Digital Commons Network^™

Using X-Ray Crystallography To Inform Foldamer Drug Design, Isabella Garcia

Undergraduate Student Symposium

Foldamers are synthetic peptide oligomers that fold into well-defined three-dimensional structures, such as alpha-helices and beta-sheets. The goal of this project is to discover the solid-state confirmation of beta-amino acid foldamers that have potential as therapeutic molecules. This research represents an interdisciplinary collaboration between KAIST, a research university in South Korea, and NSU. We solved the single-crystal X-ray structures of two foldamers and then visualized their secondary structures and intermolecular contacts using Mercury. The first foldamer crystallized in the space group P2₁2₁2 and demonstrated a continuous alpha-helical secondary structure, with the residue side chains evenly distributed …

Navigating Inclusive Education In The United Arab Emirates: Perspectives From School Leaders And Teachers, Daisy Loyd, Rhoda Myra Garces-Bacsal, Zachary Walker, Najwa Mohamed Alhosani, Hala Elhoweris, Osha Almuhairy, Ahmed Mohamed, Rachel Alison Takriti, Sana Butti Al Maktoum, Amani Mohamed, Hamdah Jibar Alhashmi

All Works

Inclusive education in the United Arab Emirates (UAE) has been promoted through ambitious policies. These have included a mandated change in nomenclature alongside strengthening the infrastructure and professional development to support inclusive education in mainstream schools. Perspectives from school leaders and teachers across the country about these changes and their implementation have not been captured. This research used a qualitative methodology to explore school leaders' and teachers' perspectives of inclusive education policy and practice. Participants included 22 school leaders and 57 teachers from public and private schools across the seven Emirates of the UAE. Data were collected through group and …

A Critical Narrative Review Appraisal Of The 2025–2030 Dietary Guidelines: Scientific Strengths, Conceptual Gaps, And Overlooked Dimensions Of Sustainability And Health Equity, Dimitrios Papandreou, Azza Alsuwaidi, Zainab Taha, Constantinos Giaginis, Georgios K. Vasios, Eleni P. Andreou

All Works

The 2025–2030 Dietary Guidelines introduce an important shift in public health nutrition, emphasizing minimally processed foods, higher protein intake, greater inclusion of full-fat dairy, and a food-based advice centered on “real food” consumption. While several of these recommendations align with accumulating evidence, particularly the discouragement of ultra-processed foods and added sugars, substantial concerns remain regarding their internal coherence, population-level applicability, risk of misinterpretation, as well as environmental footprint. This critical narrative review evaluates whether the scope, emphasis, and framing of the new guideline components are proportionate to the strength, consistency, and context of the underlying evidence. Using a novel framework …

Rethinking Behavior - Spring 2026, Midwest Symposium For Leadership In Behavior Disorders

ReThinking Behavior Magazine

A magazine for professionals serving children and youth with behavioral needs.

Spring 2026, Volume 9, Issue 3

Book Review: Emplacing Globalisation: Contextualising Mission In Settings Of People, Places And Processes By John Cheong And Jonathan Ro, David H. Campbell

Great Commission Research Journal

No abstract provided.

The Effectiveness Of The Educational Program “Istedamah” (Sustainability) Based On Green School Principles In Shaping Basic Education Students’ Perceptions Of Sustainable Development Dimensions And Their Practices In Achieving Carbon Neutrality In The School Environment In The Sultanate Of Oman, Huda Mubarak Al Dayri, Abdullah. K. Ambusaidi

Journal of Educational and Psychological Studies

The study aimed to identify the effectiveness of “ Istedamah” educational programme, which is based on green school principals, in promoting the perceptions of sustainable development dimensions and their practices related carbon neutrality among basic education students in the Sultanate of Oman. The study used quasi-experimental approach based on one group pre- and post -test design. The sample consisted of 106 male and female students selected randomly from schools that apply green school project. Two tools were used to gather data: A scale of students’ perceptions of the dimensions of sustainable development, (consisted of 26 statements) and practices for achieving …

Structural Silence: When Ai Infrastructure Fails Speakers Of Underrepresented Languages, Avijit Roy, Proma Roy

Publications and Research

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools—training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures—encodes a set of assumptions that systematically disadvantages speakers of underrepresented languages before a single model is trained. This paper examines those assumptions through the lens of Bengali, one of the world’s most widely spoken languages with roughly 285 million speakers (Ethnologue, 2025; International Communication and Leadership School, 2026), and the structural barriers that emerge when attempting to build AI-assisted educational tools for Bengali-speaking learners in low-connectivity …

Comparative Analysis Of Traffic Impact Study Accuracy Using Turning Movement Volume Counts., Kathryn Riopel

Master of Engineering Theses

Traffic forecasting and modeling has been a significant subject of debate within the transportation sector as of the last decade. Transportation agencies and planners use traffic forecasting and modeling in order to predict current and future transportation needs, as well as the impacts of proposed developments. In particular, state departments of transportation (DOTs) and city level agencies often require developers to conduct Traffic Impact Studies (TISes) in order to gauge necessary transport network enhancements to handle new demand from proposed developments. The standards by which these TISes are conducted and judged varies widely, but primarily consists of existing traffic counts, …

From Our Stories, To Our Streets (Osos), Jennifer Lopez

Student Publications

Youth traffic safety persists as a critical planning challenge and public health concern in New Mexico, where pedestrian fatality rates consistently rank among the highest in the United States (National Highway Traffic Safety Administration, NTSA, Governors Highway Safety Association, GHSA, 2023; Baca, 2025). Students (children), due to their daily interactions with roadways, whether when they walk, bike, ride transit, or drive/get driven to and from school, are among the most vulnerable groups within the transportation system (Ferenchak, N. Wesley, M., 2017. For many students in Albuquerque, the commute to or from school is one of the most dangerous parts of …

Rhetorical Violence: The Rhetorics Of Women's Counter Political Violence, Kylie Rowland

Department of English: Dissertations, Theses, and Student Research

Protest is omnipresent, and yet not much is known about the efficacy and impacts of its different rhetorical strategies—particularly so for marginalized populations, whose available means of protest are already limited by both material realities and assumptions that govern who and what can make waves. This bind is further exacerbated by gendered and racialized civility politics that determine which bodies can be disruptive and which already are considered “violent.” To address this gap, I turn toward the under-researched rhetorical potentials of marginalized people’s—specifically women and queer folks’—use of symbolic protest “violence,” which works to strategically make evident the larger ongoing …

A Software-Defined Radio Testbed For Mimo-Ofdm Communications, Chaz G. Maschman

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we implement a testbed for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems via GNU Radio. Specifically, we implement a configurable framework for the construction of MIMO-OFDM software-defined radio (SDR) systems as a GNU Radio module. The GNU Radio MIMO-OFDM module consists of multiple algorithmic blocks necessary for implementation of a MIMO-OFDM system. This includes a library for the generation of orthogonal or pseudo-random pilot sequences, amendments to the Schmidl-Cox protocol for MIMO synchronization, and the creation of click-and-drag GNU Radio blocks implementing the conversion of arbitrary data sent via external programs to MIMO-OFDM frames, the initial …

Watersmart Farms - Desalination: Community Desalination, Sharmina Begum, Wendell Ela, John K. Ruprecht

Natural resources commissioned reports

The Wheatbelt is a vast agricultural region in southwestern Western Australia, covering approximately 25 million hectares, of which 17 million are cleared. It is a key contributor to the state’s economy, supporting broadacre grain production, livestock (such as sheep, beef, and pigs) grazing, mixed farming, and numerous small rural communities. However, decades of extensive land clearing for agriculture have significantly altered the natural hydrological balance. These changes have disrupted local water cycles, increased recharge post-clearing, increased evaporation, and contributed to widespread soil salinity.

Compounding these land-use impacts, the region has also experienced a long-term drying trend due to climate change. …

Sounding W. B. Yeats’S “Cuchulain Comforted”, David Sergeant

School of Law, Humanities and Social Sciences

No abstract provided.

Visits Related To Alcohol, Substance Use, And Mental Health In New Mexico’S Emergency Departments: Social Determinants Of Health And Discharge Disposition, 2021-2024, Samuel L. Swift, Maya Stefanovic, Dylan Pell

Population Health Research and Scholarship

No abstract provided.

The Impact Of Social Emotional Learning On Chicanx Students And Their Educators, Alesia Renee Camacho

Chicana and Chicano Studies ETDs

My research on the impact of social emotional learning on Chicanx students and their educators aims to analyze how the planned implementation of a carefully curated curriculum centering on the students’ culture and lived experiences makes for a better overall experience. For this study, I use the interpretive paradigm as it allows me to explain how education can serve as a tool to illuminate the injustices that students and their families face in their lives and, in turn, how these injustices are addressed in Ethnic Studies courses. Chicana/o/x Studies scholars have long advocated for utilizing students’ knowledge, experiences, perspectives, and …

Weakly Supervised Video Anomaly Detection And Localization With Spatio-Temporal Prompts, Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension. However, most anomalous events tend to occur in localized spatial regions rather than the entire video frames, which implies existing frame-level feature based works may be misled by the dominant background information and lack the interpretation of the detected anomalies. To address this dilemma, this paper introduces a novel method called STPrompt that learns spatio-temporal prompt embeddings …

Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing …

Trace: Securing Smart Contract Repository Against Access Control Vulnerability, Chong Chen, Lingfeng Bao, David Lo, Yanlin Wang, Zhenyu Shan, Ting Chen, Guangqiang Yin, Jianxing Yu, Zibin Zheng, Jiachi Chen

Research Collection School Of Computing and Information Systems

Smart contract vulnerabilities have led to billions of dollars in economic losses. Among these, improper Access Control, which allows unauthorized users to execute restricted functions, is particularly prevalent and has caused significant financial damage. Smart contract repositories contain source code, documentation, configuration files, and other artifacts necessary for building and deploying smart contracts. GitHub hosts numerous open-source repositories of this kind, which serve as intermediate artifacts in development and require compilation and packaging to produce deployable contracts. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can …

Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Research Collection School Of Computing and Information Systems

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …

Stacked From One: Multi-Scale Self-Injection For Context Window Extension, Wei Han, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data acquisition and computational costs. To address this challenge, we propose SHAREDLLM, a novel framework based on multi-grained context compression and query-aware information acquisition. SHAREDLLM comprises two stacked short-context LLMs: a lower model serving as a compressor and an upper model acting as a decoder. The lower model compresses long inputs into compact, multi-grained representations, which are then forwarded to the upper model for context-aware processing. …

Dragging With Geometry: From Pixels To Geometry-Guided Image Editing, Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou

Research Collection School Of Computing and Information Systems

Interactive point-based image editing serves as a controllable editor, enabling precise and flexible manipulation of image content. However, most drag-based methods operate primarily on the 2D pixel plane with limited use of 3D cues. As a result, they often produce imprecise and inconsistent edits, particularly in geometry-intensive scenarios such as rotations and perspective transformations. To address these limitations, we propose a novel geometry-guided drag-based image editing method—GeoDrag, which addresses three key challenges: 1) incorporating 3D geometric cues into pixel-level editing, 2) mitigating discontinuities caused by geometry-only guidance, and 3) resolving conflicts arising from multi-point dragging. Built upon a unified displacement …

Dreamcs: Geometry-Aware Text-To-3d Generation With Unpaired 3d Reward Supervision, Xiandong Zou, Ruihao Xia, Hongsong Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation — leading to geometric artifacts, such as the Janus face problem and geometric incompleteness, due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the …

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

Reducing Class-Wise Performance Disparity Via Margin Regularization, Beier Zhu, Kesen Zhao, Jiequan Cui, Qianru Sun, Yuan Zhou, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data—posing concerns for reliable deployment. While prior efforts have explored empirical remedies, a theoretical understanding of such performance disparities in classification remains limited. In this work, we present Margin Regularization for performance disparity Reduction (MR2 ), a theoretically principled regularization for classification by dynamically adjusting margins in both the logit and representation spaces. Our analysis establishes a margin-based, class-sensitive generalization bound that reveals how per-class feature variability contributes to error, motivating the use of larger margins for “hard” classes. Guided by this insight, MR2 optimizes …

Managing Reproducibility Debt In Scientific Software: A Practical Framework, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Scientific software includes end-user applications, modelling tools, research software for publications, and production systems for real users. It plays a key role across various scientific disciplines by enabling large-scale computation, simulation, and data analysis. Unlike commercial software, scientific software is often developed in dynamic research environments with limited engineering practices, documentation, or testing. This makes it fragile and difficult to reproduce results, even when code and data are available, conditions in which Reproducibility Debt (RpD) accumulates. This paper presents the Reproducibility Debt Management Framework (RpD-MF), which is grounded in evidence from a systematic literature review, practitioner interviews, and a global …

Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the …

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

Be Responsible In Your Answers! Monitoring Out-Of-Domain Behaviors In Domain-Specific Llms, Boquan Li, Chenzhe Lou, Zhe Ren, Peixin Zhang, Zirui Fu, Jun Sun, Yaowen Zheng

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have accelerated the rapid development of chatbot web applications in various domains, such as coding, biomedicine and psychology. Compared to general LLMs like ChatGPT, domain-specific LLMs require a greater sense of responsibility. For instance, if a programming LLM casually answers medical or psychological questions, it not only misleads the public but also poses legal risks. This highlights new demands for monitoring and preventing such irresponsible behaviors. Existing efforts attempt to monitor LLMs from multiple aspects, such as lying, jailbreaks, and toxic content, while overlooking out-of-domain behaviors. In this work, we propose an innovative LLM domain monitoring …

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …