The title of the project is "Effective Cardiopulmonary Resuscitation in Prone Position: A Training Program for Operating Room Professionals." This is a DNP student.

ANSC PhD Defense: Issabelle Ampofo (MSc)When: Friday, October 24, 2025 | 11:00 a.m.-12:00 p.m.Location: Hybrid. In-Person: WITE 115 (York Room) Virtual: https://s.uconn.edu/ampofo (https://s.uconn.edu/ampofo)If you require an accommodation to participate in this event, please contact Dr. Breno Fragomeni at 860-486-1069 orbreno.fragomeni@uconn.edu (mailto:breno.fragomeni@uconn.edu)at least 5 days in advance of the seminar

Examining the Effects of the POWER Program on Internalizing Behaviors of High School Students with Disabilities. Department of Educational Psychology.

Resource Input Management in Container-Grown Petunias to Reduce Water Use and Leachate   Container-grown production depends on frequent fertigation to maintain optimal water and nutrient levels. In some scenarios where water volumes exceed crop requirements, an excess of nutrient runoff rich in nitrate-nitrogen and phosphate-phosphorus can contaminate our water sources and generate environmental degradation, which generate the need of improved irrigation practices while achieve sustainable horticultural production. The goals of this research were to 1) Quantify the difference in water consumption between the two automated irrigation systems throughout the growing season, and calculate the cost savings achieved through reduced water use; 2) Estimate the gray water footprint of three types of irrigation combined with two fertilizer rates for greenhouse production of Petunia milliflora F1 (Picobella Pink) and compared the environmental impacts of these practices; 3) Estimate water savings, plant growth, ornamental quality, and leachate reduction when irrigating petunia plants at low container capacities; 4) Assess if chitosan as a substrate amendment combined with reduced container capacity could result in marketable quality petunias at the completion of production cycle and after a two-week postharvest period; 5) Assess if mycorrhizae applied as a substrate amendment during germination, combined with reduced container capacity, could result in marketable quality petunias at the completion of production cycle. The first study showed that weight-based precision irrigation reduced water consumption by 21-26% and costs by 24-28% compared to time-based systems. The second study reported that mist irrigation consumed five times more water than drip or subirrigation systems, with phosphate-phosphorus serving as the more sensitive environmental indicator due to its lower regulatory threshold. Subirrigation systems eliminated leachate entirely, resulting in zero GWF. The third study established that maintaining substrate at 70% container capacity reduced water use by 21-26% without compromising flower coverage. At 40% CC, irrigation water use efficiency reached 3 g·L⁻¹. The fourth and fifth studies registered that chitosan reduced water use and improved post-harvest heat tolerance, while arbuscular mycorrhizal fungi improved performance only under severe stress (40% CC). These findings demonstrate that integrating precision irrigation technologies with moderate deficit irrigation and strategic biostimulant applications can substantially reduce environmental impact while preserving marketable quality in ornamental production.

Container-grown production depends on frequent fertigation to maintain optimal water and nutrient levels. In some scenarios where water volumes exceed crop requirements, an excess of nutrient runoff rich in nitrate-nitrogen and phosphate-phosphorus can contaminate our water sources and generate environmental degradation, which generate the need of improved irrigation practices while achieve sustainable horticultural production. The goals of this research were to 1) Quantify the difference in water consumption between the two automated irrigation systems throughout the growing season, and calculate the cost savings achieved through reduced water use; 2) Estimate the gray water footprint of three types of irrigation combined with two fertilizer rates for greenhouse production of Petunia milliflora F1 (Picobella Pink) and compared the environmental impacts of these practices; 3) Estimate water savings, plant growth, ornamental quality, and leachate reduction when irrigating petunia plants at low container capacities; 4) Assess if chitosan as a substrate amendment combined with reduced container capacity could result in marketable quality petunias at the completion of production cycle and after a two-week postharvest period; 5) Assess if mycorrhizae applied as a substrate amendment during germination, combined with reduced container capacity, could result in marketable quality petunias at the completion of production cycle. The first study showed that weight-based precision irrigation reduced water consumption by 21-26% and costs by 24-28% compared to time-based systems. The second study reported that mist irrigation consumed five times more water than drip or subirrigation systems, with phosphate-phosphorus serving as the more sensitive environmental indicator due to its lower regulatory threshold. Subirrigation systems eliminated leachate entirely, resulting in zero GWF. The third study established that maintaining substrate at 70% container capacity reduced water use by 21-26% without compromising flower coverage. At 40% CC, irrigation water use efficiency reached 3 g·L⁻¹. The fourth and fifth studies registered that chitosan reduced water use and improved post-harvest heat tolerance, while arbuscular mycorrhizal fungi improved performance only under severe stress (40% CC). These findings demonstrate that integrating precision irrigation technologies with moderate deficit irrigation and strategic biostimulant applications can substantially reduce environmental impact while preserving marketable quality in ornamental production.

This thesis focused on developing a Swift UI iOS application with an embedded large language model and retrieval-augmented generation for branded food category classification and contextualized explanations for food additives.

Improving management recommendations for Alternaria leaf blight and head rot of broccoli using fungicide resistance monitoring and population genetics   Alternaria leaf blight and head rot (ABHR) is a disease of broccoli that appears in the seedling stage and continues up to harvest. Even minimal black spots on the heads make them unmarketable. In the Eastern US, ABHR is mainly caused by the fungal pathogen Alternaria brassicicola. Other species, including A. japonica, A. alternata, and A. brassicae, can also contribute to disease development. Conventional broccoli growers manage the disease with fungicide applications. They mainly use Quinone-outside inhibitor (QoI) and Succinate dehydrogenase inhibitor (SDHI) fungicides. However, recent reports suggest that resistance may be present in Georgia, New York, and Virginia. Resistance to fungicides occurs when a fungus evolves and acquires a heritable reduction in sensitivity to an anti-fungal agent, such as through mutations. In this study, we sought to investigate if resistance exists in Alternaria populations in the Eastern US, as reports suggest. The standard method used to test Alternaria sensitivity is through spore germination assays. This method is laborious and time-consuming, and measurements can be subjective. The process limits its application to small numbers of isolates. To evaluate many isolates from different regions, a high-throughput method is needed. We developed a microplate assay based on optical density measurements to indirectly estimate inhibition of spore germination by fungicides. Primers to amplify and sequence regions with resistance-conferring mutations for SDHI fungicides were also developed and validated. We used the new high-throughput method to screen more than 600 Alternaria spp. isolates collected in Connecticut, Georgia, Massachusetts, New York, and Virginia between 2019 and 2023. Isolates that showed reduced sensitivity to azoxystrobin (QoI) and boscalid (SDHI) in the microplate assay were further screened for resistance mutations. Results indicated that A. brassicicola, the most abundant species in the Eastern US, was sensitive to azoxystrobin, but multiple resistant isolates to boscalid were detected. Most A. alternata isolates were resistant to both fungicides. The G143A mutation, which confers complete resistance to QoIs, was detected in A. alternata isolates. The H143A mutation, which confers resistance to SDHI, was detected in both species. These results partially explain why fungicides failed to control ABHR. They enable us to make more informed management decisions. Organic growers mainly rely on cultural practices to manage ABHR. Understanding the population biology of the pathogen may help us identify effective cultural practices. We conducted a population genetics study to investigate the structure of A. brassicicola populations in organic farms in Connecticut. High to moderate genetic richness and diversity were observed in most fields in 2022 and 2023. No evidence of recombination was observed, suggesting populations are mainly reproducing asexually. Since we found no evidence of sexual reproduction, we wanted to determine if high genetic diversity was due to multiple introductions of the pathogen. Our data suggests that A. brassicicola has a high dispersal ability. This supports the hypothesis that high diversity results from multiple introductions. These introductions may occur through airborne dispersal of spores or human-mediated dispersal of contaminated seed.

Improving management recommendations for Alternaria leaf blight and head rot of broccoli using fungicide resistance monitoring and population genetics Daniel G. Cerritos Garcia PhD Candidate Plant Science and Landscape Architecture Department Major Advisor: Sydney E. Everhart

Structural variation mechanisms and their rates in inbred mice

COMMITTEE Dr. Kari Adamsons  Dr. Beth Russell  Dr. Florrie Fei-Yin Ng, Dept of Educational Psychology, The Chinese University of Hong Kong

Xinhao Wang from Nutritional Sciences presents his dissertation on an integrated strategy to reduce strawberry waste using bio-based nanocomposite coatings and a deep learning-powered quality monitoring system. This research addresses the critical challenge of postharvest strawberry loss by developing a comprehensive, dual-domain solution. The findings offer promising sustainable strategies to reduce food waste, enhance food safety, and improve quality management in the perishable food supply chain.

This is a doctoral dissertation defense in Kinesiology

A genomic investigation of selection, evolution, and climate sensitivities in Northern sand lance, Ammodytes dubius Oceanography

This dissertation focused on deep learning methods for large-scale dish classification and nutrient estimation through ingredient-guided RGB-D imaging supported by vision-text contrastive learning and Swift UI iOS application development.

COMMITTEE Dr. Eva Lefkowitz  Dr. Keith Bellizzi  Dr. Amanda Denes

This dissertation focused on the design and development for fluorescent sensor array for the foodborne pathogenic bacterial and biofilm identification with machine learning techniques. it also includes the investigation of interfacial biofilm monitoring and quantification for better pathogenic biofilm control and food safety.

This dissertation defense presents a unified, learning-based framework that enhances the accuracy, robustness, and interpretability of Distribution System State Estimation (DSSE) and cyber–physical event analytics. The research develops multi-fidelity framework for uncertainty-aware state estimation and spatio-temporal deep learning architectures for detecting, classifying, and localizing cyberattacks and physical events. Validation on IEEE benchmark feeders and a real-world 2,135-node real distribution system demonstrates significant improvements in scalability, resilience, and transparency compared with traditional methods.