Digital Commons Network^™

Reflections And Revisionism: Rethinking The U.S. Response To Covid-19, Wendy E. Parmet

Michigan Law Review

A review of In Covid’s Wake: How Our Politics Failed Us. By Stephen Macedo and Frances Lee.

Fourth Amendment Anti-Theory, Matthew Tokson

Michigan Law Review

A review of The Digital Fourth Amendment: Privacy and Policing in Our Online World. By Orin Kerr.

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

Michigan Law Review

A review of AI Snake Oil.By Arvind Narayanan and Sayash Kapoor.

Automated Optimization Of Membrane Bioreactor Performance Using Machine Learning, Janessa Cayabyab, Isabella Lopez, Oluwatomisin Opaleye, Jessica Stone

Bioengineering Senior Theses

Membrane bioreactors (MBRs) are increasingly employed in biomanufacturing due to their ability to produce high-quality effluent and support complex biological processes. However, membrane fouling remains a critical operational challenge, driving increased energy consumption, frequent cleaning cycles, elevated transmembrane pressure (TMP), and accelerated membrane degradation. Conventional operator-driven cleaning strategies are reactive by nature and insufficient for managing the dynamic, nonlinear fouling behavior characteristic of full-scale MBR systems.

This study presents a data-driven machine learning pipeline for predicting TMP and fouling behavior in an industry-scale MBR system, with the goal of optimizing cleaning schedules and extending membrane lifespan. A Random Forest regression …

Leveraging Ict Adoption And Electricity Access For Sustainable Agricultural Productivity Among Nigerian Households, Menghan Huang, Qin Chen, Oluwayemisi Kadijat Adeleke, Mamdouh Abdulaziz Saleh Al-Faryan

The Philippine Agricultural Scientist

This study investigated ICT adoption (mobile phone and internet usage), access to electricity, and their impact on household agricultural productivity in Nigeria, Africa’s largest economy. Utilizing Wave 4 (2018 to 2019) Living Standards Measurement Study–Integrated Survey on Agriculture (LSMS–ISA) data covering 4,980 household heads, this study applied logit regression and Propensity Score Matching (PSM) for estimation, alongside a Cobb-Douglas production function to analyze input contributions to agricultural output. The findings revealed that the age, residential location, educational level, and access to electricity of household heads significantly determine technology adoption among farming households. Furthermore, ICT adoption exhibits a significant, positive relationship …

Marker-Assisted Identification Of Hybrids In Polyembryonic ‘Carabao’ Mango (Mangifera Indica L.) Breeding In The Philippines, John Edward L. Felipe, Kyla Andrea G. Mendoza, Benjamin Christian J. Esguerra, Carolyn E. Alcasid, Gino R. Hernandez, Eureka Teresa M. Ocampo

The Philippine Agricultural Scientist

Varietal improvement of the Philippine ‘Carabao’ mango (Mangifera indica L.) through conventional breeding is constrained by polyembryony, which makes it difficult to distinguish zygotic hybrids from nucellar seedlings at early developmental stages. This study evaluated the utility of simple sequence repeat (SSR) markers for early and accurate identification of hybrids in polyembryonic ‘Carabao’ mango crosses. Genomic DNA was isolated from 24 parental accessions and 61 putative hybrid seedlings using a modified CTAB protocol that yielded DNA suitable for PCR-based marker analysis. Eighty-one SSR markers were screened across 29 parental combinations, of which 64 markers (79.0%) were polymorphic in at …

Effect Of Leaf Harvesting Frequency On Cassava Leaf Production And Tuber Yield Of Binulak, Lakan 1 (Upl Cv-2), And Sultan 6 (Nsic 2003 Cv-25), Fides Marciana Z. Tambalo, Rosa Buena A. Capuno, Hiyasmin Rose L. Benzon, Jayson F. Garcia, Lizette Sahar N. Arcillas, Rona Joy C. Tud

The Philippine Agricultural Scientist

Cassava (Manihot esculenta Crantz) is a vital staple crop in many developing countries, valued for its adaptability and dual-purpose use of roots and leaves. This study investigated the effects of leaf harvesting frequency on the leaf and tuber yields of 3 locally grown cassava varieties: Binulak, Lakan 1, and Sultan 6. The experiment followed a split plot in a Randomized Complete Block Design with 4 treatments varying in leaf harvesting frequency and initiation. Cassava leaves were harvested at 4, 6, 8, or 10 mo after planting (MAP), and tubers were harvested at 10 MAP. Results indicated that leaf yield …

Tempus Est Philippine Madrigal Singers, Arwin Q. Tan

Perspectives in the Arts and Humanities Asia

No abstract provided.

Learning About Land Through Indigenous Storywork, Critical Information Literacy, And Archival Research At The Northern Shores Storywork Project Site, Kel Sassi, Morgan Raether, April E. Lindala

Language Arts Journal of Michigan

This article takes a critical approach to an Information Literacy and Leadership Institute (Institute) for Upper Peninsula K-12 educators with the goal of coming to terms with the dispossession of Indigenous peoples in the region. Participants learned about critical information literacy in relation to archival materials about settler colonization and resource extraction and created curriculum materials for their classrooms. Facilitators designed institute activities with Archibald’s Indigenous Storywork principles in mind and analyzed the outcomes through this framework. The focus on land was reflected in teacher materials, but could be integrated more deeply in future institutes.

Intouch Week Of June 1, 2026, New York Medical College

InTouch

• Annual NYMC Golf Tournament Recognizes Excellence in Leadership and Service
• NYMC Announces 2026-2027 Translational Science Initiative Recipients
• Suicidality Is Key Predictor of Treatment-Resistant Focal Epilepsy
• TCDM Celebrates Class of 2026 Commencement
• Top It Off: 20% Off Select Hats
• Student Spotlight: Inspired to Innovate, Driven to Heal 
• Alumni Spotlight: A Passion for Performing Arts and Physical Therapy
• Alumni Spotlight: Two Lives in One Career

Extensive And Intensive Margin Labor Supply On Ride-Sourcing Platforms, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show …

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 …

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 …

“Grandpa, Can You Speak Nicer?”: Envisioned Chatbot Roles And Design Tensions In Intergenerational Communication Conflicts, Tianyi Zhang, Emran Bin Elias Poh, Yueyue Hou, Yi-Chieh Lee, Renwen Zhang, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Intergenerational conversations often break down when differences in tone, language, or expectations lead participants to feel dismissed or misunderstood. In this work, we explore how people envision AI-driven chatbot interventions for addressing communication problems in text-based intergenerational family chat. We conducted a scenario-based design interview with 10 pairs of family members from different generations, in which participants designed chatbot interventions that varied in intervention target and timing. Our findings show that participants expect chatbots to perform multiple themes of intervention, including mediating understanding, providing emotional support, offering evaluative commentary, and guiding interaction through behavioral suggestions. These expectations varied systematically across …

The Stars Align: Modeling User Rating Calibration With Sparse Semantic Review Features, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

User ratings are often treated as comparable across users, although identical scores may reflect different experiences. We study whether ratings can be viewed as user-specific discretizations of a shared semantic continuum derived from review text. Our method maps reviews into sparse semantic features with a sparse autoencoder and learns user-specific filters for each rating level. On Amazon Electronics, the learned embeddings align along a shared low-dimensional rating axis. Users differ mainly in how they anchor and partition this continuum, while preserving its overall ordinal structure. These findings support a semantic view of calibration beyond scalar bias correction.

“From Remembering To Shaping”: Narrating Shared Experiences By Co-Designing Cultural Heritage Artifacts In Collaborative Vr, Yushang Yang, Fanxu Meng, Fiona Fui-Hoon Nah, L. C. Ray

Research Collection School Of Computing and Information Systems

The ways people remember and recall places reveal an invisible aspect of cultural heritage (CH), reflecting how individuals and communities relate to these places. Heritage is communal, emerging through collaboratively constructed narratives rather than individual records. To probe how people may share collective memories, we designed an immersive two-person workflow for collaboratively co-designing 3D artifacts and environments in virtual heritage locations, using Generative AI (GenAI) to instantiate these intangible memories. Observations of the co-creation process revealed that participants merged prompts and model placements when negotiating different perspectives. They used spatial operations to compose scenes, and also to express personal and …

Not Too Early, Not All At Once: Design Tensions In Ai-Mediated Self-Disclosure In Online Dating, Pei-Hua Tsai, Tianyi Zhang, Emran Bin Elias Poh, Anthony Tang, Yung-Ju Chang

Research Collection School Of Computing and Information Systems

Online dating relies on self-disclosure, yet initial conversations are fragile: users must navigate uncertainty around timing, boundaries, and reciprocity with little shared context. While advances in AI raise the possibility of mediating disclosure, how such support might reshape the experience of early-stage relational disclosure remains underexplored. We conducted 29 semi-structured interviews to examine how daters envision AI-mediated self-disclosure in online dating. Our findings surface recurring design tensions rather than simple opportunities or risks. Participants welcomed guidance that could pace disclosure, support reflection, and reduce social awkwardness, but stressed preserving agency and authorship. They valued interpretive assistance for sense-making of ambiguous …

Context Matters: Auditing Gender Bias In T2i Generation Through Risk-Tiered Use-Case Profiles, Jose Luis Luna Campoverde, Yankun Wu, Xiaofei Xie, Noa Garcia

Research Collection School Of Computing and Information Systems

Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting to be integrated into higher-impact pipelines. Since generated images tend to reinforce stereotypes, producing representational erasure via “default” depictions and shaping perceptions of who belongs in certain roles, a growing body of work has proposed metrics to quantify gender bias in T2I outputs. Yet existing evaluations remain fragmented. Metrics are often reported without a shared view of what they measure, what assumptions they entail, or how their results should be interpreted under different deployment contexts. This limits the usefulness of gender …

Rc-Nf: Robot-Conditioned Normalizing Flow For Real-Time Anomaly Detection In Robotic Manipulation, Shijie Zhou, Bin Zhu, Jiarui Yang, Xiangyu Zhao, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the …

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 …

Design And Optimization Of A Bluff Body For Energy Harvesting Of Transverse Galloping Induced By Low-Speed Wind Using Bernstein Polynomial Equations, Youssef Wael Abdelmoneim

Theses and Dissertations

In this thesis, a computational framework is proposed for optimizing the aerodynamic shape of bluff bodies used in galloping-based wind energy harvesters. The system targets low-wind speed environments, where normal wind turbines are not effective, offering a potential alternative to batteries used for powering small electronic devices such as wireless sensors. The design relies on the galloping effect, where airflow around a bluff body induces transverse oscillations that drive an energy conversion mechanism. To generate efficient bluff body geometry, the Class-Shape Transformation (CST) method is used to define a wide range of candidate shapes with minimal design parameters. These shapes …

The Other Within: Stigmatization And Marginalization Of Hui Muslims In The Novel Musilin De Zangli (Muslim Funeral), Flávio Fraga Da Silva

Perspectives in the Arts and Humanities Asia

This article offers a case study of the protagonist of the novel Musilin de Zangli by Huo Da, as a representation of the stigmatization and marginalization of the Hui Muslim community in Beijing in the early 1960s. Drawing on this portrayal, the novel’s subliminal and allegorical dimensions, and an examination of the mechanisms of “domestic othering” operative in Maoist China, the study discusses how the text advances a veiled critique of the abuses and excesses experienced by this community in the period leading up to, and including, the Cultural Revolution. By situating the text within its broader sociocultural and historical …

Beyond Hostile Islands: The Pacific War In American And New Zealand Fiction Writing, Jay Jomar Quintos

Perspectives in the Arts and Humanities Asia

No abstract provided.

Insular Yet Global: Selected Essays Of Fernando Zialcita, Maria F. Mangahas

Perspectives in the Arts and Humanities Asia

No abstract provided.

Roots Of Tide Exhibition, Ning Zhou

Perspectives in the Arts and Humanities Asia

No abstract provided.

Interview With Karina A. Bolasco, Jonathan Chua

Perspectives in the Arts and Humanities Asia

No abstract provided.

Navigating Oer Support Without Drowning In Ai, Lydia Burrage-Goodwin, Christine Moynihan

Joseph P. Healey Library Publications

This was a presentation at the June 2026 Boston Library Consortium at Connecticut College.

UMB Healey Librarians Lydia Burrage-Goodwin and Christine Moynihan talk about what experiences they have had with faculty using OER and AI, which led them to develop ethics guidelines to support librarians who work with faculty authors. Attendees learned about creating AI use statements for OERs, using AI transparency logos, and applying open licenses to fully AI generated content as well as OER adaptations.

A Proactive Food Demand Forecasting-Inventory Management Approach Under Weather Disruptions, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Jun Shen

All Works

Effective demand forecasting has become crucial to strengthening system resilience, reducing food waste, and achieving sustainability in food systems. Despite recent advances in leveraging machine learning for food demand forecasting, most existing models remain static and assume stable demand patterns, posing a challenge for adapting to demand changes during disruption events. This paper develops a proactive approach that leverages demand forecasting outputs and weather disruption flags to guide inventory replenishment, ensuring adaptability to varying demand conditions across three weather disruption events while reducing waste. This paper first uses a stacking model to predict next-day demand for a food retailer, leveraging …

The Association Between Ethical Ai Use And Well-Being Among Young Adults In The Uae: A Structural Equation Modeling Approach, Areej Elsayary, Zeina Hojeij, Lames Abdul Hadi

All Works

This study examines the association between ethical AI use and young people’s emotional, social, and psychological well-being in the United Arab Emirates (UAE), where the number of hours spent on GenAI serves as a moderator. Framed within the Theory of Planned Behavior and aligned with the Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 13 (Climate Action), this research examines how responsible digital engagement is associated with both individual mental health and broader digital sustainability. A Structural Equation Modeling approach assessed how ethical AI behaviors are associated with well-being. A total of 204 participants, predominantly …

Modeling Turbulent Accretion Flows Around Black Holes: Azimuthal Flux Derivative Density Curves In 3d Mhd Simulations, Sasmitha Purushothaman, Matthew D. Duez, Pavan Chawhan

University Honors Theses

When modeling two-dimensional axisymmetric accretion disks around Kerr black holes, setting the azimuthal terms of the MHD equations to zero causes the loss of turbulence and dynamo effects. In this paper, we use three-dimensional models to identify the turbulence that arises from azimuthal flux derivatives (AFDs) and create probability density functions to characterize them. We establish a pipeline for calculating the AFDs, creating a distribution, and normalizing it. Then, we plot the dependence of the conditional probability of the AFDs on density, magnetic field strength, and velocity for the continuity and magnetic induction equations. Future research can take this information …