Physical Sciences and Mathematics Commons^™

A Note On The Hull And Linear Complementary Pair Of Cyclic Codes, Zohreh Aliabadi, Tekgül Kalayci

Turkish Journal of Mathematics

The Euclidean hull of a linear code C is defined as C ∩ C⊥ , where C⊥ denotes the dual of C underthe Euclidean inner product. A linear code with the trivial hull is called a linear complementary dual (LCD) code. Apair (C,D) of linear codes of length n over the finite field Fq is called a linear complementary pair (LCP) of codes ifC ⊕ D = Fnq. More generally, a pair (C,D) of linear codes of the same length over Fq is called a linear ℓ -intersectionpair of codes if C ∩D has dimension ℓ as a vector space …

Low-Emission Beef Production In The Southern Rangelands Of Western Australia: An Analysis Of Herd Structure And Stocking Rate Experiencing Droughts, Christophe D'Abbadie

Animal Production and Livestock Research Articles

Reconciling profitable cattle production with rangeland health and reduced emissions is a key challenge in the southern rangelands of Western Australia (WA). Stocking rate and herd structure selection are crucial decisions to achieve this balance. This study assessed the emission profiles of three contrasting herd structures (weaner production, live export, and slaughter production), and three stocking rates within a herd–carbon accounting modelling framework. The analysis considers the impact of varying drought frequencies on these cattle production systems. Herd models were developed for the semiarid southern WA rangelands. Stocking rates were set at 100%, 80% and 66% of the government recommended …

Computational And Experimental Advances In Nuclear Magnetic Resonance For High Resolution Structures, Ryan Toomey

Theses and Dissertations

Since its inception, nuclear magnetic resonance (NMR) has been a valuable tool for determining chemical structure. In recent years, the field of NMR has been advanced forward by the ability to calculate theoretical parameters with increasing accuracy and efficiency. These calculations are compared to experimental data to produce high resolution structures. The progression of these applications has been made possible by improved instrumentation, data processing methods, probe and experiment design, better quality functionals and basis sets, as well as increased computational power. This research is especially relevant with the emergence of artificial intelligence, which has great potential to expedite steps …

Evaluating The Cost Of Classifier Discrimination Choices For Iot Sensor Attack Detection, Mathew Nicho, Brian Cusack, Shini Girija, Nalin Arachchilage

All Works

The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but …

Errata: The Product Of Distributions And Stochastic Differential Equations Arising From Powers Of Infinite Dimensional Brownian Motions, Un Cig Ji, Hui-Hsiung Kuo, Hara-Yuko Mimachi, Kimiaki Saito

Journal of Stochastic Analysis

No abstract provided.

Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams

Faculty Publications

A numerical method for evolving the nonlinear Schrödinger equation on a coarse spatial grid is developed. This trains a neural network to generate the optimal stencil weights to discretize the second derivative of solutions to the nonlinear Schrödinger equation. The neural network is embedded in a symmetric matrix to control the scheme’s eigenvalues, ensuring stability. The machine-learned method can outperform both its parent finite difference method and a Fourier spectral method. The trained scheme has the same asymptotic operation cost as its parent finite difference method after training. Unlike traditional methods, the performance depends on how close the initial data …

Supplementary Files For "Impact Of Snow Accumulation On Structural Integrity: Present And Future Perspectives", Kenneth Pomeyie, Brennan Bean

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Evaluating the impact of weight exerted by settled snow (i.e., snow load) on structures poses numerous statistical challenges, including missing data, biased distribution parameters, and the influence of climate change. This dissertation aims to address challenges related to the use both direct and indirect measurements of snow load (or equivalently, snow water equivalent), as well as the anticipated impact of climate change on future extreme snow loads. The first paper within this dissertation investigates short-term snow loads by comparing various techniques for estimating extreme values of short-term snow accumulations. Additionally, the first paper includes a comparative analysis of short-term and …

Design And Implementation Of An Opioid Scorecard For Hospital System-Wide Peer Comparison Of Opioid Prescribing Habits: Observational Study, Benjamin Slovis, Soonyip Huang, Melanie Mcarthur, Cara Martino, Tasia Beers, Meghan Labella, Jeffrey Riggio, Edmund Pribitkin

Jefferson Hospital Staff Papers and Presentations

BACKGROUND: Reductions in opioid prescribing by health care providers can lead to a decreased risk of opioid dependence in patients. Peer comparison has been demonstrated to impact providers' prescribing habits, though its effect on opioid prescribing has predominantly been studied in the emergency department setting.

OBJECTIVE: The purpose of this study is to describe the development of an enterprise-wide opioid scorecard, the architecture of its implementation, and plans for future research on its effects.

METHODS: Using data generated by the author's enterprise vendor-based electronic health record, the enterprise analytics software, and expertise from a dedicated group of informaticists, physicians, and …

Minimal Separating Sets In Surfaces, Christopher Nelson Aagaard

Dissertations and Theses

Given a connected topolgical space X, we say that L ⊆ X is a minimal separating set if removing L from X gives a disconnected surface, butremoving any proper subset of L leaves the surface connected. We classify which embeddings of topological graphs are minimal separating in an orientable surface X with genus g, and construct a computer program to compute the number of such embeddings, and the number of topological graphs which admit such an embedding for g ≤ 5.

Analyzing Student Prompts And Their Effect On Chatgpt’S Performance, Ghadeer Sawalha, Imran Taj, Abdulhadi Shoufan

All Works

Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link between these strategies and the model’s response accuracy, the existence of individual prompting tendencies, and the impact of gender in this context. Our students used ChatGPT to solve five problems related to embedded systems and provided the solutions and the conversations with this model. We analyzed the …

Integrated Environmental Vulnerability Assessment And Adaptation Strategies For Coastal Areas Under Sustainable Development, Lien-Kwei Chien, Yu-Chi Li, Chia-Feng Hsu

Journal of Marine Science and Technology

This research focuses on the holistic management and environmental vulnerability of coastal areas in Taiwan within the framework of sustainable development. With economic and social growth gravitating towards coastal regions, the strain on the natural environment is increasing. Therefore, discovering a balance between economic progress and environmental conservation is paramount. To decipher the vulnerability of Taiwan's coastal zones, this study first defines ‘Integrated Environmental Vulnerability of Coastal Areas.’Key vulnerability factors were identified across environmental, social, and economic dimensions. Seven core determinants were determined using the Fuzzy Delphi method: biodiversity, coastal erosion, water pollution, population density, population aging, land utilization, and …

Evalution Of The Ability To Infer Tilt Angle And Size Distributions Of Fish Using A Broadband Scientific Echosounder Based On Simulation, Jing Liu

Journal of Marine Science and Technology

The biological information, such as species, size, and tilt angle, is crucial for converting the echo data into biomass information in acoustic surveys. Typically, the information can be obtained through trawl net sampling or underwater camera observations. However, both methods have some limitations. To overcome these limitations, scientists have utilized inversion methods with multi-frequency and broadband echosounders to derive biological information about fish, plankton, and krill. However, evaluating the reliability and accuracy of these inversion methods has been challenging due to the difficulty in obtaining accurate biological information. In this study, a numerical simulation method was used to generate fish …

A Forest Management Evaluation System For Small Private Forest Landowners, Pete Bettinger, Taeyoon Lee, Krista Merry, Daniel Drummond

The Journal of Extension

When small private forest landowners have a need to address jointly economic and sustainability objectives, efficiency in both respects becomes important given limitations on the land, budget, time, and other resources that are available. The suite of forest management options available to a landowner may be vast and complex, therefore a tool to assist and inform their potential management activities can be of value. The eYield model was developed as an application (app) to assess forest management options on many different computing devices, from cellphones to desktop computers. Within eYield, a person can define a management situation, specify prices and …

Overview For Exploring The Multisensory Landscape, Brent Chamberlain, Richard Smardon

Landscape Architecture and Environmental Planning Faculty Publications

Land use change has had a fundamental impact on the livelihoods of people throughout the world. This Special Issue focuses on the research being conducted at the intersection of this land use change and the importance of maintaining landscapes that enrich humanity and our engagement with nature. Within this Special Issue, we explored the value of landscapes that heighten the senses. Visual Resource Stewardship is an area of research that closely aligns with understanding how changes in the environment may be perceived and experienced. Understanding the tools, processes, and theories involved helps us to better understand how land use change …

Locating Diversity In Reservoir Computing Using Bayesian Hyperparameter Optimization, Whitney Lunceford

Theses and Dissertations

Reservoir computers rely on an internal network to predict the future state(s) of dynamical processes. To understand how a reservoir's accuracy depends on this network, we study how varying the networ's topology and scaling affects the reservoir's ability to predict the chaotic dynamics on the Lorenz attractor. We define a metric for diversity, the property describing the variety of the responses of the nodes that make up reservoir's internal network. We use Bayesian hyperparameter optimization to find optimal hyperparameters and explore the relationships between diversity, accuracy of model predictions, and model hyperparameters. The content regarding the VPT metric, the effects …

Density-Dependence Inside A Marine Protected Area Increases Natural Mortality And Stunts The Growth Of A Spiny Lobster, Simon De Lestang, Emma Jade Tuffley

Fisheries Research Articles

Sustainable fisheries management often requires the modelling of stocks under unfished conditions, when the influence of population densities on animal growth and mortality can be substantial. This can be especially true for species such as spiny rock lobster, which are very habitat specific. Using western rock lobster (Panulirus cygnus) tag-recapture data from adjacent and similar fished and unfished areas, the key life history parameters of natural mortality and growth were examined and compared under different population density scenarios. In an area representative of virgin biomass levels, lobsters exhibited reduced growth rates and a substantially higher rate of natural mortality …

Supplementary Files For: Quantifying The Impact Of Rain-On-Snow Induced Flooding In The Western United States, Emma Watts, Brennan Bean

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Serious flooding can happen when rain falls on snow, which we call a rain-on-snow (ROS) event. Increasing our understanding of the behavior of floods resulting from ROS events can help us design better systems to manage flood water and prevent it from causing damage. This thesis explores how ROS events affect streamflow in the Western United States by examining the weather conditions that precede a streamflow surge. We classify stream surges as ROS or non-ROS induced based on these weather conditions, which helps us separate floods caused by ROS events from those caused by other factors. By comparing these different …

Tracing Atlantic Sea Scallops Using Radio Frequency Identification (Rfid) Technology, Will Shoup, David Rudders, Jonathon Peros

The Journal of Extension

Radio Frequency Identification (RFID) represents a technology that has the potential to enhance many aspects of the Atlantic sea scallop fishery. Driven by fishery management and market forces, fishery product traceability benefits fisheries managers, consumers, and fishermen. In order to demonstrate the capabilities of RFID technology in the scallop fishery, a solution is proposed that would help establish clear Chain of Custody (CoC) so that the scallop supply chain can be better documented. Implementation and acceptance of any new technology will hinge on effective communication and extension efforts that can leverage the multi-benefit aspects of adopting RFID into the fishery.

Cyberattack Detection And Handling For Neural Network-Approximated Economic Model Predictive Control, Jihan Abou Halloun, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Cyberattacks on control systems can create unprofitable and unsafe operating conditions. To enhance safety and attack resiliency of control systems, cyberattack detection strategies can be developed. Prior work in our group has sought to develop cyberattack detection strategies that are integrated with an advanced control formulation known as Lyapunov-based economic model predictive control (LEMPC), in the sense that the controller properties can be used to analyze closed-loop stability in the presence or absence of undetected attacks. In this work, we consider neural network-approximated control laws, concepts for mitigating cyberattacks on such control laws, and how these ideas elucidate concepts in …

Profit Considerations For Nonlinear Control-Integrated Cyberattack Detection On Process Actuators, Keshav Kasturi Rangan, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Prior research from our group developed a control-integrated active actuator cyberattack detection strategy. This strategy continuously probed for cyberattacks by updating target steady-states at every sampling time and then moving the process state toward these over the subsequent sampling period. Attacks were fagged if a Lyapunov function around the target steady-state did not decrease over a sampling period. This strategy had the benefit of ensuring safety of the process until an attack was detected. However, the continuous probing for attacks could decrease profit from the process compared to not probing for the attacks, which could limit the attractiveness of the …

Lyapunov-Based Cyberattack Detection For Distinguishing Between Sensor And Actuator Attacks, Dominic Messina, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Control-theoretic cyberattack detection strategies are control strategies where control theory can be used in the design of the detection policies and analysis of stability properties with and without cyberattacks. This work provides a step toward understanding how to diagnose cyberattacks using control-theoretic cyberattack detection mechanisms. Specifically, we analyze the conditions under which a control-theoretic cyberattack detection strategy developed in our prior work to handle detection of simultaneous actuator and sensor attacks can be extended to distinguish between whether attacks are occurring on sensors or actuators. We present and evaluate heuristic concepts for attempting to diagnose sensor attacks; these again demonstrate …

Angular Momentum Flow Without Anything Carrying It, Yakir Aharonov, Daniel Collins, Sandu Popescu

Mathematics, Physics, and Computer Science Faculty Articles and Research

Transfer of conserved quantities between two remote regions is generally assumed to be a rather trivial process: a flux of particles carrying the conserved quantities propagates from one region to another. However, we demonstrate a flow of angular momentum from one region to another across a region of space in which there is a vanishingly small probability of any particles (or fields) being present. This shows that the usual view of how conservation laws work needs to be revisited.

Methane Fluxes In Tidal Marshes Of The Conterminous United States, Ariane Arias-Ortiz, Jaxine Wolfe, Scott D. Bridgham, Sara Knox, Gavin Mcnicol, Brian A. Needelman, Julie Shahan, Ellen J. Stuart-Haëntjens, Lisamarie Windham-Myers, Patty Y. Oikawa, Dennis D. Baldocchi, Joshua S. Caplan, Margaret Capooci, Kenneth M. Czapla, R. Kyle Derby, Heida L. Diefenderfer, Inke Forbrich, Gina Groseclose, Jason K. Keller, Cheryl Kelley, Amir E. Keshta, Helena S. Kleiner, Ken W. Krauss, Robert R. Lane, Sarah Mack, Serena Moseman-Valtierra, Thomas J. Mozdzer, Peter Mueller, Scott C. Neubauer, Genevieve Noyce, Katrina V. R. Schäfer, Rebecca Sanders-Demott, Charles A. Schutte, Rodrigo Vargas, Nathaniel B. Weston, Benjamin Wilson, J. Patrick Megonigal, James R. Homquist

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Methane (CH4) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre-industrial times. Wetlands account for a large share of global CH4 emissions, yet the magnitude and factors controlling CH4 fluxes in tidal wetlands remain uncertain. We synthesized CH4 flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH4 emissions. This effort included creating an open-source database of chamber-based GHG fluxes (https://doi.org/10.25573/serc.14227085). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH4 …

Data Analysis On Predicting The Top 12 Fantasy Football Players By Position, Alan Abadzic, Jacquelyn Cheun, Milan Patel

SMU Data Science Review

Fantasy football enthusiasts rely on rankings populated by their platform of choice to draft winning teams and make strategic roster decisions. This study presents a comprehensive analysis of player performance data to forecast the top 12 fantasy points performers per position for the upcoming season. Leveraging machine learning techniques and historical data, our model identifies key performance indicators and trends to inform player evaluations. Insights gleaned from positional trends, breakout candidates, risk assessment, and matchup analysis offer a competitive edge. By addressing limitations, ethical considerations, and avenues for future research, this study contributes to the advancement of fantasy sports analysis …

Enhancing Imputation Accuracy: A Multi-Faceted Approach For Missing Data In Chicago Arrest Records, Steve Bramhall, Jae Chung, Nicholas Mueller

SMU Data Science Review

This paper introduces a novel approach to enhance the imputation process for missing data, utilizing crime records from Chicago with arrests as the target feature. Robust imputation techniques are crucial in the era of burgeoning datasets for generating reliable insights. Our core objective is to present an innovative method that improves imputation techniques, augmenting model performance and bolstering the reliability of analytical outcomes. Leveraging numeric crime data, we establish a Gradient Boosting (GBM) baseline model, then introduce ensemble methods including Random Forest and Decision Trees for further refinement. By systematically exploring multiple imputation processes, we establish a baseline for comparative …

Geospatial Temporal Crime Prediction Using Convolution And Lstm Neural Networks: Enhancing The Las Vegas Cardiff Model, Corey D. Holmes, Christian Orji, Chris Papesh

SMU Data Science Review

According to the Department of Justice, more than half of violent crimes go unreported to law enforcement in the United States (Kollar et al., 2018). This data gap reduces the opportunity to implement proven solutions in the areas with the greatest need. In 1996, Dr. Shepherd developed the Cardiff Model with the aim of bringing together hospitals, law enforcement, and community leaders through the sharing of data. We partnered with ongoing efforts to implement the Cardiff Model in Las Vegas, Nevada. Our goal was to provide a geospatial temporal model that can predict the next 30 days of crime. By …

Rethinking Retrieval Automated Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez

SMU Data Science Review

This study explores the utilization of Retrieval Augmented Fine-Tuning (RAFT) to enhance the performance of Large Language Models (LLMs) in domain-specific Retrieval Augmented Generation (RAG) tasks. By integrating domain-specific information during the retrieval process, RAG aims to reduce hallucination and improve the accuracy of LLM outputs. We investigate the use of RAFT, an approach that enhances LLMs by incorporating domain-specific knowledge and effectively handling distractor documents. This paper validates previous work, which found that RAFT can considerably improve the performance of Llama2-7B in specific domains. We also expand upon previous work into new state-of-the-art open-source models and other datasets with …

Enhancing Shap With Multi-Core Parallelization And Distributed Computation, Matthew David, William Jones, Hayley Horn

SMU Data Science Review

In recent years, the adoption of complex machine learning algorithms, often perceived as “black box” models, has grown exponentially across various disciplines. However, the lack of understanding regarding how these models come to their predictions often fosters skepticism and mistrust. In response to the demand for transparency and interpretability, Explainable AI techniques, such as SHapley Additive exPlanations (SHAP), have emerged as powerful tools for comprehending and trusting these algorithms. However, SHAP has an exponential computational demand O( x2 ), where x is the number of features. This becomes increasingly problematic with the larger datasets standard in most industries. Many frameworks …

Performance Studies Of An Axial Flow Waterjet Pump Using An Unsteady Reynolds-Averaged Navier-Stokes Model, Stephen E. Monroe, Junfeng Wang, Chunlei Liang

Northeast Journal of Complex Systems (NEJCS)

In this study, an Unsteady Reynolds-Averaged Navier-Stokes (URANS) model is demonstrated its suitability for studying the flow and performance of open marine propellers and waterjet pumps. First, the accuracy of the URANS model is validated by studying turbulent flow past counter-rotating propellers (CRPs). Specifically, experimental data from Miller (1976) is employed for comparison against the URANS results. Subsequently, URANS is used to study the flow and performance of an Office of Naval Research (ONR) axial flow waterjet pump (AxWJ-2). Due to the large number of degrees of freedom for both simulations, parallel computations over 80 cores are performed. For the …

A Marine Knowledge System For Ocean Affairs: Integrating Data, Evaluating Usage, And Enabling Sustainable Marine Management, Yu-Jen Pan

Journal of Marine Science and Technology

This study presents the evolution and assessment of the Marine Knowledge Education System (MKES), designed to improve user acceptance among students in professional marine science courses in Taiwan. The MKES leverages real-world maritime cases from the General Coast Guard Administration and is built upon existing technologies like cloud services, social networks, and data analysis tools. The technology acceptance model (TAM) provides the theoretical underpinning for the assessment of user confidence. Data was collected from 190 participants through purposive sampling. Path analysis confirmed all hypothesized relationships within the TAM with statistical significance (p < 0.001). Additionally, paired-sample t-tests revealed a significant increase in student acceptance of the MKES after integrating it into the marine science curriculum. These findings underscore the capacity of the MKES as a digital learning tool to enrich course pedagogy and improve student learning outcomes, thereby offering valuable support in advancing the education of professional marine managers.