VictorJuiz/thesis huggingface dataset:
India's Low Carbon Electricity Futures

Decarbonizing its electricity sector through ambitious targets for wind and solar is India’s major strategy for mitigating its rapidly growing carbon emissions. In this dissertation, I explore the economic, social, and environmental impacts of wind and solar generation on India’s future low-carbon electricity system, and strategies to mitigate those impacts. In the first part, I apply the Multi-criteria Analysis for Planning Renewable Energy (MapRE) approach to identify and comprehensively value high-quality wind, solar photovoltaic, and concentrated solar power resources across India in order to support multi-criteria prioritization of development areas through planning processes. In the second part, I use high spatial and temporal resolution models to simulate operations of different electricity system futures for India. In analyzing India’s 2022 system, I find that the targets of 100 GW solar and 60 GW wind set by the Government of India that are likely to generate 22% of total annual electricity, can be integrated with very small curtailment (approximately 1%). Further, I find that flexibility strategies that include increasing the size of the balancing area (moving from state level to regionally coordinated scheduling and dispatch), lowering the minimum generation levels of thermal plants, and increasing inter-regional transmission capacity are the most effective in decreasing production costs and renewable energy curtailment. In the final part of this dissertation, I examined the effects of different mixes and targets of wind and solar installed capacities on overall system cost and avoided emissions in 2030. I find that the value of renewable energy decreases with increasing penetration across all mixes of wind and solar, with value of solar decreasing faster with higher penetration than wind. In India, the limited correlation of wind and solar generation profiles with load leads to a relatively small conventional generation capacity being avoided by renewable energy. The data sets, models, and tools developed through these analyses can be used to evaluate future low carbon electricity systems, and develop strategies and policies to ensure that integration of wind and solar is cost-effective and socially and environmentally sustainable.

Advisor: Callaway, Duncan
Modes of Loss: al-Andalus in the Arabic Poetic Imagination

This dissertation examines the ways medieval and modern Arab poets utilize the space and image of al-Andalus to express nostalgia, mourning, and loss. This project combines archaeological and art historical sources with medieval and modern historical and literary texts to document the physical reality and social fabric of medieval Spain. These materials along with textual analyses of poetry from Ibn Zaydūn, ʿAbd al-Wahhāb al-Bayātī, and Maḥmūd Darwīsh, among others, form the basis of my multi-theoretical approach to understanding the impact of loss upon an individual and their respective communities. I argue that while such poetic works deviate from the tradition of the Arabic elegy in structural terms, they are unified in terms of content, especially in portraying al-Andalus as both a lost beloved and an idealized site for mourning. This absent signifier transcends temporal, geographic, and linguistic boundaries to become endemic to Arab cultural memory and identity./nI show how these medieval and modern poetic texts creates hybrid documents of history, allegory, and fantasy to express tragedy and hope, memories of the past, and dreams of the future. The events of 1492 in which Nasrid Granada, the last Muslim stronghold in al-Andalus, was handed over to King Ferdinand and Queen Isabella, are considered a major turning point in Arab cultural history. It is not surprising that this loss, which has been reasonably expressed in medieval Arabic, continues to haunt modern Arabic poetry and still resonates in the imagination of late modern Arab poets. Twentieth-century authors throughout the Arab world have utilized the image of al-Andalus in their poetry./nThis project offers alternative approaches to intertextuality in Arabic literature by introducing concepts of materiality and visuality. I put architectural and landscape theories in dialogue with anthropological theories of loss and nostalgia to analyze the psychological, physical, and sensory states of the author as they (re)claim possession of the spaces of loss that are no longer in their control. By drawing connections between the literary, visual, and material cultures of al-Andalus and the contemporary Middle East this dissertation employs interdisciplinary methods for understanding the concept of loss, home, and identity.

Advisor: Siddiq, Muhammad
Taming Evasions in Machine Learning Based Detection Pipelines

This thesis presents and evaluates three mitigation techniques for evasion attacks against machine learning based detection pipelines. Machine learning based detection pipelines provide much of the security in modern computerized system. For instance, these pipelines are responsible for the detection of undesirable content on computing platforms and Internet-based services, such as malicious software and email spam. By its adversarial nature, the security application domain exhibits a permanent arms race between attackers who aim to avoid, or evade, detection and the pipeline's maintainers whose aim is to catch all undesirable content./nThe first part of this thesis examines a defense technique for the concrete application domain of comment spam on social media. We propose content complexity, a compression-based normalized measure of textual redundancy that is mostly insensitive to the underlying language used and adversarial word spelling variations. We demonstrate on a real dataset of tens of millions of comments that content complexity alone achieves 15 percentage points higher precision than a state-of-the-art detection system./nThe second part of this thesis takes a quantitative approach to evasion and introduces one machine learning algorithm and one/nlearning framework for building hardened detection pipelines. Both techniques are generic and suitable for a large class of application domains. We propose the convex polytope machine, a non-linear large-scale learning algorithm which aims at finding a large-margin polytope separator and thereby decrease the effectiveness of evasion attacks. We show that as a general purpose machine learning algorithm, the convex polytope machine displays an outstanding trade-off between classification accuracy and computational efficiency. We also demonstrate on a benchmark handwritten digit recognition task that the convex polytope machine is quantitatively as evasion-resistant as a/nclassic neural network./nWe finally introduce adversarial boosting, a boosting-inspired framework for iteratively building ensemble classifiers that are hardened against evasion attacks. Adversarial boosting operates by repeatedly constructing evasion attacks and adding the corresponding corrective sub-classifiers to the ensemble. We implement this technique for decision tree sub-classifiers by constructing the first exact and approximate automatic evasion algorithms for tree ensembles. For our benchmark task, the adversarially boosted tree ensemble is respectively five times and two times less evasion-susceptible than regular tree ensembles and the convex polytope machine.

Advisor: Joseph, Anthony D
Tygar, J. D.
Proposed Improvements to the MCAA Method for Quantifying Construction Loss of Productivity

Project changes are often encountered in construction industry and can hurt labor productivity, which in turn can jeopardize project success for all parties. Contractors and owners frequently fail to agree on the merit, responsibility and impact of changes at the time of their occurrence. This failure to agree becomes a major source of claims and litigation./nThere are several ways to calculate productivity loss caused by changes. One method to quantify loss of productivity is the MCAA (Mechanical Contractors Association of America) Factor method. The MCAA method has been in use for over forty years, and has gained wide acceptance in the construction industry and before various Courts and Boards of contract appeals. But the model has been rejected in several recent claims. Some researchers commented that this method has several deficiencies that limited its use./nThis dissertation aims to make improvements to the existing MCAA model. We document the MCAA model’s history, identify typical mistakes made in its application, and compare it with other studies and previous legal case decisions. Suggested improvements to the model are then offered./nFirst, based on the observations and analysis of this dissertation, we recommend the following when using the MCAA model: 1) Establish causation for EACH Factor and explain clearly when, where, who, and how productivity was affected; 2) Use fewer Factors rather than more Factors; and 3) Do not blindly rely on the Loss of Productivity (LOP) damage percentages contained in the MCAA manual./nSecondly, this research proposes structural improvements on the MCAA model for its use in a LOP claim: 1) suggested use of a cause-effect visualization tool to establish causation; 2) improved definitions by explaining what each Factor means and how that Factor hinders labors productivity; and 3) suggestions for quantification of LOP impact for each Factor./nFinally, this dissertation proposes procedures and guidelines to use the MCAA method in LOP analysis./nThis dissertation will help parties (both the contractor and the owner) better understand the MCAA method in estimating productivity loss so that the measure of the loss of productivity can be less subjective and more transparent. With the improvements provided in this dissertation, LOP can be better quantified and LOP disputes can be solved more quickly and reasonably.

Advisor: Ibbs, William
A Systems Study on Combined Heat and Power Fuel Cell Systems: A Techno-Economic-Environmental Assessment

Increasing global energy demands and climate change concerns are necessitating the development of energy conversion systems with high efficiency and minimal environmental impact. Fuel cell technologies offer a promising role in addressing energy security and carbon emissions due to their efficient energy conversion process and clean emissions profiles in comparison to fossil fuel combustion based-systems. Combined heat and power fuel cell systems (CHP FCS) offer great potential from both an economic and environmental standpoint because they can produce both electricity and useful heat onsite. Many barriers to wide-scale implementation of CHP FCS exist, including 1) a lack of detailed modeling studies of CHP FCS with sensitivity to different system design specifications, 2) the high manufacturing costs of fuel cells, and 3) a clear understanding of the life-cycle benefits of fuel cells. Given the interdependencies between design specifications, costs, and operational characteristics are vital to the success of CHP FCS, a full systems study is needed for proper analysis and robust outcomes. This work expands the scope of existing studies to develop an integrated assessment to target current adoption barriers and access the viability of fuel cell technologies for CHP applications. This work accesses system design and performance, identify opportunities for cost reduction, and evaluate the lifetime costs and benefits to provide a platform for wide-scale adoption of fuel cell technologies in CHP applications.

Advisor: Dornfeld, David
Pruitt, Lisa
When “Unheard Sound” (Re)Sounds: Affective Listening, Ethical Affects, and Embodied Experience in Sikh Sabad Kīrtan

In Sikh religious practice, listening to sung sacred poetry (sabad kīrtan) is the chief means of worship and a central part of everyday life for Sikhs around the world. While it is the sacred-text (sabad) that is held as primary and inviolable, and music is regarded as secondary and flexible, it is the musical rendering of sabad that is the most widely practiced worship activity. In this dissertation I explore the combined role of sabad and music in the lived experience of congregants participating in sabad kīrtan. Based on ethnographic research, I propose that sabad kīrtan listening is a primarily affective practice that also constitutes an epistemic site where ethicality is experienced as embodied sensation rather than as mentalist reasoning; that sabad kīrtan occasions are listening ecologies where affect becomes imbued with ethicality. My investigation explores how music heightens those ethically-imbued affective experiences—that is, the ways in which music works on the sensorium to deepen such sensations. I focus on three types of sensations that I found to be particularly intensified among congregants in different kīrtan occasions, namely feelings of affection, and of awe, and the experience of aura. I analyze the role of musical sound from the three main contemporary musical genres of sabad kīrtan – the most popular “light” genre, the fast growing AKJ (Akhand Kīrtani Jatha) genre, and the historical, in revival, “classical” genre. Thus, highlighting the contributions of diverse musical means in rich experiences among congregants, I critique the objectification of musical sound in recent scholarship on sabad kīrtan, and the binarized authenticity discourses among a few Sikh musicians as well as scholars focused on the classical genre. I argue that the multifaceted affective work of musical sound is contextual even within a tradition, i.e., in its different musical sub-collectivities. Shifting the focus from musicians to listeners, I draw attention to the crucial interpretive work of reception in the representation of traditions and assessment of change over time, proposing what I term “multiple authenticities in motion.”

Advisor: Wade, Bonnie C.
Simulating Bioclogging Effects on Dynamic Permeability, Infiltration, and Subsurface Geochemistry

Bioclogging in rivers can detrimentally impact aquifer recharge. This is particularly so in dry regions, where losing rivers are common, and where disconnection between surface water and groundwater (leading to the development of an unsaturated zone) can occur. Reduction in riverbed permeability due to biomass growth is a time-variable parameter that is often neglected, yet permeability reduction from bioclogging can introduce order of magnitude changes in seepage fluxes from rivers over short (i.e., monthly) timescales. To address the combined effects of bioclogging and disconnection on infiltration, I developed numerical representations of bioclogging processes within 1D, 2D, and 3D variably-saturated flow models representing losing-connected and losing-disconnected rivers subject to Mediterranean climatic effects on riverbed sediments. I tested bioclogging formulations using a synthetic and field case study informed with biological data obtained from the Russian River, California, U.S.A. My findings show that modeled biomass growth reduced seepage for losing-connected and losing-disconnected rivers. However, for rivers undergoing disconnection, infiltration declines occurred only after the system was fully disconnected. Before full disconnection, biologically-induced permeability declines were not significant enough to offset the infiltration gains introduced by disconnection. The two effects combine to lead to a characteristic infiltration curve where peak infiltration magnitude and timing is controlled by permeability declines relative to hydraulic gradient gains. Biomass growth was found to hasten the onset of full disconnection; a condition I term ‘effective disconnection’. Biomass growth leading to sediment pore-space clogging and consumption of river Carbon and Nitrogen reveal fundamental controls on bioclogging and river nutrient cycling based on riverine sediment conditions. These results show that river infiltration can respond dynamically to bioclogging and subsequent permeability declines that are highly dependent on river connection status.

Advisor: Rubin, Yoram
Learning of Task-specific Control Policies for Industrial Robots

Today's industrial robots are designed to be able to execute versatile tasks like a human. When deploying the robots to production lines, however, they only need to perform well for a narrowly defined task. A natural question to ask is whether the robots can tailor themselves for different working conditions. This dissertation focuses on developing learning and optimization algorithms that allow robots to achieve higher overall performance for a particular application. The difficulties of this work arise from the facts that 1) most robots have no end-effector sensors, 2) high robot precision and productivity may compromise the robot service life, and 3) robot trajectories in a single application may variate. In regards to these issues, this dissertation proposes a probabilistic approach to optimize the robot models. The approach solves various parameter learning problems in sensor-limited robots by Bayesian inference. Additionally, a trajectory optimization algorithm is introduced to minimize the robot life cost along a robot path. This dissertation also presents policy learning methods that can mimic the standard iterative learning controller for a group of robot motion. Experimental results on FANUC industrial manipulators show that the proposed methods effectively adjust the control policies for different tasks and make the robots outperform the traditional ones. In addition, they perform comparably to the commercial solutions while having the advantage of not requiring an additional learning action every time when the trajectory is changed. A number of subspace learning based Q-filters are also introduced for removing the undesired effects during the learning process. All algorithms are designed to meet industrial needs such as light computation. Thus, they can be integrated into commercially available robots without special hardware and software requirements.

Advisor: Tomizuka, Masayoshi
Structural and Functional Characterization of the Proteasomal Deubiquitinase Rpn11

The 26S proteasome is responsible for selective protein degradation in eukaryotic cells. Polyubiquitin chains mark proteins for degradation by the proteasome, but before degradation can occur, ubiquitin chains must be removed by the intrinsic proteasomal deubiquitinase Rpn11. My graduate work primarily focused on understanding how Rpn11 is regulated so it only removes ubiquitin chains from substrates that have been committed to degradation. To answer this question, I developed an Rpn11 purification strategy and solved the crystal structure of Rpn11 alone and in complex with ubiquitin. These structures revealed several features that control the activity of Rpn11 toward ubiquitin substrates. I showed that Rpn11 is promiscuous in its activity toward different ubiquitin substrates, which explained how the enzyme is able to remove ubiquitin molecules from the wide range of proteasome substrate proteins. Comparison of our ubiquitin-bound and ubiquitin-free structures revealed that a critical loop changes conformation when ubiquitin binds to Rpn11 to form a catalytically active state and that substrate translocation may accelerate this transition, thereby activating the enzyme./nRpn11 is part of a proteasome sub-complex called the lid. In the isolated lid Rpn11 is inhibited to reduce off pathway deubiquitination prior to proteasome assembly, but the mechanism of Rpn11 inhibition in the lid was not understood. We used cryo-EM to determine the atomic structure of the isolated lid which explained how Rpn11 is inhibited and how it becomes activated when incorporated into the proteasome. Our structure revealed that Rpn11 is held in a conformation that fully occludes its active site and prevents the enzyme from accessing ubiquitin substrates. Mutagenesis of inhibitory residues in the lid release Rpn11 from its inhibited state and activate the enzyme. Similarly, when the lid incorporates into the proteasome, Rpn11 is released from its inhibited state and adopts a fully extended, open conformation seen in the 26S proteasome.

Advisor: Martin, Andreas
Trapped in Dualization: Labor Market and Social Protection for Low-income Workers from the Perspective of Public Benefit Use

Labor markets have been segmented typically based on skill-levels of workers in the United States and in many other economically advanced countries. Since welfare reform 1996, the welfare system in the United States has increasingly linked labor market participation to the eligibility for public benefits. This development signals a shift in public responsibility for economic security and social protection to the private sector and employers. However, welfare recipients are mostly low-income workers and, as outsiders in the labor market, they often have disadvantages, which include limited employment benefits. Therefore, the relationship between labor market and social protection for low-income workers has continuously increased economic inequality and disparity in social protection in the United States./nThis dissertation research draws on the theoretical concepts of dualization embedded in the structures of the labor market and social protection derived from the welfare state. Focusing on economic disparities and social protection for low-income workers, this study analyzes public benefit use by low-income workers and the effects of changes in economic conditions. Specifically, the analyses focus on the extent to which low-income workers use public benefits and the way these choices are related to public and private employment sectors and employer-based private benefits in particular. It also examines how different characteristics of individual, family and household, and other labor market components are associated with public benefit use by low-income workers; the analyses include the differences between household and state levels regarding the public benefit use. Statistical analyses for these inquiries involve cross-sectional and multilevel modeling of large scale data sets./nThe findings of this study show that many low-income workers cannot obtain economic security and adequate social protection through employers, given current labor market structure. Also, these findings indicate that the employment conditions and benefits provided through employers are already determined by the low-level segmentation in the labor market, rather than the public and private sectors in which low-income workers are engaged. Thus, the dualization process has increased the inequality between insiders and outsiders, which strengthens the social divide.

Advisor: Gilbert, Neil