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Bat tracking across Europe
No full textNew technology answers questions about the seasonal migration of bat
Antarctic Wide Subglacial Hydrology Modeling
No full textGlobal sea levels are rising due, in large part, to the increase in the rate of melting of ice sheets as a direct result of climate change. A large fraction of uncertainty in future sea level rise comes from limited understanding of basal conditions under ice sheets. Specifically, subglacial hydrology, which describes the volume and movement of meltwater underneath glaciers and resulting basal water pressures. This system exerts a strong control on ice basal sliding and the rate of ice shelf melt, both of which can destabilize glaciers and lead to enhanced sea level rise. Here, we present subglacial hydrology model results of the full Antarctic Ice Sheet derived using the Glacier Drainage System model. We examine water pressures of the distributed drainage system, the distribution of the channelized system, and freshwater discharge across grounding lines of major drainage basins in the steady state subglacial system under present day conditions
Barriers to Machine Learning Adoption in Regulated Electric Utilities
No full textMachine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, and ease of mitigation. Findings reveal that trust in ML systems, workforce readiness, and regulatory compliance remain critical issues. In this research, the TOE framework is extended to regulated industries, providing utility leaders with strategies for addressing barriers and policymakers with insights into adaptive regulatory reforms. By focusing on the unique challenges of regulated utilities, this study provides a roadmap for leveraging machine learning (ML) to modernize operations while maintaining compliance, security, and public trust
Mosquito Classification and Explainability from Image Data via Deep Learning Techniques
No full textAccording to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, only a select few act as primary vectors of disease. Most disease transmission is attributed to mosquitoes from three key genera: Aedes (Ae.) , Anopheles (An.) , and Culex (Cx.) Within these groups, specific species are associated with particular diseases—for instance, An. gambiae is a major malaria vector in Africa, while An. stephensi plays a similar role in India. Ae. aegypti is known for spreading dengue, yellow fever, chikungunya, and Zika, whereas Cx. nigrip is a carrier of West Nile virus and various types of encephalitis.
Because not all mosquitoes are capable of transmitting disease, identifying the vectors during an outbreak becomes a critical first step in disease control. Public health teams often deploy mosquito traps across affected regions, capturing hundreds of specimens. However, only a fraction of these are vectors, and it becomes essential to accurately identify them to estimate population density and transmission risk. Currently, this identification process relies heavily on visual examination by trained taxonomists, who must inspect each specimen under a microscope. This method is not only time-consuming but also mentally demanding, placing significant strain on personnel responsible for the classification and documentation of trapped mosquitoes. In this dissertation, we present a comprehensive, interpretable, and field-ready AI framework for mosquito classification that bridges the gap between academic research and real-world vector surveillance efforts.
The first stage of the work, we examine the feasibility of classifying the gonotrophic stages (i.e., reproductive states) of mosquitoes from three medically significant genera— Aedes , Anopheles , and Culex A novel dataset was collected from 139 adult female mosquitoes across all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1,959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures ( Resnet50 , MobileNetV2 , EfficientNet-B0 , and ConvNeXtTiny ), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 93.59% with better decision boundaries. We also assessed the explainability of our AI model, by implementing Grad-CAMs - a technique that highlights pixels in an image that were prioritized for classification. We observe that the highest significance was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. This work establishes the potential of machine learning in addressing entomological classification tasks beyond species identification.
As a next step, we expanded existing work on adult mosquito classification and segmentation by incorporating a new vector species, Aedes scapularis , and refining segmentation labels for enhanced anatomical precision. Leveraging transfer learning, both the species classification and segmentation models were retrained on augmented datasets, demonstrating measurable improvements in generalization and predictive performance. This work also highlights the flexibility and adaptability of our deep learning pipeline to accommodate new species and updated annotations. As our global image database continues to grow with contributions from diverse geographic locations, this retraining strategy allows the model to continuously learn new features while retaining previously acquired knowledge. This ensures long-term scalability and robustness of the system, enabling it to evolve alongside real-world vector surveillance needs. We further bridge research and application by developing an interactive web-based dashboard that allows users, such as vector control teams or citizen scientists, to upload mosquito images and receive immediate classification and segmentation feedback. This platform marks a crucial step toward operationalizing AI tools for public health interventions.
After achieving encouraging results from our preliminary work, we focused to the problem of early detection of invasive mosquito species, focusing on a real-world case involving Anopheles stephensi in Madagascar. Using over 1,400 expertly labeled larval images from 8 mosquito species across 3 genera, we trained and evaluated binary and multi-class deep learning models at various taxonomic levels. A field-submitted image from Antananarivo, captured through the NASA GLOBE Observer app, was consistently classified as An. stephensi across all trained models with high confidence, a result confirmed through test-time augmentation and cross-validation. This study illustrates the power of deep learning in interpreting noisy, field-captured data and supporting early-warning surveillance for emerging vectors.
Continuing this stephensi classification thread, we explored the application of advanced deep learning architectures for the classification of Anopheles stephensi mosquitoes in both larvae and adult stages, addressing the significant challenge of class imbalance in ecological datasets. Given the practical significance of detecting this particular vector among many other mosquitoes in nature, we focus our study in this paper on class imbalance. Specifically, our dataset is imbalanced (just like it will be in nature), consisting of 1195 images of stephensi mosquitoes and 6021 images that are not stephensi mosquitoes, both of which are taken via modern smartphones in varying backgrounds. We employed three state-of-the-art models — MobileNetV2 , EfficientNet-B1 , and NasNetMobile — and applied class balancing techniques such as down-sampling and focal loss to emphasize the minor class data. We assessed their performance on several performance metrics. Our findings reveal that NasNetMobile outperformed the other models in larvae classification, achieving 97.66 % accuracy, while EfficientNet-B1 excelled in adult mosquito classification with 99.62 % accuracy. The implementation of focal loss effectively mitigated class imbalance, significantly improving sensitivity and precision across all models. Additionally, Grad-CAM visualizations confirmed that the models focused on biologically relevant features, enhancing interpretability. This work highlights the potential of deep learning techniques in improving mosquito surveillance and vector control strategies, ultimately contributing to public health initiatives aimed at combating malaria and other mosquito-borne diseases.
Together, this dissertation form a cohesive pipeline for mosquito classification that not only achieves high accuracy but also promotes explainability and usability. The integration of reproductive stage detection, species-level classification, anatomical segmentation, single vector detection, and real-world deployment via a user dashboard represents a holistic approach to entomological surveillance. Each phase—from dataset design to interactive tooling—was built with considerations for public health deployment, particularly in resource-limited settings where timely mosquito identification can be the difference between containment and outbreak. This work stands as a step forward in the development of intelligent surveillance systems that empower public health authorities to respond proactively to vector-borne threats in an era of climate change, urban expansion, and global vector migration
The Ruling Relations of Care: An Institutional Ethnography of High-Risk Prenatal Care Experiences
No full textDisrespect during pregnancy, labor, and delivery is common in the United States. The Centers for Disease Control and Prevention has estimated that approximately one in five women overall, and over 30% of Black women, report being ignored, dismissed, or verbally mistreated during their care. Public health efforts have primarily focused on individual-level interventions to improve patient-provider communication or reduce provider bias; however, these strategies have been critiqued for their limited scope and effectiveness in shifting outcomes. Instead, researchers and advocates have called for an examination of the structures that shape clinical encounters, including policies, protocols, workflows, and organizational culture. This Black feminist-informed Institutional Ethnography examines how institutional policies and discourses shape Black women’s experiences of high-risk prenatal care within a high-volume safety-net clinic. Data include semi-structured interviews with patients, providers, staff, and reproductive justice leaders; approximately 140 hours of observation; and review of key clinical and institutional texts. Findings were organized across six meta-themes: (1) Constructing the ‘High-Risk’ Pregnancy; (2) Profit and Loss: The Commodification of Pregnancy Risk; (3) Navigating Invisible Tethers to the Bureaucratic State; (4) The Labor of Care: Black Women’s Refusal and Compliance Work; (5) Consequences of Noncompliance: Enforcing Order in Disordered Systems; and (6) Envisioning the Future of Care. Together, these themes trace how pregnancy risk is constructed and coordinated, commodified and contested, and ultimately reimagined. The study highlights how institutional conditions, rather than individual behaviors, can produce experiences of (dis)respectful care and offers insights into policy and practice changes that can support care rooted in reproductive justice
Reducing Data Requirements in Polymer Science: Deep Neural Networks For Predicting Surface Tension of Copolymer Compatibilizers
No full textIn polymer chemistry, compatibilization involves adding a substance often a block or graft copolymerto stabilize polymer blends that would otherwise not mix well, leading to rough structures and weak me- chanical properties. Compatibilizers improve miscibility and reduce interfacial tension, which is critical for applications such as mixed-waste polymer recycling. Sequence-controlled polymers offer unique potential by combining the tunable chemistry of synthetic polymers with the precise, function-driven design of biological macromolecules, but unlike proteins, they lack large, evolution-shaped datasets to guide discovery. This research develops a deep learning framework to predict the surface tension of sequence-controlled copolymer compatibilizers across varying concentrations. Measuring surface tension experimentally is time-consuming, so we evaluate several machine learning approaches and find deep neural networks most effective, achieving an R2 of 0.91. To address data scarcity, we propose a priming-and-tuning strategy, where a model trained on a low-fidelity sequence–property dataset is efficiently fine-tuned for high-fidelity predictions under new con- ditions. This reduces data requirements, accelerates the design of polymer compatibilizers, and demonstrates the broader potential of AI-guided molecular desi
Building Insight One Tray at a Time: A Systematized Review of Sandtray Expressive Arts on Counselor Trainees’ Self-Awareness
No full textThis study examines the impact of sandtray expressive art interventions on the self-awareness of counseling students, recognizing the vital role self-awareness plays in counselor development. It is essential for counselors to practice self-awareness so that they may connect effectively with clients, make ethical decisions, and enhance their professional competence. Self-awareness is best identified as a transformative and reflective integration process. While traditional methods like journaling and supervision are commonly studied, the use of sand tray therapy as an expressive art intervention has received limited attention. This review synthesizes existing literature on self-awareness and sandtray expressive art interventions in counselor education, identifying key findings and gaps in current research. Notably, there is a lack of studies focusing on the efficacy of a single expressive art intervention. This gap presents an opportunity for further exploration into how specific interventions, such as sand tray therapy, affect self-awareness in counseling students. The purpose of this systematized review is to investigate whether the use of sandtray expressive art interventions enhance self-awareness among counselor trainees, with the hypothesis that students using these interventions will show increased self-awareness. Findings reveal that four major themes emerge from masters-level counseling students using sandtray intervention, having a positive effect on their self-awareness
Round Table (Part 1) Book Review: \u3cem\u3eFrom Discrimination to Death: Genocide Process Through a Human Rights Lens\u3c/em\u3e
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Exploring The Impact of Menopause on Human Voice: A Comparative Analysis Between Premenopausal and Postmenopausal Women.
No full textExploring The Impact of Menopause on Human Voice: A Comparative Analysis Between Premenopausal and Postmenopausal Women. Background and Objective: Menopause affects various aspects of women\u27s health, including voice. This study aims to explore the relationship between menopause and voice disorders. Methods: We conducted a retrospective study involving 334 females, divided into two groups based on menopausal status. Data including smoking history, demographic variables, stroboscopic notes, and standardized questionnaires (Reflux Symptom Index, Voice Handicap Index, Voice Catastrophization Index, Dyspnea Index, Cough Severity Index, and Eating Assessment Tool), were collected and analyzed where Categorical variables were compared using the χ2 test while continuous variables were compared using the Mann-Whitney U test. Results: Dysphonia, pain in the throat, chronic cough and MTD were more common in premenopausal women versus postmenopausal. Laryngeal lesions and Vocal fold atrophy were more common in postmenopausal women. Conclusion: Menopause affects voice. More research should focus on exploring the mechanisms of these changes