Digital Commons Network^™

Examining The Disclosure Of Sensitive Information Through Mobile Applications: A Privacy Calculus And Warning Experiment On Location-Based Services, Dwayne A. Ford

CCAC Theses and Dissertations

Smartphones and mobile applications have become all but ubiquitous in society. These applications provide a plethora of functions both in standalone and network configurations. Many of these popular applications utilize Location-Based Services (LBS) to deliver value to the user. Whether for navigation, transportation or social interactions, sharing information is essential when using these applications. While LBS applications provide various benefits, the sharing of location data also creates significant privacy risks. In many cases, users are unaware of the real risks and continue to share their location data in exchange for the benefits the application provides.

The problem identified in this …

Development Of A Phishing Risk Exposure Taxonomy On Mobile Devices In The Healthcare Industry, Christopher P. Collins

CCAC Theses and Dissertations

Phishing emails accessed on mobile devices present substantial risks to healthcare organizations when employees often operate under high cognitive load and with limited cybersecurity training. Despite widespread security awareness initiatives, healthcare workers continue to engage with phishing content on mobile platforms, posing threats to organizational data. Given the high value of healthcare data and the increasing sophistication of phishing schemes targeting healthcare professionals, there is a pressing need to enhance their ability to recognize phishing indicators on mobile devices.

This study developed and validated a Healthcare Workers Phishing Risk Exposure (HWPRE) taxonomy, designed to classify healthcare workers based on their …

An Evaluation Of Data Protection And Privacy Issues Introduced By Byod In Financial Institutions, Andy Miguel Santana

CCAC Theses and Dissertations

The idea of “bring your own device” (BYOD) allows organizational employees to conduct their tasks or processes on their own personal devices, has increased organizational efficiency significantly while allowing employees more flexibility. However, this approach also introduces major concerns about the security of organizational data as employees take their devices everywhere with them, opening more opportunities for unauthorized access to important data. Another major concern is the privacy of employee personal data. As many organizations implement BYOD, employees worry that with organizational monitoring and device management, their personal data is at risk as well. The problem this study tackles is …

Architectural Technical Debt Migrating Object Oriented Systems To Modular Architectures, Lionel Standridge

CCAC Theses and Dissertations

Architectural Technical Debt (ATD), a subset of Technical Debt (TD), arises when outdated architectural decisions present significant challenges to the maintenance and evolution of legacy object-oriented monolithic systems. These systems tend to have tightly coupled components and rigid dependencies, making it difficult to scale, adapt, and modernize. This dissertation investigated strategies for managing ATD during the transition from monolithic architectures to modular systems. By identifying the root causes of ATD in a legacy objectoriented system and evaluating various decomposition strategies, this research proposed a framework to guide practitioners in reducing ATD and improving a system’s modularity. Using quantitative metrics, the …

A Vision Transformer Based Assistive System For Dermatological Diagnosis In Systemic Lupus Erythematosus, Syeda Lamima Farhat

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) is a complex and often underdiagnosed autoimmune disease that affects multiple organs and presents with a wide range of symptoms-ranging from fatigue and joint pain to life-threatening organ damage. One of its most visible and diagnostically significant indicators is the Butterfly Malar Rash (BMR), a distinctive facial rash that often resembles other common dermatological conditions like rosacea, acne, eczema, and fifth disease. This overlap can lead to misdiagnosis or delayed detection, especially in busy clinical environments. To assist dermatologists in distinguishing BMR from similar facial rashes, this study explores the development of an AI-powered image classification …

Program And Proceedings: Nebraska Academy Of Sciences 1880–2025, 145th Anniversary Year, One Hundred-Thirty-Fifth Annual Meeting

Nebraska Academy of Sciences: Programs and Proceedings

Program

Aeronautics and Space Science

Biological and Medical Sciences

Biology

Chemistry

Earth Sciences

Science Education

Anthropology

Applied Science and Technology

Physics and Engineering

Forensic Sciences

Ecology, Sustainability, and Environmental Science

Maiben Lecture: Mary Ann Vinton, "State of the Academy"

Friends of Science Awards: David Crouse and Daniel Sitzman

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Quantitative Preventive Approaches To Diabetes: Mathematical Modeling And Analysis, Rushi P. Bhatt

Theses, Dissertations and Culminating Projects

The rising prevalence of diabetes presents a pressing global health concern, necessitating effective control strategies. This study aims to construct a mathematical model to analyze the influence of diverse factors on blood sugar levels, with a focus on identifying optimal methods for maintaining healthy glucose levels. Employing ordinary differential equations (ODE), the model investigates variables including leptin resistance, fat mass, glucose, insulin resistance, beta cell mass, daily physical activity, and dietary intake. Utilizing parameter estimates from existing literature, the model’s framework is established, and simulation results elucidate the intricate interplay between lifestyle choices and blood glucose dynamics. Furthermore, the model …

Exploring Coupled Land Use -- Climate Change Impacts In The Northeast United States From 2020-2025 With Implications For Bombus Habitat Suitability, Archana Prasad

Theses, Dissertations and Culminating Projects

The combined impacts of land use and climate change pose significant challenges to bumblebee populations in the Northeast United States (NEUS), a region characterized by dense urbanization, diverse landscapes, and exposure to climate extremes. As urban and suburban areas expand, the loss of agricultural lands and open spaces alters habitat availability for bumblebees, which rely on grasslands, meadows, and forest edges for nesting and foraging. Simultaneously, the NEUS is experiencing an increase in extreme climate events, including prolonged heatwaves, heavy precipitation, and shifts in temperature patterns. Using CMIP6 climate model projections, we analyzed trends in nine climate indices. Our results …

Brain Tumor Classification Using Deep Learning: Custom Cnn Vs. Resnet50, Rayen Bentemessek

ICT

The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection …

Fake News Detection Using Machine Learning Models., Anne Higgins

ICT

This project investigates the use of machine learning to detect fake news, addressing the societal and political challenges posed by the widespread dissemination of false and misleading information. Using automated classification techniques, the project analyses news content to predict the likelihood of intentional deception. The methodology follows the CRISP-DM framework, encompassing data preparation, model development, and evaluation. By leveraging machine learning, the study aims to support organisations, governments, and digital platforms in mitigating misinformation, while also considering ethical, interpretability, and strategic implications. The findings provide actionable insights for enhancing content moderation and reducing the influence of disinformation in digital media …

Comparative Evaluation Of Ai-Generated Synthetic Data And Real-World Data Performance In Predictive Analytics., Corey Louise Hughes

ICT

There are growing restraints when it comes to Real World Data (RWD), these include topics such as privacy regulations, ethical concerns, and the cost of collecting the data, and they have drove an interest in AI-generated synthetic data as a potential alternative in predictive analytics. This project examines the possibilities of synthetic data and if it can act as a reliable substitute for RWD in predictive modelling. This project uses Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic reproductive health data and evaluates its predictive performance against RWD using linear regression and key metrics, including Mean Absolute …

Identifying Skills Gaps And Labour Market Trends Using Machine Learning: A Data Analysis Of Tendencies Within The Irish Online Job Market., Stephen Ray

ICT

The Irish labour market is a dynamic system characterised by overlapping and distinct demands. The skills required to maintain competitiveness within today's labour market are constantly evolving in response to the needs of corporations, non-governmental organisations, and capital. Labour market participants, fulfilling specific roles, contribute to a skill-based relationship where supply and demand operates in new and innovative ways. With the advent of the internet, the labour market has shifted towards a digital environment, leading to the rise of online job advertisement platforms that cater to geographically defined regions. These platforms attract vast numbers of users and prospective employees daily …

Tour Demand Forecasting In Ireland: Development And Evaluation Of Classical, Deep Learning, And Hybrid Models, Ruben Elias Charleston Montfort

ICT

Tourism plays a significant role in global economies by supporting employment, infrastructure, and national development. As international travel continues to grow, accurate tourism demand forecasting has become increasingly important for effective planning and decision-making. In Ireland, tourism is a key economic sector attracting millions of visitors annually. For tour operators such as Irish Day Tours, reliable demand forecasting is essential for optimizing logistics, resource allocation, marketing strategies, and customer satisfaction. Advances in machine learning and deep learning techniques offer new opportunities to improve forecasting accuracy and support data-driven decision-making within the tourism industry.

Zinc Or Swim: Investigating Zinc Pollution From Restoration Materials, Megan R. Jensik

Honors Undergraduate Theses

Zinc is an essential nutrient, but can be toxic at high concentrations, negatively affecting various biological functions. Gabions are galvanized metals coated in zinc that are used for many human purposes, including coastal restoration efforts attempting to avoid using plastic materials. However, gabions can degrade in marine environments, potentially leaching metal into the water and soil. There is a knowledge gap regarding the occurrence and impacts of zinc pollution in the areas where these gabions are deployed, including the Indian River Lagoon (IRL). To address this, sediment samples were collected from the IRL and analyzed for their physical and chemical …

The Relevance Of Dnajb1 Protein Dimerization Against The Human Neurodegenerative Diseases, Erina Kotreli, Trang Le, Szymon Ciesielski

Dean's Leadership Council Library Research Prize

All biological functions in living organisms depend on the activity of proteins, long molecules that must twist and fold into specific shapes to become functional. A subset of proteins known as molecular chaperones is essential for maintaining cellular health by assisting other proteins in folding correctly and refolding those damaged by stress. When proteins misfold and clump together, they can form toxic aggregates linked to many neurodegenerative diseases, including Alzheimer’s disease (AD), one of the leading causes of death in the United States. In AD, these protein aggregates form organized, elongated structures referred to as amyloid-like fibrils, which damage neurons …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

Understanding The Challenges And Satisfaction Among The Medical Professional Preceptors, Cornelia Ko

West Chester University Doctoral Projects

This study evaluates the Penn Medicine Family Medicine Clerkship program through a mixed-methods approach, exploring the challenges and factors influencing the satisfaction of medical professional preceptors when precepting students. A quantitative survey with 54 preceptor participants reveals the top challenges preceptors face: workload, conflicts with patient care, and limited clinical space. Despite this, there is strong support for the program, with 89% of preceptors enjoying the role and 85% expressing willingness to continue to precept for the next three years. Finally, data analysis shows no statistical correlation between preceptor motivation with gender, ethnicity, age group, years of serving, or total …

Noaa Oceanic And Atmospheric Research Ocean Carbon Observing Science Plan Fy25 To Fy35, United States National Oceanographic And Atmospheric Administration

National Oceanographic and Atmospheric Administration: Technical Reports and Related Materials

United States National Oceanographic and Atmospheric Administration oceanic and atmospheric research ocean carbon observing science plan for fiscal years 2025 through 2035.

Nebraska Water Center Annual Report 2025

Nebraska Water Center: Administrative Materials

Training Nebraska's future water professionals, research publications, water sciences laboratory impact and know your well curriculums and community impact

Impact Of An Herbicide Treatment In An Annual Grass Invaded Sagebrush Steppe, Jordan Dino Santia

Undergraduate Scholarship

Globally, rangelands are being threatened by invasive annual grasses (IAGs) that alter fire regimes, drive vegetation type conversion, and alter ecosystem function. This study evaluates vegetation responses to an Imazapic treatment applied in 2020 to control a population of Taeniatherum caput-medusae, an IAG, that flourished following a severe wildfire in 2013. The site, located on a Bureau of Land Management (BLM) grazing allotment east of Klamath Falls, Oregon, has been monitored annually using the Assessment, Inventory, and Monitoring (AIM) protocol since the Summer of 2022. Over this period, very little T. caput-medusae has been detected while bare ground has …

Igc Oral Presentation Summary: Xxv Igc Congress, S. Ray Smith, Echo Elizabeth Gotsick, J. R. Weinert-Nelson, Alayna Jacobs, Christopher D. Teutsch, Will Fleming, D. R. Woodfield, James L. Klotz, Jeff Lehmkuhler, Greg S. Halich, Brittany Davis, Brittany Hendrix, Krista Lea

IGC Proceedings (1977-2023)

This paper will give an overview of the XXV International Grassland Congress thematic, keynote, and plenary sessions. The theme of the XXV IGC Congress meeting was “Grasslands for Soil, Animal, and Human Health.” We had many people attending, presenting, and participating in all these sessions. Within a month after the conclusion of the Congress, the proceeding’s papers presented at the meeting will be available on the IGC app. By the end of 2023 all papers will be available for download through any search engine at internationalgrasslands.org.

Circadian Variation In Mgmt Promoter Methylation And Expression Predicts Sensitivity To Temozolomide In Glioblastoma, Maria F. Gonzalez-Aponte, Yitong Huang, William A. Leidig, Tatiana Simon, Omar H. Butt, Marc D. Ruben, Albert H. Kim, Joshua B. Rubin, Erik D. Herzog, Olivia J. Walch

Mathematics Sciences: Faculty Publications

Purpose Recent studies show that glioblastoma (GBM) is more sensitive to temozolomide (TMZ) in the morning. In cells, inhibiting O6-Methylguanine-DNA-Methyltransferase (MGMT) abolished time-dependent TMZ efficacy, suggesting that circadian regulation of this DNA repair enzyme underlies daily TMZ sensitivity. Here, we tested the hypotheses that MGMT promoter methylation and protein abundance vary with time-of-day in GBM, resulting in daily rhythms in TMZ efficacy.

Methods We assessed daily rhythms in MGMT promoter methylation in GBM in vitro and retrospectively analyzed MGMT methylation status in human GBM biopsies collected at different times of day. Next, we measured MGMT and BMAL1 protein …

Spatiotemporal Economic Impact Analysis Of The Taal Volcano Eruption Using Electricity Consumption And Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Analyzing the spatiotemporal dimension of the economic impacts of disasters is critical for providing timely and proportionate support. However, traditional economic impact measures often lack spatiotemporal details. Hence, we estimated the daily electricity consumption (EC) loss using high-frequency EC data and analyzed temporal dimensions of the immediate impacts of Taal Volcano’s eruption on January 12, 2020. Subsequently, we computed the nighttime light (NTL) change using high-resolution NTL data to analyze the spatial distribution of these impacts. The temporal analysis revealed two EC loss peaks. The first peak coincided with the power outage and decreased energy demand, followed by a brief …

Frozen Commons: Food Security And Food Sovereignty In Rural Alaskan Communities And Tribes, Catey Selby, Weiwei Mo

Honors Theses and Capstones

This project explored the challenges, needs, and potential solutions related to food security and food sovereignty in rural Alaskan communities. These communities have long relied on traditional subsistence practices like fishing, hunting, and foraging, but those practices have been increasingly affected by climate change, regulatory restrictions, and commercial overfishing. Key issues identified included shorter hunting seasons, changes in animal migration, unsafe ice conditions, habitat loss, complex land ownership, and permitting barriers. Large-scale commercial fisheries also put additional pressure on subsistence resources, reducing access to traditional food sources.

The project focused on identifying key stakeholders—such as government agencies, tribal organizations, and …

Potential Of Green Synthesis Of Silver And Gold Nanoparticles For The Management Of Metabolic Associated Steatotic Liver Disease: A Mini Review, Atiqah Suhaili, Ya Chee Lim, Mei Ann Lim, Lee Hoon Lim, Mohammad Mansoob Khan, Monowarul Mobin Siddique

ASEAN Journal on Science and Technology for Development

Metabolic-associated steatotic liver disease is a metabolic disorder with limited pharmacological treatment options and only one approved medication to date. MASLD can be asymptomatic in its earlier stage and hence, prevention of this disease progression is the most effective approach. Recently, green-synthesized nanoparticles, particularly silver (AgNPs) and gold nanoparticles (AuNPs), have gained interest due to their eco-friendly production, biocompatibility, and bioactive properties. This review highlights the biosynthesis of AgNPs and AuNPs using medicinal plant extracts and their reported biological effects relevant to MASLD management including anti-hyperlipidemic, anti-diabetic, antioxidant and anti-inflammatory activities. While findings are promising, further mechanistic studies and preclinical …

How Do Selected Biomedical And Health Sciences Journals React To Submissions Of Artificial Intelligence (Ai) Assisted Manuscripts?, Misa Mi, Lin Wu, Yingting Zhang, Wendy Wu

Library Scholarly Publications

Background and Objectives: Generative artificial intelligence (GenAI) increasingly impacts research and scholarly communication. Given the evolving application of ChatGPT and other AI tools in scholarly communications, health sciences librarians must become cognizant of any existing journal publishing guidelines for AI-created or assisted manuscripts. The study aims to examine how scholarly biomedical and health sciences journals and publishers respond to submissions of these manuscripts and what requirements or policies have been put in place to guide and instruct authors on AI use.

Methods: We first retrieved and consolidated a list of journals representing disciplines in biomedical and health sciences from four …

Multi-Attribute Characterization Of Polymeric And Lipid-Based Drug Delivery Systems By Column Switching Liquid Chromatography-Mass Spectrometry, Brady W. Drennan

Chemistry & Biochemistry Dissertations - Archive

Drug delivery systems (DDS) are an increasingly relevant technology, owing to their ability to provide controlled release and to enable the use of otherwise toxic or labile therapeutics. However, these modalities are complex, containing a multitude of species with diverse chemical and physical properties. Their characterization, which involves the assessment of critical quality attributes (CQAs), requires numerous analytical techniques. The high-throughput demands of the biopharmaceutical industry have driven the need to consolidate these characterizations into a single analytical method. Consequently, novel analytical technologies are needed to support multi-attribute characterization. A multimodal liquid chromatography–mass spectrometry (LC–MS) system was developed to facilitate …

Σ-Ary, Minnesota State University Moorhead, Mathematics Department

Math Department Newsletters

No abstract provided.

Artificial Intelligence In Science And Society: The Vision Of Usern, Tommaso Dorigo, Gary D. Brown, Carlo Casonato, Artemi Cerda, Joseph Ciarrochi, Mauro Da Lio, Nicole D'Souza, Nicolas R. Gauger, Steven C. Hayes, Stefan G. Hofmann, Robert Johansson, Marcus Liwicki, Fabien Lotte, Juan J. Nieto, Giulia Olivato, Peter Parnes, George Perry, Alice Plebe, Idupulapati M. Rao, Nima Rezaei, Fredrik Sandin, Andrey Ustyuzhanin, Giorgio Vallortigara, Pietro Vischia, Niloufar Yazdanpanah

All Peer-Reviewed Publications

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in …