Digital Commons Network^™

Mapping The Single-Cell Differentiation Landscape Of Osteosarcoma, Danh D Truong, Corey Weistuch, Kevin A Murgas, Prasad Admane, Bridgette L King, Jes Chauviere Lee, Salah-E Lamhamedi-Cherradi, Jyothishmathi Swaminathan, Najat C Daw, Nancy Gordon, Vidya Gopalakrishnan, Richard G Gorlick, Neeta Somaiah, Joseph O Deasy, Antonios G Mikos, Allen Tannenbaum, Joseph Ludwig

Faculty, Staff and Student Publications

Purpose: The genetic intratumoral heterogeneity observed in human osteosarcomas poses challenges for drug development and the study of cell fate, plasticity, and differentiation, which are processes linked to tumor grade, cell metastasis, and survival.

Experimental design: To pinpoint errors in osteosarcoma differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs mesenchymal stem cells toward adipogenic and osteoblastic fates. Incorporating preexisting chondrocyte data, we applied trajectory analysis and non-negative matrix factorization to generate the first human mesenchymal differentiation atlas.

Results: This "roadmap" served as a reference to delineate the cellular composition of morphologically complex osteosarcoma tumors and quantify …

Cluster-Based Toxicity Estimation Of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability By Using Whole Dose-Volume Histograms For Cohort Risk Stratification, Seyedmohammadhossein Hosseinian, Mehdi Hemmati, Cem Dede, Travis C Salzillo, Lisanne V Van Dijk, Abdallah S R Mohamed, Stephen Y Lai, Andrew J Schaefer, Clifton D Fuller

Faculty, Staff and Student Publications

PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible.

METHODS AND MATERIALS: The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients …

Grace Periods In Comparative Effectiveness Studies Of Sustained Treatments, Kerollos Nashat Wanis, Aaron L Sarvet, Lan Wen, Jason P Block, Sheryl L Rifas-Shiman, James M Robins, Jessica G Young

Faculty, Staff and Student Publications

Researchers are often interested in estimating the effect of sustained use of a treatment on a health outcome. However, adherence to strict treatment protocols can be challenging for individuals in practice and, when non-adherence is expected, estimates of the effect of sustained use may not be useful for decision making. As an alternative, more relaxed treatment protocols which allow for periods of time off treatment (i.e. grace periods) have been considered in pragmatic randomized trials and observational studies. In this article, we consider the interpretation, identification, and estimation of treatment strategies which include grace periods. We contrast natural grace period …

A Lower Bound On The Multicolor Size-Ramsey Numbers Of Paths In Hypergraphs, Deepak Bal, Louis Debiasio, Allan Lo

Department of Mathematics Faculty Scholarship and Creative Works

The r-color size-Ramsey number of a k-uniform hypergraph H, denoted by Rˆr(H), is the minimum number of edges in a k-uniform hypergraph G such that for every r-coloring of the edges of G there exists a monochromatic copy of H. In the case of 2-uniform paths Pn, it is known that Ω(r²n)=Rˆr(Pn)=O((r²logr)n) with the best bounds essentially due to Krivelevich (2019). In a recent breakthrough result, Letzter et al. (2021) gave a linear upper bound on the r-color size-Ramsey number of the k-uniform tight path Pn^(k); i.e. Rˆr(Pn^(k))=Or,k(n). At about the same …

The Impact Of Perceived Administrative Support On Teacher Burnout, Madison L. Kelly

Electronic Theses and Dissertations

Teachers are crucial to many facets of life (Güneyli, 2012). Due to current occupational requirements and emotional demands, many educational professionals experience burnout (Roethler, 2021). Burnout occurs when daily occupational challenges overpower one's ability to maintain a healthy emotional state and meet professional demands (Roethler, 2021). Previous studies have been conducted on the levels of teacher stress and burnout (Agyapong et al., 2022; Chang, 2009; Lindqvist et al., 2021; Skaalvik & Skaalvik, 2021), while few studies explored the impact of administrative support and its contribution to burnout within the teacher population (Howard et al., 2017; Jacob, 2007; McCray-Davis, 2022; Tickle …

Elucidation Of Molecular Mechanisms Underlying Tumor Necrosis Factor (Tnf) Release From Mast Cells, Tolulope Eunice Ayo

Dissertations

Mast cells are one of the major producers of tumor necrosis factor-alpha (TNF), and mast cell-derived TNF (MC-TNF) is associated with pathological conditions such as rheumatoid arthritis, inflammatory bowel diseases, and allergic asthma. However, the molecular machinery underlying TNF release from mast cells has been unclear, despite the conserved nature of exocytosis. To delineate the exocytic machinery required for TNF release from mast cells, we systematically dissected the exocytic fusion machinery (e.g., Munc13, Munc18, and VAMP families) expressed in mast cells, using a combination of CRISPR-dependent knockout, RNA interference, confocal microscopy, lentiviral rescue experiments, and cell-based assays. Out of the …

Delta-Shaped Approximation Based Homotopy Analysis Method For Nonlinear Poisson-Type Partial Differential Equations, Cyril Ocloo

Dissertations

This research aims to solve nonlinear Poisson-type partial differential equations (PDEs) by the approach of the homotopy analysis method (HAM) incorporated with approximate particular solutions (APS) using Delta-shaped basis (DSB) approximations.

With the inclusion of the h auxiliary parameters, we tackle nonlinear problems by studying the mathematical characteristics of the h curve. This is to ensure the numerical convergence of the HAM.

In the solution process, we use the homotopy analysis method to convert a nonlinear PDE into linear inhomogeneous PDEs, which are solved using the method of approximate particular solutions with DSB.

A proper value of the h is …

Variation In Zero Plane Displacement And Roughness Length For Momentum Revisited, Ashvath Singh Kunadi, Richard P. Silberstein, Sally E. Thompson

Research outputs 2022 to 2026

Zero plane displacement height (d0) and momentum roughness length (z0m), describe the aerodynamic characteristics of a vegetated surface. Usually, d0 and z0m are assumed to be constant functions of the physical characteristics of the surface. Prior evidence collected from the literature and our examination of flux tower data show that d0 and z0m vary in time at sites with tree and shrub canopies, but not grasslands. The conventional explanations of these variations are based on linear functions of wind velocity and friction velocity, with little theoretical basis. This study explains the variation in aerodynamic parameters by matching four analytical canopy …

Upscaling Relative Permeability And Capillary Pressure From Digital Core Analysis In Otway Formation: Considering The Order And Size Effects Of Facies, Masoud Aslannezhad, Mohammad Sayyafzadeh, David Tang, Zhenjiang You, Stefan Iglauer, Alireza Keshavarz

Research outputs 2022 to 2026

Digital Core Analysis (DCA) has emerged as a crucial instrument in reservoir characterization in recent times. With the advent of high-resolution micro-CT imaging, it is now possible to visualize the three-dimensional microstructures of in-situ pores and flow patterns within rocks. DCA offers several notable benefits over traditional techniques, such as a higher density of measurements, faster processing times, and the preservation of rock samples. It also demonstrates considerable flexibility with challenging core conditions and can derive numerous parameters from each individual sample. The objective of this work is to utilise DCA data from Otway formation to enhance reservoir characterisation and …

Explainable Ai For Cybersecurity Automation, Intelligence And Trustworthiness In Digital Twin: Methods, Taxonomy, Challenges And Prospects, Iqbal H. Sarker, Helge Janicke, Ahmad Mohsin, Asif Gill, Leandros Maglaras

Research outputs 2022 to 2026

Digital twins (DTs) are an emerging digitalization technology with a huge impact on today's innovations in both industry and research. DTs can significantly enhance our society and quality of life through the virtualization of a real-world physical system, providing greater insights about their operations and assets, as well as enhancing their resilience through real-time monitoring and proactive maintenance. DTs also pose significant security risks, as intellectual property is encoded and more accessible, as well as their continued synchronization to their physical counterparts. The rapid proliferation and dynamism of cyber threats in today's digital environments motivate the development of automated and …

Fatigue Performance Of 3d-Printed Poly-Lactic-Acid Bone Scaffolds With Triply Periodic Minimal Surface And Voronoi Pore Structures, Hamed Bakhtiari, Alireza Nouri, Majid Tolouei-Rad

Research outputs 2022 to 2026

Bone scaffolds serve a crucial role in tissue engineering, particularly in facilitating bone regeneration where natural repair is insufficient. Despite advancements in the fabrication of polymeric bone scaffolds, the challenge remains to optimize their mechanical resilience. Specifically, research on the fatigue behaviour of polymeric bone scaffolds is scarce. This study investigates the influence of pore architecture on the mechanical performance of poly-lactic-acid (PLA) scaffolds under quasi-static and cyclic compression. PLA scaffolds with a 60% porosity were fabricated using extrusion-based 3D printing in various designs: Gyroid, Lidinoid, Fischer–Koch, IWP, and Voronoi. Results demonstrated that Gyroid scaffolds had the highest compressive strength …

Evolution Mechanism Of Organic Macromolecular Structure During Lignite Pyrolysis, Ying Shi, Yanming Zhu, Zhenjiang You, Wu Li, Yang Wang, Yu Song

Research outputs 2022 to 2026

Understanding the mechanisms governing coal pyrolysis reactions, particularly in relation to the coal macromolecular structure, is crucial for improving energy efficiency and facilitating the transition of coal into a clean energy source. This study employs cutting-edge techniques, such as High-Resolution Transmission Electron Microscopy (HRTEM) and Thermogravimetric Analysis coupled with Fourier Transform Infrared Spectroscopy and Mass Spectrometry (TG–FTIR–MS), to analyze lignite and its residual solid samples subjected to pyrolysis across a range of temperatures from 300 °C to 1100 °C. The variational characteristics of the macromolecular structure during coal pyrolysis are examined. Utilizing molecular dynamics simulations, we analyze the evolution mechanism …

Phthalocyanine-Enabled Technologies For Water Treatment And Disinfection Strategies, Hooralain Bushnaq, Catherine Munro, Sisi Pu, Amir Razmjou, Masoumeh Zargar, Giovanni Palmisano, Srinivas Mettu, Ludovic F. Dumée

Research outputs 2022 to 2026

Water treatment and disinfection are critical factors in ensuring a safe and sustainable water supply. Phthalocyanines (Pcs), a class of versatile and robust organic compounds, have garnered significant attention for their application in various water treatment processes. This review paper offers a groundbreaking investigation of Pc-enabled technologies within the domain of water treatment applications. The core aim of this review is to meticulously analyze the chemical and optical properties of Pcs, elucidate their photosensitization mechanism and establish the crucial connection between their chemical structure and photodynamic efficacy. The various modes of Pcs application in water treatment processes - whether in …

Primary School Students' Perceptions And Developed Artefacts And Language From Learning Coding And Computational Thinking Using The 3c Model, David A. Martin, Peter Curtis, Petrea Redmond

Research outputs 2022 to 2026

Background: A resurgence in teaching coding in primary school classrooms has led to a pedagogical swing towards using physical computing and coding to develop students' use of algorithms, computational thinking, and problem-solving skills. Two obstacles impede the optimal development of these objectives: the availability of a suitable pedagogy and an instructional sequencing model for primary school teachers to effectively present coding and computational thinking concepts and skills to students in alignment with their developmental stage. Objective: This study aims to address both obstacles by introducing the 3C Model, a newly developed instructional sequence grounded in established pedagogies and designed to …

Comparison Of Transport Models In Dense Plasmas, Zachary A. Johnson, Luciano G. Silvestri, George M. Petrov, Liam G. Stanton, Michael S. Murillo

Faculty Research, Scholarly, and Creative Activity

We compare a variety of models used for the calculation of transport coefficients in dense plasmas, including average-atom models, models based on kinetic theory, structure matching effective potentials, and pair-potential molecular dynamics. In particular, we focus on the parameter space investigated in the second charged-particle transport coefficient code comparison workshop [Stanek et al., Phys. Plasmas 31, 052104 (2024)]. Each model is based on the self-consistent output of our average-atom calculations. Ionic transport properties are generated from implicit electron pair matched molecular dynamics simulations, bypassing the need for either dynamical electron simulations or on-the-fly electronic structure calculations. These matched pair potentials …

Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao

Research Collection School Of Computing and Information Systems

This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac …

“Academic Publishing Is A Business Interest”: Reconciling Faculty Serials Needs And Economic Realities At A Carnegie R2 University, Rachel E. Scott, Anne Shelley, Chad E. Buckley, Cassie Thayer-Styes, Julie A. Murphy

Faculty and Staff Publications – Milner Library

Introduction: This article explores faculty conceptions of academic publishers, their willingness to circumvent paywalls and share content, and their understanding of who holds the responsibility to pay for this body of scholarly work to which they all contribute.

Methods: The authors conducted semi-structured interviews with 25 faculty at their Carnegie R2 university to explore scholars’ perspectives with respect to the costs of serials and the responsibilities of the University and library in support of scholarly publishing.

Results: Participants reported a broad spectrum of perspectives with respect to circumventing publisher paywalls and offered nuanced practices for interacting with paywalled content. They …

Routing Optimisation For Towing A Floating Offshore Wind Turbine Under Weather Constraints, Frédéric Le Pivert, Adán López-Santander, Matthew J. Craven, Adam Roberts

School of Engineering, Computing and Mathematics

This paper presents a methodology for optimising routing for towing of fully assembled Floating Offshore Wind Turbines using a purpose-built ship simulator to generate datasets describing dynamics for a towing arrangement together with the engine data of the ship, and using such dataset and historical metocean data to perform multi-objective route optimisation for the tow using NSGA-II evolutionary algorithm. The work introduces the new ship simulator and the modelling of the platform VolturnUS-S, including the discussion of a comparative experiment between the model in the ship simulator and a 1:70 scale model in a wave tank. This is then followed …

A Hierarchical Framework For Interpretable, Safe, And Specialised Deep Reinforcement Learning, Ammar Abbas

Doctoral

Safety-critical systems, which are crucial for human safety and the environment, are difficult to control and operate. Traditional controllers need precise models of these complex systems, which is hard to develop. \acrfull{drl} offers a potential solution by learning from interactions rather than detailed models, but it faces limitations such as non-transparent decision-making and an expensive, unsafe learning process. Additionally, a key challenge in DRL is ensuring effective decision-making in rare situations.

This thesis proposes a novel approach called the \acrfull{prop_frame} that enables safe and reliable control of critical systems. SRLA combines probabilistic modelling with reinforcement learning to create an interpretable …

Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang

Research Collection School Of Computing and Information Systems

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate …

Synergizing Large Language Models And Pre-Trained Smaller Models For Conversational Intent Discovery, Jinggui Liang, Lizi Liao, Hao Fei, Jing Jiang

Research Collection School Of Computing and Information Systems

In Conversational Intent Discovery (CID), Small Language Models (SLMs) struggle with overfitting to familiar intents and fail to label newly discovered ones. This issue stems from their limited grasp of semantic nuances and their intrinsically discriminative framework. Therefore, we propose Synergizing Large Language Models (LLMs) with pre-trained SLMs for CID (SynCID). It harnesses the profound semantic comprehension of LLMs alongside the operational agility of SLMs. By utilizing LLMs to refine both utterances and existing intent labels, SynCID significantly enhances the semantic depth, subsequently realigning these enriched descriptors within the SLMs’ feature space to correct cluster distortion and promote robust learning …

A Survey On Neural Question Generation : Methods, Applications, And Prospects, Shasha Guo, Lizi Liao, Cuiping Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The survey begins with an overview of NQG’s background, encompassing the task’s problem formulation, prevalent benchmark datasets, established evaluation metrics, and notable applications. It then methodically classifies NQG approaches into three predominant categories: structured NQG, which utilizes organized data sources, unstructured NQG, focusing on more loosely structured inputs like texts or visual content, and hybrid NQG, drawing on diverse input modalities. This classification is followed …

Speaker Verification In Agent-Generated Conversations, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks. However, the ability to personalize the generated utterances to speakers, whether conducted by human or LLM, has not been well studied. To bridge this gap, our study introduces a novel evaluation challenge: speaker verification in agent-generated conversations, which aimed to verify whether two sets of utterances originate from the same speaker. To this end, we assemble a large dataset collection encompassing …

Towards Gradient-Based Time-Series Explanations Through A Spatiotemporal Attention Network, Min Hun Lee

Research Collection School Of Computing and Information Systems

In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos.

Environmental Sustainability In Universities Of The Gulf South, Carol T. Lunn

LSU New Orleans Theses and Dissertations

Universities and colleges are uniquely qualified to serve as community showcases on incorporating environmental sustainability across education, research, operations and leadership. Campuses can serve as living laboratories that benefit students, employees and the local community. The Whole Institution Approach to sustainability promotes active engagement of the entire school in a holistic way. Leadership plays a key role in embedding sustainability across curriculum, research, operations and community engagement.

The purpose of this study was to research universities of the Gulf South to determine what environmental sustainability practices they have in place and examine if there are trends related to university characteristics. …

Indefinability Of Effective Stress For Unsaturated Soils, Xiong Zhang, Sandra Houston

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

For decades, researchers have searched for an unsaturated soil effective stress that performs analogously to effective stress for saturated soils and provides some simplifications over net stress and suction approaches. A recent debate is whether problems identified with unsaturated soil effective stress can be resolved via elastoplastic analyses. Effective stress equations are reviewed and categorized as being based on volume change, shear strength, yield, or degree of saturation. These equations are evaluated for consistency with saturated soil effective stress expectations using the Barcelona basic model (BBM), simplified into an integrated elastoplastic framework using the modified state surface approach (MSSA). Under …

Doubly Differential Cross Sections For Ionization In Proton Collisions With Atomic Hydrogen: Energy And Angular Distribution Of Emitted Electrons, C. T. Plowman, K. H. Spicer, N. W. Antonio, M. S. Schöffler, Michael Schulz, I. Bray, A. S. Kadyrov

Physics Faculty Research & Creative Works

We use the two-center wave-packet convergent close-coupling approach to ion-atom collisions to calculate the energy and angular distribution of electrons emitted in proton collisions with atomic hydrogen. Results are provided across a wide range of intermediate energies where many competing reaction channels make calculations challenging. The present data consistently agree with the available experimental measurements and improve upon previously available results based on perturbative and classical methods. Furthermore, we extend the range of electron angles and energies over which theoretical data are available for the doubly differential cross section for ionization. This provides strong evidence that at the level of …

Novel Emergent Phases In A Two-Dimensional Superconductor, Simrandeep Kaur, Hemanta Kumar Kundu, Sumit Kumar, Anjana Dogra, Rajesh Narayanan, Thomas Vojta, Aveek Bid

Physics Faculty Research & Creative Works

In this letter, we report our observation of an extraordinarily rich phase diagram of a LaScO₃/SrTiO₃ heterostructure. Close to the superconducting transition temperature, the system hosts a superconducting critical point of the Infinite-randomness type characterized by an effective dynamical exponent ν z that diverges logarithmically. At lower temperatures, we find the emergence of a magnetic field-tuned metallic phase that co-exists with a quantum Griffiths phase (QGP). Our study reveals a previously unobserved phenomenon in 2D superconductors—an unanticipated suppression of the QGP below a crossover temperature in this system. This concealment is accompanied by the destruction of the …

A Model Of Information Visualization Interpretation, Michael Gene Hilgers

Business and Information Technology Faculty Research & Creative Works

Since the groundbreaking work by Cleveland and McGill in 1984, studies have revealed the difficulties humans have extracting quantitative data from visualizations as simple as bar graphs. As a first step toward understanding this situation, this paper proposes a mathematical model of the interpretation effort of a bar graph using concepts drawn from eye tracking. First, three key areas of interest (AOIs) are identified, and fixations are modeled as random point clouds within the AOIs. Stochastic geometry is introduced via random triangles connecting fixations within the adjacent key visual regions. The so-called landmark methodology provides the basis for the probabilistic …

An Efficient Neutrosophic Approach For Evaluating Possible Industry 5.0 Enablers In Consumer Electronics: A Case Study, Mai Mohamed, Asmaa Elsayed, Bilal Arain, Jun Ye

Neutrosophic Systems with Applications

With the use of cutting-edge technologies like artificial intelligence (AI), robotics, and the Internet of Things (IoT), Industry 5.0 represents a breakthrough move towards a sustainable and human-centered industrial future. Industry 5.0 endeavors to transform industries such as consumer electronics by emphasizing sustainability and collaboration, in contrast to its predecessors, who only concentrated on automation and efficiency. Along with improved manufacturing efficiency and product innovation, this change in the consumer electronics sector also redefines the human-machine interaction. This paper proposes a novel hybrid integrating model that combines the Entropy Weight Method (EWM), Best-Worst Method (BWM), and an acronym in Portuguese …