Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 56521 - 56550 of 721882

Full-Text Articles in Entire DC Network

Concord Or Inharmony? Searching For Democracy In Nicolaus Cusanus' De Concordantia Catholica, Kevin Treadway May 2024

Concord Or Inharmony? Searching For Democracy In Nicolaus Cusanus' De Concordantia Catholica, Kevin Treadway

Chronos

No abstract provided.

Go to article

Building Community For Completion: Doctoral Students’ Perceptions Of Technology Integration Within Dissertation Committee Collaboration, Scott A. Fillman, Barbara Holmes, Dejuanna Parker, Kent Willis May 2024

Building Community For Completion: Doctoral Students’ Perceptions Of Technology Integration Within Dissertation Committee Collaboration, Scott A. Fillman, Barbara Holmes, Dejuanna Parker, Kent Willis

The Journal of Advancing Education Practice

The COVID-19 pandemic highlighted the prevalence of technology-mediated collaboration and mentorship between dissertation committee members and doctoral student scholars. Qualitative research methods were used to explore the role of technology for collaboration and building community within dissertation committees, focusing on dissertation scholars’ perspectives. The study was based on one overarching research question: How do doctoral students describe the integration of technology for collaborating with dissertation committees? Doctoral scholar participants described the importance of technological literacy within dissertation committees, most indicating that the COVID-19 pandemic exacerbated the importance of fluency with technology. Other participants portrayed the importance of technological literacy within …

Go to article

Differences Between Male And Female Welding Students’ Tinkering Self-Efficacy, Kjersti Decker, Michael Pate, Tyson Sorensen, Michelle S. Burrows, Katie N. Kraus, Don Edgar May 2024

Differences Between Male And Female Welding Students’ Tinkering Self-Efficacy, Kjersti Decker, Michael Pate, Tyson Sorensen, Michelle S. Burrows, Katie N. Kraus, Don Edgar

Online Journal for Workforce Education and Development

Welding, agricultural mechanics and blue-collar trades have traditionally been perceived to be reserved for males, yet many females in recent years have enrolled in training programs and have entered these careers (England, 2010). In previous research, females have indicated lower levels of tinkering self-efficacy and confidence in mechanics settings (Baker & Krause, 2007). This study examined difference between male and female welding student’s perceptions of welding technology, tinkering self-efficacy, and perceptions of learning welding technology. Students were engaged in designated tinkering activities throughout the semester to promote developing tinkering abilities. Activities included but were not limited to GMAW and …

Go to article

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins May 2024

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins

SMU Data Science Review

This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …

Go to article

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte May 2024

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …

Go to article

Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma May 2024

Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma

SMU Data Science Review

This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.

Go to article

Baseball Decision-Making: Optimizing At-Bat Simulations, Varun Gopal, Krithika Kondakindi, Nibhrat Lohia, Morgan Williams May 2024

Baseball Decision-Making: Optimizing At-Bat Simulations, Varun Gopal, Krithika Kondakindi, Nibhrat Lohia, Morgan Williams

SMU Data Science Review

Pitch selection in baseball plays a crucial role, involving pitchers, catchers, and batters working together. This practice, dating back to early baseball, has seen teams try various methods to gain an advantage. This research aims to use reinforcement learning and pitch-by-pitch Statcast data to improve batting strategies. It also builds on previous statistical work (sabermetrics) to make better choices in pitch selection and plate discipline. The dataset used, including over 700,000 pitches for each full season and 200,000 pitches for the COVID-shortened 2020 season, encompasses a wealth of crucial metrics including pitch release point, velocity, and launch angle. This study …

Go to article

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford May 2024

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford

SMU Data Science Review

This paper provides updated forecasts of energy demand in Texas and recognizes the impact of sustainable energy. It is important that the forecasts of the adoption of sustainable energy are reexamined after Winter Storm Uri crippled the Texas power grid and left many without power. This storm highlighted the issues the Texas power grid had and has continued to struggle with in supplying the state with energy. This paper will offer an overview of the relevant literature on the adoption of sustainable energy and relevant events that have occurred in the state of Texas that will give the reader the …

Go to article

Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava May 2024

Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava

SMU Data Science Review

Electroencephalography (EEG) or brainwave signals serve as a valuable source for discerning human activities, thoughts, and emotions. This study explores the efficacy of EXtreme Gradient Boosting (XGBoost) models in sentiment classification using EEG signals, specifically those captured by the MUSE EEG headband. The MUSE device, equipped with four EEG electrodes (TP9, AF7, AF8, TP10), offers a cost-effective alternative to traditional EEG setups, which often utilize over 60 channels in laboratory-grade settings. Leveraging a dataset from previous MUSE research (Bird, J. et al., 2019), emotional states (positive, neutral, and negative) were observed in a male and a female participant, each for …

Go to article

Building Effective Large Language Model Agents, Sydney Holder, Shreyash Taywade May 2024

Building Effective Large Language Model Agents, Sydney Holder, Shreyash Taywade

SMU Data Science Review

The advancement of large language models (LLMs) has significantly expanded the influence of artificial intelligence across various sectors. This paper explores building LLM agents to power applications and examines what is necessary to build an efficient and helpful AI assistant. The research investigates the core components necessary to create specialized agents, facilitate collaboration in problem-solving, and improve human task performance. The development and application of tools designed to augment the capabilities of LLM agents are also explored. The paper addresses the potential risks of the unknowns, such as hallucinations, which can compromise the success of agent-based solutions within LLM applications. …

Go to article

Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young May 2024

Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young

SMU Data Science Review

Abstract. “Growth strategies that are purpose-led, customer-centric, experience-driven, data/AI-enabled, and technology-scaled require new mindsets…” (Cornfield, 2021). What can we take from this? Business growth and customer experience are inextricably tied together. Therefore, thriving, as an organization, is dependent on reimagining enterprise operations through modern, scalable data and AI technologies. Our study aims to enhance support operations with emerging AI capabilities, including OpenAI’s LLM models, built on self-attention mechanism transformer architecture, and tailored for business needs through prompt engineering. Our research uses Markov Decision Process and the Q-learning algorithm to evaluate synthetically created support incidents. Through this set of methods, our …

Go to article

Game Recommendation Analysis Using Steam Profiles And Reviews, Robert Blue, Luis Garcia, Jacob Turner May 2024

Game Recommendation Analysis Using Steam Profiles And Reviews, Robert Blue, Luis Garcia, Jacob Turner

SMU Data Science Review

Smaller game studios are at a disadvantage when it comes to getting their product noticed by users. This study aims to provide insights on how recommendation engines work so that these smaller studios can have their games noticed on Steam. Steam is one of the largest video game distribution services and they have a recommendation engine which promotes games to its user base. This study utilized user information such as number of games played, the type of games, and the hours played and created recommendation engines to identify the qualities in the game that are driving recommendations.

Go to article

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn May 2024

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn

SMU Data Science Review

As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …

Go to article

Privacy’S Commodification And The Limits Of Antitrust, Jeffrey L. Vagle May 2024

Privacy’S Commodification And The Limits Of Antitrust, Jeffrey L. Vagle

Arkansas Law Review

This Article argues that the buying and selling of personal data forms what Debra Satz calls a “noxious market,” and, thus, any regulation of information privacy should not accept or depend upon its commodification but should stand on its own. This Article proceeds in three parts. Part I first lays out the history and effects of data commodification, arguing that the market created by this commodification is noxious and undesirable. Part II examines the renewal of antitrust’s purpose as a regulatory tool, especially in the context of its use in the regulation of large technology firms. Finally, Part III argues …

Go to article

Smart Mathematical Model For Variable Refresh Rate Prediction Algorithm, Hsin Hsueh Li, Chien-Hui Wen May 2024

Smart Mathematical Model For Variable Refresh Rate Prediction Algorithm, Hsin Hsueh Li, Chien-Hui Wen

Defensive Publications Series

This publication describes a smart mathematical model that involves compensating and calibrating the display to mitigate brightness and color differences when transitioning between different refresh rates for variable reference rate (VRR) compensation.

Go to article

Insect Abundance And Richness Response To Ecological Reclamation On Well Pads 5–12 Years Into Succession In A Semi-Arid Natural Gas Field, Michael F. Curran, Jasmine Allison, Timothy J. Robinson, Blair L. Robertson, Alexander H. Knudson, Bee M. M. Bott, Steven Bower, Bobby M. Saleh May 2024

Insect Abundance And Richness Response To Ecological Reclamation On Well Pads 5–12 Years Into Succession In A Semi-Arid Natural Gas Field, Michael F. Curran, Jasmine Allison, Timothy J. Robinson, Blair L. Robertson, Alexander H. Knudson, Bee M. M. Bott, Steven Bower, Bobby M. Saleh

Extension Research

Natural gas extraction is a critical driver of the economy in western North America. Ecological reclamation is important to ensure surface disturbance impacts associated with natural gas development are not permanent and to assist native biota. Previous studies in semi-arid natural gas fields within Sublette County, Wyoming, USA have shown insects respond favorably to 1–3-year-old well pads undergoing reclamation compared to older successional reference vegetation communities dominated by Wyoming big sagebrush (Artemisia tridentata spp. Wyomingensis). Here, we examined well pads which were initially seed 5, 8, 10, 11, and 12 years prior to our study. We used a free, image-based …

Go to article

Investigating Bias In Mortgage-Rate Machine Learning Models, Will Kalikman May 2024

Investigating Bias In Mortgage-Rate Machine Learning Models, Will Kalikman

Computer Science Senior Theses

Banks and fintech lenders increasingly rely on computer-aided models in lending decisions. Traditional models were interpretable: decisions were based on observable factors, such as whether a borrower's credit score was above a threshold value, and explainable in terms of combinations of these factors. In contrast, modern machine learning models are opaque and non-interpretable. Their opaqueness and reliance on historical data that is the artifact of past racial discrimination means these new models risk embedding and exacerbating such discrimination, even if lenders do not intend to discriminate. We calibrate two random forest classifiers using publicly available HMDA loan data and publicly …

Go to article

Minimal Specialization: The Coevolution Of Network Structure And Dynamics, Annika King May 2024

Minimal Specialization: The Coevolution Of Network Structure And Dynamics, Annika King

Theses and Dissertations

The changing topology of a network is driven by the need to maintain or optimize network function. As this function is often related to moving quantities such as traffic, information, etc., efficiently through the network, the structure of the network and the dynamics on the network directly depend on the other. To model this interplay of network structure and dynamics we use the dynamics on the network, or the dynamical processes the network models, to influence the dynamics of the network structure, i.e., to determine where and when to modify the network structure. We model the dynamics on the network …

Go to article

Engineering Thermodynamics, Paul J. Marchese May 2024

Engineering Thermodynamics, Paul J. Marchese

Open Educational Resources

This collection of assignments is designed for an introductory thermodynamics class. It includes comprehensive readings that cover the fundamental concepts, problem sets to reinforce learning through practical application, and YouTube videos that provide detailed explanations and visual demonstrations of the material. These resources can be utilized in a regular classroom setting or for independent study, offering flexibility to accommodate different learning environments and preferences.

Go to article

Towards A New Discourse On Success In Alternative Education, Samuel Thomas Settelmeyer May 2024

Towards A New Discourse On Success In Alternative Education, Samuel Thomas Settelmeyer

Dissertations and Theses

Although researchers agree that Alternative Education (AE) within the United States is an essential set of schools and programs that do things beyond traditional education, they do not agree on the purpose for these efforts. To understand how researchers can connect between existing perspectives and consider new ways that they can discuss success in the future, I interviewed 16 students and 15 staff from three different AE schools within the same state. Through thematic analysis, I found students and staff to describe success as an amalgamation of individual and common conceptions, requiring individual effort and support, a commitment to daily …

Go to article

Efficient And Chemoselective Access To Highly Functionalized Arenes By C-H Deprotonative Generation Of Aryne Intermediates, Bryan Edward Metze May 2024

Efficient And Chemoselective Access To Highly Functionalized Arenes By C-H Deprotonative Generation Of Aryne Intermediates, Bryan Edward Metze

Dissertations and Theses

Arenes are a ubiquitous structural motif occurring in numerous organic pharmaceuticals, agrochemicals, secondary metabolites, and functional materials. As such, studies on the characterization, functionalization, and properties of arenes have been a central pillar of organic chemistry since its inception. Early methods to synthesize complex benzenoid rings focused on ring functionalization rather than ring construction due to the high accessibility of very simple arenes and the wide range of aromatic substitution reactions discovered in the late 19th century. As the importance of arenes was recognized more methods of building complex arenes emerged. Both classical and modern approaches are still somewhat limited, …

Go to article

Automatic Measurement Of Dialogue Engagingness In Multilingual Settings, Amila Ferron May 2024

Automatic Measurement Of Dialogue Engagingness In Multilingual Settings, Amila Ferron

Dissertations and Theses

Expansive use of large language models (LLMs) as dialogue systems brings increased importance to the evaluation of the responses they generate. Although evaluation of qualities such as coherence and fluency are readily possible with well-established automatic metrics, engagingness is often measured with human evaluation -- a process that can be costly and slows the pace of development. Existing automatic metrics for engagingness have low to moderate correlation with human annotations, evaluate the response without the conversation history, are complicated to implement, or are designed for a specific dataset. Moreover, they have been tested exclusively on English conversations. Given that dialogue …

Go to article

Detecting Drifts In Data Streams Using Kullback-Leibler (Kl) Divergence Measure For Data Engineering Applications, Jeomoan Francis Kurian, Mohamed Allali May 2024

Detecting Drifts In Data Streams Using Kullback-Leibler (Kl) Divergence Measure For Data Engineering Applications, Jeomoan Francis Kurian, Mohamed Allali

Engineering Faculty Articles and Research

The exponential growth of data coupled with the widespread application of artificial intelligence(AI) presents organizations with challenges in upholding data accuracy, especially within data engineering functions. While the Extraction, Transformation, and Loading process addresses error-free data ingestion, validating the content within data streams remains a challenge. Prompt detection and remediation of data issues are crucial, especially in automated analytical environments driven by AI. To address these issues, this study focuses on detecting drifts in data distributions and divergence within data fields processed from different sample populations. Using a hypothetical banking scenario, we illustrate the impact of data drift on automated …

Go to article

Gardner-Webb’S Department Of Natural Sciences Receives Funds For Undergraduate Researchers, Office Of University Communications May 2024

Gardner-Webb’S Department Of Natural Sciences Receives Funds For Undergraduate Researchers, Office Of University Communications

Gardner-Webb NewsCenter Archive

North Carolina Independent Colleges and Universities (NCICU) awarded two of 11 Undergraduate Research Program Grants to students in the Gardner-Webb Department of Natural Sciences. Tuyet “Snow” Anh Nguyen, a chemistry major, and Kyndal Elaina Jackson, a biology major, received over $500 each to complete their research in the 2024 Fall Semester.

Go to article

Explorando Las Ciencias Exactas: Teoría Y Aplicaciones En El Mundo De Los Números, Fabrício Moraes De Almeida, Daniel Méndez De La Cruz, Patricia Del Carmen Gerónimo Ramos, Josué Ojeda Montejo, Cristo Leon May 2024

Explorando Las Ciencias Exactas: Teoría Y Aplicaciones En El Mundo De Los Números, Fabrício Moraes De Almeida, Daniel Méndez De La Cruz, Patricia Del Carmen Gerónimo Ramos, Josué Ojeda Montejo, Cristo Leon

STEM for Success Resources

Las ciencias exactas, compuestas por las matemáticas, la física y otras, nos ofrecen un marco fundamental para comprender el universo y su región fronteriza. En este fascinante viaje nos adentraremos en el mundo de los números, explorando tanto sus fundamentos teóricos como sus aplicaciones prácticas en diferentes áreas de un amplio espectro. Por lo tanto, el libro presenta los conceptos teórico-prácticos en los resultados obtenidos por los distintos autores y coautores en la producción de cada capítulo. Por encima de todo, Atena Editora ofrece divulgación científica con calidad y excelencia, esenciales para asegurar protagonismo entre las mejores editoriales de Brasil …

Go to article

Collisional Damping In Plasmonic Wakefield Accelerators, Maxime Pindrys May 2024

Collisional Damping In Plasmonic Wakefield Accelerators, Maxime Pindrys

Honors Scholar Theses

We investigate the previously proposed role of collisional damping in plasmonic wakefield accelerators. Wakefields driven in a doped semi-conductor will exist in differing regimes dependent on the driving beam’s intensity. At low intensities, the mobility of free carrier electrons is limited. Here, wakefields will be small if the mean free path of an electron is short compared to the quiver amplitude of a free electron. After reaching a threshold intensity, conduction electrons in the semiconductor will be driven to such high speeds that their coulomb cross section will drop sharply. Here the resulting wakes will resemble those in a hollow …

Go to article

Jamaican Teachers’ Perceptions Of The Impact Of The Tablets In School Project On Their Literacy-Teaching Practices, Valri Leonie Morgan May 2024

Jamaican Teachers’ Perceptions Of The Impact Of The Tablets In School Project On Their Literacy-Teaching Practices, Valri Leonie Morgan

Walden Dissertations and Doctoral Studies

There have been many innovations targeting literacy teaching and learning in small countries such as Jamaica, but changes to literacy teaching practices are rarely sustained beyond the life of the initial projects. The purpose of this study was to understand teachers’ rationale for changes to literacy teaching practices that were and were not sustained beyond the life of the initial Tablets in Schools (TIS) project of 2014 in Jamaica. Grounded in Lewin’s and Rogers’s change theories, the study’s conceptual framework communicated that impactful innovation leads to sustainable change. The two research questions sought to establish how primary school teachers explained …

Go to article

Design And Implementation Of Non-Isolated High Gain Dc-Dc Converter For Photovoltaic Application, Rajesh R May 2024

Design And Implementation Of Non-Isolated High Gain Dc-Dc Converter For Photovoltaic Application, Rajesh R

Theses and Dissertations

Fossil fuel-based power plants emit 25% of greenhouse gases and 40% of carbon emissions globally. Therefore, several countries focus on deploying RES-based electricity production (such as solar and wind). As per the International Energy Agency report, solar PV might be the lowest-cost option for generating electricity in the future. Solar energy is highly emission-free, but it is still a key challenge in procuring the maximum amount of energy from solar PV due to its non–linear characteristics. Therefore, a DC-DC converter must integrate with solar PV to increase the output voltage level. Many existing topologies exist in the literature, but the …

Go to article

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley May 2024

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley

Computer Science Senior Theses

The growth of the commercial aviation industry has yielded many interesting problems in the field of Operations Research, many of which are now able to be solved as both technology and mathematical optimization improve. A particularly interesting problem in airport operations re- search is the Aircraft Gate Assignment Problem (AGAP), which seeks to create a feasible match- ing between planes and flights at an airport. This problem is well-suited to modeling with Integer Programming, and has attracted research since the 1970s. Researchers of the AGAP have considered many different objectives, ranging from airline-focused objectives to more passenger-focused objective functions. In …

Go to article

Welfare Maximization In The Airplane Problem, Alina Chadwick May 2024

Welfare Maximization In The Airplane Problem, Alina Chadwick

Computer Science Senior Theses

Given a set of passengers and a set of airplane seats, the goal of the airplane problem is to sit passengers in seats in a way that maximizes the sum of their total welfare, that is, the total happiness of the passengers in the plane. We aim to maximize their welfare subject to three constraints and how much they care about each constraint being satisfied: a group constraint (where passengers may want to sit together), a constraint on where in a row passengers want to sit (i.e. a window seat, a middle seat, or an aisle seat), and finally a …

Go to article

First Prev Next Last