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GROUP THREAT AND THE POLICING OF MOBILITY: NEIGHBORHOOD VISITING PATTERNS AND RACIAL DISPARITIES IN ARREST RATES
No full textResearch on group threat has identified the racial composition of neighborhoods as significant for understanding spatial variation in racial bias and discrimination. A distinct but related strand of research has found disproportionate rates of arrests of non-Whites in predominantly White neighborhoods. This past work has generally operationalized racial composition in residential terms. Here, I explore the role of the racial composition of everyday mobility patterns in predicting racial threat. I propose that just as White neighborhoods experiencing growing non-White residential populations exhibit patterns of group threat, the same dynamic may occur in White neighborhoods experiencing large influxes of non-White everyday visitors. Drawing on arrests and cell phone mobility data from New York City, I find that "threatened" neighborhoods, characterized by a high proportion of non-Hispanic White residents and a disproportionately high rate of Black and Hispanic visitors, experience an exceptionally greater number of Black and Hispanic arrests. Further, I find that the heightened rate of non-White visitors into these White “threatened” neighborhoods explains much, if not all, of the relationship between neighborhood racial composition and racially disparate arrest rates. Overall, this study offers a novel perspective on how policing practices intersect with the racialized control of space in urban environments
EVALUATING THE RELATIONSHIP BETWEEN ILLNESS PERCEPTION, EXPECTATIONS REGARDING AGING, HEALTH LOCUS OF CONTROL, AND MEDICATION ADHERENCE AMONG OLDER ADULTS WITH CHRONIC CONDITIONS
No full textBackground: Medication adherence among older adults with chronic conditions is a critical determinant of health outcomes. However, adherence behaviors are influenced by multiple psychosocial factors. This study examined the relationship between illness perception, expectations regarding aging, health locus of control, and medication adherence among older adults with chronic conditions in Jordan. Methods: A cross-sectional correlational study was conducted with 120 older adults (≥65 years) diagnosed with at least one chronic condition and receiving prescription medication. Participants were recruited from outpatient clinics at King Abdullah University Hospital in Irbid, Jordan, using convenience sampling. Data were collected via self-report and using validated Arabic versions of the Medication Adherence Report Scale (MARS-5), Brief Illness Perception Questionnaire (BIPQ), Expectations Regarding Aging Survey (ERA-12), and Multidimensional Health Locus of Control (MHLC) Form A. The data was analyzed by descriptive statistics, non-parametric tests, Spearman correlations, and multiple linear regression.Results: Participants reported moderate to high medication adherence (M = 21.84, SD = 3.16). Illness perception was negatively associated with adherence (β = –0.221, p = .024), while belief in powerful others (e.g., healthcare providers) positively predicted adherence (β = 0.286, p = .008). Internal and chance health locus of control, expectations regarding aging, and demographic variables were not significantly associated with adherence. The final regression model explained 13.1% of the variance in adherence scores (Adjusted R² = .092). Conclusions: Illness-related beliefs and trust in healthcare providers are significant predictors of medication adherence among older adults in Jordan. Interventions aiming to enhance adherence should focus on addressing maladaptive illness perceptions and fostering strong patient-provider relationships. Sociodemographic factors and general aging expectations were not predictive, suggesting that adherence interventions should prioritize psychosocial rather than demographic targeting.2027-06-2
STRATEGIES TO CONTROL BLISTERING AND BROWNING IN LOW-MOISTURE PART-SKIM MOZZARELLA CHEESE BAKED ON PIZZA
No full textMost food-service retailers serving pizzas with LMPS Mozzarella cheese utilize forced-air ovens for rapid baking. A forced-air oven is an oven in which hot air is continuously blown on the pizza from above and sometimes below as the pizza travels down a short, timed conveyor. This oven is economically beneficial for retailers due to the quick baking of pizzas. However, this method of baking has some drawbacks. Surface browning of the cheese, a Maillard reaction occurring on the cheese-air interface, is much more prevalent when using forced air ovens due to their high heating rate (Thorton, 2022). Surface browning can be considered a defect, if excessive. Thus, producers and retailers of LMPS Mozzarella are interested in developing strategies to control surface browning on pizzas baked in forced-air ovens
Oral History Interview, Jeff Henriques (2503)
No full textIn his three 2025 interviews, Academic Staff Award Winner Jeff Henriques discusses his time spent at UW, first as a PhD student and then as a longtime lecturer in the psychology department. To learn more about this oral history, download & review the index first (or transcript if available). It will help determine which audio file(s) to download & listen to.In his three 2025 interviews, Academic Staff Award Winner Jeff Henriques discusses his time spent at UW, first as a PhD student and then as a longtime lecturer in the psychology department. He shares his dissertation research and the process of getting his PhD. Henriques discusses his varying appointments and commitments throughout his years at UW; this includes his time as a statistician in the School of Nursing, along with the courses he taught: Intro to Psychology, Basic Statistics for Psychology, and Abnormal Psychology. He speaks about his love for teaching, his methods of helping students understand content, and his involvement in UW’s Teaching Academy and its executive board, which encourages discourse between teachers and students. In his second interview, he discusses his typical work day, changes in student engagement, the evolution of course management systems and technology in the classroom, COVID, and Wisconsin’s Act 10. In his third interview, he discusses his involvement in campus committees and their relationships to his classroom techniques, including the Teaching Academy, the Letters and Science Diversity Committee, and Academic Technology. He mentions his attempts to address student achievement gaps through employing intentional teaching methods and creating an environment of belonging. He mentions hiring processes, noting the tendency toward choosing internal hires and the need for academic staff to have a larger voice in curriculum decisions alongside faculty. Finally, he expresses gratitude for the Academic Staff Teaching Award and shares his thoughts on retirement. This interview was conducted for inclusion into the UW-Madison Archives and Records Management Oral History Program as part of the Academic Staff Award Winners Project
Hydroclimatic Drivers on Groundwater Nitrate Variability in Western Wisconsin
No full textresearch posterWisconsin relies extensively on groundwater usage for both agricultural production and private use. Under a changing climate, significant precipitation and drought events are becoming more severe. Nitrate (NO3-) has become a prominent contaminant of concern due to its exceptional solubility in water, its ability to persist in aquatic systems, and its potential for adverse health effects such as methemoglobinemia in infants. In agriculturally intensive areas, nitrate concentration levels are linked with the usage of manure and certain fertilizer types. Regular monitoring of groundwater quality is essential to assess nitrate concentration amounts for potentially hazardous contamination levels. In partnership with a regional farmer-led watershed council, nearly one hundred wells have been sampled across western Wisconsin since 2018 to monitor groundwater quality at both seasonal and interannual timescales. In this time, the sampling region has experienced the wettest precipitation year on record along with three consecutive years of drought conditions. Nitrate concentration is then coupled with hydrological parameters such as recharge precipitation intensity to determine how climatic extremes can affect wells with greater fluctuations in their contaminant amounts. Wells within the sampling area can vary in depth and either draw water from the Prairie du Chien dolostone or Jordan sandstone aquifers. A sizable portion of the wells sampled have shown stable nitrate levels, with some showing slight increases or decreases during the testing period that could be correlated with overall precipitation patterns. Final nitrate analysis will be reported back to the farmer-led council to inform and support regional efforts towards sustainable land management and agricultural practices which aim to reduce nitrate and other contaminants from leaching into groundwater
Unraveling PEG-Biomolecules Interactions Through Raman Spectroscopy
No full textColor poster with text, images, charts, photographs, and graphs.Polyethylene glycol (PEG) is a flexible, non-toxic polymer. It is considered biologically inert and has numerous applications in medicine and industry. PEG is often attached to drug molecules in a process called PEGylation to enhance their stability and solubility, decrease the immune response, and increase circulation time throughout the body. Recently, PEGylated lipids have been included as an ingredient in COVID-19 vaccines. Additionally, PEG molecules of variable sizes are commonly used for studying the effects of molecular crowding and confinement on the conformation and function of proteins and nucleic acids. Despite being considered biologically inert, recent studies have shown that PEG interacts with biomolecules such as proteins. To gain a deeper understanding of PEG-protein interactions, we are using Raman Spectroscopy to investigate the effect of PEG of variable sizes on the vibrational modes of amino acids and proteins. This vibrational spectroscopic technique identifies unique fingerprints of molecules based on the inelastic scattering of monochromatic light. We will present the preliminary results of our study.UW-Eau Claire Foundation; Mayo Clinic; University of Wisconsin--Eau Claire Office of Research and Sponsored Program
Food Safety Net & Nutrition Incentive Programs : WI Farmers' Markets SNAP Matching Program with Statewide Implications : Part II
No full textColor poster with text, images, charts, photographs, and graphs.Food insecurity is a significant issue facing many American households. The Supplemental Nutrition Assistance Program (SNAP) provides increased access to food for families in need. Additionally, fruit and vegetable (FV) consumption has been shown to improve health and reduce the risk of a variety of chronic diseases. However, poor nutrition among children and adults, including low FV intake have contributed to rising rates of obesity in America. It is particularly challenging for low-income households to purchase/eat the recommended amount of FV. Farmers’ markets offer a wide variety of fresh, local and healthy foods, especially FV, but data show that low-income households are much less likely to shop at farmers’ markets. The Eau Claire Downtown Farmers’ Market (ECDFM) sponsors a Market Match Program (MMP) incentivizing This presentation uses 2023 and 2024 survey data to highlight the many benefits of the MMP, while also exploring a variety of barriers to using the MMP mapped to corresponding changes that might reduce these barriers. Our results show most SNAP shoppers do not regularly shop at the market with the most reported barriers being limited market hours/locations, limited SNAP benefits running out and not remembering. We also provide demographic characteristics for all survey respondents.University of Wisconsin--Eau Claire Office of Research and Sponsored Program
CLINICAL COMPETENCE AND CLINICAL ENVIRONMENT: A COMPARATIVE STUDY OF NURSE EXTERNS AND NON-EXTERNS
No full textABSTRACT CLINICAL COMPETENCE AND CLINICAL ENVIRONMENT: A COMPARATIVE STUDY OF NURSE EXTERNS AND NON-EXTERNS by Sherri Ann Hanrahan The University of Wisconsin-Milwaukee, 2025Under the Supervision of Professor Julia Snethen Background: Student nurse externship programs historically increase during nursing shortages or patient care demands. Today’s healthcare environment faces challenges, including significant nursing shortages and rising patient acuity, making it essential to examine externship perceptions. This study compares how externship participation impacts perceived clinical competence and evaluations of the clinical environment versus non-externs.Methods: A quasi-experimental, cross-sectional correlation design was conducted with 46 nursing students (35 externs, 11 non-externs) from two Midwestern baccalaureate programs. Participants completed two validated self-report measures via Qualtrics or in-person surveys. Descriptive statistics summarized characteristics. Between-group differences were tested using ANCOVA, controlling for age, healthcare experience, BSN track, and weekly clinical hours. Pearson’s r and Spearman’s ρ examined relationships between perceived competence and clinical environment; moderation analyses explored interaction effects of externship status and environmental factors. Results: Externs reported slightly higher adjusted mean scores than non-externs across general-performance, core nursing skill, and advanced skills, yet none reached statistical significance (p > .05). ANCOVA showed no main effect of externship participation on overall perceived competence (F(1,40) = 1.72, p = .20, η² = .04), and effect sizes were uniformly small (η² < .06), indicating limited practical impact. SECEE Learning Opportunities and Preceptor Facilitation subscales demonstrated moderate positive correlations with overall competence (r ≈ .30, p < .05), but externship status did not significantly moderate these relationships. Stratified analyses revealed a strong association between Core Nursing Skills and overall competence among externs (ρ = .929, p < .001). Conclusion: Although externship participants rated competence marginally higher in some domains, externship participation alone did not significantly improve perceived clinical competence. Competence development appears multifactorial, shaped by personal, educational, and environmental interactions. Strengthening externship models through structured, supportive environments may enhance measurable gains. Future multi-site studies, larger samples, and longitudinal designs are warranted to assess externship effects on NCLEX-RN® outcomes.2027-12-2
Harnessing Advanced Data Analytics To Improve Early Detection And Diagnosis Of Rare Medical Conditions
No full textHealthcare experts and care providers continually seek innovative approaches to enhance care delivery. As a discipline, health has always been about enabling perfect care for everyone. With medicine shifting toward a future where value-based care becomes the norm, the role of early detection and intervention in health problems will become increasingly important. Most physicians are already aware that early detection and intervention can profoundly impact the health outcomes of any patient, especially those with diagnostically challenging diseases where timely management can prevent irreversible complications.Many health conditions have high mortality rates, not due to a lack of treatment options but because of ambiguous onset patterns and delayed diagnoses. Rare and multisystem diseases pose significant challenges, often leading patients through an odyssey of diagnosis, a prolonged and frustrating journey involving multiple doctor visits, extensive testing, misdiagnoses, and ineffective treatments before the correct condition is identified. For these patients, early and accurate detection can mean the difference between a manageable condition and one that significantly impairs their quality of life. Traditionally, doctors rely on extensive training, expertise, and experience to diagnose conditions. However, when medical diagnoses are difficult to achieve based on clinical information alone the inability to obtain quick and accurate answers from medical professionals can be a frustrating process. Recognizing these challenges, the healthcare sector is undergoing a transformation driven by its ability to record and analyze massive amounts of data. The rapid digitalization of healthcare has led to an exponential growth in Electronic Health Records (EHRs), imaging data, and patient-reported outcomes, collectively forming what is now referred to as Big Data in medicine. The sheer volume, variety, and velocity of medical data can empower healthcare providers with data-driven decision support systems, facilitating faster and more accurate diagnosis. However, making sense of this vast and complex information requires sophisticated analytical tools capable of identifying hidden patterns, correlations, and predictive markers. To this end, the goal of the two essays in this dissertation is to leverage advanced analytical methods when there is a need to deal with the breadth and complexity of information in early diagnosis. By designing, refining, and applying cutting-edge Machine Learning (ML) algorithms, integrating real-world medical data, and incorporating domain expertise, I aim to develop explainable and trustworthy data-driven tools that serve clinicians. In the first essay, I propose a data-driven machine learning framework to identify patients at high risk of developing Venous thromboembolism (VTE) before they undergo major hip or knee surgery. Leveraging electronic health records from over 390,000 patients who underwent major orthopedic surgery, I employed a genetic algorithm for guided feature selection and trained a fully connected deep neural network to identify high-risk patients for VTE development. My findings reveal several noteworthy insights. Traditional risk scoring tables, commonly used by physicians to assess high-risk patients, do not incorporate a comprehensive range of risk factors and are less effective than advanced machine learning techniques in differentiating between low- and high-risk individuals. Furthermore, this study identifies previously unrecognized risk factors for VTE, contributing to a broader understanding of disease prediction. The findings also offer practical considerations that may aid clinicians in optimizing VTE prophylaxis strategies. In the second essay, I propose a machine learning and network analytics approach for the preemptive identification of patients with autoimmune diseases (ADs). Given that (ADs) arise not just from individual causes but from complex interactions among several factors, the first phase of this study focuses on extracting a realistic and comprehensive understanding of the patient journey using network analytics techniques. In the second phase, the extracted knowledge from three comorbidity networks, covering more than 9,000,000 visits in our study cohort, is combined with medical record features to form an expanded input, which is then fed into several machine learning (ML) models for preclinical disease prediction. In the third step, we prioritize interpretability alongside predictive accuracy by employing Explainable Boosting Machines (EBMs), a modeling approach that helps approximate the behavior of our high-performing but less transparent classifiers. Results show that ML methods trained on these augmented features outperform previous methods trained on conventional features. The proposed model can be paired with different machine learning and deep learning classifiers to achieve high accuracy. The findings and insights from this study assist physicians in optimizing the timing of treatment administration, potentially maximizing efficacy while reducing adverse events associated with (ADs).2027-06-2
EVALUATION OF AGGREGATE DURABILITY USED IN TRANSPORTATION INFRASTRUCTURE UTILIZING MACHINE LEARNING
No full textDurability of coarse aggregate is essential for the long-term performance of pavement and concrete infrastructure in cold regions. In Wisconsin, repeated cycles of freezing and thawing cause mass loss, particle breakage, and degradation of aggregate used in pavement foundations and concrete mixtures. The Wisconsin Department of Transportation evaluates durability by testing several individual size fractions of each aggregate source and combining the results into weighted freeze-thaw and weighted sodium sulfate soundness values. These procedures require extensive specimen preparation and separate testing of multiple size fractions, resulting in a labor-intensive and time-consuming process for state laboratories.This study investigates whether the results from a single size fraction can be used to accurately predict the full weighted durability values that the Wisconsin Department of Transportation currently calculates from several size fractions. The work focuses on quarried sedimentary coarse aggregates from Wisconsin and examines three prediction tasks: (1) predicting the weighted freeze-thaw value used when the largest size fraction is absent in the gradation, using only the smallest size fraction and absorption; (2) predicting the weighted freeze-thaw value that normally requires three size fractions, using only the middle size fraction and absorption; and (3) predicting the weighted sodium sulfate soundness value using the smallest soundness fraction together with freeze-thaw information. Machine learning regression models were developed for each task using support vector regression, random forest regression, and gradient boosting regression. The models were trained on the designated training subsets and evaluated on independent testing subsets, and cross-validation within the tuning process was used to ensure stable and reliable predictive performance. Model accuracy was quantified using the coefficient of determination and the root mean square error. The results show that the required weighted freeze-thaw and sodium sulfate soundness durability values can be predicted with high accuracy using only one size fraction combined with absorption, without the need to perform all laboratory tests normally required by the Wisconsin Department of Transportation. Support vector regression consistently provided the most accurate and stable predictions across all tasks. These findings demonstrate that durability evaluation for quarried sedimentary aggregate can be substantially streamlined, reducing laboratory workload while maintaining reliable assessment of material performance.2027-12-2