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Editorial: Jewish Cultural Scholarship and Scholars—and Their Institutions—Under Duress
Get PDFThe editors of Jewish Folklore and Ethnology (JFE) express concern about intimidation and estrangement of Israeli and Jewish professors in the international academic community after the surprise attack by Hamas in southern Israel on October 7, 2023. The editors point out that these types of hurtful antisemitic incidents against Israeli and Jewish academics preceded the attack, and they call on their colleagues and administrators to voice their outrage at the harassment, boycotts, and violence against Israeli and Jewish academics in addition to the suppression of Jewish studies, and their adverse effects on scholarship as well as Jewish students and their communities. The editors review statements by folkloristic and ethnological organizations regarding after the attack and express disappointment at lack of support for Israeli and Jewish students, staff, and faculty members. The editors reaffirm JFE’s commitment to advance research in Jewish cultural studies, use education to combat hate and ignorance, and not hold contributors to a political litmus test
The Rise and Fall of the Nabob: Narrating the Emerging Empire in Eighteenth-Century England
No full textThe emergence of the British Empire in India saw the rise and fall of nabobs, merchants-turned-officers who returned to Britain with unexplained wealth and threatened to disrupt social order. This article explores the nabob as it was imagined in fiction and performed on stage. The nabob as a character provides a lens through which to explore the emergence of the empire. The essay also suggests that these imaginaries spilled over into other contexts, including the trial of Warren Hastings. The failure of the trial, it is suggested, marked the end of the nabob’s career as a villain on and off stage
Creeping Desires: Queer Masculinity in “The Youth Who Wanted to Learn What Fear Is” (ATU 326)
No full textDespite recent scholarly interest in gender, sexuality, and transgression in the fairy tale, few scholars have considered representations of masculinity in magic tales. ATU 326, “The Youth Who Wanted to Learn What Fear Is,” is a relatively uncommon tale that focuses on the troublesome masculine performance of its male protagonist. Through analyzing several versions of the tale, this article argues that the boy protagonist consistently enacts a queer masculine performance that reveals the anxieties surrounding hegemonic masculinity and queer desires. This queer reading offers transgressive possibilities that complicate “straight” readings of fairy-tale masculinity for both academics and popular audiences
Development Of Durable Zwitterionic Hydrogel Coatings
No full textThis dissertation investigates the potential of zwitterionic hydrogels as superhydrophilic coatings, with a focus on their antifouling, biocompatible, and durable performance. Recognized for their unique hydration mechanisms that resist surface biofouling, zwitterionic materials hold promising applications across diverse fields. The introductory chapter outlines the key attributes of these materials, emphasizing their unique charge-balanced structure and resistance to fouling. Subsequent chapters present innovative strategies to improve coating durability while preserving antifouling efficacy. These advancements enable the application of zwitterionic coatings to a variety of substrates, demonstrating their reliability and effectiveness in different environments. Overall, these works integrate multiple advanced strategies to develop zwitterionic hydrogel coatings, effectively addressing challenges in scenarios demanding antifouling, durability, and compatibility, and demonstrating their suitability for practical applications across various fields
Towards Improving The Adoption Of Graphql Among Software Practitioners
No full textGraphQL is a query language and web API runtime that was developed by Facebook in 2012, open-sourced in 2015, and moved to the GraphQL Foundation in 2019. It defines a client-server paradigm that solves a number of challenges observed in previous protocols such as Representational State Transfer (REST). A powerful feature of GraphQL is its type-safe nested queries that empower clients to request precisely the data they need, thereby addressing issues of data over-fetching and under-fetching. Prior studies primarily focused on comparative assessments and migration feasibility from GraphQL to REST in addition to automation of testing, and federation solutions. Nonetheless, to the best of our knowledge, no studies have yet investigated the trends of the software community’s interest in GraphQL domain topics and the challenges they face. Additionally, despite the wide spectrum of the GraphQL domain, the difficulty software developers encounter while learning GraphQL, and the resource investment necessary for software organizations to adopt GraphQL, few scientific research works focused on the effective adoption of GraphQL. To address this research gap, this dissertation aims to improve the adoption of GraphQL among various software practitioners. Our target practitioners list includes software developers, library builders, software organizations, educators, and researchers.To achieve our objective, we conduct three studies as follows –(i) We investigate what GraphQL topics software practitioners discuss, their challenges, interests, and mapping to the architecture of the GraphQL ecosystem where one of our findings indicates that GraphQL Java and Development Tools are among the difficult GraphQL topics. We also noted that tools aiming to auto-generate GraphQL from existing systems such as Prisma are difficult and unpopular. Such findings motivate us to (ii) propose an approach to automate the migration of applications to support GraphQL using source-code static analysis and code generation with a prototype implementation in the Java programming language. Lastly, we identify null-safety as an incompatibility between the GraphQL specification and the Java programming language, and therefore, (iii) we propose a tool to automate and enhance the null-safety of Java methods using code generation combined with an optional type hierarchy. Our results identify a broad range of 14 GraphQL topics that software developers discuss online on StackOverflow with varying sizes, contents, difficulty, and interests. Our findings indicate that Apollo, the reference GraphQL implementation, is among the most popular topics, especially on the client side of the GraphQL architecture. A second finding shows that topics representing basic GraphQL concepts such as queries, mutations, and resolvers are the least difficult. A third finding suggests that backend (server-side) implementations of GraphQL are less difficult and less popular than their front-end (client-side) counterparts. However, despite the popularity of GraphQL Java , a programming language predominantly utilized for backend development, it is a difficult topic. As this finding motivates our second study, we propose an approach to automate the migration of programming-language APIs (particularly Java) to GraphQL, without requiring third-party domain knowledge expertise. We evaluate GraphQLify’s prototype implementation in Java on 9 open-source projects to assess the correctness and migration performance. We also compare it with one state-of-the-art solution, OASGraph, and show GraphQLify’s ability to preserve type safety. As part of our effort to improve GraphQL in the Java domain, our third study focuses on creating a Java tool (i.e., Optional4J) to automate the null safety of Java methods. That is because the GraphQL schema allows nullable and non-null types whereas, all Java types are nullable. Our quantitative results validate the correctness of Optional4J’s code generation to reduce boilerplate and show it outperforms its Java-native counterpart. For user evaluation, we conducted a survey with 15 developers from a well-known company and our results indicate developers find Optional4J useful and easy to use
Exclusion And Education: Disability And Personhood In Public School
No full textThis study examined how the interpersonal relationships and daily practices within schools shaped disabled students’ personhood. The school experiences of the students in this study were heavily influenced by their placement within special education classrooms. I conducted a 10-month ethnographic study of three special education classrooms in a racially and ethnically diverse and low-income middle school located in a working-class suburb of Detroit. In addition to participant-observation ethnography, I interviewed students and teachers and held a focus group with students. Participants totaled 41, including 32 youth and nine adults. Of the 32 youth, 17 were educated in isolated special education classrooms. Data collection focused on these students. Twelve of these 17 youth were multiply-marginalized students, whose racial or ethnic background and disability status placed them at greater risk for discrimination and negative outcomes in the school setting. Approaching this inquiry through ethnographic and phenomenological methods and theoretical frameworks from anthropology and cultural studies regarding personhood, disability, and race, findings from this study can be grouped into three themes: social relations, spatiotemporal relations, and futurity and personhood. Social relations among students: Despite common perceptions of disability as limiting relational capacity, the disabled and isolated students in this study cared for each other in ways that affirmed each other’s personhood. These relations were complex and contained traces of care and neglect. This person-extending care, despite overwhelming isolation from the larger school, was practiced in the context of ambiguity, without assurances that this care would be reciprocated or guaranteed in the future and with the knowledge that some students desired very much to exit the classrooms in order to participate more fully in the wider social world of the school. Spatiotemporal relations: Spatiotemporal practices of special education create forms of inclusion and exclusion. These spatiotemporal practices should be understood in the larger context of disability in the history of U.S. social services. In this school, spaces were inaccessible not through physical barriers but because of school practices that limited the mobility and access of student and thus opportunities for peer relationships. Analyzing the construction of certain school spaces as White spaces and others as abled spaces, I found that spatial exclusion contributed to constructions of disabled and racialized personhood as limited and to constructions of disabled students as non-learners. Futurity, citizenship, and personhood: Cultural perceptions of disabled youth as future failed adults, in conjunction with choices regarding classroom practices and how to spend educational time, worked to figure disabled youth as outside of the citizen-building project of contemporary education. Based on an analysis of three field trips, students’ emplotment on a temporal trajectory as either permanent children or future failed adults show how sociocultural perceptions of disability and racialized perceptions of Black youth interfere with child-personhood and adult-personhood. This study contributes empirical data and theoretical depth to the study of personhood and the cultural construction of disability. Empirical data focused on the phenomenological experiences of disabled youth provide specificity regarding the embodied of disability and sociocultural conceptions of disability. This specificity works against theorizing of disability as always-already negative and locates the stigma of disability within sociocultural conceptions. In doing so, this study also contributes to the theorization of personhood and relationality in the context of segregation. In spaces of segregation and carcerality, important social relations can be found, and these relations are rich in care as well as in neglect. Another contribution of this study is the focus on school as an important site for sociocultural formations of disability, viewing what happens in schools and through special education as an effect of American culture rather than a product of schooling. By drawing out the mismatch between the ideological projects of U.S. schooling and cultural conceptions of disability, changes to schooling can be based on firmer theoretical ground. These findings also raise implications for educational policy and school-based social work. Specifically, special education policymakers must endeavor to end the reliance on isolated special education classrooms as a pedagogical practice. This requires that our schools are made fully accessible, spatially and socially, so that all students are able to engage within all of the spaces of schools. Through their work in providing psycho-social assessments and evaluations of students’ social and adaptive skills, school social workers contribute to how disability is made meaningful in society. School social workers can become stronger advocates for disabled students by reinforcing the acceptance of disability as part of the breadth of human diversity and working to create inclusive social environments. This will require shifting some professional focus away from teaching normative behavior to individual disabled students and toward school culture and policies. School social workers and educators can work together to develop and extend the concept of neurodiversity as a way to promote and expand disability cultural capital. In addition to providing avenues for disabled students to connect with larger social and political movements for disability pride, this will also help promote an open and clear discussion of disability, in plain language, in schools
Fault And Cyberattack Diagnosis And Handling Via Large Language Models And State Prediction For Manufacturing And Quantum Systems
No full textIn the digitalization era and Smart Manufacturing, companies are harnessing the power of artificial intelligence (AI) and machine learning (ML) across multiple sectors, including process engineering optimization, process control and fault detection, to enhance efficiency and engineering decision making. Although AI and ML are widely used in anomaly detection and handling, there are still areas where it has been less explored. One of the major areas where AI’s potential in manufacturing needs to be characterized is with respect to the applications of large language models (LLMs) in manufacturing troubleshooting for fault/attack handling. A second major area where the potential of AI and ML in manufacturing is not fully characterized is with respect to how the use of AI/ML impacts the security of process operation. In this thesis, we address these gaps.
With respect to leveraging LLMs in anomaly handling, we provide investigations into how an industry could move toward enhanced troubleshooting for fault/attack diagnosis and handling where an engineer can provide queries on an encountered issue/anomaly and receives a reply from an LLM model that includes the reasons behind a fault or attack and how to fix it. However, a major challenge for the “enhanced troubleshooting” workflow is that it would require that the LLM always returns verifiably correct outputs without being fine-tuned. Improvements can be performed by grounding the LLM’s responses on proprietary data sourced from vectorized sources through a process called retrieval augmented generation (RAG). Through this process documents that are similar to the user’s query will be retrieved from a vector database to assist the LLM’s model in generating a response that aligns with the user’s query. However, the enhanced troubleshooting strategy has two major challenges related to the relevancy and the accuracy of the retrieved information and the response generated by the LLM model, respectively. In this thesis, we focus on one challenge related to the relevancy problem of the information retrieved from a centralized vector store. Although there are various approaches for addressing the relevancy problem, a company may want to try to assess which strategy would perform best in a constantly growing literature. A common approach for validating the performance of different techniques for utilizing LLMs is to benchmark their performance with a substantial dataset. However, because LLMs are less explored for critical applications like engineering troubleshooting, there is no current database available to be used in the evaluation strategy. Therefore, in this thesis we aim to provide guidance for an industry on how to generate their own dataset to test various enhanced troubleshooting workflows and to examine the effect of the questions generated in various ways on the success of these workflows using an identified success assessment metric.
Next, we seek in this thesis to provide initial steps toward formulating the components of the enhanced troubleshooting workflow from a control-theoretic perspective which might provide steps toward generating guarantees for the enhancement of the relevancy of the information retrieval as well as the accuracy of the LLM’s output. This thesis focuses on providing initial concepts toward this in the domains of fault and cyberattack handling.
In addition to the empirical and control theoretic LLM-based strategies for anomaly diagnosis and handling, this thesis also develops control-theoretic detection and handling strategies for anomalies and more specifically for cyberattacks on sensors. In this anomaly detection and handling direction, we first introduce the concept of cyberattack handling for classical and quantum systems using model-based control. We do this through discussing the extension of an active detection strategy elaborated in our group prior work for chemical processes under an advanced control formulation known as Lyapunov-based economic model predictive control (LEMPC). This strategy probed for cyberattacks by modifying thesteady-state around which the LEMPC was designed, as well as the associated Lyapunov function and Lyapunov-based stability constraints, at random times to check whether the modified Lyapunov functions decreased over the subsequent sampling period. In this thesis, we investigate whether this type of active detection policy can be generalized to other economic model predictive control (EMPC) formulations. In another work in our group, a ‘directed randomization’ strategy based on randomly selecting between two predefined control actions at every sampling time was developed to attempt to force an attacker to reveal himself when continuously attacking the sensor of a process. This thesis provides initial steps toward the extension of such detection mechanism to other cyberphysical systems such as quantum systems. Next, we considered the theme of this thesis, regarding the integration of AI/ML in the design of anomaly detection and we focus on the issue of having the advanced controller fully replaced by learned models such as neural networks (NN) and how that affect the security of the process in the presence of an undetected attack. More specifically, we extend a passive detection strategy elaborated in our group’s prior work to the case of an NN that approximates an LEMPC. We then elaborate a cyberattack handling strategy integrated with reachability analysis and inspired by the NN repair for attempting to ensure safety of controllers for one sampling period after undetected attacks. We then examine the potential conservatism differences between the LEMPC-based safety strategy and reachability-based method, and consider an extension of the repaired input concept to fighting back against attackers
Nonconvex Optimization Methods Under Inexact Information
No full textThis thesis focuses on the design and convergence analysis of algorithms for solving nonconvex optimization problems under inexact first-order information. We introduce Inexact Reduced Gradient (IRG) methods for general smooth functions and Inexact Gradient Descent (IGD) methods for functions with relative and absolute errors. Additionally, we develop Inexact Proximal Point and Inexact Proximal Gradient methods for weakly convex functions. Our methods improve the performance of standard inexact proximal point methods, inexact proximal gradient methods, and inexact augmented Lagrangian methods by approximately 2.5 to 10 times in terms of iteration complexity for image processing tasks. Moreover, we propose new derivative-free optimization methods for smooth functions, addressing both noiseless and noisy settings. Our derivative-free methods demonstrate greater stability than standard finite-difference-based methods with fixed intervals, the implicit filtering algorithm, and the random gradient-free algorithm when handling small noise. They also outperform production-ready solvers such as Powell, L-BFGS-B, and COBYLA from the SciPy library in highly noisy problems. Finally, we highlight the crucial role of rigorous convergence analysis in improving the training and generalization of deep neural networks for image classification tasks
Predicting Warranty Claims Using Iiot-Driven Machine Learning: Enhancing Quality In Automotive Assembly
No full textIn the rapidly evolving landscape of modern automotive manufacturing — characterized by increasingly complex assembly lines and extensive industrial IoT integration maintaining exceptional product quality while minimizing warranty claims has emerged as a formidable challenge. Addressing this critical need, our research introduces a multifaceted machine learning methodology that leverages high-dimensional sensor data collected from diverse assembly stations to preemptively identify potential warranty issues during the vehicle production process. Central to this approach is the deployment of advanced long-short-term memory (LSTM) neural networks, adept at capturing the complex temporal fault patterns that traditional quality control systems often overlook. By proactively detecting early indicators of system malfunctions, our method not only minimizes false alarms but also optimizes cost-based sensor tolerance limits across the assembly line, thereby achieving an essential balance between precise fault detection and the reduction of false alerts. In addition with this innovative strategy, we also present a novel transfer learning framework designed to improve quality control in scenarios marked by limited data availability, particularly when introducing new vehicle configurations. This framework employs robust domain adaptation techniques, notably the Correlation Alignment (CORAL) method, to transfer established knowledge from models trained in legacy vehicle configurations to new production scenarios characterized by variations in sensor setups, station tools, and vehicle dynamics. For instance, the transition from a four-door to a two-door configuration exemplifies the domain shift challenge, one that our methodology addresses by integrating sensor data and sophisticated machine learning algorithms. Empirical evaluations conducted on extensive real-world automotive datasets underscore the efficacy of our combined approach, yielding high fault detection accuracy and an area under the receiver operating characteristic curve, alongside significant improvements in precision and recall metrics. The convergence of proactive fault detection and adaptive transfer learning in our framework illustrates a paradigm shift from conventional, reactive quality control methods to a more predictive and resource-efficient strategy that enhances operational efficiency, reduces warranty related costs, and promotes overall product excellence. Furthermore, by harnessing cutting-edge machine learning paradigms and employing robust domain adaptation techniques, our framework not only mitigates immediate challenges associated with manufacturing variability but also anticipates future complexities arising from rapidly evolving industrial technologies. This forward-thinking approach fosters greater resilience and adaptability in production systems, thereby reinforcing the strategic significance of predictive analytics in maintaining a competitive edge within the automotive sector. In summary, our integrated methodology marks a significant step forward in achieving intelligent, efficient, and cost-effective quality control in high-tech automotive assembly environments, setting a new benchmark for scalable and adaptive quality assurance in dynamic production systems
The Eye Within: Self-Censorship And The Internalization Of State Surveillance Amongst Arab Americans
No full textThis thesis examines how Arab Americans experience and navigate state surveillance, particularly in relation to political expression and civic engagement. Based on in-depth interviews with twenty participants in Metro Detroit, the study employs thematic analysis grounded in Foucauldian panopticism and Selod’s framework of racialized surveillance. Findings reveal that surveillance is not merely structural but deeply embodied—shaping how individuals speak, move, and interact, both online and offline. Participants reported strategic self-censorship, calculated silences, and the development of what this study terms “digital double-consciousness”—a constant negotiation between solidarity and self-protection. While prior literature emphasized political withdrawal post-9/11, this study finds that recent geopolitical events, particularly the Gaza war, have spurred renewed activism despite surveillance risks. Demographic variations by age, gender, and immigration status shape these responses. Rather than silence, Arab Americans exhibit nuanced forms of political engagement—deliberate, contingent, and deeply aware of the risks embedded in visibility