Digital Commons Network^™

Empowering Engineering Students To Become More Effective And Self-Regulated Learners Through Course-Integrated Learning Strategies Intervention: A Pilot Study In A Solid Mechanics Course, Huihui Qi, Richard Eugene Vallejo Jr., Changkai Chen, Minju Kim, Alex M. Phan, Marko Lubarda, Celeste Pilegard, Curt Schurgers

Psychology Faculty Articles and Research

Learning is a lifelong process exercised within and beyond the classroom and a vital skill in almost all technical professions. Engineers, in particular, are impacted by rapidly evolving technologies and practices that require continuous learning and adaptation long after their training and the initial transition into their professional careers. However, despite the critical role of learning in their academic success and profession, engineering students experience academically rigorous and challenging courses with very little emphasis or conscious focus on learning strategies that power effective learning. Often-used learning strategies such as rereading, highlighting, repetition, and memorization are intuitive for many students, yet …

Board # 414: Nsf S-Stem: Developing An Ecosystem Of Stem Success For Built Environment Scholars, Melanie Villatoro, Muhammad Ummy, Hamidreza E. Norouzi, Masato R. Nakamura, Daeho Kang

Publications and Research

The National Science Foundation S-STEM program, Developing an Ecosystem of STEM success for Built Environment Scholars (Award Number 2150432), focuses on supporting and developing scholars in the majors relating to the Built-Environment which include Civil Engineering Technology, Construction Engineering Technology, Electrical Engineering Technology, Mechanical Engineering Technology and Environmental Control Technology. The intent of the project is to implement evidence-based effective practices and assess the impact of these practices, degree attainment, and entry into the U.S. workforce or graduate programs in STEM. Students are provided faculty mentors and opportunities to engage in cohort building activities that include field trips, research, workforce …

Shaping Future Innovators: A Curriculum Comparison Of Data Science Programs In Leading U.S. And Chinese Institutions, Elizabeth Milonas, Qiping Zhang, Duo Li

Publications and Research

Background: In recent years, the data revolution has sparked widespread interest in Data Science (DS) across numerous fields and disciplines [1]. As society becomes more reliant on data-driven decisions, the demand for professionals equipped to handle data challenges is rising. The need for skilled workers in diverse roles and capacities continues to grow as these demands evolve [2,3]. To meet the demand for skilled workers, training in the latest data science technologies has become a priority in colleges across the US and China. These colleges have established comprehensive data science programs to ensure students graduate with the expertise required in …

The Epistemologization Of Scientific Knowledge: Scientific Textbooks And Education In The 18th And 19th Centuries, Brian P. Bieganski

USF Tampa Graduate Theses and Dissertations

Philosophy, despite its proclivities towards history and contextualization, often neglects to properly note various characteristics of ideas. In particular, there seems to be a kind of blindness to a key component of how ideas come into being: namely, the role of writing that leads to the preservation and transmission of ideas. Textbooks, for instance, are a key instrument for containing, disseminating, and archiving knowledge of a specific domain that can be used to introduce those complex concepts to newcomers and the unspecialized. As such, a textbook is an epistemic device used for transmitting a condensed constellation of ideas, concepts, formulas, …

Reimagining Musical Collaboration: Ai And Music Real-Time Communication (M-Rtc), The Future Of Musical Creativity, Dana Kemack Goot

Journal of the Association for Technology in Music Instruction

This project explored the intersection of Music Real-Time Communication (M-RTC), artificial intelligence (AI), and machine learning (ML) in online musical collaboration. Presented at the 2023 National CMS/ATMI conference, the project served as a preliminary inquiry into how online musicking technologies impact human-to-human interactions and sought out the capabilities in creativity in music technology. The live performance featured three musicians performing from different geographic locations using an AI-generated composition and an AI-powered accompaniment application. The project demonstrated the creative potential of integrating AI and ML into M-RTC, expanding the possibilities for remote collaboration, composition, and performance. The musicians navigated both the …

Agenda (Pdf), Department Of Orthopaedic Surgery

Dept. of Orthopaedics: Robert D. D’Ambrosia Lectureship & Research Day

No abstract provided.

First Insight On The Genesis Of Iron Oxide-Hydroxide Biospeleothems In Rancho Chico Lava Tube, Mexico, Rafael López-Martínez, Elizabeth Solleiro-Rebolledo, Rocio Alcántara-Hernández, Teresa Pi Puig, Sergey Sedov, Daisy Valera Fernández, M. Nayeli Luis-Vargas, Yasmany Lima Vera, Jesús Solé, Ramon Espinasa-Pereña

International Journal of Speleology

Iron is a common element in lava tube systems, typically observed as thin crusts or inferred from the reddish coloration of secondary mineral deposits. In this study, we describe iron oxide-hydroxide biospeleothems discovered in the Rancho Chico Lava Tube, located within the Xalapa Monogenetic Volcanic Field in Mexico. We adopt an integrative approach combining petrography, soil micromorphology, scanning electron microscopy, mineralogy, and 16S rRNA gene metabarcoding. DNA sequencing reveals the presence of microorganisms such as Acidithiobacillus, Gallionella, and members of the Leptospirillaceae family, which may be involved in the oxidation of Fe²⁺ within the lava tube. The source …

A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater

SMU Data Science Review

Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in …

Enhancing News Article Generation With Ai Tools, Adam Ercanbrack, Christopher Johnson, Max Pagan, Nibhrat Lohia

SMU Data Science Review

Abstract. This paper aims to present a comparative analysis of a custom-built AI Journalist Assistant and ChatGPT 4.0 for news article generation. The goal is to evaluate the performance of each model based on accuracy, speed, ethical safeguards, and relevance, particularly in the context of journalism. While ChatGPT is widely used for general-purpose content creation, its reliance on older data and potential for plagiarism presents challenges in the fast-paced, high-stakes world of news reporting. To address these issues, we will design and implement an AI Assistant using Retrieval-Augmented Generation (RAG) techniques, focusing on real-time data access, bias reduction, and plagiarism …

Predictive Modeling Of Colorectal Cancer Risk: Leveraging Health, Demographic, And Socioeconomic Factors For Targeted Screening, Anish Bhandari, Shawn Deng, Michael Olheiser, Robert Slater

SMU Data Science Review

Colorectal cancer (CRC) remains a significant public health concern, affecting millions in the United States and worldwide. This study investigates the risk factors associated with CRC using data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and aims to develop predictive models to identify high-risk individuals for targeted screening and increased awareness. The dataset integrates CRC incidence data from the National Cancer Institute with socioeconomic indicators from U.S. Census Bureau, linked by zip code. We employ Logistic Regression and Neural Network models to predict CRC risk, incorporating health, demographic, and socio-economic features. While the results suggest that …

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

The Prevalence And Impact Of Discourse In Social Media Networks: The 2024 Presidential Election, Kyle Kuberski, Xavier R. Mojica, Gwonchan J. Yoon, Brad Klein, Bivin P. Sadler

SMU Data Science Review

Abstract. Social Media platforms serve as central hubs for global discourse, where political dialogue is widely shared and echoed. This exchange shapes civic participation and influences electoral outcomes, often with both intended and unintended consequences. In its inception, social media platforms served as message boards for the masses, yet manipulation and exploiting of systems via bot usage has made platforms susceptible to outside forces. Thus, false narratives and an artificial sense of consensus are endemically augmented. As for the 2016 and 2020 U.S. presidential elections, it is important to investigate the prevalence of evolved bot activity in both political and …

Molecular Quantum Particle Algorithm (Mqpa): Hybrid Quantum-Classical Learning For Molecular Property Prediction, Jessica T. Mcphaul, Bivin Sadler Ph.D.

SMU Data Science Review

Classical machine learning models and quantum kernel methods often struggle to capture quantum-coherent molecular features under the constraints of noisy intermediate-scale quantum (NISQ) hardware, limiting both predictive accuracy and scalability.

This paper introduces the Molecular Quantum Particle Algorithm (MQPA), a hybrid quantum–classical framework designed to achieve chemically accurate property prediction by integrating handcrafted molecular descriptors with parameterized quantum circuits. Molecular inputs, expressed as SMILES strings, are processed via RDKit and encoded through angle-based quantum gates with entangling layers in Qiskit [1]. Quantum parameters are optimized using simultaneous perturbation stochastic approximation (SPSA) [2], while classical regression layers leverage Adam [3] with …

Through The Eyes Of A Guard: Watson Kirkconnell And Great War Internment In Canada, Daniel R. Meister

Canadian Military History

Histories of internment in Canada have paid much attention to internees’ experiences but far less to those of the guards. Correcting this trend, this article offers a detailed case study of one soldier, Watson Kirkconnell, who worked at the Fort Henry and Kapuskasing internment camps. Drawing on his valuable but heretofore unutilised archive of material, this article provides a vivid social history of the camps through Kirkconnell’s eyes. Nuancing the historiography of wartime internment, this article demonstrates how commandants determined the experiences of both internees and guards alike and, at the same time, that staff could sometimes mitigate their superior’s …

The Enigmatic Samuel Aughey: Nebraska’S Pastor Naturalist, Hugh H. Genoways, Margaret R. Bolick, Mary Anne Andrei

Zea E-Books Collection

Samuel Aughey at age 39 became the first Professor of Natural Science at the new University of Nebraska in Lincoln, serving from 1871 to 1883. He was an ordained pastor of the Evangelical Lutheran Church, with an addendum degree from Pennsylvania College (now Gettysburg College) in 1856 and an A.M. degree two years later. His early pastoral work was in Pennsylvania, but in 1864 he became a home missionary to a church in Dakota City, Nebraska. Aughey’s career at the university was marked with successes and honors, but also criticism and questions about his basic knowledge of the sciences. While …

Why Real Impact Stories Of Forest-Dependent Communities Are Essential In Carbon Marketing, Andrew Mwape

School of Natural Resources: Faculty Publications

As the global carbon market matures, the pressure on companies and project developers to prove the integrity and social value of their carbon offset initiatives has never been greater. At the heart of this growing scrutiny lies a key opportunity: to move beyond metrics and tell authentic, human-centered stories that reflect the real impact of carbon projects; especially those rooted in forest landscapes.

Auroral Hemispheric Power Asymmetry During Geomagnetic Storms: A Multi-Database Investigation Using Ae And Supermag Indices, Melvin J. Reyes Lozada

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Melvin J. Reyes Lozada on June 20, 2025.

A Beacon Of Hope In Public Health, June 20, 2025, Tisa Mason Ed.D.

Heart of a Tiger Columns

No abstract provided.

Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu

Journal of System Simulation

Abstract: To address the negative impact of source-load uncertainty on the stable operation of the grid, a two-stage optimization scheduling strategy for the microgrid participation of electric vehicles based on the vehicle-to-grid (V2G) mode is proposed. In the first stage, the charging and discharging costs of electric vehicles as well as the load fluctuation target are determined taking into account the battery losses. Through a zero-sum game, we objectively weigh the interests of both vehicle owners and the microgrid, utilizing the mobile energy storage characteristics of electric vehicles to optimize the load curve and integrate renewable energy; in the second …

Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei

Journal of System Simulation

Abstract: In order to solve the problem that UAV aerial images have a large number of small target samples but little extractable feature information, which is not conducive to improving the accuracy of aerial target detection, an improved small target detection algorithm for aerial photography based on YOLOv8s is proposed. The algorithm applies deformable convolution to the feature extraction module of the backbone network to adaptively capture the details of the target at different locations and scales. The feature information at different scales of the backbone network is extracted and enhanced by the feature collection module in the multilevel information …

Generation Of Non-Maxwell-Boltzmann Energy Distributions During Thermalization Of Interacting Baths, Roberto Onofrio, Bala Sundaram

Data and Datasets

data associated with all the figures in the publication.

Development Of A Near Terahertz Backward Wave Oscillator Using Standard Waveguide, Alexander Glick

Electrical and Computer Engineering ETDs

There is a demand for terahertz (THz) frequency radiation sources. Applications include, but are not limited to, imaging for medical and security purposes, biochemical and organic spectroscopy, and velocimetry. Historically, there was a limited supply of THz devices due to technological limitations. In recent years much progress has been made to reduce this “gap” in supply and demand for THz sources. This work proposes a vacuum electronic device that produces high power, extremely high frequency radiation in the G-band, by utilizing a backward wave oscillator (BWO) based on WR3 standard waveguide. This device is compact, fundamentally simple, and has great …

A Comprehensive Systematic Review Of Machine Learning Applications In Assessing Land Use/Cover Dynamics And Their Impact On Land Surface Temperatures, Rasool Vahid, Mohamed Aly

Environmental Dynamics Faculty Publications and Presentations

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the …

2025-06-20 Agenda Book, Morehead State University. Board Of Regents.

Morehead State Board of Regents Agenda Books and Meeting Minutes

Agenda booklet for the Board of Regents meeting of June 20, 2025, which includes the minutes of the meeting held on March 28, 2025.

Integrated Decision Support System For Optimizing Time And Cost Tradeoffs In Linear Repetitive Construction Projects, Ahmed Gouda Mohamed, Ali Hassan Ali, Ahmed Adel Abdelhady

Civil Engineering

No abstract provided.

Integrated Decision Support System For Optimizing Time And Cost Trade Offs In Linear Repetitive Construction Projects, Ahmed Gouda Mohamed

Civil Engineering

time and cost performance. Traditional scheduling techniques often struggle to effectively address these complexities. This paper aims to enhance project optimization by introducing a metaheuristicbased Time-Cost Trade-off (TCT) framework specifically designed for repetitive project environments. Unlike previous studies that focus solely on single-algorithm applications, this research evaluates two metaheuristic optimization strategies—Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)—within a consistent problem setting. The framework employs both algorithms, which are independently assessed for their effectiveness in tackling the Linear Repetitive Project Time-Cost Trade-off (LRPTCT) problem. The methodology utilizes task decomposition alongside the Line of Balance (LOB) scheduling technique, facilitating a more …

Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng

Journal of System Simulation

Abstract: To eliminate the influence of parameter perturbations and external disturbances on the wheel angle tracking control performance of steer-by-wire (SbW) system, a fractional-order integral terminal sliding mode control scheme based on a finite-time disturbance observer is proposed. A sliding modebased second order finite-time disturbance observer (FDO) is designed to precisely estimate the total disturbance of the SbW system, and the estimated total disturbance is compensated into the system control input to reduce the wheel angle tracking error. A fractional-order fast integral terminal sliding mode control (FOFITSMC) scheme is designed to ensure fast convergence of the wheel angle tracking error …

Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge

Journal of System Simulation

Abstract: In order to improve the visual obstacle avoidance ability of substation robots in complex environments, a robot visual obstacle avoidance method based on spatiotemporal networks is proposed. The method utilizes traditional image processing techniques to enhance road information and designs a lightweight deep convolutional neural network structure to extract road features from a spatial domain perspective; based on the spatial characteristics of the road, a long short-term memory network is introduced to mine the changes in the road from a temporal perspective, and a classification regression prediction structure is used to predict the robot's obstacle avoidance direction and angle; …

Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao

Journal of System Simulation

Abstract: To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms …

Research On Behavior Control Techniques For Autonomous Vehicles Based On Parallel Behavior Tree Architecture, Jianchao Yuan, Shuo Yang, Qi Zhang, Ge Li

Journal of System Simulation

Abstract: Aiming at the problem of high collision rate and low efficiency of traditional serial behavior tree in autonomous vehicle control, a solution based on improved parallel behavior tree architecture is discussed to achieve safe behavior control. A safety behavior control strategy under dynamic road conditions is proposed, and behavior models for observation, decision-making, and movement are constructed, as well as their temporal constraint relationships; an improved parallel behavior tree control architecture is proposed, which achieves parallel execution and real-time interaction of behaviors through parallel control nodes, improving the real-time performance of decision control. The results show that compared with …