Digital Commons Network^™

Medical Monitoring Working Group Meeting Minutes, Mike Mcanulty, Eric Hassler

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Environmental And Socioeconomic Effects Of Roundtable On Sustainable Palm Oil Certification: A Systematic Review And Meta-Analysis, Kibrom T. Sibhatu, Matin Qaim

All Peer-Reviewed Publications

Palm oil is the most widely produced and consumed vegetable oil worldwide, but its production is associated with deforestation and other environmental and social problems. Roundtable on Sustainable Palm Oil (RSPO) is the only internationally-recognized voluntary certification standard aimed at mitigating such problems. Many studies examine RSPO’s effects on particular outcomes in specific contexts, but a consolidated global assessment is lacking. Here, we systematically review the literature and identify 53 original studies covering RSPO effects in various countries of Asia, Africa, and Latin America. Where data availability permits, we also conduct meta-analysis. Results reveal that RSPO can lead to environmental …

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation. We evaluate four prominent SOLMs using various prompt strategies and parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation …

Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation seeks to recommend a bundle of related items to users to improve both userexperience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items.CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learningframework, significantly improving SOTA performance. It does, however, have two limitations: (1) the twoview formulation does not fully exploit all the heterogeneous relations among users, bundles, and items; and(2) the “early contrast and late fusion” framework is less effective in capturing user preference and difficultto generalize to …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Air: Improving Agent Safety Through Incident Response, Zibo Xiao, Jun Sun, Junjie Chen

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise. In this work, we introduce AIR, the first incident response framework for LLM agent systems. AIR defines a domain-specific language for managing the incident response lifecycle autonomously in LLM agent systems, and integrates it into the agent's execution loop to (1) detect incidents via semantic checks grounded in the current environment state and …

Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen

Research Collection School Of Computing and Information Systems

The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full-dataset poisoning and introduce transformations that break code compilability. In this paper, we introduce FunPoison, a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. FunPoison leverages reusable statement-level templates with automatic repair and conservative safety checking to ensure side-effect freedom, while a type-aware synthesis module preserves type correctness, suppresses static-analysis warnings, and …

Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji

Research Collection School Of Computing and Information Systems

Video Corpus Moment Retrieval (VCMR) requires models to efficiently retrieve and precisely locate specific moments relevant to natural language queries within a massive, untrimmed video corpus. However, existing discriminative approaches typically rely on shallow visual-textual feature matching mechanisms, which often struggle to capture fine-grained semantic differences. To address this limitation, we propose Video-GAR, a novel framework that reframes the conventional retrieval task from superficial matching to generative understanding, positing that the capability for query reconstruction evidences deep semantic comprehension. Specifically, Video-GAR orchestrates three synergistic components: To overcome the computational efficiency bottleneck, we construct a Bi-Mamba backbone that leverages the linear …

Zero-Shot Transfer Of Foundation Time-Series Forecasters To Datacenter Operational Telemetry: Mapping A Domain Nobody Gets To Study, David Pace Jr

LSU New Orleans Theses and Dissertations

This thesis asks how best to forecast real datacenter operational telemetry. Can pretrained foundation time-series forecasters do it zero-shot, or do trained classical baselines perform best? On a single-site benchmark of eight audited targets and four horizons (32 slices), the zero-shot foundation forecasters Moirai, Chronos, and TimesFM win 26 of 32 slices against multivariate classical baselines. However, the foundation wrappers operate per-channel on the target history, while classical baselines consume the full feature tensor. When the same classical models are retrained univariate, the foundation lead persists (27 of 32 slices) and a 3-seed rerun of a classical challenger reproduces it; …

The Geologic History Of The Chehalis Forearc Basin, Washington State, Usa, R. L. Moe, J. Bershaw, L. M. Staisch, A. R. Streig

Geology Faculty Publications and Presentations

The Chehalis basin is located between the Cascade arc and the Coast Range in southwest Washington State. It consists of sedimentary and volcanic rocks deposited throughout the Cenozoic and is underlain by the Siletzia terrane, a thick, large igneous province accreted in the Eocene. Here, we constrain evolution of the Chehalis basin depocenter using isochore maps derived from isostatic gravity anomalies, borehole data, and stratigraphy for several time periods: the Eocene (47.6–36.8 Ma), the Oligocene (36.8–20 Ma), and the Neogene (20–0 Ma). Our results suggest that local subsidence is driven by deformation on faults that bound and intersect the basin. …

Statistical Evaluation Of A New Orleans Historic Rainfall Dataset (1840–1893), Arielle C. Filostrat

LSU Master's Theses

This study evaluates the climatic credibility of a newly identified historical rainfall dataset for New Orleans, Louisiana, spanning 1840–1893. The New Orleans Historical Dataset (NOHD) is compared to a modern observational record from the Audubon rain gauge (1893–2024) to determine whether nineteenth-century rainfall observations are consistent with known precipitation behavior in the region. Establishing the reliability of early records is critical for extending climatological baselines and improving understanding of long-term rainfall variability.

Historical rainfall observations were first evaluated through archival review using the New Orleans Medical and Surgical Journal and other nineteenth-century meteorological records to verify notable rainfall events. Daily …

A Robust Hybrid Security Framework: Integrating Multi-Layered Text Encryption With Barcode-Based Steganography, Mohamed Sayed, Talaat M. Wahbi, Farooq Abdalwahab Haboub

BAU Journal - Science and Technology

The widespread use of the Internet is causing increasing security concerns regarding online communications. One method for achieving secure communication between authorized parties is steganography. We herein employ multilevel technologies, including compression, encryption, barcoding, and steganography to secure a secret text message. Type I multilevel steganography is used with a two-level setup. The first level uses enhanced least significant bit (secure LSB-L1) image steganography; the output is a stego-image file, the cover is an image file, and the secret data in this level is English text. The output from the first level is encrypted using the RSA algorithm, and the …

Unmasking Twitter Bots: An Applied Machine Learning Approach, Rayane El Raba’A, Layal Abu Daher

BAU Journal - Science and Technology

The rapid growth of social networks has led to increased challenges, such as fraud, cyberbullying, and the spread of automated accounts (bots). Detecting anomalies within these networks is essential to maintaining security and trust. This study explored machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Logistic Regression for anomaly detection in social networks, specifically focusing on Twitter bot identification, By applying AI-driven data mining techniques to a dataset of 37,438 Twitter bot accounts dataset, the research evaluates the effectiveness of these models in detecting unusual patterns. XGBoost achieved the highest accuracy (84.9%), with an ROA_AUC of 0.87, …

Enhanced E-Commerce Recommendation Experience With Collaborative Sentiment Analysis And Ranked Content-Based Filtering, Sandi El Zein, Farah Hamze, Amani Merhi, Lynn Noureddine, Lama Affara

BAU Journal - Science and Technology

The rise of online shopping has made informed purchasing decisions increasingly difficult, as consumers face an overwhelming number of product choices and struggle to manually evaluate specifications and user reviews. This paper presents an AI-powered, sentiment-aware product recommendation tool that effectively aligns user preferences with real-world customer feedback from online reviews. The proposed system utilized Instruct ABSA deep learning models for feature extraction and DeBERTa-v3 for sentiment analysis to turn reviews into interpretable scores that would nominate optimal products. An interactive Rasa-based chatbot interface, TopPickAI, was developed to give a seamless user experience, educate users on product features, and conversationally …

A Mathematical Model For Managing Diphtheria Outbreaks In Nigeria Via Vaccination, Enhanced Surveillance, Effective Quarantine, And Social Distancing Measures, Hycienth Ortser Orapine, Nyiutya Cephas Ine, Dekera Jacob Washachi, Ali Audu Baidu, Lubem Matthew Kwaghkor

Mathematical Modelling and Numerical Simulation with Applications

Diphtheria is a highly infectious respiratory and skin disease that poses a significant threat to global public health. In Nigeria, children and adults with low immunity remain at high risk due to recurring annual outbreaks in the last decade, with a death rate of as high as 68.8\%. This paper presents a deterministic compartmental model to evaluate the effectiveness of interventions implemented by the Nigerian government. The model's mathematical and epidemiological validity is established through proofs of non-negativity and boundedness. The basic reproduction number ($R_0$) is derived, and stability analysis confirms that the diphtheria-free equilibrium is locally and globally asymptotically …

Principal Leadership As A Predictor Of Teacher Performance: Evidence From An Indonesian Elementary School, Irminapinem Pinem, Antonius Remigius Abi, Hamela Sari Sitompul

Jurnal Pendidikan Sains

Teacher performance is a critical determinant of educational quality and student achievement, making the identification of factors that influence teacher effectiveness an important area of educational research. Among these factors, principal leadership has been recognized as a key organizational variable that shapes teachers’ professional practices and instructional performance. This study aimed to examine the relationship between principal leadership and teacher performance at State Elementary School Raya Kahean, Simalungun Regency, Indonesia. The study employed a quantitative correlational research design involving 52 teachers selected through total sampling. Data were collected using two structured questionnaires measuring principal leadership and teacher performance. The principal …

The Effect Of Emotional Intelligence And Logical-Mathematical Intelligence On Students’ Mathematical Problem-Solving Ability, Zulkyfli Siduppa, Sutji Rochaminah, Muh Hasbi, Dasa Ismaimuza

Jurnal Pendidikan Sains

Mathematical problem-solving ability is an essential competency in mathematics education because it supports students’ reasoning, critical thinking, and decision-making skills. This study aimed to examine the influence of emotional intelligence and logical-mathematical intelligence on the mathematical problem-solving ability of Grade XII students at SMA Negeri 1 Tolitoli. The study employed a quantitative survey design involving 143 students from the Mathematics and Natural Sciences (MIPA) program during the 2023/2024 academic year. Data were collected using an emotional intelligence questionnaire, a logical-mathematical intelligence questionnaire, and a mathematical problem-solving test on algebraic function limits. All instruments were validated and demonstrated satisfactory reliability, with …

Tumor-Immune Dynamics With Memory And Time Delay: A Fractional-Order Model With Ctla-4 Regulation, Mutaz Mohammad, Mohyeedden Sweidan, Alexander Trounev, Fathalla Rihan

All Works

This study develops a fractional-order tumor-immune interaction model incorporating Caputo memory effects, delayed immune activation, and CTLA-4 checkpoint regulation. The model describes the coupled dynamics of tumor cells, CD4^+ T cells, IFN-γ, and CTLA-4, and extends classical integer-order tumor-immune models by accounting for hereditary immune responses and biologically motivated latency effects. Theoretical properties, including positivity, boundedness, equilibrium structure, and fractional-order stability, are examined to establish the biological and mathematical consistency of the model. The delayed fractional system is then investigated computationally by comparing several numerical methods, including finite difference discretization, Daubechies wavelet collocation, Euler wavelet collocation, and a predictor-corrector scheme. …

Hydroclimatic Whiplash Across The Contiguous United States: Characterizing Wet–Dry Transitions In Pdsi And Phdi, James Lam, Thomas Christopher Piechota

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Hydroclimatic whiplash, characterized by rapid transitions between wet and dry conditions, poses a growing challenge for water resource management and ecosystem stability. However, the spatial and statistical characteristics of wet–dry transitions across the contiguous United States are not fully understood. This study analyzed Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) records from 344 NOAA climate divisions spanning 1895–2024. Whiplash was defined as a transition between severe drought and severe wetness within 12-month rolling windows. Statistical descriptors including lag-1 autocorrelation, variance, and skewness were evaluated for whiplash and non-whiplash windows to assess differences between them. Whiplash hotspots …

Conditionals And Modalities In Constructive Quantum Logics, Juan P. Aguilera, Guillaume Massas

Mathematics, Physics, and Computer Science Faculty Articles and Research

We investigate logics that generalize both intuitionistic logic and quantum logic. In earlier work, we introduced Ex-logic, an extension of Holliday's fundamental logic that coincides with the intersection of orthologic and the implication-free fragment of intuitionistic logic. In this paper, we add an implication connective to Ex-logic and axiomatize iEx-logic, the intersection of full intuitionistic logic and orthomodular logic with the implication connective interpreted as the Sasaki hook. As a consequence, we obtain a characterization of the lattice of logics extending iEx-logic as the product of the lattice of intermediate logics and the lattice of orthomodular logics. We also explore …

An Interactive Multi-Objective Programming Approach For Optimizing Fully Trapezoidal Spherical Fuzzy Linear Programming Problem With Application, Sultan S. Alodhaibi, Hissah Ibrahim Almuzini, Hamiden Abd El-Wahed Khalifa

Neutrosophic Systems with Applications

In this paper, a linear programming framework with completely uncertain parameters is investigated by employing trapezoidal spherical fuzzy numbers (TrSFNs). The proposed formulation incorporates a spherical fuzzy (SF) decision environment in which the optimization process simultaneously maximizes the degree of positive membership while minimizing the corresponding neutral and negative membership degrees. By utilizing the concept of the α -cut associated with TrSFNs, the original fully fuzzy linear programming problem is transformed into an interval-valued linear programming model with confidence levels. To rank and compare the resulting interval objective values, an interval ordering approach based on the decision maker's preferences—considering the …

Exploiting Uncertainty Of Computational Methodology In Optimizing User Interface In Human-Computer Interaction, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Human-computer interaction (HCI) evaluation and optimization of user interfaces (UIs) constitute a complex multi-criteria decision-making challenge, marked by conflicting evaluation dimensions, subjective expert judgments, and inherent uncertainty in user experience assessment. Traditional evaluation approaches, such as heuristic expert reviews and user satisfaction surveys, rely on sharp, binary classifications that fail to capture the gradual and overlapping nature of human cognitive and affective states. This limitation necessitates a more robust uncertainty-aware methodology that can model the true complexity of HCI evaluation. This paper proposes a hybrid mathematical model that integrates various Multi-Criteria Decision Making (MCDM) techniques of Entropy, and Simple Additive …

Recursive Neutrosophic Superhypergraphs With Illustrative Applications, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran

Neutrosophic Systems with Applications

Finite hypergraphs generalize ordinary graphs by permitting each hyperedge to join any nonempty set of vertices, and thus provide a natural model for truly multiway interactions. To represent hierarchical and multi-layer structure, SuperHyperGraphs iterate the powerset operation so that set-valued entities created at one level can be treated as vertices at higher levels. Independently, recursive hypergraphs allow edge recursion: an edge may contain not only vertices but also lower-level edges, yielding nested (and possibly self-referential) incidence controlled by a specified recursion depth. In this work we introduce and axiomatize Recursive Neutrosophic SuperHyperGraphs, a unified framework that combines vertex …

Modelling Of Dust Devil Flow Dynamics: Effects Of The Sharpness Parameter On Velocity And Pressure, Deepanshu Kumar, Hossein Jafari

Mathematical Modelling and Numerical Simulation with Applications

This work discusses the development of a straightforward model of dust devils, demonstrating a method for estimating wind speed and pressure. The current model incorporates momentum equations and the mass conservation equation for steady, axisymmetric, inviscid, and incompressible flow. In this model, the radial velocity is first considered, which is restricted in both the radial and axial directions. The sharpness parameter is also incorporated into the radial velocity formulation, as described by Vatistas model. Using the radial velocity as a foundation, we derive the azimuthal and axial velocities. The study further evaluates the pressure. Notably, for large values of the …

Dynamical Behavior, Extinction And Persistence In A Stochastic Delayed Two-Strain Epidemic Model With A Generalized Crowley-Martin Incidence Rate, Amina Allali, Dounia Bentaleb, Saida Amine

Mathematical Modelling and Numerical Simulation with Applications

This paper develops and analyzes a novel delayed stochastic SIR epidemic model with two interacting strains and general incidence functions. By establishing the existence and uniqueness of a positive global solution, the well-posedness of the model under stochastic perturbations is ensured. Extinction occurs when the stochastic reproduction number falls below unity, as demonstrated through Itô calculus and martingale convergence theorems, while persistence is guaranteed under the Crowley–Martin incidence when it exceeds one. Numerical experiments based on the Positive Preserving Truncated Euler–Maruyama (PPTEM) scheme confirm the analytical predictions and highlight the influence of time delay and noise intensity on the long-term …

Tumor–Immune Dynamics With Memory And Time Delay: A Fractional-Order Model With Ctla-4 Regulation, Mutaz Mohammad, Mohyeedden Sweidan, Alexander Trounev, Fathalla Rihan

Mathematical Modelling and Numerical Simulation with Applications

This study develops a fractional-order tumor-immune interaction model incorporating Caputo memory effects, delayed immune activation, and CTLA-4 checkpoint regulation. The model describes the coupled dynamics of tumor cells, CD4$^{+}$ T cells, IFN-$\gamma$, and CTLA-4, and extends classical integer-order tumor-immune models by accounting for hereditary immune responses and biologically motivated latency effects. Theoretical properties, including positivity, boundedness, equilibrium structure, and fractional-order stability, are examined to establish the biological and mathematical consistency of the model. The delayed fractional system is then investigated computationally by comparing several numerical methods, including finite difference discretization, Daubechies wavelet collocation, Euler wavelet collocation, and a predictor-corrector scheme. …

Stochastic Analysis Of Epidemic Size And Peak Infection In An Svir Model With Imperfect Vaccine And External Source Of Infection, María Gamboa Pérez

Mathematical Modelling and Numerical Simulation with Applications

The dynamics of an infectious disease outbreak are studied under a stochastic framework in a closed population where individuals are homogeneously mixed. A vaccination program is implemented in the community prior to the start of the epidemic. The administered vaccine is imperfect, meaning that vaccinated individuals may still become infected upon contact with infected individuals. The mathematical Susceptible-Vaccinated-Infected-Recovered (SVIR) model describing the evolution of the epidemic is formulated as an absorbing, continuous-time, three-dimensional Markov chain with compartments for vaccinated, susceptible, infected and recovered individuals. The aim of this study is to characterize two fundamental epidemiological quantities: the maximum number of …

Numerical Method For Strongly Variable-Density Flows At Low Mach Number: Flame-Sheet Regularisation And A Mass-Flux Immersed Boundary Method, Matheus P. Severino, Fernando F. Fachini, Elmer M. Gennaro, Daniel Rodríguez, Leandro F. Souza

Mathematical Modelling and Numerical Simulation with Applications

A low-Mach-number flow, in the laminar regime, has intrinsically two characteristic spatial scales for a given time scale, or two characteristic temporal scales for a given spatial scale, and these dual scales are very different due to the disparity between the flow and acoustic speed. Therefore, low-Mach-number flows impose mathematical and computational challenges in their description. Standard numerical methods for compressible flows, which are typically designed for problems with a single dominant spatial and temporal scale, require alternative approaches, such as preconditioning techniques or solvers tailored for low-Mach-number equations. The present work introduces a simplified fluid dynamics model for flows …