Digital Commons Network^™

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

Contextual Scaffolding And Self-Efficacy: Supporting Computer Skill Development Among Blind Learners In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Inclusive computer literacy education efforts, broadening the participation of blind or visually impaired (BVI) individuals, have gained traction in recent years. Existing literature investigating these efforts primarily draws evidence from affluent Global North contexts, where accessibility resources and legal frameworks are relatively more mature. Little is known about the in-situ teaching and learning challenges faced by trainers and BVI students, respectively, in resource-constrained, multicultural Global South countries like India. To address this knowledge gap, we conducted a four-month contextual inquiry at two computer training centers catering to 94 BVI students in India. We notably observed a rigid, experience-driven training environment …

Voxvista: Enhancing Screen Reading Experience For Online User Comments, Yash Prakash, Akshay Kolgar Nayak, Mohammed Shoaib Alyaan, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

Exploring Marshall–Olkin Models Through Bibliometric And Topic Modeling Approaches Uses Latent Dirichlet Allocation (1981-2025): A Study Based On Scopus Data, Humberto Llinás, Brian Llinás, Carlos López, Daniela Nuñez

Computer Science Faculty Publications

The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. …

Exploring Large Language Models For Trustworthy Use: Insights From Research And Development, Sandeep Kalari, Sahithi Padidela, Vikas Ashok, Ravi Mukkamala

Computer Science Faculty Publications

Large Language Models (LLMs) are increasingly being adopted in a wide variety of domains, including sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. We have experimented with several strategies to address these challenges. First, we developed BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into the Qwen 2.5 LLM workflow. Second, we developed PrivAware, a multilayered privacy-enforcement framework, using a fine-tuned Flan-T5 model with self-attention masking, to safeguard data while maintaining high utility. Both systems …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

A Survey Of Ai Methods For Geometry Preparation And Mesh Generation In Engineering Simulation, Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Franck Ledoux, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich, Yongjie Jessica Zhang

Computer Science Faculty Publications

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons …

Dynamic Social Impact Theory And The Consolidation And Dispersal Of Clusters In Squid Game: The Challenge, Adrienne Darrah, Robert J. Darrah

Iowa Journal of Communication

This study utilizes a dynamic social impact theory (DSIT) framework to analyze human influence on the popular Netflix television series Squid Game: The Challenge. A British reality competition, Squid Game: The Challenge is comprised of ten-episodes and features 456 contestants vying for $4.56 million. As contestants self-organize into social alliances, their shared attitudes, beliefs, and values transform the physical space into a dynamic social space. The creation of clusters through these alliances, the correlation between contestant attitudes and behaviors, and the consolidation of contestant attitudes and behaviors reflective of alliance needs is reinforced by the physical proximity of contestants, the …

Research News, Publications, 2026, Volume 1, Soumyajit Majumdar

Publications (2015-)

Publications that appeared during the period of January 1 - March 31, 2026.

Prickle4 Drives Microenvironmental Remodeling And Resistance To Parp Inhibition In Idh-Mutant Glioma, Ju Yang, Hua Yang, Yifan Yuan, Chenyang Zhang, Ziwei Fu, Yanyan Chen, Yinghong Xiong, Shuyu Chen, Kexin Ling, Ying Liu, Jason T Huse, Bo Chen, Timothy A Chan, Zengxin Qi, Zhao Zhang, Xiuping Liu, Yuxiang Wang

Faculty, Staff and Student Publications

Mutations in isocitrate dehydrogenase (IDH) genes sensitize gliomas to PARP inhibition (PARPi) by inducing epigenetic reprogramming of DNA damage repair circuits. However, tumors treated with PARPi eventually relapse despite initial responsiveness. In this study, it is demonstrated that the anti-angiogenic agent lenvatinib synergizes effectively with PARPi, resulting in substantial tumor regression and significantly extended survival. Genomic analysis of tumors reveals that PARPi induces widespread transcriptomic changes that are predominantly pro-inflammatory, thereby promoting tumor angiogenesis. Prickle4, a planar cell polarity protein, is identified as a critical mediator of PARPi-induced neovascularization. Targeting Prickle4 effectively overcomes PARPi resistance in these tumors. Collectively, these …

Digital Communication Strategies For Culinary Medicine In Cancer Prevention And Survivorship: Information-Seeking Habits And Content Preferences Of Online Cooking Media Among Adults Participating In Four Cancer Prevention And Control Cohort Studies, Margaret Raber, Sara Fares, Maria Vazquez, Larkin Strong, Carrie Daniel-Macdougall, Lorna Mcneill, Karen Basen-Engquist

Faculty, Staff and Student Publications

Introduction

Poor diet and excess weight have been linked to increased risk for at least 13 types of cancer. Culinary medicine utilizes experiential cooking skill development to improve individuals’ capacity for healthy eating. Digital communication strategies offer pathways for scalable culinary medicine interventions, but little research has explored how online cooking tools could be leveraged for cancer prevention messaging. We conducted a cross-sectional survey study exploring online cooking information-seeking habits and content preferences among participants in four cancer prevention and control cohorts to inform future digital culinary medicine interventions.

Methods

A cross-sectional survey study was conducted with a convenience sample …

Freeze-Dried, Not Frozen: Lyophilized Mesenchymal Stromal Cell-Derived Extracellular Vesicles And Therapeutic Function In Neuroinflammatory Models, Janet R Ashley, Aidan M Collier, Scott D Olson, Charles S Cox

Faculty, Staff and Student Publications

Background: Mesenchymal stromal cell (MSC)-derived extracellular vesicles (EVs) offer a promising acellular therapy for immune modulation, but clinical translation is hindered by variability and storage constraints. Lyophilization (freeze-drying) could enable shelf-stable EV therapeutics, although its effects on biological activity remain incompletely defined.

Study design: We compared the immunomodulatory effects of frozen and lyophilized EVs from bone marrow (BM), adipose tissue (AD), and umbilical cord (UC) MSCs using lipopolysaccharide-stimulated rodent splenocytes and microglial models and human peripheral blood mononuclear cells (PBMCs). Variables included batch scale (small vs large), cytokine priming, and MSC source. Tumor necrosis factor alpha (TNF-α) secretion was measured …

Single-Cell Lineage Trajectory Defines Cyclin-Dependent Kinase Inhibitor-Sensitive Cells-Of-Origin In Esophageal Squamous Cell Carcinoma, Kyung-Pil Ko, Jie Zhang, Sohee Jun, Jae-Il Park

Faculty, Staff and Student Publications

Background and aims: Understanding the cells of origin is essential for overcoming therapy resistance and improving early intervention strategies in esophageal squamous cell carcinoma (ESCC). Despite recent advances in genomic profiling, the precise cellular hierarchies and molecular programs driving ESCC initiation remain poorly defined.

Methods: We utilized machine learning-based single-cell trajectory analysis on 4-nitroquinoline 1-oxide-induced murine models and genetically engineered organoids to identify cellular lineages during tumorigenesis. Combined with gene regulatory network analysis, we identified transcriptional drivers of tumor initiation and employed transcriptome-based drug repurposing to predict compounds targeting these initiating populations.

Results: Our analyses revealed multiple distinct epithelial clusters …

Engineering Mutation Clones In Mammalian Cells With Crispr/Cas9, Zijun Huo, Jian Tu, Rachel Shoemaker, Dung-Fang Lee, Ruiying Zhao

Faculty, Staff and Student Publications

CRISPR, Clustered Regularly Interspaced Short Palindromic Repeat, as a powerful genome engineering system, has been widely accepted and employed in gene editing of a vast range of cell types. Compared to zinc finger nucleases (ZFNs) or transcription activator-like effector nucleases (TALENs), CRISPR shows a less complicated process and higher efficiency. With the development of different CRISPR systems, it can be used not only to knock out a gene but also to make precise modifications, activate or repress target genes with epigenetic modifications, and even for genome wide screening. Here we will describe the procedure of generating a stable cell line …

Contemporary Opportunities And Potential Of Auger Electron-Emitting Theranostics, Seok-Yong Lee, H Charles Manning

Faculty, Staff and Student Publications

Recent breakthroughs in radiopharmaceutical (RP) therapy have emerged interest in employing Auger electron (AE)-emitting radionuclides as potential agents for precise theranostics. AE provides energy with exceptional localization due to their short tissue penetration range (TPR, < 10 nm), rendering them particularly effective for targeting nuclear DNA in tumor cells. In this context, AE-emitting radionuclide therapy (AE-emitting RLT) enables the targeted destruction of tumor cells while reducing harm to adjacent healthy tissue, a significant challenge in this field. Preclinical and early clinical investigations reveal the efficacy of AE-emitting RLTs in the theranostics of diverse malignancies, such as glioblastoma, prostate cancer, and neuroendocrine tumors. Notwithstanding these developments, challenges and limitations persist regarding dosimetry, delivery efficiency, and the treatment of radiotoxicity. A new paradigm is being developed to tackle the obstacles encountered by integrating molecular target markers (e.g., PARP) that function near the nucleus to improve the intranuclear delivery efficiency of AE-emitting radionuclides. Novel radiochemical methods such as these have facilitated the more stable and efficient labeling of biomolecules with AE-emitting radionuclides. Also, recent advances in DNA-molecular targeting, nanoparticles, nucleic acid/protein engineering, click- or bioorthogonal conjugation chemistry, and artificial intelligence (AI)-based structure modeling present concrete opportunities to overcome these limitations. Moreover, the integration of diagnostic imaging companion platforms employing theranostic radioisotope pairings facilitates real-time assessment of therapeutic efficacy and biodistribution, resulting in the formulation of enhanced treatment regimens. This review summarizes the prior development, recent advancements, barriers in clinical implementation, and future perspective of AE-emitting RLTs.

Nsd2 Inhibitors Rewire Chromatin To Treat Lung And Pancreatic Cancers, Jinho Jeong, Simone Hausmann, Hanyang Dong, Kacper Szczepski, Natasha M Flores, Andy Garcia Gonzalez, Liyang Shi, Xiaoyin Lu, Joanna Lempiäinen, Moritz Jakab, Liyong Zeng, Tourkian Chasan, Eric Bareke, Rui Dong, Emma Carlson, Reinnier Padilla, Dylan Husmann, Julia Thompson, Gerry A Shipman, Emily Zahn, Courtney A Barnes, Laiba F Khan, Liz Marie Albertorio-Sáez, Eva Brill, Vishnu Udayakumar Sunita Kumary, Matthew R Marunde, Danielle N Maryanski, Cheryl C Szany, Bryan J Venters, Carolina Lin Windham, Michal Eligiusz Nowakowski, Iwona Czaban, Mariusz Jaremko, Michael-Christopher Keogh, Kang Le, Michael J Soth, Benjamin A Garcia, Łukasz Jaremko, Jacek Majewski, Pawel K Mazur, Or Gozani

Faculty, Staff and Student Publications

NSD2 catalyses the epigenetic modification H3K36me2 (refs. 1,2) and is a candidate convergent downstream effector of oncogenic signalling in diverse malignancies3–5. However, it remains unclear whether the enzymatic activity of NSD2 is therapeutically targetable. Here we characterize a series of clinical-grade small-molecule catalytic NSD2 inhibitors (NSD2i) and show that the pharmacological targeting of NSD2 constitutes an epigenetic dependency with broad therapeutic efficacy in KRAS-driven preclinical cancer models. NSD2i inhibits NSD2 with single-digit nanomolar half-maximal inhibitory concentration potency and high selectivity over related methyltransferases. Structural analyses reveal that the specificity of NSD2i for NSD2 …

Host Modulation Therapy In Periodontitis: From Established Therapies To Emerging Technologies, Aonjittra Phanrungsuwan, Jiaqi Huang, Neeraja Dharmaraj, Alejandra Cobos Perez, Omid Veiseh, Simon Young, Chun-Teh Lee

Faculty, Staff and Student Publications

Periodontitis is a biofilm-induced chronic inflammatory disease, characterized by gingival inflammation and alveolar bone loss. According to a national survey, approximately half of the U.S. adults are affected by periodontal disease. To effectively prevent and treat periodontitis, it is essential to address its underlying causes. The primary etiological factors include polymicrobial synergy and dysbiosis of the oral microbiota, and a dysregulated immune response. The standard therapeutic approach, mechanical removal of biofilm through debridement, sometimes demonstrates limited efficacy, particularly in cases of severe periodontitis, which may require adjunctive or additional therapy. Emerging evidence indicates that periodontal tissue destruction is initiated by …

Uncertainty-Guided Test-Time Optimization For Personalizing Segmentation Models In Longitudinal Medical Imaging, Jaehee Chun, Austin Castelo, Mckell Woodland, Caleb O'Connor, Mais Al Taie, Mohamed Eltaher, Aashish Gupta, Bilel Daoud, Shanli Ding, Jeddy Bennett, Anirban Maitra, Matthew A Firpo, Kimberly Kirkwood, Eugene J Koay, Kristy K Brock

Faculty, Staff and Student Publications

Background: Accurate and consistent image segmentation across longitudinal scans is essential in many clinical applications, including surveillance, treatment monitoring, and adaptive interventions. While personalized model adaptation using patient-specific prior scans has shown promise, current approaches typically rely on fixed training durations and lack mechanisms to determine optimal stopping points on a per-patient basis, particularly in the absence of validation labels.

Purpose: We propose an uncertainty-guided test-time optimization (TTO) framework that dynamically adjusts the personalization duration for each patient using a validation-free stopping criterion based on predictive uncertainty.

Methods: Our framework personalizes a generalized segmentation model using patient-specific prior imaging and …

Preclinical Ischemic Stroke Multicenter Trials (Prism) Collective Statement: Opportunities, Challenges, And Recommendations For A New Era, Cenk Ayata, Philip M Bath, Anna M Planas, Stuart M Allan, Johannes Boltze, Ryan P Cabeen, Claire L Gibson, Marilyn J Cipolla, Marcio A Diniz, Stefano Fumagalli, Fahmeed Hyder, Raymond C Koehler, Arthur Liesz, Sarah K Mccann, Tim Magnus, Louise D Mccullough, Emily S Sena, Simone Beretta, Jaroslaw Aronowski, Francesca Bosetti, Clinton B Wright, Patrick D Lyden, Lauren H Sansing

Faculty, Staff and Student Publications

Preclinical stroke research faces a critical translational gap, with animal studies failing to reliably predict clinical efficacy. To address this, the field is moving toward rigorous, multicenter preclinical randomized controlled trials (mpRCTs) that mimic phase 3 clinical trials in several key components. This collective statement, derived from experts involved in mpRCTs, outlines considerations for designing and executing such trials. mpRCTs offer advantages such as increased sample sizes, robust statistical design, incorporation of heterogeneity, and standardized protocols, but they face challenges in finding the right balance between standardization and heterogeneity, appropriate stroke model selection, and outcome measures, as well as the …

A Quantum Walk Through Sustainability: Nonlinear Thinking For A Nonlinear World, Sanjay Wahal

Trade & Scholarly Monographs

We are living through a planetary inflection point. Conventional sustainability thinking-rooted in linear logic, siloed disciplines, and incremental fixes-no longer suffices. The climate crisis is nonlinear. Our response must be as well.

In this book, Dr. Sanjay Wahal, global systems strategist and founder of Decarbonization, LLC, delivers a visionary, paradigm-shifting framework that draws inspiration from quantum theory to reimagine how we think, act, and lead for climate action.

This is not a book about quick fixes or silver bullets. It is a meditation on coherence, emergence, and transformation. It is for those who believe sustainability is not just a policy …

Legal Ethics Of Ai Snake Oil: Navigating The Hype, Harm, And Hope Of Legal Ai, Drew Simshaw

Scholarly Works

This paper argues that the legal ethics of AI extend far beyond competence and hallucinations. It shows how AI often functions as a mirror, exposing deeper ethical questions about institutional incentives, lawyer wellbeing, access to justice, and AI's broader social and environmental impacts.

Exploring And Increasing Women’S And Girls’ Recreational Ocean Access In A Coastal Community In The Tropical Majority, Florence Barraud

School of Law, Humanities and Social Sciences Theses

Vast gender inequalities restrict women’s and girls’ access to ocean spaces, particularly in the tropical majority. Globally, 68% of women cannot swim, compared to 43% of men. In many coastal communities, women are key stewards of highly biodiverse yet vulnerable marine environments that people rely upon for food security, livelihoods, culture, and coastal protection. Being able to swim is imperative for enabling access to the dominant environment and a core recreational, educational, and economic space. This study aimed to explore and increase women’s and girls’ recreational ocean access in the Maldives, a tropical majority island nation.Utilising a pragmatic feminist approach, …

Safe Family Therapy Activities And The Emotional Well-Being Of Parents Of Autistic Children, Gamze Camci

School of Law, Humanities and Social Sciences Theses

This thesis addresses a critical gap in the existing literature on the emotional well-being of parents of autistic children by applying SAFE family therapy activities grounded in the Narrative Attachment Therapy approach through a three-cycle action research design. Stage 1 involved 3 parents as primary participants, with their children present during selected sessions. Stage 2 included 2 families; the primary focus was on 3 parents across these families. Stage 3 engaged 5 therapists. Data were collected through interviews, diaries, questionnaires and therapy recordings. Data from interviews and journals were analysed thematically, whereas questionnaires were analysed idiographically. The first and second …

A 20th Century Historical Perspective On Congenital Syphilis And Lessons For Today., Jane F. Knapp, Robert Schremmer

Manuscripts, Articles, Book Chapters and Other Papers

No abstract provided.