Digital Commons Network^™

Geographies Of Trauma And Healing: Resistance, Resilience, ‘Ustawi Wa’, And The Lived Experiences Of African Women Refugees From The Ehagl Region In Canada, Rosemary Njeri Kimani-Dupuis Dr., Rosemary Kimani-Dupuis

Theses and Dissertations (Comprehensive)

This dissertation explores the lived experiences of African women refugees from the East and Horn of Africa and Great Lakes (EHAGL) region resettled in Ontario, Canada, with particular focus on the complex intersections of trauma, resilience, and healing within global refugee governance. It asks: How do the gendered and spatial dynamics of refugee protection and Canadian settlement systems sustain structural violence against African women refugees, and how do their transnational experiences challenge and reshape dominant frameworks of care, well-being, and institutional response?

Through a multi-scalar analysis—macro (legal governance frameworks), meso (institutional actors), and micro (women’s narratives)—the study explores how trauma …

The Experiences Of Mothering With An Intimate Partner Violence-Related Brain Injury, Emily L. Sebben Miss

Theses and Dissertations (Comprehensive)

One in three Canadian women will experience intimate partner violence (IPV) in their lifetime, and 75% of them have a probable brain injury (BI) (Haag et al., 2022). Despite the recent growth of IPV-BI research, many survivors remain undiagnosed and many aspects remain unexplored. The experiences of being a mother with an IPV-related BI has been left untold. These gaps leave frontline workers with little guidance in supporting survivors who are specifically mothers. This research reports on a qualitative study exploring the lived experiences of mothering with an IPV-BI. The study uses a constructivist worldview and hermeneutic phenomenology to amplify …

How Can University-Community Partnerships Strengthen Food Literacy To Improve Food Access And Food System Resilience In Rhode Island?, Jocelyn Furtado

Student Publications

Nearly two out of every five households in Rhode Island experience difficulties accessing adequate and nutritious food, falling into the category of food insecure. While it is not the responsibility of a sole organization to reverse this complex systemic issue, collaborative efforts are a step toward combating food insecurity and systems resilience through improving food literacy. When accomplished within a community, adequate food literacy can combat food insecurity. To the same effect, food insecurity may cause a lack of food literacy.

To hone in on the relationship of food literacy as it relates to food insecurity and food systems resilience, …

Tickborne Illness, Children's Mercy Kansas City

Clinical Pathways

No abstract provided.

Food Resource Efficiency Decision Support Hub: Consumer Survey Report, Tracey O'Connor, Mina Sadeghzadeh, Julia Lessa Feitosa Virgolino, Jennifer Attard

Publications

FRED is focused on informing policy on food waste prevention using a total food system sustainability approach.

Consumers sit at a critical point of food value networks as the decision-makers about food purchasing, household storage and management, and (non-)consumption. It was critical therefore to understand the consumer context, their concerns and needs regarding the food system, and their food purchasing and waste attitudes and behaviours.

Novel Sex Work Anti-Discrimination Protections, Chi Adanna Mgbako, Christine Eldabh, Katie Falk, Emma Pennie

Faculty Scholarship

Sex workers throughout the world experience ubiquitous and acute discrimination in every facet of their lives due to deeprooted stigma. Societal fear and hatred of sex workers often lead to their exclusion from healthcare services, financial institutions, and accessible housing, as well as their marginalization by the carceral state, family law systems, media, and politicians. Between 2022 and 2024, the Australian jurisdictions of the Northern Territory, Queensland, and Victoria passed groundbreaking legislation codifying novel anti-discrimination protections for sex workers. These laws are the first in the world to explicitly protect sex workers against discrimination in accommodation, employment, government services, and …

Isapa 2025: Program Guide, Catherine Carty, Gerard Masdeu Yelamos

Publications

ISAPA 2025 is a landmark international event that brings together global leaders, researchers, practitioners, and advocates in Adapted Physical Activity to ignite systemic change under the unifying theme “Shifting the Dial: From Awareness to Transformation.” Hosted in Kerry, Ireland, under the patronage of UNESCO, the symposium integrates five major events into one dynamic platform.

The program reflects IFAPA’s mission to advance inclusion, research, professional excellence, and international cooperation, while also contributing meaningfully to the implementation of global frameworks such as the UN Convention on the Rights of Persons with Disabilities (CRPD), the Sustainable Development Goals (SDGs), the Paris 2024 Call …

Mixing-Controlled Combustion Of Ethanol Enabled By Prechamber Ignition (Pc-Mcc): A Preliminary Experimental Demonstration, Jared Zeman, Ziming Yan, Michael Bunce, Adam Dempsey

Mechanical Engineering Faculty Research and Publications

This numerical study focuses on assessing the key design parameters of interest for use of an active prechamber igniter as an ignition assistance device to enable mixing-controlled combustion (MCC) of ethanol (E100) in a heavy-duty Caterpillar C9.3B engine. Computational fluid dynamic (CFD) simulations of a baseline diesel and prechamber retrofitted C9.3B at a gross indicated mean effective pressure (IMEPg) of 5 bar and 1800 rpm are carried out using CONVERGE. In particular, the sizing of the prechamber volume, sizing of total orifice cross sectional area (orifice diameter), and location of the prechamber relative to a centrally mounted common rail direct …

Resilience As Accusation: A Critical Examination Of Individual Resilience Training For Burnout Mitigation, Jacqueline Christianson, Bonnie Sommers-Olson, Jessica Leiberg, Dana Kaminstein

College of Nursing Faculty Research and Publications

Burnout, a syndrome of work-related exhaustion and cynicism, is prevalent among nurses and is associated with workplace stressors. Resilience training programs are a prevalent method of burnout mitigation employed by healthcare institutions that aim to improve or alter how individuals respond to chronic stressors. Through the lens of General Systems Theory, we describe resilience training as a method of individualizing a systemic problem by problematizing a response to chronic stress exposure. Resilience training may furthermore serve as a mechanism which allows subversion of institutional responsibility for nurses’ well-being in the workplace. We describe several suggestions for nurses to resist being …

The Munster Statement Om Principerna För Forskningsetik Och Forskningsintegritet (Swedish), Ingenium

Dissemination

INGENIUM är en allians av tio lärosäten från tio europeiska länder. Europauniversitetet strävar efter att göra det möjligt för varje medlem i nätverket att erbjuda högkvalitativa studie- och kvalifikationsprogram med digitala komponenter som delas inom INGENIUM Alliance.

Vid universitet och högskolor läggs allt större vikt vid att främja god forskningssed inom alla aspekter av forskning1,2, inräknat allt från grundforskning till tillämpad forskning och kommersialisering. Forskningsetik (FE) och forskningsintegritet (FI) stödjer forskningsverksamhet och excellens och anses utgöra en viktig grund för att forskare ska kunna lita på varandra och på forskningsresultaten3 samt stärka allmänhetens förtroende för vetenskap och vetenskapliga resultat4. Men …

The Munster Statement On The Principles Of Research Ethics And Research Integrity (English), Ingenium

Dissemination

INGENIUM is an alliance of ten institutions of higher education from ten European countries. This European University seeks to enable each member of the network to offer high-quality study and qualification programmes with digital components that are shared within the INGENIUM Alliance.

In Higher Education Institutions there is a growing emphasis on promoting good research practices in all aspects of research1,2, including everything from fundamental research to applied research to commercialisation. Research ethics (RE) and research integrity (RI) underpin research activity and excellence and are considered a critical component of the basis for researchers to trust each other as well …

Η Δήλωση Munster Για Τις Αρχές Δεοντολογίας Της Έρευνας Και Ακεραιότητας Της Έρευνας (Greek), Ingenium

Dissemination

Το INGENIUM είναι μια συμμαχία δέκα ιδρυμάτων τριτοβάθμιας εκπαίδευσης που εκτείνονται σε ισάριθμες ευρωπαϊκές χώρες. Αυτό το Ευρωπαϊκό Πανεπιστήμιο επιδιώκει να καταστήσει ικανό κάθε μέλος του δικτύου να προσφέρει υψηλής ποιότητας προγράμματα σπουδών και κατάρτισης με ψηφιακές συνιστώσες που είναι κοινά στο πλαίσιο της συμμαχίας INGENIUM.

Στην τριτοβάθμια εκπαίδευση σημειώνεται αυξανόμενη έμφαση στην προώθηση ορθών ερευνητικών πρακτικών σε όλες τις πτυχές της έρευνας1,2, συμπεριλαμβανομένων όλων των τομέων από τη βασική έως την εφαρμοσμένη έρευνα και την εμπορική εκμετάλλευσή της. Η δεοντολογία της έρευνας (ΔτΕ) και η ακεραιότητα της έρευνας (ΑτΕ) στηρίζουν την ερευνητική δραστηριότητα και την αριστεία και θεωρούνται κρίσιμες …

Munsterin Julkilausuma Tutkimusetiikan Ja Hyvän Tieteellisen Käytännön Periaatteista (Finnish), Ingenium

Dissemination

INGENIUM on kymmenen Eurooppalaisen korkeakoulun verkosto, joka antaa verkoston jäsenille mahdollisuuden tarjota korkealaatuista koulutusta, hyödyntäen INGENIUM-allianssin yhteisiä digitaalisia opintokokonaisuuksia.

Korkeakouluissa painotetaan yhä enemmän hyvien tutkimuskäytäntöjen edistämistä kaikilla tutkimuksen osa-alueilla1,2 perustutkimuksesta soveltavaan tutkimukseen ja kaupallistamiseen. Tutkimusetiikka ja hyvä tieteelinen käytäntö tukevat tutkimustoimintaa ja huippuosaamista, ja niitä pidetään tärkeänä osana tutkimuksen perustaa, jonka avulla tutkijat luottavat toisiinsa ja tutkimusansioihin 3ja vahvistavat yleisön luottamusta tieteeseen ja tieteellisiin tuloksiin4. Koska ohjeita on kuitenkin lukuisia, on välttämätöntä laatia lausunto, joka mahdollistaa johdonmukaisen lähestymistavan tutkimusetiikkaan ja hyvään tieteelliseen käytäntöön koko INGENIUMissa.

Koska tutkimusetiikka ja hyvä tieteellinen käytäntö ovat merkityksellisiä kaikkien tieteenalojen tutkimuksessa, on erityisen tärkeää, että …

Knowing What To Eat And When To Eat: Reading The Food Offering Text (1 Corinthians 8:1–11:1) From A Myanmar Christian Perspective, Hram Hu Lian

The Asbury Journal

Food offerings play a vital role in the socio-religious life of the people of Myanmar. This is mainly because food is served as a part of worship in religious settings (Nat worship, Ahlu pwe, and religious festivals), as well as a part of regular interactions in a social setting (workrelated dinner parties, dinner parties at a Buddhist neighbor’s house, and non-religious social gatherings). In this context, knowing what to eat, and when to eat, becomes crucially important for Myanmar Christians as often times a person encounters various questions in regard to food offerings such as; Should a Christian participate in …

Journal In Entirety

The Asbury Journal

No abstract provided.

Journal In Entirety

The Asbury Journal

No abstract provided.

Blessed Be The Holiness Tie That Binds, Don Thorsen

The Asbury Journal

No abstract provided.

Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models, George Ku

Master's Projects

Coral reefs can be primarily found in tropical and sub-tropical regions of our oceans, providing a thriving habitat for millions of species. Marine sponges, which can be frequently found in coral reefs, play a critical role that contributes to the maintenance of these ecosystems, including the recycling of nutrients through water filtration. However, rising ocean temperatures and acidification due to climate change have resulted in the bleaching and death of coral reefs worldwide. In order to preserve these reefs and the sponges that depend on them, scientists have been performing studies on their biodiversity. This includes collecting numerous images of …

Ribomoe: An Application Of Mixture Of Experts On Artificial Riboswitch Classification, Hainian Audrey Long

Master's Projects

Urban water sewage is a potential health concern due to its possibility to spread contagious RNA viruses such as Coxsackievirus B3. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. To address this, this study proposes a novel viral detection method using synthetic riboswitches that bind to the target virus and trigger a reporter gene, amplifying the detection signals. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features. This model used …

Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed

Master's Projects

Although recent trends indicate that LLMs outperform traditional methods in solving complex problems with enhanced reasoning, there has been barely any progress in replicating the quality of diagnoses like those of actual human doctors. The identification of an accurate diagnosis with thorough reasoning is still a significant challenge, even with advanced AI models. The process of performing accurate diagnosis remains challenging due to a lack of transparency in state-of-the-art models existing today, a lack of explanation in the diagnosis process, an emphasis on results rather than reasoning, and a lack of foundational knowledge in models, along with limited exploration of …

Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms, Kush Patel

Master's Projects

Transformer architectures have emerged as powerful tools for time series forecasting, excelling at capturing complex temporal dependencies across multivariate inputs. However, these models are highly susceptible to adversarial attacks such as the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM), which can significantly degrade predictive performance through small, targeted perturbations. This work integrates dynamic attention mechanisms, adaptive masking modules that introduce controlled variability into attention pathways, into a transformer forecasting model to enhance robustness against such attacks. Using two distinct datasets, we compare the performance of a standard transformer and a dynamic attention-enhanced transformer under both clean and …

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …

Rul Estimation Of N-Cmapss Turbofan Engines Using Deep Learning With Customized Penalty And Expanded Sensors, Prathmesh Pethkar

Master's Projects

Accurate prediction of Remaining Useful Life (RUL) for aircraft engines is important to enhance maintenance efficiency and flight safety. For this project, a solution to RUL prediction on NASA's N-CMAPSS data set, mimicking realistic engine degradation under simulated full-flight scenarios, is being proposed. For addressing the high-dimensional noisy sensor data challenge, a new feature engineering pipeline was utilized. Models trained on healthy data predict normal sensor behavior, and the discrepancy between these predictions—referred to as residual features—is a measure of degradation. To handle the size and computational demands of the dataset, training was conducted on Google Cloud Platform using GPU-supported …

Medilightrag: A System For Medical Query Response Using Fine-Tuned Llms And Graph Based Retrieval, Rajiv Karthik Reddy Kodimala

Master's Projects

The exponential increase in medical data has created a greater demand for precise and efficient information retrieval systems. Existing Large Language Models (LLMs) face domain-specific difficulties such as sophisticated medical jargon, situational comprehension, and the continual advancement of healthcare knowledge. To tackle these challenges, we present MediLightRAG, an innovative two-stage system which integrates parameter-efficient fine-tuning of Large Language models with LightRAG’s graph-based retrieval. The first stage focuses on enabling accurate resource-efficient model adaptation for the medical domain through QLoRA fine-tuning. In the second stage, LightRAG’s two-tiered retrieval architecture that combines graph-based indexing with dynamic knowledge retrieval is employed to enhance …

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar

Master's Projects

In this thesis, we present a comparative analysis of the use of transformerbased and Large Language Model (LLM) models for State of Health (SoH) and Remaining Useful Life (RUL) prediction of lithium-ion batteries. With electric cars and renewable energy systems based on batteries at the forefront, the need to predict degradation accurately in order to enhance the performance and reduce maintenance costs has become imperative. Most traditional prediction methods lag the complex and non-linear characteristics of degradation in batteries, and hence the usage of sophisticated methods becomes a necessity. The research employs the CALCE dataset, which includes long-horizon cycling data …

Introducing Justice And Rights To Students Of Economics, Mark D. White

Publications and Research

Most economics instruction is focused on outcomes, whether evaluated in terms of individual or collective welfare or well-being. This is consistent with economics’ roots in classical utilitarianism, and no consideration is usually given to the process by which these results obtain, which is a deontological concern of right and wrong. This can seem odd to economics students, especially in the context of trade-offs in which one party is benefited at the expense of another, or aggregate welfare is maximized while some individuals are harmed, with no consideration in either of rights, desert, or justice. This chapter argues for the importance …

Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana

Master's Projects

Exponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We …

Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder

Master's Projects

Accurately predicting protein-protein interactions (PPIs) is essential for understanding cellular function and advancing biomedical discovery. We model PPIs as graphs, where nodes represent proteins and edges denote interactions. Using interaction data from the STRING database, we use two samples of it, namely the benchmark datasets—SH27K and SH148K—filtered by confidence score and annotated by interaction mode (multiple relations). In this project, we present EvoRGCN, a graph-based machine learning framework for PPI prediction that integrates both sequence-level (ESM-2 embeddings) and network-level information. We incorporate various Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, and Relational Graph Convolutional Networks. Our …

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana

Master's Projects

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein …