Spartan Daily, October 30, 2025

Volume 165, Issue 30https://scholarworks.sjsu.edu/spartan_daily_2025/1073/thumbnail.jp

Global tracking of marine megafauna space use reveals how to achieve conservation targets

The recent Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals but no clear pathway for how zero loss of important biodiversity areas and halting human-induced extinction of threatened species will be achieved. We assembled a multi-taxa tracking dataset (11 million geopositions from 15,845 tracked individuals across 121 species) to provide a global assessment of space use of highly mobile marine megafauna, showing that 63% of the area that they cover is used 80% of the time as important migratory corridors or residence areas. The GBF 30% threshold (Target 3) will be insufficient for marine megafauna’s effective conservation, leaving important areas exposed to major anthropogenic threats. Coupling area protection with mitigation strategies (e.g., fishing regulation, wildlife-traffic separation) will be essential to reach international goals and conserve biodiversity

The Impact of Artificial Intelligence on Data Privacy: A Risk Management Perspective

Purpose: The purpose of this paper is to increase the artificial intelligence (AI) ethical literacy of information governance professionals by explaining how AI intensifies familiar data privacy issues by virtue of its dependency on data and ability to create new personal information, to explicate emerging privacy enhancing methods and to show their continuity with existing privacy and information governance principles. Design/methodology/approach: The paper uses an interdisciplinary design research methodology that extends current governance frameworks by combining information science, information governance and applied ethics concepts. Three information sources were referenced: 1) academic papers; 2) standards and best practices published by governmental and nongovernmental organizations and professional associations; and 3) white papers, market research and vendor reports. The literature review was informed by real-world implementation knowledge and anecdotal evidence to identify privacy risks when using AI. Useful tools, techniques and governance approaches to manage and mitigate the risks associated with digital privacy and ethics when using AI are identified and discussed. Findings: The paper analyzes the relationship between different approaches to AI (e.g. symbolic-deductive, machine learning and deep learning) and levels of privacy risks. It identifies risk reduction methods (e.g. differential privacy) and relates these to extant privacy principles such as data minimization. Finally, the paper shows the continuity between information governance practices and newly emerging AI governance and risk frameworks. Originality/value: The authors present useful tools and techniques and discuss them from a business perspective, using the lens of information governance to mitigate AI-related privacy risks. The authors also discuss how design techniques and technologies can help minimize data collection of sensitive information and can be used to anonymize sensitive data when training AI models

Improving Communication and Job Satisfaction: Utilizing Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) for Interventional Service Areas

Purpose: Improved communication and job satisfaction among staff are essential for those working in high-stress critical care environments like the cardiac catheterization laboratory (Cath Lab). The purpose of this investigation was to utilize the quality improvement initiative Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) to improve attitudes and job satisfaction in the Cath Lab. Method: A total of 30 subjects participated in a TeamSTEPPS educational intervention. After obtaining informed consent, a demographic survey, a pre- and post-test TeamSTEPPS Teamwork Attitudes Questionnaire (T-TAQ) and Job Satisfaction Survey (JSS) were administered. The post-test included open-ended questions for feedback that were analyzed using manifest content analysis. Results: Qualitative findings indicated improvements in attitudes towards teamwork and job satisfaction. The qualitative results highlighted effective communication strategies promoted teamwork and patient safety. Teamwork ultimately improved by empowering staff to provide the best care, and positive teamwork impacted job satisfaction. Results of the quantitative analysis showed no significant difference between the mean T-TAQ and JSS scores on pre- and post-tests after the TeamSTEPPS educational intervention. Study limitations included a high leadership turnover, a small sample size and less than 4 months between the pre- and post-tests. Conclusion: This project highlights the critical role of teamwork and strategic communication in enhancing job satisfaction and ultimately better patient care in the interventional service areas, giving valuable insights for future practice and research in this area

Sirilla: Predicting Traffic Flow via Stacked Decentralized Federated Learning

Billions of people today rely on traffic predictions to optimize their travels. Digital mapping services deliver accurate predictions by learning from vast troves of historical data. Impressive as these systems are, their assumptions do not always apply. They depend on an endless flow of sensitive user data to a central authority, a stable Internet connection, and trustworthiness on both sides of the traditional client-server model. This thesis explores a novel architecture which bucks those assumptions. In the proposed model, traffic data remains on edge devices which individually train models via federated learning. Beyond the obvious privacy benefits, this architecture enables traffic prediction to occur in scenarios which the current paradigm struggles to support. In particular, in disaster scenarios such as earthquakes or hurricanes, evacuees may have no recourse in a dangerous environment without a system such as this

Interactions Between a High Intensity Wildfire and an Atmopsheric Hydraulic Jump in the Case of the 2023 Lahaina Fire

On 8 August 2023, a grass fire that started in the city of Lahaina, Hawai’i, grew into the deadliest wildfire in the United States since 1918. This wildfire offers a unique opportunity to explore the impact of high heat output on an atmospheric hydraulic jump and a downslope wind event. We conducted two WRF-SFIRE simulations to investigate these effects: one incorporating fire–atmosphere feedback and the other without it. We compare these WRF-SFIRE simulations to all available data regarding the fire event, including high resolution satellite data, weather station data from other parts of Maui and the Hawaiian Archipelago, and official reports from the Maui Police Department, Fire Department, and the Fire Safety Research Institute. Our findings revealed that, in the uncoupled simulation, the hydraulic jump moved inland significantly earlier than in the coupled simulation. This altered the wind pattern near the fire front in the uncoupled simulation, accelerating its lateral spread. The results suggest that fire–atmosphere interactions and their influence on near-fire circulation may be more intricate than previously understood. Specifically, while fire-induced wind acceleration is often linked to enhanced fire spread, this study highlights that, in cases where the lateral fire spread is dominant, fire-induced circulation may reduce cross-flank flow and inhibit the fire growth

Beyond peak water security: Household-scale experiential metrics can offer new perspectives on contemporary water challenges in the United States

The U.S. has moved beyond peak water security. Infrastructural degradation, institutional inertia, and climate change are reducing the ability of households and communities to benefit from near-universal safe, adequate, affordable, sustainable water services. Yet, current supply-side research tools, that focus largely on system performance, are not equipped to measure the prevalence and lived experiences of household water insecurity, thus limiting the evidence available to policymakers, utilities, and communities to make decisions about water services. We discuss how demand-side metrics, such as household-level water insecurity scales validated for high-income contexts, such as the U.S., can help stakeholders to better identify local variation in user water issues, guide resource allocation, and improve hazard and disaster response. Targeted infrastructure investments informed by these metrics can enhance water security, reduce reliance on emergency social services, and promote public health and economic vitality. To address 21st-century water challenges effectively, we must integrate experiential measures into local, regional, and national water assessments

Working Memory in Major Depressive Disorder: A Meta-Analysis of Structural and Functional Brain Differences

Major depressive disorder (MDD) is one of the most prevalent mental health disorders in the US and is characterized by depressed mood and loss of interest. Many MDD patients also experience difficulties with executive function, including working memory. Neuroimaging studies reveal structural and functional brain differences between MDD patients and healthy controls that may underlie these working memory difficulties, but findings have been mixed. A quantitative synthesis of neuroimaging data through a meta-analytic approach offers a promising avenue to identify commonalities across studies. We conducted separate meta?analyses of structural and functional magnetic resonance imaging (MRI) data, with the latter focused on studies utilizing the N-back task. We predicted reduced volume in individuals with MDD relative to controls in the hippocampus, prefrontal cortex, and anterior cingulate cortex, whereas we had no strong predictions regarding functional differences due to mixed and often contradictory findings in the literature. No significant effects were found for either the structural or functional meta-analyses when using a statistically conservative approach. Follow-up exploratory analyses revealed reduced volume in depressed individuals in the left superior frontal gyrus and right fusiform gyrus, as well as hyperactivity in the left anterior cingulate cortex. Should these findings be replicated in the future, it would suggest that these neural differences may explain reduced working memory abilities in MDD, potentially supporting the development of customized interventions targeting these regions. Keywords: Working Memory, Major Depressive Disorder, Neuroimaging, Meta-Analysi

Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis

Background: The opioid crisis remains a critical public health challenge, with opioid use disorder (OUD) imposing significant societal and health care burdens. Web-based communities, such as the Reddit community r/OpiatesRecovery, provide an anonymous and accessible platform for individuals in recovery. Despite the increasing use of Reddit for substance use research, limited studies have explored the content and interactions of self-disclosure and social support within these communities. Objective: This study aims to address the following research questions: (1) What content do users disclose in the community?; (2) What types of social support do users receive?; and (3) How does the content disclosed relate to the type and extent of social support received? Methods: We analyzed 32,810 posts and 324,224 comments from r/OpiatesRecovery spanning 8 years (2014‐2022) using a mixed method approach. Posts were coded for recovery stages, self-disclosure, and goals, while comments were categorized into informational and emotional support types. A machine learning–based classifier was used to scale the analysis. Regression analyses were conducted to examine the relationship between post content and received support. Results: The majority of posts were made by individuals using opioids (7225/32,810, 22.0%) or in initial recovery stages (less than 1 mo of abstinence; 27.7%). However, posts by individuals in stable recovery (abstinence for more than 5 years) accounted for only 1.8%. Informational self-disclosure appeared in 88.3% (n=28,977) of posts, while emotional self-disclosure was present in 75.6% (n=24,816). Posts seeking informational support (19,153/32,810, 58.4%) were far more common than those seeking emotional support (779/32,810, 2.4%). On average, each post received 9.88 (SD 11.36) comments. The most frequent types of support were fact and situational appraisal (mean 5.62, SD 6.82) and personal experience (mean 4.88, SD 5.98), while referral was the least common (mean 0.61, SD 0.50). Regression analyses revealed significant relationships between self-disclosure and received support. Posts containing informational self-disclosure were more likely to receive advice (β=0.17, P\u3c.001), facts (β=0.30, P\u3c.001), and opinions (β=0.11, P\u3c.001). Emotional self-disclosure predicted higher levels of emotional support (β=0.17, P\u3c.001) and personal experiences (β=0.07, P\u3c.001). Posts from individuals in the addiction stage received more advice (β=−0.06, P\u3c.001) but less emotional support (β=−0.05, P\u3c.001) compared with posts from individuals in later recovery stages. Conclusions: This study highlights the role of self-disclosure in fostering social support within web-based OUD recovery communities. Findings suggest a need for increasing engagement from individuals in stable recovery stages and improving the diversity and quality of social support. By uncovering interaction patterns, this study provides valuable insights for leveraging online support groups as complementary resources to traditional recovery interventions

Increased melt from Greenland’s most active glacier fuels enhanced coastal productivity

Seasonal phytoplankton blooms in Greenland’s coastal waters form the base of marine food webs and contribute to oceanic carbon uptake. In Qeqertarsuup Tunua, West Greenland, a secondary summertime bloom follows the Arctic spring bloom, enhancing annual primary productivity. Emerging evidence links this summer bloom to subglacial discharge from Sermeq Kujalleq, the most active glacier on the Greenland Ice Sheet. This discharge drives localized upwelling that may alleviate nutrient limitation in surface waters, yet this mechanism remains poorly quantified. Here, we employ a high-resolution biogeochemical model nested within a global state estimate to assess how discharge-driven upwelling influences primary productivity and carbon fluxes. We find that upwelling increases summer productivity by 15–40% in Qeqertarsuup Tunua, yet annual carbon dioxide uptake rises by only ~3% due to reduced solubility in plume-upwelled waters. These findings suggest that intensifying ice sheet melt may alter Greenland’s coastal productivity and carbon cycling under future climate scenarios