Physical Sciences and Mathematics Commons^™

Novel Digital Twin Deployment Approaches: Local And Distributed Digital Twin, Shahid Rauf, Fazal Muhammad, Akhtar Badshah, Hisham Alasmary, Muhammad Waqas, Sheng Chen

Research outputs 2022 to 2026

The Digital Twin (DT) technology is considered as a backbone in the Industrial 4.0 revolution as it is playing a vital role in the digitization of various industries. A DT is a virtual representation of a physical entity, thus having the ability to simulate real data generated at physical space to optimize, estimate, control, monitor and forecast states/configurations. Despite enormous benefits, DT technology has several implementation challenges. Although deploying DT on edge or cloud platforms yields a plethora of services, its implementation in both spaces faces certain limitations. These limitations include latency, data communication overload, transmission energy consumption, privacy concerns, …

Embodied Ai For Challenging Rearrangement Tasks In The Context Of Service And Assistive Robots, Mariia Khan

Theses: Doctorates and Masters

Embodied AI explores intelligent agents that learn through interaction with their environment, aiming to replicate human-like learning processes. Achieving this requires agents capable of understanding a scene via various sensors, reasoning about their actions, and reacting accordingly. These abilities are necessary for service domestic robots to assist humans in their day-to-day activities. Embodied AI tasks can include but are not limited to: visual exploration, visual navigation, instruction following and embodied question answering, which typically consider static (unchanging) environments, where objects do not move over time. This thesis addresses one of the most challenging Embodied AI tasks – visual room rearrangement, …

Assessing Environmental Performance Through Environmental Management Initiatives, Green Extrinsic And Intrinsic Motivation, And Resource Commitment In Malaysian Hotels, Ataul K. Patwary, S. Mostafa Rasoolimanesh, Roslizawati C. Aziz, Muhammad U. Ashraf, Md Mahmudul Alam, Shafique U. Rehman

Research outputs 2022 to 2026

The level of dedication that an organization has toward the preservation of the natural environment is reflected in its environmental performance. This study evaluates the role of green extrinsic and intrinsic motivation and resource commitment on Malaysian hotel employees’ environmental performance. It also examines the mediating roles of green extrinsic and intrinsic motivation and resource commitment in the relationship between green management initiatives and hotel environmental performance. Data was collected from the Four- and five-star hotel employees and tested the models and hypotheses of this study by employing partial least squares-structural equation modeling. Green extrinsic and intrinsic motivation significantly influence …

Gans And Synthetic Financial Data: Calculating Var*, David E. Allen, Leonard Mushunje, Shelton Peiris

Research outputs 2022 to 2026

Generative Adversarial Neural nets (GANs) are a new branch of machine learning techniques. A GAN learns to generate new data from the training data set. We examine the characteristics of the fake financial data using GANs trained on samples of daily S&P 500 and FTSE 100 index values. GANs feature two competing neural networks in a game theoretic context. The Generator net generates pseudo data that is presented to the discriminator net which then attempts to distinguish between the real and the fake data. This facilitates unsupervised learning on the dataset. The generative network generates data sets, while the discriminative …

Natural Hydrogen Soil Gas Emissions Near Harvey, Perth Basin: A Comparative Study Of Survey Methods, Krista Davies, Peter W. Haines, Charmaine Thomas, Leon Normore

Research outputs 2022 to 2026

Quantifying natural hydrogen soil gas emissions and their controlling factors is critical for assessing hydrogen storage viability and improving exploration strategies. This study provides the first detailed comparison of hydrogen emissions near the DMP Harvey-2 well in the Perth Basin and compares the effectiveness of repeated point surveys and autonomous monitoring in capturing emission variability. Hydrogen concentrations ranged from 0 to >1000 ppm in point surveys, with transient pulses up to 598 ppm detected through continuous monitoring, indicating episodic release. Repeated point surveys successfully captured the overall range of emissions but failed to resolve dynamic processes, such as abrupt flux …

S-Methyl Cysteine Sulfoxide And Its Potential Role In Human Health: A Scoping Review, Caroline R. Hill, Alex Haoci Liu, Lyn Mccahon, Liezhou Zhong, Armaghan Shafaei, Lois Balmer, Joshua R. Lewis, Jonathan M. Hodgson, Lauren C. Blekkenhorst

Research outputs 2022 to 2026

Higher intakes of cruciferous and allium vegetables are associated with a lower risk of cardiometabolic-related outcomes in observational studies. Whilst acknowledging the many healthy compounds within these vegetables, animal studies indicate that some of these beneficial effects may be partially mediated by S-methyl cysteine sulfoxide (SMCSO), a sulfur-rich, non-protein, amino acid found almost exclusively within cruciferous and alliums. This scoping review explores evidence for SMCSO, its potential roles in human health and possible mechanistic action. After systematically searching several databases (EMBASE, MEDLINE, SCOPUS, CINAHL Plus Full Text, Agricultural Science), we identified 21 original research articles meeting our inclusion criteria. These …

Joint 3d Beamforming-And-Trajectory Design For Uav-Satellite Uplink Covert Communication, Jihong Yu, Yuting Cai, Shihao Yan, Yun Li, Jingjing Wang, Jiahao Liu, Jianping An

Research outputs 2022 to 2026

In this paper,we study uplink covert communication in a space-air system,where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV,we develop a decoupling …

Zero Day Ransomware Detection With Pulse: Function Classification With Transformer Models And Assembly Language, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

Finding automated AI techniques to proactively defend against malware has become increasingly critical. The ability of an AI model to correctly classify novel malware is dependent on the quality of the features it is trained with and the authenticity of the features is dependent on the analysis tool. Peekaboo, a Dynamic Binary Instrumentation tool defeats evasive malware to capture its genuine behaviour. The ransomware Assembly instructions captured by Peekaboo, follow Zipf's law, a principle also observed in natural languages, indicating Transformer models are particularly well-suited to binary classification. We propose Pulse, a novel framework for zero day ransomware detection with …

Sources Of Underwater Noise, Christine Erbe, Alec J. Duncan, Alexander Gavrilov, Montserrat Landero, Robert D. Mccauley, Iain Parnum, Evgeny Sidenko, Chandra Salgado-Kent, Evgeny Sidenko

Research outputs 2022 to 2026

The marine soundscape, whether in coastal areas or the deepest ocean, in the tropics or at the poles, contains a myriad of sounds. Sounds may be grouped by their origin into geophony (e.g., wind, precipitation, waves, earthquakes, volcanoes, and ice), biophony (e.g., invertebrates, fishes, and marine mammals), and anthropophony (e.g., port construction, mineral and hydrocarbon exploration and production, renewable energy installation, and shipping). This chapter gives a brief overview of the geophony and biophony, and then focuses on the anthropophony. The sounds of boats and ships of various types, marine seismic surveys, drilling, dredging, pile driving, windfarms, geotechnical site investigations, …

Finding Time-Proximity Communities In Temporal Heterogeneous Information Networks, Yifu Tang, Chengfei Liu, Lu Chen, Rui Zhou, Jianxin Li

Research outputs 2022 to 2026

Community search in heterogeneous information networks (HINs) often neglects temporal dynamics, yielding structures that poorly reflect real-world interactions. We introduce the Temporal HIN Community Search (THCS) problem and propose a novel core model that captures both structural cohesiveness and temporal relevance. Our model uses a time span constraint to ensure interaction recency and a query interval for flexible temporal exploration, filtering irrelevant connections while preserving structural density. We develop two efficient online algorithms—Center-based Sliding Window search and Incremental Center Expansion—that exploit meta-path symmetry and dynamic connectivity tracking. For frequent queries, we design a Temporal HIN Core Interval-Index (TCI-Index), organising minimal …

Fuel Consumption Prediction Using Bayesian Neural Networks, Syarifah Diana Permai, Jurike V. Moniaga, Zener Sukra Lie, Ferry Jie

Research outputs 2022 to 2026

Transportation is one of the necessities of life. Because humans need transportation to move from one location to another. Transportation requires fuel. On the other hand, fuel consumption is important and must be controlled. This is because fuel can come from both renewable and non-renewable energy sources, depending on the type and process of its formation. Several factors influence the fuel efficiency of a car, including the type of engine, vehicle weight, aerodynamics, driving habits, and other vehicle conditions. This research aims to predict car fuel consumption and identify the factors that affect fuel consumption. Several Machine Learning and Statistical …

Pfedrag: A Personalized Federated Retrieval-Augmented Generation System With Depth-Adaptive Tiered Embedding Tuning, Hangyu He, Xin Yuan, Kai Wu, Ren Ping Liu, Wei Ni

Research outputs 2022 to 2026

Large Language Models (LLMs) can undergo hallucinations in specialized domains, and standard Retrieval-Augmented Generation (RAG) often falters due to general-purpose embeddings ill-suited for domain-specific terminology. Though domain-specific fine-tuning enhances retrieval, centralizing data introduces privacy risks. The use of federated learning (FL) can alleviate this to some extent, but faces challenges of data heterogeneity, poor personalization, and expensive training data generation. We propose pFedRAG, a novel Personalized Federated RAG framework, which enables efficient collaborative fine-tuning of embedding models to address these challenges. The key contribution is a new Depth-Adaptive Tiered Embedding (DATE) architecture, which comprises a Global Shared Layer, combined using …

Ecological Resources Of A Heavily Modified And Utilised Temperate Coastal Embayment: Cockburn Sound, Peter J. Mitchell, Daniel E. Yeoh, Kurt N. Krispyn, Claire N. Greenwell, Sorcha Cronin-O’Reilly, Delphine B.H. Chabanne, Glenn A. Hyndes, Danielle Johnston, David V. Fairclough, Claire Wellington, Alan Cottingham, Gary Jackson, Jeffrey V. Norriss, Matias Braccini, Hector Lozano-Montes, Chandra P. Salgado Kent, Erin Clitheroe, Alissa Tate, James W. Penn, Marion Massam, Neil R. Loneragan, James R. Tweedley

Research outputs 2022 to 2026

Coastal environments and their associated biota provide numerous environmental, economic and societal services. Cockburn Sound, a temperate embayment on the lower west coast of Western Australia, is immensely important for the State and adjacent capital city of Perth. However, urbanisation and associated terrestrial and marine development has the potential to threaten this important ecosystem. This study collated published and unpublished data to review the current state of the ecological resources of Cockburn Sound and describe how they have changed over the past century. Post-WWII, the embayment began undergoing pronounced anthropogenic changes that limited oceanic water exchange, increased nutrient load, modified …

Cui-Derived Cuxo Thin Films Development Via Thermal Oxidation For Lead-Free Perovskite Solar Cells, Md Jakir Hossen, Md Shahinuzzaman, M. S. Jamal, Suhana Mohd Said, S. F.W.M. Hatta, Md Helal Miah, Mayeen Uddin Khandaker, Mohammad Nur-E-Alam, Mohammad Aminul Islam

Research outputs 2022 to 2026

The development of low-cost, stable, and efficient hole transport layers (HTLs) is crucial to the advancement of lead-free perovskite solar cells. In this work, a solution-based oxidation process is employed to convert spin-coated copper iodide (CuI) films into mixed-phase copper oxide (CuxO) thin films comprising Cu₂O and CuO. Oxidation occurs under moderate temperatures (200–300°C), leading to uniform, compact, and highly conductive p-type films with tunable bandgaps. Structural, morphological, and optical analysis confirms successful phase transformation, and measurements of Hall effect reveal maximum mobility of holes of 26.7 cm² V⁻¹ s⁻¹ for double-coated films annealed at 300°C. Device …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

On-Device Recommender Systems: A Comprehensive Survey, Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, Chengqi Zhang

Research outputs 2022 to 2026

Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recommender systems work by collecting user-item interaction data in a cloud-based data center and training a centralized model to perform the recommendation service. However, such cloud-based recommender systems (CloudRSs) inevitably suffer from excessive resource consumption, response latency, as well as privacy and security risks concerning both data and models. Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), …

Deriving Best Practice Principles And Evaluation Criteria For Eia Follow-Up Governance, Charlene Smith, Angus Morrison-Saunders

Research outputs 2022 to 2026

Robust governance structures are crucial for effective Environmental Impact Assessment (EIA) follow-up, a critical component of best practice. EIA follow-up, with its five elements of monitoring, evaluation, management, participation and governance involves understanding project outcomes to inform ongoing management, enhance decision-making, and improve future EIA applications. EIA follow-up governance incorporates all the processes, mechanisms and arrangements required to enable the implementation of EIA follow-up. Despite its importance, implementation of EIA follow-up is still overlooked. The paper proposes best practice principles and evaluation criteria for EIA follow-up governance with a jurisdictional (macro) level focus, building upon existing EIA follow-up best practice …

Toward Privacy-Preserving Data Sharing - An Australian Healthcare Perspective, Kimley Foster, Nectarios Costadopoulos, Arash Mahboubi, Sabih Ur Rehman, Md Zahidul Islam

Research outputs 2022 to 2026

The rise of big data has brought increased urgency to the importance of privacy-preserving data sharing in healthcare. In Australia, health records exist in various databases; however data sharing is limited. While many consumers and healthcare professionals recognise the advantages of sharing data for research and health care services, misgivings about privacy and security persist. This study examined current perspectives on data sharing, investigating the trust level in privacy preserving data sharing tools and techniques among healthcare professionals and organisations, and their openness to adopting technology for secure data sharing. We incorporated participants from various healthcare professions across Australia. We …

Enhancing Cybersecurity Through Autonomous Knowledge Graph Construction By Integrating Heterogeneous Data Sources, Hatoon Alharbi, Ali Hur, Hasan Alkahtani, Hafiz Farooq Ahmad

Research outputs 2022 to 2026

Cybersecurity plays a critical role in today’s modern human society, and leveraging knowledge graphs can enhance cybersecurity and privacy in the cyberspace. By harnessing the heterogeneous and vast amount of information on potential attacks, organizations can improve their ability to proactively detect and mitigate any threat or damage to their online valuable resources. Integrating critical cyberattack information into a knowledge graph offers a significant boost to cybersecurity, safeguarding cyberspace from malicious activities. This information can be obtained from structured and unstructured data, with a particular focus on extracting valuable insights from unstructured text through natural language processing (NLP). By storing …

Protocol For An Integrative Meta-Analysis Of The Application Of Machine Learning Algorithms In The Prediction Of Chronic Disease Risks And Outcomes, Ebenezer Afrifa-Yamoah, Emmanuel Peprah-Yamoah, Enoch Odame Anto, Victor Opoku-Yamoah, Eric Adua

Research outputs 2022 to 2026

Background: Precise risk prediction of chronic diseases is essential for effective preventive care and management. Machine learning (ML) is a promising avenue to enhance chronic disease risk prediction; however, a comprehensive assessment of ML performance across various chronic diseases, populations, and health settings is needed. Methods: This meta-analysis aims to synthesize evidence on the performance of ML techniques for predicting the risks and outcomes of chronic diseases. A literature search was conducted through PubMed, Web of Science, Scopus, Science Direct, Medline, and Embase. Studies applying ML techniques to predict chronic disease risks or outcomes and reporting performance metrics were included. …

Automated Methods For Estimating Blood Alcohol Concentration Level From Facial Cues, Ensiyeh Keshtkaran

Theses: Doctorates and Masters

This thesis investigates different approaches for detecting alcohol intoxication in drivers by analysing facial video data. Tackling this issue necessitates the creation of a novel dataset to overcome the limitations of existing datasets. The dataset constructed in this study is the first to include RGB video recordings of individual faces at varying levels of alcohol intoxication during simulated driving, featuring 60 participants with BAC levels ranging from 0 to 0.165 g/100ml. The constructed dataset not only supports this thesis, but also offers the broader scientific community a valuable resource for further study and development.

Building on this, this thesis presents …

Toward Embodied Navigation Through Vision And Language, Muraleekrishna Gopinathan

Theses: Doctorates and Masters

Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and predicting the layout of the environment for better planning.

First, we …

Machine Learning For Computer-Aided Diagnostics From Complex Medical Images, Afsah Saleem

Theses: Doctorates and Masters

Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorptiometry (DXA). Among these, lateral spine DXA scans, commonly used for osteoporosis screening, offer a cost-effective and low-radiation opportunity for opportunistic CVD risk …

Pygoscelis Penguins Prefer Different Oceanographic And Terrestrial Habitats During The Austral Summer, Katherine L. Gallagher, Rachel Herman, Katherine Walton, Michael S. Dinniman, Heather J. Lynch

OES Faculty Publications

Pygoscelis penguins interact with both terrestrial and marine environments, especially during the austral summer when they raise their chicks. Accessible ice-free ground spaces serve as locations for penguins to build colonies and successfully raise chicks while the surrounding marine environment serves as the foraging grounds for adult birds. Penguins must be able to navigate through the waters around their colonies to forage for Antarctic krill (Euphausia superba), their primary prey during the austral summer. Krill, whose distributions are driven partially by the surrounding physics and partially by their behaviors, must also be plentiful in the waters near the …

The Relative Contribution Of Deep And Shallow Benthic Sources To Iron Supply In The Ross Sea, With Specific Emphasis On The Ross Bank, Blair J.W. Greenan, Michael S. Dinniman, Dennis J. Mcgillicuddy Jr., Peter N. Sedwick, Stefanie L. Mack, Walker O. Smith Jr.

OES Faculty Publications

Summer primary productivity in the Ross Sea is limited by the availability of dissolved iron (DFe) in the euphotic zone. Previous studies have suggested that benthic sources dominate the supply of DFe to the upper water column in the southern Ross Sea polynya. The purpose of this study is to investigate whether these benthic sources are derived from shallow banks or deeper areas of the continental shelf. Our study combines analysis of in situ observations near Ross Bank with a physical ocean model simulation in an overlapping summertime period to investigate the vertical supply of DFe. Two passive tracer dyes …

Sandy Subterranean Estuaries Minimize Groundwater Nitrogen Pollution Impacts On Coastal Waters, Xiaogang Chen, Isaac R. Santos, Jinzhou Du, Bochao Xu, Joseph J. Tamborski, Ding He, Neven Cukrov, Christian J. Sanders, Jianan Liu, Peiyuan Zhu, Yan Zhang, Ling Li

OES Faculty Publications

Widespread anthropogenic activities pollute groundwater that eventually seeps out to the coastal ocean. Here, we resolve nutrient transformations and fluxes in 11 sandy subterranean estuaries (STEs) with contrasting nutrient sources and development trajectories. Coastal groundwater nitrogen pollution stems from sewage discharge and land use change. Anthropogenically derived groundwater nutrient fluxes with high N/P ratios (∼170) accounted for 22%–61% of riverine inputs into China's coastal waters, providing an additional source of nutrients that can fuel coastal eutrophication and algal blooms. Sandy STEs remarkably attenuated ∼84% of nitrogen pollution, minimizing the impact of submarine groundwater discharge (SGD) on coastal water quality. Hence, …

The Δ¹³C Signature Of Dissolved Organic And Inorganic Carbon Reveals Complex Carbon Transformations Within A Salt Marsh, Meagan J. Eagle, Kevin D. Kroeger, John W. Pohlman, Joseph J. Tamborski, Zhaohui Aleck Wang, T. W. Brooks, Jennifer O. Keefe Suttles, Adrian Mann

OES Faculty Publications

Coastal wetlands have high rates of atmospheric CO₂ uptake, which is subsequently respired back to the atmosphere, stored as organic matter within flooded, anoxic soils, or exported to the coastal ocean. Transformation of fixed carbon occurs through a variety of subsurface aerobic and anaerobic microbial processes, and results in a large inventory of dissolved carbon. Carbon source and the roles of aerobic respiration, sulfate reduction, and methane cycling were evaluated within salt marsh peat and the underlying sandy subterranean estuary. There is a large increase in dissolved inorganic carbon (DIC, 7,350 ± 3,900 μmol L^-¹), dissolved organic carbon …

Dust Deposition To The Sargasso Sea: A Comparison Of Estimates Using Aluminum In The Surface Ocean Versus Aerosols And Rainwater, T. E. Williams, P. N. Sedwick, B. M. Sohst, K. N. Buck, S. Caprara, R. J. Johnson, D. C. Ohnemus, J. A. Resing, L. E. Sofen, A. Tagliabue, B. S. Twining

OES Faculty Publications

As a major component of mineral aerosol that partially dissolves in seawater, aluminum (Al) has been used to estimate time-averaged dust fluxes to the ocean, based on dissolved aluminum (DAl) concentrations and an assumed replacement time of DAl (tau DAl) in the surface mixed layer, along with the fractional solubility (Als) and mass abundance of Al in dust. We apply this method in the Bermuda region using seasonal water-column DAl concentrations, and compare our results with fluxes estimated from Al measured in Bermuda aerosols and rain. Inventories of DAl over the maximum mixed-layer depth of 200 m and empirically derived …

From Wind To Whales: Potential Hydrodynamic Impacts Of Offshore Wind Energy On Nantucket Shoals Regional Ecology, Eileen E. Hofmann, Jeffrey R. Carpenter, Qin J. Chen, Josh T. Kohut, Richard L. Merrick, Erin L. Meyer-Gutbrod, Douglas P. Nowacek, Kaustubha Raghukumar, Nicholas R. Record, Kelly Oskvig

OES Faculty Publications

The National Academy of Sciences, Engineering, and Medicine convened a committee in June 2023 to assess the potential hydrodynamic and ecological impacts from offshore wind energy development in the Nantucket Shoals region, with particular attention to impacts on the critically endangered North Atlantic right whale (Eubalaena glacialis) that forages on zooplankton aggregations in the region. The assessment suggested that the effects of offshore wind energy development will be difficult to distinguish from the effects of natural variability and climate change in this region. The Consensus Study Report recommendations highlight observational and modeling studies that will advance understanding of …

High Antarctic Coastal Productivity In Polynyas Revealed By Considering Remote Sensing Ice-Adjacency Effects, Hilde Oliver, Jessica S. Turner, Alexandre Castagna, Henry Houskeeper, Heidi Dierssen

OES Faculty Publications

Ocean color-based estimates of Antarctic net primary productivity (NPP) have indicated low nearshore productivity in ice-adjacent waters, contrasting with coupled physical–biogeochemical models. To understand this discrepancy, we assessed satellite records of polynya NPP by comparing field data with two satellite imagery datasets derived using different processing schemes. Our results indicate historical underestimation of chlorophyll a for imagery obtained using default atmospheric correction processing within approximately 100 km of ice-covered coastlines due to adjacency effects. Using radiative transfer modeling, we find that biases in ocean color polynya observations due to adjacency effects correspond to the high albedo of ice and snow. …