Digital Commons Network^™

Estimation Of Spo2 Levels And Heart Rate From Ppg Signals In The Presence Of Motion Artifacts, Chizhong Wang

Dissertations

In the rapidly advancing world of health technology, wearable devices are at the forefront, fundamentally changing how individuals monitor their health. The key to this transformation is photoplethysmography (PPG), a non-invasive optical method that measures blood flow variations through the skin by detecting changes in light absorption with each heartbeat. This capability makes PPG essential for monitoring vital signs such as heart rate and blood oxygen saturation(SpO₂). Furthermore, the utility of PPG signals has been extended beyond traditional health metrics to include human activity classification, offering a holistic perspective on an individual's physical health and activity levels, in …

Order-Picking Strategies And Efficiency Models For The Fulfillment Of Multi-Line E-Commerce Grocery Orders, Zijia Wang

Dissertations

The fulfillment process for Buy Online Pickup from Store grocery orders (BOPS-Grocery) is particularly challenging, given the large item count per order and the low-profit margins. This research investigates the BOPS-Grocery model and pursues the following objectives: (i) Defining and specifying the BOPS-Grocery fulfillment process. Differentiating the process from classical warehouse order picking., identifying performance objectives, and characterizing design options and facility layout. (ii) Modelling and developing order picking algorithms specific to the straight aisle section. Formulated as an order hatching problem with picker movement minimization. No order splitting, and (iii) modeling and developing order picking algorithms specific to the …

Numerical Techniques For Improving Simulations Of Tropical Cyclones, Yassine Tissaoui

Dissertations

The increasing frequency and intensity of tropical cyclones (TCs) due to climate change pose significant challenges for forecasting and mitigating their impacts. Despite advancements, accurately predicting TC rapid intensification (RI) remains a challenge. Large eddy simulation (LES) allows for explicitly resolving the large eddies involved in TC turbulence, thus providing an avenue for studying the mechanisms behind their intensification and RI. LES of a full tropical cyclone is very computationally expensive and its accuracy will depend on both explicit and implicit dissipation within an atmospheric model. This dissertation presents two novel numerical methodologies with the potential to improve the efficiency …

Large Deviation Theory In Stochastic Processes: Applications To Biological Modeling, Moshe C. Silverstein

Dissertations

This dissertation delves into developing and applying stochastic models to analyze complex biological systems. It leverages Large Deviation Theory (LDT) to gain insights into these systems, focusing on two key examples: neural networks and calcium signaling dynamics. Traditional deterministic methods frequently fail to capture biological processes' randomness and inherent variability. Meanwhile, many stochastic approaches struggle to be mathematically tractable or provide accessible insights. The approach introduced in this study provides rigorous mathematical frameworks to enhance understanding of these stochastic behaviors while remaining tractable and insightful.

A stochastic model for a random biological neural network is constructed that addresses the dependencies …

Efficient Numerical Methods For Monge-Ampere Type Equations, Jake S. Brusca

Dissertations

Current numerical methods for Monge-Ampere-type equations and Optimal Transport problems face challenges when handling higher dimensions and large-scale data. This dissertation aims to develop and analyze efficient, highly parallelizable numerical algorithms for solving the Monge-Ampere equation to address these issues. Two approaches are employed:

(1) The discretization method is enhanced by introducing an integral repre-sentation of the Monge-Ampere operator. This integral can be discretized using higher-order quadrature, yielding a more efficient, higher-order monotone scheme that allows for narrower stencils. An additional advantage of this discretization is its natural extension to arbitrary dimensions.

(2) The nonlinear solvers for this scheme are …

The Potential For Automated Insect Classification Using Infrared Remote Sensing, Rachel Deahl

Theses

Insects play an important role within ecosystems, occupying almost all trophic levels and providing crucial services such as pollination and decomposition. In addition, they can directly and negatively impact humans as crop pests, parasites, and disease vectors. Thus, it is essential to understand the spatial and temporal dynamics of insect populations and communities. Various forms of remote sensing, such as LiDAR, have been used to capture data on insects but often are costly and require advanced expertise. The objective of this thesis is to evaluate a cost-effective methodology that balances information loss. This thesis aims to outline the discrimination required …

Questioning Reality: The Progressive Development Of Modern Physics, Joshua Lancman

STEM Month

Humanity has a tendency to divide time. The past is distinct from the present which is entirely separate from the future. In supposedly 20-20 vision history is neatly divided into different sections, distinct eras with sharp lines between them. What is present and in the future is always modern. What is past is something else with another name.

Yet time is not divided so neatly. We know this living through it: years and decades blend into one another in a non-uniform progression. To divide human history into separate eras is a necessary simplification, as it helps to ascribe order onto …

Ai And The Future Of Marketing Education Through The Lens Of The Space Merchants, Jasmin Cowin, Cristo Leon, Sabra Brock, Xavier Oviedo Torres

STEM for Success Resources

Artificial Intelligence (AI) is transforming professional marketing practices as well as marketing education. This paper employs the science fiction novel “The Space Merchants” by Pohl and Kornbluth as a metaphorical framework for critically examining AI integration in marketing curricula. The authors review existing literature examining the impacts of AI on marketing practices and education. Furthermore, they establish a qualitative methodology analyzing a real-world case study, current applications of AI in marketing education, and discuss ethical frameworks surrounding the use of AI. Utilizing “The Space Merchants” satirical portrayal of consumerism, they reflect on AI’s potential to commodify marketing education, homogenize student …

Elusive Shift: How Role-Playing Games Forged Their Identity [Reseña], Edgar Meritano Corrales

Journal of Roleplaying Studies and STEAM

Elusive Shift: How Role-Playing Games Forged Their Identity by Jon Peterson traces the history and evolution of role-playing games (RPGs), from their roots in wargames to their establishment as a distinct genre. The book examines how RPGs have shifted from focusing on simulating physical conflict to emphasizing character creation and collaborative storytelling, exploring the role of the game master, ethical decision-making within the game world, and the philosophical implications of the gaming experience. Peterson also explores the maturity of RPGs over time, highlighting the development of invisible systems and the elusive shift that has solidified the unique identity of RPGs. …

Estética Relacional De Los Juegos De Rol., Juan M. Díaz De La Torre

Journal of Roleplaying Studies and STEAM

El presente escrito son los primeros esbozos de una investigación mayor, la cual busca establecer que los juegos de rol son una forma artística fundamentada en la llamada estética relacional propuesta por Nicolas Bourriaud. Hubo trabajos previos que alimentaron este artículo[1]. La idea central es pensar al acontecimiento estético como la fase más alta de un proceso de agregación en donde diferentes dimensiones de la vida de los jugadores y planos existenciales (juego, reglas, mundo social, mundo narrativo, etc) se aglomeran para generar la experiencia estética. En este sentido, lo estético en los juegos de rol es algo …

Las Figuras De Juegos De Rol Como Soporte Para La Apropiación De Personajes, Roberto A. García Madrid, Blanca E. López Pérez

Journal of Roleplaying Studies and STEAM

El desarrollo de juegos de rol implica la consideración de diversos elementos, como temática, historia y personajes. Un aspecto es la utilización de figuras en miniatura, que actúan como avatares físicos en la narración del juego. Estas figuras contribuyen a la identificación entre jugadores y personajes, así como a la construcción de escenas y al entendimiento del desempeño de los personajes.

Cada juego ofrece diversas opciones de avatares, con distintos niveles de abstracción y representación. El mercado de figuras proporciona una amplia variedad de personajes con diferentes poses y técnicas de pintura, enriqueciendo la experiencia de construcción avatares. Identificar los …

Journal Of Roleplaying Studies And Steam (Jrpssteam) Vol. 3 [2024], Número 1 (Issue 1)., Miguel A. Bastarrachea-Magnani, Edgar Meritano, Mauricio Rangel Jiménez, Cristo Leon, James Lipuma Dr.

Journal of Roleplaying Studies and STEAM

Editorial Lento pero seguro, seguimos adelante

Estamos muy contentos de seguir con este proyecto que ha tenido gran éxito, contamos con registro de descargas de más de 130 instituciones universitarias de 74 países, además de posicionarnos poco a poco como una revista académica de gran nivel gracias a las políticas estrictas que seguimos número tras número.

En esta ocasión los trabajos presentados dan muestra de la madurez que estamos alcanzando como revista, como primer artículo tenemos Las figuras de juegos de rol como soporte para la apropiación de personajes, texto que nos habla de la gran relevancia que tienen las …

Empirical Exploration Of Software Testing, Samia Alblwi

Dissertations

Despite several advances in software engineering research and development, the quality of software products remains a considerable challenge. For all its theoretical limitations, software testing remains the main method used in practice to control, enhance, and certify software quality. This doctoral work comprises several empirical studies aimed at analyzing and assessing common software testing approaches, methods, and assumptions. In particular, the concept of mutant subsumption is generalized by taking into account the possibility for a base program and its mutants to diverge for some inputs, demonstrating the impact of this generalization on how subsumption is defined. The problem of mutant …

Sustainable And Cost-Effective Work Zone Scheduling On Two-Lane Highways With Managed Lanes, Ahmed Z. Edrees

Dissertations

Roadway maintenance projects greatly influence the roadway capacity, resulting in potential traffic disturbances captured by delays. Additionally, costs associated with these projects tend to be exorbitantly extensive. Most agencies and planners try to find a solution that minimizes roadway maintenance costs, traffic delays, crash risks, and environmental impact.

Work zones on two-way two-lane roadway typically avoids high-demand periods. Lane-closure scenario is commonly used and converts the open lane into a phantom intersection, alternating two-direction movements on one lane with the help of a flagger or a temporary signal. Alternatively, using shoulders as temporarily managed lanes allows for simultaneous two-way movements …

Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain

Dissertations

The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these …

Integrating Laser Charging And Drones For Secure Edge Computing, Weiqi Liu

Dissertations

Drone-mounted base stations (DBSs) have emerged as a promising solution to enhance the flexibility and coverage of wireless networks, potentially revolutionizing communication systems. This dissertation explores the integration of DBSs into 5G and beyond networks, focusing on methodologies to optimize their deployment and performance. A laser charging-enabled DBS framework is proposed to extend flight time and enhance network coverage. By leveraging laser charging technology, the DBS can receive continuous energy transmission from a ground-based charging station while providing communication services to users. The framework is formulated as an optimization problem to jointly maximize flight time and communication data rate, while …

On The Ubiquity, Properties And Evolution Of Small-Scale Magnetic Flux Ropes In The Heliosphere, Hameedullah Farooki

Dissertations

The solar wind is a plasma constantly blowing out from the Sun with a large-scale magnetic field having significant local complexity at small scales. Small-scale magnetic flux ropes (SMFRs), plasma structures with twisted field lines, are an important element of this complexity. This dissertation contributes several studies that further our understanding of SMFRs. The first study applies machine learning to measurements from Wind labeled by the presence of SMFRs and magnetic clouds (MCs). MCs were distinguished from non-MFRs with an AUC of 94% and SMFRs with an AUC of 89% and had distinctive plasma properties, whereas SMFRs appeared to be …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

Interaction Of Particles With Plasma And Shock Produced By Pulsed Spark Discharge, Shomik Mukhopadhyay

Dissertations

Interactions of powders with high-temperature plasma and shockwaves occur in diverse scenarios, such as nuclear blasts, accidental industrial dust explosions, solid propellant combustion in explosive charges and when removing contaminants from surfaces. Electrostatic Discharge (ESD), known for generating shock and plasma, is a promising lab-scale technique for simulating these interactions. Studies with ESD involved placing powders near a spark-producing gap between electrodes and observing mechanical and chemical processes like particle motion and ignition. A limited range of spark conditions and material properties have been tested, which facilitated the development and validation of preliminary computational models describing this system. Significant gaps …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Information Theoretic Bounds For Capacity And Bayesian Risk, Ian Zieder

Dissertations

In this dissertation, the problem of finding lower error bounds on the minimum mean-squared error (MMSE) and the maximum capacity achieving distribution for a specific channel is addressed. Presented are two parts, a new lower bound on the MMSE and upper and lower bounds on the capacity achieving distribution for a Binomial noise channel. The new lower bound on the MMSE is achieved via use of the Poincare inequality. It is compared to the performance of the well known Ziv-Zakai error bound. The second part considers a binomial noise channel and is concerned with the properties of the capacity-achieving distribution. …

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …

A Molecular Dynamics Study On The Destruction Mechanism Of Per And Polyfluoroalkyl Substances Due To Ultrasound Technology, Bruno Bezerra De Souza

Dissertations

Per- and polyfluoroalkyl substances (PFAS) are a group of stable synthetic chemicals that are highly persistent and harmful pollutants to the environment and human health. PFAS have caused a strong public and regulatory response due to their ubiquitous presence in the environment and toxicity to humans. The application of ultrasound is one of the most effective treatment technologies for the mineralization of PFAS in contaminated water. However, this technology is treated as a black box causing the inability to be optimized. Therefore, there is a pressing need to investigate the intricate dynamics of PFAS degradation under ultrasound to unlock its …

Molecular-To-Continuum Scale Modeling Of Aerosols: Atmospheric Application And Beyond, Ella Ivanova

Dissertations

Aerosol modeling is critical for various applications, such as climate forecasting, air quality, and human health impact assessment. During their lifetime, aerosols undergo a complex evolution, usually divided into several stages — formation, processing, transport, and removal — that occur on different scales. Thus, the choice of modeling methods depends on the stage considered. For example, certain stages of particle formation may require nano-scale modeling while aerosol-cloud interactions span from microscale to mesoscale. This study examines the modeling of aerosol behavior over a wide range of scales, from nano to microscale, with implications to mesoscale.

This dissertation focuses on two …

Microplastic Abundance And Removal In Stormwater Using Green Infrastructure, Meghana Parameswarappa Jayalakshmamma

Dissertations

Microplastics (MPs) are increasingly recognized as a pervasive environmental contaminant, conveying other contaminants, accumulating in aquatic organisms, and posing ecological risks. The existing research has primarily focused on MPs in wastewater treatment plant effluents in surface waters. However, the role of stormwater systems in MP transport has been less explored. Given the rise in MPs from urban sources, stormwater runoff from urban areas may serve as a critical conduit for transporting land-based MPs into aquatic environments. Green infrastructures (GIs) have been recognized for mitigating flooding and improving water quality from minor storms by trapping stormwater pollutants. Yet, the capability of …

A Machine Learning-Assisted Steering And Scheduling Framework For Big-Data Scientific Workflows On Heterogeneous Computing Platforms, Yijie Zhang

Dissertations

In next-generation scientific applications, the exponential growth of big data necessitates advanced techniques for efficient data storage, processing, and analysis. This has led to the construction of intricate computing workflows, managed and orchestrated by powerful engines in big data systems as exemplified by Hadoop. As scientific applications increasingly shift towards simulation-centric approaches, traditional methodologies face new challenges in accommodating the complexity of extreme-scale numerical modeling with numerous tunable parameters. To address these challenges, this dissertation propose to develop a machine learning-assisted framework that enables autonomous computational steering of scientific simulations and optimized execution of big-data workflows on heterogeneous platforms. This …

Development Of Super Hydrophobic Membranes And Their Applications, Sumona Paul

Dissertations

Effective liquid phase separation is vital across industries, especially in aviation, chemicals, fuels, and textiles. Membrane technology offers advantages over traditional methods like distillation, with low energy consumption and protection for heat-sensitive molecules. Membrane performance is assessed based on factors such as materials, surface modification, and interactions. This research targets superhydrophobic membranes for liquid-liquid filtration and antiwetting omniphobic membranes for membrane distillation, focusing on properties like contact angle and thermal resistance.

This study presents a highly hydrophobic membrane achieved by immobilizing carbon nanotubes (CNTs) onto a PTFE microfiltration membrane. It's designed for dewatering organic-water mixtures with trace water content via …

Neural Operator For Accelerating Coronal Magnetic Field Computations In Bifrost Mhd Model, Yutao Du

Theses

The application of the Tensorized Fourier Neural Operator (TFNO) to significantly enhance the computational efficiency of coronal magnetic field calculations within the Bifrost Magnetohydrodynamics (MHD) model is introduced in this study. Leveraging simulated data from the European Sunrise Science Data Center, the TFNO? an extension of the Fourier Neural Operator (FNO) that incorporates tensor decomposition for improved handling of high-dimensional data?is employed to solve time-varying partial differential equations (PDEs) over a 3D domain. The performance of the TFNO is compared with traditional machine learning methods, including Vision Transformer and CNN-RNN (encoder-decoder) architectures, to demonstrate its accuracy, computational efficiency, and scalability. …

Marking Estimation In Petri Nets Using Dynamic Mode Decomposition, Aditya Kale

Theses

Petri Nets (PNs) are a well-established framework for modeling and analyzing complex systems with interacting, concurrent processes. This thesis extends traditional Petri Net methodologies by integrating Dynamic Mode Decomposition (DMD), a technique originally developed for fluid dynamics, to analyze Continuous Petri Nets (CPNs). By applying DMD to CPN marking evolution, this research constructs a reduced-order model that captures its dynamics through dynamic modes and eigenvalues, enabling prediction of future markings without detailed knowledge of transition firings or underlying deterministic models. The principal contribution of this research is extending the kit of tools available for analysis of CPN dynamics, providing insights …