Digital Commons Network^™

Differential Treatment Effects On Β-Cell Function Using Model-Based Parameters In Type 2 Diabetes: Results From The Glycemia Reduction Approaches In Diabetes: A Comparative Effectiveness Study (Grade), Kristina M Utzschneider, Mark Tripputi, Nicole M Butera, Andrea Mari, Samuel P Rosin, Mary Ann Banerji, Richard M Bergenstal, Necole Brown, Anders L Carlson, Ralph A Defronzo, Michaela R Gramzinski, Tasma Harindhanavudhi, Alexandra Kozedub, William I Sivitz, Michael W Steffes, Ashok Balasubramanyam, Neda Rasouli

Faculty, Staff and Students Publications

Objective: To evaluate how model-based parameters of β-cell function change with glucose-lowering treatment and associate with glycemic deterioration in adults with type 2 diabetes (T2D).

Research design and methods: In the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE), β-cell function parameters derived from mathematical modeling of oral glucose tolerance tests were assessed at baseline (N = 4,712) and 1, 3, and 5 years following randomization to insulin glargine, glimepiride, liraglutide, or sitagliptin, added to baseline metformin. Parameters included insulin secretion rate (ISR), glucose sensitivity (insulin response to glucose), rate sensitivity (early insulin response), and potentiation. Linear mixed-effects …

Factors Affecting Students Persistence To Enrol In Stem Education, Nurul Yasmin Nor Huzir, Noraini Ahmad, Nor Aini Hassanuddin, Sarah Yusoff, Nur Syazana Rosly

Malaysian Journal of Computing (MJoC)

Science, technology, engineering, and mathematics (STEM) education is vital in producing a highly skilled and knowledgeable Malaysian workforce to meet the demand for its industries. Malaysia aims to be a developed country that initiates the technology itself instead of just being a user. Despite this ambitious goal, STEM education in Malaysia faces challenges due to low enrolment in STEM elective subjects. This declining trend is transparent in secondary school since students are given a choice to enrol in STEM education or any other stream provided. While STEM elective subjects are viewed as tough and difficult, there is a tendency that …

Distributed Denial Of Service Ddos Framework In Software Defined Networking Sdn A Comprehensive Review Challenges And Future Directions, Kanqi Xie, Mohamad Yusof Darus, Boxun Liao, Nan Ding, Azlin Ramli

Malaysian Journal of Computing (MJoC)

Distributed Denial of Service (DDoS) attacks represent a major threat to network security.. In response, this paper examines the potential of countering DDoS attacks through the integration of Software-Defined Networking (SDN). SDN separates the network control logic from the underlying routers and switches, which facilitates the communication between software components. Moreover, the synergy of SDN with Machine Learning (ML) and Deep Learning (DL) technologies provide a promising approach for effective threat mitigation. This systematic review explores the evolving landscape of information security defense frameworks within the context of Internet of Things (IoTs) security. Over the past five years, numerous articles …

Climate Change Detection In Four Locations In Peninsular Malaysia For Green Sustainability Awareness, Noor Kesuma Mohd Yazid, Norshahida Shaadan, Firdaus Mohamad Hamzah, Nurain Ibrahim, Mahayaudin M.Mansor

Malaysian Journal of Computing (MJoC)

Climate change impacts ecosystems, often resulting in the extinction of many species and their habitats and harm to human health. Preserving and protecting green spaces is essential to maintaining ecosystem health. This study aims to detect and examine climate change and its impact on temperature and rainfall at four locations in Malaysia to increase green environment sustainability awareness. The methodology involved investigating the pattern of climate variation using several visualization tools, the Mann-Kendall test, the EWMA control chart, and the Kruskall-Wallis test. Data on monthly average temperature and rainfall amount for the 30 years between 1989 and 2018 was obtained …

Localization Module In User Experience Questionnaire For Cross Cultural Website Design, Nurhayati Mohd Husni, Nur Atiqah Sia Abdullah

Malaysian Journal of Computing (MJoC)

The importance of User Interface (UI) and User Experience (UX) in ensuring the practicality and usability of websites for users is well-recognized. The User Experience Questionnaire (UEQ) is widely employed to evaluate these aspects of a product, ensuring linguistic and cultural appropriateness for its target locale based on country, region, or language. However, the UEQ lacks a module specifically designed to assess the localization properties of a product. This research aims to extend the UEQ by introducing a localization module that adheres to cross-cultural design principles. The study involved a systematic literature review to identify potential items for the localization …

Integrated Cybersecurity Framework For Enhanced Threat Detection And Incident Response In The Digital Era, Azlin Ramli, Mohamad Yusof Darus, Yusnani Mohd Yussoff, Badri Azni, Kanqi Xie

Malaysian Journal of Computing (MJoC)

This research presents a novel cybersecurity framework aimed at improving threat detection and incident response in today's complex digital environment. The framework integrates three key components: advanced threat detection, accelerated incident response, and continuous risk assessment, adopting a holistic and adaptive approach. It leverages machine learning (ML) and artificial intelligence (AI) to proactively identify and counter evolving cyber threats, moving beyond traditional reactive systems. The advanced threat detection element utilizes AI-driven analytics to spot anomalous patterns and forecast potential vulnerabilities, thus enhancing threat visibility. The accelerated incident response streamlines automated responses to common threats, significantly cutting response times. Complementing these …

Examining The Impact Of Feature Selection Techniques On Machine And Deep Learning Models For The Prediction Of Covid 19, Hafiza Zoya Mojahid, Jasni Mohamad Zain, Marina Yusoff, Abdul Basit, Abdul Kadir Jumaat, Mushtaq Ali

Malaysian Journal of Computing (MJoC)

Feature selection is a vital preprocessing step for identifying the most informative features in complex datasets, enhancing the efficiency and accuracy of machine learning models. Its applications extend across various domains, including big data analytics, finance, chemometrics, medical diagnostics, biological research, intrusion detection systems, and renewable energy solutions. In medical contexts, feature selection serves a dual purpose: it reduces dimensionality while simultaneously improving the comprehension of disease etiology. This study delves into key variable selection methods—specifically Recursive Feature Elimination (RFE), Principal Component Analysis (PCA) and Least Absolute Shrinkage and Selection Operator (LASSO). We evaluate the interaction of these methods with …

Measuring The Extent Of Cyberbullying Comments In Facebook Groups For Mosul University Students, Kanaan J. Brakas, Mafaz Alanezi

Mesopotamian Journal of CyberSecurity

The widespread utilization of social media platforms such as Facebook, Twitter, and Instagram inside academic organizations has become fundamental for student correspondence and joint effort, yet it has simultaneously prompted an increase in cyberbullying incidents. Cyberbullying is embodied by offensive and harmful comments that sabotage the casualty's prosperity. This study plans to resolve this issue by fostering an extensive module to distinguish cases of cyberbullying inside Facebook groups of Mosul University students. Our methodology starts with the collection of data in the Arabic language, which is then exposed to careful manual handling to label comments and eliminate clamor-like copies and …

Dagchains: An Improved Blockchain Structure Based On Directed Acyclic Graph Construction And Distributed Mining, Abbas Mahdi, Furkan Rabee

Mesopotamian Journal of CyberSecurity

Blockchain has revolutionized cryptocurrency and completely changed the management of data and transactions in the digital world because of its decentralized nature, improved transparency, increased security measures, ability to facilitate commercial trading between untrusted parties, and contribution to preventing fraudulent activity. However, the primary issue with blockchain systems is their limited scalability, as they can only process a maximum of 30 transactions per second (TPS), like Ethereum and Bitcoin. In this paper, we introduce an approach using the Nakamoto protocol and the Directed Acyclic Graph (DAG) to develop an improved infrastructure known as a DAGchains that can increase the processing …

A Dynamic Dna Cryptosystem For Secure File Sharing, Kanaan J. Brakas, Mafaz Alanezi

Mesopotamian Journal of CyberSecurity

The digital age relies heavily on file sharing, which has become more important with the increasing use of digital data and the Internet. Along with this increasing importance come major and widespread security issues, especially for files containing sensitive or vital data, such as those related to commercial, military, or healthcare sectors. The most common and effective way to protect the security and privacy of shared files containing sensitive information is still encryption. With the development of high-efficiency devices and massive processing capabilities, traditional encryption methods are increasingly under pressure from modern computer security threats, particularly quantum computing. This study …

A Novel Multi-Layered Secure Image Encryption Scheme Utilizing Protein Sequences, Dynamic Mealy Machines, 3d-As Scrambling, And Chaotic Systems, Radhwan Jawad Kadhim, Hussein K. Khafaji

Mesopotamian Journal of CyberSecurity

The swift rise in multimedia transmission through insecure channels has made the study of information security critically important. Image encryption holds significant importance in this context, hence necessitating the improvement of the encryption algorithms. This research introduces a Protein-Driven Mealy Machine Image Encryption with Multi-Layer Protection (PMIE-MLP) algorithm, an innovative cryptographic system to improve image security that uses a dynamic protein-based Mealy machine, a novel 3D-AS scrambling, and a chaotic system. An encryption framework comprises key generation and six protection layers: substitution, four layers of diffusion, and confusion. These layers attempt to address the confusion and diffusion principles of Shannon’s …

Color Image Encryption Based On A New Symmetric Lightweight Algorithm, Ala’A Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi

Mesopotamian Journal of CyberSecurity

Many lightweight algorithms, such as the tiny lightweight algorithm, have significant weaknesses, mainly due to the lack of substitution boxes, effective confusion mechanisms, or both. In today's world, enhancing encryption and secure transmission has become increasingly vital. This paper presents a newly developed lightweight algorithm for color image encryption that is based on a new symmetric block cipher structure. The method starts by transforming each pixel channel value into a 24-bit binary number. A new F-function is introduced in this block cipher to improve diffusion and confusion. Additionally, a 3D Hindmarsh-Rose model is used to generate a dynamic 6-bit S-Box …

Enhancing User Authentication Through The Implementation Of The Forestpa Algorithm For Smart Healthcare Systems, Nurul Syafiqah Zaidi, Al-Fahim Mubarak Ali, Ahmad Firdaus, Adamu Abubakar Ibrahim, Adamu Abubakar Ibrahim, Mohd Faizal Ab Razak

Mesopotamian Journal of CyberSecurity

The machine learning-based authentication model for smart healthcare systems represents a crucial step in addressing the needs of an ever-evolving healthcare industry. The need to protect sensitive patient data, ensure regulatory compliance, and reduce medical errors, especially in the context of telemedicine and remote monitoring, underscores the importance of such systems. Traditional authentication methods frequently lack sufficient security, resulting in potential breaches. Relying solely on usernames and passwords, without supplementary authentication measures, exposes systems to advanced security attacks. As it involves patients’ health and human lives, it is important to provide additional authentication, fast machine learning-based authentication models and high …

Healthcare Security In Edge-Fog-Cloud Environment Using Blockchain: A Systematic Review, Zaid J. Al-Araji, Mahmood S. Alkhaldee, Ammar Awad Mutlag, Zaid Ali Abdulkadhim, Zaid Ali Abdulkadhim, Sharifah Sakinah Syed Ahmad, Namaa N. Hikmat, Ayad Yassen, Abdullah A. Ibrahim Al-Dulaimi, Najwa N. Hazem Al-Sheikh, Ammar Hashim Ali

Mesopotamian Journal of CyberSecurity

Context: Our domain of expertise is healthcare security, which protects the sensitive information of patients and the integrity of the healthcare services provided to them. As health record digitization grows, along with the adoption of advanced technologies, data protection becomes more complex and vital. Realizing the transformative potential of blockchain (BC) in healthcare security requires critical exploration into the prevailing centralization of sensitive patient information, shedding traditional paradigms, and embracing the digital decentralization enabled by the BC realm. Objectives: The purpose of this study was to analyse prior research and provide a comprehensive overview of the literature on BC-based healthcare …

A Stacked Ensemble Classifier For Email Spam Detection Via An Evolutionary Algorithm, Salam Al-Augby, Hasanen Alyasiri, Fahad Ghalib Abdulkadhim, Zahraa Ch. Oleiwi, Zahraa Ch. Oleiwi

Mesopotamian Journal of CyberSecurity

Email communication is a crucial aspect of modern interactions. With the growing volume of spam emails, there is a pressing need for more effective antispam filters to detect unwanted messages. Existing spam detection techniques often fall short, prompting researchers to leverage machine learning and artificial intelligence to enhance online security. This study introduces an advanced spam detection technique using an ensemble learning approach. First, key features are extracted from both spam and non-spam emails via the term frequency-inverse document frequency (TF-IDF) method. Several classification algorithms, including cubist, naïve Bayes, support vector machine, rpart, and ctree, are applied to classify emails …

Optimized Deep Learning Model Using Binary Particle Swarm Optimization For Phishing Attack Detection: A Comparative Study, El-Sayed M. El-Kenawy, Marwa M. Eid, Hussein Lafta Hussein, Ahmed M. Osman, Ahmed M. Osman

Mesopotamian Journal of CyberSecurity

Phishing attacks manipulate users to disclose critical information, resulting in cybersecurity risks. Traditional phishing detection algorithms usually have large false positive rates and poor feature selection, degrading performance. This paper presents an optimized phishing detection framework that integrates binary particle swarm optimization (BPSO)-based feature selection (FS) with deep learning models. Six deep learning architectures were evaluated on the selected feature subset to identify the most effective model for accurate phishing classification. BPSO was used to select suitable attributes on a public Kaggle dataset with 10,000 samples, comprising phishing and legitimate website data with 48 attributes. NumDots, UrlLength, IpAddress, and NoHttps …

Ensure Privacy-Preserving Using Deep Learning, Abeer Dawood Salman, Ruqayah Rabeea Al-Dahhan

Mesopotamian Journal of CyberSecurity

Deep learning has emerged as a powerful approach for treating complex real-world challenges. However, the performance of the deep learning models is heavily reliant on access to large volumes of high-quality training data—an aspect often constrained by privacy concerns. Ensuring data availability while preserving user confidentiality remains a pressing issue. In response, cryptographic techniques like homomorphic encryption (HE), which are grounded in strict mathematical principles, present hopeful solutions for securing data on digital platforms without compromising its usability for learning models. It performs computations on encrypted data without revealing the underlying plaintext. The main attractive feature of this technique is …

An Artificial Intelligence-Based System For Detecting Meta Fake Profiles Via Gradient Boosting And Multilayer Perception, Ruaa A. Al-Falluji, Marwan Ali Albahar, Ahmad Mousa Altamimi

Mesopotamian Journal of CyberSecurity

The rise of metaverse platforms has renewed interest in detecting fake profiles, which pose a significant threat to digital ownership and asset transactions within these virtual environments. If digital ownership is not guaranteed, platforms risk missing the point of the metaverse. Current supervised learning techniques for fake profile detection often struggle to maintain acceptable accuracy and interpretability in practice. To address this problem, this study investigates the application of two machine learning models, multilayer perceptron and gradient boosted trees, for detecting fake profiles, with model evaluation performed via two Explainable AI (XAI) techniques, Local Interpretable Model-agnostic Explanations (LIME) and SHapley …

Anti-Cyber Childhood Exploitation: An Online Game Chat Monitoring System, Saja J. Mohammed, Awos K. Ali, Ibrahim M. Ahmed

Mesopotamian Journal of CyberSecurity

Despite its revolutionary benefits, the Internet has been utilized to abuse children through chat in online gaming. Exhibiting harmful content can negatively impact children's psychology and behaviour, particularly during their developmental years. This paper examines the psychological effects of online predation and the growing risks posed by Internet predators (with focus on children under 15 years old). This paper proposed an Anti-Cyber Childhood Exploitation (A2CE) system, a comprehensive framework designed to detect and prevent three major forms of online abuse: psychological manipulation, cyberbullying, and online grooming. Leveraging advanced Natural language processing (NLP) techniques, A2CE analyses online conversations in real time. …

Integrating Data Visualizations To Enhance Effectiveness Of Mitigation Reports, Eric A. Kowalik

Master's Theses (2009 -)

No abstract provided.

Strengthening The Paediatric Clinical Trial Ecosystem To Better Inform Policy And Programmes, James A. Berkley, Judd L. Walson, Glenda Gray, Fiona Russell, Zulfiqar Ahmed Bhutta, Per Ashorn, Shane A. Norris, Ebunoluwa A. Adejuyigbe, Rebecca Grais, Bernhards Ogutu

Institute for Global Health and Development

The first WHO Global Clinical Trials Forum was convened in November, 2023 to develop a shared vision of an effective global clinical trial infrastructure. The Paediatric Clinical Trials Working Group was formed to provide perspectives, identify challenges, and propose solutions to strengthen the paediatric clinical trials ecosystem. Participants represented paediatric disciplines, including infectious diseases, nutrition, neonatology, pharmacology, oncology, neurodevelopment, public health, and policy. Childhood diseases have profound lifelong effects on health, livelihoods, and societies. Investment in early childhood results in highly cost-effective changes to lifelong health, productivity, and human capital returns. Yet, there remain substantial gaps in knowledge on the …

Justification Of Permissible Discharges Of Water Users Into Water Bodies, Mariia S. Stroganova, Ivan V. Antonov, Andrey V. Epifanov

CHEMISTRY AND CHEMICAL ENGINEERING

The purpose of this work is developing an algorithm for ecological and technological rationing of the permissible load on water bodies using geoinformation systems. The algorithm takes into account the self-purification of aquatic ecosystems using a three-component model. Initially, the article provides an overview of methods for determining the permissible discharge masses of pollutants for developing new approaches to rationing. The algorithm of ecological and technological rationing of the load on water bodies and geoinformation systems for its implementation is proposed. The main models used to assess the ecological state of an aquatic ecosystem are considered. A three-component model of …

Lie-Ability: A Technical Analysis Of The Application Of Section 230 Immunity To Chatbot Outputs, Abigail Drummond

St. John's Law Review

(Excerpt)

This Note argues that under the material contribution test for determining service provider liability under Section 230(c), software developers are likely covered under the CDA for civil and criminal liability for chatbot and other generative AI outputs. Part I will review the legislative purpose and subsequent case law of Section 230(c) of the CDA. Part I will also discuss the material contribution test, the prevailing standard for determining service provider liability developed by the Ninth Circuit and generally adopted across federal courts. Part II will provide an overview of how chatbots work and present the state of chatbot regulation …

Improving Solar Air Heater Performance By Using New Novelty Perforated V-Shaped Barriers Experimental Solar Air Collector, Absorber Surface, V-Shaped, Friction Factor, Sajjad Tariq A. Shafi, Mohammed K. Al-Saadi, Ameer Abed Jaddoa

Terra Joule Journal

To enhance the heat transfer rate and the thermal performance of solar air heaters, specific methodologies such as artificial roughness, barriers, and obstructions should be employed to augment the heat exchange between the working fluid and the absorber surface. In this study, perforated V-shaped obstructions were utilized, featuring circular, hexagonal, square, rectangular, and triangular geometries. These obstructions were securely affixed to the absorber plate within a channel measuring 150 × 50 × 5 cm, increasing the exit temperature of the air traversing through the channel. The experimental investigation comprises six distinct cases conducted in November in Baghdad, Iraq, aimed at …

Optimizing Wind Turbine Performance: The Impact Of Atmospheric Factors And Advanced Control Strategies, Alaa Abdulhady Jaber, Latif Ibraheem, Harshil Patel

Terra Joule Journal

Wind energy, a cornerstone of renewable energy solutions, provides a sustainable means of meeting global energy demands while minimizing environmental impact. This study investigates the influence of meteorological factors—wind speed, temperature, air pressure, and turbulence—on wind turbine performance and energy yield. Numerical techniques employed in the work include Euler's Method, Runge-Kutta 4th Order (RK₄), and Physics-Informed Neural Networks, which were applied to simulate dynamics for turbine performance optimization. In addition, the Jensen's Wake Model has been applied for wake effect analysis and optimization of turbine spacing in wind farms. Moreover, yaw and pitch control strategies have been investigated …

The Impact Of Emergency Haying On Grassland Birds In The Conservation Reserve Program Habitats Of Northwest Iowa, Claire Widmer, Ai Wen, Kenneth Elgersma, Mark Vandever

Research in the Capitol

Data was collected on breeding grassland bird communities in northwest Iowan Conservation Reserve Program (CRP) fields. Within those fields, CRP grasslands in drought areas could be hayed or grazed for emergency management to harvest supplemental forage for livestock. We compared the bird communities in emergency hayed versus undisturbed grasslands to understand how the drought induced emergency haying affected the grassland birds. Using distance sampling, we found that significantly higher bird richness was found in undisturbed habitat areas compared to the emergency managed hayed areas in the same CRP parcel. In addition, we found that bird density in the emergency managed …

Investigating Teachers’ Attitudes Toward Using Assistive Technology In Teaching Students With Hearing Impairments, Salma Masoud

Jordan Journal of Applied Science-Humanities Series

This study aims to investigate Jordanian teachers’ attitudes towards using assistive technology to teach students with impaired hearing. The research was conducted during the academic year 2022-2023, utilizing one data collection tool: an online questionnaire distributed to 347 special education teachers from Al-Amal Schools for the Deaf in Jordan. The data was analyzed using SPSS, with mean, standard deviation, and percentage calculations. The results indicate that teachers have positive attitudes toward the use of assistive technology for students with impaired hearing.

Degree Of Control By Faculty Members Over Their Online Classes At Yarmouk University: A Student Perspective, Khaled Al-Jaraidah, Nouwar Al-Hamad

Jordan Journal of Applied Science-Humanities Series

This study aimed to examine the degree to which faculty members at Yarmouk University control their online classes from the students' perspective and to explore the impact of variables such as gender, educational level, and specialization. The study sample consisted of 462 students, selected using a convenience sampling method. The study employed a descriptive survey method, and to achieve its objectives, a questionnaire consisting of 40 items was designed, covering three domains. The results revealed that the degree of control perceived by students regarding faculty members' management of their online classes was high. The study also found statistically significant differences …

19th Annual Research In The Capitol [Program], March 31, 2025, University Of Northern Iowa. University Honors Program. Iowa State University. Honors Program. University Of Iowa. Honors Program.

Research in the Capitol Programs

Program of research presentations given at the Capitol by students from the University of Northern Iowa, Iowa State University, and the University of Iowa.

Faculty Senate Meeting Agenda And Minutes, March 31, 2025

Faculty Senate Minutes and Agendas

Agenda and minutes from the Wright State University Faculty Senate Meeting held on, March 31, 2025.