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Full-Text Articles in Engineering

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang Jan 2026

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang

Computer Science and Engineering Dissertations

Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning …

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Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling Jan 2026

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

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A Macrocognitive Design Taxonomy For Simulation-Based Training Systems: Bridging Cognitive Theory And Human-Computer Interaction, Jessica M. Johnson Jan 2026

A Macrocognitive Design Taxonomy For Simulation-Based Training Systems: Bridging Cognitive Theory And Human-Computer Interaction, Jessica M. Johnson

Virginia Digital Maritime Center (VDMC) Faculty Publications

Simulation-based training systems are increasingly deployed to prepare learners for complex, safety-critical, and dynamic work environments. While advances in computing have enabled immersive and data-rich simulations, many systems remain optimized for procedural accuracy and surface-level task performance rather than the macrocognitive processes that underpin adaptive expertise. Macrocognition encompasses higher-order cognitive processes that are essential for performance transfer beyond controlled training conditions. When these processes are insufficiently supported, training systems risk fostering brittle strategies and negative training effects. This paper introduces a macrocognitive design taxonomy for simulation-based training systems derived from a large-scale meta-analysis examining the transfer of macrocognitive skills from …

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Microwave Synthesis And Characterization Of Fe2ptge Nanoparticles, Ally T. Fennell, David Carnevale Jan 2026

Microwave Synthesis And Characterization Of Fe2ptge Nanoparticles, Ally T. Fennell, David Carnevale

Chemistry Theses

Magnetic, semiconductor, and spintronic materials are vital to the functionality of devices from household appliances and sensors to medical and computing systems. This study discusses a novel synthesis and characterization of nanoscale Fe₂PtGe nanoparticles. The material was synthesized using microwave irradiation (175oC, 5 minutes) and characterized using XRD, XRF, TEM, and VSM. XRD of the synthesized particles correlates to a cubic crystal structured, indicating a face-centered cubic (fcc) platinum structure with iron and germanium incorporated in a disordered manner. XRF confirms the presence and relative ratios of iron, platinum, and germanium species. TEM imagery shows spherical nanoparticles with an average …

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A Comprehensive Statistical And Regional Analysis Of Lng-Powered Marine Vessels On Ghg Mitigation Strategies Considering Existing Bunkering Stations And Gwp Values, Canberk Hazar, Onur Yuksel, Murat Bayraktar Jan 2026

A Comprehensive Statistical And Regional Analysis Of Lng-Powered Marine Vessels On Ghg Mitigation Strategies Considering Existing Bunkering Stations And Gwp Values, Canberk Hazar, Onur Yuksel, Murat Bayraktar

Journal of Marine Science and Technology–Taiwan

This study aims to quantify and analyse emissions from marine vessels that can operate on liquefied natural gas (LNG) but continue to use conventional fuels, largely due to the limited availability of LNG bunkering stations (BSs) over long distances. Four regions have been identified as having high concentrations of LNG-fueled vessels but limited access to operational BSs: the West Coast of the United States of America (USA), South Africa–Good Hope–Madagascar, Northwest Africa, and Brazil. This selection is based on the geographical distribution of these ships and the existing infrastructure. Hourly greenhouse gas (GHG) emissions have been calculated by considering the …

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Unveiling Microplastic Removal And Characteristics In Wastewater From Two Municipal Wastewater Treatment Facilities In Indonesia, Nurul Setiadewi, Prayatni Soewondo, Cynthia Henny Jan 2026

Unveiling Microplastic Removal And Characteristics In Wastewater From Two Municipal Wastewater Treatment Facilities In Indonesia, Nurul Setiadewi, Prayatni Soewondo, Cynthia Henny

Applied Environmental Research

Wastewater treatment plants (WWTPs) are considered an entrance pathways for microplastic (MP) pollution in aquatic environments. This study reveals the removal and characteristics of MPs in wastewater from two municipal WWTPs in Indonesia. The influent contained 17.1 ± 5.65 particles L-1 (WWTP A) and 15.45 ± 4.31 particles L-1 (WWTP B), whereas the effluent contained 1.41 ± 0.01 and 1.5 ± 0.16 particles L-1. The removal efficiency was 91.75% for WWTP A and 90.32% for WWTP B, with no statistically significant difference (p > 0.05). WWTP A employed advanced treatment units, whereas WWTP B used a conventional pond-based system. MPs were …

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Assessment Of The Influence Of Human Activities On The Occurrence Of Forest Fires In Thailand Via Multiple Linear Regression (Mlr), Sittipong Ruktamatakul, Jirarat Insuk, Benjaporn Pinwongpet, Sarisa Ruktametakul, Pornpis Yimprayoon Jan 2026

Assessment Of The Influence Of Human Activities On The Occurrence Of Forest Fires In Thailand Via Multiple Linear Regression (Mlr), Sittipong Ruktamatakul, Jirarat Insuk, Benjaporn Pinwongpet, Sarisa Ruktametakul, Pornpis Yimprayoon

Applied Environmental Research

Forest fires represent one of the most critical environmental challenges in Thailand, with impacts varying depending on forest type, fuel characteristics, terrain conditions, fire intensity, and the frequency of fire occurrence on the same landscape. While forest fires can contribute to ecosystem degradation, biodiversity loss, and the depletion of natural resources, such effects are not uniformly severe across all forest ecosystems. Understanding the human-induced factors contributing to forest fire occurrence is crucial for developing effective prevention strategies and promoting sustainable forest management. This study aimed to identify the anthropogenic factors influencing forest fire areas in Thailand via multiple linear regression …

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Effect Of Chromium Doping On The Uv- And Sunlight-Driven Photocatalytic Performance Of Srtio3, Ro’Ikhatul Jannah, Dianisa Khoirum Sandi, Fahru Nurosyid, Risa Suryana, Didier Fasquelle, Yofentina Iriani Jan 2026

Effect Of Chromium Doping On The Uv- And Sunlight-Driven Photocatalytic Performance Of Srtio3, Ro’Ikhatul Jannah, Dianisa Khoirum Sandi, Fahru Nurosyid, Risa Suryana, Didier Fasquelle, Yofentina Iriani

Applied Environmental Research

Chromium (Cr)-doped strontium titanate (SrTiO3, STO) photocatalysts with compositions of SrTi1-xCrxO3 (x = 0, 5, and 10%) were prepared via the coprecipitation method. This study aimed to investigate the effects of Cr doping on structural, morphological, and optical properties. Furthermore, this work aimed to examine the photocatalytic performance of pure and Cr-doped STO against methylene blue (MB) degradation under ultraviolet (UV) and sunlight exposure. X-ray diffraction confirmed the formation of the cubic STO phase and the insertion of the Cr dopant in the STO structures. Furthermore, Cr doping reduced the lattice constant, crystallite size, and average particle size and narrowed …

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Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei Jan 2026

Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling CO2 channeling in heterogeneous reservoirs remains a major challenge for both enhanced oil recovery (EOR) and secure geological storage. AMPS-HPAM copolymers exhibit high-temperature resistance and brine tolerance compared with conventional HPAM gels, making them well suited for the harsh environments associated with CO2 injection. Chromium-based crosslinkers (CrAc and CrCl3) were investigated because sulfonic acid groups in AMPS can coordinate with trivalent chromium ions, enabling dual ionic crosslinking and the formation of a robust gel network. While organic crosslinked AMPS-HPAM gels have been widely studied, the behavior of chromium-crosslinked AMPS-containing systems, particularly their gelation kinetics under …

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Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao Jan 2026

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO2, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO2-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and 1H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

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Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier Jan 2026

Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier

Electrical and Computer Engineering Faculty Research & Creative Works

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) …

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Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan Jan 2026

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

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Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan Jan 2026

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

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New Metrics For Disambiguating Feature Overlap And Catastrophic Forgetting In Incremental Learning Contexts, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch Jan 2026

New Metrics For Disambiguating Feature Overlap And Catastrophic Forgetting In Incremental Learning Contexts, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Catastrophic forgetting remains a central challenge in lifelong learning, where newly acquired knowledge interferes with previously learned tasks, degrading performance over time. Mitigation strategies such as rehearsal and regularization have been proposed, but both introduce limitations, either by retaining old data or by constraining model updates in ways that may impair learning. Complicating matters, recent findings show that feature-space overlap between tasks can produce similar performance drops even in models that memorize data, making it difficult to distinguish true forgetting from representational interference. Current accuracy-based metrics fail to disentangle these effects, undermining diagnostic clarity. In this work, we introduce the …

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Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria Jan 2026

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

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Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch Jan 2026

Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article explores the problem of fixed-time consensus tracking (FT-CT) for nonlinear multi-agent systems utilizing the a periodically intermittent control (AIC) strategy. In contrast to existing control algorithms, the proposed algorithm utilizes the AIC strategy instead of the conventional continuous-time control strategy, effectively reducing the consumption of communication resources. Moreover, the problem of intermittent FT-CT is well handled by proposing the average control rate of the AIC strategy. Two theorems based on the cases of directed and undirected graphs are proposed, respectively. Finally, the validity of these results is confirmed through numerical simulations on a general nonlinear system and a …

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Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch Jan 2026

Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article studies the practical predefined-time synchronization (PPTS) for complex networks (CNs) under deception attacks based on the asynchronously intermittent event-triggered control (AIE-TC). Notably, AIE-TC effectively integrates the advantages of asynchronously intermittent control (AIC) and event-triggered control, where AIC provides each subsystem node with independent control and rest intervals. Besides, all synchronization errors of the CNs converge to an adjustable neighborhood within the predefined time by designing a bounded time-varying function into the controller. Moreover, this article considers that the transmission network is subjected to stochastic deception attacks modeled by a Markov process, which captures the state-driven dynamic transition characteristics …

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Evaluation Of A Novel Re-Crosslinkable Preformed Particle Gel For Conformance Control In Ultra-High Temperature Reservoirs, Yanbo Liu, Tao Song, Caleb Kwasi Darko, Thomas P. Schuman, Mingzhen Wei, Baojun Bai Jan 2026

Evaluation Of A Novel Re-Crosslinkable Preformed Particle Gel For Conformance Control In Ultra-High Temperature Reservoirs, Yanbo Liu, Tao Song, Caleb Kwasi Darko, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

Preferential fluid flow remains a major challenge in subsurface energy production and gas storage operations, resulting in excessive water production in mature oil fields, reduced heat extraction in geothermal reservoirs, and low sweep and storage efficiency in CO2-EOR projects. Polymer gels are widely used to mitigate high-permeability channels; however, conventional systems exhibit limited plugging efficiency and short lifetimes in ultra-high-temperature reservoirs due to poor thermal stability. This study presents a novel ultra-high-temperature-resistant preformed particle gel (UHT-PPG) developed for conformance control in reservoirs with temperatures of 150–275 °C and severe super-K or channeling problems. The material was evaluated in …

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Sadqn-Based Residual Energy-Aware Beamforming For Lora-Enabled Rf Energy Harvesting For Disaster-Tolerant Underground Mining Networks, Hilary Kelechi Anabi, Samuel Frimpong, Sanjay Madria Jan 2026

Sadqn-Based Residual Energy-Aware Beamforming For Lora-Enabled Rf Energy Harvesting For Disaster-Tolerant Underground Mining Networks, Hilary Kelechi Anabi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a …

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Determining The Electric Field In A 10 Ns Pulsed Plasma In Fuel-Air Mixtures Using Efish, Md Ziaur Rahman, Christopher J. Kliewer, Brian D. Patterson, Chunqi Jiang Jan 2026

Determining The Electric Field In A 10 Ns Pulsed Plasma In Fuel-Air Mixtures Using Efish, Md Ziaur Rahman, Christopher J. Kliewer, Brian D. Patterson, Chunqi Jiang

Bioelectrics Publications

Transient plasma ignition (TPI) utilizes non-equilibrium plasmas, produced by nanosecond high-voltage pulses, to improve lean-fuel combustion performance and reduce emission. It is known that the relatively high reduced electric field (E/N) in TPI plays an important role in generating energetic electrons and facilitating energy-efficient radical productions, resulting in reliable ignition for lean combustion. Determining the reduced electric field in the discharge is hence important for the understanding of the TPI process and ultimately allowing for the control of the plasma chemistry. This study reports spatiotemporally resolved measurements of the electric field (E) in a 10 ns pulsed plasma that is …

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Ammonia Synthesis By Nanosecond Pulsed Atmospheric Pressure Plasma Jets Impinging On Water, Zach Caudell, Lynnet Rich, Olga Pakhomova, Chunqi Jiang Jan 2026

Ammonia Synthesis By Nanosecond Pulsed Atmospheric Pressure Plasma Jets Impinging On Water, Zach Caudell, Lynnet Rich, Olga Pakhomova, Chunqi Jiang

Bioelectrics Publications

Developing energy-efficient technologies for carbon-neutral ammonia (NH₃) synthesis is critical for decentralized fertilizer production and global decarbonization. This study investigates generating NH₃ from water using a nanosecond pulsed atmospheric pressure plasma jet (ns‑APPJ) operating in either N₂ or dry air. The plasma jet reactor employed approximately 250 ns, up-to-22 kV pulses at 500 Hz to sustain a nonequilibrium discharge impinging directly on static liquid water. The kinetics, energy efficiency, and product selectivity of NH3 formation were quantified as functions of the pulse voltage, repetition frequency (PRF), and gas flow rate. NH₃ production increased linearly with treatment time and scaled strongly …

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Detecting And Repairing Conflicting Constraints In Co-Trained Physics-Informed Neural Networks For Composite Curing Processes, Cooper J. Evans Jan 2026

Detecting And Repairing Conflicting Constraints In Co-Trained Physics-Informed Neural Networks For Composite Curing Processes, Cooper J. Evans

Dissertations, Master's Theses and Master's Reports

Composite materials have become a critical component of modern manufacturing, especially in the automotive and aerospace industries. The curing process for these composites has been modeled using a variety of partial differential equations representing the heat transfer and composite curing kinetics. Optimizing the applied temperature profile is critical for maximizing the efficiency and capacity of composite part manufacturers. Constraints must be placed on the inputs and outputs of the model, including but not limited to, the applied temperature profile, part temperature, and final degree of cure. Conflicting sets of constraints are easy to unknowingly impose due to the highly coupled …

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On The Influence Of Faraday Waves On Transport In Resonant Acoustic Mixing, Preston David Silverstein Jan 2026

On The Influence Of Faraday Waves On Transport In Resonant Acoustic Mixing, Preston David Silverstein

CGU Theses & Dissertations

Resonant acoustic mixing (RAM) uses low frequency high acceleration oscillatory forcing to combine fluids, particles, and powders. Although progress has been in adjacent RAM fields, there are still gaps in understanding how Faraday surface instabilities affect momentum transport to the bulk fluid and particles therein. This work investigates the energy pathways that an oscillatory mixer has and connects surface deformation to bulk rotational flow and particle forcing. This study is conducted in three phases. In phase 1, a thermodynamic first- and second-law analysis couples the surface features with the scale of subsurface rotational features. Experimental surface measurements showed that for …

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Machine Learning For Predicting Prosthetic Limb Movements, Jessica Alexandra Cegarra Arraiz Jan 2026

Machine Learning For Predicting Prosthetic Limb Movements, Jessica Alexandra Cegarra Arraiz

Theses and Dissertations

This thesis develops and evaluates a deep learning-based prediction model capable of identifying intended limb movement from surfaced electromyography (sEMG) signals using sequence learning techniques. sEMG signals change over time due to multiple factors such as muscle fatigue or user variability. Traditional prosthetics control methods rely on static feature extraction, ignoring how signals change over time, thereby limiting their ability to capture the temporal changes of muscle activity. As a result, these approaches often lead to poor accuracy, robustness, and generalization. Limited experimental validation has been conducted on sequence-based machine learning approaches using temporal sEMG data from publicly available datasets …

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Revisiting The Life Cycle Of Margalefidinium Polykrikoides Group Iii, Eduardo Pérez-Vega, Kenneth N. Mertens, Pjotr Meyvisch, Margaret R. Mulholland Jan 2026

Revisiting The Life Cycle Of Margalefidinium Polykrikoides Group Iii, Eduardo Pérez-Vega, Kenneth N. Mertens, Pjotr Meyvisch, Margaret R. Mulholland

OES Faculty Publications

Dinoflagellates produce cysts as a strategy to withstand environmental stressors, with nutrient depletion generally considered a key trigger for cyst production. Resting cysts are thick-walled, typically composed of one to several layers, and characterized by a prolonged dormancy period. In contrast, pellicle cysts possess a thin, single wall and exhibit no dormancy or a markedly shorter dormancy than resting cysts of the same species. Margalefidinium polykrikoides produces pellicle and resting cysts, whereas its congener, M. fulvescens, has been shown to produce pumpkin-like structures. Using phase-contrast microscopy, time-lapse microscopy, FlowCam, and attenuated total reflection Fourier transform infrared microspectroscopy (ATR μ-FTIR), …

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Gradient-Based, Post-Optimality Sensitivity Analysis With Respect To Parameters Of State Equations, Gene Hou, Jonathan Degroff Jan 2026

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 …

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Uncertainty-Aware Estimation, Planning, And Control For Tracking Multiple Drifting Patches In Flow Fields, Daniel O. Akanji, Krishnanand N. Kaipa, Cong Wei Jan 2026

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 …

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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 Jan 2026

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 …

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Radical-Based Oxidative Pretreatment Enhances Biofuel Production From Lignocellulosic Biomass Via Hydrothermal Liquefaction, João Vitor Dos Santos, Louis C. Bondurant, Patrick G. Hatcher Jan 2026

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 …

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Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine Jan 2026

Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine

ASEAN Journal on Science and Technology for Development

In this research, extraction of polysaccharide compounds from Cactus (Opuntia Ficus Indica) leaves in both fresh and dehydrated condition by solvent precipitation method using three types of water, acid (HCL) and NaOH. Before extraction, the physicochemical properties were examined to determine optimum yield (%) of extracted polysaccharide by optimization of Box-Behnken Design (BBD) of response surface methodology (RSM). The polysaccharide-based acrylamide hydrogel was prepared by free radical polymerization. The functional and structural characterization was done by FTIR, XRD and SEM for examination of extracted polysaccharide as raw polymer backbone in hydrogel preparation and prepared hydrogel as adsorbent for metal removal. …

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