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Biostimulant Applications in Watermelon Production
No full textWatermelon is a water-intensive vegetable crop that is sensitive to drought and requires adequate nutrient supply for good yield and fruit quality. It is an important vegetable crop grown on 142,000 acres in the United States and 586 acres throughout Utah. Utah experienced its driest year on record in 2020 during a five-year drought (2019 to 2023). This affected local ecosystems, including the Great Salt Lake, which experienced record-low water levels. The drought also negatively affected the agricultural sector, including watermelon growers. Higher-than-average fertilizer prices in recent years have also threatened watermelon production. Growing awareness of agriculture\u27s water use and its contribution to nutrient pollution in waterways has prompted investigations into reducing water and fertilizer applications to achieve more sustainable watermelon production in Utah. Plant biostimulants have been shown to increase drought tolerance and enhance nutrient efficiency and may be a novel method to combat these challenge
Improving Ground Cover Crop Fractional Vegetation Mapping via Causality-Based Deep Representation Learning
No full textSemantic segmentation and deep learning methods have rarely been applied to fractional vegetation cover (FVC) segmentation tasks due to the lack of publicly available datasets for training deep learning models. FVC is a key indicator for assessing vegetation distribution, crop density, and crop responses to water availability and fertilizer application, yet conventional field-based measurement methods are time consuming, costly, labor intensive, and may lack the accuracy required for critical applications such as drought stress evaluation and water productivity. In this paper, we introduced causality-based deep learning techniques for FVC segmentation on a publicly available RGB dataset that consists of four ground cover crops: Phyla nodiflora L., Cynodon dactylon, Frankenia thymifolia Desf., and Oxalis stricta L. By separating causal from spurious correlations in pretrained features, using the stepwise intervention and reweighting (SIR) method at different encoder stages reduced confounding bias and enabled the models to learn more generalizable and task-relevant features. Extensive experiments on the FVC dataset, conducted with and without causality learning, showed that the proposed FCN + ResNet-50 model with causality learning and data augmentation achieved an accuracy of 94.80%, a precision of 94.97%, a recall of 94.35%, and an F1-score of 94.62%, which outperformed non-causal baselines and state-of-the-art transformer-based models including SegFormer and Mask2Former
Children and Youth with Special Health Care Needs (CYSHCN) Regional Support Centers (RSC): A framework for effectively engaging stakeholders
No full textThe three University Centers for Excellence in Developmental Disabilities (UCEDDs) in New York served as Children and Youth with Special Health Care Needs (CYSHCN) Regional Support Centers (RSC). The RSCs aimed to increase the capacity of local CYSHCN programs to connect with and support CYSHCN and their families in their community. RSCs conducted activities in the areas of family engagement, training and educational materials development, and technical assistance. RSCs engaged in ongoing dialogue with CYSHCN and their families to learn about experiences to inform systemic improvements through focus groups, interviews, and surveys. This article describes a statewide framework to serve and support CYSHCN and their families, which promotes data-driven quality improvement that incorporates family engagement at all levels. Data from conversations with families of CYSHCN informed systemic improvements through the following themes: 1) a disability diagnosis impacts and puts demands on the entire family; 2) parents of CYSHCN must develop and communicate detailed understanding of their children’s needs regarding health care, education, child care, and other supports; 3) parents often experience difficulty navigating systems when seeking services and supports; and, 4) community inclusion is difficult to achieve due to limited understanding and support. Informed by feedback from families, RSCs provided local CYSHCN programs with ongoing, individualized technical assistance to develop outreach strategies and resources. RSCs are a viable option to engage parents and improve services to CYSHCN in local communities
Psychological Correlates of Audiological and Self-Report Indicators of Hyperacusis in Adults With Misophonia
No full textPurpose:
Misophonia and hyperacusis are distinct sound sensitivity conditions that can co-occur and are linked to heightened emotional distress and impaired functioning. This study investigated the convergence between audiometric-based and self-reported indicators of hyperacusis and examined how each related to misophonia severity and psychological outcomes in adults with misophonia.
Method:
Participants (N = 60) completed self-report measures and underwent loudness discomfort level (LDL) testing.
Results:
Analyses revealed discrepancies between self-reported and LDL-defined hyperacusis, with no significant associations between LDL-based classifications and psychological or misophonia-related outcomes. In contrast, hyperacusis identified via a standardized self-report measure was significantly associated with heightened emotional and behavioral reactivity in misophonia, as well as greater severity and elevated internalizing symptoms, including depression, anxiety, and stress.
Conclusions:
Findings raise concerns about the clinical utility of LDLs as standalone indicators in misophonia populations. They highlight the value of subjective assessments for identifying clinically relevant variables associated with hyperacusis and misophonia comorbidity. Our results support that self-report tools may better capture the lived experience and psychological features associated with co-occurring hyperacusis and misophonia. However, until more research is established, we recommend employing audiometric and self-report metrics to assess for hyperacusis in this population. Future research should focus on identifying psychometric measures that can accurately distinguish between the two conditions and on developing standardized protocols for assessing hyperacusis in individuals with misophonia
Identifying Priorities to Guide Extension Water Quality Programming in Utah
No full textUtah State University Extension water quality faculty conducted a statewide water quality needs assessment to identify priority contaminants and resource gaps. The results found that elevated nutrient levels and sedimentation were the top concerns where additional resources would be beneficial. In addition, insufficient funding for improvements and low public engagement were identified as key challenges in addressing water quality issues. Results revealed current unmet needs with regional variation to inform Extension resource allocation and programming
Growth and Development of Lettuce and Mizuna Under URRM-Representative Conditions
No full textThe Utah Reusable Root Module (URRM) is a zero-discharge plant growth system under development for operation in microgravity aboard the International Space Station. Qualification of the URRM requires defining expected crop performance under representative operating conditions to evaluate nominal biological behavior. This study presents experimental results documenting the growth of mizuna (Brassica rapa var. japonica) and Outredgeous lettuce (Lactuca sativa L.) under URRM-representative conditions defined by the use of a silicone rubber fringe top cover for root-zone containment. Ground-based experiments were conducted using the Greenhouse System, URRM Simulator – Replicate 1, and URRM Simulator – Replicate 2, all filled with peat-based growth media maintained at approximately 60% volumetric water content (θᵥ = 0.60 cm³ cm⁻³) and fertigated with the same nutrient solution formulated for URRM operations. All growth metrics reported here correspond to initial planting cycles established without residual root material at the planting surface. Thus, the results do not reflect potential effects associated with sequential replanting in previously occupied positions. Crop establishment and growth were evaluated through emergence, early leaf development, canopy diameter, plant height, fresh shoot biomass, and variability metrics across developmental stages. Emergence and early seedling development under URRM-representative conditions were comparable to uncovered controls, indicating that the silicone rubber fringe containment approach did not impede plant establishment. Canopy diameter proved to be a practical, non-destructive, and easily measurable indicator of early development through approximately 12 days after seeding (DAS). Following establishment, plant height and fresh shoot biomass were used to characterize continued vegetative growth. From 10 to 24 DAS, plant height increased steadily in both crops, with the most rapid increase occurring between 17 and 24 DAS; thereafter, lettuce exhibited a reduced rate of vertical growth consistent with a sigmoidal pattern. Fresh shoot biomass accumulated rapidly in both crops; however, mizuna reached harvest-relevant biomass by 24 DAS, whereas lettuce required approximately one additional week (28–31 DAS) to reach comparable final biomass under conservative light conditions (300–500 µmol m⁻² s⁻¹ PPFD). These results provide a data-informed reference describing expected mizuna and lettuce growth under URRM-representative conditions and support evaluation of system performance during qualification testing and early operations
General Education Subcommittee Minutes January 8, 2026
No full textCall to Order Approval of Minutes - December 4, 2025 Course Approvals/Removals/Syllabi Approvals New Business Adjourn: 9:30 a
VegClassify: A Hybrid Deep Learning–NDVI Greenspace Classification Tool and GUI
No full textVegClassify is a hybrid, high-resolution greenspace classification tool integrating a fine-tuned deep learning model with automated NDVI threshold optimization. The tool improves accuracy and reduces subjectivity in vegetation classification using NAIP imagery. This graphical user interface (GUI) was developed to extend and simplify the use of the previously established greenspace image classification tool. The GUI provides an accessible, user‑friendly environment for running the VegClassify workflow without requiring users to interact directly with Python scripts or command‑line tools. It supports loading NAIP or other high‑resolution imagery, running deep‑learning–assisted NDVI threshold optimization, visualizing classification outputs, and exporting vegetation masks.
The underlying Python codebase that implements the VegClassify hybrid deep learning–NDVI threshold method was originally published in:
Wang, H., Zhao, X., Gholami, S., McGinty, C., Chamberlain, B., & Qi, X. (2026). A Hybrid Deep Learning and NDVI Threshold Approach for High-Resolution Urban Greenspace Classification. Urban Forestry & Urban Greening, 129332.
This GUI serves as an application‑layer wrapper for that Python tool, enabling broader accessibility for research, planning, education, and applied landscape analysis
Aspen Graffiti are Close to Trails and on Large-Diameter Trees
No full textKey results
1. Close to the trail
75% of all graffiti was within 60 feet of the trail.
2. On the largest trees
Graffiti was concentrated on the largest diameter aspen (larger than 8 in diameter).
3. Higher growth in marked aspen
Aspen with graffiti had 24% higher radial growth, relative to unmarked aspen. This is likely because people carve the healthiest aspen
General Education Subcommittee Agenda, January 8, 2026
No full textCall to Order Approval of Minutes - December 4, 2025 Course Approvals/Removals/Syllabi Approvals New Business Depth Courses and Engineering Discussion Additional Items Adjourn: 9:30 a