Physical Sciences and Mathematics Commons^™

When It Comes To Scientific Information Extraction And Llms, Less Is More, Sameer Shaik

Theses and Dissertations from DePaul University

The scientific literature continues to expand rapidly, making manual extraction of structured scientific facts increasingly impractical. Traditional Machine Learning and Natural Language Processing (NLP) pipelines require large expert-annotated datasets, which are costly to produce. Novel Large Language Models (LLMs) face challenges in long-context scientific reasoning, hallucinations, and entity linking. This thesis investigates ELSIE-Blob, a domain-aware preprocessing method that segments scientific articles into compact text “blobs” containing components of entity relations (here, polymer names, melting point indicators, and numerical values). We test whether blob-based input allows lightweight, consumer-hardware-accessible LLMs to extract polymer–melting point (polymer–Tm) pairs accurately without training data. Experiments using …

Draft 2026 Interim Site-Wide Groundwater Monitoring Quality Assurance Project Plan, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Gw241011 And Gw241110: Exploring Binary Formation And Fundamental Physics With Asymmetric, High-Spin Black Hole Coalescences, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, R. Espinosa, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang

Physics & Astronomy Faculty Publications

We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO–Virgo–KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, nonnegligible spin–orbit misalignment, and unequal mass ratios between their constituent black holes. These properties are characteristic of binaries in which the more massive object was itself formed from a previous binary black hole merger and suggest that the sources of GW241011 and GW241110 may have formed in dense stellar environments in which repeated mergers can take place. As the …

Attorneys And Ai: How Lawyers Use Artificial Intelligence And Analyze Its Impacts, Matthew I. Hall, Christian Turner, Eddie A. Gomez Schieber, Nathaniel Kite, Ari Schlesinger

Scholarly Works

AI systems are testing lawyers' professional ethics obligations of competence, confidentiality, and candor. In the legal profession, the widespread availability of AI systems presents opportunities, like improving the review of documents during the discovery stage of a lawsuit, and challenges, illustrated by the handful of high-profile incidents where lawyers submitted legal briefs in court citing and describing fictitious cases based on AI-generated output. We conducted interviews with 44 legal professionals in the U.S. to understand how attorneys are making sense of AI technology and the impacts these technologies are having on their profession, legal ethics, and legal institutions. We describe …

Substituted Piperidines As (Alpha4)3(Beta2)2 Nicotinic Acetylcholine Receptor Positive Allosteric Modulators: In Vitro Pharmacology, Nataly Sanchez, Josue Gaona, Wilder Felix, Nathalia Menegasso, Brisa Cea, Ganesh Thakur, Ayman K. Hamouda

School of Medicine Student Publications and Presentations

Nicotinic acetylcholine receptors (nAChRs) play a crucial role in the pathogenesis of neurodegenerative diseases such as Parkinsonʼs disease (PD). In PD, degeneration of dopaminergic and cholinergic neurons impairs neurotransmitter release, leading to cognitive and motor dysfunction. Current treatments provide only symptomatic relief, highlighting the need for novel therapeutic strategies. The (α4)₃(β2)₂ subtype of nAChRs is expressed on affected neurons, making it a promising druggable target for enhancing neurotransmitter release and providing neuroprotection. To explore the therapeutic potential of positive allosteric modulators (PAMs) of (α4)₃(β2)₂ nAChRs, we evaluated chemical derivatives of CMPI [3-(2-chlorophenyl)-5-(5-methyl-1-(piperidin-4-yl)-1H-pyrazol-4-yl)isoxazole]. We used two-electrode voltage-clamp recordings from Xenopus laevis …

Synthesis And Evaluation Of Bodipy-, Azabodipy- And Chlorin-Based Conjugates For Imaging And Photodynamic Therapy Of Egfr-Overexpressing Cancers, Simran Dhingra

LSU Doctoral Dissertations

This dissertation discusses the design of EGFR-targeted small-molecule agents that unite molecular imaging with photodynamic therapy. Chapter 1 frames the clinical need for early detection and selective treatment and surveys BODIPY, aza-BODIPY, and chlorin scaffolds as tunable chromophores for near-infrared imaging and photosensitization. It also outlines EGFR as a validated target and compares two ligand classes used throughout this work: the tyrosine kinase inhibitor erlotinib and the cyclic peptide KLARLLT.

Chapter 2 describes the synthesis of a near-IR BODIPY bearing either one or two erlotinib units. Docking and SPR show specific interactions with the EGFR kinase domain, and both conjugates …

A Revised Chronostratigraphy Of The Triassic-Jurassic Moenave Formation, Western Usa: Implications For Timing Of Continental Climate Change, Celina A. Suarez, Glenn R. Sharman, Jordan Oefinger, Asher Rea Boudreaux, Dennis Mmasa, James L. Crowley, Michael T. Mohr, Adam D. Marsh, Andrew R.C. Milner, Lisa E. Park Boush

Geosciences Faculty Publications and Presentations

The Moenave Formation of the Colorado Plateau region of western USA preserves the Triassic-Jurassic transition, a time period that saw one of the most significant climate disruptions in Earth's history. During this time, major carbon (C)-cycle perturbations due to carbon release from the emplacement of the Central Atlantic Magmatic Province (CAMP) caused rapid climate change prompting the end-Triassic mass extinction (ETE). Here we present a Bayesian agemodel generated from new C-isotope chemostratigraphic records paired with highprecision detrital zircon U-Pb geochronology to constrain the stratigraphic placement of the ETE and Triassic-Jurassic boundary within the Moenave Formation. Maximum depositional ages of detrital …

Be+ Assisted, Simultaneous Confinement Of More Than 15000 Antihydrogen Atoms, Tim Tharp

Physics Faculty Research and Publications

Antihydrogen, the bound state of a positron and an antiproton, is the only pure anti-atomic system ever studied. It is produced exclusively in the laboratory, as it has never been observed in nature. This unique system is of great interest for searching for tentative differences between matter and antimatter. Antihydrogen has been routinely trapped since 2010 and accumulated since 2017, enabling, for example, the first precision spectroscopic study of the anti-atom in 2018 and the first observation of the influence of gravity in 2023. Here we report an eight-fold increase in the trapping rate of antihydrogen, enabled by sympathetic cooling …

Neural Network Ensemble For Computing Cross Sections Of Rotational Transitions In H2O + H2O Collisions, Bikramaditya Mandal, Dmitri Babikov, Phillip C. Stancil, Robert C. Forrey, Roman V. Krems, Naduvalath Balakrishnan

Chemistry Faculty Research and Publications

Water (H₂O) is one of the most abundant molecules in the universe and is found in a wide variety of astrophysical environments. Rotational transitions in H₂O + H₂O collisions are important for modeling environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally inelastic transitions in collisions of complex molecules such as water. The proposed methodology utilizes data computed with a mixed …

Draft 2026 Interim Site-Wide Surface Water Monitoring Quality Assurance Project Plan, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Butte Priority Soils Operable Unit Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp) Revision 0, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Spatio-Temporal Gcn With Softmax Classifier For Skeleton-Based Human Action Recognition, Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov

Chemical Technology, Control and Management

Skeleton-based human action recognition is an important research area with many practical applications. Most existing methods rely on single representations of skeletal sequences, which cannot totally obtain all the complex features of human movements. This paper presents LFHAR (Latent Features for Human Action Recognition), a new framework that uses multiple spatio-temporal latent representations to improve the extraction of action features. Our method captures how skeletal poses change over time and combines motion information from both individual joints and connected body parts. The proposed approach applies graph-based processing to each skeleton frame in a sequence, then arranges the resulting graph features …

An Efficient Self-Supervised Learning Framework For Swarm Robot Trajectory Analysis, Brooklyn Berry, Gaukhar Nurbek, Juan Manuel Perez, Richard Tapia, Qi Lu, Yifeng Gao

Computer Science Faculty Publications

Swarm robotics leverages groups of autonomous robots to perform complex tasks collaboratively. Recently, there has been growing interest in the Self-Supervised Learning framework (SSL) for social robots, yet very little research has been done on designing a SSL framework for foraging swarm robots systems. In this paper, to address the two challenges above, we proposed a novel efficient self-supervised learning framework. Specifically, we proposed 1) a shared weight multi-instance based encoder-decoder structure for model pre-training; and 2) an embedding series compression strategy to reduce the space cost in inference stage. Experiments show our system can match the performance of standard …

Spatio-Temporal Dynamics Of Malaria Vector Niche Overlaps In Africa, Eric Ali Ibrahim, John Odindi, Mark Wamalwa, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Malaria remains a significant public health challenge in sub-Saharan Africa, with transmission heightened by the dynamics of primary and secondary mosquitoes infected with Plasmodium parasites. Regions where both vector types co-exist face heightened likelihood of intensified malaria transmission. Hence, understanding vectors' ecological interactions, especially their niche overlaps in geographic or environmental space, is crucial for targeted malaria control and elimination strategies. We employed a dynamic cellular automata (CA) model to map niche overlaps among primary (Anopheles gambiae complex, An. funestus group) and secondary (An. pharoensis, An. coustani) malaria vectors across African, using open-access environmental and vector occurrence datasets sourced from …

Climate-Driven Potential For Tularemia In East Africa: Skill Testing And Ecological Consistency Of A Transferred Risk Model, Komi Mensah Agboka, Allan Muohi Ngángá, Bonoukpoè Mawuko Sokame, Steve Soh Bernard Baleba, Tobias Landmann, Elfatih M. Abdel-Rahman, Chrysantus M. Tanga, Souleymane Diallo

All Peer-Reviewed Publications

Tularemia, a neglected zoonosis, remains underreported in Africa despite growing concern over its climate-driven expansion. This study aims to quantify the specific contribution of climate to tularemia risk using a climate attribution framework. We trained a Least Squares Dummy Variable (LSDV) fixed-effects panel model on United States (U.S.) county-level tularemia incidence data from 2011–2020 (n = 500, R² = 0.90), incorporating only climatic predictors: cumulative temperature, cumulative precipitation, and their respective variabilities. The climate-only model explained 86% of variance in the training data, demonstrating strong climate influence on tularemia disease dynamics. We then applied the model to East Africa, using …

Catch Me If You Can: Microplastics Hidden In Plain Sight, Karin Mattsson, Pere Masque, Thomas Maes, Marc Metian

Research outputs 2022 to 2026

Microplastic (MP) pollution is widely recognised as a pressing environmental issue, yet certain major sources remain under-addressed. This paper focuses on three significant but often overlooked contributors: paint particles, textile fibres, and tire wear particles (TWP). Together, these sources account for over 25 % of global MP emissions, nearly 3 million tonnes annually, yet they are rarely singled out in research or regulation. Each presents unique analytical and regulatory challenges due to their complex compositions, diffuse emissions, and strong ties to daily life. We enumerate current knowledge on their environmental pathways, emission estimates, and detection methods, highlighting the need for …

Carbon–Neutral Granular Filtration For Sustainable And Safe Rainwater Harvesting In Urban Malaysia, Mohammad Nur-E-Alam, Md Helal Uddin, Saurav Dixit, Boon Kar Yap, Mohammad Tariqul Islam, Nowshad Amin

Research outputs 2022 to 2026

This work aims to design a granular filter-based carbon–neutral rainwater treatment system. Granular filters are capable of cleaning rainwater while leaving a carbon–neutral trace to promote environmentally friendly rainwater collection techniques by examining the filtration performance and impact on the environment. The elimination of pollutants and impurities from collected rainwater through granular filters highlights the significance of meeting water quality requirements. The system uses three filters (F1, F2, and F3) for the treatment process where palm (oil) fruit shell-activated carbon, coconut shell-activated carbon, coarse sand, and fine sand are used as raw materials. The physicochemical quality of rainwater (harvested at …

Problems In Extremal Graph Theory And Spectral Graph Theory, Fareeha Jamal

Dissertations

Spectral graph theory is a subfield of algebraic graph theory that studies the matrices associated with graphs. It lives at the nexus of Linear Algebra and Combinatorics. Many intriguing results in the domains of Matrix Theory and Combinatorics have come from studying the eigenvalues of graph matrices; in fact, several open problems in both areas have been resolved. Beyond its theoretical appeal, spectral graph theory has found meaningful applications in theoretical chemistry, particularly in the mathematical classification of chemical graphs. These classifications underpin quantitative structure–property relationships (QSPRs), facilitating the prediction of physicochemical properties such as enthalpy of vaporization, molar refractivity, …

Advancing Security Safeguards In Large Language Models Through Multi-Agent Systems, Mohammed Rashed Alnuaimi

Theses

This thesis focused on enhancing the safe use of Large Language Model (LLM) through the innovative use of a Multi-Agent System (MAS). As LLMs like ChatGPT became essential to our everyday interactions, the need to maintain the safe use of these systems increased. This research thoroughly assessed the current security measures in place for LLM, pointed out their limitations and developed new and more effective security strategies. The core of the proposed solution was a MAS designed to ensure that all data processed by LLM met guidelines including Privacy, Confidentiality, and Ethical standards before reaching the user. The system involved …

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Theses

Increasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three …

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

Cobalt Layered Double Hydroxide As Advanced Electrode Materials For Supercapacitors And Electrocatalysis, Wafa Sultan Alaryani

Theses

Due to the high demand for sustainable energy storage options and efficient energy conversion devices, extensive research has been conducted on various electrode materials. This thesis is concerned with the fabrication of electrode material mainly using cobalt-based material, particularly cobalt layered double hydroxides (Co-LDH) with the combination of nitrogen-doped carbon nanotubes (N-CNTs) to be used in supercapacitors and oxygen evolution reactions (OERs) applications. The main objective is to enhance the electrochemical performance of supercapacitors and to develop a novel electrode material for a more stable and efficient OER in alkaline medium.

The methodology involved a one-step hydrothermal procedure to synthesize …

Detecting Data Poisoning Attacks In Medical Imaging: A Study On Chest X-Ray Classification Tasks, Akhila Abdulla Asgar

Theses

This thesis examines the vulnerability of AI medical imaging models to adversarial threats, with a specific focus on data poisoning attacks in chest X-ray classification. The study begins with a Systematic Literature Review (SLR) to assess the existing adversarial attacks and defenses in medical imaging, revealing a significant research gap in studies exploring data poisoning attacks in the medical domain. Based on our literature search, an efficient and lightweight defense, namely friendly noise defense, against data poisoning has not been investigated in medical imaging classification tasks. Hence, in this work, we investigated its effectiveness on the chest X-ray dataset, and …

Determination Of Variations In Orbital Parameters Of Sample Of Satellites Through Spacecraft Tracking, Asma Ali Alasmari

Theses

The continues increasing density of Resident Space Objects (RSOs) in Earth’s orbit highlights the crucial need for Space Situational Awareness (SSA). This thesis studies the orbital changes in two regions, active low Earth orbit (LEO) satellites and inactive geostationary Earth orbit (GEO) satellites. For LEO satellites we used publicly available data (two line element TLE from Space-Track) and ASTRIAGraph. We verified the orbital elements series and time ordered them, applied a Hampel (median+MAD) mask to semi-major axis, eccentricity vector and inclination to find outliers and exclude them from event detection. The events are then detected using time normalized slopes and …

The Simulation Of Hyperspectral Observations By The Upcoming Spacecraft "Arab Satellite 813", With The Radiative Transfer Model Sciatran, Sara Maher Alhasan

Theses

The Arab Satellite 813, which is scheduled to be launched in 2025/2026 by the UAE Space Agency, is set to increase earth observation and monitor the climate across the middle East and North Africa (MENA) region. This study uses the SCIATRAN radiative transfer model which was originally developed for the Envisat mission to simulate the hyperspectral observation specifically tailored for the satellite's payload. Using calibrations that were used by the previous research in their SCIATRAN V4.6 study and adjusting parameters like the Leaf Area Index (LAI), a simulation which agreed robustly with the benchmarks set by the previous research was …

Startup Success Forecasting Through Machine Learning: A Comprehensive Analysis Of It Startups, Khaled Abdulla Alhassani

Theses

Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success?

This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), …

Enhancing It Security Management With An Advanced Intrusion Detection System Based On Machine Learning And Explainable Ai, Hanan Aslam Al Zubaidi

Theses

The fast changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today's advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.

The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, …

How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability …

Building Confidence For Class Participation, Tamas Makany, Ivy Seow

Research Collection Lee Kong Chian School Of Business

What happens to students’ critical thinking when half the class filters their thoughts through AI? During a recent debate on AI policy in education, one student mentioned they routinely run their ideas through ChatGPT before speaking up. When I asked who else did the same, more than half the class raised their hands.

Image Captioning Through The Lens Of The Gricean Maxims: Generating Meaningful And Relevant Image Descriptions, Annika Lindh

Doctoral

Image captioning models enable us to automatically generate natural language image descriptions for previously unseen images. It combines the two fields of computer vision and natural language generation, allowing models to interpret the con tent of an image and communicate that knowledge through natural language text.

Research into image captioning has the potential benefit of reducing the gap in digital information availability between fully sighted individuals and those who are visually impaired. However, automatically generated captions often fail to provide the required level of detail and specificity to achieve this goal. Furthermore, current standard evaluation methods are insufficient at measuring …