Environmental and Occupational Health Sciences Institute
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Electrostatic phenomena associated with peeling tape
No full textTriboelectrification or contact-electrification is a widely documented process that describes the generation of electrical charge from contacting and separating surfaces. Though it is not well defined, and its mechanisms remain widely debated despite decades of speculation and study, there are many examples of this behavior. As early as Picard’s observations of flashes of light from mercury rolling from a barometer, we have been aware of intriguing interactions in rubbing surfaces. It is responsible for the shock experienced when removing a wool sweater and the fascinating volcanic lightning made possible through granular collisions in ash plumes. In this dissertation, we discuss three projects investigating phenomena associated with triboelectricity. The first chapter examines the relationship between stick-slip events and triboelectricity in separating surfaces. Using the simple act of peeling adhesive tape, we establish protocols for visualizing stick-slip and charge patterns using electrically charged toners. We identify reproducible trends in the patterns on the tape and the substrate from which it was peeled, and by varying the substrate materials, we show that peeling tape from different materials (e.g., polymethylmethacrylate and polytetrafluoroethylene) may pre-determine certain characteristics by generating specific electrostatic and stick-slip patterns. This method may be valuable for classifying surfaces in industry or laboratory experiments based on their stick-slip signatures. In the second chapter, we examine complex electrostatic patterns from mated surfaces, including the complementary but distinct differences between surfaces. We classify notable signatures achieved with this technique. We discuss a computational model in which particles interact with each other and a charged surface. We show that varying the particles’ electrical polarity will replicate the two general categories of patterns we see in experimental surface separation. We directly compare the results of the model with experimental findings. We contextualize our simulation and experimental results with questions that remain unanswered and with future steps. In the last chapter, we evaluate three-dimensional structures formed from electrostatic interactions in granular materials. In micro-gravity environments, where electrostatic interactions dominate over gravity, granular tendrils have been identified, similar to those found in highly charged systems in laboratory experiments. We compare debris attracted to equipment on Mars in 2022 to formations composed of toner to motivate this work, adding to it chains of granular materials formed on triboelectrically charged adhesive tape.Ph.D.Includes bibliographical reference
Risk factors for social and emotional loneliness in community-dwelling adults 60 years and older: A systematic review
No full textAbstract
Objective: The objective of this review was to identify the risk factors for social and emotional loneliness in community-dwelling adults 60 years and older. Introduction: Emotional and social loneliness are two distinct phenomena that can exist separately or co-exist. Emotional loneliness is the subjective, painful feeling that represents a lack of meaningful interpersonal attachments. Social loneliness is the lack of a social network of friends, family, or neighbors. Loneliness is more common in older adults and has a negative impact on quality of life. Inclusion criteria: This review considered studies that include people aged 60 years and older. This review considered studies that explore modifiable and non-modifiable risk factors for emotional and social loneliness. The review included studies that identify social and/ or emotional loneliness as the primary or secondary outcomes. The review considered epidemiological study designs including prospective and retrospective cohort studies, case control studies and analytical cross-sectional studies. Methods: MEDLINE (Ovid), Embase, Cochrane Database of Systematic Reviews, Web of Science and CINAHL (EBSCO) were searched for eligible studies. Sources of unpublished studies and gray literature were also searched. Following the search, potential studies were assessed against the inclusion criteria. Eligible studies were critically appraised using the JBI critical appraisal instruments. Findings were presented in narrative form and were used to create clinical practice recommendations. ResultsTen studies were included in the systematic review. The total number of participants in all ten studies was 138,672. The studies included in this review were published between 2009 and 2021. The participants were aged 60 and above. Based on the totality of the evidence being female, living alone, having low socioeconomic status, poor mental and physical health were associated with loneliness. ConclusionHealthcare professionals should screen older adults for the identified risk factors for loneliness. Keywords: older adults; emotional loneliness; social loneliness; risk factorsD.N.P.Includes bibliographical referencesIncludes vit
Enhancing audit quality and risk assessment: three essays on the role of machine learning and exogenous data sources
No full textThe advent of innovative technologies, such as data analytics, machine learning, and natural language processing (NLP), along with the rapid expansion of Big Data, has profoundly transformed the landscape of the auditing professions (Warren et al., 2015; Yoon et al., 2015; Issa et al., 2016; Appelbaum et al., 2017; Gepp et al., 2018; Sun & Vasarhelyi, 2018). Previous studies have underscored the promising benefits and application contexts of emerging artificial intelligence (AI) and data analytics in audit practice (Dechow et al., 2011; Barboza et al., 2017; No et al., 2019; Bertomeu et al., 2021; Nasir et al., 2021). However, there are still significant concerns that continue to exist when it comes to using machine learning and data analytics to improve audit quality and risk assessment. Some of the concerns that have been raised include the extraction of information that may not be relevant to the objectives and contexts of the audit, the need to integrate both internal and external data sources to improve decision-making, and the potential for these technologies to be used inappropriately. The three essays in this dissertation aim to address these concerns by illustrating: 1) how to use machine learning toolkits and data analytics properly to extract relevant information from varying data sources, and 2) how to enhance assurance by bridging external information with traditional audit evidence and financial figures.Ph.D.Includes bibliographical reference
Variational autoencoders, optimal transport, and cooperative communication
No full textVariational Autoencoders (VAEs) are a class of generative models that have gained significant attention for their ability to learn complex data distributions in an unsupervised manner. VAEs have been successfully applied in various domains, including image generation, anomaly detection, and representation learning, due to their robust framework for encoding high-dimensional data into a lower-dimensional latent space. Despite their success, VAEs often encounter challenges related to optimization and the quality of generated samples. This dissertation explores the enhancement of VAEs through the integration of Entropy-regularized Optimal Transport (EOT) theory and its subsequent application in the domain of cooperative communication which formalizes a single problem comprised of interactions between two processes: action selection (teaching) and inference (learning). By recasting VAEs as an EOT problem, we proposed a novel framework that improves the model expressiveness, leading to more flexible learning processes and higher-quality generated samples. Our results on synthetic and real datasets support our statements. The second part of this dissertation considers the reversed process. We formalized a new theory of cooperative communication, which generalized the previous EOT-based cooperative communication models. We draw a connection to VAEs framework. This approach offers new theoretical and computational gains in the study of human-human and human-machine cooperation. We carry out a series of empirical simulations to support and elaborate on our theoretical results. Overall, the models and theory developed in this work contribute to the understanding of the connection between VAEs and EOT and the gap between advanced machine learning techniques and practical communication systems, offering a novel methodology with potential extensions to various applications in both fields.Ph.D.Includes bibliographical reference
Three essays on governmental and nonprofit accounting
No full textThis dissertation focuses on municipalities’ fiscal health and donations to small charities. The dissertation intends to provide insights for both academia and practitioners, especially those who have strong interests in those areas. Filing for Chapter 9 bankruptcy has historically been rare for local governments (LGs). The 1st study focuses on improving the prediction of fiscal distress in LGs using ML. It employs six ML algorithms to analyze financial and socioeconomic indicators from LGs across 49 US states from 2015 to 2017. Empirical results demonstrate that the Exactly Balanced Bagging Classifier, when paired with an optimal undersampling ratio, generally outperforms other ML algorithms in predicting Resource Flow Deficiency. Specifically, it enhances prediction performance, achieving F1 scores between 52.85% and 58%, while logistic regression yields F1 scores ranging from 2.22% to 48%. Additionally, key features such as net asset fluctuation, unfunded liabilities of governmental activities (GA), revenue-to-expense ratio of GA, and general fund balance prove crucial for out-of-sample predictions.
The 2nd study examines how governance affect donations to small charities based on Form 990-EZ. Following prior literature, this paper forms five governance constructs via Exploratory Factor Analysis (EFA) to measure the governance level of all observations: Management, Board, Transparency, Policy, and Volunteerism. Board, Policy, and Transparency constructs are positively associated with donations. Volunteerism has a negative impact on donations because volunteers, as rational people, may not make contributions when they already contribute time and effort to the organizations. In summary, the results imply that effective governance has strong positive signal effects on donors.
The 3rd study explores fiscal distress of LGs in New York State from 2012 to 2022 using two approaches. It compares methodologies for assessing fiscal distress of 919 LGs and employs five dimensions of solvency: Long-run Solvency, Cash Solvency, Budget Solvency, Service-level Solvency, and Revenue Structure Solvency. Socioeconomic factors are also considered. The study finds that Budget Solvency, Cash Solvency, and Service Solvency are significant indicators of fiscal status, aligning with both academic frameworks and policymakers' perceptions. Statistical measures like AIC, BIC, and pseudo R-squared validate the effectiveness of these dimensions over the Financial Condition Index (FCI).Ph.D.Includes bibliographical reference
D. Russell Connor Collection of Benny Goodman Audio Recordings
No full textIn 2009, The Institute of Jazz Studies received a grant from the Andrew W. Mellon Foundation to digitize two of its most significant bodies of sound recordings: the Benny Carter and Benny Goodman Collections. The D. Russell Connor collection of Benny Goodman audio recordings contains 396 reel-to-reel tapes of rare and unreleased performances compiled over four decades and donated by D. Russell Connor, a Benny Goodman biographer and discographer. It represents the most complete collection of Goodman recordings anywhere and includes reels from Goodman's personal archive, as well as those of many Goodman researchers and collectors worldwide.
In June 2011, the digitization process was completed. Project staff included: Ed Berger (Project Director), Ryan Maloney, Robert Nahory, Vincent Pelote, Joe Peterson, Scott Wenzel. Project Engineers: Duke Markos, Seth B. Winner
Understanding Pascal’s Addition Property and Counting Pizza Combinations
No full textThis is Analytic 3 (of 4) of "Last steps to the ’Aha!’ - Recognizing the Isomorphism: A Series of Four Analytics"
This Analytic examines the students’ continued mathematical journey as they unravel the connections between the combinatorial problems they have been solving and the addition property inherent in Pascal’s Triangle. Specifically, the students apply their understanding of pizza topping combinations to grasp the addition principle that allows Pascal’s Triangle to grow - each number is the sum of the two numbers diagonally above it. This principle mirrors the process of determining the number of pizza combinations when an additional topping is available, demonstrating the kind of structural understanding that develops over time in mathematical investigations (Maher, 2002).
During this exploration, the students perceive that each entry in Pascal’s Triangle correlates to a specific set of pizza combinations, depending on the number of available toppings. They explore this relationship dynamically, theorizing about the addition of toppings and their combinatorial consequences. This process of connecting different representations and contexts aligns with Goldin’s (2014) perspectives on mathematical engagement and problem-solving.
The Analytic captures events where the students, building on their previous experiences with towers, see structural similarities between their past explorations and the current pizza problem. Through reflection and dialogue, they develop a clearer vision of the mathematical patterns that underpin their analysis, particularly the concept of exponential doubling as it relates to the range of possible pizza combinations. This development of structural understanding echoes the findings of longitudinal studies on mathematical reasoning (Krupnik, 2020; Teehan, 2019).
Problem Statement:
“A local pizza shop has asked us to help design a form to keep track of certain pizza choices. They offer a plain pizza that is cheese and tomato sauce. A customer can then select from the following toppings: pepper, sausage, mushrooms, and pepperoni. How many different choices for pizza does a customer have? List all the choices. Find a way to convince each other that you have accounted for all possible choices. Suppose a fifth topping, anchovies, were available. How many different choices for pizza does a customer now have? Why?”
References
Maher, C. A. (2002). How students structure their own investigations and educate us: What we’ve learned from a fourteen-year study. In Proceedings of the Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 31-50).
Krupnik, V. (2020). Early development and application of proof-like reasoning: Longitudinal case studies. Rutgers, The State University of New Jersey. PhD dissertation.
Teehan, K. (2019). A longitudinal case study tracing growth in mathematical understanding through the lens of the Pirie-Kieren theory. Rutgers University. Dissertation.
Pirie, S., & Kieren, T. (1994). Growth in mathematical understanding: How can we characterize it and how can we represent it? Educational Studies in Mathematics, 26(2-3), 165-190.
Goldin, G. A. (2014). Perspectives on emotion in mathematical engagement, learning, and problem-solving. International handbook of emotions in education, 391-414
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No full textThis thesis explores safety, precarity and danger, as well as the structures that promise and forbid meaningful connections across the boundaries of race, gender, class, and species. Part I takes up these ideas in the context of sexual violence and heterosexual relationships; Part II in the context of environmental crisis; and Part III in the context of gentrifying urban space.M.F.A.Includes bibliographical reference
Analyzing and interpreting deoxyribonucleic acid from multiple donors using a forensically relevant single-cell strategy
No full textThe complexity of interpreting forensic DNA stains from multiple donors, often a cacophony of signals arising from unknown numbers of contributors (NOC), artifacts, and noise, presents considerable challenges. Traditional methods in forensic analysis rely on probabilistic interpretations prone to inefficiencies and potential inaccuracies due to mixed profiles and partial information of each donor. This study introduces a novel, forensically relevant single-cell strategy to address these issues. It used metrics for electropherogram (EPG) signal quality and statistical tests to optimize the process from extraction to interpretation. Our method combines multiple approaches of direct-to-PCR extraction, microfluidic DEPArray™ technology, and Model-Based Clustering (MBC) algorithms, to improve the reliability and interpretability of forensic DNA analysis. We evaluated four direct-to-PCR extraction treatments across 102 single buccal cells, measuring allele detection rates, peak heights, peak height ratios, and peak height balance for short tandem repeat (STR) markers. Statistical tests identified significant EPG metric variations among treatments. The Arcturus® PicoPure™ extraction method showed the most efficient, exhibiting the lowest median allele drop-out rate and the highest median average peak height, among others. We then adjusted reagent concentrations, specifically phosphate buffer saline (PBS) and proteinase K. Results indicated that decreased PBS concentrations from 1X to 0.5X or 0.25X significantly improved EPG quality metrics, while variation of proteinase K concentrations yielded negligible impact.
Leveraging DEPArray™ technology, the research further incorporated a semi-automated single-cell strategy that was optimized for the forensic pipeline. Furthermore, an innovative framework was developed to streamline the interpretation of single-cell electropherograms (scEPGs) using MBC and a novel algorithm, EESCIt (Evidentiary Evaluation of Single Cells). This framework clustered scEPGs by genetic origin calculates likelihood ratios (LR) for each cluster and averaged these across all clusters to provide a comprehensive weight of evidence summary. Our analysis revealed that 99% of comparisons provided log LR values greater than 0 for true contributors, regardless of the mixture complexity. It significantly expanded the applicability of DNA forensics.
In summary, this thesis demonstrates the feasibility and statistical robustness of a single-cell forensic DNA strategy. It provides an integrated approach for the probabilistic interpretation of forensic DNA data from multiple donors, improving both the efficiency and reliability of forensic DNA analysis.Ph.D.Includes bibliographical referencesIncludes vit
Integrative modeling of macroscopic and microscopic pathophysiology Parkinson’s Disease
No full textIn this thesis, we present a comprehensive exploration of various aspects of Parkinson’s Disease (PD), with a focus on unraveling underlying molecular
mechanisms and investigating potential therapeutic interventions. We begin
with a literature review covering genes, proteins, and pathways linked to ge
netic PD, alongside clinical trial data. Utilizing this knowledge, we develop
a Quantitative Systems Pharmacology (QSP) model capturing the complexity
of PD pathophysiology. Within this model, we develop a computational tool
that attempts to simulate metabolite concentration dynamics, offering insights
into disease progression. Additionally, we design a tool for analyzing protein
interactions within the PD Map from the University of Luxembourg, identi
fying proteins in PD-related compartments and their functions. This projects
provide a holistic framework for understanding PD’s molecular underpinnings
and offer insights into potential therapeutic strategies.M.S.Includes bibliographical reference