Digital Commons Network^™

Ml Model To Better Identify Instances Of Bullying Faced By Members Of The Lgbtq+ Community, Arslan Bisharat

Master's Theses

Cyberbullying poses a significant threat to online communities, with the LGBTQ+ community facing disproportionately higher rates of harassment. While existing cyberbullying detection systems have made progress in identifying general instances of online harassment, they often fail to capture the nuanced and context-dependent nature of LGBTQ+-targeted bullying. This thesis presents a novel approach to this challenge by developing SpectrumNet, an LGBTQ+-centric transformer-based model for cyberbullying detection. Our research was conducted in two phases. In Phase 1, we evaluated the effectiveness of pre-trained transformer models (RoBERTa, BERT, and GPT-2) in identifying LGBTQ+-related cyberbullying. Building on these findings, Phase 2 introduced SpectrumNet which …

A Qualitative Analysis Of Open Banking In Poland: The Perspective Of Market Experts, Sylwia Ciechanowska

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: The purpose of this article is to present an in-depth analysis of the development of open banking in Poland based on interviews conducted with market experts representing financial institutions and regulatory authorities. The article identifies both the benefits and implementation barriers of open banking, with particular emphasis on the quality of APIs (application programming interfaces), system interoperability, and the impact of regulatory frameworks on service innovation and security.

Methodology: This qualitative study was conducted using structured interviews with market experts representing banks, fintech companies, regulatory authorities, and independent industry consultants. The selection of experts was purposive, with …

Exploring The Factors That Influence K-12 Teachers’ Use Of Open Educational Resources, Yu-Chun Kuo, Yu-Tung Kuo, Hungwei Tseng

Research, Publications & Creative Work

The features of open educational resources (OERs) have the potential of enhancing teaching effectiveness and student learning experiences in K-12 education. Encouraging K-12 teachers to use OERs requires an understanding of the factors that have an influence on teachers’ decisions to adopt or use OERs in their teaching. In this regard, this study explored the factors related to how teachers perceived their use of OERs, including the perceived ease of use of OERs, perceived usefulness of OERs, intention to adopt OERs, attitudes, OER self-efficacy, and so on. The relationships of several proposed factors were explored. The participants were teachers from …

The Impact Of International Baccalaureate (Ib) Courses: Experience And Outcome Differences For Full Ib Diploma Earners Vs. Non-Diploma Earners In An Urban School In The Us, Christopher Jennens, Nilufer Guler

Research, Publications & Creative Work

This study examines differences in educational experiences and outcomes for high school(HS) students who participated in the International Baccalaureate (IB) Diploma Program and earned full diplomas compared to those who participated in the IB Diploma Program but did not earn the diploma. ACT scores, after-high school placement, and HS graduation rates of 256 participants were collected. 122 participants completed an online survey, and 12 participated in interviews. The results revealed that the educational outcomes and experiences for diploma and non-diploma earners were vastly similar. Diploma earners had significantly higher ACT scores, but their college entrance and other academic outcomes were …

Digital Twins In Healthcare: A Comprehensive Review And Future Directions, Hamid Khoshfekr Rudsari, Becky Tseng, Hongxu Zhu, Lulu Song, Chunhui Gu, Abhishikta Roy, Ehsan Irajizad, Joseph Butner, James Long, Kim-Anh Do

Faculty, Staff and Student Publications

Digital Twin (DT) technology has emerged as a transformative force in healthcare, offering unprecedented opportunities for personalized medicine, treatment optimization, and disease prevention. This comprehensive review examines the current state of DTs in healthcare, analyzing their implementation across different physiological levels-from cellular to whole-body systems. We systematically review the latest developments, methodologies, and applications while identifying challenges and opportunities. Our analysis encompasses technical frameworks for cardiovascular, neurological, respiratory, metabolic, hepatic, oncological, and cellular DTs, highlighting significant achievements such as population-scale cardiac modeling (3,461 patient cohort), reduced atrial fibrillation recurrence rates through patient-specific cardiac models, improved brain tumor radiotherapy planning, advanced …

Combining Biology-Based And Mri Data-Driven Modeling To Predict Response To Neoadjuvant Chemotherapy In Patients With Triple-Negative Breast Cancer, Casey E Stowers, Chengyue Wu, Zhan Xu, Sidharth Kumar, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I Tamir, Gaiane M Rauch, Thomas E Yankeelov

Faculty, Staff and Student Publications

Purpose

To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC).

Materials and Methods

In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN …

The Comparison Of Human And Machine Performance In Object Recognition, Gokcek Kul

School of Psychology Theses

Deep learning algorithms have advanced significantly due to better hardware and large training sets, with some claiming they now match or exceed human performance. Recent findings indicate improvements in robustness against Out-Of-Distribution benchmark sets and alignment with human categorisation behaviour. This thesis evaluates various machine learning models to ensure a fair comparison with human performance using principles from comparative psychology. Chapter 2 introduces a psychologically grounded framework for comparing human and machine object categorisation performance, focusing on different presentation times and task complexities. The results indicate that while recent machine learning models have reached or exceeded human performance under optimised …

“Fish In Different Water, To Survive Or To Thrive?” Understanding Direct-Entry Pre-Sessional Chinese Students’ Learning Experiences During Covid-19 And Its Aftermath, Lingyuan Osgathorpe

School of Law, Humanities and Social Sciences Theses

This thesis explores the learning experiences of direct-entry pre-sessional Chinese students in the UK during the COVID-19 pandemic and its aftermath, using Pierre Bourdieu’s theoretical framework. Through the metaphor of students as "fish in different water", the research examines how these learners navigate and adapt to new educational environments that often starkly contrast with their prior experiences.The study employs a two-stage semi-structured interview process with student participants, first in China during their online pre-sessional courses and later in the UK during their degree programmes. All interviews were conducted in Mandarin Chinese to ensure linguistic and cultural accessibility. Additionally, expert interviews …

Unraveling Complex Temporal Patterns In Ehrs Via Robust Irregular Tensor Factorization, Ren Yifei, Linghui Zeng, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium V Bhavani

Faculty, Staff and Student Publications

Electronic health records (EHRs) contain diverse patient data with varying visit frequencies. While irregular tensor factorization techniques such as PARAFAC2 have been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose REPAR, an RNN REgularized Robust PARAFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs Recurrent Neural Networks (RNNs) for temporal regularization and a low-rank constraint for robustness, enabling precise patient subgroup identification and improved clinical decision-making in noisy EHR data. …

Explainable Diagnosis Prediction Through Neuro-Symbolic Integration, Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu

Faculty, Staff and Student Publications

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models …

Rank Aggregation Methods And Tools In Genomic Data Analysis, Wenping Zou, Savannah Mwesigwa, Sayed-Rzgar Hosseini, Zhongming Zhao

Faculty, Staff and Student Publications

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. …

Global Research Trends And Focus On Immunotherapy For Endometrial Cancer: A Comprehensive Bibliometric Insight And Visualization Analysis (2012–2024), Yachen Xu, Tao Wang, Xiaojing Liang, Jie Yang, Yuxiang Zhang, Shan Bao

Faculty, Staff and Student Publications

Background: This study conducted a novel systematic bibliometric and visualization analysis of global literature on immunotherapy for endometrial cancer (EC) to explore dynamic trends, research hotspots, and emerging topics, providing valuable references for future research.

Methods: Articles and reviews on EC immunotherapy published between 2012 and August 2024 were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric tools, CiteSpace and VOSviewer, were used to analyze clustering patterns and research dynamics.

Results: A total of 861 articles were contributed by 5,331 authors from 1,392 institutions across 58 countries or regions, involving 1,823 keywords. China demonstrated outstanding performance in this …

Characterization Of Additive Gene-Environment Interactions For Colorectal Cancer Risk, Claire E Thomas, Yi Lin, Michelle Kim, Eric S Kawaguchi, Conghui Qu, Caroline Y Um, Brigid M Lynch, Bethany Van Guelpen, Kostas Tsilidis, Robert Carreras-Torres, Franzel J B Van Duijnhoven, Lori C Sakoda, Peter T Campbell, Yu Tian, Jenny Chang-Claude, Stéphane Bézieau, Arif Budiarto, Julie R Palmer, Polly A Newcomb, Graham Casey, Loic Le Marchandz, Marios Giannakis, Christopher I Li, Andrea Gsur, Christina Newton, Mireia Obón-Santacana, Victor Moreno, Pavel Vodicka, Hermann Brenner, Michael Hoffmeister, Andrew J Pellatt, Robert E Schoen, Niki Dimou, Neil Murphy, Marc J Gunter, Sergi Castellví-Bel, Jane C Figueiredo, Andrew T Chan, Mingyang Song, Li Li, D Timothy Bishop, Stephen B Gruber, James W Baurley, Stephanie A Bien, David V Conti, Jeroen R Huyghe, Anshul Kundaje, Yu-Ru Su, Jun Wang, Temitope O Keku, Michael O Woods, Sonja I Berndt, Stephen J Chanock, Catherine M Tangen, Alicja Wolk, Andrea Burnett-Hartman, Anna H Wu, Emily White, Matthew A Devall, Virginia Díez-Obrero, David A Drew, Edward Giovannucci, Akihisa Hidaka, Andre E Kim, Juan Pablo Lewinger, John Morrison, Jennifer Ose, Nikos Papadimitriou, Bens Pardamean, Anita R Peoples, Edward A Ruiz-Narvaez, Anna Shcherbina, Mariana C Stern, Xuechen Chen, Duncan C Thomas, Elizabeth A Platz, W James Gauderman, Ulrike Peters, Li Hsu

Faculty, Staff and Student Publications

Background: Colorectal cancer (CRC) is a common, fatal cancer. Identifying subgroups who may benefit more from intervention is of critical public health importance. Previous studies have assessed multiplicative interaction between genetic risk scores and environmental factors, but few have assessed additive interaction, the relevant public health measure.

Methods: Using resources from CRC consortia, including 45,247 CRC cases and 52,671 controls, we assessed multiplicative and additive interaction (relative excess risk due to interaction, RERI) using logistic regression between 13 harmonized environmental factors and genetic risk score, including 141 variants associated with CRC risk.

Results: There was no evidence of multiplicative interaction …

The Tripod-Llm Reporting Guideline For Studies Using Large Language Models, Jack Gallifant, Majid Afshar, Saleem Ameen, Yindalon Aphinyanaphongs, Shan Chen, Giovanni Cacciamani, Dina Demner-Fushman, Dmitriy Dligach, Roxana Daneshjou, Chrystinne Fernandes, Lasse Hyldig Hansen, Adam Landman, Lisa Lehmann, Liam G Mccoy, Timothy Miller, Amy Moreno, Nikolaj Munch, David Restrepo, Guergana Savova, Renato Umeton, Judy Wawira Gichoya, Gary S Collins, Karel G M Moons, Leo A Celi, Danielle S Bitterman

Faculty, Staff and Student Publications

Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes …

Phase 1 Trial Of Hypofractionated Stereotactic Re-Irradiation In Combination With Nivolumab, Ipilimumab, And Bevacizumab For Recurrent High-Grade Gliomas, Solmaz Sahebjam, Raju R Raval, Peter A Forsyth, Heiko Enderling, Nam D Tran, John A Arrington, Robert Macaulay, Haley K Perlow, Joshua D Palmer, Jayeeta Ghose, Prajwal Rajappa, Pierre Giglio, Zihai Li, Arnold B Etame, Sepideh Mokhtari, Ruben J Cruz-Chamorro, Menal Bhandari, Ram Thapa, Timothy J Robinson, Dung-Tsa Chen, Hsiang-Hsuan Michael Yu

Faculty, Staff and Student Publications

Background: Our previous clinical investigation suggested that hypofractionated stereotactic re-irradiation (HFSRT) and PD-1 blockade may act synergistically to enhance the immune response against glioma. This subsequent trial investigated the dual blockade of CTLA4 and PD-1 in combination with HFSRT and bevacizumab.

Methods: This phase I study enrolled eligible patients with bevacizumab-naïve recurrent glioblastoma or anaplastic astrocytoma. Participants received nivolumab, ipilimumab, and bevacizumab concurrently with HFSRT (3000 cGy in 5 fractions). Subsequently, nivolumab, ipilimumab, and bevacizumab were administered for a total of 4 cycles followed by nivolumab and bevacizumab until progression. The primary end point of this study was the safety …

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Evaluation Of Quantum Contouring Algorithms For Treatment Planning On Mr Abdominal Images, Rachel Glenn, Tucker Netherton, Adrian Celaya, Mohamed Eltaher, Beatrice Riviere, Eugene J Koay, David Fuentes

Faculty, Staff and Student Publications

Introduction: Quantum computing is increasingly being investigated for integration into medical radiology and healthcare applications worldwide. Given its potential to enhance clinical care and medical research, there is growing interest in evaluating its practical applications in clinical workflows.

Methods: We developed an evaluation of quantum computing-based auto-contouring methods to introduce medical physicists to this emerging technology. We implemented existing quantum algorithms as prototypes tailored for specific quantum hardware, focusing on their application to auto-contouring in medical imaging. The evaluation was performed using a medical resonance imaging (MRI) abdominal dataset, comprising 102 patient scans.

Results: The quantum algorithms were applied to …

Quantile Regression Based Method For Characterizing Risk-Specific Behavioral Patterns In Relation To Longitudinal Left-Censored Biomarker Data Collected From Heterogeneous Populations, Minjae Lee, Belinda M Reininger, Kelley Pettee Gabriel, Nalini Ranjit, Larkin L Strong

Faculty, Staff and Student Publications

There are many studies aimed at promoting positive lifestyle behaviors to reduce lifetime risk of cancer and related diseases. However, assessing these modifiable behaviors through statistical modeling is challenging because of the multidimensionality of interrelated measurements that may dramatically differ between at-risk individuals. Taking into account this heterogeneity while considering the multidimensionality of behavior changes is fundamental to tailoring interventions to their needs. Biomarkers that identify high-risk individuals may help validate proximal measures, but the number of validated methods that link biomarkers to multiple behavioral measurements by determining their dynamic relations with disease risks is limited to just a few, …

Characterizing Spatial Immune Architecture In Metastatic Melanoma Using High-Dimensional Multiplex Imaging, Joel Eliason, Santhoshi Krishnan, Yasunari Fukuda, Matias A Bustos, Dan Winkowski, Sungnam Cho, Akshay Basi, Regan Baird, Elizabeth A Grimm, Michael A Davies, Dave S B Hoon, Arvind Rao, Jared K Burks, Suhendan Ekmekcioglu

Faculty, Staff and Student Publications

Introduction: Immune checkpoint inhibitors (ICIs) have significantly improved survival for patients with metastatic melanoma, yet many experienceresistance due to immunosuppressive mechanisms within the tumor immune microenvironment (TIME). Understanding how the spatial architecture of immune and inflammatory components changes across disease stages may reveal novel prognostic biomarkers and therapeutic targets.

Methods: We performed high-dimensional spatial profiling of two melanoma tissue microarrays (TMAs), representing Stage III (n = 157) and Stage IV (n = 248) metastatic tumors. Using imaging mass cytometry (IMC) and multiplex immunofluorescence (mIF), we characterized the phenotypic, functional, and spatial properties of the TIME. Cellular neighborhoods were …

Challenging Assumptions: Ai And Teacher Assessment Identity Formation, Nadia Ollington

Australian Journal of Teacher Education

This article explores the ways in which teacher educators can strengthen the developing teacher assessment identity of preservice teachers who are at the precipice of Industry 4.0, where work with Artificial Intelligence (AI) and assessment will require flexible thinking. This reflective case study narrative outlines a teacher educator’s experiences in a novel assessment task using Generative AI for technology enhanced learning in an Australian postgraduate teaching program. In the task, those who held positive beliefs and emotions about AI were more likely to engage in flexible thinking skills than those who were more critical of the AI. To ensure that …

Changes In Mentor Teacher Participation In Postgraduate Secondary Initial Teacher Education: Climatic Conditions Preceding Workforce Reform., Alison L. Hilton, Chad Morrison, Felicity I. Mclure, Kathryn G. Dehle, Sandi Fielder

Australian Journal of Teacher Education

Professional Experience placements provide invaluable opportunities for pre-service teachers to connect their expanding knowledge to teaching practice. When done well, these experiences are underpinned by purposeful and continuous guidance from experienced mentor teachers. Significantly, the participation and engagement of Australian mentor teachers in this process is voluntary in nature, meaning the system relies on teachers ‘opting-in’ to mentoring. This research examines mentor teacher participation within Secondary-level initial teacher education courses and highlights issues relating to overall mentor teacher (and host school) engagement. Analysis of placement data over a five-year period to 2021 illustrates a significant change in participation with a …

Raising Information Literacy Of Biology Pre-Service Teachers: Study On Three Problem Solving Methods, Muhammad Saefi, Hadi Suwono, Rifka Fachrunnisa, Widi Cahya Adi, Herawati Susilo, Ahmad Kamal Sudrajat

Australian Journal of Teacher Education

Integrating information literacy teaching methods with subject-content curricula to develop prospective biology teacher students' abilities is still problematic. This quasi-experimental research, with a nonrandomized pretest-posttest control group design, intends to compare the influences of challenge-based learning (CBL), problem-based learning (PBL), and problem-solving through classroom discussion (PCD) – as the control- in improving information literacy. This study involved 183 students from three classes of freshmen and three sophomores in the biology education program. The results showed that CBL significantly improved students' information literacy compared to PBL and PCD across five indicators: 1) identifying the required information, 2) attaining the required information …

Deep Learning For Precise Mri Segmentation Of Lower-Grade Gliomas, Wusat Ullah, Hamza Naveed, Saalam Ali

Sustainable Machine Intelligence Journal

Brain tumors represent a significant public health issue worldwide, affecting individuals across all age groups and leading to severe neurological and cognitive deficits. Lower-grade gliomas (LGGs), classified by the World Health Organization (WHO) as grade II or III, are characterized by more diffuse infiltration into brain tissue compared to high-grade gliomas but exhibit a slower growth rate. Precise evaluation of tumor resection and detection of residual tumor cells are critical, as incomplete resection is associated with an increased risk of disease recurrence. This study reviews an automated, deep learning-based approach for brain tumor segmentation in Magnetic Resonance Imaging (MRI) using …

A Hybridized Cnn-Lstm-Mlp-Knn Model For Short-Term Solar Irradiance Forecasting, Doaa El-Shahat, Ahmed Tolba

Sustainable Machine Intelligence Journal

The accurate prediction of solar irradiance is essential for maximizing renewable energy production. Despite some researchers struggling to achieve sufficient prediction accuracy and model quality, we are working on developing a forecasting model for solar radiation using advanced artificial intelligence techniques. Solar energy offers a sustainable alternative to fossil fuels and has a wide range of applications, making the development of an effective forecasting model crucial. This paper proposes a hybrid model that combines deep learning and machine learning methods, denoted as CNN-LSTM-MLP-KNN. By combining Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN), …

Enhancing Software Effort Estimation In Healthcare Informatics: A Comparative Analysis Of Machine Learning Models With Correlation-Based Feature Selection, Muhammad Abid, Sama Bukhari, Muhammad Saqlain

Sustainable Machine Intelligence Journal

Software effort estimation is one of the most crucial processes in the management of software projects predominantly related to the healthcare industry. It involves the prediction of efforts needed to develop and endorse different software applications. To render clinical projects on time within the budget range, flawless projection with efficient planning is incumbent. This paper discloses the techniques that utilize machine learning models for ameliorating software effort estimation by using biomedical datasets, including Breast Cancer Wisconsin, COVID-19, Sleepy Drivers EEG Brainwave, Heart Disease Prediction and Food Nutrition. All of these datasets are cleaned and prepared by handling missing values, converting …

The Economic Cost Of Exclusion: How Trump's Second-Term Immigration Policies Threaten California's Economy, Kit Johnson

Faculty Articles

The second Trump administration has pursued an array of immigration enforcement initiatives—from denaturalization and visa scrutiny to termination of humanitarian programs and aggressive workplace raids. Legal scholars have sharply debated these measures, but almost entirely through constitutional, statutory, and rights-based frames. This Article exposes a critical omission in immigration law scholarship: the economics of large-scale noncitizen removal. Drawing on major empirical studies of historic enforcement episodes—including Mexican repatriation between 1929-1934, Japanese internment during World War II, Arizona’s immigration restrictionism from 2007-2012, and federal immigration enforcement efforts of 2008-2015—this Article demonstrates that broad noncitizen removals may reduce the employment and wages …

Optimal Timing Of Organs-At-Risk-Sparing Adaptive Radiation Therapy For Head-And-Neck Cancer Under Re-Planning Resource Constraints, Fatemeh Nosrat, Cem Dede, Lucas B Mccullum, Raul Garcia, Abdallah S R Mohamed, Jacob G Scott, James E Bates, Brigid A Mcdonald, Kareem A Wahid, Mohamed A Naser, Renjie He, Aysenur Karagoz, Amy C Moreno, Lisanne V Van Dijk, Kristy K Brock, Jolien Heukelom, Seyedmohammadhossein Hosseinian, Mehdi Hemmati, Andrew J Schaefer, Clifton D Fuller

Faculty, Staff and Student Publications

Background and purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing of re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC).

Materials and methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for …

Endocrine Responses To Two Distinct Foraging Contexts In The Bonobo (Pan Paniscus), Lauren Mitchell

Master's Theses

In gregarious animals, a social structure characterized by female dominance is relatively rare. However, bonobos (Pan paniscus), one of the two species most closely related to humans, are characterized by partial female dominance, with females forming close social bonds. In contrast, chimpanzees (Pan troglodytes), a close relative of the bonobo, are characterized by strong male-male bonding and social dominance with female chimpanzees lacking close social bonds. There is a lack of consensus concerning the reasons for these behavioral differences in Pan, specifically, the physiological processes responsible for female dominance in bonobos. The role of hormones …

2025 Merc Annual Report, David B. Naff, Jesse Senechal, Paula L. Ogston-Nobile

MERC Publications

This is the annual report of the Metropolitan Educational Research Consortium (MERC) at Virginia Commonwealth University for the 2024-2025 academic year. It includes vignettes depicting MERC activities in the past year that align with each of its five principles: research, relevance, rigor, multiple perspectives, and impact. It concludes with a discussion of MERC's commitment moving forward.

“With Love, From Me To You:” Replacing The Unconstitutional All-Comer Provision With Favorable Conflict Of Laws Principles To Still Confer The Postmortem Right Of Publicity, Jackson Polish

Case Western Reserve Law Review

No abstract provided.