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Meeting Communities Where They Are: City of Minneapolis 2040 Plan Community Re-Engagement
No full textThis report addresses community re-engagement surrounding the Minneapolis 2040 Comprehensive Plan. The City of Minneapolis Department of Community Planning and Economic Development (CPED) engagement efforts significantly stalled after 2019 due to staffing changes in the City, the murder of George Floyd, and the COVID-19 pandemic. Our capstone project aimed to re-establish community connections and learn preferred methods to engage with community groups and sustain long-term relationships. Our report synthesizes the academic literature on community engagement and peer city engagement practices, which highlight the importance of diverse and adaptable engagement methods, transparency, and accountability to community input. Our team conducted interviews with community engagement practitioners in Minneapolis and organizational leaders from the Native American, Youth, and African American communities. Our qualitative analysis revealed four key findings: 1) community members have minimal understanding of the 2040 Plan and its impact; 2) community leaders feel City action on identified priorities is lacking; 3) the lapse in engagement weakened the meaningful relationships built during prior planning cycles; and 4) navigating changing city dynamics within the comprehensive planning period poses challenges for both City staff and community members. Based on these findings and peer city analysis, we recommended both lower and higher cost interventions. On the lower cost end, we recommend CPED attend events already happening in the community, leverage existing community organizations as trusted partners, and break down complex plan information into understandable pieces. Our higher-cost recommendations include seeking funding for a dedicated full-time community engagement specialist within CPED and determining preferred engagement methods and frequency of communications with each community. There is a critical need for CPED to do continuous community engagement. Our report and accompanying one-pagers provide both a theoretical framework and the community perspective for this ongoing engagement to occur.Seigfreid , Ann; Kirsner, Elizabeth; Danielzuk, Kaytlyn; Yetvin, Will. (2025). Meeting Communities Where They Are: City of Minneapolis 2040 Plan Community Re-Engagement. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/272024
The Power of Representation: Shaping Library Collections and Programming for Diverse Voices
No full textPresentation part of Academic and Research Libraries Division (ARLD) Day 2025 conference, May 2, 2025.The Arts, Humanities, and Areas Studies Department of the University of Minnesota Libraries consists of eight liaison/subject librarians covering a myriad of disciplines in the arts, humanities, and social sciences. While our liaison responsibilities and academic backgrounds vary, collectively, we identify information gaps in our collections and strive to acquire materials by and about underrepresented groups and cultures. Similarly, some of us engage with marginalized communities through library programming and events. Panel members will discuss four questions: 1) how they inform themselves about underrepresented groups in their disciplines or geographic regions; 2) how they identify gaps within the collections and prioritize next steps; 3) how they source materials covering underrepresented groups via international vendors, book fairs, and other means; and 4) how they create programs and events geared toward supporting underrepresented groups. The panel will provide audience members with resources and recommendations for diversifying their own collections and library programming.Vetruba, Brian; Barraza, Paloma; Grant, Malaika; Tillett, Aubree; Ultan, Deborah; Ye, Shuqi. (2025). The Power of Representation: Shaping Library Collections and Programming for Diverse Voices. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/272009
Minutes: Senate Committee on Information Technologies: March 3, 2025
No full textIn these minutes: Update on Course Works; Discussion Regarding Permanent Opt-Out Feature for the New Course Works Progra
AI-Based Auto-Detection of Non-Penetrating Traumatic Brain Injury Using Advanced Imaging Techniques
No full textFaculty Advisor: Yuk ShamThis research was supported by the Undergraduate Research Opportunities Program (UROP).Sharma, Tanya. (2025). AI-Based Auto-Detection of Non-Penetrating Traumatic Brain Injury Using Advanced Imaging Techniques. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/271270
Supporting Data for Laser ablated sub-wavelength structure anti-reflection coating on an alumina lens
No full textThe images are VK4 files generated by a Keyence VKX-3000 confocal microscope. The code "Pyramid stacking.py" is a custom code written by Calvin Firth with which the images were analyzed.These are images and the code that were used to analyze the data provided for the paper "Laser ablated subwavelength structure antireflection coating on an alumina lens."Hanany, Shaul; Cray, Scott; Dietterich, Samuel; Dusing, Jan; Firth, Calvin; Koch, Jurgen; Lam, Rex; Matsumura, Tomotake; Sakurai, Haruyuki; Sakurai, Yuki; Suzuki, Aritoki; Takaku, Ryota; Wen, Qi; Wienke, Alexander; Yan, Yan. (2025). Supporting Data for Laser ablated sub-wavelength structure anti-reflection coating on an alumina lens. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/X07S-DD66
Minutes: Senate Committee on Academic Freedom and Tenure: January 24, 2025
No full textIn these minutes: Discussion of Concerns with the Administrative Hiring Task Force; Collegiate Personnel Plans and Long-term Appointments Justifications; Discussion of Work Plan for Post Tenure Review IssuesUniversity of Minnesota: Senate Committee on Academic Freedom and Tenure. (2025). Minutes: Senate Committee on Academic Freedom and Tenure: January 24, 2025. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/271474
Modelling the Incentive Structures Created by Earnings Limits in Social Security Disability Insurance Policy for the Blind
No full textSocial Security Disability Insurance (SSDI) in the United States is an insurance program offering financial support to workers who become disabled. There are limits placed on how much a disabled person can earn while receiving SSDI. If any SSDI beneficiary earns past the Substantial Gainful Activity (SGA) threshold, they lose their entire SSDI cash benefit, thus creating a cash cliff and a disincentive for earning. The National Federation of the Blind has been advocating in the United States Congress for the Blind Americans Return to Work Act (BARWA), a reform bill that will amend the Social Security Act. BARWA will remove the cash cliff and replace it with a 2-for-1 phase-out so that each dollar earned above SGA results in a $0.50 reduction in SSDI benefits. To inform the policy debate, this project mathematically models the incentive structure created by current SSDI rules and the proposed reforms. SSDI earnings limit policies for non-blind recipients are controlled by the Social Security Administration, so neither Congressional action nor bill is applicable to that program. Data for this analysis applies specifically to SSDI for the blind.Salisbury, Justin M.H.. (2025). Modelling the Incentive Structures Created by Earnings Limits in Social Security Disability Insurance Policy for the Blind. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/270464
Designing Nanomaterials to Fight Hunger and Pollution (2025-04-18)
No full textDr. Christy L. Haynes, Department Head, Distinguished McKnight University Professor, Department of Chemistry, University of Minnesota Twin Cities; Spring 2025 Symposium; Friday, April 18th, 2025; Chem 200, 3:00 p.m.; Host: Dr. Melissa Maurer-Jones; Refreshments will be served!Haynes, Christy L; University of Minnesota Duluth. Department of Chemistry and Biochemistry. (2025). Designing Nanomaterials to Fight Hunger and Pollution (2025-04-18). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/272307
Minutes: Senate Committee on Information Technologies: April 7, 2025
No full textIn these minutes: Draft Alternative Credentials Task Force Report; Course Retention (Canvas) and Media Retention (Kaltura) PoliciesUniversity of Minnesota: Senate Committee on Information Technologies. (2025). Minutes: Senate Committee on Information Technologies: April 7, 2025. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/272455
Develop informatics solutions to deliver relevant information for clinical decision making that improve the management of cardiovascular disease risk of breast cancer patients
No full textUniversity of Minnesota Ph.D. dissertation. January 2025. Major: Health Informatics. Advisor: Rui Zhang. 1 computer file (PDF); x, 84 pages.In the realm of breast cancer treatment, balancing the efficacy of therapy against the risk of treatment toxicity particularly cardiotoxicity as one of the more morbid complications of treatment poses a significant clinical challenge. This thesis presents an innovative approach to addressing the issue of breat cancer-related cardiotoxicity by integrating health informatics with real world data from Electronic Health Records (EHRs) and other health information technology (e.g., imaging reports from cardiovascular information systems) to improve cardiovascular disease risk management in breast cancer patients. Our approach leverages deep learning algorithms and a range of natural language processing (NLP) approaches in order to extract, analyze, and interpret complex clinical data to enhance decision-making processes in healthcare settings for this vulnerable group of patients.At the core of this body of research is the development and application of transformer-based deep learning methods, which are specifically tailored to extract targeted information from clinical texts in EHRs. By creating a specialized cancer domain vocabulary, the study demonstrates the enhanced performance of these models in accurately identifying relevant clinical data, such as patient demographics, treatment details, and cancer phenotypes. This approach significantly advances the precision and reliability of extracting important clinical information, which often is a crucial step in developing robust predictive models.
The thesis further explores the generalizability of these NLP algorithms across different healthcare institutions via external validations, a vital consideration given the varied nature of EHRs. Through cross-institutional evaluations, the research establishes the portability of these models, ensuring their effectiveness in diverse clinical environments. This aspect of the study is critical in validating the broader applicability of the developed methodologies in various healthcare settings.
Central to the thesis is the creation of predictive models for assessing the risk of heart disease in breast cancer patients. Utilizing a deep learning approach, specifically LSTM-D models, the study effectively harnesses longitudinal EHR data to predict cardiovascular risks associated with cancer treatments. We find that these models outperform traditional methods, offering a more nuanced understanding of patient-specific risk factors and temporal patterns in treatment responses.
The thesis' findings underscore the potential of integrating advanced data analysis tools in clinical decision-making, particularly in the context of breast cancer treatment. By providing a more detailed and personalized risk assessment, the research contributes significantly to the field of personalized medicine, enhancing the quality of patient care and treatment outcomes. Overall, this thesis bridges a critical gap in healthcare informatics by developing and validating innovative methodologies for extracting and analyzing EHR data. The research marks a significant step towards more informed and personalized breast cancer treatment, highlighting the transformative potential of health informatics in managing complex disease interactions and improving patient outcomes.Zhou, Sicheng. (2025). Develop informatics solutions to deliver relevant information for clinical decision making that improve the management of cardiovascular disease risk of breast cancer patients. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/271693