Digital Commons Network^™

Serotype-Specific Pneumococcal Invasiveness: A Global Meta-Analysis Of Paired Estimates Of Disease Incidence And Carriage Prevalence, Katherine Gallagher, Fredrick Odiwour, Christian Bottomley, John Ojal, Aisha Adamu, Esther Muthumbi, Eunice Kagucia, Laura Hammitt, Sergio Massora, Betuel Sigaúque

Population Health, East Africa

Background Serotype-specific estimates of pneumococcal invasiveness used in pneumococcal carriage transmission models to predict changes in disease incidence post-vaccination are largely derived from high-income settings. We conducted a systematic review of carriage prevalence and invasive pneumococcal disease (IPD) incidence to calculate case–carrier ratios (CCRs) in different income settings.

Methods We conducted a systematic search of MEDLINE, Embase, and Global Health databases on March 14, 2022, to identify publications on pneumococcal carriage prevalence or IPD incidence; we requested individual-level data from authors of relevant texts. Serotype-specific CCRs, calculated as IPD incidence divided by carriage prevalence, were pooled across settings using random …

Fare-Free Public Transportation: A Systematic Review Of Outcomes, Joseph S. Lightner, Nancy Ziegler, Tyler Prochnow, Sammi Ford, Jordan A. Carlson, Mallory Moon, Jannette Berkley-Patton, Steven Chesnut, Amanda Grimes

Manuscripts, Articles, Book Chapters and Other Papers

Introduction

Methods

Wayne E. Sabbe Arkansas Soil Fertility Studies 2025, Nathan A. Slaton

Arkansas Agricultural Experiment Station Research Series

Rapid technological changes in crop management and production require that the research efforts be presented in an expeditious manner. The contributions of soil fertility and fertilizers are major production factors in all Arkansas crops. The studies described within will allow producers to compare their practices with the university’s research efforts. Additionally, soil-test data and fertilizer sales are presented to allow comparisons among years, crops, and other areas within Arkansas.

Comparison Of Analytical Techniques From The Extraction Of Bioactive Compounds From Kratom, Curry, Ginger, And Turmeric, Riley Bruno

Honors Program: Senior Projects (Public)

Plants, herbs, and spices have been used as medicines for thousands of years. Early civilizations often attributed healing properties of plants to magical or divine forces. However, as chemistry and analytical technology advanced between the 16th and 18th centuries, scientists began to understand that bioactive compounds within the plants actually caused these effects. Today, natural products remain extremely important in drug discovery. As the number of newly developed synthetic drugs declines, there has been renewed interest in identifying biologically active molecules from plants. Bioactive molecules are a group of diverse chemical compounds that stimulate a response in living tissues. The …

Tourniquet Tester, Tyler Offerman, Thuy Nguyen, Ryan Ly, Cody Hoeptner

Biomedical Engineering

The Tourniquet Tester is a device intended to be used as a standardized testing device for tourniquets. Due to the lack of a standard testing device for testing tourniquets that can produce accurate and reproducible results, it is difficult to test the efficacy of tourniquets currently being sold on the market. A standard testing device that can produce consistent and reproducible results would allow tourniquet manufacturing companies to properly test the efficacy of their product and develop proper guidelines and directions for users of the product. Medical professionals and military personnel who may use tourniquets in their line of work …

Driver Behavior Analyzer 2.0: A Modular Framework For Interpretable Driver Safety Analysis From Obd-Ii And Gps Telemetry, Sangwhan Cha, Venkata Sundar Kamesh Durvasula

Harrisburg University Other Works

Driver behavior analysis plays a central role in advancing road safety and enabling data-driven driver feedback. Although commercial telematics platforms offer sophisticated analytics, they are frequently expensive, proprietary, and optimized for enterprise-scale use. At the same time, low-cost On-Board Diagnostics II (OBD-II) adapters make telemetry collection widely accessible, but they typically do not provide higher-level behavioral interpretation.

In this paper, we present Driver Behavior Analyzer 2.0 (DBA 2.0), an offline-first, modular analytics framework that converts OBD-II and GPS telemetry into interpretable safety insights. DBA 2.0 supports ingestion of telemetry logs in CSV and JSON formats, data normalization, rule-based detection of …

Cpap Belly Prevent, Alek Corpuz, Emma Hoffman, Danielle Tran

Biomedical Engineering

Neonates with low gestation periods often face complications such as respiratory distress syndrome, or RDS, which increases risk for complications such as necrotizing enterocolitis. Use of bCPAP helps facilitate regular breathing, but can also force air into the stomach and consequently cause abdominal distention. Clinicians currently use an already inserted feeding tube to vent the accumulated air, however this interrupts the feeding process.

The PreVent device was developed to allow for simultaneous feeding and venting. The dual function minimizes interruptions to NICU nurse workflow and improves capability to relieve abdominal distention before the onset of adverse complications.

Newborn Spinal Needle, Allison Inouye, Kyle Morton, Evan Russell

Biomedical Engineering

Spinal punctures, or lumbar punctures, are important diagnostic procedures used to collect cerebrospinal fluid (CSF) to diagnose issues in the central nervous system (CNS) such as meningitis. While these procedures are routinely performed on adult and neonatal patients, the current tools are not optimized for use on newborns. Existing spinal needles are often modified versions of adult devices, leading to challenges in fluid collection efficiency, user ergonomics, and patient safety when used in neonatal care.

The Newborn Spinal Needle Project aims to design and develop a spinal needle specifically optimized for neonatal anatomy and clinical workflow. The goal is to …

An Intersectional Framework For Effectively Mentoring Latinas In Stem Fields, Rosalynn A. Vega

Anthropology Faculty Publications

While Latinas are underrepresented across all areas of academia, this problem is especially severe in the science, technology, engineering, and mathematics (STEM) fields. This chapter offers a “best practices,” step‑by-step guide for how to effectively mentor Latinas in STEM fields—drawing from interdisciplinary concepts, culled from critical race studies, statistics, and anthropology—and personal experience as a Latina in STEM, mentor, and mentee. Crenshaw’s (1989) theory of intersectionality, Farmer’s (1999) notion of structural violence, and multivariate statistical modeling is used to demonstrate that intersectional inequality can be traced back to social structures. Multi‑gender and multiethnic mentoring by individuals who are motivated to …

Interest Fit Beyond The Riasec: The Comprehensive Assessment Of Basic Interests-O*Net (Cabin-Net), Chu Chu, Kevin A. Hoff, Zihan Liu, Nicholas F. Heimpel, Anthony Greco, Frederick L. Oswald, James Rounds

Research Collection Lee Kong Chian School Of Business

Over the past half-century, Holland’s RIASEC model has dominated vocational interest research. Although the RIASEC categories effectively capture general occupational themes, their breadth obscures meaningful variability across underlying basic interests, which are more refined interest scales. In this research, we adapted the Comprehensive Assessment of Basic Interests (CABIN; Su et ., 2019) to be used alongside O*NET for assessing interest fit, resulting in the 60-item CABIN-NET that measures 20 basic interest scales organized under RIASEC domains. In Study 1, we refined items for the CABIN-NET using a nationally representative adult sample (N = 768). In Studies 2 and 3, we …

A Narrative Study On The Resilience Of Women In Technology Careers, Gina J. Baldridge

Electronic Theses and Dissertations

In spite of the promotion of science, technology, engineering, and mathematics (STEM) education and careers, there is a lack of women entering the STEM workforce and majoring in STEM subjects. Women can experience gender bias and workplace marginalization in STEM careers, but have learned coping strategies that have contributed to their technology career resilience. The purpose of this narrative qualitative study was to explore how women working as individual performers and leaders in technology companies told their career stories of resilience through the perspective of change acceptance, confidence, and communication, three factors in Kumar’s CR8 model of resilience. The model …

Transformative Technology Or Blatant Theft: Reassessing Fair Use In The Era Of Generative Ai, Tsai-Lin Hsieh, Tsai-Fang Chen

Cardozo Law Review

The rapid proliferation of generative artificial intelligence (“AI”) applications in recent years has sparked a global “AI frenzy.” Yet, the rise of new AI technologies has also raised concerns about copyright infringement, particularly regarding whether generative AI developers should be held liable for using copyrighted works as training inputs and generating outputs that compete with original works. In response, AI developers have invoked a long line of technological fair use precedents to defend against any potential copyright infringement claims. The purpose of this Article is to offer insights into whether generative AI is a transformative technology that deserves fair use …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

Cuentos (In)Creíbles: Ethnography As Faithful Witnessing For Transborder Epistemologies, Sarah Gallo, Melissa Adams Corral

Teaching and Learning Faculty Publications

Through the careful analysis of the border-crossing epistemologies that are collaboratively shared and validated by a fifth grader and ethnographer in liminal classroom spaces, we identify key methodological approaches for researchers working with border crossers to document the co-production of knowledge among researchers and participants, to engage in active reflection with trusted thought partners, and to understand the possibilities for personal epistemic expansion.

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Homogeneous Syringe-Sharing Network, Seun Ale, Que Thi Nguyet Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model assumes homogeneous mixing among syringe-sharing agents, without any form of heterogeneity in the agents interactions or syringe-sharing attitude. All syringe-sharing PWID are treated as identical in terms of their interaction frequency and syringe-sharing probability. Interactions are generated dynamically using proximity-based sampling at each timestep (one day), allowing agents to form syringe-sharing interactions based on spatial closeness. The number of daily interaction events is fixed at the population level, and each syringe-sharing agent has the same probability …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (M4), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned distinct syringe-sharing probabilities, reflecting variation in risk-taking behaviour across structural groups within the syringe-sharing …

Sustainable Pathways For Fish Waste Oil Valorization Into Biofuel: Process Synthesis And Case Study, Hany A. Elazab, Radhi H. Alazmi, Abdelrahman El-Bagalaty, Hossam Tammam, Mahmoud El-Sayed, Mohamed Omar, Fathi Shokry, Mamdouh A. Gadalla, Fatma H. Ashour, Thokozani Majozi

Chemical and Biochemical Engineering Faculty Research & Creative Works

Biodiesel is a promising, sustainable alternative to fossil fuels such as petrol and diesel. Currently, biodiesel can be produced from edible plant oils and non-edible sources and wastes. Notably, fish waste oil is a sustainable resource for transesterification reactions to produce biodiesel. This research proposes a general process design methodology to investigate the potential of biodiesel production from fish waste oil as a pathway for waste-to-energy. The methodology integrates Pinch Analysis principles and process simulation to optimize the energy efficiency of a process design. Real data are collected on fish waste from fish industries in Egypt, focusing on three regions …

Kras4a And Kras4b Show Distinct Lipid-Dependent Regulation Of Ras-Raf Membrane Dynamics, Konstantia Georgouli, Jeremy O.B. Tempkin, Liam G. Stanton, Tomas Oppelstrup, Rebika Shrestha, Timothy S. Carpenter, Fikret Aydin, Xiaohua Zhang, Harsh Bhatia, Yue Yang, Que N. Van, Pedro Andrade Bonilla, Gulcin Gulten, Debanjan Goswami, Francesco Di Natale, Joseph R. Chavez, Joseph Y. Moon, Gautham Dharuman, Nicolas W. Hengartner, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

KRAS4a and KRAS4b are important regulators of signaling, and their interactions with the plasma membrane are dynamic and influenced by lipid composition. KRAS 4a and 4b have nearly identical globular domains but differ in their membrane-associated hyper variable region (HVR). The functional distinctions between these isoforms remain unclear, particularly with regards to their dependence on specific lipids and the membrane environment. Previous work showed that the membrane orientation of KRAS4b affects its ability to bind to RAF kinase RBDCRD and that the KRAS–RBDCRD complex adopts different poses on the membrane as well as influences the size and composition of the …

Diagnostic Trends And Service Enhancements In Deaf Mental Health: A Longitudinal Analysis, Kent Schafer, Steve Hamerdinger, Frank Wu, Rie Sakai Bizmark, Charlene Crump

JADARA

A longitudinal study investigated mental health diagnostic trends and patterns among Deaf and Hearing individuals using a comprehensive dataset from Alabama Department of Mental Health spanning 2004-2024 (Deaf=4,547; Hearing=2,056,188). Chi-square analysis revealed significant proportional differences in diagnoses between the groups. Deaf individuals showed significantly higher proportions of psychotic (12.93% vs. 8.33% Hearing), mood (20.76% vs. 18.84% Hearing), and personality (0.58% vs. 0.43% Hearing) disorders. These findings suggest that increased access to specialized and competent mental health services for individuals who are Deaf, particularly following the increase of staffing patterns for the Office of Deaf Services in 2015,contributed to more accurate …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

Exploring The Lived Experiences Of Black Women In Undergraduate Stem Core Expansion Pathways, Lizzie L. Evans Martinez Alvarez

Electronic Theses and Dissertations

The use of social incongruence in the film Hidden Figures, which juxtaposed with STEM education as the most powerful in contrast with Black women as the least powerful, supported the argument for the specific problem of practice to examine the unmet need for socially and ethnically diverse student populations in U.S. STEM higher education and careers. The purpose of this hermeneutic phenomenological research study was to identify and analyze the lived experiences of Black women regarding barriers they perceive in the STEM educational and career decision-making processes, while exploring transformational pathways to STEM academic success. The study specifically analyzed …

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

Identifying And Mitigating Api Misuse In Large Language Models, Terry Yue Zhuo, Junda He, Jiamou Sun, Zhenchang Xing, David Lo, John Grundy, Xiaoning Du

Research Collection School Of Computing and Information Systems

API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development. While LLMs demonstrate impressive code generation capabilities, their interactions with complex library APIs are often error-prone, potentially leading to software failures and vulnerabilities. In this paper, we conduct a large-scale study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java, using three representative LLMs (StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot). Based on extensive manual annotation of 3,209 method-level and 3,492 parameter-level misuses, we identify and categorize four recurring misuse types by building on …

Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning ability, while being able to reject unknown samples that are drawn from different distributions of the learned classes. This capability is crucial to the safety of deploying CIL models in open worlds. However, despite remarkable advancements in the respective CIL and OOD detection, there lacks a systematic and large-scale benchmark to assess the capability of advanced CIL models in detecting OOD samples. To …

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

Exploring Neural Network Structure Code Reuse In The Open-Source Community For Improving Maintenance, Xiaoning Ren, Yuekun Wang, Chongyang Liu, Yueming Wu, Qiang Hu, Lijun Zhang, Yinxing Xue

Research Collection School Of Computing and Information Systems

Neural networks (NNs) have rapidly advanced, demonstrating exceptional performance across various fields, leading to a surge in open-source NN projects. The complexity and rapid growth of these projects pose significant challenges for maintenance within the open-source community. Given that NN architecture code is the core asset of NN projects, understanding its reuse in the open-source community is essential for effective maintenance, such as reducing redundancy and identifying potential intellectual property violations. While prior studies have examined code reuse in open-source projects, they have two key limitations: They do not specifically address NN structure code, and they rely on manually selected …

Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host-pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer …

Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu

Research Collection School Of Computing and Information Systems

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization …

Compositions Of Variant Experts For Integrating Short-Term And Long-Term Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user’s immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommendations. Through an empirical study conducted on diverse real-world datasets, we have observed and quantified the existence and impact of both short-term (immediate and transient) and long-term (enduring and stable) preferences on users’ …

Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) face two key challenges, heterogeneity and heterophily, which often degrade performance. Existing approaches either focus narrowly on specific meta-paths, limiting their expressiveness, or are expressive but cannot effectively leverage higher-order neighbors. In this paper, we propose the Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which combines local independent filtering to adaptively handle meta-path subgraphs with varying homophily ratios, and global hybrid filtering to capture high-order neighbor interactions with linear computational complexity. On five heterogeneous graph benchmarks—DBLP, ACM, IMDB, AMiner, and Yelp—H2SGNN consistently outperforms strong baselines, for example, achieving +1.0% Macro-F1 and +1.3% Micro-F1 on IMDB. It …