Digital Commons Network^™

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 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 …

Applying The Matching Law To Major League Baseball (Mlb), Christopher Watkins, Vincent Berardi

Psychology Faculty Articles and Research

The application of the generalized matching equation (GME) has been detailed in a variety of sports, including football, basketball, and others. However, only a limited number of studies have focused on Major League Baseball (MLB), and they typically have examined ≤ 5 players and/or focused on a single behavior. This paper increases the generalizability of such work by using newly available, state-of-the-art data from thousands of players to explore the GME in several scenarios within three aspects of a baseball game - defense, pitching and batting. We found that the GME accurately summarized response allocation in most scenarios, with r …

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 …

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, …

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 …

Factor Analysis Of Neuropsychological Attention Measures With Mild Traumatic Brain Injury Patients, Elizabeth Mateer

Clinical Psychology Dissertations

This study explored the factor structure of commonly used neuropsychological tests to assess attention in individuals with mild traumatic brain injury (mTBI). The study aimed to clarify the relationships among tests traditionally used to assess different components of attention, including the NAB Numbers & Letters Test, Paced Auditory Serial Addition Task (PASAT), Trail Making Test A & B, Digit Span, Arithmetic, and Coding. Exploratory factor analysis (EFA) with an oblimin rotation procedure was used to identify the factor structure. The EFA suggested that a four-factor solution was optimal. These factors were identified as: (1) visual attention and psychomotor speed, (2) …

Social Support Networks’ Influence On First-Generation Professionals’ Career Success, Melissa-Ann M. Lagunas

Clinical Psychology Dissertations

This dissertation examines how perceived social support influences career success among first-generation professionals (FGPs)—individuals who are first in their families to pursue white- collar careers. Drawing on Seibert et al.'s (2001) Social Capital Theory of Career Success, I investigate direct and indirect pathways through which social support affects three dimensions of career success: sense of belonging, professional flourishing, and career satisfaction, while exploring mediating roles of perceived support network quality and resilience. A sample of 230 FGPs (Mage = 31.9 years, SD = 5.32) completed a 62-item online survey hosted on Qualtrics. Participants represented diverse professional disciplines classified into five …

The Impact Of Coping Strategies On The Perceived Stress And Well-Being Of People With Disabilities During The Covid-19 Pandemic, Angela G. Maccarrone

Clinical Psychology Dissertations

The COVID-19 pandemic significantly and negatively impacted those around the world, particularly marginalized groups such as people with disabilities (PWDs). What remains unclear from existing published research is the exact relationship between perceived stress of the pandemic, coping, and overall well-being in PWDs. Prior research and theoretical models of coping suggest that active coping processes are associated with more positive outcomes versus avoidant coping strategies, which have shown less favorable outcomes on well-being. Due to the varying ways in which the pandemic affected PWDs, coping might serve as a moderator, amplifying the negative relationship between perceived stress and well-being. Building …

“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 …

Exploring Inclusive Education And Listening In Relation To Children's Experiences And Teachers' Perspectives, Ramazan Merk

School of Law, Humanities and Social Sciences Theses

This thesis addressed the field of inclusive education and the importance of listening from the perspectives of both children and teachers. Drawing on experiences in pre-school and reception classrooms in England, I explored the essence of inclusive education and effective implementation strategies to meet the needs of children, including those with diverse needs. Emphasis was placed on the necessity for teachers to acknowledge and address each child's individual needs when developing early years learning environments. Moreover, the study underlined the important role of active listening in promoting communication and understanding between teachers and children as a cornerstone of inclusive pedagogy. …

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 …

Shooting For The Stars: The Place Of International Economic Law In The International Legal Order, Valentina Vadi

Indian Journal of International Economic Law

In the universe of international law, the galaxy of international economic law (IEL) consists of various legal instruments, institutions, and experts that – like stars, planets, and vast clouds of dust – are all bound together by gravity. Like cosmonauts, international economic courts ‘must look for reliable reference points in the known legal cosmology.’2 This article aims to contextualise the galaxy of international economic law within the universe of international law, discuss its role within the same, and identify some reference points that can help adjudicators navigate by the stars.

Generalizations Of Some Q-Product Identities Of Ramanujan And Others, Timothy J. Huber, James Mclaughlin, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

By considering certain limiting cases of a WP-Bailey chain discovered by Andrews, and also limiting cases of certain classical summation formulae for basic hypergeometric series, we derive new expressions for certain Lambert series in terms of basic hypergeometric series. In some cases, the resulting series involve an arbitrary Bailey pair. This allows for the derivation of new basic hypergeometric expansions for some q-products and series that Ramanujan expressed in terms of Lambert series. Some of Ramanujan’s identities are extended to more general relations containing one or more free parameters.

Design Of A Laser Writing Instrument For Selective Local Patterning Of Vanadium Oxide Phases, Junjie Li

Faculty Publications

No abstract provided.

Biology-Informed Inverse Problems For Insect Pests Detection Using Pheromone Sensors, Thibault Malou, Nicolas Parisey, Katarzyna Adamczyk-Chauvat, Elisabeta Vergu, Béatrice Laroche, Paul André Calatayud, Philippe Lucas, Simon Labarthe

All Peer-Reviewed Publications

Most insects have the ability to modify the odor landscape in order to communicate with their conspecies during key phases of their life cycle such as reproduction. They release pheromones in their nearby environment, volatile compounds that are detected by insects of the same species with exceptional specificity and sensitivity. Efficient pheromone detection is then an interesting lever for insect pest management in a precision agroecological culture context. A precise and early detection of pests using pheromone sensors offers a strategy for pest management before infestation. In this paper, we develop a biology-informed inverse problem framework that leverages temporal signals …

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

All Peer-Reviewed Publications

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

Global Strategies To Manage Huanglongbing (Hlb) And Its Vectors: Insights And Implications For The Mediterranean Region, Meritxell Pérez-Hedo, Mark S. Hoddle, Fernando Alferez, Lochy Batista, George A.C. Beattie, Shourish Chakravarty, Paul Holford, Fathiya Mbarak Khamis, Inusa Jacob Ajene, Aruna Manrakhan, Paul H. Fourie, Maritza Luis, Eduardo Osorio-Hernández, Mamoudou Sétamou, Alejandro Tena, Pedro Takao Yamamoto, Tara Wade, Changyong Zhou, Nian Wang, Lukasz L. Stelinski, Alberto Urbaneja

All Peer-Reviewed Publications

Huanglongbing (HLB), also known as citrus greening disease, poses a severe threat to the global citrus industry. This disease is caused by three species of bacteria, ‘Candidatus Liberibacter asiaticus’, ‘C. L. africanus’, and ‘C. L. ameri-canus’. The primary pathogen vectors are the Asian citrus psyllid (Diaphorina citri Kuwayama, Hemiptera: Psyllidae) and the African citrus psyllid (Trioza erytreae Del Guercio, Hemiptera: Triozidae). The disease is incurable, and its management relies on reducing vector densities and the adverse effects of bacterial infections on host trees. Managing psyllid vec-tors, especially D. citri, has proven exceptionally challenging due to its rapid spread and subsequent …

An Assessment Of Vegetable Production Constraints, Trait Preferences And Willingness To Adopt Sustainable Intensification Options In Kenya And Uganda, Rose N. Okoma, Evanson R. Omuse, Daniel M. Mutyambai, Dennis Beesigamukama, Marius F. Murongo, Sevgan Subramanian, Frank Chidawanyika

All Peer-Reviewed Publications

Global food production systems are under pressure due to population increase, limited farmland, biotic and abiotic constrains, and ongoing climate change. Sustainable intensification is needed to increase agricultural productivity with minimal adverse environmental and social impacts. Vegetable-integrated push pull (VIPP) technology coupled with black soldier fly (BSF) frass offer such opportunities to smallholder farmers. However, farmers’ vegetable preferences and willingness to adopt these innovations remain unknown and are variable across various geographic scales. Focus group discussions (FGDs) and in-person interviews with smallholder farmers were conducted to assess vegetable production constraints and select vegetables to be integrated into VIPP coupled with …

Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach, Franck B.N. Tonle, Henri E.Z. Tonnang, Milliam M.Z. Ndadji, Maurice Tchoupe Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy

All Peer-Reviewed Publications

Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key …

Morphometric Characterization Of Invasive Diaphorina Citri Infesting Citrus And Orange Jasmine In Ghana, Kodwo Dadzie Ninsin, Maxwell Kelvin Billah, Bernard Agyeman Boateng, Jonathan Osei-Owusu, Fred Kormla Ablormeti, Samuel Opuni-Basoa, Aboagye Kwarteng Dofuor, George Edusei, Steve B.S. Baleba, Owusu Fordjour Aidoo

All Peer-Reviewed Publications

Morphometric analysis plays a crucial role in the accurate identification and classification of insect pests, which is essential for implementing effective management strategies. The Asian citrus psyllid (Diaphorina citri Kuwayama; Hemiptera: Liviidae), an invasive species and a known vector of ‘Candidatus Liberibacter asiaticus’ and ‘Ca. Liberibacter americanus,’ the bacterial pathogens associated with Huanglongbing (HLB), has recently emerged as a pest of concern in Ghana. Despite its significance, little is known about the biological traits of field populations in the region. In this study, we examined morphometric variation in D. citri populations infesting citrus and orange jasmine (Murraya paniculata) across different …

A Review On Knowledge And Information Extraction From Pdf Documents And Storage Approaches, Salvador D. Atagong, Henri Tonnang, Kennedy Senagi, Mark Wamalwa, Komi M. Agboka, John Odindi

All Peer-Reviewed Publications

Introduction: Automating the extraction of information from Portable Document Format (PDF) documents represents a major advancement in information extraction, with applications in various domains such as healthcare, law, or biochemistry. However, existing solutions face challenges related to accuracy, domain adaptability, and implementation complexity. Methods: A systematic review of the literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine approaches and trends in PDF information extraction and storage approaches. Results: The review revealed three dominant methodological categories: rule-based systems, statistical learning models, and neural network-based approaches. Key limitations include the rigidity of rule-based …

Performance Of The Dssat Manihot-Cassava Model For Cassava Cultivation In The Recôncavo Baiano, Diego M. De Melo, Paola De F. Bongiovani, Fabio L.S. Costa, Patricia Moreno-Cadena, Alexandre B. Heinemann, Julian Ramirez-Villegas, Mauricio A. Coelho Filho

All Peer-Reviewed Publications

The aim of the present study was to calibrate the DSSAT MANIHOT-Cassava model with information from cassava varieties grown in the Recôncavo Baiano region, Bahia state, Brazil. The database used to calibrate the model was obtained in the dry sub-humid tropical climate in Cruz das Almas city, from 2019 to 2020. The model was calibrated with experimental data obtained under irrigated and rainfed conditions for the BRS Novo Horizonte and Eucalipto varieties. The calibration was carried out by adjusting parameters related to the characteristics of each variety. Model performance was evaluated with statistical indices that indicate the precision and accuracy …

Spatial Analytics To Elucidate The Incubation Period And Drivers Of Visceral Leishmaniasis: Case Of Turkana County In Kenya, Kennedy Senagi, Maureen Nzilani, Evans Omondi, David P. Tchouassi, Tobias Landmann, Damaris Matoke-Muhia, Emmanuel Okunga, Barrington Gesimba, Elfatih M. Abdel-Rahman, Dawn Maranga, Joseph M. Ndungu, Daniel Masiga

All Peer-Reviewed Publications

Introduction: Visceral leishmaniasis (VL) is a severe and neglected tropical disease of public health concern. VL is fatal if not treated. Kenya has experienced multiple outbreaks of the disease since 2017. The underlying drivers of the disease risk dynamics, as well as the incubation period, are not well understood. Methods: We implemented statistical (spatial logistic regression and Bayesian spatial) and machine learning (random forest, support vector machine, AdaBoost, logistic regression, and extra trees) models to estimate the incubation period and predict areas of low/high risk in Turkana County, an endemic VL foci in Kenya. Two-year (2019–2020) patient data were sourced …

Predicting Food Prices In Kenya Using Machine Learning: A Hybrid Model Approach With Xgboost And Gradient Boosting, Benard O. Ogol, Evans Omondi, John Olukuru, Betsy Muriithi, Kennedy Senagi

All Peer-Reviewed Publications

Introduction: Food price volatility continues to be a significant concern in Kenya's economic development, posing challenges to the country's economic stability. Methodology: This study examines the application of machine learning methods, employing a hybrid approach that combines XGBoost and gradient boosting, to predict food prices in Kenya. The food prices data from the World Food Programme, covering the period from January 2006 to September 2024, as well as currency exchange rates data from the Central Bank of Kenya in US dollars (USD) and inflation rates data, were collated and preprocessed to be ready for analytics and machine learning. The augmented …

Integrating Machine Learning And Symbolic Regression For Predicting Damage Initiation In Hybrid Frp Bolted Connections, Sherif Sorour

Mechanical Engineering

No abstract provided.

Automated Vulnerability Scanning And Security Audit Framework For Maritime Systems, Avanthika Vineetha Harish

School of Engineering, Computing and Mathematics Theses

The maritime industry is a well-established sector, with the shipping industry dating back hundreds of years. The devices in this sector differ from those in the Information Technology (IT) sector in their usage, operations, hardware, andprotocols. Technological advancements have introduced new challenges in the sector, including the increased risk of cyber attacks. To effectively identify, respond to, and analyse these threats, it is essential to understand the cyber securityvulnerabilities in maritime systems and system-of-systems.Currently, there are no standardised methods or automated tools for conducting cyber security testing of maritime systems. This thesis presents an automated vulnerability scanning and security audit …

Generated Image Quality Assurance And Generative Adversarial Network Augmentation For Machine Learning, Dirk Holscher

School of Engineering, Computing and Mathematics Theses

The advancement of machine learning is heavily dependent on the quality of data used for training models. This thesis explores the enhancement of data quality and augmentation techniques using Generative Adversarial Networks (GANs), pecifically focusing on the Pix2Pix architecture. The research addresses the critical challenges of improving image quality, optimizing hyperparameters, and detecting fake images generated by GANs, aiming to enhance the robustness and eliability of machine learning models. This work presents a collection of metrics for measuring data quality for both structured and unstructured data. Furthermore, it defines data quality for machine learning, divided into three quality levels. Advanced …

A Protocol Agnostic Electrically Transparent Distributed Switch Fabric For It/Ot Testbenches, Wesley Andrews

School of Engineering, Computing and Mathematics Theses

This thesis presents the design and development of custom hardware, based upon RF (Radio Frequency) MEMS (Micro Electro-Mechanical System) switch technology and a grounded, conductor backed, edge coupled, coplanar waveguide transmission line design to facilitate protocol-agnostic, electrically transparent switching of different hard-wire protocols, such as Ethernet, USB (Universal Serial Bus), CAN (Controller Area Network) bus, RF and more within an IT/OT (Informational/Operational Technology) environment. This is achieved through a distributed, decentralised switch fabric composed of individual switch fabric nodes, which achieves fully reconfigurable IT/OT environments at the electrical level. Traditional IT/OT environments consist of diverse systems involving computers, network switches, …

Anomaly Detection Based Post Gripping Perception For Industrial Logistics Robotics, Firas Zoghlami

School of Engineering, Computing and Mathematics Theses

The adoption of low volume high mix manufacturing lines in global production sites has lead to a great need for flexible automation solutions. Flexible intelligent robots as an example are increasingly deployed in new industrial environments, such as the intra-logistics, in order to increase the automation level of the mentioned manufacturing lines. The lack of structure and the high dynamic of such industrial environments lead to potential performance deterioration related to unexpected events that are not recognized by the robot. Detecting such changes in the robot environment is believed in this thesis to be possible with anomaly detection techniques which …