Digital Commons Network^™

Increasing Efficiency And Flow In Primary Care Clinics Using Triage Protocols Based On The Donabedian Model, Verneita Mun

Doctor of Nursing Practice Projects

Purpose

This project evaluated the effectiveness of triage protocols based on the Donabedian model in increasing efficiency and flow in primary care clinics. Patients experienced extended wait times in primary care clinics, resulting in aggravated outcomes and dissatisfaction with care delivery. Healthcare providers also experienced stress and burnout due to workload and operational inadequacies. These inefficiencies mandated a quality improvement approach that addressed patient satisfaction, workflow optimization, and resource management.

Methods

This project used structured triage protocol based on the Donabedian model, which addressed the dimensions of structure, process, and outcomes. The proposed solution was disseminated through a quality improvement …

Bacterial Adaptation To Radiofrequency Electromagnetic Fields Based On Experiences From Ionizing Radiation, Ilham Said-Salman, Seyed Mohammad Javad Mortazavi, Sami El Khatib, Seyed Alireza Mortazavi, Lembit Sihver

Informatics and Engineering Systems Faculty Publications

Bacteria, part of the three domains of life (Eukarya, Archaea, and Bacteria), are constantly exposed to man-made electromagnetic fields, which often exceed the intensity of natural electromagnetic sources. In response to this exposure, bacteria have developed various defensive and resistant traits. This article presents an overview of both historical and recent research on how bacteria adapt to common sources of Radiofrequency Electromagnetic Fields (RF-EMF). The widespread use of mobile phones and Wi-Fi, both utilizing Radiofrequency (RF) radiation, raises potential public health concerns, which have been addressed by international organizations like the World Health Organization (WHO). Understanding how bacteria adapt to …

Satellite-Based Forest Structure Metrics As A Predictive Tool For Biodiversity In Hyperdiverse Tropical Forests: A Test Of The Habitat Heterogeneity Hypothesis In The Tropics, David Luther, Marconi Campos-Cerqueira, Aline Medeiros, Aidan Mccarthy, Emilia Roberts, Xiaoxuan Li, Paulo Bobrowiec, Jared Wolfe, Gabriel Augusto Leite, Tomaz Nascimento De Melo, José W. Ribeiro, Thiago Bicudo, Konrad Wessels

Michigan Tech Publications

Tropical forests hold the most species yet face the greatest threats and knowledge gaps. To improve tropical biodiversity knowledge we combined satellite-based LiDAR with in situ bird, mammal, and acoustic soundscape data, via camera and audio recorders, at the Biological Dynamics of Forest Fragments Project in the Amazon rainforest and tested the habitat heterogeneity-diversity hypothesis as a predictor of alpha diversity in tropical forests. GEDI spaceborne LiDAR was used to assess the predictive power of vertical forest structure on acoustic diversity and species diversity in lowland tropical forests. In 4 months, we detected 201 bird and 35 mammal species representing …

Conference Calls And Information Spillover: The Role Of Analyst Participation, Amanda Awyong, Young Jun Cho, Holly I. Yang

Research Collection School Of Accountancy

We examine the role of conference calls in creating information spillover within firms. We find that analyst participation in conference calls is positively associated with subsequent revisions in management forecasts, consistent with analysts' questions prompting managers to collect additional information. We find that this effect strengthens when analysts pose more questions on new topics and when they ask questions with abnormally positive or negative tones. We also find that analyst participation has greater effects when analysts have more experience and higher forecasting ability. Further analyses demonstrate that analyst participation is associated with higher accuracy in subsequently revised forecasts. Overall, our …

“Megadeal” Subsidies, Local Spillovers And Corporate Innovation, Yoojin Lee, Shaphan Ng, Aruhn Venkat

Research Collection School Of Accountancy

We examine whether the largest place-based, firm-specific corporate subsidies (“Megadeals”) awarded by state and local governments affect local firms’ innovation. First, we document that 1) subsidy firms innovate in the subsidized county and 2) subsidy firms bring inventors from other counties into the subsidized county, consistent with subsidy firms generating new knowledge locally. In our main test, we use a stacked cohort design with stringent fixed effects to document that local firms increase patenting following a Megadeal. Cross-sectionally, effects are increasing 1) in subsidy firm innovativeness, 2) in the technological closeness of subsidy firms and local firms, 3) when subsidy …

Attention To Detail: How Do Information Users Process Exhibits In Form 10-K?, Stephanie F. Cheng, Yimeng Li, Pengkai Lin

Research Collection School Of Accountancy

Form 10-K offers a setting for studying how users process complex, multi-layered disclosures: managerial narratives in the main file alongside separate exhibits, such as contracts and certifications, that provide unfiltered detail. Drawing on rational inattention theory, we investigate how users allocate limited attention across these components. Users typically begin with the main file and selectively access exhibits when the main file appears shorter, less readable, or less confident, indicating higher perceived information loss. This pattern strengthens for exhibits that offer more detail on topics discussed in the main file and among institutional investors and time-constrained users. Exhibit access persists beyond …

The City As Showroom: Singapore And The Rise Of The "Urban Solutions" Industry, Sol Andrew Stokols

Research Collection College of Integrative Studies

This article uses the concept of the “city as showroom” to describe an emergent logic shaping urban development that stems from the growing importance of “urban solutions” as an industry. Focusing primarily on Singapore, the article examines how certain urban districts and high-profile sites are conceptualized as “showrooms” for the infrastructure and urban technologies they aim to export around the world. The idea of the “city as showroom” combines aspects of the curation of a city’s image through showcase districts, as well as “testbed urbanism” seen in certain smart-city projects. Singapore's need to reproduce itself as a model of urban …

Moving Beyond Entry Modes: Toward A New Paradigm Of International Involvement In An Era Of Digitalization And Global Transitions, Noman Shaheer, Sali Li, Liang Chen, Keith D. Brouthers, Dan Li, Peter Liesch

Research Collection Lee Kong Chian School Of Business

We seek to reconceptualize internationalization strategy by shifting focus from static, single-entry mode choices to dynamic constellations of multiple involvement modes. In an era of rapid technological change enabled by artificial intelligence, blockchain, and robotics, geopolitical volatility fueled by trade wars and violent conflicts, and increasing sustainability demands, multinational corporations (MNCs) need to rethink how they are involved with diverse stakeholder expectations, prioritize flexibility and reversibility, and embed co-creation and legitimacy alongside efficiency. Extending Brouthers et al. (2022), we integrate transaction cost economics with resource-based, institutional, opportunity, and ecosystem perspectives to explain how MNCs configure and reconfigure various constellations of …

The External Influence Of International Business Scholarship: Growth, Stabilization, Or Decline?, Gokhan Ertug, Andrew Delios, Yi Li, Zhengchu Zhang

Research Collection Lee Kong Chian School Of Business

The influence of international business (IB) scholarship is an active debate (e.g., Bello & Kostova, 2012; Cantwell et al., 2014, 2016; Doh et al., 2023; Tung et al., 2023). Although IB research has expanded in the number of publications, in its sophistication of research design, and in its thematic breadth over the past decades, questions remain regarding its influence beyond the core ecosystem of IB journals. Anecdotally, a pessimistic view has become more commonplace. The concern is that IB has become increasingly self-referential, relying more heavily on IB-related theory extensions and context-specific settings, which could limit its resonance in the …

Settlement Manipulation In Prediction Markets, David Dai, Ruizhe Jia, Shihao Yu

Research Collection Lee Kong Chian School Of Business

Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Polymarket's five-minute Bitcoin contract, settlement-time spot order flow spikes, causing large price reversals after settlement. Manipulators capture a large amount of profit, mostly from retail. Manipulation is largely absent in the fifteen-minute contracts: lengthening the contract horizon removes it, providing the market-design remedy our model and evidence support.

Partial Insect-Based Meal Inclusion In The Diets Of Ross® 308 Broilers Upregulates Growth And Immune Related Genes, Victor K. Rotich, Isaac M. Osuga, Mathew G. Gicheha, Shaphan Y. Chia, Jandouwe Villinger, Anderson N. Maina, Jinhua Xiao, Dennis Beesigamukama, Chrysantus M. Tanga

All Peer-Reviewed Publications

This work investigated the effects of black soldier fly ( Hermetia illucens L.) larvae meal (BSFLM) on growth- and immune-related gene expression in Ross 308 broilers. Birds were fed diets containing soybean meal (SBM), fishmeal (FM), combinations with BSFLM, or BSFLM alone. Growth, organ weights, immune genes [interleukin-2 (IL-2), interleukin-6 (IL-6), tumor necrotic factor alpha (TNF-α), interferon-gamma (IFN-γ)] and growth-related genes [chicken growth hormone (cGH), insulin growth factor 1 (IGF-1)] expression were measured. Our results demonstrate that birds fed FM+BSFLM had significantly higher final liveweight, average daily weight gain (ADG), and low feed conversion ratio. Birds fed FM+BSFLM showed highest …

Comparing Allometric Models To Machine Learning Models For Aboveground Biomass Estimation In Agroforestry Systems In Kenya, Samuel Irungu Kigotho, Kennedy Senagi, John Olukuru, David Masereti Makori, Elfatih M. Abdel-Rahman, Evans Omondi

All Peer-Reviewed Publications

This study compared traditional allometric models with machine learning (ML) techniques for accurately estimating aboveground biomass (AGB) in six Acacia species within Kenyan agroforestry systems. Using tree diameter at breast height (DBH) and total height as inputs, the research evaluated allometric models (Chave’s, Brown’s, and Henry’s) against ML models, including Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR). This research advances the field by benchmarking machine learning and classical allometric models at the species level within Kenyan agroforestry systems and employing SHapley Additive exPlanations (SHAP) for interpretability analysis. Model performance was validated using …

Feedback Loops: What Am I Missing?, Patrick Barry

Articles

In May, 2024, The Wall Street Journal published an article titled “For Conversations You Dread, Try a Chatbot.” Here are the opening lines: Many people worry about the outsize role that AI may eventually play in our lives. But what if employing an AI program could actually help us with issues that cause fear and anxiety? We’ve found that it can be a surprisingly effective tool for figuring out how to approach emotionally charged conversations.

The Wake Of Addiction: Pharmacological Strategies For Sleep Disturbances In Stimulant Use Disorders, A Systematic Review, Isabella G Bourtin, Douglas J Calvillo, Jessica C Badawi, Joy M Schmitz, Scott D Lane, Jin Ho Yoon, Heather E Webber

Faculty, Staff and Student Publications

Background: A systematic review was conducted following PRISMA guidelines to identify human studies investigating pharmacological interventions and reported sleep outcomes among individuals with CUD or MUD.

Methods: PubMed and APA PsycInfo were searched from inception to January 2025 and risk of bias was assessed. Articles were included if they included human participants with either cocaine or methamphetamine dependence, administered a pharmacological treatment, and reported night-time sleep as an outcome using at least one rigorous measurement tool. Articles were excluded if they included animals, did not include pharmacological intervention (e.g., supplements or behavioral treatments), or only assessed baseline sleep or if …

Assessment Of Knee Laxity After Retrograde Intramedullary Nailing For Periprosthetic Distal Femur Fractures: A Cadaveric Study., Joshua P Rainey, Lucas A Anderson, Ian M Duensing, Jeremy M Gililland, Patrick J Kellam

Faculty, Staff and Student Publications

Objectives: Periprosthetic distal femur fractures have grown in volume because the rate of primary total knee arthroplasty (TKA) has increased. These injuries are increasingly treated with intramedullary nailing (IMN), but knee instability after retrograde IMN has not been well studied. A cadaveric study was performed to investigate TKA balance after retrograde femoral IMN. The main hypothesis was that retrograde femoral IMN preparation would lead to increased flexion space laxity.

Methods: A medial parapatellar approach and measured resection TKA technique were performed in 8 cadaveric knees. A tensioner was used to measure the flexion and extension spaces in millimeters and the …

Incidence And Risk Factors For Stillbirth In Kenya, Mozambique And The Gambia: The Precise Cohort Study, Grace Mwashigadi, Joseph Akuze, Angela Koech, Hawanatu Jah, Anna Roca, Geoffrey Omuse, Moses Mukhanya, Marleen Temmerman, Amina Abubakar, Patricia Okiro

Centre of Excellence in Women and Child Health

Background

Methods

Terminal Digit Preference And Threshold Avoidance In Digital Blood Pressure Measurements During Pregnancy: Secondary Analysis Of Data From The Clip And Precise Cohorts, Peter Dadelszen, Akshdeep Sandhu, Jeffrey Bone, Rahat Qureshi, Olukayode Dada, Ashalata Mallapur, Marleen Temmerman, Marie-Laure Volvert, Joseph Waiswa, Moses Mukhanya, Angela Koech

Centre of Excellence in Women and Child Health

Background:Screening for, detecting, and managing pregnancy hypertension is a core function of antenatal care. To reduce both training requirements and the risks of measurement error in blood pressure (BP) values, automated and semiautomated BP devices have been validated in pregnant women with normal BP and pregnant women with hypertension and introduced for serial antenatal measurement of BP.

Objectives:The study aimed to (1) determine whether or not repeated BP measurements reduced the presence of terminal digit preference and (2) discern whether or not there was evidence of threshold avoidance in the Community-Level Interventions for Preeclampsia (CLIP) trials compared with …

Anatomical Domain Shifts: Test-Time Heterogeneous Adaptation For 3d Human Pose Prediction, Qiongjie Cui, Pan Zhou, Jingjing Chen, Na Zhao

Research Collection School Of Computing and Information Systems

The research frontier in human pose prediction (HPP) is advancing toward continual test-time adaptation (TTA), where models must self-adapt to dynamic test distributions. To date, the homeostatic continual TTA remains the sole viable solution, which isolates the model parameters and update domain-sensitive ones. Despite mitigating full-body domain gaps, human anatomical heterogeneity (domain shifts often localize to specific regions) is ignored. This anatomical-agnostic approach forces uniform parameter adaptation across kinematically distinct segments, causing: over-adaptation of stable regions and under-adaptation of shift-prone articulations. To address it, we introduce TT-HA, a novel Test-Time Heterogeneous Adaptation that implicitly estimates domain changes for anatomical segments, …

A Novel Hierarchical Multi-Agent System For Payments Using Llms, Donghao Huang, Joon Kiat Chua, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language model (LLM) agents, such as OpenAI’s Operator and Claude’s Computer Use, can automate workflows but unable to handle payment tasks. Existing agentic solutions have gained significant attention; however, even the latest approaches face challenges in implementing end-to-end agentic payment workflows. To address this gap, this research proposes the Hierarchical Multi-Agent System for Payments (HMASP), which provides an end-to-end agentic method for completing payment workflows. The proposed HMASP leverages either open-weight or proprietary LLMs and employs a modular architecture consisting of the Conversational Payment Agent (CPA - first agent level), Supervisor agents (second agent level), Routing agents (third agent …

Patchfuzz: Patch Fuzzing For Javascript Engines, Junjie Wang, Zhihua Xie, Xiaofei Xie, Xiaoning Du, Xiangwei Zhang

Research Collection School Of Computing and Information Systems

Context: Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScript (JS) engines with considerable success, these efforts have been limited to using ordinary test cases or publicly available vulnerability PoCs (Proof of Concepts) as seeds, and the sustainability of these approaches is hindered by the challenges associated with automating the PoC collection. Objective: To address these limitations, we propose an end-to-end sustainable approach for JS engine patch fuzzing, named PatchFuzz. Method: It automates the collection of PoCs of a broader range of …

To Wait Or To Transfer? A Three-Level Optimization Framework For Intermodal Transfer Coordination In First Train Timetabling And Bus Bridging Services Management, Hao Li, Liujiang Kang, Norman Weik, Huijun Sun, Qingying Lai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

This study addresses the integrated optimization of the first train timetabling and bus bridging service design (FTT-BBSD) for morning transfer challenges, two critical but interdependent passenger services in the public transit system. In contrast to most existing studies and conventional approaches, this study explicitly models the influence of passenger path choices and transfer mode selections on FTT-BBSD. Through a novel dual-level network representation that integrates subway and bus systems, we formulate the FTT-BBSD problem as a mixed-integer nonlinear programming model. The model simultaneously determines subway and bus timetables and bridging line deployment to minimize total travel time for all first …

Hydpn: A Hybrid Deep Reinforcement Learning, Programming, And Neighborhood Operations Framework For Integrated Scheduling On Parallel Batch Processing Machines, Yuqi Wang, He Luo, Guoqiang Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments …

When Politics Meets Digital Assets: Gender Identity Salience And Nft Pricing After Roe V. Wade, Xiang Liu, Yao Zhao, Ping Fan Ke

Research Collection School Of Computing and Information Systems

Major sociopolitical events can reshape public attention toward identity-related issues, potentially influencing valuation patterns in digital markets where identity-related characteristics are embedded in digital assets. Using the overturning of Roe v. Wade as an exogenous policy shock, this paper examines how gender attributes represented in non-fungible token (NFT) avatars affect market outcomes. Using transaction data from six major avatar-based NFT collections traded on Etherscan in 2022, we apply a quasi-experimental design combining propensity score matching and a difference-in-differences model. The results indicate that the policy shock significantly increased the resale prices of NFTs representing female avatars. These findings suggest that …

On-The-Fly Generation-Quality Enhancement Of Deep Code Models Via Model Collaboration, Weifeng Sun, Naiqi Huang, Meng Yan, Zhongxin Liu, Hongyan Li, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

The growing prominence of deep code models in automating software engineering tasks is undeniable. However, their deployment encounters significant challenges in on-the-fly performance enhancement, which refers to dynamically improving the performance of deep code models during real-time execution. Conventional techniques, such as retraining or fine-tuning, are effective in controlled pre-deployment scenarios but fall short when adapting to on-the-fly adjustments post-deployment. CodeDenoise, a notable on-the-fly performance enhancement technology, leverages uncertainty-based methods to identify misclassified inputs and applies an input modification strategy to rectify classification errors. While effective for classification tasks, this approach is inapplicable to generative tasks due to two key …

Scaling Up Multi-Agent Reinforcement Learning For Large Agent Teams And Long-Horizon Tasks: A Survey, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) empowers multiple autonomous agents to acquire effective policies for collaborative problem-solving. Over the last decade, MARL has seen significant advancements, with numerous algorithms achieving impressive performance across various benchmarks and real-world applications. Nevertheless, the scalability of multi-agent systems, in terms of the number of agents and the length of the task horizon, remains a critical consideration for applying MARL methods to complex problem-solving. Given that a dedicated review of the existing approaches and challenges in scaling up multi-agent systems remains largely absent, this survey aims to bridge this gap by delivering a comprehensive review of MARL …

A Pruning-Based Question-Answering For Interactive Video Search: A Simple Baseline, Yu Tong Cheng, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

There are various factors affecting the performance of video search. An imprecise query will enlarge search space and reduce the discriminative power of ranking functions. This problem is further exacerbated by the presence of numerous visually or semantically similar videos in large datasets. Consequently, users need to painstakingly browse through many highly similar candidates to locate the search target, leading to increased cognitive load and inefficient searching. Ideally, engaging users through interactive questioning to resolve uncertainties in the search process is an effective strategy for progressively narrowing down the search space. However, despite rapid advances in deep learning, generating informative …

Rode: Linear Rectified Mixture Of Diverse Experts For Food Large Multi-Modal Models, Pengkun Jiao, Xinlan Wu, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have significantly advanced a variety of vision-language tasks. The scalability and availability of high-quality training data play a pivotal role in the success of LMMs. In the realm of food, while comprehensive food datasets such as Recipe1M offer an abundance of ingredient and recipe information, they often fall short of providing ample data for nutritional analysis. The Recipe1M+ dataset, despite offering a subset for nutritional evaluation, is limited in the scale and accuracy of nutrition information. To bridge this gap, we introduce Uni-Food, a unified food dataset that comprises over 100,000 images with various food labels, …

Interfold: Learning Interpretable Diffusion Manifolds Beyond Binary Samples, Alexander Vincent Lewi, Rainer Tan, Shengfeng He

Research Collection School Of Computing and Information Systems

We propose InterFold, a framework for learning and applying interpretable semantic manifolds in latent diffusion models, without requiring binary or paired supervision. Existing methods for semantic editing either rely on limited paired data or uncover only coarse, unsupervised directions that fail to capture user-specific, fine-grained attributes. InterFold addresses these limitations by learning a target attribute manifold in the H-space of diffusion models using only a set of positive, unlabeled examples. To edit a new image, InterFold projects its H-space representation toward this learned manifold through test-time optimization, enabling precise, identity-preserving modifications of complex, non-binary concepts. To make these edits effective …

Sam3-Litetext: An Anatomical Study Of The Sam3 Text Encoder For Efficient Vision-Language Segmentation, Chengxi Zeng, Yuxuan Jiang, Ge Gao, Shuai Wang, Duolikun Danier, Bin Zhu, Stevan Rudinac, David Bull, Fan Zhang

Research Collection School Of Computing and Information Systems

Vision-language segmentation models such as SAM3 enable flexible, prompt-driven visual grounding, but inherit large, general-purpose text encoders originally designed for open-ended language understanding. In practice, segmentation prompts are short, structured, and semantically constrained, leading to substantial over-provisioning in text encoder capacity and persistent computational and memory overhead. In this paper, we perform a large-scale anatomical analysis of text prompting in vision–language segmentation, covering 404,796 real prompts across multiple benchmarks. Our analysis reveals severe redundancy: most context windows are underutilized, vocabulary usage is highly sparse, and text embeddings lie on a low-dimensional manifold despite high-dimensional representations. Motivated by these findings, we …

Frozen Lvlms For Micro-Video Recommendation: A Systematic Study Of Feature Extraction And Fusion, Huatuan Sun, Yunshan Ma, Changguang Wu, Yanxin Zhang, Pengfei Wang, Xiaoyu Du

Research Collection School Of Computing and Information Systems

Frozen Large Video Language Models (LVLMs) are increasingly employed in micro-video recommendation (MVR) due to their strong multimodal understanding. However, existing apporches typically deploy LVLMs as fixed black-box feature extractors without systematically comparing alternative representation strategies. To address this gap, we present the first systematic empirical study on various feature extraction paradigms and integration strategies, along with hierarchical representations from frozen LVLMs for MVR. Extensive experiments on representative LVLMs reveal that hidden states from multiple decoder layers provide richer and more effective representations for MVR. Guided by this insight, we propose the Dual Feature Fusion (DFF) Framework, a lightweight approach …