100039 research outputs found
Sort by
NourishTank: Participant Experiences and Feedback
Get PDFNourishTank is a shark-style competition that empowers students to develop innovative, sustainable solutions for combating hunger and ensuring food security. Following the inaugural NourishTank event, participants provided valuable feedback, including an enhanced understanding of hunger issues, improved hard and soft skills, and expressed interest in volunteering. Feedback emphasized academic and personal network channels for awareness and suggestions for improvement related to program refinement and expansion of outreach efforts
Determining the Interrelationships for Dietary Intake of Lipid Oxidation Products from Thermally Abused Oils, the Gut Microbiome, and Gut Health
Get PDFDesigning a Robust Lab Scale Electrocoagulation Reactor For Removal of Micro- and Nanoplastics from Drinking Water
Get PDFThe widespread occurrence of micro and nanoplastics in drinking water sources could cause serious public health issues. These plastics particles, products of industrial operations and weathering of larger plastics, can interact with other contaminants in the environment, leading to more severe environmental pollution. Present drinking water treatment technologies were designed to remove suspended colloids. However, due to the distinct chemical and physical properties of micro and nanoplastics from conventional colloids, it is challenging for traditional chemical coagulation/flocculation/sedimentation process to achieve satisfying removal performance. This report investigates a design of a lab-scale electrocoagulation reactor for the removal of nanoplastics from drinking water sources. Synthesized polystyrene nanoplastics (246.50 ± 16.12 nm, spheres) were added to a 200 ml solution of 5 mM sodium chloride to a concentration of 1.585 mg/l. Electrode holders, printed in 3D using acrylonitrile butadiene styrene (ABS) filament (1.75 mm), were specifically designed, and made with an adjustable bar to hold the electrodes in precise, measurable vertical positions. A DC power supply and multimeter were used for precise voltage and current control during electrocoagulation. Two aluminum plates with an active surface area of 1.0 x 10-3 m2 were used as the electrodes. The design was tested under a constant voltage (5 V) but varying currents (10 mA, 25 mA, and 50 mA). After 2 hr electrocoagulation and 2 hr settling, the concentration of nanoplastics in the water column was determined using a turbidity meter. Electrocoagulation reduced the nanoplastics concentration by 83.6 %, 90.8 % and 93.9 % (n=3 x 3 trials for each current) with a current of 10 mA, 25 mA, and 50 mA, respectively, without the addition of a flocculant or coagulant. The impact of current was statistically significant. It was also observed that the pH increased in the solution from 5.5 to a stable pH of 8.3, which facilitates aluminum hydroxide formation for removal of the nanoplastics through hetero aggregation. The distribution of nanoplastics in the produced foam and the settled phase were also determined, and mass balance analysis on total nanoplastics were performed. While the volume of foam produced correlated with the current intensity, nanoplastic content in foam increased first then appeared to reach a peak. The mass balance performed across the systems with different currents recorded an average percentage recovery of 114 ± 2.2 (std error).
These findings demonstrate that electrocoagulation can be employed for removing nanoplastics from drinking water sources. This study lays the groundwork for the systematic evaluation of the impact of different water chemistries (different ionic strengths, concentration and types of dissolved organic matter, pH buffering capacity) and different plastic type (size, shape, and surface morphology) on the effectiveness of electrocoagulation. Future studies will aid in process scale up and further improving drinking water safety addressing this crucial environmental and public health problem
Policies and Programs for Water-Wise Residential Landscaping in Utah
Get PDFWater-wise landscaping is the practice of using plants in a landscape that are adapted to local conditions and only need small amounts of water. Converting yards to water-wise landscaping conserves water and is a climate adaptation action residents can take to benefit Utah as temperatures rise and drought becomes more common. Many policies and programs in Utah help residents use water conscientiously and make changes. This fact sheet provides information regarding current and future water-wise landscaping policies and programs in Utah
Curriculum Subcommittee Agenda September 5, 2024
Get PDF• Approval of Minutes – April 4, 2024
• Program Proposals
• Semester Course Approval Reviews: College of Agriculture and Applied Sciences, Caine College of the Arts, Jon M. Huntsman School of Business, Emma Eccles Jones College of Education and Human Services, College of Engineering, College of Humanities and Social Sciences, S.J. & Jessie E. Quinney College of Natural Resources, College of Science, College of Veterinary Medicine, Other
• Other Business
• Adjourn: 3:00 p
Inclusion in Disability Evaluation and Surveillance Projects: Reflections and Recommendations For Inclusive Project Teams
Get PDFDisability rights advocates emphasize “Nothing about us without us,” yet a program evaluation or surveillance team’s composition rarely reflects inclusion of the individuals from the disability populations they focus on. Individuals who have lived experience with disabilities should be present during all steps of program evaluation and surveillance projects in meaningful ways to progress the impact of disabilities work. In this paper, we describe a process used by staff at Alaska’s University Centers for Excellence in Developmental Disabilities (UCEDD) to hire, train, and work with individuals with intellectual, development disabilities (IDD) as team members. The case example for the inclusion effort was the National Core Indicators (NCI) In-Person Survey (IPS). Recruitment started in December 2020 with Zoom interviews for the NCI IPS occurring from March through August 2021. The project team included ten staff members, one of whom also was an individual who experiences an IDD (partner interviewer). Team members completed web based and Zoom training sessions. Throughout the training and onboarding process, project leads sought to modify the training and project implementation to better suit the expressed needs of all team members. To support the partner interviewer with IDD, two team members with research and program evaluation experience served as “lead” interviewers. Project leads also created a simplified version of the NCI IPS instrument for data collection. Multiple training sessions were held to acclimate the lead and partner interviewer with the team interview process and modified data collection instrument. Recommendations for improving our UCEDD program evaluation and surveillance inclusive practices were noted: Involve individuals with disabilities in every part of project planning processes; allow team members agency in selecting their projects and room for flexibility if research plans don’t work out; establish open communication and safe spaces for all team members; provide comprehensive, accessible, and equitable training; give team members a sense of timelines and trajectories of research projects with regular check-ins; adjust practices for an increasingly online work environment with COVID-19; develop accessible training, data collection and data entry systems; and invest in all team members long-term
Medium-sized Fires Burn Less Severely Than Large Fires
Get PDFResults of research in forest health and forest futures in Uta
Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, And Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I
Get PDFFrom June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (208,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication