Estimating Mariana Monitor (Varanus Tsukamotoi) Density With Multiple Methods On Islan Dåno’, Guam, And Its Relevance To Guam Rail (Hypotaenidia Owstoni) Conservation, Jack T. Christie

Cal Poly Humboldt theses and projects

The Mariana monitor (Varanus tsukamotoi), or hilitiai, is a medium-sized varanid lizard found throughout the Mariana islands. On Islan Dåno’, a small islet off the southwest coast of Guam, hilitai coexist with one of the rarest birds in the world, the Guam rail (Hypotaenidia oswtoni), or ko’ko’, which were introduced to the island in 2010. Since 2009, hilitai have been lethally removed from the island to reduce possible predation on ko’ko’. After 15 years of removal efforts, I sought to 1) estimate the abundance and density of hiltai, 2) assess the effect of removal effort and …

Wait... Homework Can Be Fun?! A Pilot Homework System Study, Caroline Brock

Cal Poly Humboldt theses and projects

Homework has been a long-debated topic in schools. Regardless of where people stand on the issue, the evidence shows that quality homework can be an effective tool to help students be successful in school. Beyond academics, homework can also be used as a tool to help students develop important foundational study skills that are critical for future success. But how can school districts get all stakeholders behind a homework system if they do not see the value of homework, or if the homework is a point of individual or familial stress. Through an action research project using mixed methods, parents …

Elementary Teachers' Knowledge Towards Teaching Physical Education, Hannah Lee Mandy

Cal Poly Humboldt theses and projects

This study examined elementary teachers’ preparation in physical education (PE) and its impact on teaching effectiveness for students, including students with disabilities. Twenty-seven teachers completed a survey and three participated in a follow-up focus group where participants were asked to expand on previous questions. Findings revealed limited PE coursework, minimal hands-on experience and low familiarity with the California Physical Education Standards in teacher preparation programs. Additionally, only 74 percent of participants completed a PE course during their teacher preparation program and focus group participants emphasized their preparation programs focused on lesson planning rather than hands on teaching, and the programs …

Large Language Models And Identity: A Reflexive Approach, Nicholas Austin Nielsen

Cal Poly Humboldt theses and projects

This project explicates theories of identity, technology, and capitalism and uses an auto ethnographic approach in order to apply that explication at a personal level. The project responds to AI rhetoric in business and pedagogy and the instability surrounding perspectives on job security, the role of technology in Higher Education, and existential representations of the human. By deconstructing identity, ideology, cultural contexts, and technologic rhetorics, the goal is to further the scholarship surrounding Large Language Model applications in pedagogy and offer a personal, therapeutic approach to feelings of economic and existential discontent and fear experienced by students and educators regarding …

Provenance Of Clastic Sedimentation In Middle Deadfall Lake And Paleoclimate Modelling Of Glacier Fluctuations At Mount Eddy, Eastern Klamath Mountains, California, Usa, Emily Stebbins

Cal Poly Humboldt theses and projects

This study reconstructs the timing, extent, and paleoclimate conditions associated with late Pleistocene glaciation in the Mount Eddy region of the eastern Klamath Mountains, northern California and investigates the provenance of clastic sedimentation in Middle Deadfall Lake, just east of Mt Eddy. Using a multi-pronged approach that combines geomorphic mapping, sediment core analysis, X-ray fluorescence (XRF) geochemistry, tephrochronology, and glacier modeling using PalaeoIce 2.0, this research provides new insights into glacial dynamics and climate variability in a region with limited glacially derived paleoclimate datasets, as well as past volcanic eruption deposits.

Field and GIS-based geomorphic mapping identified three generations …

Zika Virus Encephalitis Causes Transient Reduction Of Functional Cortical Connectivity, Shannon C Agner, Lindsey M Brier, Jeremy D Hill, Ethan Y Liu, Annie Bice, Rachel M Rahn, Shengxuan Chen, Joseph P Culver, Robyn S Klein

2020-Current year OA Pubs

SIGNIFICANCE: Determining the long-term cognitive impact of infections is clinically challenging. Using functional cortical connectivity, we demonstrate that interhemispheric cortical connectivity is decreased in individuals with acute Zika virus (ZIKV) encephalitis. This correlates with decreased presynaptic terminals in the somatosensory cortex. During recovery from ZIKV infection, presynaptic terminals recover, which is associated with recovered interhemispheric connectivity. This supports the contribution of synapses in the cortex to functional networks in the brain, which can be detected by widefield optical imaging. Although myeloid cell and astrocyte numbers are still increased during recovery, RNA transcription of multiple proinflammatory cytokines that increase during acute …

Spatial Analysis Of American Chestnut (Castanea Dentata) Distribution And Ecology In Kentucky’S Federal Lands, Jacob Raymond Pease

Murray State Theses and Dissertations

Anthropogenic factors have been the cause of the decline of tree species across the globe. From unsustainable logging and development to disease and improper management, threats to forests have taken their toll. The American chestnut (Castanea dentata) was once an integral basal tree species in Appalachian forests, but chestnut blight drove the species to functional extinction by the mid-1900s. The recent creation of genetically modified chestnuts gives hope for future restoration of the species throughout its historical range. Despite blight resistance, effective restoration will require optimal site selection for finding and planting chestnuts, which will maximize limited resources. …

An Examination Of Middle School Enrollment And Participation In The National Ffa Organization, Melanie Dorminey

Murray State Theses and Dissertations

Middle school agricultural education programs and opportunities for middle school students to be members of the National FFA Organization were first made available in 1988. The Perkins V Act of 2018 provided the first federal funding to support middle school career and technical education programs. In conjunction with the federal funding provided by the Perkins V Act of 2018, the National FFA Organization adopted a national middle school membership affiliation program where middle school FFA chapters could pay a flat fee for all students enrolled in middle school agricultural education courses over the traditional membership of paying per individual student. …

The Link Between Poor Student Reading Proficiency And College And Career Readiness, James D. Kirkland Iii

Murray State Theses and Dissertations

The ability to read proficiently is a skill often considered necessary for the success of an individual while in school and after graduating from high school. Although reading proficiency plays a large role in an individual's success, many students in our nation’s schools struggle to read and comprehend the texts they are presented with in their academic courses. The purpose of this research study was to determine whether a link existed between the reading proficiency and college and career readiness of high school agriculture students. The study used a mixed methods approach that collected both qualitative and quantitative data to …

Identifying The Agricultural Mechanics Knowledge And Skills Needed By Kentucky Agriculture Teachers, Thomas Poole Jr

Murray State Theses and Dissertations

The agricultural industry increasingly relies on complex machinery and technology, creating a need for skilled workers that are proficient in agricultural mechanics. School-based Agricultural Education (SBAE) programs play a crucial role in preparing students for careers in this field. However, many agriculture teachers feel unprepared to teach agricultural mechanics due to gaps in technical skills, self-efficacy concerns, and safety considerations. This study sought to determine the agricultural mechanics knowledge and skills that Kentucky agriculture teachers need to successfully teach agricultural mechanics in SBAE programs across the state. Using Wells et al.'s (2021) Agricultural Teacher Education and Agricultural Industry Partnership Model …

Strong Neighborhood-Prime Labelings Under Ordinary & Gaussian Integers, Micheal Arnal-Brown

Murray State Theses and Dissertations

This thesis introduces and studies the notion of a strong neighborhood-prime labeling, a strengthening of the neighborhood-prime labeling where the label 1 can be assigned to any vertex in a graph. We prove that several graph families—including paths, cycles (excluding those congruent to 2 modulo 4), caterpillars, helm graphs, closed helm graphs, gear graphs, and graphs with universal vertices—admit such labelings, and also provide results to more general classes of graphs. We extend this new labeling concept to the Gaussian integers using a spiral order- ing on Z[i] and define a Gaussian analogue of strongly neighborhood-primeness. To support this extension, …

Examining Kentucky Agriculture Teachers’ Use Of Student Credentialing In Agricultural Education Programming, Kaycee J. Palmeri, Kevin T. Wells

Murray State Theses and Dissertations

The purpose of this qualitative study was to explore Kentucky agriculture teachers’ perceptions regarding current industry credential offerings, how they operationalize the curricula, knowledge, and skills necessary to prepare for industry credential offerings, and the perceived benefits for students who earn such credentials. The current study was undergirded by human capital theory. Using random sampling across the 12 Kentucky FFA service regions, the researcher collected data from nine current Kentucky agriculture teachers via semi-structured interviews. Further, the researcher used a Qualtrics-based instrument to collect participant demographics data. The analysis of the interview data revealed eight distinct emerging themes. The researcher …

How Did The Covid-19 Pandemic Affect The Educational Level Of Students, Measured By The Act?, Davey A. Reed, Davey A. Reed

Murray State Theses and Dissertations

This study examined the relationship between socioeconomic status (SES), chronic absenteeism, and ACT performance among Alabama’s 4A public high schools, with a particular focus on how these factors evolved before and after the COVID-19 pandemic. Utilizing a longitudinal design, the research examined data from four academic years: 2019 (pre-COVID) and 2022, 2023, and 2024 (post-COVID). Data were obtained from the Alabama State Department of Education’s public report card portal and included ACT composite scores, school-level SES data, and chronic absenteeism rates. Schools were anonymized to ensure confidentiality and grouped into SES categories (Low, Mid, High) for comparative analysis. A combination …

Alabama Agriculture Teachers’ Perceptions Toward Compensation And Extended Contracts, Allison Kincer

Murray State Theses and Dissertations

Abstract

The purpose of the current study was to explore Alabama agriculture teachers’ perceptions toward compensation and extended contract grants that the state offers annually. The current study was a microreplication of Still and Solomonson’s (2024) prior study and was conceptually framed by their agriculture teacher compensation model. Using a Qualtrics-based data collection instrument, the researcher used multiple points of contact as recommended by Dillman et al. (2014) and collected usable data from 95 Alabama agriculture teachers. The researcher used descriptive statistics to analyze all data collected as part of the current study. The researcher found that respondents felt they …

Bridging Classroom And Competition: How Students With Intellectual Disabilities Learn And Apply Ffa-Related Skills, Skyler Lempert

Murray State Theses and Dissertations

Though people often hear about inclusion and differentiation in education and their classroom settings there are questions about the capability and effectiveness of students with moderate to severe disabilities to participate in intra-curricular activities such as the National FFA Organization. Often teachers are tasked with trying to find ways for students to be a part of these experiences with little education on this aspect. This research begins by examining the historical progress of special education, particularly in relation to students with moderate to severe disabilities. It then explores the complex educational needs of this population and how schools have worked …

Relationship Between School Climate And Effectiveness, Michele D. Franklin Mrs

Murray State Theses and Dissertations

Abstract

The purpose of this quasi-experimental study was to explore the relationship between school climate and school effectiveness in six rural elementary schools. School effectiveness was defined by state reading, math, and index achievement scores. The districts’ Studer Education Survey and the KSA’s Quality of School Climate and Safety survey assessment data were combined to determine each school’s climate score. The Multi-Tiered Systems of Support (MTSS) framework guided the study. The study examined a rural school district’s focus on providing a system of support for reading, math, social emotional learning (SEL), and behavior (SEB) that influenced school climate and ultimately …

Elementary School Mathematics Teacher Perspectives Of Blended Learning, Elizabeth H. Forrester

Murray State Theses and Dissertations

This qualitative case study explored the experiences and perceptions of teachers who utilize technology for instructional purposes, blended learning in elementary school instruction, and the impact of professional development (PD) on blended learning instruction. Guided by the Technological, Pedagogical, and Content Knowledge theory (TPACK), research aims to understand teachers' motivation and use of blended learning during instruction and the impact PD has had on teachers’ implementation of blended learning. Data was collected through semi-structured interviews of seven elementary school teachers in grades two through five in the state of Kentucky. Findings revealed that teachers experienced technology integration instruction during their …

Deepseek: Is It The End Of Generative Ai Monopoly Or The Mark Of The Impending Doomsday?, Malik Sallam, Kholoud Al-Mahzoum, Mohammed Sallam, Maad M. Mijwil

Mesopotamian Journal of Big Data

The rise of superintelligent open-source generative AI (genAI) heralds both extraordinary potential and unprecedented risk, exemplified by the rapid emergence of DeepSeek as a global AI innovator. This perspective article examines the dual-edged nature of open source genAI technologies, highlighting their capacity to democratize innovation while exposing critical vulnerabilities. By providing affordable, high-performing, and openly available models like DeepSeek-R1 and DeepSeek-V3, this Chinese AI company has disrupted the proprietary dominance of Western AI giants. These advancements are expected to empower researchers in resource-limited settings, foster global collaboration, and enable breakthroughs across numerous fields. Open-source AI, as illustrated by DeepSeek, has …

Coupling A Dual Tree - Complex Wavelet Transform With K-Means To Clustering The Epileptic Seizures In Eeg Signals, Raid Lafta, Rasha Hallem Razzaq

Mesopotamian Journal of Big Data

Electroencephalography (EEG) signals are routinely recorded in clinical settings for the diagnosis of epilepsy, that is, brain electrical disorders, by a neurologist. Nevertheless, both the reliability and safety of GB analysis of EEG parameters are not satisfactory. Therefore, identifying the effectiveness of EEG for diagnosis is a considerable challenge for hospitals. This study was conducted to improve the efficiency of detecting epileptic seizures in EEG signals by combining three methodologies: the double tree complex wavelet transform (DT-CWT), K means clustering, and the ChaCha20 encryption algorithm. EEG segments are initially partitioned into regular segments, each of which is further partitioned into …

A Comparative Study Of Rfid System Performance In Large-Scale Network Planning Facility, Ali Abdulqader Al Qisi, Azli Bin Nawawi, Adel Muhsin Elewe

Mesopotamian Journal of Big Data

Big data in manufacturing fields present several challenges leads to reduce profitability and missed opportunity for innovation. One of the used strategies is the use of radio frequency identification system.  considered a business strategy to increase productivity, speed up decision-making, and enhance production monitoring and control while preserving the structure and integrity of current manufacturing systems. The present research compares five artificial inelegant algorithms based on RFID system in facility layout design to investigate the fitness of each algorithm in manufacturing big data processing. The objective functions have been used are the minimum number of required readers, minimum readers overlap, …

Advances And Insights In Image Texture Analysis : A Review, Ghaith H. Alashour, Nidhal K. El Abbadi

Mesopotamian Journal of Big Data

Texture analysis is an essential step in image analysis, and subsequent applications such as medical imaging, remote sensing, and scene understanding are highly important in image processing. Although it is vital, the field presents its own set of research challenges, especially when manipulating variations in texture patterns and requirements for properties that remain unaffected by related transformations, such as rotation, scaling, and translation. This review provides an in-depth description of key activities in the field of texture analysis, including classification, segmentation, synthesis, and image retrieval, along with their strengths and limitations. The approaches are classified as structural, statistical, and model-based …

Hybrid Quantum-Assisted Deep Learning Model For Early-Stage Alzheimer’S Disease Classification Based On Mri Images, Eman A. Radhi, Mohammed Y. Kamil, Mazin Abed Mohammed

Mesopotamian Journal of Big Data

Alzheimer's disease (AD) presents significant diagnostic challenges owing to the subtle morphological similarities observed in the early stages, with traditional deep learning approaches often struggling to distinguish between the various stages of disease progression via structural Magnetic Resonance Imaging (MRI) data. Quantum computing offers unique advantages for medical image analysis, leveraging superposition and entanglement capabilities to process high-dimensional feature spaces beyond the limits of classical computation. This study introduces a hybrid quantum-classical neural network architecture (HQC-Net) for accurate four-class Alzheimer's disease classification, which uses quantum processing to detect patterns that are often invisible to classical spatial analysis methods. The proposed …

Towards Autonomous Optical Fibre Networks: High-Precision Edfa Gain And Spectral Response Prediction Via Hybrid Cnn-Lstm Deep Learning, Ibtesam Hussien Htaat, Mudhafar Hussein Ali, Abdulla Khudiar Abass

Mesopotamian Journal of Big Data

This paper introduces a hybrid CNN-LSTM architecture for automatic advantage prediction and optimization in erbium-doped fibre amplifiers (EDFAs), addressing a crucial role in maintaining signal strength over long-distance optical networks; however, existing modelling techniques face significant challenges in balancing accuracy and computational efficiency. The proposed version uniquely integrates convolutional neural networks (CNNs) for spatial-spectral function extraction and long short-term memory (LSTM) networks for temporal dynamics modelling, allowing simultaneous prediction of benefit profiles, 3 dB compression factors, and full-width half-maximum (FWHM) bandwidths from 5 input parameters: pump electricity, signal energy, fibre length, erbium concentration, and wavelength. When validated against OptiSystem simulations …

Shared Generator-Based Serverless Multimodal Federated Learning For Medical Image Analysis, Mohammed Adel Al-Shahe, Ahmed Saihood, Mustafa Asaad Hasan

Mesopotamian Journal of Big Data

Medical image analysis constitutes the foundation of the diagnosis of potential life-threatening conditions such as lung cancer. Nevertheless, AI model construction in this context is hindered by strict privacy regulations (e.g., HIPAA, GDPR), the variability of imaging protocols, and the paucity of large annotated datasets. These barriers constrain centralised machine learning and dampen interdisciplinary research. To overcome such barriers, this paper introduces shared generator-serverless federated learning (SGS-FL), a decentralised multimodal medical imaging framework. By employing a shared generator and multidiscriminator architecture, SGS-FL eliminates centralised dependency via cross-modal synthesis, while the communication burden is reduced by embedding a sharing protocol. By …

A Robust Model For Android Malware Detection Via Ml And Dl Classifiers, Omar Almomani, Areen Arabiat, Muneera Al Tayeb, Mohammed Amin Almaiah, Mansour Obeidat, Theyazn H. H. Aldhyani, Rami Shehab, Mahmood Rowad

Mesopotamian Journal of Big Data

The rapid growth of sophisticated Android malware (AM) threats is significant, as Android devices often store private and sensitive personal and financial information. These threats allow stealing of data, interference with device functioning, and network compromise. One of the greatest difficulties in efficient interception systems is ensuring a high level of detection accuracy for distinguishable AM variants. This study focuses on developing a robust Android malware detection model via machine learning (ML) and deep learning (DL) techniques. The model combines ML classifiers, which consist of logistic regression (LR) and decision trees (DTs), and a DL classifier, an artificial neural network …

Transformer-Based Genomic Classification And Simulation Of Heat-Responsive Genes In Citrus Limon, Ali Fahem Neamah, Zahraa A. Abdalkareem, Mostafa A. Mohammed, Jasim Ibrahim Ahmed, Phaklen Ehkan

Mesopotamian Journal of Big Data

Iraqi lemon trees (Citrus limon), vital for regional agriculture and food security, face intensifying threats from extreme heat caused by ongoing climate change in Iraq. Native cultivars often lack thermotolerance due to low expression of protective heat-response genes. This study addresses this critical challenge by developing an AI-assisted framework that integrates real RNA-Seq data, Transformer-based deep learning, and explainable AI to classify and simulate the function of genes associated with heat stress adaptation. The primary objective is to identify key thermotolerance genes and model their biological impact, with a specific focus on indigenous citrus varieties. Using a customized transformer architecture …

Optimized Wavelet Scatter Method For Accurate Classification And Segmentation Of Lung Nodules, Enas Hamood Al-Saadi, Ahmed Nidhal Khdiar, Nidhal K. El Abbadi

Mesopotamian Journal of Big Data

Lung cancer is a significant global health concern due to its high mortality rate. This is primarily due to the difficulty of identifying malignant growths in early-stage computed tomography (CT) images. The thing is, it's often hard to tell the difference between a benign nodule and a malignant one. This makes it tricky to figure out what's going on with the patient. That's where these computer-aided diagnosis (CAD) approaches come in. This study introduces a lightweight and interpretable framework for comprehensively analyzing lung nodules, including their detection, classification, and segmentation. This approach uses the wavelet scattering transform (WST) to extract …

Data Mining And Machine Learning-Based Healthcare Monitoring In Cloud-Iot, Sarah Amer, Rania Hazim, Wassan Kader

Mesopotamian Journal of CyberSecurity

Healthcare monitoring Cloud-IoT systems use data mining and machine learning methods to analyse patient data in real-time from linked devices. By offering insights for the early diagnosis of anomalies and individualized treatment suggestions, this strategy improves healthcare management. In this research first the Collect and Load the Clevant Heart Disease Dataset for Data Collection Process. Next, preprocess the loaded data using the Synthetic Minority Oversampling Technique (SMOTE), and then the feature extraction process is done using the Principal Component Analysis (PCA) Method. In this case, the characteristic must be extracted by feeding a specific column. The classification procedure is then …

Department Of Computer Engineering, University Of Diyala, 32001 Diyala, Iraq., Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel

Mesopotamian Journal of CyberSecurity

Android devices are rapidly being used, which makes it easy for the malware threat to rise to higher levels. This ever-growing problem has prompted the need to enhance detection systems as far as these devices are concerned. Standard techniques of machine learning (ML) are sufficient from the point of view of their speed for searching patterns and behaviors of contemporary malware; however, it is more important to have effectively enhanced methods. The purpose of this paper is to expand the utilization of Android malware identification via artificial neural networks (ANNs) and compare its efficiency with that of other ML methods. …

Exploring The Impact Of Blockchain Revolution On The Healthcare Ecosystem: A Critical Review, Maad M. Mijwil, Mohammad Aljanabi, Mostafa Abotaleb, Ban Salman Shukur, Ban Salman Shukur, Indu Bala, Kamal Kant Hiran, Ruchi Doshi, Klodian Dhoska

Mesopotamian Journal of CyberSecurity

Blockchain technology is a type of distributed ledger that provides secure and efficient storage, management, and transmission of data over a decentralized network. With its ability to ensure transparency and immutability, blockchain is increasingly adopted across various sectors ranging from finance, healthcare, and logistics to education. In healthcare, blockchain technology is attracting attention because of its potential to fundamentally transform health ecosystems. The healthcare sector has significantly benefited from blockchain technology by enhancing data security and interoperability and reducing medical errors. In this context, a set of studies highlighted the importance of blockchain in the field of healthcare, enhancing trust …