Digital Commons Network^™

Bioremediation Potential Of Indigenous Stutzerimonas Stutzeri 08tbb Isolated From Mercury-Contaminated Gold Mining Sites In Indonesia, Uun Rohmawati, Juanita Hibatullah, Achmad Rodiansyah, Yunita Messe, Dwi Nur Aini Fajriah

Applied Environmental Research

Artisanal and small-scale gold mining (ASGM) widely uses mercury (Hg) in the amalgamation process, and the resulting waste is often discharged directly into the environment, posing serious risks to ecosystems and human health because of the high toxicity of Hg. This study aimed to isolate and screen indigenous Hg-tolerant bacteria from contaminated gold mining sites in Indonesia; characterize the most effective isolate using morphological, biochemical, and molecular approaches; evaluate Hg(II) degradation performance using ICP‒OES, FTIR, and SEM analyses; and identify key metabolites involved in Hg detoxification through untargeted LC–HRMS metabolomics. A total of 51 bacterial isolates were obtained from Hg-contaminated …

Assessment Of Chromosomal Abnormalities In Hybrid Catfish In Reservoir Contaminated With Heavy Metals From Municipal Landfill, Tanadol Savigun, Natchanon Thiratanaboon, Nuttawadee Maneesan, Manop Sriuttha, Lamyai Neeratanaphan

Applied Environmental Research

In this study, the concentrations of heavy metals (As, Cd, Cr and Pb) in water, sediment, and hybrid catfish (Clarias gariepinus x C. macrocephalus) as well as chromosomal abnormalities (CAs) from a reservoir affected by leachate from a municipal landfill were compared to those in an unaffected reservoir. Heavy metal concentrations were analyzed using inductively coupled plasma‒optical emission spectrometry (ICP‒OES). Chromosomes were prepared from kidney cells by direct methods. The As, Cd and Pb concentrations in the water and sediment from both reservoirs were within the Thailand water and soil standards. The concentrations of As and Cd in the water …

Assessing Forest Fragmentation And Edge Expansion In Nan Province, Thailand, Using Landsat Data (2014–2022), Kasidit Rison, Marut Fuangarworn, Chatchawan Chaisuekul

Applied Environmental Research

The increasing pressure from land-use change and agricultural expansion in Nan Province, northern Thailand, has accelerated forest fragmentation and reduced the ecosystem service value (ESV) of the landscape. This study aims to: assess land use and forest fragmentation change in Nan Province between 2014 and 2022, and evaluate the impact of land use change on ESV. Landsat 8 OLI satellite imagery from 2014 and 2022 was used to classify land cover into five types: forest, agriculture, settlements, exposed soil, and others. Forest fragmentation was evaluated using landscape metrics such as patch number, mean patch size, edge length, and core area. …

Application Of Diatom Indices To Monitor Environmental Shifts In The Downstream Tuntang River, Central Java, Mirza Hanif Al Falah, Tri Retnaningsih Soeprobowati, Hadiyanto -, Muhammad Isnaini Ibnu Sabil, Marta Wojewódka-Przybył, Łukasz Kruszewski, Paweł Rydelek

Applied Environmental Research

Diatoms are widely recognized as sensitive bioindicators for reconstructing past water quality and ecological changes in aquatic ecosystems. In this study, multiple diatom-based indices were applied to sediment cores collected from three downstream stations of the Tuntang River, Central Java, to infer long-term environmental change in a tropical river system. A total of 38 sediment subsamples were analyzed, with dominant taxa including Fragilaria crotonensis (2.4–18.6%), Nitzschia palea (3.1–22.4%), and Aulacoseira granulata (2.0–15.8%), indicating sustained nutrient enrichment. Diatom indices consistently classified conditions as mesotrophic to eutrophic throughout the sediment profiles. The occurrence of the marine diatom Cylindrotheca closterium (up to 6.3%) …

Environmental Sustainability Performance And Efficiency In Thailand’S Rajabhat Universities : A Cross-Regional Analysis Of The Impact Rankings, Konstantinos Balis

Applied Environmental Research

Higher education institutions (HEIs) are expected to increasingly communicate their contributions to the United Nations’ Sustainable Development Goals (SDGs), with the rise of alternative sustainability rankings serving as prominent reporting instruments for institutional transparency. In Thailand, Rajabhat Universities, a network of government HEIs historically associated with their mission of teacher training and local development, have been actively participating in the Times Higher Education (THE) Impact Rankings, presenting a unique case study to explore cross-regional sustainability efforts in relation to global benchmarks. To evaluate trends, regional disparities, and engagement performance across the sustainability pillars, this study uses a descriptive quantitative design …

Initial Screening Of Halophilic Plastic-Degrading Bacteria From Mangrove Water And Sediment In Pekalongan, Indonesia, Novia Safinatunnajah, Muhammad Zainuri, Aris Ismanto, Tony Hadibarata, Kunarso -, Sugeng Widada, Hermin Pancasakti Kusumaningrum, Candra Wahyuningsih

Applied Environmental Research

Microplastic pollution in tropical mangrove ecosystems represents an increasing environmental concern, particularly in coastal regions such as Pekalongan, Indonesia, where industrial and residential activities contribute substantially to plastic waste. This study aimed to isolate and preliminarily screen halophilic bacteria from mangrove sediments and seawater collected from a plastic-impacted coastal area. Using mineral salt medium (MSM) supplemented with polyethylene glycol (PEG), six bacterial strains were obtained and evaluated for their potential to interact with polyethylene (PE) and polypropylene (PP) on the basis of gravimetric weight loss measurements. Among the isolates, Halomonas Cupida A1, Marinobacter nauticus A6.3, and Marinobacter nauticus SD5.2 exhibited …

Advancing Food Equity Through Explainable Ai (Xai): Identifying Place-Based Factors And Conditions Of Food Security, Leslie Hoglund, Hyoshin Park

Health Behavior, Policy & Management Faculty Publications

Food behaviors, food security, and their association with socioeconomic factors constitute a critical area of study with implications for public health, economic stability, and social equity. Understanding these relationships are essential for developing effective policies and interventions that promote sustainable, healthy food systems and greater food equity. This paper employs explainable artificial intelligence (XAI) methods to identify key features influencing household food behaviors. The insights gained from the XAI analysis are further utilized in conjunction with inverse reinforcement learning (IRL) to examine expert behaviors related to eating habits satisfaction. The XAI results reveal that household health conditions, spending patterns, and …

Machine Learning: Thematic Feature Grouping, And The Magnificent Seven: A Forecasting Analysis, Mirarmia Jalali, Mohammad Najand, Andrew Cohen

Finance Faculty Publications

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, …

Natural Resource Rents And Energy Poverty Nexus In Next Eleven Economies, Muhammad Salah Uddin, Ayub Ali, Zobayer Ahmed, Md Nasir Uddin Sikdar, Ahsan Habib, Abdullah Elah Al-Mahde

Finance Faculty Publications

Energy poverty (EP) remains a persistent global challenge with important implications for economic development, public health, and social welfare. While natural resources, particularly oil and gas, are often viewed as key sources of energy access, their effectiveness in mitigating EP remains underexplored in emerging economies. This study examines the relationship between natural resource rents (NRR), specifically oil rents (OR) and natural gas rents (NGR), and energy poverty (EP) in the Next Eleven (N-11) countries from 2000 to 2020. The primary objective is to assess how NRR influences EP at various levels of poverty using the Method of Moments Quantile Regression …

Comparative Machine Learning Models For Disease Risk Prediction, Mercy Mawusi Agbley

Theses, Dissertations and Capstones

Accurate prediction of disease outcomes is crucial for improving clinical decision-making and enabling early intervention. This study compares the performance of various statistical and machine learning models for clinical risk prediction using two healthcare datasets: diabetic retinopathy and heart disease. The models assessed include Logistic Regression, LASSO, k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks, Random Forests, Gradient Boosting Machines (GBM), and a stacked ensemble model. Prior to modeling, datasets were split into train and test sets. Standardization was applied to numeric features whilst categorical features were one-hot encoded. These transformations were later applied to the test set. Principal …

The Link Fantastic: Detecting And Linking Simulated, Nongravitationally-Accelerating Objects With The Rubin Observatory Pipeline, Ellie White

Theses, Dissertations and Capstones

Rubin Observatory’s Legacy Survey of Space and Time (LSST) is poised to revolutionize our understanding of the Solar System. After only a few months of regular operations, Rubin has already discovered over 11,000 new Solar System bodies, and is projected to discover millions more. With discoveries pouring in, and with the development of moving object detection pipelines for routine observing, one key question is: how effectively can Rubin detect and identify objects with anomalous trajectories? The answer is of interest not only for natural bodies in our Solar System, but also in the search for technosignatures, which could be identified …

Spatial Temporal Modeling Of Infectious Disease Patterns In Texas, Robert E. Lashbrook

Earth & Environmental Sciences Theses

The Texas Department of State Health Services monitors numerous notifiable conditions statewide, including Campylobacter, Salmonella, Shiga toxin-producing Escherichia coli (STEC), Rabies, and West Nile virus (WNV). Given the substantial health, economic, and public health burden associated with these conditions, improving prediction is an important step toward reducing their overall impact. This study evaluated whether external demographic, social, climate, and environmental data could improve prediction of county-year disease activity across Texas. County level data was analyzed using supervised machine learning models, including linear regression, ridge regression, multilayer perceptron, random forest, XGBoost, as well as K-means clustering to identify broader …

Investigating The Viability Of Fully Online Asynchronous Physics Instruction In Formal Education, Clifton W. Massey-Noel

Physics Dissertations

In this dissertation we present results from two consecutive investigations into the fully online asynchronous modality in Physics Education. We first discuss the efficacy of a fully online asynchronous section of Modern Physics held in Fall 2024. The asynchronous section is compared with four prior partially-flipped sections of the same class, two of which were held in-person, and two of which were held synchronously online. Results suggest that all three modalities can be made to be about equally as effective. We also discuss the efficacy of a fully online asynchronous section of Calculus-Based Introductory Mechanics held in Fall 2025. The …

Physics Alumni Newsletter Spring 2026, Terry Goforth

Physics Alumni Newsletter

Physics Alumni Newsletter

The Physics Alumni Newsletter is produced by the SWOSU Physics Department.

Our Engineering Physics students are recruited in fields such as electronics, aerospace, mechanical engineering, petroleum engineering and software engineering. Graduates also have careers in meteorology, architecture, education and more.

Partially Penalized Anisotropic Trilinear Ife-Pic Methods For Dc Plasma Transport Problems, Jiahui Li, Guangqing Xia, Yajie Han, Ziping Wang, Chang Lu, Xiaoming He

Mathematics and Statistics Faculty Research & Creative Works

Implicit and hybrid particle-in-cell methods are widely used for efficient simulation of DC discharge plasma transport. However, their computations require solving anisotropic elliptic equations and face challenges related to mesh geometry, non-axisymmetry, and complex interfaces. Moreover, the accuracy of particle trajectories is critical for plasma etching and erosion studies, where errors near interfaces can significantly impact simulation results. To address these challenges, this paper proposes a three-dimensional anisotropic trilinear partially penalized immersed finite element (ATPPIFE) method, which captures interfaces on Cartesian meshes and effectively reduces discontinuities at interface element faces, ensuring that particle trajectories better align with real-world behavior. Building …

Logistic-T Multinomial Mixture Model For Clustering For Microbiome Data, Wenshu Dai, Yuan Fang, Sanjeena Subedi

Mathematics & Statistics Faculty Publications

The logistic-normal multinomial distribution has been used for modelling microbiome data obtained from high-throughput sequencing technologies, which are compositional in nature. A logistic-normal multinomial distribution is a hierarchical multinomial distribution that assumes the latent variable which are the additive log-ratio (ALR) transformed proportions in a multinomial distribution follows a Gaussian distribution. Model-based clustering algorithms have also been developed for clustering microbiome data based on the logistic-normal models. However, the Gaussian assumption may violated when the ALR transformed variable exhibit heavy-tailed distributions or has outliers. Our study introduces a novel mixture of logistic-t multinomial models that effectively address these challenges. Utilizing …

Cost-Aware Predictive Routing For Vision-Language Models, Sahil Sait Naveed

Master's Projects

Vision-language models (VLMs) are increasingly used for multimodal tasks, but their inference costs vary widely across model tiers. This work presents a predictive routing framework that assigns each query to the most cost-effective VLM by using multimodal embeddings, clustering, and per-cluster model error estimates. Evaluated on six commercial VLMs, the selected router reduces average cost by about 49 percent relative to the quality-first setting on both validation and test. This comes with accuracy losses of 0.4 percentage points on validation and 0.9 on held-out test data. It also outperforms K-NN and ZeroRouter routing baselines while providing lowercost operating points than …

Ai Deployment Authorisation: A Global Standard For Machine-Readable Governance Of High-Risk Artificial Intelligence, Daniel Djan Saparning

Student Publications

Modern artificial intelligence (AI) governance lacks a formal, enforceable mechanism for determining whether a given AI system is legally permitted to operate in a specific domain and jurisdiction. Existing approaches-such as model cards, audits, and benchmark evaluations provide descriptive information about model behaviour and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS). This machine-readable, regulator-grade framework evaluates AI systems across five legally and economically grounded dimensions: Risk, Alignment, Externality, Control, and Auditability, derived from safety engineering, alignment theory, algorithmic accountability, and liability economics. ADAS produces …

Efficient Organic Molecular Crystal Structure Prediction Using The Density Functional Tight-Binding Method, Maureen M. Kitheka, Yan Jing, Yan Yao, Puja Goyal

Faculty Publications – Chemistry

Over the years, computational crystal structure prediction (CSP) for organic molecules has thrived as an area of research, spanning various scientific disciplines and having significant applications in industries such as pharmaceuticals and agrochemicals. Within the field of batteries, redox-active organic materials (ROMs) such as quinones have received increased attention as promising electrode materials for rechargeable batteries. However, experimental determination of the crystal structure of intermediate species formed during the discharge/charge cycle can often be challenging. Incomplete X-ray diffraction patterns can also lead to difficulties in crystal structure determination for ROMs used in batteries. Use of a semiempirical electronic structure method …

Fluid Agency In Ai Systems: A Case For Functional Equivalence In Copyright, Patent, And Tort, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern Artificial Intelligence (AI) systems exhibit fluid agency in multi-step workflows: lacking human-like consciousness or culpability, yet they display behavior that is (i) stochastic (probabilistic and path‑dependent), (ii) dynamic (co‑evolving with user interaction), and (iii) adaptive (able to reorient across contexts). These properties generate valuable outputs but collapse attribution, irreducibly entangling human and machine inputs. Doctrines that assume traceable provenance—authorship, inventorship, and liability—fracture under this unmappability, yielding ownership gaps and moral “crumple zones.”This Article argues that only functional equivalence stabilizes doctrine under unmappability: Where provenance is indeterminate, legal frameworks should treat human and AI contributions as equivalent for allocating rights …

Multiscale Modeling Of The Thermomechanical Behavior Of Polymeric And Molecular Organic Semiconductors, Kehinde H. Fagbohungbe

University of Kentucky Doctoral Dissertations

Organic semiconductors, derived from π-conjugated polymers and molecules, enable the development of deformable, stretchable and flexible electronics due to their tunable redox, optical, electronic and mechanical properties. However, an informed understanding of how multi-scale morphological characteristics of the polymeric and molecular semiconductors influence bulk properties that contribute to electronic and optical performance, especially under operational thermal and mechanical stresses, remains incomplete. This lack of understanding poses a challenge to scalability and commercialization of organic electronics. This dissertation develops and deploys computational modeling approaches, particularly atomistic molecular dynamics (MD) simulations, to investigate the multiscale morphological behavior of these synthetic semiconducting materials …

1-D And 2-D Hadamard Transformations For Protein Sequence Analysis, Esraa A. Ahmed, Abed M. Nasr, Marwa A. Abd Elwahaab

Mansoura Engineering Journal

Similarity/dissimilarity studies follow alignment-based or alignment-free methods. Many alignment-free methods involve selecting physicochemical properties. Most previous articles don’t explain their criteria for choosing the properties used. These heuristic approaches have, by definition, undeterministic and unreliable performance. Hence, there is a need for a powerful proxy for similarity/dissimilarity by a deterministic, reliable approach.

In this study, a physicochemical property is selected by a multi-criteria sequential selection process. The amino acids composing any protein sequence are replaced with their values of the chosen property to produce a discrete signal in the time domain.  The one-dimensional discrete Hadamard transforms our signal from the …

Attacks And Detections In Recommender Systems: A Comprehensive Analysis For Models, Progresses, And Trends, Yan Feng, Zhihai Yang, Kexin Li, Jianxin Li, Pinghui Wang, Zhiquan Liu

Research outputs 2022 to 2026

Recommender systems (RSs), as crucial components of online services, can help users efficiently obtain information they may like. In reality, RSs face long-term threats. Attackers manipulate recommendation results by injecting malicious data in order to obtain benefits. At present, research on the security of RSs lacks a comprehensive understanding of attack capabilities. Moreover, existing defense strategies have not yet been systematically associated with attack characteristics. More importantly, existing defense methods rarely focus on real unlabeled data in practical application scenarios for anomaly detection and forensics. Therefore, this survey systematically analyzes the security of RSs and provides new insights. Specifically, we …

Software Architecting In The Brazilian Industry: Results Of A Prior Survey, Valdemar Vicente Graciano Neto, Mohamad Kassab, Diana Lorena Santos, Andrey Gonçalves França, Edson Oliveira, Rafael Z. Frantz, Ahmad Mohsin, Marcos Kalinowski

Research outputs 2022 to 2026

Context: Software architecture is essential for system quality and long-term sustainability, yet little is known about how the architect role is structured in Brazilian companies.

Objective: This study examines how Brazilian organizations incorporate software architecture into their development processes, focusing on the presence and formalization of software architects, and the activities performed by them.

Method: A survey of 105 professionals from 24 states collected data on organizational profiles, architect roles, compensation, and architectural tasks. Quantitative and qualitative analyses were conducted.

Results: Architectural work is widespread but unevenly formalized. Only 41% of companies employ formal architects, and 68% delegate architectural duties …

Satellite Observations Reveal Ecosystem Resistance And Resilience To Short-Term Water Stress Driven By Dominant Vegetation Along A Rainfall Gradient In Australia, Huanhuan Wang, Qiaoyun Xie, Sally E. Thompson, Caitlin E. Moore, David L. Miller, Erik J. Veneklaas, Richard P. Silberstein, Xing Li, Jingfeng Xiao, Belinda E. Medlyn, William K. Smith

Research outputs 2022 to 2026

Climate change is projected to intensify water stress in many ecosystems and poses threats to their stability, which can be quantified through ecosystem resistance and resilience. Relevant studies mostly focused on multi-year or annual droughts, and in spatially homogeneous or species-specific ecosystems. However, resilience and resistance within complex ecosystems, where different plants exhibit different adaptations and recovery behaviours, are less understood. Using productivity data from satellite-derived GOSIF (Global Orbiting Carbon Observatory-2 Solar-Induced Fluorescence) and flux towers, we examined vegetation responses to short-term (<1 year) water stress events from 2000 to 2018 along the North Australia Tropical Transect, which spans a 1600 mm rainfall gradient and transitions from seasonal mesic to non-seasonal arid ecosystems. We define resistance as productivity maintained during stress relative to a multi-year average baseline, and resilience as the extent to which productivity recovered one year after stress relative to the same baseline. Our results show that ecosystem resistance to water stress was lowest in semi-arid regions but higher in both arid and mesic regions, while ecosystem resilience showed the opposite pattern. These spatial patterns occurred regardless of seasonality and were mainly associated with dominant vegetation type. Woody savanna-dominated mesic regions exhibited highest resistance (0.82 ± 0.13, p < 0.001) and lowest resilience (0.26 ± 0.19, p < 0.001), shrublands in arid areas had intermediate values of both resistance (0.81 ± 0.14, p < 0.001) and resilience (0.27 ± 0.22, p < 0.001), while the grasslands in semi-arid regions had low resistance (0.78 ± 0.15, p < 0.001) and high resilience (0.38 ± 0.24, p < 0.001). The highest likelihood (>75.0 %) of full recovery (i.e., exceeding baseline after one year) occurred during the wet season in …

A Comprehensive Review Of Cyber Security And Current Practices In Global Mining Critical Infrastructure, Abu Barkat Ullah, Wanli Ma, Mohiuddin Ahmed, Bazlur Rashid, Munir Ahmad Saeed, Omer Arshad, Utkarsh Raghav

Research outputs 2022 to 2026

The purpose of the study is to explore the reasons behind the low uptake of Information Security Management Standards (ISMS), Asset Management, and Business Continuity Plans despite increasing cyber threats to the mining sector. Mining companies need to modernize and automate to keep up with the ‘Fourth Industrial Revolution’, driven by disruptive technology, forcing systems and technologies to become more integrated, increasing cyber attack threats. To address this, we conducted a literature review analyzing the mining industry across various regions. The research is based on a qualitative analysis of diversified literature. The results highlighted factors behind the low uptake of …

Gradient-Based, Post-Optimality Sensitivity Analysis With Respect To Parameters Of State Equations, Gene Hou, Jonathan Degroff

Mechanical & Aerospace Engineering Faculty Publications

Design optimization is a computational tool that can enable a designer to investigate the effectiveness of a design concept in an organized format. However, this design process requires the design variables, constraints, and objective function to be properly defined and expressed in mathematical forms. Post-optimality analysis thus becomes a necessary step to investigate different variations in the problem formulation and parameters to ensure that optimization produces a stable and trustworthy outcome. One efficient way to achieve this aim is to compute the local derivative of the optimized objective function with respect to the optimization problem parameters, such as bounds on …

Uncertainty-Aware Estimation, Planning, And Control For Tracking Multiple Drifting Patches In Flow Fields, Daniel O. Akanji, Krishnanand N. Kaipa, Cong Wei

Mechanical & Aerospace Engineering Faculty Publications

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the estimated flow field while carrying per-vertex covariance, thereby quantifying uncertainty growth during advection. A flow-aware gain-scheduled linear quadratic regulator (LQR) was designed to shape the desired surge speed to take advantage of favorable currents. When the vehicle services a …

Mechanical-Medical Convergence In Heart Failure: Artificial Intelligence, Finite-Element Modeling, And 3d Printing For Diagnosis And Prognosis, Quazi Noor E. Sabrina, Quazi Md Zobaer Shah, Quazi Noor E. Sohela, Md Mahabub Hasan Mousum, Md. Moyeen Uddin Chisty, Quazi Md. Akbar Shah

Mechanical & Aerospace Engineering Faculty Publications

Heart failure remains a leading cause of global morbidity and mortality, yet routine clinical indices often miss the regional biomechanical disturbances that drive progression and shape treatment response. This State-of-the-Art review examines how finite-element (FE) modeling, additive manufacturing, and artificial intelligence (AI) are converging to improve the diagnosis, phenotyping, procedural planning, and prognostic assessment of heart failure (HF). Although these technologies have matured in structural heart disease and transcatheter intervention research, their greatest translational potential may lie in HF, where patient-specific ventricular remodeling, myocardial stress–strain heterogeneity, valve-ventricular coupling, and device-tissue interaction are incompletely captured by conventional clinical indices. We synthesize …

Radical-Based Oxidative Pretreatment Enhances Biofuel Production From Lignocellulosic Biomass Via Hydrothermal Liquefaction, João Vitor Dos Santos, Louis C. Bondurant, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

The sustainable production of biofuels from lignocellulosic biomass is a central goal in the transition to low-carbon energy systems. However, hydrothermal liquefaction (HTL), a promising thermochemical conversion pathway, is constrained by the high oxygen content and complex aromatic structure of lignin, which lowers bio-oil quality. Here, we used a model system of brown-rot-degraded white oak (Quercus alba) to test whether radical-based oxidative pretreatment could enhance HTL performance by converting lignin into more aliphatic intermediates. Oxidation was performed under simulated Fenton conditions using fixed Fe(II) (60 ppm) and two hydrogen peroxide concentrations (3 and 8 M), resulting in extensive lignin depolymerization …