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Title: ENGAGEMENT FRAMEWORK FOR SERIOUS THERAPEUTIC GAMES FOR HEALTH
Creator: Damarjian, Alex G.
Date: 2022
Description: The conventional treatment of amblyopia in pediatric patients routinely experience low patient compliance due toits limitations. Therapeutic...
Show moreThe conventional treatment of amblyopia in pediatric patients routinely experience low patient compliance due toits limitations. Therapeutic games that utilize VR technology have the potential to open new avenues of medical research and treatment. A review of the prevailing literature shows the effectiveness of VR based games for therapeutic applications and the potential for increased patient compliance. A strong component of the literature is grounded in the medical humanities, specifically the way in which thought patterns, cognitive development, and perceived social rejection affect patient engagement and treatment efficacy. In order to increase the effectiveness of therapeutic games and streamline their development, a new framework has been created using existing research into therapeutic games. This framework ensures that all therapeutic games meet certain criteria within ethics, immersion, active learning, universal accessibility, aesthetics, and medicine. When applied to game development, specifically virtual and extended reality games, it can be used to transform existing therapeutic or diagnostic models into games operating as health care tools. The result is a more effective, lower cost, more accessible treatment option with increased patient compliance and greater overall outcomes.
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Title: DOES FAMILY QUALITY OF LIFE MEDIATE THE RELATION BETWEEN AUTISM WAIVER SERVICES AND CHILD PROGRESS?
Creator: Desai, Shivani S.
Date: 2022
Description: Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder that affects a child’s language, social, and behavioral development,...
Show moreAutism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder that affects a child’s language, social, and behavioral development, and also is associated with difficulty with academics, independent completion of daily living skills, and emotion regulation. Diagnosed individuals often require comprehensive, long-term, and family-based intervention that is costly. Several states, including Maryland, have adopted Medicaid Home and Community Based Services (HCBS) waiver services that specifically serve children and young adults with ASD at no out-of-pocket cost to families. The Maryland autism waiver (AW) also includes services to support diagnosed individuals’ family members, including family consultation and respite services. Family factors, such as specific parenting behaviors and parental mental health, contribute significantly to symptom improvement in children with ASD and child development more broadly, highlighting the importance of studying family systems and targeting them in treatment. Prior research has found that AW services have a positive impact on family quality of life (FQoL), which is a multidimensional concept of family functioning. The aim of the present study was to examine if the several domains of FQoL are mediators in the relation between receipt of Maryland HCBS AW services and caregivers’ perception of their child’s improvement in several domains of functioning. The participants in this study consisted of 460 families who were enrolled in a larger study examining effects of Maryland AW services. Half of these families (n = 230) received the Maryland Medicaid AW services (n = 230) and the other half were on a registry to receive services (n = 230). Deidentified survey data were collected between 2013-2016 from caregiver informants who had a child under the age of 21 who exhibited symptoms of ASD. The survey included questions about demographics, FQoL, and their child’s progress in the areas of academics, independent living skills, social communication skills, stereotypic and repetitive behaviors, and aggressive behaviors over the past 6 months. Results of the mediation analyses revealed that FQoL in the domains of parenting, emotional well-being, and disability support services (but not in the domains of family interaction and physical/material well-being) each mediated the relations between AW services and caregiver report of improvement in all measured domains of child functioning (academics, independent living skills, social communication skills, stereotypic and repetitive behaviors, and aggressive behaviors). These findings highlight the significant role of FQoL as a mediator in the relation between waiver serves and child outcome. They also reveal the importance of increasing family quality of life when providing treatment services to children with symptoms of autism and their families.
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Title: WIDE BANDGAP FRACTIONAL POWER PROCESSING
Creator: Kundu, Aritra
Date: 2022
Description: The adoption of wide bandgap (WBG) power semiconductors can improve the performance of power converters at the expense of significantly higher...
Show moreThe adoption of wide bandgap (WBG) power semiconductors can improve the performance of power converters at the expense of significantly higher cost than Si at present time. In this thesis, an innovative Wide bandgap Fractional Power Processing (WFPP) design concept is introduced where silicon devices process the base power at a low switching frequency (2kHz or lower) while WBG devices are judiciously used to process only a fraction of the total power at a much higher frequency (tens of kHz). WFPP inverter is a design concept that splits the power processing into a low frequency Si base power processor and a high-frequency WBG fractional power processor. WBG devices are therefore judiciously used to process only a fraction of the total power to achieve both high-efficiency and lower cost than a full-WBG converter design. This thesis investigates an optimization strategy to minimize the total power loss while maintaining a reasonable THD and cost for a hybrid inverter design with comprehensive power loss analysis and calculation on fundamental and harmonics currents. Optimal selection of power sharing between the Si and WBG inverters and switching frequency are discussed in the thesis. The circulating current paths in topology with hybrid switching frequencies are also analyzed and presented in this thesis. Experimental results on a 9kW SiC/Si hybrid inverter prototype with isolated and non-isolated DC power supplies are presented to validate the design concept.
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Title: STRUCTURE AND DYNAMICS OF MODIFIED NUCLEOSOMES UNDER EPIGENETIC REGULATION
Creator: Kohestani, Havva
Date: 2022
Description: Epigenetic regulations are critical in inducing heritable phenotype changes in biological systems without alternating their core genetic DNA...
Show moreEpigenetic regulations are critical in inducing heritable phenotype changes in biological systems without alternating their core genetic DNA sequences. In vivo, reversible epigenetic mechanisms engage various molecular structures from RNAs to larger proteins. The present thesis investigates the influence of epigenetic regulatory factors such as histone protein variants and small non-coding RNAs on the dynamics and structure of nucleosome core particles. Our results show that a histone substitution is an efficient tool in increasing or decreasing the exposure of DNA to post-translational modification (PTMs) factors or larger molecular assembly elements. Substitution of canonical H2A with H2A.B alters DNA-dimer interface resulting in increased breathing and accessibility of DNA. Replacement of canonical H3 with CENP-A variant impacts the overall core-DNA dynamics with flexibility of DNA entry/ exit sites and more rigid tetramer structure. Histone substitution also affects the micro to macro level molecular communication in the nucleosome system. The long-range correlated motions are weakened in H2A.B compared to canonical NCP. We observed a reduction in effective long-range DNA-DNA and DNA-core allosteric pathways in CENP-A NCP compared to canonical and Widom NCPs. Non-coding RNAs increase the tendency of the H3 tail histones to interact with DNA and induce the structural changes in the initial ideal B-DNA of NCP. Overall, the interaction of epigenetic regulatory factors in the form of protein or nucleic acids shifts the energetic and structural properties of the original nucleosome system. As a result, the chromatin structure is prepared to generate the proper biological response throughout spermatogenesis, chromosome segregation, or PTMs assembly.
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Title: PREDICTING AND SIMULATING OUTDOOR THERMAL COMFORT-BASED HUMAN BEHAVIOR IN URBAN ENVIRONMENTS
Creator: Khan, Zahida Marzaban
Date: 2022
Description: Rapid urban growth due to a constant rise in world population has amplified the need for sustainable design development of cities. Human...
Show moreRapid urban growth due to a constant rise in world population has amplified the need for sustainable design development of cities. Human behavior, a key performance metric of sustainable design, can be rewarding for urban policies and city planning. Due to its complex nature, human behavior prediction and simulation are increasingly challenging. Complexity is associated with multiple factors, among which social and environmental factors are critical, especially in urban conditions with tall buildings that create unique microclimates. Human behavior in this study referred to human spatial behavior. This research hypothesized that the microclimatic variations at seasonal and diurnal levels affect people’s behavior in outdoor urban environments. Additionally, interdisciplinary crossover studies on novel methodologies to predict human behavior is becoming popular. Moreover, architects and urban designers are interested in human behavior simulation tools that can help them make informed design decisions through ‘what-if’ scenarios and assess their designs before execution. This doctoral research investigated the inter-relationship between Outdoor Thermal Comfort (OTC), human behavior, and urban morphology for Plazas in urban conditions with tall buildings and within a specific climate zone. The study focused on two overarching objectives: (1) to present a novel research methodology to investigate and predict OTC-based human behavior in urban conditions; and (2) to develop HuBeSIM - a human behavior simulation framework using an agent-based model (ABM) to simulate OTC integrated human behavior in outdoor spaces. Daley Plaza – an urban public space built-in 1965 in downtown Chicago — was used as (1) a case study to test the feasibility of this research methodology and (2) a pilot study to demonstrate the HuBeSIM framework. The outcome of this study shows a significant impact in the outdoor urban environments with design goals that use human behavior as a key performance indicator. The research contributes to the modeling and simulation of OTC-based human behavior in urban environments to nurture livable communities and sustainable cities. The first part of the dissertation presented a novel research methodology involving data collection through an on-site observational study for behavioral mapping, and microclimatic CFD simulations for OTC index - Physiological Equivalent Temperature (PET). The sample data consisted of two seasons, namely summer and fall, with more than 600 observations collected during the three-hour lunchtime period. The second part of the dissertation involved developing a Human Behavior SIMulation (HuBeSIM) framework in the popular computer aided design platform Rhino® and Grasshopper® (GH). This part integrated OTC using physics-based modeling and human behavior using mathematical agent-based modeling to develop a simulation framework for outdoor urban space design. The findings from the observational study revealed a moderate relationship between microclimate and human behavior in the fall, and a weak correlation in summer. The results showed that people’s behavior is not affected by PET values above 35°C. The proposed Human Behavior SIMulation framework has a high potential to develop into a comprehensive model by incorporating other behavioral factors. This study contributes to the sustainable built environment design that leads to the environmental, social, and economic upliftment of a city.
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Title: The Feasibility of Double-Skin Façades to Provide Natural Ventilation in Tall Office Buildings
Creator: Kim, Yohan
Date: 2022
Description: Many tall office buildings (i.e., buildings of or taller than 656 ft (200 m)) are on the rise around the world. The energy efficiency and...
Show moreMany tall office buildings (i.e., buildings of or taller than 656 ft (200 m)) are on the rise around the world. The energy efficiency and healthy environment of tall office buildings has become an important concern, given the current environmental challenges and health considerations. Natural ventilation has proven to be an effective passive strategy in improving energy efficiency and providing healthy environments given environmental challenges. However, such a strategy has not been commonly adopted to tall office buildings that traditionally rely on single-skin façades (SSFs), due to the high wind pressure that creates excessive air velocities and occupant discomfort at upper floors. Double-skin façades (DSFs) can provide an opportunity to facilitate natural ventilation in tall office buildings, as the fundamental components such as the additional skin and openings create a buffer to regulate the direct impact of wind pressure and the airflow around the buildings. Wind-driven natural ventilation has not been fully studied in DSFs as most previous studies focused on the stack effect. Moreover, the studies assumed that the indoor spaces are mechanically ventilated without regard to airflow behavior between the air cavities and the indoor spaces. This study investigates the impact of modified multi-story type DSFs on indoor airflow in a 60-story, 780-foot (238 m) naturally ventilated tall office building under isothermal conditions. Therefore, the performance of wind effect related components was assessed based on the criteria (e.g., air velocity and airflow distribution), with respect to opening size, number of openings per floor, cavity depth, and cavity segmentation. Computational fluid dynamics (CFD) software was utilized to simulate outdoor airflow around the tall office building, and indoor airflow at various heights in case of various DSF configurations. Two sequential CFD simulations were carried out not only to reduce computational time, but also to comprehensively analyze the impact of DSFs responding to positive and negative wind pressures on indoor airflow behavior. The CFD simulation results indicate that the outer skin opening is the more influential parameter than the others on indoor airflow behavior. On the other hand, variations of inner skin opening size help improve the indoor airflow, with respect to the desired air velocity and distributions. Despite some air vortexes observed in the indoor spaces, cross ventilation can occur as positive pressure on the windward side and negative pressure on the other sides generate a productive pressure differential. The results also demonstrate that DSFs with smaller openings suitably reduce not only the impact of wind pressure, but also the concentration of high air velocity near the windows on the windward side, compared to single-skin façades. Further insight on indoor airflow behaviors depending on various DSF configurations leads to a better understanding of the DSF design strategies for effective natural ventilation in tall office buildings. This study aims to develop a performance-based DSF design guideline to assist architects in their design of DSF components in the early design stage.
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Title: THE RELATIONSHIP BETWEEN SLUGGISH COGNITIVE TEMPO AND PERFORMANCE ON TASKS OF PROCESSING SPEED: INFLUENCE OF DEPRESSION
Creator: Kim, Jeong Hye
Date: 2021
Description: Sluggish cognitive tempo (SCT) is often associated with reported difficulties in various functional areas, including daily activities,...
Show moreSluggish cognitive tempo (SCT) is often associated with reported difficulties in various functional areas, including daily activities, emotional functioning, cognitive functioning, academic performance, and sleep. However, there are only a handful of research studies on SCT and neurocognitive functioning in adult populations, and the neuropsychological profile of SCT in adults is unclear.The purpose of this research is to investigate the effect of depression on the relationship between a self-reported measure of SCT and objective measures of SCT in adults by focusing on processing speed performance. The result of this research supports the previous notion that SCT is a novel and independent condition distinct from ADHD, and there are significantly positive relationships between symptoms of SCT and Inattention and Hyperactivity/Impulsivity. Furthermore, SCT and depression are also positively associated indicating the people who experience more symptoms of SCT report more symptoms of depression. However, results did not support the hypothesis that SCT significantly contribute to differences in performance on various types of neuropsychological tasks (WAIS PSI, Trail Making Test, and CPT-II Reaction Time) assessing processing speed after controlling for symptoms of ADHD (inattention and hyperactivity/impulsivity) and symptoms of depression. It is notable that the significant model beta weights for SCT in the final regression model suggests that the relationship between SCT and processing speed is worthy of additional investigation.
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Title: MULTICRITERIA DECISION-MAKING METHODOLOGY WITH TRADEOFF ANALYSIS FOR TRANSPORTATION BUDGET ALLOCATION
Creator: Truong, Tung Quoc
Date: 2022
Description: Transportation agencies in the United States nowadays rely on tax dollars for maintaining surface transportation infrastructure, which mainly...
Show moreTransportation agencies in the United States nowadays rely on tax dollars for maintaining surface transportation infrastructure, which mainly comes from fuel tax. However, travel behaviors are changing every day. People and businesses demand better and safer roads. Yet, consumers travel in more fuel-efficient vehicles and buy less gas, which means less revenue for fixing aging roads and highways. Meanwhile, new construction and repair costs increase for our overburdened transportation systems. Transportation agencies, therefore, must use their limited funding more wisely to optimize the service performance and minimize risks (Li, 2018). The budget allocation problem in transportation is not an easy task. The consequences of an ineffective decision in allocating resources are multi-facet both in the short- and long-term, including degrading in the condition of transportation facilities, losing public trust, and increasing backlogs in maintenance and repair. Therefore, transportation agencies are seeking more robust and comprehensive data-driven strategies that take into account of agency’s strategic goals and regulatory requirements, user expectations, nature of the asset, availability of resources, and lifecycle cost analysis in determining the optimal allocation of resources and making the best use of available funds (Li and Sinha, 2004; Sinha and Labi, 2007). The proposed research aims to utilize the concept of multicriteria decision making coupled with a holistic asset management framework to support performance-based allocations of transportation budgets and help transportation agencies achieve the future vision of the nation’s strategic planning requirements to enable sustainable management of the system. A computational study for the real-world dataset obtained by a state Department of Transportation (DOT) is conducted using the proposed budget allocation method. The results from the computational study reveal that the proposed method can derive optimal decision solutions for transportation budget allocation problems and can be utilized by transportation agencies on different scales – urban and rural, in other sectors – public and private, to effectively manage the transportation infrastructure sustainably, by effectively spending transportation budget to maximize service performance and minimize operating costs.
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Title: CARING FOR THE CAREGIVER: INTERPERSONAL FACTORS AND DEPRESSION AS PARALLEL-SERIAL MEDIATORS BETWEEN STIGMA AND SUICIDAL IDEATION
Creator: Tsen, Jonathan Y.
Date: 2022
Description: Background/Objectives: This study applied Joiner's Interpersonal PsychologicalTheory to a caregiver population, by describing relationships...
Show moreBackground/Objectives: This study applied Joiner's Interpersonal PsychologicalTheory to a caregiver population, by describing relationships among affiliate stigma, thwarted-belongingess (TB), perceived-burdensomeness (PB), and depression, and suicidal ideation (SI). Participants/Setting: 243 adult caregivers participated in this study via Prolific Academic and caregiver-related websites. Design/Main Outcome Measures: This study used a cross-sectional, survey-based design including demographics, the Affiliate Stigma Scale (α=.93), Interpersonal Needs Questionnaire-15 (α=.95), Center of Epidemiology Studies–Depression-10 (α=.90), and Depressive Symptom Inventory— Suicide Subscale (α = .91) via Qualtrics. Analyses run on SPSSv27/Hayes’ PROCESS macro. Results: Parallel-serial mediation found after controlling for covariates that the total indirect effect of affiliate stigma on SI through both TB and PB then through depression was significant, B = .0271, SE = .0062, β = .1659, 95%CI [.0152, .0393]. Conclusions: Findings indicated that affiliate stigma indirectly affected SI through both TB and PB then through depression. Interventions to improve caregiver wellbeing should capitalize on both improving interpersonal functioning and depressive symptoms in tandem in order to reduce SI risk.
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Title: Towards Understanding the Microstructure and Mechanical Properties of Additively Manufactured Ni-base Superalloys
Creator: Tiparti, Dhruv Reddy
Date: 2022
Description: Nickel-base superalloy components such as turbine discs typically undergo numerous manufacturing steps that contribute to increasing the cost...
Show moreNickel-base superalloy components such as turbine discs typically undergo numerous manufacturing steps that contribute to increasing the cost and the waste of excess materials. With advent of fusion based additive of manufacturing (AM) techniques, such components with complex geometry can be fabricated with great efficiency. However due to characteristically high energy densities, fast cooling rate, and layer-by-layer building process associated with AM; Ni-base superalloys with higher temperature performance are difficult to be fabricated by AM due to susceptibility to composition related defect formation, which is further exacerbated by anisotropic grain structures induced by the large thermal gradients present. Crack-free material can be fabricated but, in most cases, issues such as an anisotropic microstructure will prevail, and the balance of mechanical properties achieved may not be suitable for the desired applications. Several strategies exist to mitigate the challenges posed by additive manufacturing via post-processing such as hot-isostatic processing, annealing heat treatments, application of grain refining inoculants, etc. All these strategies utilized to mitigate issues with AM of Ni-base superalloys still require further study to understand their effects on the microstructure and mechanical properties. This work aims to evaluate the use of inoculant particles, and novel heat treatments on the microstructure and mechanical properties of different superalloys. First, the effect of varying amounts of CoAl2O4 inoculant ranging from 0 to 2 wt.% on the microstructure evolution of Inconel 718(IN718) fabricated by selective laser melting (SLM) was evaluated. The findings from this study indicated that additions of CoAl2O4 only resulted in a minor degree of grain refinement with slight increase in anisotropy; in addition, a CoAl2O4 ¬content above 0.2 wt.% resulted in the formation of agglomerate inclusions; and that to effectively utilize CoAl2O4 as a grain refining inoculant, process parameters must be further optimized while considering the formation of agglomerates, and other defects. Second, the application of CoAl2¬O4 was extended towards the Direct Energy Deposition (DED) of IN718. Here findings indicated that due to the modification of the thermophysical properties of the melt pool by oxide addition, an earlier onset of large columnar extending across multiple layers occurred while counteracting conditions required for equiaxed grain formation; and these CoAl2O4 were also found to exhibit a potent Zenner pinning effect that maintained the as-built grain structure despite application of extreme high treatment condition of 1200oC for 4 hrs. Third, the tensile and fatigue properties of the DED IN718 with CoAl2O4 were evaluated. Here, it was found that the addition of CoAl2O4 leads to a minor increase in tensile strength in the as-built condition attributed primarily to the fine oxide dispersion; a more modest increase in tensile strength in the heat-treated condition due to grain refinement induced by retaining the as-built grain structure; and that despite the increase in tensile strength with CoAl2O4 a corresponding increase in fatigue life did not occur. Lastly, the processing of René 65 conducted by laser-powder bed fusion(L-PBF) was done and compared to the conventionally cast and wrought material. Here, the effect of the difference in processing route in conjunction with heat treatments was evaluated to understand the creep and stress relaxation behavior. It was found that L-PBF of René 65 led to an overall improved resistance to deformation by creep and relaxation mechanism.
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Title: Quality of Life in People with Epilepsy: The Associations of Anti-seizure Medications and Biopsychosocial Variables
Creator: Thomas, Julia A.
Date: 2022
Description: People with epilepsy, on average, experience lower quality of life (QOL) than healthy controls (Taylor et al., 2011). This study examined the...
Show morePeople with epilepsy, on average, experience lower quality of life (QOL) than healthy controls (Taylor et al., 2011). This study examined the associations between specific anti-seizure medication, biopsychosocial factors, and QOL in people with epilepsy. Analysis of covariance revealed that individuals taking three or more anti-seizure medications had significantly lower QOL than those taking levetiracetam. Findings also demonstrated that when looking at biopsychosocial factors as predictors of QOL in hierarchical regression, anxiety, depression, and daytime sleepiness were significant predictors of QOL. Once these factors were entered into the model, number of medications was no longer significant. The final model predicted 59.6% of the variance in QOL. Lastly, a moderation analysis to examine the moderating effect of employment on the association between number of anti-seizure medications and QOL was not significant. Additional exploratory analyses looking at individuals who were employed versus those who were not employed were completed. These findings underscore the importance of addressing psychological health and sleep factors within the epilepsy population.
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Title: Synthesis and Photophysical Characterization of Novel Organic Triplet Donor–Acceptor Dyads for Light-Harvesting/Modulation Application
Creator: Yun, Young Ju
Date: 2022
Description: Donor–acceptor chromophoric systems (D–A) are important scaffolds for several light-harvesting/initiated processes and devices, including...
Show moreDonor–acceptor chromophoric systems (D–A) are important scaffolds for several light-harvesting/initiated processes and devices, including light-emitting diodes, photo-catalytic/redox systems, and photovoltaic cells. It has been hypothesized that for efficient photophysical processes (viz. energy/charge-transfer or excited-state interactions); it is ideal to tether the donor and acceptor chromophores into molecular dyads. To this end, I devised and synthesized several dyads by tethering an organic triplet energy donor and various polyaromatic chromophores (e.g., perylene derivatives and anthracene derivatives) onto a conjugated-/non-conjugated-linker (phenylene- and triptycene- linker, respectively). During the 4-5 years of my Ph.D., I synthesized a total of five (5) dyads: o–, p–3, and dyads 3–5. These systems were fully characterized using different spectroscopy tools/techniques. The spectroscopy investigations of the dyads have allowed me to decipher two important energy transfer pathways: through-bond and through-space with the phenylene linker and only through-space energy with the triptycene linker. Furthermore, the investigations led to the discovery that geometrical features such as face-to-face (co-facial) or slip-stacked interactions between the donor and acceptors chromophores might dictate the dynamic/kinetic of light-induced energy transfer in the dyads. Findings from my graduate research project paved the way for developing molecular engineering studies for light-harvesting/modulation applications.Subsequently, I was able to employ the dyads of my interest to achieve intramolecular and intermolecular triplet energy transfer (TEnT) triplet-triplet annihilation-based photon upconversion (TTA-PUC).
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Title: DEFAULT RISK AND MOMENTUM PREMIUM
Creator: Zhang, Yi
Date: 2022
Description: Birge and Zhang (2018) reported that combining common factors models with functions of the default risk improves models' performance to...
Show moreBirge and Zhang (2018) reported that combining common factors models with functions of the default risk improves models' performance to explain stock returns. Default risk contains firm specific information and may help to explain momentum premium that compensates investors for the firm specific risk exposures. In this paper, we confirmed that the forward-looking measure of default risk, as proposed by Birge and Zhang (2018), seems to capture some pricing information in the momentum premium. This provides an alternative to explain the underlying risks associated with the momentum strategy.
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Title: INTEGRATED DECISION SUPPORT SYSTEM FOR THE SELECTION AND IMPLEMENTATION OF DELAY ANALYSIS IN CONSTRUCTION PROJECTS
Creator: Yang, Juneseok
Date: 2022
Description: The goal of this study is to establish an objective, user-friendly, and reliable decision support system, called delay analysis selection and...
Show moreThe goal of this study is to establish an objective, user-friendly, and reliable decision support system, called delay analysis selection and implementation system (DASIS), which allows delay analysts and practitioners in the construction industry to select a type of delay analysis that is most appropriate for given conditions and to perform the selected type of delay analysis. DASIS integrates a delay analysis selection system (DASS) module and an implementation module (DAIS) that performs the type of delay analysis selected by DASS in construction projects.The model that operates the DASS module consists of (1) four different delay analysis approaches currently available to practitioners; (2) a set of 26 attributes that affect the selection of a type of delay analysis; (3) a case-base involving 3,776 cases described by these 26 attributes and their corresponding output values (i.e., the most appropriate delay analysis approach); (4) a set of 7 categories consisting of subsets of attributes; (5) the weights of the attributes and the categories; and (6) a spreadsheet designed in Microsoft Excel that performs the calculations involved in case-based similarity assessment. The implementation module is a computerized analytics and automation platform that performs the type of delay analysis selected by DASS. In developing the DASS module, 26 attributes that influence the selection of the most appropriate type of delay analysis were identified based on a thorough literature review and were organized in seven categories. These attributes were used to evaluate the four types of delay analysis (i.e., static, dynamic, additive, and subtractive analyses). Based on the results of this evaluation, a case-base of 3,776 cases was generated while considering the constraints of each category. The weights of the attributes and categories were determined by using several methods. To determine the best-fit between a target case (defined by its 26 attributes) and the 3,776 cases stored in the case-base were used to perform a case-based similarity assessment to calculate weighted case similarity scores, and to find the best-informed solution to the delay analysis type selection problem. In developing the DAIS module, the four types of delay analysis were coded in Microsoft Excel using macros programmed in Visual Basic for Applications (VBA). This automated tool performs the selected delay analysis by DASS. The fully integrated DASIS model finds the best-fit match between a target case and cases stored in the case-base by means of similarity assessment methods by using weighted case similarity scores, hence identifying the most appropriate type of delay analysis for use in the target case, performs the selected type of delay analysis and generates a report about the results of the delay analysis to the analyst instantaneously, allowing the contractual parties to settle the issues quickly. This study is the first attempt to establish an objective decision support system (DASS) to assist delay analysts by automating the selection of a type of delay analysis using combinations of well recognized and reliable attributes and similarity assessment techniques. In addition, DASS is immediately followed by DAIS in an integrated system (DASIS) that does not only do the selection of the most appropriate type of delay analysis, but that also implements the selected delay analysis, hence providing ease of use and high speed. A case study based on fictitious scenarios is presented to demonstrate and validate the research approach. The use of the entropy weight method to calculate the weights of the attributes can be considered a minor limitation of the study. Finally, DASIS can be reformulated as a web-based application that allows analysts to work online using ordinary browsers anywhere and anytime.
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Title: MEASUREMENT OF ELECTRON NEUTRINO AND ANTINEUTRINO APPEARANCE WITH THE NOνA EXPERIMENT
Creator: Yu, Shiqi
Date: 2020
Description: As a long-baseline neutrino oscillation experiment, the NuMI Off-axis $\nu_e$ Appearance (NOvA) experiment aims at studying neutrino physics...
Show moreAs a long-baseline neutrino oscillation experiment, the NuMI Off-axis $\nu_e$ Appearance (NOvA) experiment aims at studying neutrino physics by measuring neutrino oscillation parameters using the neutrino flux from the Main Injector (NuMI) beam. It has two functionally identical detectors. The near detector is onsite at Fermi National Accelerator Laboratory. The far detector is 810 km away from the source of neutrinos and antineutrinos, at Ash River, Minnesota. At the near detector, muon neutrinos or antineutrinos, before significant oscillations take place, are used to correct the Monte Carlo simulation. At the far detector, the neutrino and antineutrino fluxes after significant oscillations have happened are measured and analyzed to study neutrino oscillation. The NOvA experiment is sensitive to the values of $\sin^2\theta_{23}$, $\Delta m^2_{32}$, and $\delta_{CP}$. The latest values from the NOvA 2020 analysis are as follows: $\sin^2\theta_{23}=0.57^{+0.03}_{-0.04}$, $\Delta m^2_{32}=(2.41\pm0.07)\times10^{-3}$ eV$^2$/c$^4$, and $\delta_{CP}=0.82\pi$ with a wide 1$\sigma$ interval of uncertainty. My study is focused on the neutrino oscillation analysis with NOvA, including detector light model tuning, particle classification with convolutional neural network, electron neutrino and antineutrino energy reconstruction, and oscillation background estimation. Most of my studies have been used in the latest NOvA publication and the NOvA 2020 analysis.
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Title: Towards Trustworthy Multiagent and Machine Learning Systems
Creator: Xie, Shangyu
Date: 2022
Description: This dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of...
Show moreThis dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of Things (IoT) system, which mainly consists of two aspects: data privacy and robustness. Specifically, data privacy concerns about the protection of the data in one given system, i.e., the data identified to be sensitive or private cannot be disclosed directly to others; robustness refers to the ability of the system to defend/mitigate the potential attacks/threats, i.e., maintaining the stable and normal operation of one system.Starting from the smart grid, a representative multiagent system in the IoT, I demonstrate two works on improving data privacy and robustness in aspects of different applications, load balancing and energy trading, which integrates secure multiparty computation (SMC) protocols for normal computation to ensure data privacy. More significantly, the schemes can be readily extended to other applications in IoT, e.g., connected vehicles, mobile sensing systems.For the machine learning, I have studied two main areas, i.e., computer vision and natural language processing with the privacy and robustness correspondingly. I first present the comprehensive robustness evaluation study of the DNN-based video recognition systems with two novel proposed attacks in both test and training phase, i.e., adversarial and poisoning attacks. Besides, I also propose the adaptive defenses to fully evaluate such two attacks, which can thus further advance the robustness of system. I also propose the privacy evaluation for the language systems and show the practice to reveal and address the privacy risks in the language models. Finally, I demonstrate a private and efficient data computation framework with the cloud computing technology to provide more robust and private IoT systems.
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Title: Deep Learning Methods For Wireless Networks Optimization
Creator: Zhang, Shuai
Date: 2022
Description: The resurgence of deep learning techniques has brought forth fundamental changes to how hard problems could be solved. It used to be held that...
Show moreThe resurgence of deep learning techniques has brought forth fundamental changes to how hard problems could be solved. It used to be held that the solutions to complex wireless network problems require accurate mathematical modeling of the network operation, but now the success of deep learning has shown that a data-driven method could generate powerful and useful representations such that the problem could be solved efficiently with surprisingly competent performance. Network researchers have recognized this and started to capitalize on the learning methods’ prowess. But most works follow the existing black-box learning paradigms without much accommodation to the nature and essence of the underlying network problems. This thesis focuses on a particular type of classical problem: multiple commodity flow scheduling in an interference-limited environment. Though it does not permit efficient exact algorithms due to its NP-hard complexity, we use it as an entry point to demonstrate from three angles how the learning-based methods can help improve the network performance. In the first part, we leverage the graphical neural network (GNN) techniques and propose a two-stage topology-aware machine learning framework, which trains a graph embedding unit and a link usage prediction module jointly to discover links that are likely to be used in optimal scheduling. The second part of the thesis is an attempt to find a learning method that has a closer algorithmic affinity to the traditional DCG method. We make use of reinforcement learning to incrementally generate a better partial solution such that a high quality solution may be found in a more efficient manner. As the third part of the research, we revisit the MCF problem from a novel viewpoint: instead of leaning on the neural networks to directly generate the good solutions, we use them to associate the current problem instance with historical ones that are similar in structure. These matched instances’ solutions offer a highly useful starting point to allow efficient discovery of the new instance’s solution.
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Title: Essays on Clean Energy Finance and Cryptocurrency Market
Creator: Xie, Yao
Date: 2021
Description: This dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the...
Show moreThis dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the first essay, I investigate the heterogeneous relationship between various determinants of the clean energy market across all subsectors of the clean energy stock market. My findings reveal that VIX is the most significant predictor of all clean energy subsectors conditional volatility. During the COVID-19 stress period, economic uncertainty measures become more significant measures. The heterogeneity of clean energy market persists in the out-of-sample results. These results suggest that portfolio diversification for different clean energy subsector is necessary. In the second essay, I study the safe haven property of several volatility indexes on clean energy subsectors. I compare the current COVID-19 stress period and the time before. The results show that market volatility and commodity volatility are good safe haven assets during the COVID-19 period. But they are not safe haven assets against the clean energy subsector before the pandemic period. Among all volatility indexes, gold volatility index is the most effective safe haven assets. In the third essay, I investigate the characteristics of Bitcoin as a financial asset. A comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other markets, stress level, and investor sentiment. The empirical results show that blockchain technology, stress level and investor sentiment have strong predicting power on Bitcoin returns. In the fourth essay, I aim to study how extreme sentiment measures from Google Trend and Wikipedia Pageviews affect both traditional cryptocurrency, such as Bitcoin and stablecoin, like Tether. Our results show that Tether’s return is not affected by the extreme sentiment measures during the COVID-19 stress period which suggests that stablecoin can offer price stability.
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Title: DEEP LEARNING AND COMPUTER VISION FOR INDUSTRIAL APPLICATIONS: CELLULAR MICROSCOPIC IMAGE ANALYSIS AND ULTRASOUND NONDESTRUCTIVE TESTING
Creator: Yuan, Yu
Date: 2022
Description: For decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data...
Show moreFor decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data analysis and pattern recognition applications where analytical methods are ineffective. In recent years, Deep Learning (DL) has been proven to be an effective AI technique that can outperform other methods in applications such as computer vision, natural language processing, autonomous driving. Realizing the potential of deep learning techniques, researchers have also started to apply deep learning on other industrial applications. Today, deep learning based models are used to innovate and accelerate automation, guidance, and decision making in various industries including automotive industry, pharmaceutical industry, finance, agriculture and more. In this research, several important industrial applications (on Biomedicine and Non-Destructive Testing) utilizing deep learning algorithms will be introduced and analyzed. The first biopharmaceutical application focuses on developing a deep learning based model to automate the visual inspection process in Median Tissue Culture Infectious Dose(TCID50). TCID50 is one of the most popular methods for viral quantification. An important step of TCID50 is to visually inspect the sample and decide if it exhibits cytopathic effect(CPE) or not. Two novel models have been developed to detect CPE in microscopic images of cell culture in 96 well-plates. The first model consists of a convolutional neural network (CNN) and support vector machine(SVM). The second model is a fully convolutional network (FCN) followed by morphological post-processing steps. The models are tested on 4 cell lines and achieve very high accuracy. Another biopharmaceutical application developed for cellular microscopic images is the clonal selection. Clonal selection is one of the mandatory steps in cell line development process. It focuses on verifying the clonality of the cell culture. The researchers used to visually inspect the microscopic images to verify the clonality. In this work, a novel deep learning based model and a workflow is developed to accelerate the process. This algorithm consists of multiple steps, including image analysis after incubation to detect the cell colonies, and verify its clonality in day0 image. The results and common mis-classification cases are shown in this thesis. Image analysis method is not the only technology that has been advancing for cellular image analysis in biopharmaceutical industry. A new class of instruments are currently used in biopharmaceutical industry which enable more opportunities for image analysis. To make the most of these new instruments, a convolutional neural network based architecture is used to perform accurate cell counting and cell morphology based segmentation. This analysis can provide more insight of the cells at very early stage in characterization process of cell line development. The architecture and the testing results are presented in this work. The proposed algorithm has achieved very high accuracy on both applications, and the cell morphology based segmentation enables a brand new feature for scientists to predict the potential productivity of the cells. Next part of this dissertation is focused on hardware implementation of Ultrasonic Non-Destructive Testing (NDT) methods based on deep learning, which can be highly useful in flaw detection and classification applications. With the help of a smart and mobile Non-Destructive Testing device, engineers can accurately detect and locate the flaws inside the materials without reliance on high performance computation resources. The first NDT application presents a hardware implementation of a deep learning algorithm on Field-programmable gate array(FPGA) for Ultrasound flaw detection. The Ultrasound flaw detection algorithm consists of a wavelet transform followed by a LeNet inspired convolutional neural network called Ultra-LeNet. This work is focused on implementing the computationally difficult part of this algorithm: Ultra-LeNet, so that it can be used in the field where high performance computation resources (e.g., AWS) are not accessible. The implementation uses resource partitioning to design two dedicated pipelined accelerators for convolutional layers and fully connected layers respectively. Both accelerators utilize loop unrolling, loop pipelining and batch processing techniques to maximize the throughput. The comparison to other work has shown that the implementation has achieved higher hardware utilization efficiency. The second NDT application is also focused on implementing a deep learning based algorithm for Ultrasound flaw detection on a FPGA. Instead of implementing the Ultra-LeNet, the deep learning model used in this application is Meta-learning based Siamese Network, which is capable for multi-class classification and it can also classify a new class even if it does not appear in the training dataset with the help of automated learning features. The hardware implementation is significantly different than the previous algorithm. In order to improve the inference operation efficiency, the model is compressed with both pruning and quantization, and the FPGA implementation is specifically designed to accelerate the compressed CNN with high efficiency. The CNN model compression method and hardware design are novel methods introduced in this work. Comparison against other compressed CNN accelerators is also presented.
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Title: MARKETABLE LIMIT ORDERS AND NON-MARKETABLE LIMIT ORDERS ON NASDAQ
Creator: ZHANG, DAN
Date: 2022
Description: My research includes two parts. In the first part of my research, I classify marketable limit orders into three different types: large...
Show moreMy research includes two parts. In the first part of my research, I classify marketable limit orders into three different types: large marketable order to buy, large marketable order to sell, and small marketable order. I use dummy variance method to research the effect of the three marketable orders on standardized variance, and find that LMOB and LMOS play significant role in variance increase. The second part of my research is about modelling of time to execution and time to cancellation of Non-marketable limit orders. I construct variables and model time to execution for NLO to buy and time to cancellation for NLO to buy and NLO to sell based on exponential distribution with accelerated failure time specification. My research shows that the longer the distance of limit price to buy away from the best bid price, the longer time to execution is. The longer the distance of limit price to buy away from the best bid price or limit price to sell away from the best ask price, the longer the time to cancellation is.
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