Digital Commons Network^™

Revolutionizing Medical Implant Fabrication: Advances In Additive Manufacturing Of Biomedical Metals, Yuhua Li, Deyu Jiang, Rui Zhu, Chengliang Yang, Liqiang Wang, Lai Chang Zhang

Research outputs 2022 to 2026

Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants, offering the potential to revolutionize patient care and treatment outcomes. This article reviews the recent advances in additive manufacturing (AM) of biomedical metal implants, especially load-bearing biomedical alloys, biodegradable alloys, novel metals, and 4D printing, whose properties are systematically assessed to facilitate material selection for specific medical applications. The applications of the most cutting-edge artificial intelligence in AM and surface functional modification are also presented. This article also explores the application of AM in various medical specialties, such as orthopedics, dentistry, cardiology, and neurosurgery, demonstrating its …

The Relationship Between Visceral Fat Accumulation And Risk Of Cardiometabolic Multimorbidity: The Roles Of Accelerated Biological Aging, Tianyu Zhu, Yixing Tian, Jinqi Wang, Zhiyuan Wu, Wenhan Xie, Haotian Liu, Xia Li, Lixin Tao, Xiuhua Guo

Research outputs 2022 to 2026

Objectives: To investigate the association between visceral fat accumulation and the risk of cardiometabolic multimorbidity (CMM) and the potential roles of accelerated biological aging in this relationship. Methods: Using data from the UK Biobank, a nationwide cohort study was conducted using the available baseline body roundness index (BRI) measurement. Biological aging was assessed using the Klemera–Doubal method for biological age and the phenotypic age algorithms. The association between the BRI and CMM was estimated using the Cox proportional hazards regression model, while the roles of biological aging were examined through interaction and mediation analyses. Results: During a median follow-up of …

Interferometric Differential High-Frequency Lock-In Probe For Laser-Induced Vacuum Birefringence, R. G. Bullis, Ulrich D. Jentschura, D. C. Yost

Physics Faculty Research & Creative Works

We propose a measurement of laser-induced vacuum birefringence through the use of pulsed lasers coupled to femtosecond optical enhancement cavities. This measurement technique features cavity-enhanced pump and probe pulses, as well as an independent control pulse. The control pulse allows for a differential measurement where the final signal is obtained using high-frequency lock-in detection, greatly mitigating time-dependent cavity birefringence as an important and possibly prohibitive systematic effect. In addition, the method features the economical use of laser power and results in a relatively simple experimental setup.

Phases And Dynamics Of Quantum Droplets In The Crossover To Two-Dimensions, Jose Carlos Pelayo, George Bougas, Thomás Fogarty, Thomas Busch, Simeon I. Mistakidis

Physics Faculty Research & Creative Works

We explore the ground states and dynamics of ultracold atomic droplets in the crossover region from three to two dimensions by solving the two-dimensional and the quasi-two-dimensional extended Gross-Pitaevskii equations numerically and with a variational approach. By systematically comparing the droplet properties, we determine the validity regions of the pure two-dimensional description, and therefore the dominance of the logarithmic nonlinear coupling, as a function of the sign of the averaged mean-field interactions and the size of the transverse confinement. One of our main findings is that droplets become substantially extended upon transitioning from negative-to-positive averaged mean-field interactions. This is accompanied …

Tunable Pairing With Local Spin-Dependent Rydberg Molecule Potentials In An Atomic Fermi Superfluid, Chih Chun Chien, Seth T. Rittenhouse, S. I. Mistakidis, H. R. Sadeghpour

Physics Faculty Research & Creative Works

We explore the energy spectrum and eigenstates of two-component atomic Fermi superfluids with tunable pairing interactions in the presence of spin-dependent ultralong-range Rydberg molecule (ULRM) potentials, within the Bogoliubov-de Gennes formalism. The attractive ULRM potentials lead to local-density accumulation, while their difference results in a local polarization potential and induces the in-gap Yu-Shiba-Rusinov (YSR) states whose energies lie below the bulk energy gap. A transition from equal population to population imbalance occurs as the pairing strength falls below a critical value, accompanied by the emergence of local Fulde-Ferrell-Larkin-Ovchinnikov (FFLO)-like states characterized by out-of-phase wave functions and lower energies compared to …

Construction Of Stock Portfolios By Machine Learning Methods, Alfan Gehad Abulehia

Theses

We study the theory and application of two machine learning (ML) algorithms: Long-Short Term Memory Network (LSTM) and Random Forest. The study begins by providing an overview of mathematical foundation, highlighting the significance of mathematics in machine learning. We study and explain the mathematical details involved in these two algorithms. We also study closely related subjects which include but not limited to gradient descent, automatic differentiation, and recurrent neural network. The main ideas behind these topics form the foundation of any ML algorithms.

As an application of the ML algorithms, we focus on the U.S. stock market. We build stock …

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

Smart Traffic Intersections: Leveraging Isac And Millimeter-Waves For Advanced Vehicle Platooning, Mohammed Risal Thadathil

Theses

The rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections.

The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge …

Investigating Teachers' Perceptions On The Effectiveness Of School Inspection In Public Schools In The United Arab Emirates, Ruqaia Adel Mohammed

Theses

School inspection plays a significant role in ensuring quality education in UAE public schools. This paper investigates public school teachers’ perceptions of the effectiveness of school inspections in the United Arab Emirates (UAE) and examines the impact of teachers’ demographic factors on these perceptions. A total of 218 teachers across the UAE participated in the study. The research focused on four key domains within the School Inspection Perception Framework (SIPF): Perceived Effectiveness of School Inspections (PESI), Perceived Effectiveness of School Inspection Training (PESIT), Perceived Credibility of School Inspections (PCSI), and Perceived Usefulness of School Inspection Feedback (PUSIF). Data were collected …

Study Of Falāj Pathways And Their Unknown Extension In Al Ain City Using Geophysical Methods, Saud Mohammed Alsenaani

Theses

Historically, the city of Al Ain has relied on its Falāj systems as historic water resources for freshwater, which is vital for drinking and irrigating its oases and essential for supporting life in the region for centuries, providing the foundation for communities and farming. The remarkable rainfall in 2024 uncovered several hidden ancient Falāj systems and newly enlarged versions of previously recorded ones, highlighting the importance of ongoing research for their preservation. This research project aims to examine the current Falāj systems in Al Ain, particularly Falāj Mezyad, using geophysical methods to identify uncharted extensions and to examine current Falāj …

Study Of The Extension Of Cavities In Jebel Hafeet By Implementing Geophysical Methods, Mai Rashed Alkaabi

Theses

Subsurface cavities pose challenges to the buildings and their stability, leading to ground instability and subsidence. In Al Ain City, UAE, particularly in Jebel Hafeet, cavities within carbonate formations and sedimentary rocks present risks to infrastructure and urban development. This study employs Electrical Resistivity Tomography (ERT) and Gravity Surveys to detect, locate, and determine the depth of these cavities, enhancing geological understanding and geohazard assessment. The ERT survey, using a pole-dipole array, was conducted along eight lines to identify subsurface anomalies. It revealed high-resistivity anomalies (> 45 Ω·m), indicative of air-filled cavities, and low-resistivity zones (< 6 Ω·m), corresponding to water-saturated or clay-rich deposits. The ERT survey detected resistivity variations, highlighting anomalies ranging from 45 to 120 Ω·m at depths between 1 m and 17 m. The gravity survey, conducted at 28 stations, detected low-density anomalies, which aligned with high-resistivity ERT zones, confirming subsurface cavities at depths between 10 m and 220 m (below ground level). Five 3D gravity inversion models visualized density variations, revealing cavities at different depths for the models from 0 m to 195 m (a.s.l). The integration of ERT and gravity methods significantly improved cavity detection accuracy, demonstrating a strong correlation between low-density gravity anomalies and high-resistivity features. This study recommends increasing gravity station density, and avoiding heavy infrastructure in cavity-prone areas. The findings contribute to the geological understanding of Jebel Hafeet, providing insights for sustainable urban planning, risk mitigation, and infrastructure safety in Al Ain City.

Exploring The Relationships Between G2g, G2e And G2c E-Government Interactions And Organizational Readiness For Citizen Centricity In The Uae Government: Organizational Culture As A Moderator, Esam Al Sayed Ahmed

Dissertations

The UAE has topped the e-Gov development rankings globally over the last two decades. But during the era of the COVID-19 pandemic, certain problems befell the model of e-Gov, like no coherent strategy, inconsistencies in digital practice, inadequate collaboration, the lack of mechanisms for innovation, poor digital talent, and concerns with public trust. After the pandemic, the government released its digital strategy (2021-2025) as part of the rapid recovery plan to stimulate the economy and society. The strategy aimed at increasing sustainability and innovation and putting citizens first. Yet, the strategy can be improved in regard to how prepared government …

Exploring Transfer Learning For Deep Learning Polyp Detection In Colonoscopy Images Using Yolov8, Fabian Vazquez Jr., Jose Angel Nuñez, Xiaoyan Fu, Pengfei Gu, Bin Fu

Computer Science Faculty Publications

Deep learning methods have demonstrated strong performance in object detection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare …

Baseline Associations Of Office And Ambulatory Blood Pressure Monitoring With Cognitive Function And Dementia Prevalence, Jesus Melgarejo, Dhrumil Patil, Sokratis Charisis, Kristina P. Vatcheva, Silvia Mejia-Arango, Luis J. Mena, Claudia Satizabal, Eron G. Manusov, Sudha Seshadri, Gladys E. Maestre

School of Medicine Publications

Background: High blood pressure has been associated with dementia prevalence and incident. However, it is unclear the relationship of office and ambulatory BP monitoring with cognitive function and dementia and in particular, it remains unknown whether ambulatory BP variability relates to dementia.

Objective: To investigate the associations of office and 24-h blood pressure (BP) with cognitive function and dementia prevalence.MethodsCross-sectional population-based study of 1435 participants aged ≥40 years with office BP/24-h BP, and cognitive assessments (Mini-Mental State Examination [MMSE] and Selective Reminding Test [SRT]). Dementia was diagnosed with a clinical dementia rating ≥1.0. Statistics included logistic and linear regression models. …

Secondary School Students Attitude And Its Effects On Mathematics Achievement, Aini Shuhaimah Selamat, Zarith Sofiah Othman, Siti Salwana Mamat

Malaysian Journal of Computing (MJoC)

This study examines students' attitudes and motivation toward learning mathematics. The purpose of the study is to determine the relationship between the variables of students' attitudes toward mathematics and their motivation to learn mathematics. The study also addresses the question of how non-intelligence variables affect student achievement. To answer the research questions, a questionnaire was administered to 150 grade 4 students who were surveyed. From the results, the relationship between these two variables is shown to be significant. However, the variables did not seem to affect the students' performance. It appeared that although the students had a positive attitude towards …

Exploring Transfer Learning And Convolutional Autoencoder For Effective Kitchen Utensils Classification, Hashim Rosli, Rozniza Ali, Muhamad Suzuri Hitam, Ashanira Mat Deris, Noor Hafhizah Abd Rahim

Malaysian Journal of Computing (MJoC)

Effective classification of kitchen utensils is crucial for advancing assistive technologies and enhancing daily living for individuals with visual impairments. This study investigates the use of transfer learning and convolutional autoencoders to improve classification accuracy. We integrate pre-trained networks into an autoencoder framework to enhance feature extraction and image reconstruction. Models including ResNet50, DenseNet121, and their autoencoder variants were evaluated using precision, recall, accuracy, Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Results show that DenseNet121 outperforms ResNet50 with a classification accuracy of 72% and shorter training time. When combined with autoencoders, DenseNet121-Autoencoder achieves the highest classification accuracy …

Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White

Faculty Publications

Defense program managers utilize Earned Value Management (EVM) methodologies to measure, report, and predict the cost and schedule performance of their programs. Previous research conducted by Christensen (1996) and Kim et al. (2019) has shown varied results in the stability of EVM Estimates at Completion (EACs). Stability is defined as a 10% or less deviation from the final EAC at a specified percent completion point of the program. The Christensen (1996) and Kim et al. (2019) studies also noted that program-specific factors, such as phase, can impact the accuracy of EVM metrics. This study builds upon those works by assessing …

Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine

Faculty Publications

This article analyzes and investigates the distribution of cost growth of the Estimate at Completion (EAC) for the Work Breakdown Structure (WBS) elements of approximately 60 historical United States Acquisition Category I Research, Development, Test and Evaluation aircraft programs. Using the method of maximum likelihood in conjunction with the Akaike Information Criterion, the authors suggest that both the lognormal and Weibull distributions provide relatively good fit to EAC cost growth, with the lognormal slightly edging out the Weibull. As a summarized finding, the authors present their empirical results for the mean, coefficient of variation (CV), the 15th and 85th percentiles …

Strengthening The Paediatric Clinical Trial Ecosystem To Better Inform Policy And Programmes, James A. Berkley, Judd L. Walson, Glenda Gray, Fiona Russell, Zulfiqar Ahmed Bhutta, Per Ashorn, Shane A. Norris, Ebunoluwa A. Adejuyigbe, Rebecca Grais, Bernhards Ogutu

Institute for Global Health and Development

The first WHO Global Clinical Trials Forum was convened in November, 2023 to develop a shared vision of an effective global clinical trial infrastructure. The Paediatric Clinical Trials Working Group was formed to provide perspectives, identify challenges, and propose solutions to strengthen the paediatric clinical trials ecosystem. Participants represented paediatric disciplines, including infectious diseases, nutrition, neonatology, pharmacology, oncology, neurodevelopment, public health, and policy. Childhood diseases have profound lifelong effects on health, livelihoods, and societies. Investment in early childhood results in highly cost-effective changes to lifelong health, productivity, and human capital returns. Yet, there remain substantial gaps in knowledge on the …

The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker

Faculty Publications

Blast pressure is the primary military targeting metric for nuclear weapons. Any local conditions that affect blast pressure have the potential for altering nuclear plans, both from defensive and offensive standpoints. Understanding the impact of snow to the blast wave, therefore, provides a benefit both to military planners and to warfighters on the ground, for any operation occurring in arctic environments. No existing data provides a quantitative description of how snow on the ground affects a nuclear detonation blast wave passing over it. Similar blast waves passing over dust have experimentally proven to enhance blast pressure in a localized region.1 …

Implementation Of Machine Learning To Predict Cable Failures In Electrical Networks, Sultan Bader Albahri

Theses

Electrical networks are critical infrastructures that power industries, businesses, and households. Among their key components, electrical cables play a vital role in ensuring uninterrupted power distribution. However, cable failures due to aging, environmental factors, mechanical stress, and load imbalances pose significant challenges, leading to outages, financial losses, and reputational damage. Traditional maintenance approaches, which rely on periodic inspections and reactive repairs, have proven inadequate in preventing unexpected failures. In response to this challenge, predictive maintenance using Machine Learning (ML) has emerged as an effective solution. This research focuses on developing an ML-based predictive model to forecast cable failures in Dubai …

Network-On-Chip Packet Obfuscation And Encoding For Hardware Trojan Mitigation, Sydale John Ayi

Theses

Networks-on-Chip (NoCs) represent a transformative advancement in System-on- Chip (SoC) communication architectures, addressing the scalability and performance limitations of traditional bus-based systems. The increasing reliance on NoCs in modern SoCs has raised security concerns, particularly in untrusted fabrication environments. This thesis investigates the vulnerabilities posed by malicious hardware Trojans and untrusted IP cores, such as reverse engineering, misdirection attacks, and brute-force decryption. To counter these threats, the study introduces a robust security framework leveraging encoding and obfuscation techniques. Hamming codes are employed to detect and correct errors, ensuring data integrity and correcting misdirection attacks. Discrepancies between codewords and data words …

On Studying Transformer Networks For Volume-To-Surface Registration Of Inhomogeneous Soft Bodies For Liver Laparoscopy, Michael Adam Young

Theses

An important practical consideration in laparoscopic liver surgery is the limited visual information relative to open surgery. In laparoscopic interventions, the surgeon’s view of the liver surface is generally limited to the scene provided by a single scope with a narrow field-of-view. This limits the ability to navigate towards internal lesions previously identified through pre-procedural imaging. Surgical navigation during laparoscopy could be enhanced by registration of full preoperative liver models derived from pre-procedural imaging scans onto the partial laparoscopic view of the liver. This entails both a rigid registration to match the liver surface view to the preoperative volume, as …

Insights Into Covid-19 Dynamics Via The Integration Of Subpopulations, Vaccinations, And Bayesian Calibration In A Semi-Enclosed Community Model Framework, Meghan Rowan Childs

Theses

From the beginning of the COVID-19 pandemic, universities have experienced unique challenges due to their multifaceted nature as a place of education, residence, and employment. Previous work has used mathematical models to explore varying approaches to combating COVID-19 and tailored these models to local contexts, such as hospitals. However, previous work has not broadly incorporated pharmaceutical mitigation strategies into university models. We address this gap in research by integrating subpopulations, vaccines, and COVID-19 variants into a semi-enclosed population model framework. We further improve the quantification of uncertainty in the model parameters by implementing a formal Bayesian model calibration, fusing real-world …

An Ultra-Low-Power, Area-Efficient All-Analog Trojan With Temporal Power Supply Triggering, Roberto Ramos-Brito

Theses

With the rapid expansion of the Internet of Things and rising concerns over intellectual property protection, hardware security has become increasingly vital—particularly in defending against hardware trojans. While much of the existing research targets digital trojans, analog hardware trojans remain largely unexplored, presenting unique opportunities for developing innovative attack vectors and mitigation strategies. This thesis introduces a novel all-analog temporal trojan that functions entirely within the analog domain. The design incorporates power supply noise detection and charge-accumulator techniques to enable precise control over attack execution and payload delivery. It also leverages novel circuit techniques to reduce power and area overhead, …

The Effects Of Avatar Human-Likeness On Psychological Closeness In Virtual-Reality, Rebecca L. Chae, Hyojin Lee, Eunsoo Kim

Faculty Research, Scholarly, and Creative Activity

This research explores the impact of avatar human-likeness on psychological closeness and its downstream consequences in virtual-reality work and learning environments. Study 1 suggests that people feel greater psychological closeness to more (vs. less) humanlike avatars. Study 2 applies machine learning to virtual-reality recordings and shows that people physically move closer to more (vs. less) humanlike avatars. Study 3 extends these findings by showing that avatar human-likeness positively influences attitudes toward adopting virtual-reality in education. Finally, Study 4 shows that enhanced psychological closeness to humanlike avatars promotes trust in instructors, leading to more favorable attitudes toward virtual experience. Two additional …

Motivators And Barriers To Healthy Food Consumption: Qualitative Study With Gen Z Living In Bratislava, Emel Yarimoglu, Lucia Vilcekova

AMTP Proceedings 2025

People in Europe have been caring about physical activity, healthy eating, and healthy body weight. The study aimed to explore the motivators and barriers to healthy food consumption among university students living in Bratislava, Slovakia. Qualitative research was conducted with sixteen students, and data were collected by written text. Self-reported written data were collected by convenience sampling and analyzed via content analysis. Four motivators of healthy food consumption were revealed (personal appearance, health-consciousness, physical activity, and social), and four barriers to healthy food consumption were obtained (functional, personal, psychological, and social). This research is original since it demonstrated motivators and …

A Novel Method For Semi-Quantitative Detection Of Hpv16 And Hpv18 Mrna With A Low-Cost, Open-Source Fluorimeter, Kathryn A Kundrod, Mary E Natoli, Chelsey A Smith, Jackson B Coole, Megan M Chang, Emilie Newsham Novak, Elizabeth Chiao, Elizabeth A Stier, Jane R Montealegre, Michael E Scheurer, Philip E Castle, Kathleen M Schmeler, Rebecca R Richards-Kortum

Center for Medical Ethics and Health Policy Staff Publications

Despite global calls to eliminate cervical cancer, rates of cervical cancer incidence and mortality remain high in resource-limited settings, where it is challenging to implement and sustain screening, diagnosis, and treatment programs. The presence of high-risk HPV mRNA in cervical cells is a sensitive and specific biomarker of cervical precancer. Yet, current testing methods are too costly and complex for use in resource-limited settings. Here, we present a novel method for semi-quantitative detection of HPV16 and HPV18 mRNA with minimal infrastructure requirements. The assay relies on isothermal reverse transcription recombinase polymerase amplification (RT-RPA) with real-time fluorescence readout, demonstrated on rugged, …

How Financial Aid Affects The Odds Of Study Abroad Participation For Students Of Color, Patrick Newcomb

LSU Doctoral Dissertations

Prior research has shown that studying abroad provides students with enriching, diverse educational experiences with positive personal, academic, and professional outcomes. Despite the benefits, only a small portion of American higher education students participate in study abroad programs. Students of color disproportionately study abroad at lower rates than White students. Since cost is often cited as the primary barrier preventing education abroad, this study examined the types of financial aid associated with study abroad access and participation.

This study used data from the National Postsecondary Student Aid Study, which is a nationwide dataset designed to represent the makeup of institutions …

Approximations Of Koopman Operator Semigroups, Ibrahem Al Jabea

LSU Doctoral Dissertations

The main purpose of this dissertation is to study approximation methods for nonlinear systems using Bernhard Koopman's Global Linearization Method or Sophus Lie's method of continuous transformation groups. This approach enables the application of linear semigroup methods to a nonlinear system by focusing on the dynamics of the observables of the states, rather than directly studying the dynamics of the states. In this dissertation, we studied the pointwise semigroup and introduce the modified space $C_m(\Omega)$ and the modified Koopman-Lie semigroups. We use a splitting operator and outline a systematic approach for approximating the pointwise Koopman-Lie semigroup flows \begin{equation*} t\to T(t)g(x) …