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The College Voice, Vol. 62, No. 7 [December 3, 2025], Connecticut College

2025-2026

No abstract provided.

Molecularly Imprinted Polymer For The Removal Of Nutrients From Water: Synthesis, Application, Performance And Limitation, Emmanuella Anang, Abolade Busari

Faculty Publications

Nutrient contamination is a major contributor to eutrophication and water quality degradation worldwide. Conventional treatment technologies often lack selectivity and efficiency in complex aquatic environments, highlighting the need for advanced materials with tailored recognition capabilities. Molecularly imprinted polymers (MIPs) have emerged as promising adsorbents for nutrient remediation due to their high selectivity, stability and reusability. This review synthesizes recent progress on the synthesis strategies of MIPs. Applications of MIPs in removing phosphate, nitrate and ammonia from water are critically examined, with particular attention to adsorption performance under varying environmental conditions. The limitations of current systems, including modest adsorption capacities, incomplete …

Design & Control Of High-Power Density Gan-Based Immd For All-Electric Aircraft Application, Armin Ebrahimian

Dissertations (1934 -)

Transportation electrification has been a major focus of research across both academia and industry over the past few decades. In recent years, the rapid growth in the electric vehicle (EV) market share has demonstrated a paradigm shift, with consumers increasingly adopting EVs due to benefits such as lower operational costs, enhanced safety, and reduced environmental impact. While EVs continue to gain popularity, the electrification of aerial transportation has emerged as a promising and impactful extension of this trend. A round-trip flight from Chicago to New York burns approximately 72,000 gallons of fossil fuel—roughly equivalent to one-eighth of the annual carbon …

Building A Data-Driven Security Ai Framework Using Machine Learning Models, Christopher Chan Vi

Master's Theses

This research project explores a modern approach to Intrusion Detection System (IDS) anomaly detection by leveraging Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLM), and Explainable AI (XAI). The purpose is to assess the effectiveness of these technologies in enhancing the understanding of intrusion events. This research study addresses the challenges of threshold determination and the interpretability of anomaly detection results. The proposed solution involves an LLM-based framework with XAI capabilities, integrated with a Retrieval Augmented Generation (RAG) architecture, to provide clear explanations for detected anomalies, utilizing both custom and pre-trained datasets. The study navigated inherent challenges, including …

Experience From A Distance: Improving Transparency For The Multnomah Athletic Club, Matthew Penner

University Honors Theses

Portland is home to the largest and one of the most prestigious athletic clubs in the world: Multnomah Athletic Club. In many ways, it is the pinnacle of luxury and innovation, and over time, it finds any way to entice prospective members to pay the expensive upfront fee of $6000 and monthly membership fees exceeding $300. Due to the previous technological barrier, which was not being able to see the full extent of what amenities the club had to offer, the club faced major challenges in recruitment and marketing. Over two academic terms, a team of six computer science capstone …

Towards Resilient Data Governance: An Industry Study On Practitioner Perspectives On The Current State Of Data Governance, Anthony Mazzarella Iii

Theses and Dissertations

Experts agree that data governance is crucial for managing data as an asset, yet in practice, it remains inconsistently defined, unevenly implemented, and poorly understood. Earlier industry studies offered valuable descriptive benchmarks of governance adoption and professional roles, but provided limited theoretical insight and little empirical evidence explaining how governance functions within organizations. Building on this foundation, the current research offers a more substantial and analytically rigorous contribution by transforming descriptive observations into empirical explanations. Through a collaborative academic–industry partnership, the study utilizes a global survey of 565 respondents (348 valid cases) to analyze governance structures, motivations, challenges, and the …

An Innovation Accelerator’S Effect On Early Adopters: Advancing Cultures Of Change, Olivia A. Lemberger

Biennial Convention (CONV)

Background: According to Rogers’ Diffusion of Innovation Theory (1971), the process of adoption occurs gradually over time and usually begins with Innovators and Early Adopters, the key enablers of innovation success. As the largest healthcare workforce, it is imperative for nurses to advance their knowledge of innovation adoption, yet there are a limited number of nursing programs or healthcare systems that offer innovation-related education. In 2023, the American Nurses Association Innovation department created the first ever national Innovation Accelerator (IA) outside of academia in the United States. The nine-month IA consisted of monthly virtual meetings, multi-modal learning modules, small group …

Integrating Synaptic Transistors With Perovskite Photodetectors For Neuromorphic Applications, Sydney Zhu, Shuoyan Liu, Yiheng Zhang

Electrical and Systems Engineering Undergraduate Student Research

The convergence of neuromorphic electronics and optoelectronic materials offers a pathway toward next-generation artificial vision systems. Synaptic transistors, which emulate the adaptive plasticity of biological synapses, can process information in an energy-efficient and parallel fashion. Perovskite photodetectors, on the other hand, provide tunable band gaps, high responsivity, and facile fabrication, making them excellent candidates for light sensing. Integrating these two device classes enables direct photoresponsive computation, where optical signals are not only detected but also weighted, filtered, and stored in a single element. My research explores the electrical and optical coupling mechanisms in such hybrid devices, examining how ion migration, …

Understanding Medical Information And Emotional Support Needs In Mental Health Questions With Large Language Models, Chen Liu, William Yu Chung Wang, Gohar Khan

All Works

Purpose – This study seeks to bridge the gap between users’ multidimensional needs and the single-task capabilities of existing Mental Health Question Answering (MHQA) systems by tackling the underexplored challenge of jointly understanding medical informational needs and emotional support needs within complex consumer mental health inquiries. Design/methodology/approach – Grounded in Rhetorical Structure Theory (RST), the proposed Multi-Needs and Context Recognition (MNCR) framework decomposes mental health question understanding task into four interrelated subtasks: Medical Needs Recognition (MNR), Medical Needs-related Context Extraction (MNCE), Emotional Needs Recognition (ENR) and Emotional Needs-related Context Extraction (ENCE). A new benchmark dataset, MHQ-MedEmo, was constructed through multi-layered …

Using Artificial Intelligence Applications In Humanizing Temporary Buildings For Workers, Developing Design Standards And Involving Users, Anas Almahmoud, Hatem El Shafie

Mansoura Engineering Journal

This research focuses on artificial intelligence (AI) and its tools for assisting in architectural design and decision-making. It identifies modern AI applications for use in research and design processes, aiming to raise awareness among architects and consulting firms. This is the primary objective of the research. The study also analyzes a mobile temporary worker cabin complex in Riyadh, specifically the metro project, as a case study. The goal is to address its architectural program, improve its design, identifies the necessary design criteria and make it more humane. The core research problem lies in testing the effectiveness of using AI tools …

A Harmonious Equation: How To Facilitate The Interdisciplinary Nature Of The Arts In A High School Math Classroom, Myah Medendorp

Honors Theses

The education system is constantly evolving and advancing, and the teaching methods should advance along with it. Musical involvement has long been cited as having a positive effect on mathematical proficiency and being an easy way to boost mathematical performance but is most often used in an elementary school setting and the early learning years. Are there ways to use this scientific relationship to boost performance and engagement in a secondary school environment?

This literature review functionalization takes a closer look at this relationship and how it can be implemented effectively in a high school math classroom beyond the young …

Examining The Impact Of Social Media On The Nutrition Decisions Of College Students And Their Attitudes Toward Nutritional Guidelines, Katlyn Neibaur

Undergraduate Honors Theses

Social media is becoming an increasingly popular platform used by young people to garner information, but this information is not always accurate. Understanding the degree of influence social media has on college students’ dietary habits and attitudes toward nutritional guidelines is vital for improving nutrition literacy efforts and creating policies to protect against nutritional misinformation. This study examined the associations between the social media habits of college students, their attitudes toward nutritional guidelines, and their ability to detect nutritional misinformation. A cross-sectional survey was conducted with 457 undergraduate students at Brigham Young University. The content validity of the survey was …

Hidden Threats In Water: The Global Rise Of Emerging Contaminants, Baljit Singh, Abhijnan Bhat, Gayathree Thenuwara, Kamna Ravi, Azza Silotry Naik, Christine O’Connor, Furong Tian

Research Outputs: 2025-Present

The general spread of water safety awareness and enforcement often masks the escalating risks of emerging contaminants (ECs) that evade standard detection and monitoring techniques. Traditional monitoring infrastructures depend heavily on localized laboratory-based testing, which is expensive, time-consuming, and reliant on specialized infrastructure and skilled personnel. While specific types of ECs and detection technologies have been examined in numerous studies, a significant gap remains in compiling and commenting on this information in a concise framework that incorporates global impact and monitoring strategies. We aimed to compile and highlight the impact ECs have on global water safety and how advanced sensor …

The 'Pay-Or-Consent' Model And Competition Policy: A Case Comment On The European Commission’S Decision Against Meta Under The Digital Markets Act, Arletta M. Gorecka

Yearbook of Antitrust and Regulatory Studies

This case comment analyses the European Commission’s first non-compliance decision under the Digital Markets Act (DMA), focusing on Meta’s “pay-orconsent” model and its incompatibility with Article 5(2). The Commission held that Meta failed to provide users with a genuinely equivalent, privacy-protective alternative to behavioural advertising, thereby undermining the voluntariness of consent and commodifying user data in violation of DMA standards. The comment situates the ruling within broader EU regulatory trends, highlighting parallels with Apple’s infringement of Article 5(4) DMA and the Commission’s emerging outcomes-oriented enforcement approach. It argues that these decisions jointly clarify gatekeeper obligations, expand the meaning of “free …

Fide 2025: Eu Digital Economy: General Framework (Dsa/Dma) And Specialised Regimes With Follow-Up Considerations On “Regional Gatekeepers”, Kamil Dobosz

Yearbook of Antitrust and Regulatory Studies

No abstract provided.

The Artificial Intelligence (Ai) Economy In The Mountain West, 2025, Dre Boyd-Weatherly, Sean Curry, Krish Sharma, Kristian Thymianos, William E. Brown Jr.

Economic Development & Workforce

This fact sheet reports on the artificial intelligence (AI) readiness of five Mountain West metropolitan statistical areas (MSAs): Phoenix-Mesa-Chandler, AZ; Salt Lake City-Murray, UT; Denver-Aurora-Centennial, CO; Las Vegas-Henderson-North Las Vegas, NV; and Albuquerque, NM. Using data from the Brookings Institution's “Mapping the AI Economy” report, the MSAs are benchmarked based on their overall population, employment, talent, adoption, and innovation.

Social And Contextual Factors Influencing Hypertension Among Latino Men Living In California, Leslie Ivett Ortega

Walden Dissertations and Doctoral Studies

Hypertension remains a critical and often silent public health issue that disproportionately affects Latino men in California. This quantitative, retrospective cross-sectional study explored the social and contextual factors influencing hypertension among this vulnerable population. Using the 2019 Behavioral Risk Factor Surveillance System dataset, which included a sample size of 2,060 participants, the study examined the relationship between hypertension and key variables including primary care utilization, health insurance, smoking, physical activity, mental health, and marital status, while controlling for age, education, and income. Guided by the health belief model, binary logistic regression analyses found health insurance and primary care utilization significantly …

Unsaturated Fatty Acid Oil-Based Microdroplets: A Promising Novel Class Of Microdroplets, Ramiz S. Alejilat

Seton Hall University Dissertations and Theses (ETDs)

Droplet-based microfluidics has rapidly advanced numerous fields, including chemistry, biology, materials science, medicine, food science, and cosmetics. In these systems, fluorocarbon oil combined with fluorinated surfactants is the preferred medium for fluid encapsulation, offering exceptional stability and biocompatibility essential for sensitive biological and chemical processes. However, growing concerns about the environmental and biological risks associated with fluorinated chemicals have prompted a search for alternatives.

This study is the first to explore the use of unsaturated fatty acids derived from emu oil for microdroplet formation. We characterized droplet formation based on flow rates and the presence non-fluorinated surfactant at a certain …

2025-12-03 Letter To The Editor: Student Letter To Su Administration Regarding Interim President, Seattle University

The Spectator Online

No abstract provided.

Viability Of Widely Used Encryption Schemes In Drone Transmission, Emanuel Yasir Nelson

Cybersecurity Undergraduate Research Showcase

This paper presents throughout research on the security issues related to drone transmission. These topics were addressed and explained, in particular the aspects relating to cybersecurity, for utmost clarity. These include threats and vulnerabilities, drone transmission the impact of encryption on latency, and the details of the encryption methods AES-128, AES-256, and ChaCha20 that were used in the experiment described in the paper. Each encryption method performance was measured and outputted by the Python code developed and used in the experiment. Afterwards, the performance of each method was analyzed in relation to their decryption time, encryption time, end to end …

Efficient Routing For Software-Defined Wireless Sensor Networks: A Naïve Bayes Approach, Amine Tcherak, Samia Loucif, Mohamed Ould Khaoua

All Works

Wireless Sensor Networks (WSNs) form the backbone of Internet of Things (IoT) applications. Software-Defined Networking (SDN) is an emerging networking paradigm that extends the lifetime of WSNs by transferring the resource-intensive routing task from sensor nodes to a centralized controller. However, many SDN-based routing schemes for WSNs employ inefficient algorithms at the controller. Traditional shortest-path methods often create traffic imbalances across neighboring nodes, while Reinforcement Learning (RL)-based approaches typically generate excessive control traffic. Both issues accelerate energy depletion and reduce network lifetime. Moreover, existing algorithms frequently overlook critical factors, such as buffer occupancy, when selecting relay nodes, which can lead …

Fall 2025 Student Scholar Symposium Abstract Volume, Chapman University Center For Undergraduate Excellence

Student Scholar Symposium Abstracts and Posters

No abstract provided.

Nativism As The Engine Of Fear Toward “Illegal” Immigrants, Amilie Cai

Student Scholar Symposium Abstracts and Posters

Contemporary concerns about “illegal” immigration reveal deep-seated anxieties about race, cultural, and national identity in the United States. In this paper, I examine how xenophobia, media consumption, and Christian nationalism collectively shape fear of illegal immigrants. I build upon previous literature, such as the Group Threat Theory, which posits that dominant groups mobilize to preserve social hierarchies when their status is threatened, and the Agenda-Setting Theory, which emphasizes the media’s power to shape public perceptions. Using Wave 11 of the Chapman Survey of American Fears–a nationally representative sample of U.S. adults collected in 2025–I provide a comparative analysis of these …

Developing Accessible Narrative-Based Stem Learning Software For K-6 Braille Display Users, Dylan Ravel, Daniel Tsivkovski, Brandon Foley, Maryam Etezad, Franceli Cibrian, Ariel Han, Rajeev Joshi

Student Scholar Symposium Abstracts and Posters

This research develops a free, accessible web application that enables K-6 students who are blind or visually impaired (BVI) to learn STEM concepts using refreshable braille displays. Currently, most online learning tools are not designed for BVI students, creating a significant educational barrier.

The application interfaces with commercial braille displays and uses narrative-based learning to make STEM content approachable and engaging. By presenting material as interactive stories, students can connect with concepts while developing braille reading skills. The curriculum design prioritizes accessibility through the Accessible Rich Internet Applications (ARIA) standards and screen reader support.

The goal is to provide BVI …

Bridging Machine Learning And Islamic Scholarship: A Study In Hadith Translation And Similarity Analysis, Asiyah R. Speight

Student Scholar Symposium Abstracts and Posters

Translation of Islamic religious texts poses unique challenges requiring both linguistic and theological expertise. This study explores the application of neural machine translation (NMT) models to Arabic-English hadith translation while analyzing semantic similarity patterns across different human translations. Using the complete Sahih Bukhari corpus (7,550 hadiths) as the primary dataset, we adopt a dual approach combining transfer learning and comprehensive neural network analysis to demonstrate the critical impact of corpus size on model performance.

First, we fine-tune a pre-trained MarianMT Arabic-English translation model on the full Sahih Bukhari corpus, comparing models trained on 40 hadiths versus 7,550 hadiths. Performance is …

Selenium Nanoparticles As Versatile Delivery Tools, Amir Nasrolahi Shirazi, Rajesh Vadlapatla, Ajoy Koomer, Kyle Yep, Keykavous Parang

Pharmacy Faculty Articles and Research

Selenium nanoparticles (SeNPs) have emerged as promising metal-based nanoparticles for drug delivery due to their unique physicochemical properties, intrinsic bioactivity, and biocompatibility. SeNPs offer a lower toxicity, higher bioavailability, and flexibility to be customized for surface chemistry compared to traditional selenium compounds. Advances in synthetic strategies, including chemical reduction, green biosynthesis, and surface functionalization with polymers, peptides, or ligands, have improved their stability, targeting capability, and circulation time. SeNP-based systems have demonstrated unique anticancer, antimicrobial, and anti-inflammatory activities, as they can function as drug carriers and active therapeutic agents. The surface of SeNPs has been functionalized with ligands such as …

Siamese: Stealing Fine-Tuned Visual Foundation Models Via Diversified Prompting, Madhureeta Das, Gaurav Bagwe, Miao Pan, Kaichen Yang, Xiaoyong Yuan, Lan Zhang

Michigan Tech Publications

Visual foundation models, characterized by their robust generalization and adaptability, serve as the basis for a wide array of downstream tasks. When fine-tuned for specific tasks, these models encapsulate confidential and valuable task-specific knowledge, making them prime targets for model stealing (MS) attacks. While recent efforts have exposed MS threats in practical scenarios such as data-free and hard-label contexts, these attacks predominantly target traditional victim models trained from scratch. Fine-tuned visual foundation models, pre-trained on vast and diverse datasets and then fine-tuned on downstream tasks, present significant challenges for traditional MS attacks to extract task-specific knowledge. In this paper, we …

Imaging Of Dynamic Processes In Materials With A Laser-Wakefield Accelerator, J. Kang, R. J. Shalloo, M. J. Higgins, S. J.D. Dann, J. N. Gruse, C. I.D. Underwood, A. F. Antoine, C. Arran, C. D. Baird, M. D. Balcazar, N. Bourgeois, J. A. Cardarelli, K. Krushelnick, S. P.D. Mangles, C. D. Murphy, N. Lu, P. P. Rajeev, M. P. Selwood, D. R. Symes, C. P. Ridgers, A. G.R. Thomas, C. Thornton, Z. Najmudin, P. G. Sanders, P. D. Staublin, A. J. Shahani, M. J.V. Streeter

Michigan Tech Publications

Betatron x rays generated from laser-wakefield accelerators are a promising source for imaging dynamic processes in materials. Here, we present proof-of-concept imaging of microstructural evolution in a hypermonotectic Al-Bi alloy, which consists of liquid Bi particles in a solid Al matrix. The images capture the elongation and fragmentation of Bi particles upon isothermal annealing. Because of the femtosecond time scale of the betatron source, the images are not subject to motion blur, whereas the accessibility of the source allows for studies of long-term processes such as annealing. The high-resolution data reveal that the evolution of Bi particles is mediated by …

Emerging Tools In Plant Genome Editing, Shilpi Sharma, Naveen Kumar Saroha, Abhilasha Sehrawat, Guiliang Tang, Deepali Singh, Sachin Teotia

Michigan Tech Publications

Plant genome editing has undergone a transformative shift with the advent of advanced molecular tools, offering unprecedented levels of precision, flexibility and efficiency in modifying genetic material. While classical site-directed nucleases such as ZFNs, TALENs and CRISPR-Cas9 have revolutionized genome engineering by enabling targeted mutagenesis and gene knockouts, the landscape is now rapidly evolving with the emergence of novel systems that go beyond the conventional double strand break (DSB)-mediated approaches. Advanced and recent tools include LEAPER, SATI, RESTORE, RESCUE, ARCUT, SPARDA, helicase-based approaches like HACE and Type IV-A CRISPR system, and transposon-based techniques like TATSI and piggyBac. These tools unlock …

Artificial Intelligence In Higher Education: Opportunities And Challenges, Maytha Al-Ali, Adam Marks, Jasur Umirzokov, Noura Metawa

Iraqi Journal for Computer Science and Mathematics

While AI is often presented as a panacea for the challenges facing higher education, there is limited empirical evidence supporting its effectiveness in improving student learning and institutional performance. This gap between expectation and reality emphasizes the need for rigorous research, realistic goal-setting, and careful planning to ensure that AI technologies deliver on their promises in higher education. This study contrasts the potential utilization of AI technologies in Higher Education from the literature, against actual utilization in universities. The study also investigates the key barriers of AI implementation in higher education. This study uses a mixed-research methods approach, including case …