Digital Commons Network^™

Impact Of A Design Thinking Educational Activity On Graduate Students’ Knowledge, Confidence, And Perceived Benefits, Janice Hawkins, John Baaki, Beth Tembley, Robert J. Hawkins

Ellmer School of Nursing Faculty Publications

Background: Design Thinking is gaining recognition as an innovative and creative approach to problem solving. Though nurse leaders need problem solving tools to address health care challenges, Design Thinking concepts are not commonly taught in nursing education. To introduce graduate level nursing students to Design Thinking, we held an educational activity focused on this content as part of required coursework.

Purpose: The purpose was to describe and compare outcomes of a Design Thinking educational activity on students’ perceived knowledge, confidence, and benefits to nursing practice.

Methods: Graduate level nursing students participated in a 3-hour educational activity. After the session, …

Vulnerability Of Wetlands Due To Projected Sea-Level Rise In The Coastal Plains Of The South And Southeast United States, Luis Lizcano-Sandoval, James Gibeaut, Matthew J. Mccarthy, Tylar Murray, Digna T. Rueda-Roa, Frank E. Muller-Karger

Marine Science Faculty Publications

Coastal wetlands are vulnerable to accelerated sea-level rise, yet knowledge about their extent and distribution is often limited. We developed a land cover classification of wetlands in the coastal plains of the southern United States along the Gulf of Mexico (Texas, Louisiana, Mississippi, Alabama, and Florida) using 6161 very-high (2 m per pixel) resolution WorldView-2 and WorldView-3 satellite images from 2012 to 2015. Area extent estimations were obtained for the following vegetated classes: marsh, scrub, grass, forested upland, and forested wetland, located in elevation brackets between 0 and 10 m above sea level at 0.1 m intervals. Sea-level trends were …

Exploring The Mechanical Properties Of The Gyroid Sheet Network For The Additive Manufacturing Of Biomedical Structures: A Numerical Analysis Approach, Babak Ziaie, Xavier Velay, Waqas Saleem

Conference Papers

No abstract provided.

Understanding The Impact Of Dual Shock On Kuwait Stock Market, Talal A.N.M.S. Alotaibi

Doctoral

The emergence of the COVID-19 pandemic has caused financial markets to suffer historic losses during the first quarter of 2020 at unseen levels since the crisis of the futures markets in 1987. In January 2020, the World Health Organisation officially declared the pandemic, bringing with it significant uncertainty levels to the global economy. The COVID-19 pandemic affected global markets parallel to a simultaneous shock from the Saudi Arabian and Russian oil price wars. As the worldwide health crisis escalated, governments worldwide were forced to take measures that led to economic lockdowns, leading to significant disruption of global supply chains and …

Effect Of Plant Based Nutritional Education On A1c Levels In Type 2 Diabetic Adults, Candice Kim

Master of Science in Nursing Family Nurse Practitioner

Type 2 diabetic adults are those with a condition that prevents production of enough insulin and are also resistant to the effects of insulin itself (American Diabetes Association, 2018). According to the U.S. Department of Health and Human Services, “diabetes mellitus affects an estimated 29.1 million people in the United States, and is the 7th leading cause of death,” (Office of Disease Prevention and Health Promotion, 2022, section 3). Risk factors that affect type 2 diabetic adults include being overweight, aged 45 or older, high blood pressure, and high level of triglycerides (National Institute of Diabetes and Digestive and Kidney …

The Effects Of Aerobic Exercise To Improve Depression In Overweight Adults, Curtis Lura

Master of Science in Nursing Family Nurse Practitioner

Overweight/obese adults are at an increased risk for both physical and mental comorbidities (CDC, 2019). Depression is a common comorbidity in those that are obese (CDC, 2019) and the standard of care currently to treat depression may increase the risk of physical comorbidities because weight gain is a common side effect of antidepressants (Hall-Flavin, 2018). The aim of this capstone research paper is to present evidence that an aerobic exercise regimen can be a valid treatment option for depression.

Basal Topography And Carbon Accumulation Rate Of A Rare Northern California Coastal Fen, Sarah Leidinger

Cal Poly Humboldt theses and projects

Increasingly, the importance of peatlands as biodiversity hotpots, year-round water storage features, and significant carbon sinks are being recognized. Despite this, they are one of the most vulnerable ecosystems globally. While some peatland conservation and restoration initiatives are being developed, a greater understanding of small peatlands is needed to make management decisions. This study aims to determine the timing of peat formation, peat volume, and carbon accumulation rates of the Conspicuous Scoop Fen, a small (2.1-acre) fen near Bridgeville, California (~840 m.a.s.l.). During the summers of 2022 and 2023, I collected 39 m of peat cores at 14 sampling locations. …

Performance Activism Versus Collective Action, Grace Belt

Cal Poly Humboldt theses and projects

Collective action is how groups can change society. According to the social identity model of collective action (SIMCA), facets of group identification can motivate or discourage collective action. However, no current research has focused on motivations for engagement in performance activism (i.e., displaying support for a mobilized group without engaging in mobilization) from an ingroup perspective. The current study investigates this literature gap, focusing on the dynamics of group membership, specifically feelings of self-prototypicality, as motivation to engage in performance activism or collective action. Participants (N = 189) were made to feel they were core members (prototypical) or fringe …

The Effectiveness Of A Visual Activity Schedule In Aquatics On Skill Acquisition In Individuals With Autism Spectrum Disorder, Marcus J. Romero

Cal Poly Humboldt theses and projects

This study investigates the effectiveness of a Visual Activity Schedule (VAS) on the acquisition of aquatic skills (floating, front glide, and breath control) in individuals with autism spectrum disorder (ASD). Due to the heightened risk of drowning among people with ASD, this research aims to determine if a VAS can enhance learning and safety in aquatic environments. The study utilizes a multiple baseline design across skills, involving two male participants aged 16 and 17 years old, both diagnosed with ASD. Baseline assessments were administered and followed by intervention phases where VAS was introduced. The dependent variables were the successful completion …

Cover And Contents

Turkish Journal of Medical Sciences

No abstract provided.

Leaky Gut And Inflammatory Biomarkers In A Medication Overuse Headache Model In Male Rats, Doğa Vuralli, Hale Gök Dağidir, Elif Abbasoğlu Topa, Hayrunnisa Bolay Belen

Turkish Journal of Medical Sciences

Medication overuse is common among chronic migraine patients and nonsteroidal antiinflammatory drugs (NSAIDs)are the most frequently overused drugs. The pathophysiological mechanisms underlying medication overuse headache (MOH) are notcompletely understood. Intestinal hyperpermeability and leaky gut are reported in patients using NSAIDs. The aim of the study is toinvestigate the role of leaky gut and inflammation in an MOH model MOH model in male rats.Methods: The study was conducted in male Sprague Dawley rats. There were two experimental groups. The first group was the chronicNSAID group in which the rats received mefenamic acid (n = 8) for four weeks intraperitoneally (ip) and …

Masthead & Table Of Contents, Vol. 102, No. 3

Nebraska Law Review

No abstract provided.

Masthead & Table Of Contents, Vol. 102, No. 4

Nebraska Law Review

No abstract provided.

Digital Physicians: Unleashing Artificial Intelligence In Transforming Healthcare And Exploring The Future Of Modern Approaches, Ban Salman Shukur, Mohd Khanapi Abd Ghani, Burhanuddin Bin Mohd Aboobaider

Mesopotamian Journal of Artificial Intelligence in Healthcare

Growing global awareness that attention to health care is the basis for maintaining citizens' quality of life. Health institutions seek to increase interest in electronic care services and enhance patient results by integrating artificial intelligence techniques. Artificial intelligence tools are indispensable to diagnosis, treatment, and patient care. Integrating artificial intelligence techniques into the development of the electronic healthcare environment works to enhance public health and disease prevention and provide free services to all citizens. Designing electronic platforms raises health awareness in society, provides health programs and initiatives, and reaches homes, gardens, schools, and universities through applications based on artificial intelligence. …

Regulation By (Bad) Proxy: How Selective Application Of Transaction Cost Economics Tainted The Ftc's Proposed Ban Of Employee Noncompete Agreements, Alan J. Meese

Faculty Publications

Agencies have imperfect information about conduct they regulate. This problem is particularly acute when identical conduct has differing effects in various markets. Determining the economy-wide impact of such conduct can be difficult or impossible.

The FTC faces such a challenge. The Commission has announced a rule banning the nation’s 30 million employee noncompete agreements (“NCAs”) as unfair methods of competition under Section 5 of the FTC Act. The Commission determined that NCAs likely reduce aggregate wages, helping to establish a presumptive violation. The Commission also found that nearly all NCAs are both procedurally coercive—because employers use overwhelming bargaining power to …

Leveraging Ai And Big Data In Low-Resource Healthcare Settings, Ahmed Hussein Ali, Saad Ahmed Dheyab, Abdullah Hussein Alamoodi, Aws Abed Al Raheem Magableh, Yuantong Gu

Mesopotamian Journal of Big Data

Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have …

Agent-Interacted Big Data-Driven Dynamic Cartoon Video Generator, Yasmin Makki Mohialden, Abbas Akram Khorsheed, Nadia Mahmood Hussien

Mesopotamian Journal of Big Data

This study presents a novel method for animating videos using three Kaggle cartoon faces data sets. Dynamic interactions between cartoon agents and random backgrounds, as well as Gaussian blur, rotation, and noise addition, make cartoon visuals look better. This approach also evaluates video quality and animation design by calculating the backdrop colour's average and standard deviation, ensuring visually appealing material. This technology uses massive datasets to generate attractive animated videos for entertainment, teaching, and marketing.

Mlp And Rbf Algorithms In Finance: Predicting And Classifying Stock Prices Amidst Economic Policy Uncertainty, Bushra Ali, Khder Alakkari, Mostafa Abotaleb, Maad M. Mijwil, Klodian Dhoska

Mesopotamian Journal of Big Data

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and …

Advanced Machine Learning Approaches For Enhanced Gdp Nowcasting In Syria Through Comprehensive Analysis Of Regularization Techniques, Khder Alakkari, Bushra Ali, Mostafa Abotaleb, Rana Ali Abttan, Pushan Kumar Dutta

Mesopotamian Journal of Big Data

This study addresses the challenge of nowcasting Gross Domestic Product (GDP) in data-scarce environments, with a focus on Syria, a country facing significant economic and political instability. Utilizing a dataset from 2010 to 2022, three machine learning algorithms Elastic Net, Ridge, and Lasso were applied to model GDP dynamics based on macroeconomic indicators, commodity prices, and high-frequency internet search data from Google Trends. Among these, the Lasso regression model, noted for its variable selection and sparsity promotion, proved most effective in capturing Syria's complex economic realities, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). …

Hybrid Model For Forecasting Temperature In Khartoum Based On Cru Data, Hussein Alkattan, Alhumaima Ali Subhi, Laith Farhan, Ghazwan Al-Mashhadani

Mesopotamian Journal of Big Data

This consider leverages verifiable climatic data from the Climatic Research Unit (CRU), traversing from 1901 to 2022, to create progressed temperature forecasting models for Khartoum, Sudan. By applying state-of-the-art machine learning techniques, including Hybrid model, we aim to progress the precision of temperature forecasts in a semi-arid climate. The integration of long-term CRU data permits for the recognizable proof of climate patterns and patterns, upgrading the unwavering quality of short- and long-term forecasts. Moved forward temperature forecasting can altogether advantage basic segments empowering way better adjustment to climatic changes and extraordinary climate occasions. Our approach illustrates the potential of combining …

Deep Learning Approaches For Gender Classification From Facial Images, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Reem D. Ismail, Z. T. Al-Qaysi, Fatimah N. Ameen

Mesopotamian Journal of Big Data

Gender recognition on the facial level is considered one of the most important technologies that finds use in such fields as a personalized marketing plan, safe systems of authentication, and effective human-computer interfaces. However, it has the following challenges; variation of lighting, facial movement, and ethnic/age face images. AI and DL has been improving on the effectiveness, flexibility, and speed of the gender classification system. AI enables complex and automatic feature learning in Data, while DL is tailored for handle variants in vision-based data. In this paper, we evaluated several architectures including Efficient Net_B2, ResNet50, ResNet18, and Lightning whilst determining …

Automated Water Quality Assessment Using Big Data Analytics, Yasmin Makki Mohialden, Nadia Mahmood Hussien, Saba Abdulbaqi Salman

Mesopotamian Journal of Big Data

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest …

Anomaly-Based Ids (Intrusion Detection System) For Cyber-Physical Systems, Ahmad Muter Awaad, Khattab M. Alheeti, Abdul Kream A.H. Najem

Mesopotamian Journal of Big Data

Cyber-physical systems (CPS) are critical infrastructures that integrate physical processes with computational components. The security of CPS is paramount, as any breach can lead to severe consequences. Anomaly-based intrusion detection systems (IDS) have emerged as a promising approach to safeguard CPS against cyber threats. This paper presents an anomaly-based IDS designed specifically for CPS, leveraging machine learning techniques to establish a baseline of normal system behaviour and promptly detect deviations indicative of malicious activities. The proposed system incorporates multiple classification techniques, including KNeighbors, RandomForest, XGB, DecisionTree, SGD, SVM, LGBM, AdaBoost, Bagging, and MLP Classifier, to enhance detection accuracy and robustness. …

Using Data Anonymization In Big Data Analytics Security And Privacy, Abdulatif Ali Hussain, Ismael Khaleel, Tahsien Al-Quraishi

Mesopotamian Journal of Big Data

Big Data and Analytics mean an enormous and complex collection of very diverse information, which is processed with various technologies and methods to produce and deliver useful and valuable insights. Analytics is the science of using data, or information to extract useful and actionable insights, facts and knowledge from a collection of data it could be stated that Big Data Analytics is the best thing since every commercial data system ever built, although everybody with a more optimistic vision of technology would like to take note that there is a fine line where Everything Data crosses the boundary to something …

Empirically Assessing 510(K) Device Safety, George Horvath

Akron Law Faculty Publications

Innovations in medical device technology hold the potential to improve health outcomes across the populace. Nearly half of all medical devices that enter the U.S. market each year are regulated by the Food and Drug Administration (FDA) under the 510(k) pathway. If FDA regulation of 510(k) devices stifles innovation, health outcomes will suffer over time because beneficial devices would not reach patients; on the other hand, if regulation facilitates innovation, health outcomes could improve. Most discussions of innovation under the 510(k) pathway have been either excessively reductionist (such as claims that the pathway stifles innovation) or wholly pessimistic (notably, the …

The Patent Law Origins Of Science Fiction, Camilla Hrdy, Daniel H. Brean

Akron Law Faculty Publications

This Article reveals the surprising role of patent law in shaping the literary genre of science fiction. Drawing on previously unpublished sources, the Article shows that Hugo Gernsback—the so-called “father” of science fiction who started the first all-science-fiction magazine in 1926—believed that works of science fiction are analogous to patents. Like patents, science fiction stories can disclose useful information to the public about new inventions. Like patents, science fiction stories can influence future inventors and drive innovation. Gernsback went even further, positing that some of the inventions depicted in science fiction should themselves be patentable. In 1952, he urged Congress …

Regulating Driving Automation Safety, Matthew Wansley

Articles

Over forty thousand people die in motor vehicle crashes in the United States each year, and over two million are injured. The careful deployment of driving automation systems could prevent many of these deaths and injuries, but only if it is accompanied by effective regulation. Conventional vehicle safety standards are inadequate because they can only test how technology performs in a controlled environment. To assess the safety of a driving automation system, regulators must observe how it performs in a range of unpredictable, real world edge cases. The National Highway Traffic Safety Administration (NHTSA) is trying to adapt by experimenting …

Causal Neuro-Symbolic Ai: A Synergy Between Causality And Neuro-Symbolic Methods, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Causal Neuro-Symbolic AI combines the benefits of causality with Neuro-Symbolic Artificial Intelligence (NeSyAI). More specifically, it (1) enriches NeSyAI systems with explicit representations of causality, (2) integrates causal knowledge with domain knowledge, and (3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.

Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth

Publications

Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation [1]. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (e.g., hallucinations and confirmation biases) [2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations [3,4]. Thus, significant challenges arise when these models are required to perform …

Effect Of Inlet Velocity On The Entrance Length Of Laminar And Turbulent Flow In A Circular Pipe, Farqad T. Najim

Babylonian Journal of Mechanical Engineering

This study uses numerical simulations performed with ANSYS-FLUENT to investigate the hydrodynamic entrance length for fluid flow in a circular pipe under both laminar and turbulent conditions. The effect of inlet velocity on entrance length and pressure drop was analysed. Two physical models with diameters of 15 cm and lengths of 10 m and 13 m were employed. The numerical results were validated against theoretical predictions, showing good agreement. The results indicate that entrance length increases with inlet velocity due to the extended development of the boundary layer. For laminar flow, entrance lengths of 1.46 m, 3.85 m, and 12.54 …