Digital Commons Network^™

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 …

Examining The Impact Of A Health Course On Lifelong Learner Attitudes Toward Psychedelic And Entheogenic Substances, Dustin B. Larrazolo

Cal Poly Humboldt theses and projects

This study examined the impact of a three-week Osher Lifelong Learning Institute (OLLI) health course, “Far Out” Medicine: The Science and History of Psychedelics, on midlife and older adult learners’ knowledge, attitudes, and perspectives regarding psychedelic and entheogenic substances. Eleven midlife and older adults (ages 40 - 80; M = 59.0, SD = 15.35) completed matched pre- and post-course surveys assessing demographic characteristics, factual knowledge, attitudes, personal willingness, and social or policy views. The sample was highly educated (1 associate, 5 bachelor’s, 3 master’s, and 2 doctoral degrees) and demonstrated strong internal consistency and response convergence across repeated measures. …

Sex Work And Covid-19: An Intersectional Discussion Of U.S.-Based Sex Worker Experiences During The Pandemic, Sabrina M. Grandia

Cal Poly Humboldt theses and projects

The COVID-19 pandemic impacted all forms of industry, including that of sex work. Between October 2020 until November 2022, I did field research and interviews with nine U.S.-based sex workers throughout those months of the pandemic. Using a grounded theory approach, I asked open-ended questions about their entrance into the industry, business practices, and personal experiences before and during the pandemic. Their stories allow us to gain insight into the impact the pandemic had on them as individuals and their broader communities. My analysis highlights a prominent influence of anti-trafficking policy, politicization of the virus, and the impact of Black …

The Impact Of Guided Goal Setting On Mood And Exercise Performance In College-Age Active Individuals, Payton R. Heaney

Cal Poly Humboldt theses and projects

Mental health challenges are common in college students and student athletes, yet finding accessible and effective methods to improve mood and mood disorders remains under-researched. Mood is closely related to perceived performance, making mood improvements even more important for active college individuals. This study examined whether guided goal setting and health coaching could significantly improve mood as well as perceived performance. Sleep and nutrition were targeted as key factors due to their well-researched connection to mood. Participants of this study were 25 healthy active college individuals aged 18-35. The sports and activities represented in this study were a mix of …

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 …

Environmental And Spatial Drivers Of Quillback Rockfish (Sebastes Maliger) Growth In The Northeast Pacific Ocean From California To Alaska, Claire E. Stuart

Cal Poly Humboldt theses and projects

Quillback rockfish (Sebastes maliger) are a nearshore fishery species found across the Northeast Pacific Ocean, but recent stock assessments have identified a research gap for biological growth parameters, particularly for the southern end of their range. Age-length data compiled from California to Alaska (n=34,396) was used to generate extended von Bertalanffy models with spatial, biological, and environmental covariates, specifically region, sex, depth, and an upwelling index. The objectives of this study were to explore how 1) spatial and 2) environmental covariates affect model parameters L_∞, k, and t₀. Four total models were developed: …

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 …

Curriculum Addressing Recidivism: A Case Study Of Project Rebound’S Youth Empowerment Program, Julie Stewart

Cal Poly Humboldt theses and projects

Providing effective rehabilitation for justice-involved youth is an increasing priority within the American social justice system, yet remains a significant challenge. The Youth Empowerment Program (YEP) at Cal Poly Humboldt uses curriculum to support learning objectives related to emotional intelligence, critical thinking, and emotional maturation, with the broader goal of reducing recidivism among youth in Northern California. Although curriculum design is widely understood in education as essential for effective learning, limited research examines how youth rehabilitation programs in carceral settings use curriculum to address the developmental and learning needs of participants. Using a case study methodology, this research examines how …

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 …

A Comparative Analysis Of Perineuronal Nets In Trout And Zebrafish Models, Adnan Alyan

Cal Poly Humboldt theses and projects

Perineuronal nets (PNNs) are specialized extracellular matrix structures that surround certain types of neurons in the central nervous system. Research from the past few decades has cemented PNNs as an integral player in the closure and onset of critical periods, memory modulation, and neural plasticity. These effects are thought to occur when PNNs form around a neuron and structurally stabilize its synaptic connections. PNNs have been directly linked to behavioral changes, making them of interest to psychologists as well as neuroscientists. Several PNN components, such as Hapln1 and aggrecan such as have been found in zebrafish, a teleost fish (Kang …

Serum Resistin Levels In Patients With Acne Vulgaris And It's Correlation With Disease Severity, Marwa M. Lasheen, Magdy A. Elsohafy, Gehad M. El Mazny, Manar A. Hassan

Mansoura Medical Journal

Background: Acne is a condition characterized by inflammation of the pilosebaceous unit, and resistin—an inflammatory hormone may contribute to its pathogenesis, as studies show altered serum levels in acne patients. This work aimed to evaluate serum resistin levels in acne vulgaris patients and its association with disease severity. Methods: This Case control study involved 46 individuals had acne vulgaris, ranged in age from 18 to 35, free of systemic medication for the three months or topical treatment for the last month and 46 apparently healthy age and sex matched subjects. The patients were then split into two categories: Cases (n …

Enhancing Intrusion Detection Systems With Adaptive Neuro-Fuzzy Inference Systems, Jitender Sharma, Sonia ., Karan Kumar, Pankaj Jain, Pankaj Jain, Hussein Alkattan

Mesopotamian Journal of CyberSecurity

Network security has become increasingly critical in recent years. Among the various aspects of network security and considering several approaches to network security, intrusion detection systems (IDSs) have gained considerable attention. The prominence of this factor, among other factors of network security, is due to its ability to address the complex and uncertain nature of security breaches. Whenever data flow over the network, precise categorization of normal and malicious data is necessary. Past IDS systems lack precise categorization. Thus, the present study focuses on the use of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier to categorize network instances …

Improvement Of Internet Of Things (Iot) Interference Based On Pre-Coding Techniques Over 5g Networks, Inas F. Jaleel, Rasha S. Ali, Ghassan A. Abed

Mesopotamian Journal of CyberSecurity

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT …

Hybrid Cooperative Spectrum Structured (Hcss) Approach For Adaptive Routing In Cognitive Radio Ad Hoc Networks, Sadeem Dheyaa Shamsi, Ameer Sameer Hamood Mohammed Ali, Wameed Deyah Shamsi

Mesopotamian Journal of CyberSecurity

Cognitive radio networks provide an important function in the efficient use of the radio spectrum. Therefore, dedicated cognitive radio ad hoc networks (CRAHNs) are expected to improve communication performance in a multihop network that requires a dedicated routing protocol that considers the dynamic mobility of secondary user nodes on the basis of the random availability of primary node channels. To enhance routing in the CRAHNs environment, in this paper, a novel Hybrid Cooperative Spectrum Structured (HCSS) approach that combines cooperative spectrum sensing and spectrum-aware routing protocols can be effective in appropriate decision making for routing packets in such a network. …

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 …

Antdroidnet Cybersecurity Model: A Hybrid Integration Of Ant Colony Optimization And Deep Neural Networks For Android Malware Detection, Riyadh Rahef Nuiaa Al Ogaili, Osamah Adil Raheem, Mohamed H Ghaleb Abdkhaleq, Zaid Abdi Alkareem Alyasseri, Zaid Abdi Alkareem Alyasseri, Ali Hakem Alsaeedi, Yousif Raad Muhsen, Selvakumar Manickam

Mesopotamian Journal of CyberSecurity

Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware detection …

Enhancing Crime Detection In Video Surveillance Via A Lightweight Blockchain And Homomorphic Encryption-Based Computer Vision System, Tanya Abdulsattar Jaber

Mesopotamian Journal of CyberSecurity

Blockchain technology consists of distributed ledgers or database systems, regarded as immutable, secure, and innovative, characterized by unsupervised internal maintenance with a special security protocol used to prevent inference from malicious or third parties. The widespread use of this technology has led to deep research into the problems posed by this technology, which can be summarized in terms of computational cost and latency time. The crime detection process in video surveillance has made great progress with the use of technologies such as the Internet of Things and blockchain technologies. However, to reach high levels of security in the physical crime …

Improved Blockchain Technique Based On Modified Slim Algorithm For Cyber Security, Sarah Mohammed Shareef, Rehab Flaih Hassan

Mesopotamian Journal of CyberSecurity

The number of cybersecurity incidents is increasing, and the cost of a security breach has a catastrophic financial effect. The existing real estate information systems require more robust and resilient cybersecurity solutions to secure hardware, software, and databases. Cybersecurity often relies on user behavior. Phishing attacks and social engineering can bypass blockchain security, highlighting the need for robust user education and awareness. Many blockchain networks face challenges in handling a high volume of transactions. Solutions that work well on a small scale may not perform efficiently as the network grows, leading to delays and increased costs. A secure system is …

Robust And Efficient Methods For Key Generation Using Chaotic Maps And A2c Algorithm, Ali A. Mahdi, Mays M. Hoobi

Mesopotamian Journal of CyberSecurity

In the current digital landscape, information security has become a critical necessity given the escalating frequency and sophistication of cyberattacks across global computing networks. Cryptography is the science and practice of securing information by transforming it into a format that is unreadable or inaccessible to unauthorized parties. The strength of the cryptography algorithm lies in the strength of used encryption key. Recently, the nonlinear behavior of chaotic maps has been utilized as a random source to generate robust key stream bits for cryptographic purposes. The aim of this paper is to introduce an efficient and robust system for generating strong …

Learning Techniques-Based Malware Detection: A Comprehensive Review, Sarah Fouad Ali, Musaab Riyadh Abdulrazzaq, Methaq Talib Gaata

Mesopotamian Journal of CyberSecurity

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the threat landscape, with malwares arising as a critical concern. Advanced learning methods such as machine learning (ML), deep learning (DL), and federated learning (FL) are essential for handling complex IoT data. ML provides tools for pattern identification and detecting anomalies. DL boosts malware detection by automatically extracting features and identifying patterns. FL enables collaborative model training across decentralized devices, ensuring data privacy, which is crucial for diverse IoT systems. This comprehensive review specifically synthesizes ML, DL and FL for malware detection in the IoT environment, highlighting key …

Secured Multi-Objective Optimisation-Based Protocol For Reliable Data Transmission In Underwater Wireless Sensor Networks, Mostfa Albdair, Zainab Rustum Mohsin, Ahmed Saihood, Aqeel M. Hamad, Aqeel M. Hamad

Mesopotamian Journal of CyberSecurity

Underwater wireless sensor network (UWSN) requirements have increased beyond applications in environmental monitoring and underwater exploration to military surveillance. The complex underwater environment raises many challenges due to high propagation delays, limited bandwidth, high error rates, and dynamic underwater currents. Most traditional clustering algorithms do not consider the multifaceted requirements of UWSNs. In most cases, a single objective is optimised at the cost of other essential factors, such as energy consumption, network robustness, and data transmission reliability. This paper proposes a new UWSN protocol based on the tiger beetle optimisation (TBO) algorithm for multiobjective K-means clustering (TBO-MOK). The protocol comprises …

Enhanced Audio Encryption Scheme: Integrating Blowfish, Hmac-Sha256, And Md5 For Secure Communication, Jenan Ayad, Noor Qaddoori, Hasanain Maytham

Mesopotamian Journal of CyberSecurity

In today's communications, it is vital to protect audio data privacy before and after transfer. This work therefore introduces the enhanced audio encryption scheme (EAES), which employs the most effective techniques of cryptography and processes them to make it impossible for unauthorized persons or programs to access or change the contents of audio files. This method uses RSA to encrypt the Blowfish key, which is used for data encryption, the HMAC-SHA256 algorithm for integrity checks, and the Message-Digest algorithm (MD5) for the checking phase. The performance of the EAES is measured statistically via the MSE, PSNR, and correlation coefficient. For …

Smartphone Authentication Based On 3d Touch Sensor And Finger Locations On Touchscreens Via Decision-Making Techniques, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida, Maytham M. Hammood

Mesopotamian Journal of CyberSecurity

Smartphone authentication systems must balance security and user convenience, which is a persistent challenge in the digital realm. Traditional biometrics, such as fingerprints and facial recognition, face vulnerabilities to spoofing and environmental conditions, limiting reliability. This study introduces a novel approach by integrating three-dimensional (3D) touch sensors with finger location data for authentication. The goal is to develop a system that improves accuracy while minimizing false positives and negatives, leveraging touch pressure and spatial interaction as unique biometric identifiers. Data from 20 participants, including pressure levels, spatial coordinates, and timestamps, were analysed using Random Forest (RF) and Extreme Gradient Boosting …

Enhancing Cybersecurity With Machine Learning: A Hybrid Approach For Anomaly Detection And Threat Prediction, Adil M. Salman, Bashar Talib Al-Nuaimi, Alhumaima Ali Subhi, Hussein Alkattan, Hussein Alkattan

Mesopotamian Journal of CyberSecurity

In today's digital era, cybersecurity has become a principal concern because of the increasing frequency and advancement of cyber threats. This study explores machine learning models for detecting and predicting anomalies in cybersecurity datasets. The research evaluates models such as linear regression, decision tree, RF, gradient boosting, KNN, SVR, LSTM, and neural networks utilizing performance metrics such as accuracy, MAE and MSE. A hybrid model that integrates different learning strategies is additionally proposed to improve the predictive accuracy and strength. The results highlight the superiority of ensemble approaches, especially the hybrid model, in improving peculiarity detection capabilities. The comparative analysis …

Enhancing Traffic Data Security In Smart Cities Using Optimized Quantum-Based Digital Signatures And Privacy-Preserving Techniques, Tuqa Ghani Tregi, Mishall Al-Zubaidie

Mesopotamian Journal of CyberSecurity

Securing big data in power plants is an important and fundamental step in the infrastructure of smart cities. In addition, it becomes a barrier if it is not controlled from the beginning. Security must be a combination of fast and robust properties. This research presents a traffic security system (TSS) in a smart city (SC). It is a novel paradigm meant to improve data security and integrity by means of a multilayered method. Advanced fault analysis, dual-stage pseudonymizing, quantum key distribution via the BB84 protocol, and Falcon signatures (FS) are combined in the proposed system. TSS enhances data security by …