Digital Commons Network^™

Detecting Fake News Using Ai, Gustavo Lambert, Lucas Schultz

ICT

This is a document that presents a strategic analysis of the project “Detecting Fake News using AI”. Developed as part of the BSc (Hons) in Computing in IT at CCT College Dublin. The goal is to analyse the potential advantages, exploiting the viability and the impact of applying Artificial intelligence to check, verify and alert about misinformation found and to answer the question “How can Artificial Intelligence be leveraged to accurately detect and combat fake news while ensuring data privacy and compliance with regulations?” and “To what extent can AI-driven misinformation detection help reduce the spread of fake news on …

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification, Bernardo Gandara, Ignacio Varela

ICT

The advancement of generative AI technologies has made it increasingly difficult to distinguish synthetic images from authentic ones. This capstone project addresses the challenge by developing a binary image classification model using deep learning techniques to differentiate AI-generated images from real photographs. Guided by the CRISP-DM methodology, we employed the DeepGuardDB dataset, consisting of 13,000 balanced image samples, evenly split between real and synthetic sources. We implemented and compared three Convolutional Neural Network (CNN) architectures through transfer learning, standardising input pipelines and integrating custom classification heads. Following a performance evaluation across multiple metrics, the best-performing model was selected for further …

Proteomics Profiling Of Research Models For Studying Pancreatic Ductal Adenocarcinoma, Mathilde Resell, Hanne-Line Rabben, Animesh Sharma, Lars Hagen, Linh Hoang, Nan T. Skogaker, Anne Aarvik, Eirik Knudsen Bjåstad, Magnus K. Svensson, Manoj Amrutkar, Caroline S. Verbeke, Surinder K. Batra, Gunnar Qvigstad, Timothy C. Wang, Anil Rustgi, Duan Chen, Chun-Mei Zhao

Journal Articles: Biochemistry & Molecular Biology

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. The preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modelling, …

Metabolic And Proteomic Signatures Differentiate Inflammatory Phenotypes From Cancer And Predict Treatment Response In Patient Sera, Gabriel Cutshaw, Elena V. Demidova, Philip Czyzewicz, Elizabeth Quam, Nicole Lorang, Al Warith Al Siyabi, Surinder K. Batra, Sanjeevani Arora, Rizia Bardhan

Journal Articles: Biochemistry & Molecular Biology

Tumors shift their metabolic needs to enable uncontrolled proliferation. Therefore, metabolic assessment of cancer patient sera provides a significant opportunity to noninvasively monitor disease progression and enable mechanistic understanding of the pathways that lead to response. Here, we show Raman spectroscopy (RS), a highly sensitive and label-free analytical tool, is effective in metabolic profiling across diverse cancer types in patient sera from both pancreatic ductal adenocarcinoma (PDAC) and locally advanced rectal cancer (LARC). We also combine metabolic data with proteomic signatures to predict treatment response. Our data show RS peaks successfully differentiate PDAC patients from healthy controls. Peaks associated with …

Development Of The Mechanoresponsive Pericellular Matrix Of Chondrons, Donghee Lee, Sydney E. Greer, Andrew T. Dudley

Journal Articles: Genetics, Cell Biology & Anatomy

Physical properties of cartilage are conferred by the composition and ultrastructure of the extracellular matrix. This study focuses on the development of the pericellular matrix (PCM), a domain that directly contacts the chondrocyte and is a key regulator of biomechanical and biochemical signaling. Using three-dimensional cell culture, microfluidic cell compression platforms, and genetic mouse models, we demonstrated that collagen VI is initially assembled at the cell surface and then displaced to form a shell at the PCM-territorial matrix boundary. Cell surface-bound hyaluronan is crucial for the assembly process, and hyaluronan-aggrecan complexes drive displacement. Integrin adhesion is not required early but …

Meant To Burn: Wildfire In The West (2025), University Of Montana--Missoula. School Of Journalism

Byline

Learning to Live -- Behind the Fireline -- Nowhere to Hide -- Empowered to Fight -- Mann Gulch -- A Way of Life

The Big Four [Accounting] Law Firms: It's Past Time For The Practice Of Law To Get In The Game, Edward S. Adams, John H. Matheson

Articles

New legal services models like ABSs and accommodations like UPL waivers provide multiple positive outcomes. These business structures increase access to legal services, particularly for low and middle-income individuals who are currently priced out of them. By allowing these services to be conducted by nonlawyers, the current supply gap could be closed or completely eliminated. Also, the increased pressure on law firms from corporations and ABSs promotes free market competition. Law firms will be forced to either find a way to differentiate their expertise and services or face a serious threat due to the rejection of the traditional billable hour …

The Ole Miss, University Of Mississippi

The Ole Miss (1897-)

The Ole Miss Annual, 2025

Influence Of Deck Area And Average Daily Traffic On The Deterioration Of Bridge Superstructures., Faysal Ahamed

Graduate Theses, Dissertations, and Problem Reports (ETD)

State Departments of Transportation (DOTs) are responsible for maintaining approximately 623,000 bridges across the United States. Effective bridge management requires a comprehensive understanding of the factors influencing structural deterioration. Numerous studies have explored the effects of average daily traffic (ADT) and deck area (DA) on bridge deck deterioration. Given the interdependence between bridge decks and superstructures, it is plausible that ADT and DA significantly influence superstructure condition ratings and deterioration trends. However, their impact on superstructures remains insufficiently studied. In addition, existing studies primarily focus on overall bridge deterioration without adequately differentiating between superstructure designs and materials and maintenance authorities. …

Dementia Caregivers’ Perspectives On Technology’S Place In Care Practices: Mixed Methods Survey, Julia A. Scott, Emma Cepukenas, Mckenzie Himes, Kennedy Anderson, Kiren Grewal, An Mai, Sheila Yuter, Patricia M. Simone

Psychology

Background:

As the number of individuals diagnosed with dementia continues to grow, collective caregiving capacity is simultaneously declining. As a result, caregivers often face significant stress and burnout, which negatively impacts both their well-being and the quality of care they are able to provide. To address these challenges, various forms of technology have been developed to support caregiving responsibilities for people with dementia. Barriers to widespread adoption include the cost of technology, lack of caregiver training, limited awareness of available solutions, privacy and ethical concerns, and resistance to change from both caregivers and care recipients. By identifying these key insights, …

Kenyon College Course Catalog 2025-2026

Kenyon College Course Catalogs

No abstract provided.

Academic Freedom Under Attack: How Faculty And Students Can Protect Their Rights Situated In The Scholarly And Public Spheres, Emma Hansen

Honors Senior Capstones and Theses

This thesis project challenged me to apply my learning to the larger community and accomplish something that would make a lasting impact at Roger Williams University. When I was attempting to come up with my topic, I tussled with how my project could accomplish my main goal, to help people. With the current threats to academic freedom and institutional autonomy from the Trump Administration, I recognized that the faculty were expressing concern about the permanence of their jobs. The general feeling on campus and in the news was fear. So, I thought to myself, “How do I put these fears …

Controls On Longitudinal Stream Profile Evolution In Guadalupe Mountains National Park, Samuel Schoenmann, Lisa Tranel, Eric W. Peterson, Jonathan Boyd Thayn

Faculty Publications - Geography, Geology, and the Environment

Canyons in Guadalupe Mountains National Park are excellent locations to study the discrete boundary conditions that influence longitudinal channel profile evolution including climate, tectonics, hydrology, and lithology. The Guadalupe Mountain landscape records deep earth mantle processes in combination with surficial channel erosion and incision. Knickpoints and convex segments in longitudinal river profiles document impacts of climate change, tectonics, or surface processes. This study combines field observations with an analysis of longitudinal profile slopes and residual errors to evaluate how bedrock strength can be distinguished from features created by climate change, faulting, or subsidence in Pine Springs and McKittrick Canyons within …

Age-Friendly Technology Toolkit, Carrie Leach, Mikayla Harrison, Deborah Beard, Dell Stubblefield, Zachary Rowe, Winona Bynum, Sandra Turner-Handy, Faith Hopp, Rodlescia Sneed, Deborah Charbonneau, Rochelle Chapman, Hayley S. Thompson

Institute of Gerontology

A toolkit created for older people who are interested in connecting through technology. Content is based on recommendations from older adults who partnered on a project to increase digital inclusion.

Historicity And Axiology: Temporal Implications For Axiology, Bennett Gilbert

History Faculty Publications and Presentations

To develop the axiology of moral (or ethical) values, phenomenological analysis must look at temporality in a way that is unfamiliar to it. This way is through knowledge and experience of the actual historical past, from which it has been separated by the Heideggerian conception of historicity and its classical Husserlian analysis of time consciousness. The development of historical studies helps to make this possible. A simple sample model of the diachronesis of the awareness of ethical significance, value, and obligation is presented. It moves the analysis from a presentist focus to the field of experience in which subjects develop …

Relationship Between Transformational Leadership And Employees' Psychological Safety, Minahil Khan

SSLA Culminating Experience

This study explored how psychological safety manifests in the workplace and how it is influenced by transformational leadership. Using quantitative survey data from 101 employees—pre-dominantly based in Karachi, Pakistan—the research investigated the relationship between psychological safety and transformational leadership. It also explored whether there is a gender difference in the employees’ perception of transformational leadership and psychological safety. Findings indicate that transformational leadership has a significant positive correlation of 0.734 with psychological safety, which in turn enhances employees’ willingness to speak up and engage in creative problem-solving. Additionally results also indicated that there is no statistically significant gender difference in …

Electrocardiographic Assessment Of National-Level Triathletes: Sinus Bradycardia And Other Electrocardiographic Abnormalities, Mike Climstein, Kenneth S. Graham, Michael Stapelberg, Joe Walsh, Mark Debeliso, Kent Adams, Trish Sevene, Chad Harris

Kinesiology Faculty Publications and Presentations

Background: High-intensity endurance training induces specific cardiac adaptations, often observed through electrocardiographic (ECG) changes. This study investigated the prevalence of ECG abnormalities in national-level Australian triathletes compared to sedentary controls. Methods: A cross-sectional observational study was conducted involving 22 triathletes and 7 sedentary controls. Standard 12-lead ECGs assessed resting heart rate, ECG intervals, and axis deviation. Peak oxygen consumption was evaluated in triathletes to correlate with ECG indices and left ventricular mass, derived via echocardiography. Results: Triathletes exhibited significantly lower resting heart rates (53.8 vs. 72.1 bpm, −34%, p = 0.04), shorter QRS durations (0.088 vs. 0.107 ms, −21.6%, p …

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …

Immersive And Generative Technology: New Tools For Marketing, New Tools For Consumer Response, David W. Stewart, Gabrielle Pizzi, Shashi Matta, Federica Caboni

Marketing & Business Law Faculty Works

Immersive technologies, including AR, VR, XR, and generative technologies such as Gen‐AI, are reinventing marketing practice and consumer experience. In this article we provide an introductory review of how these technologies are (re)shaping consumer behavior in domains where the physical and digital converge, and consumers' response to them. Our review finds that while immersive technologies enhance engagement through personalization and multisensory feedback, they also raise concerns about accessibility, authenticity, and digital fatigue. Similarly, Gen‐AI, which is transforming many facets of marketing, including customer service, product development, marketing communications, and consumer research, raises new questions related to attribution, co‐creation, authenticity, and …

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

Cellular Mechanisms Of Spinal Motoneuron Hypoexcitability Underlying Dynapenia In Aging, Ibrahim Abdul Halim

Browse all Theses and Dissertations

Age-related weakness remains poorly understood as the underlying mechanisms remain unclear. While synaptic input and muscular changes have been investigated with age, intrinsic motoneuron excitability alterations are often overlooked. This thesis provides the first direct assessment of intrinsic excitability and ion channel properties of spinal α-MNs from male and female mice across three ages: young, middle aged, and old. Our findings reveal a decline in intrinsic excitability of motoneurons with age in both sexes. Mechanistic analysis shows sex specific differences: female motoneurons exhibit increased dendritic size, hyperpolarized RMP, and SK channel overactivation, whereas males show only SK overactivation with age. …

Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

Browse all Theses and Dissertations

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

Browse all Theses and Dissertations

Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh

Browse all Theses and Dissertations

Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a …

Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

Browse all Theses and Dissertations

Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …

Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala

Browse all Theses and Dissertations

Self-healing polymers, particularly vitrimers, are emerging as promising candidates in the development of advanced materials for renewable energy and aerospace structures. These materials exhibit dynamic covalent bond exchange mechanisms that enable reprocess ability, damage repair, and extended operational lifetime under harsh conditions. This study presents a density functional theory (DFT)-based computational investigation of the mechanistic pathways and energetics of bond exchange reactions in model vitrimer systems. We explore transition states, energy barriers, and thermodynamic features corresponding to associative and dissociative self-healing reactions in vitrimers. The study focuses on Diaminodiphenyl disulfide (AFD), a bifunctional molecule composed of two para-substituted aminophenyl rings …

Ab Initio Simulations For Oxidation Of An Ultra-High Temperature Ceramic (Hfb2), Wesley I. Black

Browse all Theses and Dissertations

Ultra-High Temperature Ceramics are a class of ceramics that possess high strength and melting points in excess of 3000°C. These ceramics are promising for aerospace applications, where materials need to endure high-temperature and high-stress environments. Utilizing ab initio simulations, this thesis research focuses on the atomistic details of oxidation for a typical ultra-high temperature ceramic material, namely hafnium diboride. The simulations provide energy barriers for transition from the initial to final structures via transition states on the (0 0 0 1) surface. These result in estimates for reaction rates and other thermodynamic features that are essential for assessing applicability under …

Reinforcement Learning For Adversarial Environments: Multi-Agent Hide And Seek With Multi-Modal Sensing, Christian Alejandro Carrizales

Browse all Theses and Dissertations

The development of intelligent and competitive agents in AI versus AI adversarial environments was explored through the utilization of reinforcement learning techniques with sensing modalities. A Hide-and-Seek simulation environment was developed using the Unity game engine along with the ML-Agents Toolkit. An engagement test campaign with a set of performance metrics was designed. Four AI versus AI adversarial scenarios were considered using the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) multi-agent reinforcement learning algorithms. Furthermore, the impact of sensing modalities on competing agents’ learning performance was investigated by varying the sensing capabilities of the hider and seeker, respectively. Experiments …

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

Browse all Theses and Dissertations

Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla

Browse all Theses and Dissertations

This thesis addressed two main challenges in biological data analysis: structure-preserving dimensionality reduction and synthetic data generation for small sample datasets. I proposed the Isometric Centroid Encoder (ICE), a supervised dimensionality reduction method that preserves pairwise distances between class centroids during dimension reduction. Unlike existing methods like Centroid Encoder and Super Encoder, ICE explicitly maintains geometric relationships between biological classes, achieving nearly perfect structure preservation at C dimensions (where C equals the number of classes) with strong performance even in 2D and 3D spaces. Additionally, I compared three generative models (VAE, LSH-GAN, and scDiffusion) for synthetic data generation on small …