Digital Commons Network^™

High Organofluorine Concentrations In Municipal Wastewater Affect Downstream Drinking Water Supplies For Millions Of Americans, Birdger J. Ruyle, Emily H. Pennoyer, Simon Vojta, Jitka Becanova, Minhazul Islam, Thomas F. Webster, Wendy Heiger-Bernays, Rainer Lohmann, Paul Westerhoff, Charles E. Schaefer, Elsie M. Sunderland

Graduate School of Oceanography Faculty Publications

Wastewater receives per- and polyfluoroalkyl substances (PFAS) from diverse consumer and industrial sources, and discharges are known to be a concern for drinking water quality. The PFAS family includes thousands of potential chemical structures containing organofluorine moieties. Exposures to a few well-studied PFAS, mainly perfluoroalkyl acids (PFAA), have been associated with increased risk of many adverse health outcomes, prompting federal drinking water regulations for six compounds in 2024. Here, we find that the six regulated PFAS (mean = 7 to 8%) and 18 measured PFAA (mean = 11 to 21%) make up only a small fraction of the extractable organofluorine …

Scientists’ Statement On The Chemical Definition Of Pfass, Gabriel Sigmund, Marta Venier, Marlene Ågerstrand, Ian T. Cousins, Jamie Dewitt, Miriam L. Diamond, Jennifer Field, Alex T. Ford, Shira Joudan, Stefan Van Leeuwen, Rainer Lohmann, Carla Ng, Martin Scheringer, Anna Soehl, Noriyuki Suzuki, Xenia Trier, Sara Valsecchi, Penny Vlahos, Cora J. Young, Zhanyun Wang

Graduate School of Oceanography Faculty Publications

The undersigned are scientists with expertise in per- and polyfluoroalkyl substances (PFASs) and/or the management of chemicals. We assert that the Organization for Economic Co-operation and Development (OECD) definition of PFASs is scientifically grounded, unambiguous, and well suited to identify these chemicals. We are concerned that some individuals and organizations are seeking a redefinition of PFASs endorsed by the International Union of Pure and Applied Chemistry (IUPAC) to exclude certain fluorinated chemical subgroups from the scope of the existing definition. We are concerned that this effort is politically and/or economically, rather than scientifically, motivated. An IUPAC-endorsed and potentially narrower PFAS …

Final Cruise Report: Tn401, Roxanne A. Beinart, Shawn Arellano, Craig Young

Graduate School of Oceanography Faculty Publications

No abstract provided.

Atmospheric Concentrations Of Per- And Polyfluoroalkyl Substances In Michigan's Ambient Air Using Passive Sampling, Melissa Woodward, Jitka Becanova, Simon Vojta, Rainer Lohmann

Graduate School of Oceanography Faculty Publications

As a part of Michigan's efforts to identify and address per- and polyfluoroalkyl substances (PFAS) contamination statewide, more knowledge of atmospheric PFAS concentrations is needed to better understand their atmospheric transport and impact on other environmental media. This research aimed to measure atmospheric concentrations of PFAS in Michigan's ambient air using low-cost and easy-to-use passive samplers and identify relationships with environmental factors. Passive samplers, consisting of polyurethane foam (PUF) discs and sorbent-filled polyethylene tubes (radiello-XAD samplers), were deployed for a month at 27 sites across Michigan and analyzed for both ionic and volatile, neutral PFAS. Short chain perfluorocarboxylic acids (PFCAs), …

Spatial And Temporal Trends Of Dissolved Polybrominated Diphenyl Ethers And Non-Bde Flame Retardants In The Aquatic Environment Across Countries, Lian-Jun Bao, Branislav Vrana, Rainer Lohmann, Jaromír Sobotka, Foppe Smedes, Derek Muir, Ian J. Allan, Peleg Astrahan, Terry Bidleman, Denis Crowley, Evgen Dykyi, Nicolas Estoppey, Gilberto Fillmann, Liisa Jantunen, Sarit Kaserzon, Keith A. Maruya, Brendan Mchugh, Brent Newman, Raimon M. Prats, Manolis Tsapakis, Rebecca Sutton, Mats Tysklind, Barend L. Van Drooge, Charles S. Wong, Eddy Y. Zeng

Graduate School of Oceanography Faculty Publications

Measuring dissolved concentrations of polybrominated diphenyl ethers (PBDEs) and non-BDE flame retardants on a global scale provides critical insights into the effectiveness of the Stockholm Convention. In the present study, we deployed passive sampling devices at 43 seawater and freshwater sites covering 21 countries from 2016 to 2020. The detection frequencies were 20–94% for BDE congeners and 33–42% for dechlorane plus, higher than those (0–20%) for other target compounds. The median concentrations of dissolved Σ₉PBDE (sum of BDE-28, -47, -66, -85, -99, -100, -153, -154, and -183) were 0.28 and 0.64 pg L^–1 in seawater and freshwater, …

Achievements And Challenges With Equilibrium And Kinetic Passive Sampling Of Hydrophobic And Hydrophilic Organic Compounds In Surface Waters, Kees Booij, Rachel Mackie, Branislav Vrana, Rainer Lohmann, Sarit L. Kaserzon

Graduate School of Oceanography Faculty Publications

Passive sampling in surface waters is an important method in the monitoring and risk assessment of hydrophobic and hydrophilic organic chemicals. Sampler designs can be optimized for fast equilibrium attainment (equilibrium sampling) or improved time-integrative capability (kinetic sampling). We argue that both equilibrium and kinetic sampling can be applied when aqueous concentrations do not vary with time, whereas kinetic sampling also yields useful results for time-variable concentrations that are often observed with hydrophilic compounds in surface waters. We show that these methods have similar accuracy in principle but that their dominant error sources are different: sampler-water sorption coefficients for equilibrium …

Characterization Of The Potential Long-Term Impact From Sedimentary Pfas At A Historically Contaminated Textile Waste Site, Jarod Snook, Jitka Becanova, Simon Vojta, Rainer Lohmann

Graduate School of Oceanography Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) are pervasive pollutants at historically contaminated sites throughout the United States and beyond. Two such sites in Rhode Island, USA, are textile-mill-associated waste retention ponds known to introduce PFAS contamination to the adjacent river, estuary, and eventually the Atlantic Ocean. Here, we thoroughly investigated the retention ponds as a long-term source of PFAS via water passive sampling, sediment coring, and laboratory-derived partitioning coefficients, K_d, with field sediment and water. Additional studies were performed to assess the mobility and estimate the mass fluxes of PFAS from sediment to water. Retention pond 1 was more …

Bot Detection In Social Media Using Graphsage And Bert, Abhishek Deshmukh

Master's Projects

This project details a novel bot detection system developed to battle the ever- changing challenge of disinformation, misinformation, and other bot-generated content.

The methodology employed in this project combines the text-based analytical strength of BERT (Bidirectional Encoder Representations from Transformers) with the strength of GraphSage (Graph Sample and Aggregation) for analyzing network structures. The project concatenates BERT and GraphSage vectors to create an 896-size feature embedding with a rich blend of network and text features. This project employs a Support Vector Machine to process the concatenated embeddings, as SVM works well with high-dimensional data. This project was evaluated on two …

Ai Powered Legal Decision Support System, Alisha Rath

Master's Projects

The large volume of legal cases presented by judicial professionals has made it
challenging to study and predict results. With advances in research methods and
technology, predicting law cases in a more accurate manner has become an important
trend. Prediction tools based on AI may help manage a large number of legislative
texts and documents that cannot possibly be fully read, reduce the number of cases
to be seen, and give accurate outcomes of how cases may turn out. Now, when
we look into the current AI legal prediction tools in this domain, they mostly lack
efficiency and interpretability, the …

Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham

Master's Projects

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective …

Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh

Master's Projects

The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

Cca Analysis Using Computer Vision Techniques, Rahul Thakur

Master's Projects

Coral reefs are an essential part of the marine ecosystem. They perform a wide variety of tasks, some directly and others indirectly. They can produce oxygen, absorb carbon dioxide, along with supporting ocean habitat. Crustose Coralline Algae (“CCA”) plays an important role in helping provide structural support to Coral Reef ecosystems. However, global warming is causing ocean water to become more acidic resulting in coral bleaching. This is leading to changes in coral environments and causing coral deaths at alarming rates. Object detection using computer vision techniques, specifically deep learning, can help to monitor coral reef health and identify CCA …

Mitigating Cold Start Problem Through Metadata Integration And User Preference Analysis, Prabaljit Walia

Master's Projects

Recommendation systems power the most popular platforms in the world: from content catalogs on Netflix to custom feeds on TikTok – the importance of recommendation systems is significant. Collaborative filtering, the most popular recommendation technique, is essentially based on the idea of leveraging collective user intelligence i.e., creating recommendations by finding similar users. But this technique suffers when there is not enough data in the profiles of users, formally termed as the cold start problem. This research focuses on this problem by introducing an approach that integrates metadata-driven similarity measures with profile expansion techniques. Our approach combines traditional collaborative filtering …

Coral Vision – Crustose Coralline Algae Detection With Computer Vision, Ryan Tseng

Master's Projects

Crustose coralline algae (CCA) are a group of red algae that are vital contributors to the health of coral reef ecosystems. Monitoring CCA abundance can serve as an indicator for coral reef health and improve reef conservation efforts. Autonomous Reef Monitoring Structures (ARMS) are artificial structures that can be deployed into coral reef ecosystems and retrieved to gather ecological data without harming reef structures. Traditional methods of calculating CCA abundance require manual analysis and are labor-intensive. Recent developments in computer vision and deep learning technology have provided an avenue to fully automate this task. This research aims to train a …

Ai-Enabled Anticipatory Handover Predictions In 5g Networks, Ojas Ankush Naik

Master's Projects

As users move across network cells in 5G, it is critical to maintain seamless connectivity through efficient and fast handovers. However, as 5G networks have a very dense deployment of cells and higher carrier frequencies, handovers are often more frequent and challenging, leading to failures or the ping-pong effect. In this research, we are going to use Artificial Intelligence (AI) techniques to enable predicting handover(HO) events proactively as opposed to reactively, aiming to reduce HO failures and unnecessary handovers. We develop Long-Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models to forecast future signal measurements, predict handover trigger points, and compare …

Machine Learning Based Network Traffic Classification With Cosine-Similarity Based Out-Of-Distribution Detection, Prabhat Edupuganti

Master's Projects

The changes occurring in the amount of encrypted network traffic is growing at an alarming rate. This development has created intricate problems in traffic classification which is vital for effective cybersecurity. Moreover, most frameworks seem to ignore OOD detection, model calibration and novel pattern detection as cornerstone problem areas. The due analysis is presented as a machine learning approach aimed at resolving encrypted traffic classification issues and focuses on novel OOD detection and calibration issues. Primary contributions comprise detection of out-of-distribution states using softmax scaled cosine similarity, advanced variance-based feature elimination, and lowering ECE using stringent NNs. This work demonstrates …

Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-Based Feature Selection And Ml Models, Mohith Sai Venkat Ankem

Master's Projects

Landslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine …

Adaptive Cobot Interaction Via Smartwatch Data Fusion For Car Assembly Automation, Riddhik Tilawat

Master's Projects

In modern car manufacturing, collaborative robots (cobots) work with human operators during shared workcell interactions to maximize production speed and flexibility. Collaboration between humans and robots is safe and effective only when operator intent recognition via a single wrist-worn inertial measurement unit (IMU) is accurate and low-latency. This thesis develops an IMU-only intent recognition pipeline, and is evaluated on three datasets: the public OPPORTUNITY dataset, the Sony Smartwatch Gesture dataset and a custom Samsung Galaxy watch 6 dataset. The proposed framework leverages five step sequence-to-label problems which are stepwise posed as data streams transforming raw IMU data into trainable tensors. …

Real-Time Adaptive Framework For Topic Modeling In Social Engineering Attacks, Manav Bhasin

Master's Projects

Detecting social engineering attempts is crucial for security, as these threats are becoming more frequent and increasingly exploit human vulnerabilities. This research focuses on topic modeling using conversational data from Kevin Mitnick’s ”The Art of Deception” with dialogues that illustrate various social engineering strategies. The dataset comprises manually extracted and synthetically augmented conversations to ensure natural dialogue flow. Two methodologies are presented for utterance-level and global topic extraction: prompt engineering leveraging OpenAI’s GPT-4o-mini, characterized by few-shot learning and chain-of-thought prompting, and Quantized Low Rank Adaptation (QLoRA) utilizing Mistral’s 7B instruct model for efficient fine-tuning. Through experimentation and evaluation, this study …

Simple Vs. Complex Human Activity Classification Via Hybrid Machine Learning Models, Anusha Kukreja

Master's Projects

In-depth understanding of the complexity of daily human activities is crucial for building responsive health monitoring and assistive technologies. However, limited research has focused on distinguishing activities based on their involvement level, as most existing work classifies only the type of activity performed. In this thesis, we address this gap by proposing a method to classify human activities as either simple or complex using sensor data from the Opportunity dataset. We define complex activities as those involving object interactions or multiple coordinated movements (e.g., drinking from a cup, cleaning a table), and simple activities as static or low-effort postures (e.g., …

Should Harvard And Other Large Nonprofits Be Taxed?, Reuven S. Avi-Yonah

Articles

In this installment of Reflections With Reuven Avi-Yonah, Avi-Yonah examines justifications for exempting Harvard University and other large nonprofits from tax and argues that treating those organizations like large C corporations would not be such a bad thing. The proposal to subject large university endowment investment income to the corporate tax rate of 21 percent instead of the current 1.4 percent rate has engendered significant debate. More recently, President Trump’s threat to strip Harvard University of its tax-exempt status and treat it as a taxable corporation has likewise been controversial. In their recent Tax Notes article, Harvey Dale, Daniel Hemel, …

Eliminating Qbai - One Step Forward, Two Steps Back?, Reuven S. Avi-Yonah

Articles

In this installment of Reflections With Reuven Avi-Yonah, Avi-Yonah explains what makes an effective U.S. tax regime and uses those attributes to evaluate the One Big Beautiful Bill Act. There is plenty to criticize about the One Big Beautiful Bill Act (OBBBA, P.L. 119-21), signed into law by President Trump on July 4. It increases the deficit by about $4 trillion when the interest costs from the additional borrowing are included, and by about $5.5 trillion if its provisions are made permanent in 2029, like the Tax Cuts and Jobs Act provisions were in 2025. It is distributionally skewed to …

Is The Obbba Compatible With Pillar 2?, Reuven S. Avi-Yonah

Articles

In this installment of Reflections With Reuven Avi-Yonah, Avi-Yonah compares House and Senate sections of the One Big Beautiful Bill Act and examines the potential effect on international tax relations. On May 22 the House of Representatives passed the One Big Beautiful Bill Act (OBBBA). The House version of the OBBBA extended the Tax Cuts and Jobs Act tax cuts but did not significantly change its international provisions. It did, however, contain proposed section 899, which would have imposed enhanced taxation on residents and corporations from countries that have “discriminatory or extraterritorial taxes,” defined to include primarily the undertaxed profits …

Facebook Decision Enables Irs To Seek Cwi Enforcement Against Meta, Stephen L. Curtis, Reuven S. Avi-Yonah, David G. Chamberlain

Articles

In this report, the authors explain how the Tax Court’s recent decision in the Facebook transfer pricing case — although widely viewed as a victory for the taxpayer, Meta Inc. — could instead be a Pyrrhic victory because it enables the IRS to pursue a potentially substantial periodic adjustment against the company. The views expressed in this report are solely the authors’ and do not necessarily reflect those of any other person or institution. On May 22 the Tax Court issued its opinion in Facebook. This was the third case, after Amazon and Veritas, in which the IRS tried to …

Do Specialized Medical Llms Demand A Radically New Approach Under The Eu's Medical Device Regulation, Hannah Louise Smith, W. Nicholson Price Ii

Articles

We examine the arguments made by Onitiu and colleagues concerning the need to adopt a “backward-walking logic” to manage the risks arising from the use of Large Language Models (LLMs) adapted for a medical purpose. We examine what lessons can be learned from existing multi-use technologies and applied to specialized LLMs, notwithstanding their novelty, and explore the appropriate respective roles of device providers and regulators within the ecosystem of technological oversight.

The Law Of Energy Abundance, Alexandra B. Klass, Matthew Appel

Articles

For the first time in decades, electricity demand in the United States is increasing due to the growth of data centers to power artificial intelligence, new manufacturing hubs, and transportation electrification. At the same time, coalfired power plants have been closing in response to competition from lower cost natural gas and renewable energy. Clean energy is being rapidly deployed to replace fossil fuels but not quickly enough to address concerns about demand growth and grid reliability. Accordingly, there is a growing imbalance between electricity supply and demand in many regions of the country that threatens to increase electricity prices and …

Can Side By Side Work?, Reuven S. Avi-Yonah

Articles

On June 26 Treasury Secretary Scott Bessent announced that a compromise had been reached between the United States and the rest of the G7 (Canada, France, Germany, Italy, Japan, and the United Kingdom), providing that pillar 2 of the OECD’s two-pillar global tax reform project will not apply to U.S. multinationals. As a result, proposed section 899, which would have imposed retaliatory taxation on corporations from countries that apply pillar 2 (and specifically the undertaxed profits rule) to U.S.-based multinationals, was removed from the One Big Beautiful Bill Act.

Us Policy And Pillar 2: The Evolution Of Us Tax Policy Toward Oecd Pillar 2 And Its Global Implications, Reuven S. Avi-Yonah

Articles

This paper analyzes the United States’ evolving position on OECD Pillar 2, focusing on the Trump administration’s initial rejection and subsequent negotiations to exempt US multinationals from the Undertaxed Profits Rule (UTPR). It examines the legislative and diplomatic strategies that led to international acceptance of GILTI as a valid IIR. The analysis concludes that the resulting compromise preserves US tax sovereignty while maintaining the global framework’s integrity.

The Irs Approach To Periodic Adjustments: Losing Bet Or Royal Flush?, Jeffery M. Kadet, Reuven S. Avi-Yonah, David G. Chamberlain, Stephen L. Curtis

Articles

In this article, the authors argue that GLAM 2025-001 is consistent with the terms and purposes of the commensurate with income provision, relevant legislative history, and applicable case law and that the IRS approach to commensurate with income enforcement is a valid application of discretion delegated by Congress to the Treasury under Loper Bright.