The Academic Minute

Academic Minute

Astronomy to Zoology www.academicminute.org

  1. 2d ago

    Charlotte Hogg, Texas Christian University - What Sororities Can Tell Us About Belonging

    What can sororities teach us about belonging? Charlotte Hogg, professor of rhetoric and composition at Texas Christian University, delves in the question. Faculty Bio: Dr. Charlotte Hogg is a professor at Texas Christian University specializing in rhetoric and composition. She has authored, co-authored, or co-edited five books, most recently White Sororities and the Cultural Work of Belonging. Her work has also ap­peared in Inside Higher Education, The Washington Post, College English, Rhetoric Review, Peitho, and elsewhere. She teaches women’s rhetorics and literacies, creative nonfiction, and composition. Transcript: What is it about sorority life that remains so appealing? Greek-life, even with its bad press, comprises roughly 10% of the college population nationally and as high as 50% on some campuses. I researched National Panhellenic Conference (aka historically-White) sorority life behind the highly curated recruitment videos to better understand what sorority systems can teach us about how subcultures enact belonging. Belonging seeks to erase differences for insiders, leaving others as outsiders. This kind of ideological work is done rhetorically through creating a shared space of making meaning connected to cultural values. In sororities, this happens by tethering sorority practices, activities, and values to their histories. Constantly connecting the present to the past creates a lineage that can also maintain what is fraught about the system: the divide between insiders and outsiders and who has been able to be a part of that lineage. Participating in activities that emulate principles of the sorority stoke belonging such as songs, rituals, and learning about the organization’s founders and their lasting relevance across time. Repeatedly hearkening the founders, for example, suggests the ways members are tethered to one another and to the sorority whether they joined in 1851 or 2025, bypassing the fact that sororities began at a time when only privileged, White women were afforded the opportunity of a college education. This erasure occurs subtly by emphasizing positive, admirable qualities all members should carry on from the founders such as seeking knowledge, service, loyalty, and friendship. Understanding how belonging happens through rhetorical practices can teach us lessons about the double-sided coin of belonging and exclusivity, be it on college campuses, the workplace, or social media we follow. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  2. 3d ago

    Sriniwas Pandey, Binghamton University - Platform Recommendations and Diverse Opinions on Social Media

    What happens when platforms recommend opinions instead of users? Sriniwas Pandey, lecturer at the School of Computing at Binghamton University, details this. Faculty Bio: Pandey is a lecturer in the School of Computing at Binghamton University. He received his PhD degree in system science from Binghamton University. He holds bachelor's and master's degrees in computer science engineering from UTU India and IIITDMJ India, respectively. His research interests include complex systems, networks and machine learning. His research aims to comprehend the intricacies of eccentric behavior within society, focusing on identifying the underlying factors contributing to such behaviors and its impact on network dynamics. Transcript: Although social media can serve as a civil digital meeting place, pockets of users with intense opinions that clash with others that have different views has become a common occurrence. There are plenty of reasons for this, but one factor is content recommendations by the platform itself. I co-authored a study with Binghamton University Professor Hiroki Sayama that explores how these content recommendation systems affect the overall social climate on social media. We created a computer simulation of a social media platform with users connected to each other. Each user had a default set of opinions. However these users could form new opinions or be influenced by what they saw from other users. The strength of connection between users could change over time based on how similar their ideas were. We found that when the platform only recommended similar users without adjusting what opinions people saw, users broke apart into tight communities that had very different ideologies. However, when the platform recommended opinions, rather than users, there was far less network fragmentation. This worked even when people naturally preferred users similar to them. This also led to more unusual or off-center opinions. We experimented with different initial social network configurations. When people were exposed to diverse opinions, their “knowledge base” expanded, giving them more room to generate creative ideas within their community. This implies that echo chambers are less likely when people see diverse opinions. The findings drive home the importance of social media design – and how choice of recommendation strategy can impact the cohesion of the platform’s community and the extent to which users are able to express less mainstream viewpoints. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  3. 4d ago

    Maria Steenland, University of Maryland - Stark Mortality Difference Between Pregnancy and Abortion

    There are stark mortality differences between pregnancy and abortion. Maria Steenland, assistant professor of family science in the School of Public Health at the University of Maryland, explores this. Faculty Bio: Dr. Steenland is an Assistant Professor in Family Science in the School of Public Health at the University of Maryland, College Park. She is a health services and health policy researcher focused on maternal and reproductive health policy in the United States. Her research useseconometric methods to evaluate maternal and reproductive health programs and policies, with a particular focus on Medicaid policy. The overarching goal of her research is to identify policy options to increase the equity and quality of women’s health services. Her previous researchhas examined the effect of Medicaid payment policies for immediate postpartum contraception, and expansions of Medicaid eligibility in pregnant and postpartum populations. Transcript: After Roe v. Wade was overturned by the Dobbs v. Jackson Women’s Health Organization decision in June of 2022, some advocates and academics expressed concern that abortion bans would harm maternal health. They argued that banning abortion could increase maternal mortality in part because the risk of death from childbirth is much higher than the risk from abortion. Continuing a pregnancy places significant physiologic stress on the body, which can lead to life-threatening complications, such as hemorrhage, sepsis, stroke, and heart failure. A prior study, using data from 1998 through 2005, found that the risk of death from childbirth was about 14 times greater than the risk of death from abortion. In the years since that estimate, the measurement of maternal death has improved, increasing the number of pregnancy-related deaths identified annually. At the same time, abortions are taking place earlier in pregnancy, reducing the risk of complications. Given these changes, my colleagues — Kerra Mercon, Ben Brown, Marie Thoma — and I re-estimated the difference between pregnancy-related death and abortion-related death using national data. Using figures from 2018 to 2021, we calculated a range of mortality ratios. We found that the risk of death from ongoing pregnancy was 44 to 70 times greater than the risk of death from abortion. Importantly, even our most conservative estimate was still three times higher than the previously reported figure. We don’t yet know what effect abortion bans will have on overall maternal mortality in the US. However, we can say that people who are forced to continue their pregnancies because of abortion bans will face a dramatically greater risk of death than had they been able to access an abortion. Read More: [University of Maryland School of Public Health] - Study: Risk of maternal death during pregnancy greatly exceeds risk of death from abortion This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  4. 5d ago

    Cole McFaul, Georgetown University - Pulling Back the Curtain on China’s Military-Civil Fusion

    How do we pull back the curtain on China’s use of AI in its military? Cole McFaul, senior research analyst at Georgetown University, explores this key question. Faculty Bio: Cole McFaul is a Senior Research Analyst and an Andrew W. Marshall Fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), where he mainly focuses on emerging technology competition in the Asia-Pacific and China’s science and technology ecosystem. Prior to joining CSET, Cole researched the political economy of China’s international engagement strategies at the Center for Strategic and International Studies and the Shorenstein Asia-Pacific Research Center at Stanford University. Cole holds a B.A. in Political Science and an M.A. in East Asian Studies from Stanford University. Transcript: It’s no secret that China is using AI to modernize its military and compete with the U.S. and other rivals. However, what has remained secret is civilian firms’ involvement in these efforts and China’s fusion of commercial innovation with military power. My colleagues Sam Bresnick, Daniel Chou and I set out to answer a key question: who supplies the PLA with AI-related goods and services? Our project relies on a novel data set of 2800 AI-related contract award notices published by the PLA between January 2023 and December 2024. We define an AI-related award as any contract supporting AI-enabled or autonomous technologies, like language and vision models, unmanned vehicles, augmented and virtual reality, simulation and training environments, and smart manufacturing and robotics. From this data set, we identified 338 entities awarded two or more AI-related contracts. Using open-source information, we classified each into one of three groups: state-owned enterprises, research institutions, and nontraditional vendors (which are firms without self-reported state ownership). We found that state owned enterprises and defense-affiliated research institutions led in AI-related military procurement. Institutions like AVIC, NORINCO, the Chinese Academy of Sciences, and the Seven Sons of National Defense dominate the top of the list. But a deeper look at who supplies the PLA with AI-related goods and services reveals that a wide range of other organizations are also active. Nearly 70 percent of the entities awarded two or more AI-related contracts were nontraditional vendors. Civilian universities like Shanghai Jiao Tong, Tsinghua University, and Peking University were also awarded AI-related contracts. Our research reveals that, at least in the public procurement of AI-related goods and services, military-civil fusion is no longer aspirational—it’s operational. Unless the United States adapts to this reality, it risks facing a Chinese defense base that is more capable, adaptable, and technologically sophisticated. Read More: [CSET] - Pulling Back the Curtain on China’s Military-Civil Fusion [CSET] - Civilian Tech Is Powering China’s Military This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  5. 6d ago

    Beatrice Golomb, University of California San Diego - New Diagnostic Code for Gulf War Illness

    What is Gulf War Illness and why is recognition important? Beatrice Golomb, professor of medicine at the University of California San Diego, seeks to inform. Faculty Bio: Dr. Golomb is a Professor of Medicine at UC San Diego with over 15 years of experience treating veteran patients, including veterans with Gulf War Illness (GWI). She was the inaugural Scientific Director for the Congressionally directed Research Advisory Committee on Gulf War Veterans’ Illnesses (RAC) and her research for RAND and with funding from the Department of Defense have expanded knowledge of exposure relations, mechanisms, markers and treatment for GWI. Dr. Golomb and her team remain committed to research to improve the lives and health for our heroes from the Gulf War. Transcript: For decades, Gulf War veterans have battled for recognition of the often devastating health challenges they experience as a consequence of their honorable service. Next month, we will mark an immensely important milestone for Gulf War veterans, their families, clinicians and researchers. Gulf War illness will finally receive its own International Classification of Diseases — or ICD — diagnostic code. This is more than just administrative coding. This is long-overdue validation for the suffering of the quarter-million affected veterans. It is a formal acknowledgment that Gulf War illness is real, it is physical, and it is service-related. Gulf War illness affects about one-third of the nearly 700,000 U.S. troops who served in the 1990-1991 Gulf War. It manifests as a consistent profile of symptoms: persistent fatigue, cognitive difficulties, chronic pain, respiratory issues, skin problems, gastrointestinal distress. Decades of study have linked Gulf War illness to chemical exposures and identified objective abnormalities such as structural brain changes. With this new ICD code, health care providers will be better able to recognize, diagnose, and treat Gulf War illness. Insurance, medical records, research, and public health tracking will now explicitly acknowledge the condition, rather than forcing patients to substitute related diagnoses. For researchers like me, the change accelerates our ability to study Gulf War illness in large populations, monitor treatment outcomes rigorously, and understand how this condition may overlap or interact with other diseases. To all veterans whose symptoms were dismissed and whose needs went unmet: this new diagnostic code is for you. It’s a recognition of your service. It’s a commitment to your care. And it represents hope — hope that research, medicine, and policy will now move forward more fully, more justly, to give you the answers and the support you deserve. Read More: [PNAS] - Acetylcholinesterase inhibitors and Gulf War illnesses [ScienceDirect] - Adverse effect propensity: A new feature of Gulf War illness predicted by environmental exposures [National Library of Medicine] - Mitochondrial impairment but not peripheral inflammation predicts greater Gulf War illness severity This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  6. May 22

    Khan Iftekharuddin, Old Dominion University - How Does Non-Invasive Detection of Aggressive Brain Tumor Recurrence Work

    On Old Dominion University Week: A non-invasive method for detecting an aggressive brain tumor could be key for patients. But how does it work? Khan Iftekharuddin, Professor and Eminent Scholar, delves into this. Faculty Bio: Dr. Khan Iftekharuddin is a professor and Batten Endowed Chair in Machine Learning in the department of Electrical and Computer Engineering (ECE) at Old Dominion University (ODU). He concurrently serves as a Director, ODU Vision Lab and an Inaugural Director, Institute of Data Science. Dr. Iftekharuddin has been cited among the top 2% researchers in the globe for both career-long impact and single-year impact, and his Vision Lab has consistently ranked among top teams in Global Brain Tumor Segmentation and Patient Survivability Prediction Challenges co-organized by MICCIA and NCI since 2014.Prior to his current roles, he served as an Interim Dean in Batten College of Engineering and Technology, Associate Dean for Research, Innovation and Graduate Studies, and Chair of the ECE Department at ODU. He received his MS (1991) and PhD (1995) degrees in Electrical and Computer Engineering from University of Dayton, OH. Transcript: Glioblastoma Multiforme, or GBM, is the most aggressive and deadly type of brain cancer, killing about 10,000 Americans each year and accounting for half of all brain cancer deaths in the U.S. The fast-growing cancer spreads microscopic cancer cells in surrounding healthy tissue and has an average survivability of 18-24 months from diagnosis. Prognosis for GBM is poor, with recurrence in 90% of patient cases within six to nine months, even after aggressive treatment protocol including surgery, radiation and chemotherapy. Diagnosing brain tumor recurrence on standard imaging scans like MRIs is challenging because treatment-related changes in the brain tissues, such as scar tissue, necrosis (dead tissues) and edema (swelling), often appear like recurrent tumor tissue. Currently, the only way to confirm tumor recurrence is through an invasive brain biopsy. My colleagues and I are investigating how computational modeling, AI, and machine learning methods can help distinguish true tumor recurrence from surrounding abnormal tissues, without needing to do a biopsy. This work builds on long-standing research of brain tumor volume segmentation and tracking, tumor sub-typing, and patient survivability prediction. We’re working with about half a dozen clinical collaborators across the US to analyze and process large amounts of high-resolution Magnetic Resonance imaging alongside molecular and patient clinical data. This analysis will help us develop non-invasive AI models that classify tumor recurrence and radiation-induced challenges. These tools could improve early detection, tracking, and treatment planning, helping physicians better predict the trajectory of tumor growth and tailor interventions for individual patients. Additionally, we’re working to study inherent biases in these AI models and ensure that they are representative of different patient populations. This will bolster their robustness and efficacy in clinical settings. Research Projects This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  7. May 21

    Hong Qin, Old Dominion University - How Fast Can a Viral Variant Spread?

    On Old Dominion University Week: How fast can a viral variant spread? Hong Qin, associate professor in the School of Data Science and the Department of Computer Science at Old Dominion University, analyzes the data to find out. Faculty Bio: Hong Qin is an Associate Professor in the School of Data Science and the Department of Computer Science at Old Dominion University. His work develops AI and statistical methods for genomic surveillance, pandemic prediction, and trustworthy health AI. Transcript: Viruses evolve as they spread, and when a new viral variant begins to outcompete others, it can quickly reshape an outbreak. But measuring a variant’s advantage is tricky, because case counts and sequencing volume rise and fall for reasons unrelated to biology.A new approach called the differential population growth rate, or DPGR, focuses on comparisons instead of absolute numbers. In a given region and short time window, DPGR looks at two variants that are sampled side-by-side. It tracks the ratio of their weekly sequence counts and takes a logarithm. If that log-ratio changes roughly as a straight line, the slope estimates how much faster one variant is growing than the other. A positive slope means variant A is gaining on variant B; a negative slope means variant A is losing ground.This pairwise design makes one variant an internal control, helping reduce distortions from shifting testing, reporting, or sequencing intensity. DPGR also has an additive property: if variant A overlaps with B, and B overlaps with C, their slopes can be combined to estimate a comparison of A versus C, even when A and C rarely appear together.Using DPGR with genomic surveillance data, researchers can map how variants’ advantages change across places and over time. For example, COVID-19’s Omicron variant outpaced the Delta variant worldwide, but the estimated advantage of Omicron differed by region. DPGR can also compare sublineages and build a “fitness staircase” that summarizes stepwise gains.The result? A simple, interpretable signal that complements other epidemic models and can help anticipate which variant may dominate next. Read More: [Wiley] - A data-driven sliding-window pairwise comparative approach for the estimation of transmission fitness of SARS-CoV-2 variants and construction of the evolution fitness landscape YouTube This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
  8. May 20

    Maryam Golbazi, Old Dominion University - Where the Heat Hits Hardest

    On Old Dominion University Week: When it’s hot out, some places are hotter than others. Maryam Golbazi, research assistant professor of climate science, examines why. Faculty Bio: Maryam Golbazi is a Research Assistant Professor at Old Dominion University working with the Joint Institute on Advanced Computing for Environmental Studies (JI-ACES). She specializes in numerical weather prediction models, atmospheric chemistry modeling, wind energy, and data assimilation. Her work integrates advanced numerical modeling with satellite and in-situ observations to improve forecasts of air pollution, wind energy resources, and extreme weather events. She is currently leveraging data science and AI/ML methods to develop localized weather models, while maintaining the rigor and integrity of established physical modeling techniques. With prior research experience at the National Center for Atmospheric Research and many collaborative projects, Dr. Golbazi’s research bridges science and application to address pressing environmental and energy challenges. She aims to leverage fundamental science and state of the art data-driven techniques to produce actionable insights that help protect communities, inform policy, and guide sustainable infrastructure planning. Transcript: On a summer afternoon in Hampton Roads, Virginia, the heat doesn’t feel the same everywhere. In some neighborhoods, the air lingers thick, heavy, slow to cool even after sunset. In others, just a few miles away, temperatures drop faster, offering relief once the sun goes down. These differences aren’t random. They’re shaped by concrete, roads, buildings, and the environment.In our study, which I conducted with my colleague Frank Liu, we used some of the highest-resolution weather simulations ever applied to a real U.S. city to understand how extreme heat behaves at the neighborhood scale. Instead of looking at cities from satellites, we zoomed in, down to city blocks, using advanced atmospheric models.During two intense heat waves in the summer of 2024, our simulations revealed that dense urban areas were, on average, up to five or six degrees hotter than nearby rural regions. And at night urban neighborhoods stayed warm far longer.But temperature was only part of the story.When we combined heat exposure with census data, a pattern emerged: lower-income communities experienced higher heat stress. And that translated directly into energy demand. That matters, because cooling isn’t free. For families already struggling with energy costs, extreme heat becomes both a health risk and a financial burden.As heat waves become longer and more intense, understanding where heat concentrates, and who pays the price, may be just as important as predicting the temperature itself. Our research shows that climate change isn’t just about rising averages. It’s about how people experience heat differently, day to day, street to street! Read More: [Springer Nature] - High-resolution modeling of extreme heat events with socioeconomic consideration: a real-case WRF–LES approach This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.academicminute.org

    3 min
4.3
out of 5
28 Ratings

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