143258 research outputs found
Sort by
Leaf-Based Varietal Categorization of Sweetpotato (Ipomoea batatas L. Lam.), a Potentially Healthful Vegetable, Using Image Processing and K-Means Clustering
No full textSweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected and pre-processed to extract relevant features. A distinct difference in leaf physical characteristics, i.e., leaf area, perimeter, circularity factor, breadth, and leaf ratio, between the two varieties was observed. K-means clustering algorithm identified two sweetpotato varieties as distinct clusters with centroid values (Cluster 0: Area 695627 and Cluster 1: Area 525895). Results revealed that sweet potato leaves in cluster 0 tend to have more prominent physical characteristics than in cluster 1. This result demonstrates the prospects of using machine learning and image processing techniques for in situ varietal classification of sweetpotato. The results bridge the visual characteristics and their quantitative assessment, fostering a deeper understanding of the plant's phenotype and supporting advancements in agriculture, research, and crop improvement.This research was supported by the National Institute of Food and Agriculture Department (NIFA) through the Evans Allen Grant Project, funded by the United States Department of Agriculture (USDA) (Grant no: GR015407/E/A # 20250/SI).Islam, Shahidul; Rahman, Towfiq; Islam, Md Hamidul; Momin, Md Abdul. (2025). Leaf-Based Varietal Categorization of Sweetpotato (Ipomoea batatas L. Lam.), a Potentially Healthful Vegetable, Using Image Processing and K-Means Clustering. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276903
Essays on international economics
No full textUniversity of Minnesota Ph.D. dissertation. June 2025. Major: Economics. Advisors: Manuel Amador, Timothy Kehoe. 1 computer file (PDF); xii, 187 pages.This dissertation comprises three interconnected chapters, each co-authored with Braulio Britos. The first chapter provides an extensive literature review that sets the stage and informs the analyses in the subsequent chapters. We synthesize existing research on the economic implications of climate change, with a focus on adaptation strategies that involve migration and trade. The following chapters share common features, including the role of adaptation strategies, the frictions that hinder such adaptation, and how we utilize short-run variations in weather to recover estimates of the severity of these frictions. We then use these estimated frictions to estimate the effects of new long-term productivity distributions, both over time and across space, on variables of interest. In Chapter 2, we investigate the influence of climate change on patterns of international migration, utilizing census data from Guatemala. We uncover novel empirical evidence indicating that regions experiencing higher temperatures see a decrease in migration during the subsequent year, with this effect being particularly pronounced in rural areas. We propose that elevated temperatures temporarily diminish rural productivity, consequently reducing the capacity of credit-constrained workers to afford migration costs. Thus, climate change exerts dual pressures: while diminished rural productivity potentially enhances the incentive to migrate, it simultaneously restricts individuals' financial capacity to do so. We develop and estimate a dynamic, incomplete-markets migration model featuring credit constraints and explicit migration costs, where increased temperatures negatively impact agricultural productivity. By calibrating our model to replicate the empirical temperature-migration relationship, we project future rural productivity under various climate scenarios. Our findings indicate a gradual increase in migration rates across all scenarios as workers preemptively save to afford migration, reflecting a substantial degree of anticipation. Additionally, we demonstrate that weather-contingent financial transfers, though potentially assisting in covering migration expenses, paradoxically reduce migration by providing insurance against temperature-induced income losses, thus making staying in affected areas relatively more attractive. In Chapter 3, we analyze the impacts of climate change on food prices across regions and income groups, looking into Brazilian data. As climate change alters comparative advantages in food production across goods and over space, existing trade frictions impede effective adaptation through sourcing adjustments, compelling reliance on local sourcing, and thereby pushing up food prices. Low-income households are relatively more exposed to food price fluctuations, as they tend to have higher food expenditure shares. We construct a spatial trade model that incorporates income heterogeneity and two distinct categories of food goods, characterized by varying degrees of costs associated with transportation and trade. This approach enables us to break down welfare losses attributable to climate change into specific contributions from food expenditure shares, trade shares, and productivity shifts. Leveraging Brazilian data, we estimate intranational trade relationships by observing responses to short-term weather variability, price fluctuations, and driving times between locations. Our empirical results indicate that trade costs for fresh foods are twice as sensitive to driving time as those for commodity goods, which incur relatively lower trade costs. Counterfactual analyses based on projected productivity changes reveal notable welfare losses, as well as substantial heterogeneity. The most exposed households would be willing to compromise approximately 3\% of their income to prevent anticipated productivity deterioration. Finally, we argue that investments aimed at enhancing road infrastructure emerge as an effective mitigation strategy, as they decrease trade costs, and promote integration. Households in certain states would be willing to pay up to 0.8% of their income to achieve a 10% improvement in average driving speeds nationwide.Barbosa Alves, Mauricio. (2025). Essays on international economics. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276767
How emerging technologies transform governance and market practices through disintermediation
No full textUniversity of Minnesota Ph.D. dissertation. July 2025. Major: Business Administration. Advisor: Gautam Ray. 1 computer file (PDF); x, 165 pages.This dissertation investigates how emerging technologies such as blockchain disintermediate traditional gatekeepers, thereby transforming governance and market dynamics. Across three essays, I examine how disintermediation reshapes organizational resilience, market access, and participatory governance in blockchain-based systems. The first essay finds that DAOs with more concentrated token ownership are better able to recover from cyberattacks, suggesting that certain forms of centralization can strengthen collective resilience. The second essay reveals that while NFT art platforms eliminate traditional curators, underrepresented artists still face valuation disparities, yet strategic self-curation can partially reduce these gaps. The third essay explores how the structure and content of DAO forum discussions shape voter turnout, showing that reasoned, inclusive discourse increases participation. Together, the essays argue that disintermediation alone does not guarantee equity or efficiency; rather, the quality of coordination, communication, and curation in decentralized settings becomes crucial. These findings contribute to a deeper understanding of community governance and market transformation in the age of decentralized technologies.Yang, Seonkyung. (2025). How emerging technologies transform governance and market practices through disintermediation. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277412
Building Adaptive Leaders: A Formative Evaluation of the Region V Public Health Leadership Institute Using Ripple Effect Mapping and Focus Groups
No full textObjectives: The Region V Public Health Training Center implemented an inaugural Leadership Institute (RVPHLI) from January to June 2023. This paper outlines a formative evaluation of the program to qualitatively assess its potential outcomes and influence on participants’ leadership capacity.
Design, Setting, and Participants: Thirty-eight public health and primary care professionals participated in 40 hours of online learning activities focused on adaptive leadership themes.
Main Outcome Measure: We conducted ripple effect mapping (REM) exercises and focus group discussions with 32 participants.
Results: REM analysis using the Community Capitals Framework suggested benefits to participants’ cultural, social, and human capital. The following themes emerged as takeaways from the focus groups: leaders as collaborators, new approaches to work and leadership, better understanding of individual leadership qualities and skills, current challenges, and validated existing definitions of leadership.
Conclusions: REM analysis showed participants gaining confidence and skills. They identified beneficiaries beyond themselves and shared challenges and resources. Findings will shape future RVPHLI iterations and potentially enhance development of other leadership programs in both the public health and primary care sectors.The Region V Public Health Training Center is supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under grant number UB6HP31684 Public Health Training Centers ($929 475). This information or content and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by Region V PHTC, HRSA, HHS, or the U.S. Government.Karnik, Harshada; Barbiero, Julieta; Zemmel, Danielle J.; Weiss, Nicole M.; Kulik, Phoebe K.G.; Power, Laura E.; Leider, Jonathon P.. (2025). Building Adaptive Leaders: A Formative Evaluation of the Region V Public Health Leadership Institute Using Ripple Effect Mapping and Focus Groups. Retrieved from the University Digital Conservancy, 10.1097/PHH.0000000000002150
Robotic perception and manipulation in unstructured environments with partial observations
No full textUniversity of Minnesota Ph.D. dissertation. June 2025. Major: Electrical/Computer Engineering. Advisors: Volkan Isler, Changhyun Choi. 1 computer file (PDF); x, 118 pages.This dissertation tackles the challenges of robust robotic perception and manipulation in unstructured environments characterized by uncertainty and limited sensory information. While robots excel in structured environments with controlled settings, transitioning to complex real-world scenarios calls for algorithms capable of handling unpredictable variations. Specifically, this dissertation addresses three key challenges arising from unstructured environments and partial observations: Geometric Uncertainties: The shapes, positions, and orientations of objects in unstructured environments are often unknown or unpredictable. This work explores methods to handle these uncertainties in tasks like grasping and deformable objects without precise object geometric knowledge. Through the development of force-based manipulation techniques for Velcro peeling, this dissertation demonstrates that robots can successfully peel Velcro from unknown surfaces using only force feedback, achieving 95% success rates. Additionally, a novel adaptive sampling framework for grasping moving objects improved grasping success rates by 24% over baseline methods, showing that the presented method can effectively handle geometric uncertainties in real-time. Motion Dynamics: Predicting how objects will move in response to robot actions is difficult in unstructured settings. This dissertation investigates techniques for modeling and adapting to these dynamics. The work on differentiable physics demonstrates that hidden physical parameters can be estimated through strategic action selection. Furthermore, the state decomposition particle filter estimates future Velcro states accurately, allowing peeling with less than 80% energy increase compared to optimal solutions under full observability. Sensor Ambiguities: Limited or incomplete sensory information presents another common challenge. This work develops algorithms to overcome these ambiguities, ensuring reliable perception with incomplete data. The ROW-SLAM system demonstrates robust semantic mapping in outdoor agricultural environments, achieving sub-meter accuracy in corn stalk localization despite visual occlusions, lighting variations, and sensor noise. Overall, this dissertation explores approaches from pure data-driven to pure model-based methods, as well as hybrid approaches combining both paradigms. Comparative analysis reveals that while pure approaches have merits, hybrid solutions consistently demonstrate superior performance in complex scenarios, enabling reliable robotic operation in unstructured environments.Yuan, Jiacheng. (2025). Robotic perception and manipulation in unstructured environments with partial observations. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277413
Dataset supporting Development of activators for SERCA2a for heart failure treatments
No full textOur primary aim in the present study is to discover drug-like small-molecule activators that improve SERCA function for the treatment of heart failure. We previously identified a promising hit compound, N-aryl-N-alkyl-thiophene-2-carboxamide (designated compound 1) using an NADH-coupled ATPase activity assay of the skeletal sarco/endoplasmic reticulum (SR) ATPase (SERCA1a) transporter for high-throughput screening (HTS) of a large compound library. At micromolar concentrations, compound 1 activated the Ca2+-ATPase activity and Ca2+-uptake function of the cardiac SERCA isoform (SERCA2a), using cardiac SR. Further, compound 1 increased SR Ca2+ load in cardiomyocytes. Here, we report on the medicinal chemistry development of compound 1, with the goal of improving its potency and efficacy on SERCA2a functional activities. Systemic modification of the three moieties of compound 1 resulted in a gallery of analogs that were functionally evaluated with Ca2+-ATPase activity and Ca2+-uptake measurements on SERCA2a in porcine cardiac SR. Correlation of the effects of the analogs’ chemical structure-activity relationship (SAR) revealed several analogs with improved potency and efficacy. Solubility, stability, and toxicity measurements of a subset of analogs were used to explore the lead-like potential of this chemical group. In this study, we demonstrate a path of using medicinal chemistry for developing drugs to treat heart failure.NIH R01HL139065NIH R01AR082533NIH T32AR007612Brinkmann, Marzena; Wong, Tsung-Yun; Roopnarine, Osha; Yuen, Samantha; Berg, Kaja; Cornea, Razvan; Rebbeck, Robyn; Thomas, David; Aldrich, Courtney. (2025). Dataset supporting Development of activators for SERCA2a for heart failure treatments. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/pvjn-8w51
Minutes: Senate Committee on Academic Freedom and Tenure: October 10, 2025
Get PDFIn these minutes: Committee Orientation; Resolution Related to Response to President’s Task Force on Institutional Speech Report Update; Reporting on Long-term Appointments Update; Committee Discussion: Administrative Hiring Task Force Report; Collegiate Personnel Plans Update; Academic Appointments with Teaching Functions UpdateUniversity of Minnesota: Senate Committee on Academic Freedom and Tenure. (2025). Minutes: Senate Committee on Academic Freedom and Tenure: October 10, 2025. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277099
Mechanisms of contextual modulation in primary visual cortex
Get PDFUniversity of Minnesota Ph.D. dissertation. June 2025. Major: Neuroscience. Advisors: Cheryl Olman, Audrey Sederberg. 1 computer file (PDF); viii, 135 pages.Visual perception depends critically on the brain’s ability to integrate informationacross spatial context. In this thesis, I examine the circuit mechanisms by which primaryvisual cortex (V1) implements flexible normalization to modulate local feature representations based on surrounding stimuli. I address three interrelated questions: (1) What are the cortical origins of orientation-dependent and figure ground contextual signals in V1? (2) How do network oscillations reflect and constrain mechanistic models of contextual modulation? (3) Can a biologically grounded model of E–I circuitry reproduce empirical signatures of orientation selectivity in V1? In Chapter 2, I employ ultra-high-field laminar fMRI in human V1 to disentanglefeedforward and feedback contributions to contextual modulation, revealing depth-specific BOLD profiles for distinct forms of surround influence. Chapter 3 analyzes the orientation tilt illusion and neural oscillations measured from LFP, demonstrating that contextual effects leading to altered perceptions of orientation and leading to changes in narrow-band gamma oscillations can both be explained with a biologically plausible implementation of divisive normalization. In Chapter 4, I use a stabilized supralinear network (SSN) to explore mechanisms of orientation selectivity in ferret V1 by directly comparing model predictions to single-cell measurements from two-photon calcium imaging. Together, this work bridges multiple spatial scales from synaptic circuits throughpopulation dynamics to cortical interactions across the visual hierarchy and advances our understanding of how recurrent and feedforward interactions implement context-dependent computations in visual cortex.Emerson, Joseph. (2025). Mechanisms of contextual modulation in primary visual cortex. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276757
LTAP Exchange (September 2025, vol. 33, no. 3)
Get PDFArticles include: Minnesota Mousetrap award winners: Front-mounted ditch mower and portable ground-force sign puller; Minnesota’s updated Strategic Highway Safety Plan aims to combat rising traffic fatalities; From the Director: Growing careers by growing our training program; Agencies incorporate Roads Scholar training to meet their own needs; Demo Day brings maintenance training to northeastern Minnesota; During Minnesota’s coldest months, shade matters; Don’t forget this essential winter resource (Minnesota Snow and Ice Control Handbook for Snowplow Operators); Congrats, recent Roads Scholars!; Minnesota Fall Maintenance Expo returns October 1–2; Roads Scholars: Meet two new grads!; Two Minnesotans honored with Engineer of the Year awards; Upcoming training and events (Sept. 2025 - Dec. 2025)Minnesota Local Technical Assistance Program. (2025). LTAP Exchange (September 2025, vol. 33, no. 3). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276596
Cabaret (2025-04-18 through 2025-04-26)
Get PDFUniversity of Minnesota Duluth. UMD Theatre. (2025). Cabaret (2025-04-18 through 2025-04-26). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276631