Digital Commons Network^™

Replacing Alumni Surveys With Web Searches, Farrokh Alemi, Peggy Maddox

Health Professions Education

Purpose: To examine which components of an alumni survey can be replaced with information posted on the Internet by alumni.

Methods: Surveys were conducted for four programs, including 41 alumni of Master of Health Administration, 50 alumni of Master of Science in Health Informatics, 75 alumni of Master of Science in Social Work and 90 alumni of Bachelor of Art History. Demographic, career advancement, industry, location of work, and estimated salary were available through searching the web. Program reviews was not widely available and emailing alumni allowed to supplement web-available data with comments on the program.

Results: …

Using Electro-Peroxone Process To Remediate Stabilized Clay, Elaheh Faghih Nasiri, Farhad Qaderi, S. Mustapha Rahmaninezhad

Civil Engineering Faculty Publications

Industrial processes are among human activities that cause the production of a large volume of wastewater containing organic pollutants such as phenol and its derivatives. These pollutants are among the substances that are considered a serious threat to the health of the environment, soil and humans. Soil remediation is crucial for enhancing environmental quality for both humans and other living organisms. This study investigated the use of an electro-peroxone system to remove environmental pollutants from soil. In conjunction with ozonation, the study employed electrochemically generated hydrogen peroxide using a carbon electrode, addressing concerns about transportation and storage. Experiments were structured …

Machine Learning Based Lung Disease Prediction Using Convolutional Neural Network Algorithm, M. Sahaya Sheela, G. Amirthayogam, J. Jasmine Hephzipah, S. Gopalakrishnan, S. Ravi Chand

Mesopotamian Journal of Artificial Intelligence in Healthcare

Lung disease prediction is a critical issue in today's world. However, in the past two years, the corona virus disease 2019 (COVID-19) has a broad range and, in a limited percentage of people, a notable effect on the lungs. In the past, the fuzzy logic method has been used to classify lung disease prediction, but it has faced challenges such as difficulties in identifying segmented regions and output inaccuracies. To solve the issue Convolutional neural networks are used in machine learning to predict lung condition. The preprocessing based weighted average filter gives greater weight to the central value, making its …

Modeling The Effect Of Observational Social Learning On Parental Decision-Making For Childhood Vaccination And Diseases Spread Over Household Networks, Tamer Oraby, Andras Balogh

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we introduce a novel model for parental decision-making about vaccinations against a childhood disease that spreads through a contact network. This model considers a bilayer network comprising two overlapping networks, which are either Erdős–Rényi (random) networks or Barabási–Albert networks. The model also employs a Bayesian aggregation rule for observational social learning on a social network. This new model encompasses other decision models, such as voting and DeGroot models, as special cases. Using our model, we demonstrate how certain levels of social learning about vaccination preferences can converge opinions, influencing vaccine uptake and ultimately disease spread. In addition, …

Flow And Stability Of Three-Dimensional Rotating Viscoelastic Jet, Daniel N. Riahi

School of Mathematical & Statistical Sciences Faculty Publications

We consider rotationally driven nonlinear viscoelastic jet. In contrast to previous unrealistic planar studies with no gravity effect, we investigate here realistic three-dimensional jet with gravity effect. Using theoretical and computational methods, we develop models for such jet and its stability and determine the solutions. We find three-dimensional jet with gravity effect leads to faster and thinner jet. For jet arc length higher than its exit diameter, our calculation with typical parameter values shows that jet radius is smaller by a factor of more than 1.4 and jet speed is higher by factor of more than 2 as are compared …

Assessing Concepts, Procedures, And Cognitive Demand Of Chatgpt-Generated Mathematical Tasks, Bima Sapkota, Liza Bondurant

School of Mathematical & Statistical Sciences Faculty Publications

In November 2022, ChatGPT, an Artificial Intelligence (AI) large language model (LLM) capable of generating human-like responses, was launched. ChatGPT has a variety of promising applications in education, such as using it as thought-partner in generating curricular resources. However, scholars also recognize that the use of ChatGPT raises concerns, such as outputs that are inaccurate, nonsensical, or vague. We, two mathematics teacher educators, engaged in a collaborative self-study using qualitative descriptive approaches to investigate the procedures, concepts, and cognitive demand of ChatGPT-generated mathematical tasks focused on fraction multiplication using the area model approach. We found that the ChatGPT-generated tasks were …

Brillouin Zones Of Integer Lattices And Their Perturbations, Herbert Edelsbrunner, Alexey Garber, Mohadese Ghafari, Teresa Heiss, Morteza Saghafian, Mathijs Wintraecken

School of Mathematical & Statistical Sciences Faculty Publications

For a locally finite set, 𝐴⊆ℝ𝑑 , the 𝑘 th Brillouin zone of 𝑎∈𝐴 is the region of points 𝑥∈ℝ𝑑 for which ‖𝑥−𝑎‖ is the 𝑘 th smallest among the Euclidean distances between 𝑥 and the points in 𝐴 . If 𝐴 is a lattice, the 𝑘 th Brillouin zones of the points in 𝐴 are translates of each other, and together they tile space. Depending on the value of 𝑘 , they express medium- or long-range order in the set. We study fundamental geometric and combinatorial properties of Brillouin zones, focusing on the integer lattice and its perturbations. Our …

Heuristics For Anti-Cyclotomic ℤp-Extensions, Debanjana Kundu, Lawrence C. Washington

School of Mathematical & Statistical Sciences Faculty Publications

This paper studies Iwasawa invariants in anti-cyclotomic towers. We do this by proposing two heuristics supported by computations. First we propose the Intersection Heuristics: these model “how often” the p-Hilbert class field of an imaginary quadratic field intersects the anti-cyclotomic tower and to what extent. Second we propose the Invariants Heuristics: these predict that the Iwasawa invariants 𝜆 and 𝜇 usually vanish for imaginary quadratic fields where p is non-split.

Logarithmic Algorithms For Fair Division Problems, Alexandr Grebennikov, Xenia Isaeva, Andrei V. Malyutin, Mikhail Mikhailov, Oleg R. Musin

School of Mathematical & Statistical Sciences Faculty Publications

We study the algorithmic complexity of fair division problems with a focus on minimizing the number of queries needed to find an approximate solution with desired accuracy. We show for several classes of fair division problems that under certain natural conditions on sets of preferences, a logarithmic number of queries with respect to accuracy is sufficient.

Political Social Media Use, Self-Efficacy, And Physical Participation, Paloma Ayala-Abellar

Scripps Senior Theses

Social media has become an integral part of daily life, particularly among Generation Z, transforming the way individuals engage with political movements and activism. This study explores how political social media usage influences feelings of self-efficacy and physical political participation among Generation Z in Los Angeles County. Leveraging survey data from 126 participants, the research examines the role of social media platforms—Instagram, TikTok, Facebook, and Twitter/X—in fostering self-efficacy and mobilizing individuals to engage physically with social movements. Findings reveal that frequent engagement with political content on social media, such as liking, sharing, and following, enhances feelings of self-efficacy, empowering users …

Book Review On From The Great Recession To The Covid-19 Pandemic By Professor Jerry W. Markham, Ronald Filler

FIU Law Review

A review of Professor Jerry Markham's book "From the Great Recession to the COVID-19 Pandemic."

Annotated Bibliography On The Mechanism Starting And Sustaining Atherosclerosis, Dat H. Pham, Thach Nguyen, Hao Thuy Nguyen, Marco Zuin, Duc Khiem Ngo, Hadrian Tran, Nhat H. Pham, Thinh T. Nguyen, Chau Dang, Pham Tv Le

School of Medicine Publications

Background: In the past 30 years, there has been a significant rise in coronary artery disease (CAD) and atherosclerosis, primarily attributed to dietary changes toward cholesterol-rich fast foods. Traditional research methodologies focusing on molecular biology have been insufficient in fully understanding the initiation mechanisms of these conditions. This paper reviews the literature and defines the knowledge gap for the need for a new dynamic coronary angiographic technique.

Methods: Our approach involved a comprehensive review of fluid mechanics literature to identify gaps in current CAD research. This included analyzing major works from standard physiology textbooks, fluid mechanics reviews, and bioengineering …

Reduction Of Elopement Using A Multicomponent Treatment In Young Children With Asd, Zina Eluri, Kimberly A. Zoder-Martell, Ivette Andrade, Noemi Trevino

Psychological Science Faculty Publications

Elopement, leaving a caregiver's side without permission or supervision, is a behavior that is more prevalent among children with autism as compared to their typically developing peers. With potentially fatal consequences (e.g., exposure to water, traffic, and strangers), it is reported to cause high levels of stress for caregivers. In the present study, we evaluated the assessment and treatment of elopement using a multicomponent treatment package to address the multiple functions of elopement. Treatment consisted of verbal instructions, blocking, and differential reinforcement of other behaviors with extinction (DRO w/ EXT). An ABAB reversal design was utilized to assess treatment effectiveness. …

False Discovery Rate And Localizing Power, Anderson M. Winkler, Paul A. Taylor, Thomas E. Nichols, Chris Rorden

School of Medicine Publications

False discovery rate (FDR) is commonly used for correction for multiple testing in neuroimaging studies. However, when using two-tailed tests, making directional inferences about the results can lead to vastly inflated error rate, even approaching 100% in some cases. This happens because FDR only provides weak control over the error rate, meaning that the proportion of error is guaranteed only globally over all tests, not within subsets, such as among those in only one or another direction. Here we consider and evaluate different strategies for FDR control with two-tailed tests, using both synthetic and real imaging data. Approaches that separate …

Trans-Ancestry Analysis Of Psychosis Biotypes: Shared Polygenic Risk And Unique Genomic Associations, Elliot S. Gershon, Ney Alliey-Rodriguez, Carol A. Tamminga, Matcheri S. Keshavan, Godfrey D. Pearlson, Sarah K. Keedy, David Parker, Rebekka Lencer, Scot Hill, Jeffrey R. Bishop

School of Medicine Publications

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has categorized psychosis disorders (Schizophrenia, Schizoaffective Disorder and Bipolar Disorder) into three distinct Biotypes, based on neurobiological measurements in a multi-ancestry sample. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Trans-Ancestry PRSs (TAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera TAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar TAPRSs across ancestries. In genomic analysis of Biotypes, nine genes and isoforms showed significant …

A Survey On Artificial Intelligence In Cybersecurity For Smart Agriculture: State-Of-The-Art, Cyber Threats, Artificial Intelligence Applications, And Ethical Concerns, Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, Ioannis Adamopoulos

Mesopotamian Journal of Computer Science

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review …

Benchmarking Generative Ai: A Call For Establishing A Comprehensive Framework And A Generative Aiq Test, Malik Sallam, Roaa Khalil, Mohammed Sallam

Mesopotamian Journal of Artificial Intelligence in Healthcare

The introduction and rapid evolution of generative artificial intelligence (genAI) models necessitates a refined understanding for the concept of “intelligence”. The genAI tools are known for its capability to produce complex, creative, and contextually relevant output. Nevertheless, the deployment of genAI models in healthcare should be accompanied appropriate and rigorous performance evaluation tools. In this rapid communication, we emphasizes the urgent need to develop a “Generative AIQ Test” as a novel tailored tool for comprehensive benchmarking of genAI models against multiple human-like intelligence attributes. A preliminary framework is proposed in this communication. This framework incorporates miscellaneous performance metrics including accuracy, …

Assigning Medical Professionals: Chatgpt's Contributions To Medical Education And Health Prediction, Maad M. Mijwil, Mostafa Abotaleb, Guma Ali, Klodian Dhoska

Mesopotamian Journal of Artificial Intelligence in Healthcare

Artificial intelligence is increasingly present in many applications that help humans accomplish many tasks. It can support improved results, increased productivity, and high efficiency in providing services. Systems developers seek to integrate improved artificial intelligence models into the development of healthcare services and make a significant qualitative shift in the medical field. One recently invented tool is ChatGPT, which deserves extensive advertising space. This tool provides sample answers that help both healthcare workers and patients answer all their questions. It is an important tool in medical education, increasing knowledge in making decisions that can improve the performance of medical professionals. …

Iot Revolutionizes Humidity Measurement And Management In Smart Cities To Enhance Health And Wellness, Pushan Kumar Dutta, Bhupinder Singh, Al-Sayed K. Towfeek, Jovanna Pantelis Adamopoulou, Antonis Nikos Bardavouras, Wilson Bamwerinde, Benson Turyasingura, Natal Ayiga

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper sets itself in a context of examining how IoT technology is disrupting and revolutionizing the monitoring and control of relative humidity in different spheres of smart cities with special reference to the enhancement of well-being in the information society. The authors discuss the current emergent research topics in IoT-based humidity sensors, wireless communication systems, and big data analytics for monitoring and controlling the humidity in real time. The paper also presents an outlook into various fields such as agriculture, healthcare, intelligent houses, industries, as well as the environment, and presents an indication of how managing humidity accurately enhances …

Machine Learning Techniques To Predictive In Healthcare: Hepatitis C Diagnosis, Hussein Alkattan, Bashar Talib Al-Nuaimi, Alhumaima Ali Subhi

Mesopotamian Journal of Artificial Intelligence in Healthcare

The accurate prediction and classification of medical data, such as Hepatitis C, play a significant role in upgrading symptomatic accuracy and treatment planning. This study utilized progressed machine learning algorithms, including RF, SVR, and Gradient Boosting, to analyze features and predict outcomes viably. By coordination robust preprocessing strategies and feature engineering, the models tended to missing values and categorical transformations, enabling exact predictions from complex datasets. The models accomplished high accuracy and unwavering quality in execution, as prove by comparative measurements and validation results. These discoveries highlight the potential of machine learning in increasing clinical decision-making and emphasize the require …

A Comprehensive Review On Cryptographic Techniques For Securing Internet Of Medical Things: A State-Of-The-Art, Applications, Security Attacks, Mitigation Measures, And Future Research Direction, Wamusi Robert, Asiku Denis, Adebo Thomas, Aziku Samuel, Simon Peter Kabiito, Zaward Morish, Guma Ali

Mesopotamian Journal of Artificial Intelligence in Healthcare

As healthcare becomes increasingly dependent on the Internet of Medical Things (IoMT) infrastructure, it is essential to establish a secure system that guarantees the confidentiality and privacy of patient data. This system must also facilitate the secure sharing of healthcare information with other parties within the healthcare ecosystem. However, this increased connectivity also introduces cybersecurity attacks and vulnerabilities. This comprehensive review explores the state-of-the-art in the IoMT, security requirements in the IoMT, cryptographic techniques in the IoMT, application of cryptographic techniques in securing the IoMT, security attacks on cryptographic techniques, mitigation strategies, and future research directions. The study adopts a …

Cross-Temporal Hierarchical Forecast Reconciliation Of Natural Gas Demand, Colin O. Quinn, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction …

Vulnerable Reading Practices For Ecosocial Justice In Environmental Education, Karen Nociti, Mindy Blaise

Research outputs 2022 to 2026

Environmental education has the potential to extend its transformative potential by reframing social and ecological justice as always interconnected. This paper introduces vulnerable reading as a method for unsettling anthropocentric and colonial influences on how educators conceptualise and respond to environmental precarity through a socio-ecological lens. It has emerged from a six-month walking project during which the authors developed vulnerable reading practices as they walked with young children, educators, and a weedy landscape in Boorloo (Perth), Western Australia. With a focus on reimagining pedagogies to be inclusive of multiple weedy ideas, bodies and voices, the paper uses empirical examples of …

Identifying And Promoting Qualitative Methods For Impact Assessment, Heidi Walker, Jenny Pope, Angus Morrison-Saunders, Alan Bond, Alan P. Diduck, A. John Sinclair, Brendan Middel, Francois Retief

Research outputs 2022 to 2026

Qualitative methods for impact assessment (IA) represent a broad spectrum of approaches that are important for realising effective IA practice. The purpose of this paper is to identify and promote qualitative methods that are available for use in contemporary and future (next-generation) IA processes. From an extensive literature review, an international survey (145 responses), expert interviews (48 interviewees), and a workshop attended by 27 IA practitioners, 17 qualitative method categories were identified. These were further subdivided into three classes: conventional qualitative methods, highly participatory methods, and mixed methods. Each method is described, and an indication given of how each can …

It Takes It Out Of The Textbook: Benefits Of And Barriers To Expert By Experience Involvement In Pre-Registration Mental Health Nursing Education, Brenda Happell, Sarah Gordon, John Hurley, Kim Foster, Mike Hazelton, Richard Lakeman, Lorna Moxham, Terri Warner

Research outputs 2022 to 2026

What Is Known on the Subject?: Service user involvement in mental health nursing education is beneficial in terms of attitudinal change to reduce stigma, clinical skill development and enhancing understandings of recovery-oriented practice. Service users as experts by experience have not been embedded within pre-registration nursing programs. Consequently, they remain limited in number, ad hoc and frequently tokenistic. Nurse academics responsible for the design and delivery of pre-registration mental health nursing curricula have a potentially important role in facilitating expert by experience involvement in mental health nursing education. What this Paper Adds to Existing Knowledge: Nurse academics teaching mental health …

Malware Detection With Artificial Intelligence: A Systematic Literature Review, Matthew G. Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is …

Postoperative Fluid Therapy In Enhanced Recovery After Surgery For Pancreaticoduodenectomy, Sharnice Koek, Johnny Lo, Rupert Ledger, Mohammed Ballal

Research outputs 2022 to 2026

Backgrounds/Aims: Optimal intravenous fluid management during the perioperative period for patients undergoing pancreaticoduodenectomy (PD) within the framework of enhanced recovery after surgery (ERAS) is unclear. Studies have indicated that excessive total body salt and water can contribute to the development of oedema, leading to increased morbidity and extended hospital stays. This study aimed to assess the effects of an intravenous therapy regimen during postoperative day (POD) 0 to 2 in PD patients within ERAS. Methods: A retrospective interventional cohort study was conducted, and it involved all PD patients before and after implementation of ERAS (2009–2017). In the ERAS group, a …

Optimizing Cardiac Resynchronization Therapy In Heart Failure Patients With Prolonged Qrs Duration: Insights Into Electrical And Mechanical Dyssynchrony, Zhisheng Chen, Lois Balmer, Xuerui Tan

Research outputs 2022 to 2026

Heart failure (HF) represents a critical stage of cardiac disease, associated with high morbidity and mortality. Cardiac resynchronization therapy (CRT) has become a pivotal treatment for HF patients with prolonged QRS duration. This therapy employs a biventricular pacing system to correct cardiac electromechanical dyssynchrony, thereby improving cardiac function, symptoms, and prognosis. Numerous clinical trials have consistently highlighted the benefits of CRT in this subgroup, leading to its unanimous recommendation in clinical practice guidelines. However, a significant proportion of patients do not achieve an adequate therapeutic response, despite adherence to these guidelines. As CRT treats patients by correcting cardiac electromechanical dyssynchrony, …

Developing International Guidance To Make Impact Assessment Follow-Up Happen–Reflections On An Interactive Design Process, Angus Morrison-Saunders, Jos Arts, Annette Nykiel, Bruce Muir, Richard Morgan, Patricia Fitzpatrick, Ciaran O’Faircheallaigh, Alberto Fonseca, Charlotta Faith-Ell, Ainhoa González, Jan Albert Wessels, Gesa Geißler, Luis Sánchez, Urmila Jha-Thakur, William Ross, John Glasson

Research outputs 2022 to 2026

To advance impact assessment (IA) practice worldwide the International Association for Impact Assessment (IAIA) has long promoted and published a series of international best practice principles, including the recently revised best practice principles for IA follow-up. IA follow-up refers to any kind of undertaking that seeks to ‘understand the outcomes of projects or plans’ that have been subject to IA. To support the implementation of these principles worldwide, a global guidance document has been developed (published by IAIA in 2024). The aims of this paper are to report on the approach undertaken to this guidance, applying a Delphi method, and …

The Impact Of Domain Name Server (Dns) Over Hypertext Transfer Protocol Secure (Https) On Cyber Security: Limitations, Challenges, And Detection Techniques, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Muhammad Haris, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow the analysis of DoH traffic …