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Aerocom: Winter 2026, John D. Odegard School Of Aerospace Sciences

Aerocom: John D. Odegard School of Aerospace Sciences Alumni Magazine

No abstract provided.

"Just Let Your Soul Glo": Humorous Music In Black American Comedy Films, Drew Brady

Music Theses and Dissertations

In this thesis, I inspect ways in which music has been used to create humor and meaning in black American cinema. In doing so, I address gaps in film music scholarship, as music in comedy films and in black cinema are both neglected topics. I suggest that humor in these films arises out of a particular blend of musical irony, parody, and satire, the exact combination of which is contingent on various historical, cultural, and political factors. In order to track this trend, I analyze humorous music in eight films, each representing a unique manifestation of this tradition: Watermelon Man …

How Is The Use Of Ai Impacting Nursing Students Education?, Lorna H. Strain

Honors College Theses

The use of Artificial Intelligence (AI) in education is rapidly increasing and reshaping how nursing students learn and engage with content. This thesis proposal looks at the impact of AI on the education of nursing students and explores students' perceptions of its use in their learning and education. As AI becomes more prevalent, many students are using it for study aids and content personalization, while schools are using it in simulations and textbook platforms. Many recognize its benefits, such as enhanced critical thinking, improved accessibility of materials, and more immersive learning. Concerns persist around ethical use, academic integrity, and the …

Rydberg Atom Arrays For Electric Field Sensing, James P. Hamilton

Honors College Theses

Rydberg atoms, hydrogen-like atoms that can be excited to states of high principal quantum number that carry correspondingly large electric dipole moments, have become leading candidates for precision electric field sensing. Arrays of such atoms, each trapped in a focused laser beam called an “optical tweezer,” are a particularly promising platform, since the strong dipole-dipole interactions between neighboring atoms provide a resource unavailable in a single-atom sensor. This thesis develops a theoretical and numerical framework for electric field measurement in small Rydberg atom arrays. We begin with a two-level model of a single atom, derive the Hamiltonian and equations of …

A Deal's A Deal: The Obvious Problem In Patent Law, Robin C. Feldman, Gideon Schor

Faculty Scholarship

Critics and industry alike agree that something is amiss in the market for medicines: Drug prices continue to rise, while various players point fingers, attributing the problem to different systemic inefficiencies. At the heart of this problem, however, lies the patent system, which provides the power that drives many of the strategic behaviors discussed in the literature.

A Deal’s a Deal suggests that a key element of patent law — obviousness doctrine — has been overlooked in the search for causes and solutions of the many distortions in pharmaceutical markets. This uniquely comprehensive Article examines obviousness doctrine from numerous angles: …

Exclusion By Design: Immigrant Racialization And Temporary Legal Status, Ming H. Chen, Jennifer M. Chacón, Shannon M. Gleeson

Faculty Scholarship

Migrant workers enter the United States on temporary visas more often than on green cards that lead to U.S. citizenship, and the majority come from countries whose emigrants would be classified as non-White in the United States. Even so, immigration scholarship gives short shrift to temporary statuses and often treats them as race neutral. This Article contributes to the growing body of critical migration studies by demonstrating how immigration law assigns meaning to the legal status of temporary migrants. We find that temporary legal statuses are designed in ways that unnecessarily foster social exclusion through a process of racialization and …

Access To Digital Health Technologies: Personalized Framework And Global Perspectives, Sanjiv M Narayan, Mina K Chung, Demilade Adedinsewo, Luisa C C Brant, Leslie L Davis, David Duncker, Jennifer L Hall, Janet K Han, Carolyn S P Lam, Eldrin Lewis, Joseph Loscalzo, Manlio F Márquez, Vasiliki Rahimzadeh, Fatima Rodriguez, Prashanthan Sanders, Emma Svennberg, Kenneth Stein, Mintu Turakhia, Clyde Yancy, Antonis A Armoundas

Center for Medical Ethics and Health Policy Staff Publications

The emergence and rapid adoption of digital health technologies (DHT) present unprecedented opportunities to democratize and reduce disparities in health care by monitoring health and disease at the point of care in all patients. However, limited access to DHT is becoming a major obstacle to realizing these goals. Access to DHT is influenced not only by well-recognized social determinants of health, but also by digital determinants of health, such as digital literacy and the need for broad access to digital infrastructure, as well as commercial and economic factors. Addressing these challenges and designing unbiased systems of care are essential to …

Real-Time Defect Detection In Ceramic Additive Manufacturing Using Deep Convolutional Neural Networks, Dylan Doan, Amir Armani, Marcia Golmohamadi

Faculty Research, Scholarly, and Creative Activity

Although ceramic additive manufacturing technologies have greatly advanced over the past decade, they are still far from perfect. Even small printing defects can have a severe impact on the mechanical properties of additively manufactured ceramic parts due to their low fracture toughness. The objective of this study was to detect these defects during the printing process for a new ceramic additive manufacturing technique, called ceramic on-demand extrusion (CODE). Images were collected, pre-processed, augmented, and separated based on their distinctive classes (i.e., type of defects). Deep convolutional neural networks were developed to identify multiple types of defects in real-time.

Generation Of Impact Factor-Driven Security Rating Questionnaire Using Llms For Aiot Applications, Yuna Han, Simon Shim, Deva Kumar Gajulamandyam, Yeji Choi, Hyunwoo Lee, Hangbae Chang

Faculty Research, Scholarly, and Creative Activity

Industry 4.0 has transformed industries by accelerating the adoption of artificial intelligence of things (AIoT); however, it has also led to security risks, such as data leakage. Existing data protection research focuses on network layers, whereas application-layer rating systems rely on subjective evaluations, limiting consistency and applicability. This study proposes a scalable and objective AIoT security rating framework that clarifies ambiguities in the five-question rating system of the Korean Intellectual Property Office and unifies fragmented managerial and technical rating systems. By leveraging large language models (LLMs), the framework integrates a security rating model based on 14 impact factors with automated …

The Composition Of Gases From A Diffusion Flame Above Longleaf Pine Needle Fuel Beds, David R. Weise, Thomas H. Fletcher, Timothy J. Johnson, Weimin Hao, Russell G. Tonkyn, Catherine A. Banach, Javier Palarea-Albaladejo, Mahsa Alizadeh, Stephen Baker

Faculty Publications

The gas and tar composition of a wildland fire diffusion flame from longleaf pine needles is currently relatively unmeasured and more data are needed to fill in the gap between pyrolysis data and smoke plume data, thus improving physical and chemical modeling of wildland smoke formation. A pilot experiment to measure light gas and tar composition of such a flame is described for three flame zones: persistent flame (flame base), intermittent flame, and smoke plume. Flame gases from 24 experimental fires were collected in canisters and analyzed for CO2, CO, H2, CH4, and C2 to C7 hydrocarbon gases. Other light …

Enhancing Offshore Wind Resource Assessment Through Neural Network-Based Hf Radar Data Analysis, Nikolas Martzikos, Matthew Craven, David Walker, Daniel Conley

School of Engineering, Computing and Mathematics

The increasing demand for offshore wind energy underscores the need for accurate wind speed estimation to support the design and operation of offshore wind farms. High-Frequency Radar (HFR), a widely used remote sensing technology in oceanographic research, offers promising potential for wind resource assessment, particularly in areas where conventional measurements are limited. This study explores the application of artificial neural networks (ANNs) for offshore wind speed prediction using HFR-derived data, addressing key challenges in model development and training. A key feature of this approach is the use of a decade-long dataset from the Celtic Sea, off the southwest UK coast, …

Systematic Literature Mapping: Integrated System Of Logistic Distribution For Island, Eka Suswaini, Mochammad Agung Wibowo, Ferry Jie

Research outputs 2022 to 2026

Purpose: The study aims to identify and compare various methods used to optimize supply chain distribution, with a focus on uncovering novel approaches. It particularly addresses the gap in logistics distribution models tailored for island environments, proposing the development of an integrated information system using genetic and memetic algorithms to handle uncertainties like weather and vessel availability. Design/methodology/approach: A Systematic Literature Mapping (SLM) approach was adopted. The K-Chart method was applied to categorize conceptual strategies and identify research gaps. A case study was also included to analyze logistical challenges in island settings, incorporating real data variables like transportation schedules and …

Next-Generation Virtual Environment Training For Cybersecurity In The Maritime Logistics Industry: A Position Paper, Tyler Cheng, Rory Hopcraft, Kimberly Tam, Ji-Jian Chin

School of Engineering, Computing and Mathematics

The maritime logistics industry is the most dependent on it in the modern world, yet it is not well protected against cyber risks. Recently, the maritime industry has been transitioning into industry-specific tech, and threats of cyberattacks are increasing as the adoption of technology and automation continues. One of the most effective methods to protect industries and ships from such security incidents, is to train seafarers to mitigate and handle such incidents. However, to the best of our knowledge, we did not find anything that addresses this gap. This article will present the idea and benefits of using virtual environments …

Fault Tolerant Adaptive Control Under Actuator Saturation For Robot Manipulators, D. J. López-Araujo, N. Alvarez-Jarquin, P. Borja, A. T. Becker

School of Engineering, Computing and Mathematics

In practice, actuators inherently have natural limitations that are often overlooked by theoretical control strategies, such as bounded output and discrepancies between calculated and actual responses. Consequently, reliable control techniques must be employed to ensure safe and dependable operation. We propose an adaptive control law for global regulation in the case of partial loss of effectiveness due to input faults. The proposed control strategy maintains the system at the desired configuration while accounting for the physical limitations of the actuators. Additionally, it is suitable for dealing with unknown parameters in the vector of gravitational forces, making it robust to these …

Angle-Based Formation Stabilization And Maneuvers In Port-Hamiltonian Form With Bearing And Velocity Measurements, Ningbo Li, Pablo Borja, Arjan Van Der Schaft, Jacquelien M.A. Scherpen

School of Engineering, Computing and Mathematics

This paper proposes a port-Hamiltonian approach for angle-based formation stabilization and maneuvers using bearing and velocity measurements with an underlying triangulated Laman graph. The corresponding port-Hamiltonian controller is designed using virtual couplings on the errors of angle constraints in angle space. Then, the angle constraints and agent actuators are mapped by the constraint Jacobian, which can be applied to other formation constraints. In addition, to avoid unavailable distance terms in the control law, an estimator is designed based on port-Hamiltonian theory and the property that energy is coordinate-free for different sensor modalities using bearing and velocity measurements, which permits our …

Scope 3 Carbon Emissions Assessment For Manufacturing Smes, Kourosh Halat, Stephen Childe, Nick Golding, John Summerscales

School of Engineering, Computing and Mathematics

This paper addresses the challenges facing managers of Small-andMedium-Size Enterprises (SMEs) when conducting environmental sustainability assessment at the value chain level, particularly Scope 3 Greenhouse Gases GHG) emissions. The paper investigates issues related to data availability and data quality for Scope 3 assessment and identifies dimensions of Scope 3 reliability.Utilising a series of in-depth case study analyses with SMEs in the south-west of England, UK, the paper fills a gap in supply chain sustainability research by identifying practical challenges. The findings show that SMEs encounter persistent difficulties in assessing emissions associated with purchased goods and services and downstream activities linked …

Usable Authentication: Are We There Yet?, Nathan Clarke, Steven Furnell

School of Engineering, Computing and Mathematics

With technology increasingly embedded in everyday life, the demand for secure and usable authentication methods has never been greater. Traditional password-based systems continue to dominate, despite well-known usability and security challenges. This paper explores the evolution of user authentication technologies, from secret knowledge and tokens to biometrics and emerging approaches such as Passkeys. It critically evaluates the extent to which usability has been achieved, identifying both successes—such as biometrics integrated into smartphones—and persistent issues, including inconsistent guidance, ecosystem dependence, and accessibility barriers. Drawing on academic and commercial developments, the discussion highlights the growing burden on users who must authenticate across …

From Predictions To Explanations: Explainable Ai For Autism Diagnosis And Identification Of Critical Brain Regions: 18th International Joint Conference, Biostec 2025, Kush Gupta, Amir Aly, Emmanuel Ifeachor, Rohit Shankar

School of Engineering, Computing and Mathematics

Autism spectrum disorder (ASD) is a neurodevelopmentalcondition characterized by atypical brain maturation. However, the adaptationof transfer learning paradigms in machine learning for ASD researchremains notably limited. In this study, we propose a computeraideddiagnostic framework with two modules. This chapter presents atwo-module framework combining deep learning and explainable AI forASD diagnosis. The first module leverages a deep learning model finetunedthrough cross-domain transfer learning for ASD classification. Thesecond module focuses on interpreting the model’s decisions and identifyingcritical brain regions. To achieve this, we employed three explainableAI (XAI) techniques: saliency mapping, Gradient-weighted Class ActivationMapping, and SHapley Additive exPlanations (SHAP) analysis.This framework demonstrates that …

Heat Transfer From A Partially Buried Circular Cylinder In Oscillatory Flow, H. Thomas, R. Stuhlmeier, A. G.L. Borthwick, S. Michele

School of Engineering, Computing and Mathematics

With the growing abundance of man-made cylindrical structures located on or close to the seabed, it is important to be able to assess their potential environmental impact. Herein, a model is presented of the viscous-thermal boundary layer in the vicinity of a circular cylinder resting on, or partially buried in, an otherwise flat seabed. To model the influence of wave-induced motions near such a cylinder, we assume oscillatory flow in which the water particle displacements are small with respect to the cylinder radius. A perturbation expansion is utilised to derive solutions of the boundary layer equations, leading to analytical solutions …

Diagnosing Long-Term Divergence In Build Back Better Outcomes In Low- And Lower-Middle-Income Communities, E. A. Opabola, M. Baiguera, A. Raby, T. Rossetto, A. Pomonis, E. Meilianda, Y. Idris, S. Nurdin, K. Adams, D. J. Mcgovern, N. Acikgoz, H. Mistry, F. Marafini

School of Engineering, Computing and Mathematics

Low-income communities in disaster-prone regions face persistent challenges in translating post-disaster recovery investments into sustained resilience, as they navigate trade-offs among engineering objectives, policy constraints, and evolving socio-economic needs. Despite the widespread adoption of Build Back Better (BBB) principles, evidence on the long-term sustainability of outcomes remains limited, particularly in low- and lower-middle-income contexts. This study examines the sustainability of BBB outcomes in two Indonesian case studies: Banda Aceh (approximately 18 years after the 2004 Indian Ocean tsunami) and Central Sulawesi (approximately 4 years after the 2018 earthquake, liquefaction, and tsunami). Drawing on structured housing assessments, household surveys, semi-structured interviews, …

Microbial Collagenase Activity Is Linked To Oral–Gut Translocation In Advanced Chronic Liver Disease, Shen Jin, Aurelie Cenier, Daniela Wetzel, Bethlehem Arefaine, Mar Moreno-Gonzalez, Marilena Stamouli, Merianne Mohamad, Mariia Lupatsii, Emilio Ríos, Sunjae Lee, Ane Zamalloa, Shilpa Chokshi, Adil Mardinoglu, Saeed Shoaie, Naiara Beraza, Vishal C. Patel, Melanie Schirmer

Peninsula Medical School

Microbiome perturbations are associated with advanced chronic liver disease (ACLD), but how microorganisms contribute to disease mechanisms is unclear. Here we analysed metagenomes of paired saliva and faecal samples from an ACLD cohort of 86 individuals, plus 2 control groups of 52 healthy individuals and 14 patients with sepsis. We identified highly similar oral and gut bacterial strains, including Veillonella and Streptococcus spp., which increased in absolute abundance in the gut of patients with ACLD compared with controls. These microbial translocators uniquely share a prtC gene encoding a collagenase-like proteinase, and its faecal abundance was a robust ACLD biomarker (area …

Patient And Practitioner Workshops For An Intellectual Disabilities And Epilepsy Neuropsychiatric Genetic Research Database Register: Ethical Dilemmas For Study Design, Rohit Shankar, Jon Allard, Dannielle Bilkey, Brendan Mclean, Pamela Bowman, Chloe Staples, Lance Watkins, Anna Simpson, Elizabeth Boardman, Sarah Mitchell, Sarah Lennard

Peninsula Medical School

BackgroundThere is a need for more research studies focussed on People with ID (PwID) and Epilepsy and for the design of these studies to be shaped by this complex and vulnerable population and those who care for and support them.AimsTo develop understanding as to how a future NHS genetic research database register for PwID and Epilepsy should be conducted in a way which is inclusive, suitable and appropriate for them, their carers and the NHS settings where the research would be undertaken.MethodsThree workshops were completed with 17 formal participants: (1) PwID and Epilepsy (n2); (2) carers of PwID and Epilepsy …

Designing And Evaluating Quality Transdisciplinary Work-Integrated Learning: Insights Into Practice, Denise Jackson, Claire Lambert, Sonja Coetzee

Research outputs 2022 to 2026

Higher education’s role in fostering innovation and enacting social change is increasingly important. Transdisciplinary Work-Integrated Learning (TWIL) offers opportunities to prepare students to address societal issues by transcending disciplinary boundaries and collaboratively tackling real-world global challenges. While extensive literature exists on established WIL models, research on the critical aspects of designing, implementing and evaluating impactful TWIL activities remains scarce. This paper draws together insights from 15 TWIL academics across 14 Australian universities, highlighting important design elements such as training for small group collaboration, open-ended projects, design thinking, sustained mentorship and reciprocal partnerships. Emerging challenges related to online delivery, teamwork, scalability, …

Multi-Scale Sensor-Aware Variational Autoencoders For Adaptive Iot Security: Integrating Drift Calibration And Cyberattack Detection, Md Kamal Hossain, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Existing approaches to Industrial Internet of Things (IoT) anomaly detection treat sensor drift calibration and cyberattack detection as separate problems, overlooking their strong interdependence and the compound failure modes that arise when drift dynamics are exploited by stealthy adversaries. Industrial Internet of IoT sensor networks therefore require a unified framework that jointly models both phenomena. This paper proposes the first unified multi-scale sensor-aware variational autoencoder (MS-VAE) framework that jointly models sensor drift and malicious activity within shared latent representations, enabling integrated calibration and security monitoring. The framework introduces three key components: (i) a multi-scale latent architecture that captures short-term anomalies …

Practising Digital Product Passport For Circular Supply Chain: Ecosystem Orchestration Perspective, Martin Agyemang, Daniel Faibil, Richard Asante, Cristina Iodice

Research outputs 2022 to 2026

Digital product passports (DPP) are emerging as an important tool to support industries in realising circular supply chain (CSC) practices. However, small and medium-sized enterprises (SMEs) remain constrained by various challenges in implementing DPP. Therefore, using social practice theory and ecosystem orchestration perspective, the study identifies factors essential for DPP-enabled CSC implementation by SMEs. The study proposes an actionable framework to classify and operationalise these factors. We collected data from managers and analysed it using the Best–Worst Method and Fuzzy-DEMATEL to understand the interdependence, coordination and temporal adaptation of the factors. The findings show that material factors are particularly salient …

Protomix: Unified Prototype Learning For Semantic Change Detection In Remote Sensing, Hengzhi Chen, Kun Hu, Patrick Filippi, Wei Xiang, Thomas Francis Bishop, Zhiyong Wang

Research outputs 2022 to 2026

As an emerging topic, semantic change detection (SCD) in remote sensing aims to both identify the locations of changes before and after events and determine the land cover (LC) classes of changed regions. While deep learning has demonstrated its great potential for SCD, the following two fundamental challenges remain: large variations among the same LC class as well as close similarity between different LC classes, and the severe underutilization of massive unchanged regions, which dominate remote sensing scenes but lack detailed semantic annotations. To address these issues, we propose a novel prototype learning framework for SCD, namely ProtoMix, by leveraging …

Rrto: A High-Performance Transparent Offloading System For Model Inference In Mobile Edge Computing, Zekai Sun, Xiuxian Guan, Zheng Lin, Yuhao Qing, Haoze Song, Zihan Fang, Zhe Chen, Fangming Liu, Heming Cui, Wei Ni, Jun Luo

Research outputs 2022 to 2026

Deploying Machine Learning (ML) applications on resource-constrained mobile devices remains challenging due to limited computational resources and poor platform compatibility. While Mobile Edge Computing (MEC) offers an offloading-based inference paradigm using GPU servers, existing approaches can be divided into non-transparent and transparent methods, with the former necessitating source-code modifications. Non-transparent offloading achieves high performance but requires intrusive code changes, limiting compatibility with diverse applications. Transparent offloading, in contrast, offers wide compatibility but introduces two challenges: performance degradation under MEC's low and fluctuating bandwidth, where per-operator remote procedure calls (RPCs) inflate end-to-end latency and energy consumption; and black-box logic reconstruction, which …

Selective Human Resource Practices, Psychological Safety Climate And Employee Wellbeing, Yvonne Brunetto, Matthew J. Xerri, Geremy Farr-Wharton

Research outputs 2022 to 2026

Purpose: Engineers and healthcare professionals perceive higher levels of stress compared with the general population, suggesting that past human resource management (HRM) systems have failed to manage them appropriately. This study examines whether psychosocial safety climate (PSC) acts as a psychological conduit in translating HRM practices and mediating their impact on wellbeing using the common good HRM (CGHRM) and Conservation of Resources theoretical frameworks.

Design/methodology/approach: Data were collected using surveys collected at two points – one-month apart, and then analysed using structural equation modelling (Analysis of Moment Structures) to manipulate survey data from 300 matched employees.

Findings – The variance …

Diffusion Of The Internet And Digital Divide In Post-Soviet Countries, Behrooz Asgari, Cosmos Amoah, Sudipa Majumdar

DLSU Business & Economics Review

This study explores the diffusion process of the internet in countries that were previously known as the U.S.S.R. and are currently divided into 15 countries. Utilizing the S-shaped logistic curves, this paper forecasts future trends in internet diffusion in these economies. Data varied in range, with each country having different time frames for similar variables. The logistic curves for each country determined the length of time of internet diffusion, maximum carrying capacity, and different stages of the diffusion process, namely, emerging, growth, maturity, and saturation. The results show that all countries have exceeded the emerging and growth phases, with 13 …

Common Census Methods Can Lead To Fundamentally Different Ecological Interpretations Of Coral Reef Fish Communities, Helen F. Yan, Samantha K. Crisp, David R. Bellwood, Alexandre C. Siqueira, Sterling B. Tebbett

Research outputs 2022 to 2026

In an era of large-scale stressors, scientists are racing to collate equally large datasets to answer macroecological questions. Coral reefs lie at the forefront of system-wide transformations and are thus the focus of many macroecological studies. A lack of standardised, global data across time, however, has resulted in the coalition of data from disparate sources, often comprising different survey methods. However, can we make the same ecological inferences from data collected using different survey protocols? Here, we employed two of the most common reef fish census techniques—point counts and belt transects—of varying survey effort (time and area) to assess whether …