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Nepali Speaking Bhutanese Refugees Agricultural Wisdom Reviving Traditional Practices for a Sustainable Future
Get PDFThe Bhutanese refugee community in Minnesota exemplifies the resilience of traditional agricultural practices amidst modern challenges. Many of these refugees, originating from subsistence farming backgrounds, have revitalized their cultural heritage through community gardens, growing a diverse range of organic vegetables and cereals. Their farming practices, rooted in Himalayan traditions, emphasize sustainability through techniques such as crop rotation, organic fertilization, and seed preservation. Although their agricultural wisdom aligns with global agroecological and regenerative farming movements, barriers like limited land access, language difficulties, and unfamiliarity with U.S. market dynamics restrict their growth potential. Collaborative initiatives with academic institutions, nonprofits, and local organizations have facilitated small-scale organic farming and cultural integration. However, engaging younger generations in farming and expanding market access remain pressing concerns. Recommendations include fostering cooperative models, introducing niche crops to farmers' markets, and bridging technological and linguistic divides. These efforts aim to integrate Bhutanese practices into broader climate resilience and food sovereignty frameworks, while enhancing immigrant empowerment. By blending their ancestral knowledge with modern innovations, Bhutanese farmers contribute to sustainable agriculture, biodiversity conservation, and climate justice. Their story serves as a compelling model for inclusive sustainability and the revitalization of traditional wisdom in new contexts.Dhakal, Narayan. (2025). Nepali Speaking Bhutanese Refugees Agricultural Wisdom Reviving Traditional Practices for a Sustainable Future. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277239
Data in Support of: Pollen mineralization fuels biogeochemical cycling and microbial community succession in Lake Superior
No full textThese data were collected as a part of a microcosm incubation experiment where pollen was spiked to Lake Superior water. Measurements presented in this file include raw results (pollen_leaching_chemical_data_raw.csv). The data included here contain chemical measurements and sample/time identifiers (columns) and individual measurements (rows).This dataset supports the journal article "Pollen mineralization fuels biogeochemical cycling and microbial community succession in Lake Superior" in preparation for submission to the Journal of Geophysical Research: Biogeosciences and was collected during the winter of 2024-2025. The research team (Jake Zunker and Kathryn Schreiner) collected chemical variable measurements from a microcosm incubation study in which conifer pollen was spiked into waters collected from Lake Superior water to observe chemical leaching. Data include dissolved and particulate fractions of carbon, nitrogen, and phosphorus measured in both control (lake water, no pollen) and treatment (pollen spiked into lake water) jars over a time series spanning 0 to 60 days.Zunker, Jake D; Schreiner, Kathryn M; Wood, Andrew W; Larson, Britta L; Chun, Chan Lan; Bailey, Keagan; Minor, Elizabeth C; Hendrickson, Eva; Filstrup, Christopher T. (2025). Data in Support of: Pollen mineralization fuels biogeochemical cycling and microbial community succession in Lake Superior. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/HRSJ-H838
Evidence for Biofilm-induced Self-sustaining Meandering Channels
No full textThis dataset contains all processed materials generated from three independent experimental replicates designed to investigate how synthetic biofilm alters river channel morphodynamics. The dataset includes:
1. Top-view images and videos
High-resolution time-series images (.png) and videos (.mp4) documenting river evolution throughout every flow cycle. These files capture the transition from a braided channel to a single-thread meandering, reproducing different aspects of natural rivers such as cutoff events, bank migration, and channel narrowing as biofilm cohesion develops.
2. 3D topography
Laser bed elevation measurements were collected during low cycles. These data allow for quantification and reconstruction of river evolution.
3. YOLO machine learning
A trained YOLO segmentation model for automated river morphology detection, including:
Model weights and scripts for running and extracting channel masks, centerlines, and additional metrics.
4. Processed datasets
Pre-computed .csv files summarizing:
Sediment transport measurements.
Channel width, depth, and sinuosity.
Erosion and deposition maps.
Together, these datasets provide complete and reproducible information on how synthetic biofilm influences sediment transport and channel patter evolution, enabling analysis, and broader use in geomorphology and planetary analog research.This dataset presents the complete morphological evolution of a braided river channel transitioning into a single-thread meandering channel under the influence of synthetic biofilm. It includes data from three independent experimental replicates, all conducted these experiments in a large flume at the St. Anthony Falls Laboratory, where a sand bed, constant sediment feed, and alternating high and low flow cycles were used to reproduce natural river dynamics.
The dataset contains high-resolution top-view images and videos documenting channel evolution during each flow cycle, 3D topographic measurements collected using a laser scanner, and machine learning including a trained YOLO segmentation model (weights and analysis scripts) for detecting and tracking channel morphology.
Together, these data provide comprehensive and reproducible evidence of how synthetic biofilms modify sediment transport, channel mobility, and river morphodynamics, ultimately promoting the emergence and persistence of meandering river patterns across all three replicates.This study is supported by National Science Foundation CAREER Award EAR 2236497 and Office of Navy Research Grant N00014‐23‐1‐2559.Cúñez, Esteban A; Yang, Judy Q. (2025). Evidence for Biofilm-induced Self-sustaining Meandering Channels. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/kr83-8898
Thermally accessed carbenes and benzynes derived from alkynes as a platform for synthetic methodology development
Get PDFUniversity of Minnesota Ph.D. dissertation. June 2025. Major: Chemistry. Advisor: Thomas Hoye. 1 computer file (PDF); ix, 418 pages.1H NMR (proton nuclear magnetic resonance) spectroscopy is the most widely used tool for the identification and characterization of organic compounds. Accurate referencing of 1H NMR data is particularly important for comparison of spectra of different samples of the same substance. In my first research project, I compared the effectiveness, and therefore accuracy, of the most common 1H NMR reference compound, tetramethylsilane (TMS). My findings explicitly show that TMS is a superior reference compound compared to the residual CHCl3 that is often referenced when using CDCl3 solutions (Chapter 1).The hexadehydro-Diels–Alder (HDDA) reaction is the thermal or photochemical cycloisomerization of a poly-yne system to produce an o-benzyne intermediate. The fleeting o- benzyne is trapped in situ to afford benzenoid products. Given the paramount importance of heterocycles in modern drug discovery efforts, there has been a growing impetus to incorporate heterocycles into the framework of HDDA chemistry. From this perspective, I was curious to study the reactivity of oxadiazoles with HDDA benzynes, and consequently I discovered novel oxadiazole reactivity, producing a series of 2:1 adducts (Chapter 2).
Alkynes may also be used as precursors for the generation of free carbenes, another type of high-energy, fleeting intermediate. Few examples of alkyne-derived free carbene generation have been reported. However, in 2024, Xu and Hoye demonstrated the broad synthetic utility of heteroaryl free carbenes, produced by the formal (3+2) cycloaddition between 2-alkynyl iminoheterocycles and electron-deficient alkynes. I expanded upon this work, showcasing how this 100% atom economical carbene methodology could be applied to the synthesis of complex, polycyclic cyclopropanes (Chapter 3). During the cyclopropanation study, I unexpectedly isolated a product arising from an intermolecular formal C–H insertion into a terminal alkyne. In recognizing that intermolecular trapping of free carbenes could be broadly applied to a host of trapping functionalities, I capitalized on this idea and developed a three-component coupling methodology in which free carbenes can be trapped by terminal alkynes, N-Boc carbamates, and amides (Chapter 4).Guzman, Alexander. (2025). Thermally accessed carbenes and benzynes derived from alkynes as a platform for synthetic methodology development. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276764
Responsible machine learning in child welfare and digital mental health
Get PDFUniversity of Minnesota Ph.D. dissertation. May 2025. Major: Computer Science. Advisors: Haiyi Zhu, Zhiwei Steven Wu. 1 computer file (PDF); xx, 272 pages.Machine learning-based technologies are increasingly being used to assist care work in high-stakes domains, from provisioning resources for poor families to providing mental health support for people experiencing distress. These technologies have been introduced in hopes that they improve care and decision quality. Depending on how they are designed and used, these technologies may also perpetuate harms like racial discrimination or carcerality. In order to understand how machine learning technologies can harm or help, it is necessary to understand the perspectives of people who use these technologies or are impacted by them. Yet, in many high-stakes domains, the perspectives of impacted people ---especially those who are marginalized or do not have the ability to directly influence the design of these technologies--- remain overlooked. This dissertation presents case studies of evaluating and designing machine learning technologies in child welfare and digital mental health through both quantitative and qualitative methods with people who may be impacted by machine learning technologies. Within child welfare, I explore how existing algorithmic decision-making tools exacerbate harms. First, I evaluate a particular algorithm used in the child welfare system to understand how workers use it to reduce or exacerbate racial biases. Second, I engage impacted people like parents and workers to understand how these algorithmic technologies replicate further systemic harms like carcerality. I then explore how we might design different technologies to benefit those most marginalized by the child welfare system. Within digital mental health, I continue to explore how AI-based technologies might be designed or deployed responsibly in this space, if at all. I use participatory design to understand how digital mental health support providers approach suicide prevention online and whether they think machine learning technologies could benefit them while preventing harms to support seekers. Finally, based on suggestions from mental health support providers, I design and evaluate conversational agents that simulate people in distress to help train new support providers. This work aims to showcase ways to understand how machine learning technologies exacerbate systemic harms and how we might design them better.Stapleton, Logan. (2025). Responsible machine learning in child welfare and digital mental health. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277399
Career Trends of University of Minnesota School of Public Health Alumni: Baseline Survey Results for a Longitudinal Study
Get PDFObjective: This work describes baseline results of the University of Minnesota (UMN) School of Public Health (SPH) Career Trends Survey (CTS), allowing for comparison to future CTS data.
Design and Setting: The UMN SPH CTS was fielded using multiple methods, including paper and online, from January to March 2021.
Participants: All US-based (at time of survey) UMN SPH alumni for whom the school maintained contact information were eligible to complete the survey. In total, 8817 alumni received the survey and 1966 responded (22% response rate).
Main Outcome Measure: We examined the proportion of graduates’ first jobs by job sector over time, the proportion of graduates who switched job sectors over time, and how closely graduates’ first jobs related to public health over time.
Results: Graduates overwhelmingly reported that their first jobs were either “somewhat related” or “strongly related” to public health, but a smaller proportion of graduates in the 2010s reported their first jobs being “strongly related” to public health compared to graduates from the first decades for which we have data.
Conclusions: Data suggest a noteworthy trend: proportionally fewer of our recent public health graduates are going into governmental public health. Though a more nationally representative dataset is still needed, our results are a crucial step forward in determining how to mitigate the staffing up difficulties faced by many public health agencies.The authors received no financial support for the research, authorship, and/or publication of this article.Weiss, Nicole M.; Leider, Jonathan P.; Kaltved, Darren; Thao, Kablia; Pettigrew, Melinda. (2025). Career Trends of University of Minnesota School of Public Health Alumni: Baseline Survey Results for a Longitudinal Study. Retrieved from the University Digital Conservancy, 10.1097/PHH.0000000000002138
Grounding Assumptions: Building Our Classroom Culture Together
Get PDFThe following guide was developed in partnership with the Digital Education and Innovation Team within the College of Education and Human Development. Created by the First-Gen Institute–College of Education and Human Development, University of Minnesota.Falldin, M.; Xy, X.. (2025). Grounding Assumptions: Building Our Classroom Culture Together. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276612
Nutrient management for irrigated crops in MN: Corn, potatoes, & edible beans
Get PDFRuntime 33:03Today on the Nutrient Management Podcast we discuss nutrient management for irrigated crops in Minnesota. How many irrigated acres are there in Minnesota? What are some of the benefits and risks of irrigation? In what ways can risks be mitigated - through timing, cover crop adoption and / or other general soil health practices? What should growers know about the latest irrigation research in Minnesota? If a farmer is considering adopting irrigation practices, what should they focus on first? This and much, much more on today's episode.Kaiser, Daniel; Rosen, Carl; Sharma, Vasu. (2025). Nutrient management for irrigated crops in MN: Corn, potatoes, & edible beans. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276618
Episode 303 - Improving Calf Health: What Total Serum Protein Levels Are Telling Us - UMN Extension's The Moos Room
Get PDFRuntime 20:49In this episode, Brad shares insights from recent dairy science meetings and dives deep into total serum protein (TSP) levels in calves—a key indicator of successful colostrum management and passive transfer of immunity. He reviews data from multiple studies, including work by Dave Casper in Illinois and a Midwest study on beef-on-dairy cross calves. The results show improvements in TSP levels over time, but highlight that a significant number of calves—especially male and crossbred calves—still arrive at calf ranches with low TSP and signs of poor health. Brad also shares findings from University of Minnesota research, including a 20-year dataset from Waseca involving nearly 6,000 Holstein calves, showing correlations between TSP, growth rates, and calf survival. He questions the rigid cutoffs for TSP and emphasizes a more nuanced view based on outcomes like average daily gain and long-term milk production.The episode wraps with details on two upcoming calf care workshops in Minnesota this summer (July 29 in Rochester and August 5 in Eden Valley) where farmers, consultants, and educators can learn about colostrum management, TSP testing, pain mitigation, and more. Calf Care Workshop - Tuesday, July 29, 2025, Rochester, MN (https://extension.umn.edu/event/calf-care-workshop-rochester); Calf Care Workshop - Tuesday, August 5, 2025, Eden Valley, MN (https://extension.umn.edu/event/calf-care-workshop-eden-valley).Heins, Brad; Krekelberg, Emily. (2025). Episode 303 - Improving Calf Health: What Total Serum Protein Levels Are Telling Us - UMN Extension's The Moos Room. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276591
A Computational Tool for the Reliable Prediction of Pavement Performance based on Temperature Data
Get PDFThis study presents a computational tool for predicting pavement performance using long-term temperature data from thermocouple trees embedded in three flexible and two rigid pavement sections at the MnROAD facility. The research leverages spectral and probabilistic analyses to assess thermal behavior and its impact on pavement condition. Temperature measurements, supplemented by weather data, were processed to address missing data and artifacts using compressed sampling, ensuring a uniform 15-minute sampling interval. Spectral analysis techniques based on Fourier Transform and Wavelet Analysis with Generalized Harmonic Wavelets were used to model pavement layers as a cascade of filters, revealing the time-varying behavior of the filters' gain and phase shift, which indicates that they are sensitive to aging, moisture, and compaction. Wavelet analysis provided superior temporal resolution for detecting transient thermal phenomena. A probabilistic framework using Markov Chain Monte Carlo (MCMC) methods estimated thermal diffusivity coefficients, achieving residuals below 1.17 degree C and robust uncertainty quantification. The results highlight distinct thermal responses across pavement layers, with asphalt showing uniform behavior and base/subgrade layers exhibiting environmental sensitivity. Interfaces between layers displayed significant time-dependent changes, potentially linked to densification. Implemented as a modular Python package with Jupyter notebook examples, publicly available on GitHub, the tool offers a scalable solution for pavement monitoring. This research demonstrates that thermocouple-derived temperature data, when analyzed with advanced computational methods, provides reliable indicators of pavement degradation, supporting data-driven infrastructure management decisions.dos Santos, Ketson; Marasteanu, Mihai; Zhao, Zifeng; Duarte, Joao G. C. S.; Custis, Simon. (2025). A Computational Tool for the Reliable Prediction of Pavement Performance based on Temperature Data. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276872