Digital Commons Network^™

Roundtable On Warhol V. Goldsmith: An Introduction To Warhol V. Goldsmith, Amy Adler, Lionel Bently, Susan Bielstein, Johanna Burton, Martha Buskirk, Noam M. Elcott, Jane Ginsburg, Branden W. Joseph, Joan Kee, Liz Linden, Rebecca Tushnet, Graeme Williams, Winnie Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Vehicular Traffic Flow Prediction Via Decentralized Federated Meta-Learning, Andrew Selvia, Ankur Singh, Wencen Wu

Faculty Research, Scholarly, and Creative Activity

Traffic flow prediction (TFP) has become a crucial problem for billions of people who rely on traffic predictions to optimize their travels every day. In this paper, we present a decentralized federated meta-learning (DFML) framework for traffic flow prediction. Within this framework, traffic detectors spread across a road system can form federations with nearby peers in order to predict traffic collaboratively. A node that assembles a federation assumes the role of receiver while the remainder are assigned as broadcasters. The broadcasters learn model parameters from local data and then broadcast those models to receivers, thereby enriching the available information from …

A Comparative Multivariate Analysis Of Var And Deep Learning-Based Models For Forecasting Volatile Time Series Data, Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin

Faculty Research, Scholarly, and Creative Activity

The existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), Simple Exponential Smoothing (SES), deep learning models, and, most notably, univariate Long Short-Term Memory (LSTM) built based on univariate variable where the next lag of time series is leveraged for forecasting the next cycle of data. This paper takes this line of research to the next level by focusing on forecasting time series data based on “multivariate” modeling and analysis. To have a better insight of the performance of various deep learning-based models when multivariate …

Tiktok As Social Communication Tool To Encourage Participation In Volunteering Projects, Belén Moreno-Albarracín, Tania Blanco-Sánchez

Faculty Research, Scholarly, and Creative Activity

Social media are regarded as tools of communication and social influence. Notwithstanding this, TikTok’s majority audience in 2023 is made up of members of generations Z and Alpha, associated in previous studies with a participatory crisis. Given this contradiction, a research question is proposed: do young people use TikTok to consolidate social values such as solidarity? To answer it, the main objective is to analyse the use of TikTok as an instrument for youth participation in volunteer projects. A mixed methodology is used, based on a content analysis of the 150 videos uploaded to TikTok with the hashtag #voluntariado and …

Cross-Cultural Adaptation Of The Voice-Related Experiences Of Nonbinary Individuals - Veni To Brazilian Portuguese, Isabela Dos Santos, Mara Behlau, Grace Shefcik, Pei Tzu Tsai, Vanessa Veis Ribeiro

Faculty Research, Scholarly, and Creative Activity

Purpose: This study aimed to translate and cross-culturally adapt the “Voice-related Experiences of Nonbinary Individuals” (VENI) to Brazilian Portuguese (BP). Methods: Cross-cultural adaptation was performed based on the combined guidelines of the World Health Organization’s (WHO) Translation Recommendations and the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). The process included five stages: a) Translation of the instrument into BP by a translator specialized in the construct and a non-specialist, both native BP speakers and fluent in English; b) Synthesis of the two translations by consensus; c) Back-translation by a translator specialized in the construct and a non-specialist, …

Enhancing Product Designing With The Help Of Artificial Intelligence, Dhyeykumar Nikalwala, Minnie H. Patel

Faculty Research, Scholarly, and Creative Activity

In the ever-changing world of Business, making a successful product design requires quickly trying out new ideas and listening to what users think. With the integration of Artificial Intelligence (AI), the process of rapidly generating content from existing data becomes easier, accelerating the creation of innovative product concepts. To use this cutting-edge technology to build novel products a framework is essential. The paper aims to propose a novel framework that caters to a customer-centric integration of AI in Product Design. The paper goes beyond theoretical explanation, implementing the framework to iteratively design 3 diverse products. The implementation also highlights the …

Incorporating Uas Traffic Management Into Wildland Firefighting Operations: Initial Findings Of Subject Matter Expert Interviews, Deborah L. Bakowski, Lynne Martin, Gil Dustin, Chris Mariano

Faculty Research, Scholarly, and Creative Activity

Uncrewed Aircraft Systems (UASs) are being utilized throughout the disaster and emergency response domain, including in wildland firefighting operations. While UASs can offer safety benefits in comparison to crewed aircraft, such as removing the human pilot from the vehicle so that they are not exposed to the same risks and the ability to operate in low-visibility conditions, they are not without tradeoffs. For example, it can be challenging for UAS pilots (UASPs) to build situation awareness of the airspace in which their UAS is operating. In order to address some of the challenges associated with using UASs and provide greater …

Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu, William B. Andreopoulos

Faculty Research, Scholarly, and Creative Activity

This paper presents a novel approach to enhancing the Llama language model for generating customized resumes tailored to domain-specific job descriptions. Unlike traditional methods that rely heavily on extensive fine-tuning, we implement scalable adapter modules to minimize parameter adjustments. This approach preserves the model's inherent ability to generate coherent and contextually appropriate language across diverse tasks, ensuring that its general linguistic capabilities remain intact. Additionally, we employ a prompting strategy to dynamically create a diverse and comprehensive dataset, ensuring high relevance to various job roles. Using a dataset of 10,000 job descriptions and resumes, our approach resulted in a 20% …

Study Of Using Variational Quantum Linear Solver For Solving Poisson Equation, Nithin Reddy Govindugari, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

Quantum computing is promising in speeding up the system of linear equations (SLE) solving process. However, its performance is limited by noise. The variational quantum linear solver (VQLS) algorithm is expected to be more resilient to noise than gate-based quantum computing algorithms. This is because error correction is not available yet and VQLS is based on cost function minimization. In this paper, the gate insulator Poisson equation is solved using VQLS. The results are compared to technology computer-aided design (TCAD) results and gate-based quantum algorithm results. We show that, even without error-free qubits, the IBM-Q quantum computer hardware can solve …

Tcad Structure Input File Generation Using Large Language Model, Le Minh Long Nguyen, Albert Lu, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

In this paper, we study the possibility of using Large Language Models (LLMs) to create Technology Computer-Aided-Design (TCAD) structure generation input files. LLMs are machine learning models trained on vast amounts of text data from the Web and are designed to understand, generate, and interact with humans through natural languages such as English. However, unlike programming languages with abundant training examples on the Web, TCAD examples are scarce. In this work, by using TCAD Sentaurus Structure Editor (SSE) as an example, 7000 nanowire data are generated to fine-tune open-source models (Llama 2 and 3) to obtain chatbots that can generate …

The Applicability Of Llms In Generating Textual Samples For Analysis Of Imbalanced Datasets, Saroj Gopali, Faranak Abri, Akbar Siami Namin, Keith S. Jones

Faculty Research, Scholarly, and Creative Activity

In machine learning class imbalance is a pressing issue, where the model is biased towards the majority classes and underperforms in the minority classes. In textual data, the natural language processing (NLP) model bias significantly reduces overall accuracy, along with poor performance in minority classes. This paper investigates and compares the performance of transformer-based models, such as Multi-head Attention with the data levels and algorithmic levels approaches and BERT (Bidirectional Encoder Representations from Transformers) with LLM-based data augmentation. The research utilized the approaches, such as Random Over Sampler, Synthetic Minority Over-sampling Technique (SMOTE), SMOTEENN, data augmentation at word level, class …

The Role Of Paradata In Algorithmic Accountability, Ciaran B. Trace, James A. Hodges

Faculty Research, Scholarly, and Creative Activity

This chapter examines how the doings of the algorithm (instantiated through its operations, actions, and steps) and its accompanying algorithmic system are revealed and explored through an engagement with the paradata created as a part of this data-making effort. In doing so, the chapter explores how the concept of paradata helps us understand how information professionals and domain stakeholders conceptualize accountable algorithmic entities and how this influences how they emerge as documented and describable entities. Two complementary frameworks for capturing and preserving paradata for accountability purposes are examined in the process. The first is associated with diplomatic theory and archival …

Multi-Robot Source Seeking And Field Reconstruction Of Spatial-Temporal Varying Fields, Deepak Talwar, Thinh Lu, Divyam Sobti, Wencen Wu

Faculty Research, Scholarly, and Creative Activity

To effectively respond to environmental disasters, real-time monitoring and pollution control rely heavily on the ability to estimate, predict, and reconstruct constantly changing environmental conditions across different locations. In this research, we develop a multi-robot source-seeking and field reconstruction framework that enables a multi-robot group to detect multiple sources within a spatial-temporal varying field and reconstruct the field in real time. The strategy consists of two parts: source seeking and field reconstruction. The source-seeking part features a gradient-based source-seeking controller that directs the multi-robot group toward local sources and a destination selection algorithm that navigates the multi-robot formation beyond the …

Ipeds Data Feedback Report, Georgia Southern University

IPEDS Feedback Reports

No abstract provided.

European Union Sanctions Impact On The Sector Stock Prices – The Ukrainian War Effect?, Patrycja Chodnicka, Piotr Jaworski, Marcin Kot

Journal of Banking and Financial Economics

The aim of this paper is to analyze the current impact of the implementation of the European Union sanctions related to the Ukrainian War on the abnormal rates of return on the stock prices of companies listed on the stock exchanges. It was hypothesized that the implementation of the European Union sanctions related to the Ukrainian War is causing the varied abnormal rates of return on the stock prices of companies listed on the stock exchanges, taking into consideration the type of sector and the geographical proximity to the military conflict. The analysis used panel data event studies prepared using …

Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch

Economics Faculty Research & Creative Works

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO₂ emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were …

Advance (Spring 2024), Georgia Southern Division Of Continuing And Professional Education

Division of Continuing & Professional Education

No abstract provided.

Jiann-Ping Hsu College Of Public Health Magazine, Georgia Southern University

College of Public Health: News & Publications

• Message from the Dean: Stuart H. Tedders, PhD
• JPHCOPH Research
• Our AHEC Work
• Passing the Torch: Mentor to Innovator
  • Dr. Joseph Telfair
  • Dr. Tilicia Mayo-Gamble
• The MHA is BACK
• Life After JPHCOPH
• College Milestones
• CAR: Center for Addiction Recovery
• CPHPR: Center for Public Health Practice & Research
• Office of Practice and Research: Continued Strong Funding for in FY 2024
• Eagles Talking about the Public's Health (ETAPH)
• Georgia Southern Student Scholars Symposium (GS4)
• Congratulations to our Students of the Year!

A Structure From Motion Photogrammetric Method For Continuously Measuring And Tracking The Deformations Of Soils During Triaxial Testing, Xiaolong Xia

Doctoral Dissertations

"Measuring soil deformation characteristics during triaxial testing is an important part of characterizing the stress-strain behavior of both saturated and unsaturated soils. The total and local deformations and volume changes of a soil specimen are indispensable parameters that need to be measured and are of great significance for understanding volume change and shear strength characteristics of the soil. Over the past several decades, numerous efforts have been made to measure the soil deformations during triaxial testing. However, it remains a great challenge for researchers to measure the volume-changes of unsaturated soils due to the existence of the air phase.

This …

Comprehensive Investigation Of Using Tire-Derived Aggregate In Chip Seal Subjected To Various Traffic And Environmental Conditions, Alireza Pourhassan

Doctoral Dissertations

"Chip seal is one of the most cost- and performance-effective pavement preservation treatments commonly used in the United States. Using crumb rubber obtained from scrap tires as a partial or full replacement for mineral aggregate has been proposed as a sustainable solution to reduce the negative environmental impacts of scrap tire accumulation and excessive consumption of natural resources. However, there was no data available regarding the performance of rubberized chip seal under traffic loads and environmental conditions. An extensive study using laboratory specimens and conditioning as well as field implementation and assessments were conducted to bridge this gap. Different traffic …

Characterization Of The Acquisition Parameters Of A Submersible Gamma-Ray Computed Tomography System, Zhongmin Jin

Doctoral Dissertations

"The purpose of this work is to study a component of a submersible gamma-ray computed tomography (CT) system used in the non-destructive testing of irradiated nuclear fuels. The first section of this study proposes two acceleration approaches for rapidly modeling a transmission-type gamma-ray tomography system. The first relies on Monte Carlo simulations with a monodirectionally biased source sampled from a sub-volume of the whole source volume. This method estimates the real count rate using analytical correction factors. The second way of acceleration is based on deterministic calculations that use the Beer-Lambert law and detector response characteristics. It shows that both …

Novel Uses Of Distributed Optical Fiber Sensing For Geo-Mechanical Monitoring, Samuel Vincent Nowak

Doctoral Dissertations

"Rock mass monitoring in underground excavations is crucial for ensuring workplace safety and efficiency. As mines become deeper and more complex, the need to monitor larger volumes of rock during mining becomes impractical with existing instruments due to high costs. Distributed Optical Fiber Sensing (DOFS) is a powerful technology with advantages like long sensor length, real-time measurement capabilities, ease of installation, low cost, and distributed measurement. However, its application in rock mass monitoring is limited, especially in the mining industry. This study addresses a major gap in DOFS technology, specifically the inability of long-range distributed optical fiber strain sensors to …

Design, Synthesis, And Characterization Of Complex Chalcogenides For Energy Storage And Energy Conversion Applications, Srikanth Balijapelly

Doctoral Dissertations

"Through this investigation, complex chalcogenides with the combination of main group metals, transition metals, and rare earth metals have been synthesized using the building block approach and their structure-property relationships are evaluated. The main emphasis of research is on rationally designing new materials for applications in sodium and lithium ion conducting solid electrolytes, cathodes, thermoelectrics, and nonlinear optics. Along with the experimental studies, theoretical calculations are also employed to better understand the physicochemical properties of the synthesized compounds.

The first part of the research will discuss designing alkali ion containing complex chalcogenides using the building block approach. This investigation resulted …

Understanding Catalyst Design Principles In Transition Metal Mixed Anionic Chalcogenides For Electrocatalytic Energy Conversion, Ibrahim Abdullahi

Doctoral Dissertations

"This research focused on the synthetic design of transition metal mixed anionic chalcogenide catalysts containing various ligand types around the central metal atom (chalcogen anion and chalcogen-based organic ligand) generating diverse crystal structure types applied for water splitting and carbon dioxide reduction reactions (CO₂RR).

A series of catalysts were synthesized starting with isolated metal complexes (ME_n) with a central metal core (M = Co, Ni, Cu, and Cr) through molecular clusters, to bulk nanostructured solids of similar M-E coordination. Bis(dichalcogenoimidodiphosphinato) were employed as ligands in the metal complexes, and anionic chalcogen (E = S, Se, and …

Semiconductor Physics Based Signal Integrity Analysis For 3d Ic, Ze Sun

Doctoral Dissertations

"Three-dimensional integrated circuits (3DICs) are becoming increasingly popular in high-speed electrical systems. 3DICs are made by stacking multiple dies vertically and connecting them with through-silicon vias (TSVs). In this dissertation, three studies were carried out to model the performance of 3DICs in extreme working conditions and the signal integrity (SI) performance of 3DICs.

First, an in-house Monte Carlo particle simulator was developed to characterize extreme working conditions (ESD/EMP) for ICs at the microscopic level instead of the macroscopic level. The ability of the simulator was demonstrated by modeling a voltage regulator diode under forward and reverse bias. The diode simulation …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …

Lithospheric Drip And Mantle Thermal Upwelling Beneath Central Asia And The Southern Great Plains Of North America: Evidence From Receiver Function Imaging, Yangyang Liao

Doctoral Dissertations

"Investigating the depth of mantle transition zone (MTZ) discontinuities provides insights into mantle dynamics, including regional-scale downwelling of mantle lithosphere (i.e., lithospheric drip) and upwelling of thermal materials (i.e., mantle plume). In our study, we employ receiver functions to image the 410 km (d410) and 660 km (d660) mantle discontinuities and to ascertain thickness of the MTZ beneath Central Asia and the Great Plains in United States. The findings unveil a significantly thickened transition zone, exhibiting an average thickness of 284 km beneath East Sayan. When considered alongside geochemical observations, our results indicate that the descent of the lithosphere is …

Exploring Seismic Anisotropy In The Crust And Mantle Of The Tibetan Plateau And Analyzing Seismic Velocity Variations In Yellowstone, Cong Shen

Doctoral Dissertations

"Seismology is a scientific discipline that utilizes seismic waves to investigate the deep structures within the Earth's interior, which are otherwise inaccessible to direct observation by humans. These seismic waves can be categorized into two types: body waves and surface waves, each with distinct propagation modes and behaviors, carrying valuable information about their travel paths. The first paper concentrates on the analysis and inversion of body waves produced by large seismic events. It employs shear wave splitting and receiver function methods to explore the geodynamic structure of the south-central Tibetan Plateau, near the plate boundary. The investigation covers the depth …

Kinetic Modeling And Simulations Of Plasma-Surface-Dust Interactions For Lunar Exploration, David Clifford Lund

Doctoral Dissertations

"Due to the recent relevance of NASA’s focus on lunar missions, a better understanding of the lunar plasma-surface-dust environment is vital for their success. The lunar surface, devoid of an atmosphere and a global magnetic field, is directly exposed to solar radiation and solar wind plasma. This exposure leads to the electrical charging of the lunar surface due to the bombardment of solar wind plasma and the emission/collection of photoelectrons. Therefore, this research developed accurate and efficient computational models of plasma-surface-dust interactions and extend a legacy simulation code, namely the Immersed-Finite-Element Particle-in-Cell (IFE-PIC), to a parallel version: Parallel Immersed-Finite-Element Particle-in-Cell …

Investigation Wettability Contact Angle Using 2d Imaging Technology, Saleh Alsayegh

Doctoral Dissertations

"Investigation wetting preference of tight carbonate reservoir rocks and several other unconventional reservoir rocks, and their relation to oil production, is becoming noticeably important for field development in Kuwait, as in many future unconventional tight carbonate reservoirs in the Middle East. The overall objective of this investigating and modeling study is to discover many fundamental relationships from totality wettability preference as a tool for reservoir recovery predictions and management. The investigation includes advancing position of water wet preference into smaller capillaries of the matrix voids and incrementally more oil-wet into larger capillaries of natural fractures (NF). Also, investigating water/ oil …