Digital Commons Network^™

Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback

Doctoral Dissertations and Master's Theses

College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …

Modeling And Numerical Analysis Of The Cholesteric Landau-De Gennes Model, Andrew L. Hicks

LSU Doctoral Dissertations

This thesis gives an analysis of modeling and numerical issues in the Landau-de Gennes (LdG) model of nematic liquid crystals (LCs) with cholesteric effects. We derive various time-step restrictions for a (weighted) $L^2$ gradient flow scheme to be energy decreasing. Furthermore, we prove a mesh size restriction, for finite element discretizations, that is critical to avoid spurious numerical artifacts in discrete minimizers that is not well-known in the LC literature, particularly when simulating cholesteric LCs that exhibit ``twist''. Furthermore, we perform a computational exploration of the model and present several numerical simulations in 3-D, on both slab geometries and spherical …

Reducing Nursing Documentation Burden: Evaluation Of An Electronic Health Record Optimization Plan, Jessica Collins

DNP Projects

Background: UK HealthCare transitioned to a new enterprise electronic health record (EHR) system, offered by Epic Systems Corporation, in June 2021. Approximately 2,000 inpatient nurses use the EpicCare Inpatient Module in the 1,086 licensed bed facilities. Compared to other academic medical centers, UK HealthCare nurses take more time documenting in this EHR inpatient module’s Basic Assessment Flowsheet (documentation burden) and have a longer delay between assessment and documentation (timeliness) potentially contributing to nursing dissatisfaction with using this new EHR.

Purpose: The purpose of this project was to evaluate the effectiveness of a phase of the Epic Nurse Well-Being Project, a …

Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath

Electronic Theses and Dissertations

In this thesis, the Weighted Newton-Raphson Method (WNRM), an innovative optimization technique, is introduced in statistical supervised learning for categorization and applied to a diabetes predictive model, to find maximum likelihood estimates. The iterative optimization method solves nonlinear systems of equations with singular Jacobian matrices and is a modification of the ordinary Newton-Raphson algorithm. The quadratic convergence of the WNRM, and high efficiency for optimizing nonlinear likelihood functions, whenever singularity in the Jacobians occur allow for an easy inclusion to classical categorization and generalized linear models such as the Logistic Regression model in supervised learning. The WNRM is thoroughly investigated …

Optimizing Metal 3d Printing, Abram Brown, Lauren Novak

Williams Honors College, Honors Research Projects

Partnered with Lauren Novak we are doing our senior design project on optimizing metal 3D printing with the company nVent. A brief scope of the project is that nVent uses a metal 3D printer to create tooling that is used in a hydraulic pressed and used to create iterations of prototypes during the design process. We will be researching how to decrease the amount the time of the 3D print while doing load tests with v­blocks, wiping tools, and a rib tool. The tests will be on the tooling made using different parameters including infill density, infill geometry, wall thickness, …

Basins Of Attraction And Metaoptimization For Particle Swarm Optimization Methods, David Ma

Honors Projects

Particle swarm optimization (PSO) is a metaheuristic optimization method that finds near- optima by spawning particles which explore within a given search space while exploiting the best candidate solutions of the swarm. PSO algorithms emulate the behavior of, say, a flock of birds or a school of fish, and encapsulate the randomness that is present in natural processes. In this paper, we discuss different initialization schemes and meta-optimizations for PSO, its performances on various multi-minima functions, and the unique intricacies and obstacles that the method faces when attempting to produce images for basins of attraction, which are the sets of …

An Unsupervised Machine Learning Algorithm For Clustering Low Dimensional Data Points In Euclidean Grid Space, Josef Lazar

Senior Projects Spring 2024

Clustering algorithms provide a useful method for classifying data. The majority of well known clustering algorithms are designed to find globular clusters, however this is not always desirable. In this senior project I present a new clustering algorithm, GBCN (Grid Box Clustering with Noise), which applies a box grid to points in Euclidean space to identify areas of high point density. Points within the grid space that are in adjacent boxes are classified into the same cluster. Conversely, if a path from one point to another can only be completed by traversing an empty grid box, then they are classified …

Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora

Computer Science and Engineering Theses

This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while …

Assessing Carbon Sequestration Of Mixed Hardwood Forests Through Optimizing Harvesting Strategies And Biomass Utilization For Biochar, Bibek Aryal

Graduate Theses, Dissertations, and Problem Reports

This study investigated the long-term carbon stock of central Appalachian mixed hardwood forests under several harvesting strategies. The strategies were optimized to maximize both long-term carbon sequestration and timber supply during harvest using Mixed-Integer Linear Programming (MILP) models. Clear-cut (CC), Partial cut (PC), and mixed harvesting methods to unharvested conditions over 190 years were studied. Initially, harvested forests showed lower sequestration rates than unharvested forests but eventually surpassed them, with CC showing the highest rates over time. Younger forests, particularly those aged 85 to 130 years, exhibited peak carbon sequestration rates. Regarding carbon stock, the unharvested scenario initially had the …

Discrete Event Simulation (Des) In Swapping/Charging Of Battery Electric Vehicles In Underground Mining, Albert Einstein Amponsem

Masters Theses

"With climate change concerns escalating, international agreements such as the Kyoto Protocol, the US Presidential Policy, and the Paris Agreement aim to reduce greenhouse gas (GHG) emissions, targeting significant reductions by 2050. The mining sector, a notable contributor to GHG emissions primarily through diesel-powered material haulage, emits approximately 68 million tons of CO₂ annually. Transitioning to Battery Electric Trucks (BETs) presents a viable mitigation strategy by replacing diesel trucks with electric alternatives, thus eliminating CO₂ emissions.

However, the effectiveness of BETs hinges on optimized battery swapping and charging procedures. This study employs Discrete Event Simulation (DES), a computational …

Dig Limits Optimization Using Binary Integer Linear Programming Method In Open Pit Mines, Hussam Naif Altalhi

Masters Theses

"Dig limits optimization is the process for classifying different materials (e.g., ore, stockpile material, and waste) into appropriately sized contiguous zones for open pit mining. The efficient determination of dig-limits is crucial for profitable and sustainable resource extraction in mining. Previous research has focused on defining dig-limits manually or using optimization approaches, but these methods are limited to only handling two material destinations (ore and waste). Thus, there is a need for operations research methods that consider the selectivity of mining equipment and can optimize dig-limits for metal mining operations with more than two material destinations. Consequently, the objective of …

Identifying The Shortest Log Trucking Routes And Optimizing Those Constrained By Low-Weight Bridges In Mississippi, Swagat Attreya

Theses and Dissertations

Timber haulage in Mississippi incurs the greatest portion of logging expenses because of a myriad of closed and posted (restricted) bridges. This study utilized Dijkstra's algorithm method in ArcGIS Pro to derive 129 feasible shortest optimal trucking routes between 46 harvest sites and 32 softwood sawmills in Mississippi. Among these routes, 30 of them had restricted bridges along the way; however, only 13 viable alternative routes were identified due to distance and weight restrictions. The additional trucking distance for alternative routes ranged between 1.5 to 12.9 miles, whose effect on transportation cost was determined using a Mixed Integer Linear Programming …

Economy Of Scale Of Energy Intensity In Aquifer Storage And Recovery (Asr), Alyson Haley Rapp

Theses and Dissertations

More water utilities are adopting Aquifer Storage and Recovery (ASR) to balance long-term water supply and demand. Due to large implementation and operation costs, ASR projects need to be optimized, particularly for energy use, which is a major operating expense. This study examines the relationships among energy use, recharge, and recovery at two ASR projects in the western United States. The major finding is an economy of scale for recovery processes, but not for gravity-fed recharge processes. The economy of scale found is as follows: the energy intensity recovered decreases with volume. This suggests it is more energy-efficient to recover …

Imerys: Tube Mill Optimization Project, Ryan Waltman, Dalton Beasley, Dyson Beasley, Tristan Mcmichael

Senior Design Project For Engineers

The Tube Mill Optimization Project is in partnership with Imerys for Tube Mill 81 at their Marble Hill site in Georgia. Tube Mill 81 is a dry ball mill that operates 24/7 and makes an intermediary product for Plant 3. Tube Mill 81 needs quality improvement and a production rate increase to meet demand. Imerys’s quality specification is between a particle size of 12-18 microns and an acceptable production rate of 5 tons per hour. This project focuses on the development and implementation of three solutions: increase the amps on the separator to increase production, replace missing classifier blades in …

Extremum Seeking Control Algorithms For Extremely High Frequency Antenna System Pointing, Scott Shore

Mechanical Engineering ETDs

The research examines the application of extremum seeking control (ESC) algorithms to ground station antenna pointing for extremely high frequency (EHF) communication systems. ESC algorithms search for local maxima or minima by locating where an objective function gradient goes to zero. With wireless communication expanding into higher frequencies, the ground station pointing requirement is increasing. ESC presents a method which utilizes available equipment and information to perform ground station pointing. Additionally, ESC algorithms do not rely on assumptions or approximations needed for other techniques. The dissertation demonstrates ESC algorithm feasibility for the ground station pointing problem, benchmarks the ESC algorithms …

Cost Minimizing Energy Management Control Scheme For Microgrids Considering Dynamic Electricity Prices, Levi T. Miller

All Graduate Theses and Dissertations, Fall 2023 to Present

As countries develop and technology improves, the world is using more energy than ever before. This fact along with several other political, social, and economic factors has resulted in simultaneous energy and climate crises. A partial solution to both problems is bringing clean energy sources of electricity closer to the customers who use that energy. A microgrid is a smaller version of the national electric grid where smaller electricity generators are networked with local consumers and controlled independently of the main grid. Because control of electricity sources and loads are transferred to local controllers, the flexibility with which they can …

Optimization Of Hydraulic Cylinder Positioning For Wind Tower Hydraulic Erection System, Anbesh Rawal

Master's Theses

Wind produced electricity is a rapidly growing field with wind towers serving as critical components. Conventionally, special cranes are commonly used for wind tower erection. This study explores the use of hydraulic cylinders for small-scale wind tower erection. The thesis aims to find the optimal position of hydraulic cylinder connections in a hydraulic erection system for safe and energy-efficient tower erection and retraction and to enhance the performance and longevity of the system.

Structural analysis was conducted to investigate various hydraulic cylinder positioning configurations, ensuring minimal force exertion while maintaining structural integrity. The study included the selection of locally available …

Deep Reinforcement Learning For The Design Of Structural Topologies, Nathan Brown

All Dissertations

Advances in machine learning algorithms and increased computational efficiencies have given engineers new capabilities and tools for engineering design. The presented work investigates using deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to design 2D structural topologies. Three unique structural topology design problems are investigated to validate DRL as a practical design automation tool to produce high-performing designs in structural topology domains.

The first design problem attempts to find a gradient-free alternative to solving the compliance minimization topology optimization problem. In the proposed …

Utilization Of Integer Programming For Scheduling Maintenance At Nuclear Power Plants, Timothy Gallacher

Doctoral Dissertations

This thesis develops a thought that naturally explores three specific motifs for solving the complexities of scheduling maintenance at Nuclear Power Plants (NPP). The first chapter of this paper will develop the initial thought around creating a schedule for a given work week, including all the various constraints inherent to this problem. Such constraints include but are not limited to personnel availability, allowable component out-of-service time, and the Plant Risk Assessment. The objective function being to minimize the total cost of worker’s compensation for that given week.

The second chapter addresses the question of whether this simple schedule can be …

On The Development Of A Sensing System Methodology To Evaluate The Viscoelastic Properties Of Soft Tissues As A Means Of Disease Prognosis, Shashank S. Kumat

Mechanical and Aerospace Engineering Dissertations

Identification of tissue viscoelastic properties could provide valuable information for assessing its healthiness or disease state. Current technologies present challenges to access and perform localized tissue assessment in confined spaces in the human body through contact indentation/palpation. As such, there is a need for a diagnostic system capable of measuring tissue relaxation response at the local site by accessing the tissue through a natural orifice. This dissertation presents a strain gauge-based uniaxial micro-force sensor, part of the aforementioned system, capable of measuring tissue response data in confined human space environments. A sensing system design methodology is developed and presented. The …

Parametric Optimization Of A Wing-Fuselage System Using A Vorticity-Based Panel Solver, Chino Cruz

Master's Theses

Aerodynamic topology optimization is a useful tool in the aerodynamic design pro-
cess, especially when looking for marginal gains within a design. One example is
a turboprop racer concept aircraft that is designed with the goal of breaking world
speed records. An optimization framework was developed with the intention of later
being applied to this design. In the early design stages, the optimization framework
must focus on quicker methods of drag estimation, such as a panel codes. The large
number of design variables in topology optimization can exponentially increase func-
tion evaluations and thus computational cost. A vorticity-based panel solver …

Surrogate-Assisted Simulation-Optimization Framework For Groundwater Management In A Multi-Aquifer System, Melika Mani

LSU Master's Theses

Uncontrolled groundwater exploitation can lead to aquifer depletion, land subsidence, and saltwater intrusion. Effective groundwater management is challenging due to the intricate nature of subsurface hydrogeology and spatiotemporally variable pumping, especially in a multi-aquifer system. To ensure sustainable withdrawal, multi-objective optimization is an effective tool for balancing management goals and drawdown effects. However, running simulation-optimization using detailed groundwater models is computationally expensive, pushing decision-makers to decide based on limited scenarios. In this study, a hydrogeological framework was constructed for the Capital Area, Louisiana, allowing for individual assessment of each unit to better understand each aquifer's condition. Moreover, a surrogate-assisted simulation-optimization …

Ai Application In Architecture In Uae: Developing And Testing Advanced Optimization Model For A Parametric Shading Structure As A Retrofit Strategy Of A Midrise Residential Building Façade In Downtown Abu Dhabi, Anwar Ghaleb Ahmad

Theses

Artificial intelligence is a phenomenon that influences every aspect of our lives. AI applications have already started to change the methods in different disciplines. Architecture is one of the disciplines that is highly affected by the developments of AI technologies. With the United Arab Emirates heading to employ new technology to lead the country and region development, it is important to explore and develop the application of AI in the strategic disciplines of the country in which the built environment is essential. This study aimed to develop and evaluate an advanced model script (to be used as a tool) using …

A Globalized Optimization Schema For Automated Fiber Placement Processing Parameters, Matthew John Godbold

Theses and Dissertations

Automated Fiber Placement is an advanced manufacturing technique for industrial-scale composite structures. Advanced robotics coupled with composite manufacturing results in faster and more consistent results than previously obtained through hand layup. The complexity and interconnectedness of the automated fiber placement process provides a difficult challenge for traditional modeling techniques. Modeling within automated fiber placement currently utilizes physics-based modeling to inform the translation of a design to a manufacturing plan. The intricacy of the automated fiber placement process dictates that attempts at modeling or optimizing these processes are often limited in their scope. Physics-based modeling for manufacturing typically involves numerous interacting …

Reduced Bias User Preference Methods For Determining The Pareto-Optimal Solution Point, Dylan Thomas Johnson

Master's Theses (2009 -)

Engineering design is filled with tradeoffs between competing objectives such as performance, mass, cost, and schedule. A designer must navigate these complex multi-objective problems and deliver the right solution for their application. Multi-objective optimization techniques are powerful and widely used; however, a key drawback to these techniques is that they often output a set of equivalent solutions called the Pareto Front. The designer must perform an additional multi-objective down selection on the Pareto Front to determine a single Pareto-optimal solution point for their design. Existing Pareto Front processing techniques either use traditional infinitely adjustable weights, which can yield results that …

Simulation-Based Optimization Of A Dc Microgrid: With Machine-Learning-Based Models And Hybrid Meta-Heuristic Algorithms, Tyler Van Deese

Theses and Dissertations

The field of economic dispatch (ED) focuses on optimizing power flow in a power system to minimize costs. It has the potential to significantly enhance system effectiveness, and efficiency, and reduce operating costs. Various techniques have been employed to tackle this problem, each with its own strengths and weaknesses. One promising approach is simulation-based optimization (SBO), which allows for accurate modeling of system interactions and improved representation of expected results. However, SBO requires running numerous simulations to identify an optimal solution, and there is a possibility of not achieving the global optimum. This work aims to address these challenges using …

Water Quality Monitoring And Mapping Using Rapidly Deployable Sensor Nodes, Mohamed Abdelwahab

Theses and Dissertations

Efficient and continuous monitoring of water quality parameters plays a pivotal role in responding to pollution incidents and ensuring the safety of both human consumption and ecological resources. This research introduces an affordable and dependable in-situ water quality sensor package designed for seamless continuous monitoring, providing essential data to facilitate informed decision-making in water resource management. The sensor package enables comprehensive on-site assessment of key water characteristics, including pH, temperature, turbidity (measured in NTU), and total dissolved solids (TDS, measured in ppm). Spatial interpolation techniques, specifically Kriging, are employed to extrapolate variable values at unobserved locations based on nearby measurements. …

Early Development Of C3ar1-Targeting Chimeric Antigen Receptor T Cells For The Treatment Of Glioblastoma Multiforme, Cameron Fraser

Electronic Theses, Projects, and Dissertations

Glioblastoma multiforme is the most aggressive type of glioma, demonstrating extremely low long-term survival despite modern therapies. Chimeric antigen receptor T cells have shown extreme levels of success in the treatment of B cell lymphomas through persistent anti-tumor activity. Prior research has demonstrated the therapeutic potential in targeting the C3a-C3aR1 pathway as it acts in an autocrine loop, maintaining the proliferation and survival of cancer stem cells within the tumor. Here, we reorient the treatment to target C3aR1 for the treatment of glioblastoma multiforme. In order to achieve this, Jurkat immortalized T cells will express various chimeric antigen receptor designs …

Developing A Smart And Sustainable Public Transportation System: A Case Study In Camden, New Jersey, Zahra Vafakhah

Theses and Dissertations

The transportation sector is a major contributor to air pollution and Greenhouse Gas (GHG) emissions. As a significant source of emissions, public transportation presents an opportunity for mitigation through electrification. However, transitioning to an electric bus fleet necessitates substantial investments in bus procurement and charging infrastructure. To address the associated costs, this study introduces a mixed-integer linear mathematical model developed to optimize the location of on-route fast charging stations within bus networks. The central objective of this optimization formulation is to minimize the overall cost of establishing the charging infrastructure. The study employs a real-world case study focusing on a …

Acoustic Waveform Optimization For Three-Dimensional Object Geometries, Justin Ng

Doctoral Dissertations

In recent years, deep learning (DL) has become an increasingly important tool for many different types of classification, identification, and related problems including inverse radar and sonar applications. This thesis studies the degree to which radar and sonar systems may be optimized for DL algorithms in a theoretical setting. Most of the current existing literature found on using DL involve solving a full inverse scattering problem (ISP), that is to determine the properties and/or geometry of the scatter from nearly complete measurements of the scattered field. Methods suitable for use in two-dimensional space have been proposed and demonstrated with varying …