square Construction and the Column Augmented Concatenation. Both of these constructions take existing good group testing matrices to create test matrices of larger dimensions. These new matrices are easy to nd for the practical small parameter range we are most interested in. We also address and prove asymptotic results of our Latin Square Construction. In case of the Column Augmented Concatenation the asymptotic results depend greatly on the codes used for the construction. The second part of our work is to address possible ways of augmentation of the Latin Square Construction. Here we explore the di erence in augmentation based on the properties of the starting matrix. In the appendices we give tables of best matrices coming from our constructions with xed, small column weights. We also give a list of the known best 2-disjunct matrices for small row numbers.
PH.D in Applied Mathematics, May 2014
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Title: NEURAL ADAPTIVE CONTROL STRATEGY FOR HYBRID ELECTRIC VEHICLES WITH PARALLEL POWERTRAIN
Creator: Gurkaynak, Yusuf
Date: 2011-04-20, 2011-05
Description: In a hybrid electric vehicle (HEV) with parallel powertrain, the system can be controlled by splitting the required power between the electric...
Show moreIn a hybrid electric vehicle (HEV) with parallel powertrain, the system can be controlled by splitting the required power between the electric propulsion machine and internal combustion engine (ICE) to meet specific goals related to fuel consumption, efficiency, performance, and/or emissions. This power splitting scenario, which is of great hybridization importance, is in fact the control strategy or energy management of the hybrid vehicle. Performance of the system depends on the control strategy, which needs to be robust, stable, reliable, and independent from uncertainties. This Ph.D. research is focused on model based control strategies, which are proposed for parallel hybrid powertrains, showing significant advantages in performance and fuel economy. If a model based control strategy is used to develop the hybrid power management algorithm, the accuracy of the model data needs to be high for proper control. Therefore, this type of management method is parameter sensitive. Implementing system identification features into this algorithm reduces the effect. As a result, the proposed controller algorithm learns the existing component parameters while operating. Furthermore, combining the base controller with an online tuner, which simultaneously optimizes the controller for current conditions, will improve the performance of the power management. In addition, this Ph.D. thesis presents a novel neural adaptive equivalent consumption minimization strategy (ECMS) and applies it to a hybrid representative sport utility vehicle (SUV) with parallel powertrain. The ECMS is a model based optimal control strategy and is based on the minimization of both fuel consumption and battery charge usage by introducing the equivalent coefficient between them. Proper operation of the controller depends on the accuracy of the model. It also depends on the correct selection of the equivalent coefficient. In this Ph.D. thesis, specific neural network structures are proposed for both coefficient selections by drive cycle recognition and for precise model building by system identification. This thesis also presents a novel fast solution method of ECMS algorithm for real time applications.
Ph.D. in Electrical Engineering, May 2011
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Title: GROWTH, INACTIVATION, AND SURVIVAL OF SALMONELLA ON SESAME SEEDS DURING TAHINI PROCESSING AND REFRIGERATED STORAGE OF TAHINI
Creator: Zhang, Yangjunna
Date: 2016, 2016-05
Description: Salmonella can survive for long period of time in low-moisture foods and cause human illness after consumption of contaminated foods. Recently...
Show moreSalmonella can survive for long period of time in low-moisture foods and cause human illness after consumption of contaminated foods. Recently, sesame seeds and tahini (sesame seeds pasta), have been identified as unusual sources of salmonellosis. Controlling specific steps during tahini processing and storage of tahini may minimize the risk of Salmonella contamination. This study examined the fate of Salmonella in different steps of tahini processing and refrigerated storage of tahini. A four serovar cocktail of Salmonella was used for inoculation of sesame seeds and tahini. Bacterial populations were determined by aerobic plate counts on both selective and non-selective media. Water activity of samples was determined during processing and storage. For the soaking step during tahini processing, unhulled dry sesame seeds with aw of 0.1 were inoculated with Salmonella, held 24 h, and then soaked in water at ambient temperature. Populations were monitored at 0, 18, 22 and 24 h. Salmonella decreased by 2 - 3-log CFU/g during drying and then increased by 5 log CFU/g after rehydration. Separately, inoculated de-hulled seeds with two different initial aw were roasted at three different temperatures (95, 110, and 130 ºC) for 90 min. Both the Salmonella populations and aw were determined at 10 min intervals during roasting. Finally, inoculated sesame seeds were processed into tahini. As a comparison, the same quantity of uninoculated roasted sesame seeds were processed into tahini, and then inoculated post-processing. Tahini was stored at 4 ºC for 17 weeks. Approximately 8 log CFU/g of Salmonella was detected initially in the sesame seeds prior to roasting with a 1-log CFU/g reduction after 20 min at 95 ºC when the aw of seeds decreased quickly. For different roasting temperatures, the aw declined much faster and stopped decreasing during roasting, while populations continue decreasing throughout the whole roasting. To investigate survival of Salmonella during refrigerated storage of tahini, approximately 9 log CFU/g of Salmonella was inoculated onto sesame seeds or into processed tahini without significant reduction throughout 17 weeks (p > 0.05). These results suggest that Salmonella contamination can be an issue at any step of tahini manufacture.
M.S. in Food Safety and Technology, May 2016
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Title: EFFICIENT SCORING AND RANKING OF EXPLANATION FOR DATA EXCHANGE ERRORS IN VAGABOND
Creator: Wang, Zhen
Date: 2014, 2014-05
Description: Data exchange has been widely used in big data era. One challenge for data exchange is to identify the true cause of data errors during the...
Show moreData exchange has been widely used in big data era. One challenge for data exchange is to identify the true cause of data errors during the schema translation. The huge amount of data and schemas make it nearly impossible to find “the” correct solution. Vagabond system is developed to address this problem and use best-effort methods to rank data exchange error explanations base on the likelihood that they are the correct solutions. Ranking done on scoring functions that model some aspects of explanation sets. Examples of these properties include complexity(size of explana- tion), and side effect size(number of correct data values that will be affected by the changes). The thesis introduced three new scoring functions to increase the applicability of Vagabond under various data exchange scenarios. We prove that the monotonicity property required by Vagabond may not hold for some of the new scoring functions, so a new generic ranker is also introduced to efficiently rank error explanations for these new scoring functions as well as for future scoring functions that have boundary property. We can efficiently compute upper or lower bounds on the score of partial solutions. We also completed some performance experiments on the new scoring functions and the new ranker. The experiment result proves that the new scoring functions introduced in this thesis have a scalable performance.
M.S. in Computer Science, May 2014
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Title: IMPACT OF INOCULUM LEVEL ON THE TRANSFER OF SALMONELLA SEROVARS FROM CONTAMINATED ALMOND BUTTER TO FOOD CONTACT MATERIALS
Creator: Zheng, Yue
Date: 2012-11-19, 2012-12
Description: Outbreaks of salmonellosis associated with nut butter have raised public concerns of sanitation issues in nut butter processing. The high fat,...
Show moreOutbreaks of salmonellosis associated with nut butter have raised public concerns of sanitation issues in nut butter processing. The high fat, low-moisture characteristics of nut butters significantly affect the efficiency of regular cleaning and sanitizing programs, allowing cross-contamination issues to persist on processing equipment. Besides organic matter in food soil, microbial load could also be a factor affecting efficacy of chemical sanitizers. The FDA Food Code (2005) require a 5-log bacterial reduction in testing the efficacy of a chemical sanitation method. As a result, evaluation of microbial transfer is essential for establishing methods for equipment sanitation. The first study of this thesis (Section 4.1) evaluated the survival of Salmonella serovars in almond butter at 25 ± 2 oC. This was achieved by investigating the survival of Salmonella Tennessee and Salmonella Oranienburg in inoculated almond butter sample for up to two weeks. These Salmonella serovars were inoculated into creamy almond butter separately and stored at 25 ± 2 oC. Results showed that Salmonella populations decreased slowly and could survive in almond butter for at least two weeks. This study also investigated the effect of initial inoculum level, contact time, food-contact material and bacterial serovar on the subsequent potential for transfer of bacteria to equipment surfaces (Section 4.2). Almond butter inoculated with Salmonella Oranienburg and Tennessee at different inoculums levels (~3, 6, 9 log CFU/g) were spread on 16 cm2 coupons made of polyethylene, polyurethane, Delrin and stainless steel. Microbial analysis was conducted after physically removing all visible nut butter with laboratory wipes. Results for S. Oranienburg and Tennessee followed similar trends of microbial transfer with regards to inoculum level and food-contact surface. The amount x of Salmonella transferred to a food-contact surface was dependent on initial inoculation levels. An average of 0.88 ± 0.22, 1.53 ± 0.15, and 4.59 ± 0.06 log CFU S. Tennessee per 16 cm2 were transferred to the four different food-contact surface types for low, medium, and high inoculum level, respectively. An average of 1.25 ± 0.24, 2.08 ± 0.08, and 4.55 ± 0.35 log CFU S. Oranienburg per 16 cm2 were transferred to the same four different foodcontact surfaces for low, medium, and high inoculum levels, respectively. The third part of this study (Section 4.3) determined the transfer of Salmonella from contaminated food-contact coupon surfaces to almond butter. Uninoculated almond butter was applied on the surface of previously contaminated food-contact coupon surfaces. More than 5 log CFU/16 cm2 Salmonella could transfer to clean almond butter after immediate contact (within 5 min) with the contaminated area. These findings help advance our understanding of factors affecting microbial transfer between nut butters and processing equipment surfaces. This research can be used to support future cleaning and sanitation studies for nut butter processing equipment.
M.S. in Food Safety and Technology, December 2012
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Title: EXPERIMENTAL EFFECTS OF SOCIAL MEDIA ON BODY DISSATISFACTION AND EATING PATHOLOGY: UPWARD VERSUS DOWNWARD COMPARISONS
Creator: Badillo, Krystal Elizabeth
Date: 2019
Description: Research has been limited in assessing the different impacts of social media platforms on body dissatisfaction, apart from Facebook. In...
Show moreResearch has been limited in assessing the different impacts of social media platforms on body dissatisfaction, apart from Facebook. In addition, most studies have measured social media use by only assessing time. This study aimed to test experimental effects of social comparison on body dissatisfaction (BD) and desire to engage in eating behaviors. In addition, a mediation model was tested of the association between social media use and BD via social comparison. Participants viewed one of two live public Instagram profiles and were asked questions that facilitated individuals comparing their appearance to the Instagram profile. A total of 74 women completed questionnaires regarding social media use, body image, and eating disorder psychopathology. It was found that regardless of condition, desire to eat decreased after profile views, but there was no change in BD. The mediation model suggests that there was a significant indirect effect of increased BD and importance of Instagram through social comparison. Results suggest that, contrary to earlier work, overall social media use may not negatively impact BD as previously thought. Rather, it appears that trait level factors such as social comparison negatively influence BD while using social media.
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Title: Developing Novel Optimization Algorithms Applied To Building Energy Performance and Indoor Air Quality
Creator: Faramarzi, Afshin
Date: 2021
Description: Residential and commercial buildings account for 23% of global energy use. In the United States, space heating, cooling, and lighting energy...
Show moreResidential and commercial buildings account for 23% of global energy use. In the United States, space heating, cooling, and lighting energy use accounts for 38%, 9%, and 7% of building energy consumption, which results in 54% of the total energy consumption of the building. Energy efficiency improvements in buildings require consideration of optimal design, operation, and control of building components (e.g., mechanical and envelope systems). We can address this task by taking advantage of computational optimization methods throughout the design, operation, and control processes.Non-gradient metaheuristic optimization methods known as metaheuristics are some of the most popular and widely used optimization methods in Building Performance Optimization (BPO) problems. Conventional metaheuristics usually have simple mathematical models with low rate of convergence. On the other hand, high-performance metaheuristic optimizers are efficient and usually have a fast rate of convergence, but their mathematical models are hard to understand and implement. As such, researchers are usually not inclined to employ them in solving their problems. To this end, we aimed at developing optimization algorithms which borrow simplicity from conventional methods and efficiency from high-performance optimizers to solve problems fast and efficiently while being welcomed by users from throughout the world. Therefore, the overarching objective of this work is defined to first develop novel optimization algorithms which are simple in mathematical models and still efficient in solving optimization benchmark problems and then apply the methods to building energy performance and indoor air quality (IAQ) problems. In the first objective of this work, which is the development phase, two continuous optimization methods and one binary optimizer are developed and are separately described in three different tasks. The first method called Equilibrium Optimizer (EO) is a simple method inspired by the mass balance equation in a control volume. The second optimization method called Marine Predators Algorithm (MPA) is a more complicated method compared to EO and is inspired by widespread foraging strategies between marine predators in the ocean ecosystem. Finally, the third method is the binary version of an already developed equilibrium optimizer called Binary Equilibrium Optimizer (BEO). The second objective of the dissertation is the application phase which focuses on the application of the developed methods and other widely used methods in research and industry for solving the almost new BPO and IAQ problems. The results showed that the developed methods were able to either reach more energy-efficient solutions compared to the other methods or to show a considerably faster rate of convergence compared to other methods in the problems in which the optimal solutions are similarly obtained by different methods.
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Title: AI IN MEDICINE: ENABLING INTELLIGENT IMAGING, PROGNOSIS, AND MINIMALLY INVASIVE SURGERY
Creator: Getty, Neil
Date: 2022
Description: While an extremely rich research field, compared to other applications of AI such as natural language processing (NLP) and image processing...
Show moreWhile an extremely rich research field, compared to other applications of AI such as natural language processing (NLP) and image processing/generation, AI in medicine has been much slower to be applied in real-world clinical settings. Often the stakes of failure are more dire, the access of private and proprietary data more costly, and the burden of proof required by expert clinicians is much higher. Beyond these barriers, the often typical data-driven approach towards validation is interrupted by a need for expertise to analyze results. Whereas the results of a trained Imagenet or machine translation model are easily verified by a computational researcher, analysis in medicine can be much more multi-disciplinary demanding. AI in medicine is motivated by a great demand for progress in health-care, but an even greater responsibility for high accuracy, model transparency, and expert validation.This thesis develops machine and deep learning techniques for medical image enhancement, patient outcome prognosis, and minimally invasive robotic surgery awareness and augmentation. Each of the works presented were undertaken in di- rect collaboration with medical domain experts, and the efforts could not have been completed without them. Pursuing medical image enhancement we worked with radiologists, neuroscientists and a neurosurgeon. In patient outcome prognosis we worked with clinical neuropsychologists and a cardiovascular surgeon. For robotic surgery we worked with surgical residents and a surgeon expert in minimally invasive surgery. Each of these collaborations guided priorities for problem and model design, analysis, and long-term objectives that ground this thesis as a concerted effort towards clinically actionable medical AI. The contributions of this thesis focus on three specific medical domains. (1) Deep learning for medical brain scans: developed processing pipelines and deep learn- ing models for image annotation, registration, segmentation and diagnosis in both traumatic brain injury (TBI) and brain tumor cohorts. A major focus of these works is on the efficacy of low-data methods, and techniques for validation of results without any ground truth annotations. (2) Outcome prognosis for TBI and risk prediction for Cardiovascular Disease (CVD): we developed feature extraction pipelines and models for TBI and CVD patient clinical outcome prognosis and risk assessment. We design risk prediction models for CVD patients using traditional Cox modeling, machine learning, and deep learning techniques. In this works we conduct exhaustive data and model ablation study, with a focus on feature saliency analysis, model transparency, and usage of multi-modal data. (3) AI for enhanced and automated robotic surgery: we developed computer vision and deep learning techniques for understanding and augmenting minimally invasive robotic surgery scenes. We’ve developed models to recognize surgical actions from vision and kinematic data. Beyond model and techniques, we also curated novel datasets and prediction benchmarks from simulated and real endoscopic surgeries. We show the potential for self-supervised techniques in surgery, as well as multi-input and multi-task models.
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Title: Child Temperament, Attachment, and Loneliness: The Mediating Effects of Social Competence
Creator: Evans, Lindsey M
Date: 2021
Description: Chronic loneliness is a risk factor associated with adverse psychological, physical, and academic outcomes. Converging evidence suggests that...
Show moreChronic loneliness is a risk factor associated with adverse psychological, physical, and academic outcomes. Converging evidence suggests that young children experience and can reliably report on their own loneliness. Due to the significant negative sequalae associated with childhood loneliness, it is critically important to examine risk factors for child loneliness. The aims of this study were two-fold: (a) to examine if temperament (i.e., negative affect, effortful control, and inhibitory control) and attachment security assessed at 4 years of age predict loneliness at age 6; and (b) to determine if social competence at age 5 mediates the relation between temperament and attachment security at age 4 and loneliness at age 6. Participants included a diverse sample of 796 4-year old children, about half of whom were male. At age 4, temperament was assessed with the Rothbart Child Behavior Questionnaire and three inhibitory control tasks, and attachment security was assessed with the Attachment Q-Sort. At age 5, the Social Skills Rating Scale was used to assess social competence, and, at age 6, loneliness was assessed with the Loneliness and Social Dissatisfaction Questionnaire. Results of hierarchical regression analyses indicated that lower levels of effortful control and inhibitory control at age 4 significantly predicted higher levels of loneliness at age 6. Also, lower levels of negative affect and higher levels of effortful control and attachment security at age 4 significantly predicted higher levels of social competence at age 5. However, social competence at age 5 did not predict loneliness at age 6. There was no evidence that social competence at age 5 mediated the relation between age 4 temperament, attachment security and age 6 loneliness. These findings reveal that early self-regulation is associated with later child-reported loneliness and that intervention for children who struggle with cognitive regulation may be effective in decreasing risk for later loneliness.
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Title: Intelligent Job Scheduling on High Performance Computing Systems
Creator: Fan, Yuping
Date: 2021
Description: Job scheduler is a crucial component in high-performance computing (HPC) systems. It sorts and allocates jobs according to site policies and...
Show moreJob scheduler is a crucial component in high-performance computing (HPC) systems. It sorts and allocates jobs according to site policies and resource availability. It plays an important role in the efficient use of system resources and users satisfaction. Existing HPC job schedulers typically leverage simple heuristics to schedule jobs. However, the rapid growth in system infrastructure and the introduction of diverse workloads pose serious challenges to the traditional heuristic approaches. First, the current approaches concentrate on CPU footprint and ignore the performance of other resources. Second, the scheduling policies are manually designed and only consider some isolated job information, such as job size and runtime estimate. Such a manual design process prevents the schedulers from making informative decisions by extracting the abundant environment information (i.e., system and queue information). Moreover, they can hardly adapt to workload changes, leading to degraded scheduling performance. These challenges call for a new job scheduling framework that can extract useful information from diverse workloads and the increasingly complicated system environment, and finally make well-informed scheduling decisions in real time.In this work, we propose an intelligent HPC job scheduling framework to address these emerging challenges. Our research takes advantage of advanced machine learning and optimization methods to extract useful workload- and system-specific information and to further educate the framework to make efficient scheduling decisions under various system configurations and diverse workloads. The framework contains four major efforts. First, we focus on providing more accurate job runtime estimations. Estimated job runtime is one of the most important factors affecting scheduling decisions. However, user provided runtime estimates are highly inaccurate and existing solutions are prone to underestimation which causes jobs to be killed. We leverage and enhance a machine learning method called Tobit model to improve the accuracy of job runtime estimates at the same time reduce underestimation rate. More importantly, using TRIP’s improved job runtime estimates boosts scheduling performance by up to 45%. Second, we conduct research on multi-resource scheduling. HPC systems are undergoing significant changes in recent years. New hardware devices, such as GPU and burst buffer, have been integrated into production HPC systems, which significantly expands the schedulable resources. Unfortunately, the current production schedulers allocate jobs solely based on CPU footprint, which severely hurts system performance. In our work, we propose a framework taking all scalable resources into consideration by transforming this problem into multi-objective optimization (MOO) problem and rapid solving it via genetic algorithm. Next, we leverage reinforcement learning (RL) to automatically learn efficient workload- and system-specific scheduling policies. Existing HPC schedulers either use generalized and simple heuristics or optimization methods that ignore workload and system characteristics. To overcome this issue, we design a new scheduling agent DRAS to automatically learn efficient scheduling policies. DRAS leverages the advance in deep reinforcement learning and incorporates the key features of HPC scheduling in the form of a hierarchical neural network structure. We develop a three-phase training process to help DRAS effectively learn the scheduling environment (i.e., the system and its workloads) and to rapidly converge to an optimal policy. Finally, we explore the problem of scheduling mixed workloads, i.e., rigid, malleable and on-demand workloads, on a single HPC system. Traditionally, rigid jobs are the main tenants of HPC systems. In recent years, malleable applications, i.e., jobs that can change sizes before and during execution, are emerging on HPC systems. In addition, dedicated clusters were the main platforms to run on-demand jobs, i.e., jobs needed to be completed in the shortest time possible. As the sizes of on-demand jobs are growing, HPC systems become more cost-efficient platforms for on-demand jobs. However, existing studies do not consider the problem of scheduling all three types of workloads. In our work, we propose six mechanisms, which combine checkpointing, shrink, expansion techniques, to schedule the mixed workloads on one HPC system.
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Title: FACTORS INFLUENCING INDIVIDUALS’ PROVISION OF AUTONOMY SUPPORT TO THEIR PARTNERS WITH CHRONIC PAIN: A PATH ANALYSIS MODEL BASED ON SELF-DETERMINATION THEORY
Creator: Ivins-Lukse, Melissa N.
Date: 2021
Description: Receiving autonomy support from a relationship partner has been associated with increased physical activity among individuals with chronic...
Show moreReceiving autonomy support from a relationship partner has been associated with increased physical activity among individuals with chronic pain (ICP), but no studies have explored what factors may influence partners’ use of an autonomy supportive interpersonal style with an ICP. Self-determination theory (SDT) posits that contextual, perceptual, and individual factors influence how much individuals use an autonomy supportive interpersonal style through the mediators of basic psychological need satisfaction and autonomous motivation. The present study used path analysis to test a SDT model of the relationships between a contextual factor (autonomy support from health care provider), a perceptual factor (partner’s perception of ICP motivation for physical activity), an individual factor (partner catastrophizing about ICP’s pain), and the sequential mediators of relationship need satisfaction and autonomous motivation with respect to the dependent variable of partners’ use of an autonomy supportive interpersonal style. 176 partners of ICPs completed a cross-sectional survey including the Health Care Climate Questionnaire, partner-report revised Behavioral Regulation in Exercise Questionnaire, Pain Catastrophizing Scale – Significant Other version, Need Satisfaction Scale, Motivation to Help, and Interpersonal Behaviours Questionnaire-Self. The proposed model demonstrated poor fit to the data: χ2 (10) = 31.949, p < 0.001), RMSEA = 0.11 (90% CI = .07 to .16, p = 0.01), CFI = 0.81, and SRMR = .10. While the overall model was not supported, most individual pathways in the model were significant. Alternative analyses were conducted to identify a model with acceptable fit.
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Title: Numerical Analysis and Deep Learning Solver of the Non-local Fokker-Planck Equations
Creator: Jiang, Senbao
Date: 2022
Description: This thesis is divided into three mutually connected parts. ...
Show moreThis thesis is divided into three mutually connected parts. In the first part, we introduce and analyze arbitrarily high-order quadrature rules for evaluating the two-dimensional singular integrals of the forms \begin{align*} I_{i,j} = \int_{\mathbb{R}^2}\phi(x)\frac{x_ix_j}{|x|^{2+\alpha}} \d x, \quad 0< \alpha < 2 \end{align*} where $i,j\in\{1,2\}$ and $\phi\in C_c^N$ for $N\geq 2$. This type of singular integrals and its quadrature rule appear in the numerical discretization of fractional Laplacian in non-local Fokker-Planck Equations in 2D. The quadrature rules are trapezoidal rules equipped with correction weights for points around singularity. We prove the order of convergence is $2p+4-\alpha$, where $p\in\mathbb{N}_{0}$ is associated with total number of correction weights. We present numerical experiments to validate the order of convergence of the proposed modified quadrature rules. In the second part, we propose and analyze a general arbitrarily high-order modified trapezoidal rule for a class of weakly singular integrals of the forms $I = \int_{\R^n}\phi(x)s(x)\d x$ in $n$ dimensions, where $\phi$ and $s$ is the regular and singular part respectively. The admissible class requires $s$ satisfies three hypotheses and is large enough to contain singular kernel of the form $P(x)/|x|^r,\ r > 0$ where $P(x)$ is any monomial with degree strictly less than $r$. The modified trapezoidal rule is the singularity-punctured trapezoidal rule plus correction terms involving the correction weights for grid points around singularity. Correction weights are determined by enforcing the quadrature rule to exactly evaluate some monomials and solving corresponding linear systems. A long-standing difficulty of these types of methods is establishing the non-singularity of the linear system, despite strong numerical evidence. By using an algebraic-combinatorial argument, we show the non-singularity always holds and prove the general order of convergence of the modified quadrature rule. We present numerical experiments to validate the order of convergence. In the final part, we propose \emph{trapz-PiNN}, a physics-informed neural network incorporated with a modified trapezoidal rule and solve the space-fractional Fokker-Planck equations in 2D and 3D. We verify the modified trapezoidal rule has the second-order accuracy for evaluating the fractional laplacian. We demonstrate trapz-PiNNs have high expressive power through predicting solutions with low $\mathcal{L}^2$ relative error on a variety of numerical examples. We also use local metrics such as point-wise absolute and relative errors to analyze where could be further improved. We present an effective method for improving performance of trapz-PiNN on local metrics, provided that physical observations of high-fidelity simulation of the true solution are available. Besides the usual advantages of the deep learning solvers such as adaptivity and mesh-independence, the trapz-PiNN is able to solve PDEs with fractional laplacian with arbitrary $\alpha\in (0,2)$ and specializes on rectangular domains. It also has potential to be generalized into higher dimensions.
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Title: Pain and Life Satisfaction: The Role of Emotional Support and Social Participation in Adults with Pediatric-Onset Spinal Cord Injury
Creator: Oh, Alison J.
Date: 2023
Description: Individuals who acquire a spinal cord injury (SCI) during their pediatric years are unique from individuals who acquire an SCI during...
Show moreIndividuals who acquire a spinal cord injury (SCI) during their pediatric years are unique from individuals who acquire an SCI during adulthood due to the heightened risks for developing secondary health conditions and chronic pain. These concerns may significantly impede developmental milestones and experiences, such as social participation, that may decrease life satisfaction. Thus, research on pain characteristics (e.g., frequency of pain, duration of pain, level of current pain, and level of general pain) alongside the role of emotional support and social participation on life satisfaction in adults with pediatric-onset SCI is needed. This study sample consisted of 205 individuals with pediatric-onset SCI who were former patients at pediatric hospitals in three metropolitan cities. Participants were individuals who were 19 years of age or older at the time of the interview, with the age of injury at 18 years or prior. Associations between demographic, pain, and injury characteristics, emotional support, social participation, and life satisfaction were assessed via bivariate Pearson correlations and a 3-block hierarchical multiple linear regression with LS as the primary outcome. Although pain characteristics and emotional support were not associated with life satisfaction among adults who sustained a pediatric-onset SCI, social participation was positively associated with life satisfaction. These findings suggest that clinicians should evaluate various pain characteristics and highlight the role of social participation when treating individuals with SCI.
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Title: Phase field modeling and computation of vesicle growth or shrinkage
Creator: Tang, Xiaoxia
Date: 2023
Description: Lipid bilayers are the basic structural component of all biological cell membranes. It is a semipermeable barrier to most solutes, including...
Show moreLipid bilayers are the basic structural component of all biological cell membranes. It is a semipermeable barrier to most solutes, including ions, glucoses, proteins and other molecules. Vesicles formed by a bilayer lipid membrane are often used as a model system for studying fundamental physics underlying complicated biological systems such as cells and microcapsules. Mathematical modeling of membrane deformation has become an important topic in biological and industrial system for a long time. In this thesis, we develop a phase field model for vesicle growth or shrinkage based on osmotic pressure that arises due to a chemical potential gradient. This thesis consists of three main parts.In the first part, we establish a phase field model for vesicle growth or shrinkage without flow. It consists of an Allen-Cahn equation, which describes the evolution of the phase field parameter (the shape of the vesicle), and a Cahn-Hilliard-type equation, which simulates the evolution of the ionic fluid. The model is mass conserved and surface area constrained during the membrane deformation. Conditions for vesicle growth or shrinkage are analyzed via the common tangent construction. We develop the numerical computing in two-dimensional space using a nonlinear multigrid method which is a combination of nonlinear Gauss-Seidel relaxation operator and V-cycles multigrid solver, and perform convergence tests that suggest an $\mathcal{O}(t+h^2)$ accuracy. Numerical results demonstrate the growth and shrinkage effects graphically and numerically, which agree with the conditions analyzed via the common tangent construction.In the second part, we present a model for vesicle growth or shrinkage with flow. The dynamical equations considered are an Allen-Cahn equation, which describes the phase field evolution, a Cahn-Hilliard-type equation, which simulates the fluid concentration, and a Stokes-type equation, which models the flow. The numerical scheme in two-dimensional space includes a nonlinear multigrid method comprised of a standard FAS method for the Allen-Cahn and Cahn-Hilliard part, and the Vanka smoothing strategy for the Stokes part. Convergence tests imply an $\mathcal{O}(t+h^2)$ accuracy. Numerical results are demonstrated under zero velocity boundary condition and with boundary-driven shear flows, respectively.In the last part, we give an unconditionally energy stable and uniquely solvable finite difference scheme for the model established in the first part. The finite difference scheme is based on a convex splitting of the discrete energy and is semi-implicit. One key difficulty associated with the energy stability is due to the fact that some nonlinear energy functional terms in the expansion is neither convex nor concave. To overcome this subtle difficulty, we add auxiliary terms to make the combined term convex, which in turn yields a convex–concave decomposition of the physical energy. As a result, both the unique solvability and energy stability of the proposed numerical scheme are assured. In addition, we show the scheme is stable in the defined discrete norm.
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Title: DEVELOPING FUSION BACTERIOCINS FOR ERADICATING PSEUDOMONAS AERUGINOSA BIOFILMS
Creator: An, Sungjun
Date: 2022
Description: The opportunistic pathogen Pseudomonas aeruginosa is a leading cause of morbidity and mortality in cystic fibrosis patients and...
Show moreThe opportunistic pathogen Pseudomonas aeruginosa is a leading cause of morbidity and mortality in cystic fibrosis patients and immunocompromised individuals. Due to its remarkable ability to resist antibiotics, eradicating P. aeruginosa has become increasingly difficult. As previously reported, we have successfully engineered a colicin-secretion system that kills target biofilm cells rapidly and selectively in multispecies biofilms as well as demonstrated the potential of using live microorganisms engineered to produce antimicrobial colicin protein to treat biofilm-associated infections. In this study,we constructed a fusion colicin-pyocin that could target P. aeruginosa by DNase activity of colicin E2. The newly engineered bacteriocin-secretion system upon the shift in target, maintained biofilm inhibition capacity. Both during biofilm formation and after its development, the system was able to suppress the P. aeruginosa biofilm. This result opened up the possibility that it could be used for novel live biotherapeutics. A further study was conducted to overcome the challenge of requiring an exogenous inducer. We applied the concept of Quorum-Sensing signal that recognize autoinducer as a trigger of fusion colicin-pyocin producing genetic circuit so that it automates the production and secretion of fusion colicin-pyocin as soon as the genetic circuit senses the target population growing. This study demonstrated that combining the domains of colicin and pyocin could broaden the genetic circuit target range, maintaining strain specificity, while employing the QS system could remove the fundamental problem of diffusion or degradation of extra compounds as they approach engineered cells.
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Title: PROGRAM SURVIVABILITY THROUGH K-VARIANT ARCHITECTURE
Creator: BEKIROGLU, BERK
Date: 2021
Description: Numerous software systems, particularly mission and safety-critical systems, require a high level of security during their execution....
Show moreNumerous software systems, particularly mission and safety-critical systems, require a high level of security during their execution. Enhancing software security through architecture is a highly effective method of defending against cyberattacks. The N-version is a software architecture that was developed to increase the security of software systems. In the N-version architecture, functionally equivalent versions of a program run concurrently to complete a mission or task. Each version is developed independently by a different team using only the software specifications in common. As a result, each version is expected to contain unique vulnerabilities. Due to the high cost of developing and maintaining an N-version system, this architecture is typically used only in high-budget projects requiring a high-security level. The K-variant, an alternative architecture for enhancing system security, is explained and analyzed in this thesis. In contrast to the N-version architecture, each variant is automatically generated using source-to-source program transformation techniques. Automation significantly reduces the cost of developing variants in the K-variant architecture. The K-variant architecture can help protect systems from memory exploitation attacks. Various attack strategies can be used against K-variant systems in order to increase the likelihood of a successful attack. Various attack strategies are proposed and investigated in this thesis. Furthermore, experimental studies are being conducted to investigate various defense mechanisms against proposed attack strategies. The effectiveness of each defense mechanism against various attack strategies is evaluated by using a metric of the probability of an unsuccessful attack. Additionally, various source code program transformation techniques for generating new variants in the K-variant architecture have been proposed and investigated experimentally. This thesis also describes a machine learning technique for estimating the survivability of K-variant systems under various attack types and defense strategies. To make the design of K-variant systems easier, a neural network model is proposed. With the developed tool that utilizes the neural network model, fast and accurate predictions about the survivability of K-variant systems can be obtained.
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Title: KERNEL FREE BOUNDARY INTEGRAL METHOD AND ITS APPLICATIONS
Creator: Cao, Yue
Date: 2022
Description: We developed a kernel-free boundary integral method (KFBIM) for solving variable coefficients partial differential equations (PDEs) in a...
Show moreWe developed a kernel-free boundary integral method (KFBIM) for solving variable coefficients partial differential equations (PDEs) in a doubly-connected domain. We focus our study on boundary value problems (BVP) and interface problems. A unique feature of the KFBIM is that the method does not require an analytical form of the Green’s function for designing quadratures, but rather computes boundary or volume integrals by solving an equivalent interface problem on Cartesian mesh. We decompose the problem defined in a doubly-connected domain into two separate interface problems. Then we evaluate integrals using a Krylov subspace iterative method in a finite difference framework. The method has second-order accuracy in space, and its complexity is linearly proportional to the number of mesh points. Numerical examples demonstrate that the method is robust for variable coefficients PDEs, even for cases when diffusion coefficients ratio is large and when two interfaces are close. We also develop two methods to compute moving interface problems whose coefficients in governing equations are spatial functions. Variable coefficients could be a non-homogeneous viscosity in Hele-Shaw problem or an uptake rate in tumor growth problems. We apply the KFBIM to compute velocity of the interface which allows more flexible boundary condition in a restricted domain instead of free space domain. A semi-implicit and an implicit methods were developed to evolve the interface. Both methods have few restrictions on the time step regardless of numerical stiffness. Theyalso could be extended to multi-phase problem, e.g., annulus domain. The methods have second-order accuracy in both space and time. Machine learning techniques have achieved magnificent success in the past decade. We couple the KFBIM with supervised learning algorithms to improve efficiency. In the KFBIM, we apply a finite difference scheme to find dipole density of the boundary integral iteratively, which is quite costly. We train a linear model to replace the finite difference solver in GMRES iterations. The cost, measured in CPU time, is significantly reduced. We also developed an efficient data generator for training and derived an empirical rule for data set size. In the future work, the model could be expanded to moving interface problems. The linear model will be replaced by neural network models, e.g., physics-informed neural networks (PINNs).
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Title: Implicit Theory of Willpower and Life Satisfaction Among Persons with Spinal Cord Injury
Creator: Cerny, Brian M.
Date: 2022
Description: Spinal cord injury (SCI) is a leading cause of physical disability and physical, functional, and psychosocial outcomes vary among persons with...
Show moreSpinal cord injury (SCI) is a leading cause of physical disability and physical, functional, and psychosocial outcomes vary among persons with SCI. Persons with SCI are at risk for poor psychosocial adjustment, evidenced by higher rates of mood disorders and lower reported life satisfaction (LS) when compared to the general population. LS among persons with SCI is influenced by sociodemographic, injury-related, and psychosocial factors. Implicit theory of willpower (TOW) refers to individuals’ beliefs about their capacity for self-regulation; specifically, whether or not self-regulatory capacity (i.e., willpower) is depleted with use. TOW has previously been associated with LS and other aspects of subjective well-being. This is the first study to assess TOW among persons with SCI, and aims to investigate the association between TOW and LS among persons with SCI. The study sample consisted of 156 adults with SCI who completed an anonymous online questionnaire. Associations between demographic- and disability-related factors, global perceived stress, TOW, engagement coping, disengagement coping, and LS were assessed via bivariate Pearson correlations and a 3-block hierarchical multiple linear regression with LS as the primary outcome. LS was significantly correlated with age, perceived physical health, self-reported participation, and perceived stress. After controlling for the influence of other variables, age, perceived physical health, and perceived stress were significantly associated with LS, consistent with prior work. Neither TOW nor the interaction between perceived stress and TOW were significantly associated with LS. Post hoc analyses suggest a chronic disability population may have different interpretations of the TOW construct or measure items than populations previously investigated. Clinical implications and future directions for research are discussed.
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