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EMOTION AS CURRENCY: AFFECTIVE PERSPECTIVES ON SOCIAL EXCHANGE
No full textThis dissertation explores how affect influences continued engagement in social exchanges, focusing on helping behaviors between coworkers. In organizational settings, individuals often maintain unbalanced social exchange relationships, helping certain coworkers without expecting anything in return while expecting prompt and equal reciprocation from others. This challenges traditional social exchange and equity theories, which suggest that people track the balance of exchanged favors and that unreciprocated help leads to decreased helping in future exchanges. To explain why such seemingly imbalanced relationships persist, I propose the “emotion as currency” hypothesis. This framework suggests that experienced emotions during exchanges function as a form of psychological currency, shaping individuals’ expectations of reciprocation and influencing their future helping behaviors. Specifically, when employees exchange help, the emotions they experience accumulate over time, forming an emotional ledger with their exchange partner. When faced with a new exchange opportunity, the net affect balance (the cumulative emotional currency) can serve as either a previously received benefit or an incurred cost, thereby altering perceptions of entitled reciprocation. Furthermore, gratitude expressed by the exchange partner acts as an additional form of currency, reducing expectations of reciprocation and increasing subsequent helping. I test these hypotheses through three studies: a cross-sectional survey of working adults, a behavioral experiment, and a longitudinal field study involving undergraduate students engaged in semester-long group projects. The findings contribute to a deeper understanding of how seemingly unfair social exchange relationships are sustained, shifting the focus from purely cognitive explanations to a more comprehensive understanding that includes emotional valuation. This dissertation offers a novel perspective on the role of affect in social exchange, shedding light on how helping behaviors can be sustained in the workplace
Analyzing and Enhancing Algorithmic Fairness in Social Systems and Data-Restricted Applications
No full textOver the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in applications that impact our daily lives. However, their use in high-stakes domains involving sensitive data raises significant ethical and legal concerns, particularly around algorithmic bias. Research on fairness in AI and ML (FairAI) seeks to address how the decisions made by such models may conflict with societal values. This dissertation contributes to this effort by addressing key challenges in improving algorithmic fairness within social systems and data-constrained applications, aiming to ensure ethical model deployment in high-stakes situations. Across its three parts, this dissertation begins with algorithmic development and analysis grounded in static fairness assumptions, and later revisits the limits of those assumptions, culminating in a framework that models fairness as a dynamic sequential process shaped by temporal interventions.
The first part of this dissertation proposes an algorithm to achieve both inter-group and within-group fairness in static decision-making problems. While many studies focus on fairness across different demographic groups, algorithms designed for inter-group fairness can unintentionally treat individuals within the same group unfairly. To address this issue, we introduce the notion of within-group fairness and present a pre-processing framework that satisfies both inter- and within-group fairness with minimal loss in ensemble prediction accuracy. This framework maps feature vectors from different groups to a fair canonical domain before passing them through a scoring function, preserving the relative relationships among scores within the same demographic group to guarantee within-group fairness.
The second part of this dissertation explores trade-offs in satisfying multiple fairness constraints in static data-restricted decision-making contexts. While previous research has explored trade-offs between fairness and ensemble prediction accuracy through analyzing model outputs, these studies do not consider how data restrictions impact a model's ability to satisfy fairness constraints. To fill this gap, we propose a framework that models fairness-accuracy trade-offs in data-restricted settings. Our framework analyzes the optimal Bayesian classifier’s behavior using a discrete approximation of the data distribution, allowing us to isolate the effects of fairness constraints. Our results demonstrate that this framework provides an effective, structured approach for practitioners to assess fairness constraints in decision-making pipelines.
Building on these insights, the third part of this dissertation shifts its focus to analyzing fairness from a sustainability perspective. Prior research has shown that applying fairness constraints to static, single-stage decision-making problem formulations can have negative long-term effects on disadvantaged groups. Recognizing that fairness interventions unfold over time and often involve multiple decision points rather than isolated decisions, we develop the notion of Multi-Agent Fair Environments (MAFEs)—testbeds for evaluating FairAI algorithms in temporally evolving social systems. We then present and analyze three MAFEs that model distinct social systems. We model each decision point as an agent within these MAFEs, leveraging their dynamic interactions to enable greater flexibility and more insightful analysis of system dynamics. Experimental results demonstrate the utility of our MAFEs as testbeds for developing multi-agent fair algorithms
Acyclic Cucurbit[n]uril Bearing Alkyl Sulfate Ionic Groups - Electronic Supporting Data
No full textThe methodology is detailed in the published paper and the supporting information file.This dataset contains the electronic data files that support the publication.National Science Foundation (CHE-1807486); National Institute of General Medical Sciences of the National Institutes of Health (R35GM153362)
DNA Barcoding Module in Undergraduate Biology Courses: A Comparative Analysis on Student Learning
No full textOne-Pot Ligation LAMP Assay of miRNA for Pancreatic Cancer Screening
No full textPancreatic cancer is one of the deadliest cancers due to its late diagnosis rates and low survivability. Typical diagnosis methods such as endoscopic ultrasound and magnetic resonance imaging are not very accessible or affordable. Instead, we can use miRNAs as a cancer biomarker to provide an affordable, time-efficient, and non-invasive cancer screening tool. miRNAs such as miR-21, miR-196a, and miR-221 are upregulated in the blood of patients with pancreatic cancer. In our study, we focused on using ligation-loop-mediated amplification (ligation-LAMP) to detect these biomarkers. To reduce the cost and nonspecific amplification associated with commercial mastermix, we created a buffer for LAMP by varying the amounts of polymerase, H1, H2, and filtered phenol red. Our 2x buffer successfully detected target concentrations of 1 nm, changing color during colorimetric ligation LAMP at around 50 minutes. We also included the One-Pot ligation LAMP system designed to isolate target miRNAs using magnetic beads and TRAP wax layers. Initially, the system failed due to the ineffective isothermal amplification buffer. To improve the One-Pot system, we added an isothermal amplification buffer containing 0.1% Tween 20, but this modification was also unsuccessful. Our buffer will make ligation-LAMP more affordable in future efforts to refine the One-Pot system
Solidarities, Commitment and Friendship: Japan America Student Conference (JASC) records
No full textThe author presented at the annual conference of Mid-Atlantic Region Association for Asian Studies (MARAAS), held at the University of Pittsburgh. This is a PDF version of the power point presentation slides that were shared at the panel
Chesapeake Global Collaboratory: Baltimore Harbor in Context
No full textThe recent tragic collapse of the Francis Scott Key Bridge has highlighted the critical importance of Baltimore Harbor to the region’s infrastructure, environment, and economy. In response, the University of Maryland Center for Environmental Science (UMCES), through its Chesapeake Global Collaboratory (CGC), hosted “Baltimore Harbor in Context� on October 10, 2024. The event gathered diverse stakeholders and featured plenary talks, panel discussions, and interactive breakout sessions. The meeting aimed to lay the groundwork for a broader Baltimore Harbor Summit in 2025 and to promote collaborative solutions and a shared understanding of ongoing harbor improvement efforts. Keynote speakers included Dr. Bill Dennison (UMCES), Dr. Eric Schott (Institute of Marine and Environmental Technology), and Dr. Linwood Pendleton (Ocean Knowledge Action Network), alongside local and international panelists.https://ian.umces.edu/site/assets/files/32634/chesapeake-global-collaboratory-baltimore-harbor-in-context.pd
Matlab Codes used for the analysis of micrscopy movies used in Electric field driven dynamic assembly of active colloidal aggregates
No full textSearch "Libraries Worldwide" with Primo VE
No full textChange will almost always ruffle some feathers. My institution recently migrated from WorldCat Discovery to Alma/Primo VE, and we received a lot of feedback lamenting the loss of WorldCat Discovery. This wasn't just people resistant to change, the system migration disrupted users' normal workflows. As a library focused on "just in time" services, Interlibrary Loan is an essential for many of our users. Primo VE can integrate with WorldCat, but during our migration we were discouraged to implement the integration because it causes performance issues. At most the suggestion was to create a separate search option in Primo VE to just search WorldCat, yet that would similarly complicate the research workflow. In response to the volume of feedback, I began a month-long test of the API integration, and I found that the integration did not cause performance issues. This poster will share the data I collected, the methods used, and a discussion about the pros and cons of integrating the WorldCat API as part of the default search option in Primo VE
DIARYL NITRENIUM IONS: ELECTRONIC STATE REACTIONS, MECHANISTIC STUDIES, AND PEPTIDE LABELING
No full textNitrenium ions are studied for their electronic properties, synthetic reactions, and their labeling of biomolecules. Diaryl nitrenium ions have sufficient conjugation to allow the nitrenium ions to both exist as a discrete structure and be directly observed by laser flash photolysis. In this dissertation the mechanisms and reactions of diaryl nitrenium ions will be examined. Further, the peptide WWCNDGR will be selectively labeled with a diaryl nitrenium ion.Chapter 1 reviews relevant photochemistry fundamentals and nitrenium ion basics. Chapter 2 describes the 10,11-dihydrodibenzo[b,f]azepinyl nitrenium ion 2 in comparison to the diphenyl nitrenium ion 1. It will be found that although the ethylene bridge 10,11-dihydrodibenzo[b,f]azepinyl nitrenium ion 2 raises the triplet state energy by approximately 8 kcal/mol, it’s singlet state reaction rates are only minorly decreased. Chapter 3 investigates diaryl nitrenium ions’ mechanism with H-atom donors as the parent amine has been reported in the literature as evidence of triplet state reactivity. It is concluded there are alternative mechanisms for formation of the parent amine through either reducing the nitrenium ion to the neutral radical or through direct hydrogen atom abstraction from the singlet state. Chapter 4 examines the antiaromaticity of 3,6-dibromocarbazolyl nitrenium ion’s mechanism with different electron donors and nucleophiles. It is concluded that the 3,6-dibromocarbazolyl nitrenium ion either gets trapped by a nucleophile or it gets reduced by an electron donor or the parent pyridinium ion to form the neutral radical. The neutral radical either gets protonated to form the parent amine or dimerizes to form the N-N dimer. Finally, chapter 5 shows that the diaryl nitrenium ion, N-(4,4’-dibromodiphenyl)nitrenium ion, can selectively label tryptophan on the peptide WWCNDGR