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Integrated Design And Trade Study Of A Sub-Ambient Ammonia Fuel Storage And Delivery System For Narrow-Body Commercial Aircraft, Ethan Taylor

Graduate Studies Theses and Dissertations 2026

This study develops an integrated design framework for a sub-ambient anhydrous-ammonia fuel storage and delivery system for narrow-body commercial aircraft, using the Boeing 737 MAX 8 as the baseline platform. Ammonia was selected because it contains no carbon in its molecular structure, benefits from an established global production and distribution infrastructure, and offers more practical storage conditions than several alternative carbon-free energy carriers. However, its relatively low energy density, toxicity, material-compatibility requirements, and combustion hazards require substantial modifications to power conventional commercial aircraft fuel storage and delivery systems.

The study proposes an integrated design and initial analysis of a sub-ambient …

Memory-Efficient Acceleration For Emerging Applications Via Hardware/Software Co-Design, Shilin Tian

Graduate Studies Theses and Dissertations 2026

Emerging artificial-intelligence and data-intensive scientific workloads increasingly face a memory wall: irregular access patterns and large intermediate data volumes make data movement, rather than arithmetic, the primary constraint on performance and energy efficiency. This dissertation develops a memory-centric hardware/software co-design methodology that jointly reshapes algorithms, architectures, and dataflows to retain frequently reused data on chip. The methodology is demonstrated through three accelerators and an RTL design tool. VITA replaces multi-head attention in vision-transformer-based 3D human mesh recovery with hardware-friendly average pooling and maps the resulting operators to a reconfigurable datapath, achieving 5.05-fold and 69.12-fold speedups over a state-of-the-art GPU and …

Investigation On Selective Catalytic Reduction For Retrofit Of Commercial Aviation Aircraft Engines, Connor M. Wall

Graduate Studies Theses and Dissertations 2026

As the aviation industry continues to grow, the ever-increase of emissions from the sector becomes a growing concern. While the International Civil Aviation Organization (ICAO) set a goal of net-zero carbon emissions by 2050, there are other emissions like contrails and NOx that must still be addressed. Selective Catalytic Reduction (SCR) has been used in the transportation and power generation industry for over two decades and has been an effective solution for NOx reduction. Even with the success SCR has had in other industries, its capabilities in the aviation industry have been trivially investigated. Aircraft engines pose an unique challenge …

Volumetric Fluorescence Microscopy For High-Throughput And High-Sensitivity Imaging: From Single Molecules To Tissues, Le-Mei Wang

Graduate Studies Theses and Dissertations 2026

Fluorescence microscopy is an indispensable tool in the biological sciences, enabling researchers to investigate intricate subcellular structures, particularly for volumetric studies. However, conventional optical microscopy for volumetric imaging remains fundamentally constrained by imaging speed and throughput. To bypass traditional serial z-scanning, we introduce an axially scan-free method using a phase layer cake to modulate the system's point spread function. This approach projects volumetric information onto a 2D plane in a single shot, offering high flexibility in tuning axial depth alongside simultaneous multicolor imaging with high spatial resolution and sensitivity. This dissertation divides these technical advancements into cellular and tissue imaging …

Two Papers Exploring Cognitive Fit Theory And Data Visualization In Accounting, Kelly Wellman

Graduate Studies Theses and Dissertations 2026

This dissertation examines how data visualization design influences judgment and decision making in accounting using cognitive fit theory as a unifying framework. As visualization tools such as dashboards, Excel, Tableau, and Power BI become increasingly prevalent, understanding which design choices improve performance has become critical. The first paper provides a structured literature review of cognitive fit theory from 1991 through 2025, synthesizing research across accounting and related disciplines and tracing its evolution from early graph vs tables comparisons to mor recent work incorporating task complexity, individual differences, and design-specific features. The review identifies key gaps, including limited attention to difference …

Switching Things Up: Investigating The Costs Of Switching Between Tasks Of Varying Interdependence And Factors That Influence These Transitions, Taylor A. Wilhelmy

Graduate Studies Theses and Dissertations 2026

Though researchers have indicated that there is a high probability teams will transition between tasks of varying interdependence (Benishek & Lazzara, 2019; Mesmer-Magnus et al., 2016), there is a need to further examine this idea (Smith-Jentsch, 2015; Mesmer-Magnus et al., 2016) as few have actually studied it. Expanding upon Moon et al. (2004), this study examines the costs associated with transitioning between two tasks of varying levels of interdependence (i.e., pooled and team interdependence; Saavedra et al., 1993), how these costs vary depending on the order the tasks are engaged while examining how team cognition (i.e., transactive memory systems) and/or …

Careless Responding: Testing The Theory Of Vigilance, Rusty Wilson

Graduate Studies Theses and Dissertations 2026

Careless responding (CR) has been identified as a threat to the psychometric integrity of cognitive and non-cognitive tests, with much of the current research focusing on the identification and removal of carelessness from dataset. While this research has proved fruitful in improving data quality, there has been a recent push to move towards preventing carelessness as opposed to post-hoc removal, which harms statistical power. However, the most common prevention strategy to prevent carelessness (pre-survey warnings) has shown equivocal effects. Additionally, the literature lacks an agreed upon theory to explain why carelessness occurs. To address these gaps I introduce theory from …

Michigan Academician Volume 50, Issue 2

Michigan Academician

Volume 50, Issue 2 - Abstract Issue

Tomo-Piv Study Of Baseline Flow Structures Behind A Strut Injector, Josiah Mcdermott, Connor Bell, Davide Viganò

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Stabilizing combustion in scramjet engines is a formidable challenge due to the small-time scales afforded for air-fuel mixing. Numerous studies in this area have demonstrated the potential of strut-style platforms for fuel injection and mixing enhancement, which remains an active area of research. In the Aerodynamics Research Laboratory at Missouri S&T, a strut-style injector system has recently been installed. In this study, we characterize the baseline flow structures behind this platform absent fuel injection. The wake generated by a strut itself has an appreciable impact on the resulting air-fuel mixing, which motivates its characterization. In future studies, this characterization will …

Supersonic Wind Tunnel Free Stream Turbulence Characterization Using 2-Point Focused Laser Differential Interferometry, Joseph Villarreal, Joshua Gary, Davide Vigano

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Non-intrusive laser-based diagnostics, such as Two-Point Focused Laser Differential Interfer-ometry (2-FLDI), play a crucial role in modern aerodynamic research by enabling simultaneous measurements of density and velocity in compressible flows. A 2-FLDI system has been developed and implemented for the Missouri S&T Supersonic Wind Tunnel to characterize free stream turbulence fluctuations and free stream convective velocity. Design choices that enabled the 2-FLDI to overcome low turbulence to measure free stream velocity are detailed. The free stream velocity measurements are validated against previous particle image velocimetry data, showing good agreement. Analysis of normalized velocities and density-based turbulence intensities found that the …

Temperature Compensation In Loop And Patch Fss Strain Sensors: Analysis And Experimental Validation, Swathi Muthyala Ramesh, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are arrays of conductive elements or apertures that exhibit frequency-dependent reflection and transmission properties. Their electromagnetic response is influenced by geometry and environmental conditions, making them attractive for wireless strain-sensing applications. However, temperature variations can produce frequency shifts similar to those caused by strain, reducing measurement accuracy. This work investigates the effects of intrinsic temperature compensation on two common FSS unit cell geometries—loop and patch—through comprehensive simulation analysis. The results show that loop-based cells offer superior thermal stability, while patch-based cells provide greater strain sensitivity, illustrating the trade-off between thermal robustness and mechanical responsiveness. A patch-type …

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) …

Evaluation Of Multiple Generative Large Language Models On Neurology Board-Style Questions, Mohammad Almomani, Vijaya Valaparla, James Weatherhead, Xiang Fang, Alok Dabi, Chih Ying Li, Peter Mccaffrey, Dan Hier, Jorge Mario Rodríguez-Fernández

Electrical and Computer Engineering Faculty Research & Creative Works

Objective: To compare the performance of eight large language models (LLMs) with neurology residents on board-style multiple-choice questions across seven subspecialties and two cognitive levels. Methods: In a cross-sectional benchmarking study, we evaluated Bard, Claude, Gemini v1, Gemini 2.5, ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, and ChatGPT-5 using 107 text-only items spanning movement disorders, vascular neurology, neuroanatomy, neuroimmunology, epilepsy, neuromuscular disease, and neuro-infectious disease. Items were labeled as lower- or higher-order per Bloom's taxonomy by two neurologists. Models answered each item in a fresh session and reported confidence and Bloom classification. Residents completed the same set under exam-like conditions. Outcomes included overall and …

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. …

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 …

Integrating Optical And Radiofrequency Interferometry For Enhanced Phase Sensing, Ruimin Jie, Zhaopeng Zhang, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Interferometry is a crucial investigative technique used across diverse fields to achieve high-precision measurements. It works by analyzing the phase difference between two interfering waves, which results from variations in optical path lengths within an interferometer. We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer, importantly, with the added advantage of a controllable enhancement factor. This approach is achieved through a two-step process: first, the optical phase difference is encoded into a sub-GHz radiofrequency (RF) signal using microwave-photonic manipulation; then, RF interferometry-assisted phase amplification is implemented at the destructive interference point. In …

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 …

Transformer-Customer Relationship Identification Based On Load-Switching Fluctuation Characteristics Considering Same-Feeder-Adjacent-Transformer Condition, Yanan Zhang, Gan Zhou, Yuyuan Liu, Wei Gu, Yanjun Feng, Yujue Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Accurately identifying the connectivity between transformers and downstream three-phase customers in low-voltage distribution networks is challenging, because voltage curves of different phases and nearby nodes can be weakly distinguishable, especially when adjacent transformers on the same feeder serve geographically close customers with highly similar voltage curves. This paper proposes a novel method based on load-switching fluctuation characteristics recorded by smart meters. By extracting localized current and voltage fluctuations and establishing correlation matching, the method overcomes the limited discriminability using steady-state measurements. The method operates in two stages: first, switching-induced fluctuation characteristics are extracted and matched to cluster customers by the …

Corrections To: Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems (Ieee Transactions On Instrumentation And Measurement (2026) 75 (1–12) Doi: 10.1109/Tim.2026.3676091), Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In the above article [1], a wording ambiguity appears in Proposition 4 regarding the description of the missing at random (MAR) mechanism. The published sentence states that the probability of observing the kth sample depends on the realized measurement value. This wording may be interpreted as dependence on the current unobserved value y[tk], which could suggest a missing not at random (MNAR) mechanism. The intended MAR mechanism is that the observation probability for the kth sample depends only on previously observed measurement information, such as y[tk-1], and not on the current unobserved value y[tk]. Therefore, the corrected wording clarifies that …

Perspectives Of Rural Educators On The Dependencies And Drawbacks Of Chat Gpt In Nigerian Higher Education Practices, Adedayo Olayinka Theodorio Dr, Francisca Jumoke Theodorio, Olumide Gbenga Olugbodi, Peter Adebowale Samson, Olusegun Olawale Olakotan

Journal of Educational Technology Development and Exchange (JETDE)

ChatGPT remains a valuable tool for transforming teaching strategies and supporting students’ comprehension of subject matter. However, its adoption and research remain uneven within the context of higher education in Nigeria. Specifically, there is limited empirical research that exemplifies real-world experiences regarding the dependencies and drawbacks in rural educational settings. This qualitative single-case study explored this phenomenon at a southwestern institution in Nigeria, involving a purposively selected group of educators. Data were gathered through participatory observation and focus group interviews, which were thematically analysed. The results indicated that the effective use of ChatGPT in rural higher education heavily depends on …

Longitudinal Associations And Interactions Of Adolescent Religious Deidentification And Parental Sanctification On Parent-Adolescent Relationships, Justin Hendricks, Sam A. Hardy, Michael A. Goodman, Emily De Schweintiz Taylor

Faculty Publications

In many religious families, shared religious identity is a strong source of intergenerational solidarity. However, when adolescents deidentify from their family faith, normative parent-adolescent conflict, which heightens in mid-adolescence and normally subsides in late-adolescence, may intensify and damage parent-adolescent social cohesion. Many religious parents view parenting relationships as sanctified, which typically leads to adaptive parenting, but in the context of deidentification may lead to greater distress. To understand how adolescent religious deidentification influences parentadolescent relationships, we examined the longitudinal associations and interactions of adolescent children’s religious deidentification and parental sanctification on parent-child religious conflict and parental warmth (N = 1,391 …

In Situ Measurement Of Eddy Diffusion Fluxes Of Trace Hydrophobic Organic Contaminants Across The Water-Sediment Interface In Lakes, Ying Liu, Zhiwei Liu, Rainer Lohmann, Peter Grathwohl

Graduate School of Oceanography Faculty Publications

While difficult to measure, approximating reliable eddy diffusion fluxes of trace hydrophobic organic contaminants across the water-sediment interface (WSI) is important for contaminant fate and remediation strategies. Here, we present an updated bottleneck boundary theory and a corresponding theoretical model to quantify these fluxes. Our innovative, field-based approach integrates passive sampling and eddy diffusion modeling at high vertical resolution. We performed an in situ measurement at an offshore site in Dianshan Lake, Shanghai. High-resolution passive sampling revealed vertical concentration gradients of polycyclic aromatic hydrocarbons from the sediment to the air. Vertical eddy diffusivities in the water column were calculated by …

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

Decentralized Linear Solvers: Communication Cost And Privacy, Nelson G. Brasil, Vinay A. Vaishampayan

Publications and Research

We consider the problem of multiple parties iteratively solving a system of linear equations in a decentralized manner. Specifically, we solve for $\negr{x} \in \R^n$, the $n \times n$ system of linear equations $M\negr{x} = \negr{b}$ when each party only knows their row of the matrix $M$ and a single component of $\negr{b} \in \R^n$. Our objective is to determine the tradeoff between the accuracy of the solution and the total communication cost measured in bits. A fully connected, reliable mesh network is assumed to connect the different parties. We develop a general formulation that applies to a large class …

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

Australian University Websites As Colonialities Of Gender, Emily M. Gray, Ampersand Pasley, Emma Fishwick, Mindy Blaise, Jacqueline Ullman, Maria Delaney

Research outputs 2022 to 2026

This paper offers analysis of the first phase of a research project, Understanding and Addressing Everyday Sexisms in Australian Universities. This phase involved a critical content analysis of all 39 of Australia’s public university websites, focusing on how they represent gender, absences in relation to gender and the navigability of the websites in relation to gender equity policy. Drawing on Maria Lugones’s colonialities of gender, this paper demonstrates how university websites have the potential to reproduce or renegotiate inherited institutional everyday sexisms and broader gender inequities. Themes of reconfiguring acceptable gendering, conspicuous absences, and in-built obscurity that emerged from this …

Can Environmentally Friendly Hotels Lead Customers To Green Behaviors? Evidence From The Stimulus–Organism–Response Model And Social Cognitive Theory, Saeid Nosrati, Mohtaram Rabbani, Razieh Sharifipur Shirazi, Ilkay Yorganci, Niusha Talebzadeh, Negin Bassirat

Research outputs 2022 to 2026

Purpose Using the stimulus–organism–response model and social cognitive theory, this study aims to examine how environmentally friendly hotels influence customers’ green patronage intention and green word-of-mouth. The study explored the mediating effects of hedonic value and utilitarian value and the moderating role of environmental concern. Design/methodology/approach Data were collected from 200 customers of environmentally friendly hotels in Iran and analyzed using structural equation modeling (SEM) with Mplus (version 8.3) software. Findings The results indicate that environmentally friendly hotels positively influence hedonic and utilitarian values, as well as green patronage intention and green word-of-mouth. Additionally, hedonic and utilitarian values partially mediate …

Exploring The Use Of Strategic Influencer Leadership (Sil) In Health Communication: A Cross-Cultural, Multi-Case Study, Katharina Wolf, Catherine Archer, Syafiq B. Assegaff, Joseph Nalloor

Research outputs 2022 to 2026

Purpose This study challenges the social media-centric view of influencers, arguing that health communication benefits from diverse strategic influencers beyond commercial figures. Trust, credibility and cultural resonance are essential for effective public health messaging, yet contemporary approaches often prioritise engagement metrics over strategic influence. Design/methodology/approach This paper employs a multi-case study approach to examine the strategic use of influencers in health communication across Indonesia, the United Arab Emirates and Australia during the COVID-19 pandemic. It explores how health professionals, royal representatives and a politician shaped public health messaging within culturally specific contexts. A mixed-methods approach, incorporating ethnographic research and thematic …

Effects Of Resistance Vs High Intensity Interval Training On Myokines And Cancer Cell Suppression In Breast Cancer Survivors: A Randomized Trial, Francesco Bettariga, Dennis R. Taaffe, Cristina Crespo-Garcia, Timothy D. Clay, Mauro De Santi, Giulia Baldelli, Sanjeev Adhikari, Elin S. Gray, Daniel A. Galvão, Robert U. Newton

Research outputs 2022 to 2026

Purpose: Reducing recurrence and mortality is crucial for breast cancer survivors. We investigated the effects of a 12-week resistance training (RT) vs high-intensity interval training (HIIT) program on myokines, cytokines secreted by skeletal muscle cells at rest in response to muscle contraction, and cancer cell inhibition. Methods: Twenty-eight survivors of breast cancer (age 55.5 ± 8.8 yr, body mass index 27.9 ± 5.1 kg/m², time since diagnosis 31 ± 12.3 months) were randomly allocated to a 12-week supervised moderate to high intensity RT (n = 14) or HIIT (n = 14) program 3 days per week. Resting blood …