79297 research outputs found
Sort by
Adaptive Purchase Tasks in the Operant Demand Framework
No full textVarious avenues exist for quantifying the effects of reinforcers on behavior. Numerous nonlinear models derived from the framework of Hursh and Silberberg (2008) are often applied to elucidate key metrics in the operant demand framework (e.g., Q0, PMAX), with each approach presenting respective strengths and tradeoffs. This work introduces and demonstrates an adaptive task capable of elucidating key features of operant demand without relying on nonlinear regression (i.e., a targeted form of empirical PMAX). An adaptive algorithm based on reinforcement learning is used to systematically guide questioning in the search for participant-level estimates related to peak work (e.g., PMAX), and this algorithm was evaluated across four varying iteration lengths (i.e., five, 10, 15, and 20 sequentially updated questions). Equivalence testing with simulated agent responses revealed that tasks with five or more sequentially updated questions recovered PMAX values statistically equivalent to seeded PMAX values, which provided evidence suggesting that quantitative modeling (i.e., nonlinear regression) may not be necessary to reveal valuable features of reinforcer consumption and how consumption scales as a function of price. Discussions are presented regarding extensions of contemporary hypothetical purchase tasks and strategies for extracting and comparing critical aspects of consumer demand
AGING THROUGH CALAMITIES: EXAMINING THE CUMULATIVE IMPACT OF DISASTER EXPOSURE ON THE MENTAL HEALTH OF OLDER AMERICANS
No full textAs disasters grow in frequency and intensity, their long-term mental health consequences, particularly for aging populations, remain underexplored. This dissertation investigates how cumulative disaster exposure contributes to depressive symptoms in later life, with a particular emphasis on the moderating role of geographic disparities and social support. My three-paper dissertation provides a better understanding of the relationship between natural hazard exposure, mental health, and aging. I utilized a unique longitudinal dataset that integrates disaster-related data (Spatial Hazards Events and Losses Database for the United States), including property damage and fatalities, with mental health data (REasons for Geographic and Racial Differences in Stroke) from older adults in the United States. In the first paper, I assess how varying intensities of disasters—minor, moderate, and severe—impact depressive symptoms among older adults across different time windows (1, 2, 5, 7, and 10 years) preceding their mental health assessment (in 2019). The second paper investigates how geographic context, especially the rural-urban divide moderates the relationship between disaster exposure and mental health outcomes among older adults. Finally, the third paper examines the role of social capital by examining how informal caregiving, in terms of availability, proximity, and relationship type, shapes mental health outcomes following disaster exposure in older Americans. This work demonstrates that the cumulative impact of lower-intensity disasters, particularly when coupled with limited informal caregiving support, can substantially impair psychological well-being in later life. The findings highlight the value of applying a cumulative stress framework to understand disaster resilience, especially among vulnerable older populations in the United States
Topics in Series, Integrals, and Distributions
No full textEach chapter in this dissertation focuses on some of the most fundamental objects a student may encounter in analysis: sequences, series, and integrals. The common thread that runs through each of these chapters is the connection that these fundamental objects have to \textit{Distributions} understood in the sense of generalized functions.
Chapter \ref{1} introduces the notion of separated increasing sequences . We establish that interpolation problems of the kind have solutions for all sequences of rapid decay in the sense that for all if and only if is separated for some We also give several generalizations of this result.
In chapter \ref{2}, we consider questions related to the behavior of moments . We introduce the notion of symmetrical series of order , for . We prove that when for some then several results characterizing the sequence from its moments hold. We then construct examples of sequences whose moments vanish with required density. Lastly, we construct counterexamples of several of the results valid in the case if we allow the moment series to all be \emph{conditionally convergent. }We show that for each \emph{arbitrary} sequence of real numbers there are real sequences such that%
In chapters \ref{3} and \ref{4}, we consider Frullani\u27s integral formula. In chapter 3, the existence of Frullani integral in the distributional sense is proven to be equivalent to the existence of the distributional point value at zero and Ces\`{a}ro limit at infinity. We draw connections to finite parts and Ces\`{a}ro summability culminating in applications. In Chapter 4, we discuss the different, albeit equivalent conditions provided by Iyengar and Ostrowski for the existence of Frullani Integral. We then identify other equivalent conditions and show that these conditions are solutions to a family of linear differential equations of the first order. We study the limiting behavior of these solutions at zero and infinity, providing applications of our results towards the end
Data-Driven Approaches For Vulnerability Assessment, Reinforcement Learning-Optimized Modular Reconstruction, And Digital-Twin Enabled Roadway Recovery in Post-Disaster Scenarios
No full textNatural disasters, particularly hydrological events such as floods, cause widespread damage, with coastal areas like Louisiana bearing a significant brunt. Vulnerable communities, especially those with low-income populations, often bear a disproportionate share of the consequences during and after these events. To ensure equitable and affordable recovery, it is essential that recovery strategies prioritize these communities based on their specific vulnerability levels. This dissertation proposes a comprehensive framework that addresses these challenges through three interconnected studies: (1) analysis of underlying disaster vulnerability, (2) development of modular-based housing strategies for post-disaster rebuilding, and (3) establishment of digital twin (DT) driven optimization for post-disaster recovery for transportation infrastructure systems.
The first study presents a methodology for identifying and quantifying community vulnerability to floods, addressing existing gaps in understanding the relationship between socio-demographic, infrastructural, and informational factors in disaster impact and recovery. It offers new insights into the primary determinants of community vulnerability and provides a vulnerability index for measuring and comparing risk levels across populations. The methodology has been validated using data from the 2016 Louisiana flood, revealing critical vulnerability factors that significantly affect recovery outcomes. By enabling disaster response teams and policymakers to prioritize recovery efforts for the most at-risk populations, this study advances the field of disaster resilience planning and promotes more informed decision-making.
In addition, the second study examines the potential of modular-based construction approaches to enhance post-disaster housing recovery. Post-disaster recovery efforts hinge significantly on providing permanent housing, which remains one of the most critical yet time-intensive aspects of disaster response. While modular housing presents a viable solution due to its efficiency and scalability, it is often challenged by production delays, overproduction, and misaligned delivery schedules that disrupt on-site reconstruction timelines. To address these challenges, this study proposes a deep reinforcement learning (DRL) framework designed to optimize the allocation of prefabricated modular units across multiple production facilities and construction sites. The model incorporates key constraints, including production capacity, transportation logistics, and social vulnerability prioritization, to ensure an optimized allocation strategy. A case study demonstrates that the DRL-based approach can reduce project delays by up to 46% and lower overall costs compared to conventional optimization methods. Furthermore, the study underscores the importance of aligning construction operations with social equity considerations to ensure that the most vulnerable populations receive prioritized housing support.
Beyond housing, natural disasters often inflict severe damage on critical infrastructure, particularly roadways, which serve as the backbone for overall recovery efforts, including housing reconstruction. However, monitoring and supervising roadway reconstruction manually is labor-intensive, prone to human error, and inefficient. To address these challenges, the third study proposes an innovative DT framework that utilizes audio and other sensor-based data for real-time monitoring of transportation construction projects. By leveraging deep neural network-based sound classification, the DT system enables seamless integration between physical and virtual environments, providing real-time insights into ongoing activities, progress estimation, and early identification of potential bottlenecks. Field experiments validate the system’s capability to enhance the efficiency of a construction site while reducing reliance on manual oversight, positioning it as a transformative solution for managing large-scale infrastructure projects.
By integrating these three studies, this dissertation offers a novel approach to accelerating post-disaster recovery and ultimately improving long-term resilience of infrastructure systems and communities. It highlights the vital role of advanced technologies and data-driven frameworks in strengthening community resilience, optimizing resource allocation, and streamlining construction operations. Together, these contributions lay a robust foundation for future innovations in disaster recovery and sustainable infrastructure development
Conceptualizing an Entrepreneurial Mindset in Sport for Development and Peace
No full textWithin sport for development and peace (SDP), social entrepreneurship and innovation have been noted by scholars as important for navigating resource scarcity and adjusting to pressing social issues. However, most research remains focused on organizational-level innovation rather than at the individual level. The purpose of this paper is to examine the relevance of the concept of entrepreneurial mindset for the SDP field. Following a brief overview of social entrepreneurship and innovation in SDP, the foundations of an entrepreneurial mindset are outlined. Three aspects of an entrepreneurial mindset—cognitive, emotional, and behavioral—are discussed within the SDP context. Additionally, a set of research questions are presented to advance the existing body of knowledge. We argue that entrepreneurial mindset provides a meaningful framework for the identification of specific strategies for how SDP practitioners can create more transformative organizations through their recruitment, training, and support of staff members
Governance Structures and Processes in Interorganizational Collaboratives: The Critical Role of Power and Equity
No full textInterorganizational collaboratives among human service nonprofit organizations are potential hubs for innovation and progress. The purpose of this study was to explore the role of governance in an interorganizational collaborative, the “Sport for Good Cities” initiative, which was intentionally designed to achieve collective impact. Drawing on interviews with 30 stakeholders, the findings provide important theoretical and practical insights for governance in interorganizational collaboratives in terms of the central role and challenges related to power and decision-making, backbone support, and equity and engagement. For example, the findings highlight the need to decide how collaboratives will be governed during the formative stages of interorganizational endeavors, along with governance structures and processes that deconstruct systemic and structural inequities
Mapping Collective Action: A Case Study of Identifying Assets and Actions During Community Mental Health Workshops to Address the Effects of Environmental Inequities
No full textEnvironmental changes, which have led to frequent and severe climate-related disasters, profoundly affect individuals and communities in Louisiana that display already existing disparities in vulnerability. An increasing body of evidence documents the relationship between the effects of climate change and poor mental health. This underscores the importance of utilizing an environmental justice framework to assess and innovate strategies for addressing disasters’ unequal burden. As part of a broader Community-Based Participatory Research (CBPR) project on the effects of a community-based intervention to improve mental health resilience in communities affected by disasters and crises, we included 12 churches in a community asset mapping process to identify resources within their communities and discuss actions that could improve their neighborhoods and build additional support. We conducted deductive and inductive content analysis of asset maps and field notes from 32 small groups. We found the following: (1) the church was seen as a central asset; (2) key distinctions in how participants discussed their tangible and intangible resources according to their geography, and (3) the themes of charity, resource facilitation, connecting the most vulnerable, and absence of government support typified how groups discussed possibilities of action
ASSESSING NICHE DIFFERENCES ACROSS THE DWARF SALAMANDER SPECIES COMPLEX OF THE SOUTHEAST US
No full textCharacterizing niche differences in closely related species can provide valuable insights into the extent of ecological divergence between species, and the potential evolutionary processes behind speciation. This study builds the first ecological niche models for the Eurycea dwarf salamander species complex, which includes five species with varying distributions across the southeastern United States. Ecological niche models (ENMs) were built for each species using the machine-learning algorithm, MaxEnt. We used pairwise niche overlap analyses and statistical validation techniques to investigate potential ecological drivers behind species divergence and the degree of niche overlap among the five Eurycea species. We found an overall pattern of niche divergence among this species group, with our results showing significant niche differentiation in eight out of ten pairwise comparisons. Climatic variables, particularly temperature-related metrics, were found to play a significant role in shaping species distributions but contributions varied across species. Our results provide support for the previously suggested adaptive radiation of Eurycea species in the southeast US, highlighting the importance of ecological factors in diversification. Further, our ENM results suggest novel areas for future field investigations and conservation efforts of these species
Rapid Quality Assessment of Sugarcane Using Near-Infrared Spectroscopy and Machine Learning Models
No full textSugarcane is a critical agricultural commodity, supporting sugar production, biofuel industries, and rural economies worldwide. The global sugar industry is valued at approximately 78 billion dollars annually. Cane payment systems between factories and growers in Louisiana and other major sugarcane-producing regions rely on quality parameters such as pol, Brix, and moisture, traditionally determined through wet chemistry methods. Although effective, these methods are labor-intensive, time-consuming, and vulnerable to variability. Rising levels of extraneous matter (EM), including soil and leaves, have further impacted sugar recovery, driven by climate change, mechanized harvesting, and green cane regulations. Despite its influence, EM content is not routinely measured in factories due to the lack of practical methods. This challenge is particularly critical in Louisiana, where EM levels are among the highest reported globally, potentially affecting calibration model performance.
This dissertation evaluates the application of Near-infrared (NIR) spectroscopy combined with machine learning (ML) models as a rapid and nondestructive alternative for analyzing sugarcane quality parameters and EM content. Calibration models for Brix, pol, moisture, and EM were developed using partial least squares regression (PLSR), functional regression (FR), support vector regression (SVR), and artificial neural networks (ANN). Total leaf content was predicted from shredded cane mixtures with known concentrations of cane, leaves, and soil, while soil content was predicted based on incinerated ash as the reference method.
Machine learning (ML) models achieved strong performance across most parameters, with coefficients of determination (R²) typically exceeding 90 percent and root mean square errors (RMSE) remaining below 10 percent of the measured range. FR consistently provided improved performance compared to PLSR. Improving prediction accuracy is critical because NIR predictions are directly tied to payment calculations, and any errors can affect financial transactions between growers and factories.
This research presents the first comprehensive methodology to quantify EM in sugarcane using NIR spectroscopy, addressing a longstanding gap in quality evaluation. The approach not only advances sugarcane industry practices but also offers potential applications for other crops where EM affects processing efficiency and product value. These findings support the future integration of NIR and ML models into factory operations and payment systems, promoting more accurate and economically sustainable agricultural industries
Evaluating the antibacterial activity of engineered phage ФEcSw endolysin against multi-drug-resistant Escherichia coli strain Sw1
No full textObjective: The emergence of bacteriophage-encoded endolysins hold significant promise as novel antibacterial agents, particularly against the growing threat of antibiotic-resistant bacteria. Therefore, we investigated the phage ФEcSw endolysin to enhance the lytic activity against multi-drug-resistant Escherichia coli Sw1 through site-directed mutagenesis (SDM) guided by in silico identification of critical residues. Methods: A computational analysis was conducted to elucidate the protein folding pattern, identify the active domains, and recognize critical residues of ФEcSw endolysin. Structural similarity-based docking simulations were employed to identify residues potentially involved in both recognition and cleavage of the bacterial peptidoglycan. Phage endolysin was amplified, cloned, expressed, and purified from phage ФEcSw. Pure endolysin (EL) activity was subsequently validated through SDM. Results: Our studies revealed both open and closed conformations of ФEcSw endolysin within specific residue ranges (51–60 and 128–141). Notably, the active site was identified and contains the crucial catalytic residues, Glu19 and Asp34. A time-kill assay demonstrated that the holin (HL) – EL effectively reduced E. coli Sw1 growth by 46% within 12 h. Furthermore, treatment with HL, EL, and HL-EL significantly increased bacterial membrane permeability (11%, 74%, and 85%, respectively) within just 1 h. Importantly, SDM identified a double mutant (K19/H34) of the endolysin exhibiting the highest lytic activity compared to the wild-type and other mutants (E19D, E19K, D34E, and D34H) due to increase net charge from +3.23 to +6.29. Conclusions: Our findings demonstrate that phage endolysins and HLs or engineered endolysin hold significant potential as therapeutic agents to combat multidrug-resistant bacterial infections