Environmental and Occupational Health Sciences Institute
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Evaluating the effect of stress first aid train the trainer on burnout symptoms in nurses
No full textPurpose of Project: The purpose of this project was to evaluate the effectiveness of Stress First Aid (SFA) Train the Trainer. This program is a framework that teaches participants to utilize stress reliving strategies, as well as train others. Assessment of this program will gather knowledge that aids in identifying the most effective intervention to reduce burnout in nurses. Methodology: This project was designed as a quality improvement project. Participant population included past participants of the SFA, provided by a statewide, New Jersey, organization. The mission of this organization is to enhance the wellbeing of nurses. Data was provided by the organization which was collected from CEU (continuing education unit) surveys, pre- and post- surveys and a one-time virtual focus group. Qualitative and quantitative information was collected and analyzed.
Results: Results derived from both the focus group, CEU and pre- and post-surveys, validate that Stress First Aid Train the Trainer (SFA) is a successful framework. It is simple to integrate into existing wellness programs offered in facilities, and aids in identifying overwhelmed nurses while offering ways to reduce stress and increase resilience. Furthermore, the utilization of this framework yielded results that support existing literature. Analysis of data identified effective burnout reducing interventions within the program and possible improvements that can be made.
Implications for Practice: Continuing to offer SFA would move towards finding the best intervention in reducing burnout symptoms to advance the initiative in creating a universal protocol. SFA incorporates both preventative and immediate approaches to stress. If staff is trained in SFA strategies, they will be better equipped with ways to cope with hardships associated with the job. Additionally, SFA will decrease costs within the healthcare system.D.N.P.Includes bibliographical reference
Development, implementation, and evaluation of an ICU orientation toolkit
No full textPurpose: Visitors experience high levels of stress and confusion during a loved one’s hospitalization in the Intensive Care Unit (ICU) due to the complexities of critical illness, unfamiliarity with the environment, and expectations for the plan of care. Efficient communication between visitors and the healthcare team promotes family-centered care and improves patient outcomes through increased comprehension of the ICU process. The introduction of an ICU orientation toolkit has the potential to familiarize the environment, manage expectations, and facilitate communication. This Quality Improvement (QI) project aims to improve interdisciplinary communication in the ICU and impact patient outcomes by standardizing the process of orientation for visitors through the implementation of an orientation toolkit. Methodology: The orientation toolkit was offered via a QR code displayed in patient rooms. The toolkit offers information regarding recommended times to reach the healthcare team in order to receive the most updated plan of care. In addition, a “discharge/transfer” QR code directed visitors to a Likert-type survey measuring the toolkit effectiveness. Results: Over a six-week period, twenty visitors (n = 20) participated in the survey. The majority of participants agreed that the toolkit provided information they had not received elsewhere and that it was instrumental in guiding discussions with the healthcare team. They also indicated that communication with the team members exceeded their expectations. Implications: By providing an orientation toolkit, the ICU can ensure the visitors are well-informed, comfortable, and able to provide the necessary support to the patient. Keywords: Intensive care unit (ICU), orientation toolkit, Family Satisfaction-ICU (FS-ICU) survey, communication, quality improvement (QI)D.N.P.Includes bibliographical reference
Glider-observed seasonal and spatial distributions of zooplankton in the Mid-Atlantic Bight
No full textAs secondary producers, zooplankton are essential in the energy flow within marine ecosystems, acting as a trophic link between photosynthetic primary producers and predatory organisms, such as migratory fishes and marine mammals, including the critically endangered North Atlantic right whale (Eubalaena glacialis). However, the distribution of zooplankton, and drivers of those distributions, are not well studied in the highly productive Mid-Atlantic Bight coastal shelf ecosystem. This region exhibits strong variability that occurs over multiple time scales, from seasons to years to decades, and is located within the broader U.S. Northeast shelf that is rapidly warming and is susceptible to ongoing ocean acidification. Furthermore, offshore wind construction is scheduled to begin in New Jersey coastal shelf waters within the next few years, and potential impacts of offshore wind construction and operation on the oceanography and local ecology are currently unknown. Therefore, establishing a baseline dataset of oceanographic and ecological parameters is crucial to inform not only future studies focused on determining trends in zooplankton distribution but also the offshore wind planning process toward responsible development. Autonomous underwater vehicles (AUVs) called gliders can reliably collect high-resolution data over a wider depth range and often at a lower cost compared to vessel-based sampling. Active acoustic approaches using multi-frequency echosounders make it possible for AUVs to observe marine pelagic species’ distributions more directly, and when paired with other oceanographic and ecological sensors, provide insight into how seasonal changes in ocean conditions overlap with the distribution of fish, marine mammals, and their prey. In this study, gliders were used to collect a suite of oceanographic and ecological variables covering three distinct seasons (Spring 2023, Fall 2023, Winter 2024). Variables measured and included in this analysis were temperature, salinity, depth, chlorophyll-a, pH, colored dissolved organic matter, and zooplankton abundance and biomass. From integrated glider-based acoustic and discrete tow data, small copepods were the most abundant taxa, while large copepods dominated total zooplankton biomass for all seasons. Zooplankton abundance and biomass were lowest in the spring across all depth bins. In fall, the highest depth-integrated biomass and abundance values were in the mid- and outer-shelf waters, while in the spring and winter seasons, highest values were nearshore. Average ocean temperatures were observed to be highest in the fall and lowest in the winter, while salinity was the highest in the outer-shelf waters during the spring. No statistically significant correlations were found between zooplankton abundance and biomass values and measured oceanographic variables. Future research should be directed to determine other potential physical or biological drivers of zooplankton distributions that were outside the scope of this study. Data produced here will assist in developing predictive models that could inform “hot spots” of prey distributions and respective predator feeding locations and provide a baseline from which to analyze potential impacts of offshore wind on zooplankton distributions.M.S.Includes bibliographical reference
The mathematical physics of marine particle aggregation: a computational exploration of the Smoluchowski coagulation equation
No full textMarine particle aggregation plays a crucial role in the global carbon cycle by transporting and sequestering carbon from the surface ocean to the deep sea. This thesis explores the mathematical
physics of marine particle aggregation using the Smoluchowski Coagulation Equation (SCE). The
primary goal is to evaluate the continuous deterministic version of the SCE and derive insights to
enhance future modeling efforts of how particles form. Traditional methods for solving the SCE,
such as The Method of Moments, the Sectional Approach, and the Fixed Pivot Method, offer
various advantages in certain contexts but require a thorough understanding of the mathematics of
the SCE. This study employs a direct algorithm for solving the SCE, leveraging Python's
SciPy.Integrate module to calculate gains and losses of particle concentrations and SciPy.solve_ivp
to solve the time derivative of mass-concentrations. The direct algorithm provides a transparent means to explore the fundamental dynamics described by the SCE. This approach is valuable for
introductory and exploratory modeling efforts. The numerical model uses an array of primary
particle diameters to generate 50 discreet masses, simulating the aggregation dynamics over time,
while incorporating critical microscale physical components driving encounters. Marine
aggregates, which are influenced by convergence of phytoplankton physiology, microscale
physics, and aggregation dynamics, are integral to the biological ocean pump. These aggregates
form from encounters of primary particles (cells) through complex interactions involving fluid
shear, differential settling, and Brownian motion, which are quantified by rectilinear and
curvilinear coagulation kernels. Rectilinear kernels often overestimate collision frequencies by
ignoring hydrodynamic effects, while curvilinear kernels provide more accurate predictions by
accounting for these effects. Hence simulations with rectilinear kernels show that aggregation
happens more rapidly than with simulations using curvilinear kernels.
Results indicate that particles within the 400-500 μm size range exhibit a net positive gain in mass
concentration, whereas particles smaller than 400 μm experience a net loss. The phenomenon of
"gelation," where particle mass aggregates into a single large cluster, was observed, leading to a
decrease in total observed mass. This effect underscores the need for a deeper understanding of the
SCE and the development of more complex modeling approaches to accurately capture the
dynamics of particle aggregation.M.S.Includes bibliographical reference
Maximize utilization of support-set for few-shot segmentation
No full textWe study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. In Few-shot segmentation (FSS), the support set plays a critical role as it provides target information to segment the target object in a given image. However, previous works focused on improving network architecture to get performance improvements neglecting the importance of how to utilize target features from the support set. We observed there were performance bottlenecks because of the limited utilization of the support set. Several recent methods resort to a feature masking (FM) technique to discard irrelevant feature activations which eventually facilitates the reliable prediction of segmentation mask. A fundamental limitation of FM is the inability to preserve the fine-grained texture and boundary information that affect the accuracy of the segmentation mask, especially for small target objects. We develop a simple, effective, and efficient approach to enhance feature masking (FM). We dub the enhanced FM as hybrid masking (HM). Specifically, we compensate for the loss of fine-grained texture and boundary information in FM technique by investigating and leveraging a complementary basic input masking method. Also, we observe that this feature excision through a limiting support mask introduces an information bottleneck in several challenging FSS cases, e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method (MSI), which maximizes the support-set information by exploiting two complementary sources of features to generate super correlation maps. We validate the effectiveness of our approaches by instantiating them into three recent and strong FSS methods. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves performance by visible margins and leads to faster convergence.Ph.D.Includes bibliographical referencesIncludes vit
Literatures of outrage: naturalism's hemispheric afterlives
No full textLiteratures of outrage reevaluates the influence of literary naturalism in the United States and Latin America in the 20th and 21st century. The dissertation argues that the genre’s values first articulated in the late-nineteenth-century by writers such as Émile Zola in France and later by Frank Norris and Stephen Crane in the U.S. and Eugenio Cambacérès and Federico Gamboa in Latin America — belief in the power of biological or social forces to circumscribe lives, a commitment to documenting the abjection of the downtrodden, and the drive to create outrage in readers as a means to incite political action—continue to animate fiction in the Americas long after the genre’s decline in popularity. It explores how minoritarian writers take up the major concerns of the early naturalists, appropriating many of their literary techniques while adapting them to the contexts of particular linguistic and geographic communities. Although Americanist literary critics such as Donald Pizer have pointed out that naturalism has experienced periodic resurgences in the works of canonical U.S. authors such as Don DeLillo and Cormac McCarthy, insufficient attention has been paid to the ways that minority writers in the U.S. and Latin America have built upon the history of this major literary mode. The project contends that more recent African American and Latin American writers rework the genre’s preoccupations to portray the struggles of the marginalized with more agency and complexity than said groups were granted in the past. That these writers both adapt and revise a supposedly dead genre’s anxieties about social amelioration reveals the extent to which its focus on determinism and outrage continues to have purchase across geographical lines. Furthermore, these writers, unlike some of the original writers of the genre, name rather than mystify the social forces of racism and sexism that inexorably shape their characters’ lives. In doing so, they remedy the genre’s limitations, build on its legacy, and give modern-day readers narratives to critique and combat oppression.Ph.D.Includes bibliographical reference
Towards scalable execution of scientific workflows with multi-level heterogeneity
No full textModern scientific and engineering applications increasingly rely on processing large datasets and executing complex computational workflows on diverse resources. For example, biological sciences use
computationally intensive sequencing workflows like 10x Genomics single-cell RNA-Seq by offloading
computing-intensive tasks to High-Performance Computing (HPC) systems and performing data analysis
on cloud clusters. Computational fluid dynamics uses HPC nodes to simulate the injection of jet fluids
while rendering the simulations on cloud instances. Such workflows require executing tasks with
heterogeneous computing requirements on different computational platforms. Although these workflows
enable various scientific approaches and span multiple scientific domains, they share similar requirements,
such as: (i) harnessing resources across HPC and cloud environments; (ii) having precise and finite
computational requirements; (iii) requiring coordinated execution of tasks on HPC and/or cloud platforms;
and (iv) utilizing CPUs, GPUs, or TPUs within the workflow on HPC and/or cloud. Despite these shared
requirements, each use case demands varying levels of heterogeneity across resources, tasks, and platforms.
However, existing solutions have failed to fully address these requirements, as they are often dispersed,
partial, and tailored to specific domains, platforms, and resources. Effectively harnessing heterogeneous
HPC and cloud resources present significant challenges, such as, different resource paradigms, large-scale
resource management, performance optimization, and productivity. This dissertation aims to enable the
efficient management and utilization of heterogeneous HPC and cloud resources to execute scientific
ii
workflow applications at a large scale. Specifically, we adapt computing techniques for data-intensive tasks,
integrate scalable tools for workflow execution on HPC, and enable automated resource management across
HPC and cloud platforms.
First, we addressed the resource heterogeneity aspect of the workflows by developing task HPC
awareness for image analysis pipelines to efficiently utilize multiple GPU kernels concurrently on HPC
resources, resulting in ~50% improvement compared to state-of-the-art approaches. We further investigated
the effect of different workflow designs on execution time and resource utilization. This work resulted in
improving the processing of ~22,000 satellite and airborne images in ~2 hours with ~90% GPU utilization,
compared to 16 hours previously required.
Second, we addressed task heterogeneity by designing and implementing a multi-node MessagePassing Interface (MPI) Python function executor and integrated it with the existing workflow management
system. This work enabled the execution of two real-world use cases with ~2,000 workflows on two HPC
machines, and resulted in ~99% resource utilization, compared to the 95% utilization obtained without the
developed capabilities.
Third, we addressed platform heterogeneity and resource management complexities by
designing and implementing Hydra, a novel HPC and cloud resources manager and broker with workload
management capabilities. The results of this work enabled the execution of sea-level projection use cases on
both cloud and HPC platforms. We demonstrated that Hydra can manage the execution of 80,000 tasks on
a single cloud resource and 10,000 tasks on both HPC and cloud platforms (Azure, AWS, NSF-Jetstream,
NSF-Chameleon), with average overheads of 6 seconds and throughput of ~6,000 tasks/s.
Lastly, we addressed the issue of cross-cloud resource selection for both commercial and private
cloud providers. We proposed the Hydra resource orchestrator as an extension of the Hydra resource broker
with two classes of selection algorithms while comparing their performance and selection results. Our work
offered two systematic approaches to select cost-effective or high-performance cloud resources based on
user constraints across thousands of resources efficiently and effectivelyPh.D.Includes bibliographical reference
Bentgrass Disease resistance affects fungicide scheduling and dollar spot control
No full textDollar spot caused by Clarireedia spp. is a wide-spread fungal disease of most cool- and warm-season turfgrasses. Bentgrass (Agrostis spp.) is a popular choice for high-quality playing surfaces on golf courses. Bentgrass species and cultivars vary in resistance to dollar spot. Five field trials managed as fairway turf were conducted from 2018 to 2023 to assess the impact of bentgrass disease resistance on fungicide scheduling strategies to control dollar spot. Curative fungicide scheduling has the greatest potential for reducing fungicide inputs but also the greatest risk of unacceptable disease outbreaks. Two field trials were conducted to assess the effectiveness of using damage-threshold fungicide scheduling to control dollar spot on bentgrasses with a range of disease resistance. Factorially arranged randomized complete block designs were used to evaluate bentgrass (species and cultivars) and three fungicide schedules, which included a calendar schedule and two damage-threshold schedules sprayed at 24-h or the next spray-day (NSD) after the damage-threshold was observed. The damage-threshold schedules reduced fungicide inputs up to 78% compared to the calendar schedule with the greatest reduction observed on cultivars with greater resistance to dollar spot. The 24-h damage-threshold schedule achieved acceptable control of dollar spot on all cultivars in both trials; however, acceptable control of dollar spot with the NSD schedule was only feasible on disease resistant cultivars. Unacceptable control of dollar spot resulting from delaying threshold sprays to the NSD on susceptible cultivars typically occurred when disease pressure was high.
A model-based fungicide control strategy also has the potential for improving the accuracy of fungicide scheduling and reducing fungicide use. A third field trial evaluated the feasibility of using novel action thresholds from a logistic regression model to schedule fungicide applications to control dollar spot on disease resistant (Declaration) and susceptible (Independence) creeping bentgrass cultivars. Twenty-three action thresholds from the Smith-Kerns dollar spot predictive model were used to schedule fungicide sprays, which included four risk index (RI) thresholds (20, 30, 40, and 50%); four assessments of the change in the RI over time (RI slope); and 15 combinations of RI thresholds and RI slope. The four assessments of RI slope considered a positive change in RI value over the previous 5-d, forecasted 5-d, previous or forecasted 5-d, or previous and forecasted 5-d as action thresholds to schedule fungicide applications. The trial also included calendar and curative damage-threshold schedules for comparisons. All model action threshold schedules maintained acceptable dollar spot control equivalent to the calendar schedule on the resistant cultivar Declaration, which resulted in reductions in fungicide input up to 67% compared to the calendar schedule. However, for the susceptible cultivar Independence, only the 20% and 30% RI action thresholds and the same RI thresholds combined with a positive RI slope over the previous or forecasted 5-d maintained acceptable control of disease; the 30% RI combined with RI slope reduced fungicide input 33% compared to the calendar schedule.
Anecdotal observations, albeit inconsistent, suggest that fungicides applied the previous autumn can suppress the development of dollar spot the following spring. Two field trials evaluated the impact of autumn fungicide timing and chemistry on the onset and progress of dollar spot during the following growing season on creeping bentgrass cultivars varying in resistance to the disease. Additionally, the concentration of Clarireedia spp. was evaluated in the autumn after fungicide schedules ended, as well as the subsequent spring before or at the onset of disease. Both trials evaluated non-treated controls, seven calendar schedules sprayed with a tank mixture of fluazinam and propiconazole and one sprayed with chlorothalonil. There were three additional fungicide schedules in the second trial. The first trial was conducted on ‘007’ creeping bentgrass and the second trial evaluated ‘Coho’, ‘007’, and ‘Independence’ as disease resistance (cultivar) factor. The sequential application of the tank mixture in September and October was the fungicide level with the fewest applications and was among treatments with the greatest suppression of disease during the subsequent spring in both trials. In addition, sequential applications of chlorothalonil in September, October and November had little or no effect on AUDPC in both trials. Disease onset and AUDPC during the onset period were strongly influenced by the cultivar factor, which interacted with the fungicide factor in one year of the second trial. The delay in disease onset was the greatest, and disease severity was the least on Coho creeping bentgrass. The 20% risk index threshold schedule was the only treatment that provided the greatest delay in disease onset and suppression of AUDPC across all cultivars during the subsequent season in both years of the second trial. The most effective timings of the tank mixture applied on the disease resistant cultivar Coho delayed the onset of dollar spot at least seven weeks in the second trial. Disease severity and delay in disease onset were positively and negatively correlated, respectively, with the pathogen load measured in autumn and the subsequent spring in both trials. These results support the hypothesis that suppression of pathogen load in autumn carries forward the following year reducing dollar spot. It is apparent that disease resistant cultivars have great potential for reducing fungicide inputs. Further research is needed to evaluate other fungicide chemistries and whether properly timed sprays during autumn can reduce fungicide inputs on disease susceptible cultivars the subsequent year.
Dollar spot resistant bentgrass cultivars provide flexibility when choosing fungicide scheduling strategies that maintain excellent disease control equivalent to the calendar schedule. Damage-threshold schedules provide the greatest potential for reducing fungicide input for resistance cultivars. However, using damage-threshold schedules on susceptible cultivars was not feasible because of a greater risk of unacceptable disease outbreaks. Substantial reductions in fungicide inputs on a dollar spot resistant creeping bentgrass cultivar can be realized when using higher risk index thresholds along with risk index slopes from the Smith-Kerns disease predictive model. End of the season pathogen load as influenced by cultivar, fungicide timing, and fungicide chemistry can have a significant impact on dollar spot development the subsequent year.Ph.D.Includes bibliographical reference
SDR-based emulation of machine learning-enabled spectrum sharing in 5g private networks
No full textAs 5G private networks become increasingly prevalent, efficient spectrum-sharing strategies for collocated deployments are critical. This study evaluates machine learning (ML) strategies for interference prediction and dynamic frequency/channel assignment in collocated 5G networks operating in the 3.5 GHz band. Using a Software Defined Radio (SDR) based emulation on the COSMOS testbed, we develop a spectrum management framework that leverages ML for real-time spectrum monitoring and intelligent resource allocation. By identifying spectrum overlaps and underutilized bands, the framework dynamically optimizes network performance. The system, powered by an XGBoost model with 85% accuracy, effectively predicts interference scenarios and adjusts network frequencies to enhance performance. Key performance indicators, such as Packet Error Rate (PER) and Signal-to-Interference-plus-Noise Ratio (SINR), show significant improvements following frequency reassignment, validating the effectiveness of the model-driven approach. Designed for scalability, this system is poised for application in more complex network environments involving multiple networks and channels.M.S.Includes bibliographical reference
Using phospholipids to reduce intracellular cholesterol accumulation in npc1 deficiency
No full textNiemann-Pick type C (NPC) disease is a rare genetic disorder caused by mutations in the NPC1 or NPC2 genes, leading to abnormal intracellular cholesterol accumulation in late endosomes/lysosomes (LE/LY) that ultimately cause cells to fail and causes neurological symptoms in children and teenagers. Exogenous enrichment with lysobisphosphatidic acid (LBPA), also known as bis-monoacylglycerol phosphate or BMP, either directly or via the LBPA precursor phosphatidylglycerol (PG), has been shown to reduce LE/LY cholesterol accumulation and has been investigated as a potential therapeutic intervention in NPC disease. Here we report the effects of stereoisomer configuration and acyl chain composition of LBPA on cholesterol clearance in NPC1-deficient cells. We find that S,R, S,S, and S,R LBPA stereoisomers behaved similarly, with all 3 compounds leading to comparable reductions in cholesterol accumulation in two NPC1-deficient human fibroblast cell lines. Examination of several LBPA molecular species containing one or two mono- or polyunsaturated acyl chains showed that all LBPA species containing one 18:1 chain significantly reduced cholesterol accumulation, whereas the shorter chain saturated species di-14:0 LBPA had little effect on cholesterol clearance in NPC1 deficient cells. Since cholesterol accumulation in NPC1 deficient cells can also be cleared by PG incubation, we used non-hydrolyzable PG analogues to determine whether conversion to LBPA is required for sterol clearance, or whether PG itself is effective. The results showed that non-hydrolyzable PG species were not appreciably converted to LBPA and showed virtually no cholesterol clearance efficacy in NPC1 deficient cells, supporting the notion that LBPA is the active agent promoting cholesterol clearance from the LE/LY. In summary, the stereoisomers have no effect and the size (chain length) of LBPA have effect on cholesterol clearance in NPC1-/- cells, these studies are helping to define the molecular requirements for potential therapeutic use of LBPA as an option for addressing NPC disease.M.S.Includes bibliographical reference