Digital Commons Network^™

Draft Final Bpsou Unreclaimed Sites: Ur-46 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Silver Bow Creek Conservation Area (Sbcca) Butte Reduction Works (Brw) Air Monitoring For Interim Demolition Activities – Phase Ii Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2023 Unreclaimed Sites Sampling: Ur-31 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2022 Unreclaimed Sites Sampling: Ur-07 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed (Ur) Sites New And Mahoney (Ur-23) Construction Completion Report (Ccr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System – First Quarter 2026, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System – Fourth Quarter 2025, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Fractional Bernstein Polynomial Approximations For Nonlinear Timefractional Partial Differential Equations, Reem Abdul Quzli

Theses

This thesis studies the numerical approximation of nonlinear time-fractional partial differential equations using fractional Bernstein polynomials. The main model considered is the nonlinear time-fractional foam drainage equation, in which the classical time derivative is replaced by the Caputo fractional derivative. This formulation introduces memory effects into the model and allows the present drainage behavior to depend on the previous evolution of the liquid fraction.

The proposed method approximates the solution by a finite expansion of fractional Bernstein basis functions. After substituting this approximation into the governing equation, the residual is expanded in powers of t^�� . The unknown coefficient …

Thermally Induced Color Changes In Iron Oxide Pigments Used In Architectural Coatings, Han Diep

Master's Theses

The 2025 Palisades Fire was one of the most destructive wildfires in the history of Los Angeles, destroying thousands of structures and severely impacting the communities after. This widespread destruction demonstrated an urgent need for more effective wildfire prevention strategies. Architectural coatings do not only provide aesthetics and protection against environmental conditions but can also function as a thermal indicator when a fire breaks out. This study examines the thermally induced color transformation of iron oxide pigments, commonly used in earthtone paint, to provide information relevant to CAL FIRE for improved analysis of wildland-urban interface (WUI) fire behavior and mitigation …

Content Matters, Context Matters: Unraveling Behavior Dynamics In An Online Health Community For Tobacco Cessation, Tavleen Singh, Runzhi Zhou, Kayo Fujimoto, Sahiti Myneni

Faculty, Staff and Student Publications

Objectives: The objective of this research was to examine the content and context-specific information diffusion patterns underlying communication pertaining to tobacco use from online health communities (OHCs).

Materials and methods: We utilized a mixed-methods approach comprising multidimensional qualitative coding to identify themes and communication attributes, automated text analysis leveraging advances in large language models (LLMs) to classify message content and context, and social network analysis to examine the dynamics of peer interactions in this study. Using QuitNet, an online tobacco cessation forum (n = 64 632 members, n = 2.39 million forum messages spanning 2000-2015), we extracted message-level features …

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 …

Invasion Patterns And Niche Dynamics Of The Pollinivorous Florida Calligrapher, Toxomerus Floralis (Diptera: Syrphidae) In The Afrotropical Region, Burgert Muller, John Midgley, Georg Goergen, Ali Al Jahdhami, Michelson Azo'o Ela, Terence Bellingan, Simon Cavaillès, Robert Copeland, Marc De Meyer, Martin Hauser, Allen Holmes, Ximo Mengual, Gabriel Nève, Menno Reemer, Jeff Skevington, Gunilla Ståhls, Eugène Sinzinkayo, John Smit, Axel Ssymank, Genevieve Theron, Kurt Jordaens

All Peer-Reviewed Publications

The rapid spread of Toxomerus floralis (Fabricius, 1798) (Diptera: Syrphidae) within the Afrotropical region is described. We characterise and compare the climatic niches of T. floralis in its native (Southern North America, Central America and South America) and invaded (Afrotropical Region) range to assess the potential for further expansion across Africa and beyond, and included future global climate models and socioeconomic pathways as projections. Occurrence data for native and invaded ranges were obtained from field sampling by authors, major collections of Afrotropical Syrphidae, collections records and occurrence data from the Global Biodiversity Information Facility (GBIF), including iNaturalist data. Single and …

Climate-Driven Stochastic Modelling Of Crimean-Congo Haemorrhagic Fever Transmission In Uganda, P. G. Kpatchana, J. Aduda, K. M. Agboka

All Peer-Reviewed Publications

Crimean-Congo haemorrhagic fever (CCHF) is a climate-sensitive tick-borne zoonosis that remains a significant public health concern in Uganda, where temperature and vapour pressure deficit influence tick ecology and consequently disease transmission. This study aimed to develop and analyse a climate-driven stochastic model for CCHF transmission among ticks, livestock, and humans under environmental variability in Uganda. A compartmental transmission model was formulated and extended into a stochastic differential equation framework by incorporating multiplicative environmental noise. Climate-dependent tick recruitment, development, and mortality were parameterized using district-level temperature and vapour pressure deficit data to capture spatial heterogeneity in transmission risk. Theoretical analyses based …

A(N) (Re)Awakening: Preservice Teachers Connecting Their Culture To Science Teaching Along The Us–México Border, Angela M. Chapman, William Medina-Jerez, Janine M. Schall, Uma Ganesan, Ruby Lynch-Arroyo, Miriam Ortiz, Mourat Tschoshanov

Teaching and Learning Faculty Publications

Today’s diverse science learning environments have prompted educators to adopt more culturally responsive approaches. National initiatives such as the Framework for K–12 Science Education and the Next Generation Science Standards emphasize the importance of engaging preservice teachers (PSTs) in culturally relevant curriculum design, particularly to support student populations that have been marginalized. Drawing from Testimonio scholarship, we align with scholars who elevate the value and pedagogical power of students’ voices and home experiences—stories often silenced or untold. This qualitative case study uses a three-phase approach—(1) Funds of Knowledge exploration, (2) theoretical grounding and lesson design, and (3) STEM lesson implementation …

From 5g To 6g: A Survey On Security, Privacy, And Standardization Pathways, Mengmeng Yang, Youyang Qu, Thilina Ranbaduge, Chandra Thapa, Nazatul Haque Sultan, Ming Ding, Hajime Suzuki, Wei Ni, Sharif Abuadbba, David Smith, Paul Tyler, Josef Pieprzyk, Thierry Rakotoarivelo, Xinlong Guan, Sirine Mrabet

Research outputs 2022 to 2026

The vision for 6G aims to enhance network capabilities, supporting an intelligent digital ecosystem where artificial intelligence (AI) is a key. However, the expansion of 6G raises critical security and privacy concerns due to the increased integration of IoT devices, edge computing, and AI. This survey provides a comprehensive overview of 6G protocols with a focus on security and privacy, identifying risks that have not been experienced in preceding 5G systems, and presenting mitigation strategies. While many vulnerabilities from earlier generations persist, the introduction of AI/ML introduces novel risks like model inversion and malicious manipulation of AI. Vulnerabilities in emerging …

Rvit-Fusionnet: A Local Cross-Attention Feature Fusion-Based Hybrid Framework For Brain Tumor Classification, Naima Islam, Sajeeb Kumar Ray, Md Anwar Hossain, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

Accurate brain tumor classification via MRI is essential for diagnosis and treatment. This study introduces RViT-FusionNet, a hybrid deep learning model that integrates convolutional and transformer architectures for enhanced tumor detection. The model utilizes ResNet-50 to capture textural details and a Vision Transformer for extracting global context. A Local Cross-Attention (LCA) module is proposed to align and merge these features, allowing the network to model local structures and long-range dependencies concurrently. To enhance generalization across varied imaging conditions and tumor types, a domain discriminator is included to discern spatial and domain-specific patterns, fostering the learning of domain-invariant representations. The approach …

An Integrated Analytics Model For Evaluating Strategic Investments Under Volatile Conditions, Atul Chandra, R. Hartley

Research outputs 2022 to 2026

Strategic projects are exposed to multiple uncertainties within a competitive environment that result in trade-off between two decisions, early investment that pre-empts competitors versus delay to allow resolution of uncertainties. Unlike the binary net present value (NPV) based decision to invest or abandon now, our multiple volatility real option games approach (MVR games) frames strategic investment decisions as a dynamic process for future options. It is a decision tree framework that computes and analyses strategic net present value (SNPV) using four MVR games modules and decision making. SNPV combines NPV with two offsetting real option and strategic values. Real option …

C*-Extreme Quantum Instruments; Completion And Disintegration Of Completely Positive Maps, Arghya Chongdar

Doctoral Theses

This thesis develops a comprehensive operator-algebraic framework for the study of completely positive (CP) instruments, with contributions spanning convexity theory, integration theory, completion problems, and disintegration theory. We begin by laying the foundational groundwork in the theory of C*-algebras and von Neumann algebras, introducing key structures such as CP maps, positive operator-valued measures (POVMs), and CP instruments, along with their dilation-theoretic properties. Pure and decomposable instruments are characterized via minimal bi-dilations, and CP instruments are realized as bivariate maps, providing a rigorous quantum analogue of classical joint measures. The thesis then investigates convexity-theoretic aspects of instruments. A structural characterization of …

Public Acceptance Of Meat-Reduction Policies Across Cultures: Comparing Singapore And Switzerland, Bianca Wassmann, Shu Tian Ng, Mark Chong, Angela K. Y. Leung, Michael Siegrist

Research Collection Lee Kong Chian School Of Business

Purpose – As meat-reduction policies are discussed across the globe, many are met with public resistance. Cultural values may help explain this pushback, yet their role in shaping support for food policy remains poorly understood. This study is the first to apply cultural cognition theory to food policy by examining how cultural worldviews shape the acceptance of meat-reduction interventions in Singapore and Switzerland—two economically developed countries with contrasting cultural profiles. Design/methodology/approach – In an online survey, participants (Singapore: n = 357; Switzerland: n = 495) rated their acceptance of 11 meat-reduction interventions (e.g. taxes, subsidies, labelling). We then analysed to …

Draft Final 2024 Unreclaimed Sites Sampling: Ur-46 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Deployment-Aware Deep Learning For Computer Vision: Efficient Architectures From 3d Segmentation To Mixed Reality, Bahar Uddin Mahmud

Dissertations

Deep learning has become the dominant approach for solving vision-centric problems; however, its successful deployment in real-world applications remains limited by high computational cost, data dependency, and insufficient integration with practical and human-centered environments. While state-of-the art deep learning models often achieve impressive performance in controlled settings, they frequently fail to generalize or operate efficiently under deployment constraints such as limited resources, complex data modalities, and real-time interaction requirements. These limitations motivate the need for a deployment-oriented deep learning framework that balances accuracy, efficiency, and practical usability.

This dissertation investigates the design and deployment of efficient deep learning architectures for …

African American Business Owners' Descriptions Of Experiences With Lending Practices, Wannika R. Muhammad

Electronic Theses and Dissertations

Entrepreneurship has remained an influential and unfulfilled goal for African Americans from the 1800s until now. Due to barriers in lending practices, increased business retention rates for African American business owners still need to be achieved. The lack of credit and lending resources hinders business growth and retention. Although Black-owned firms contribute significantly to the U.S. economy, they remain classified as underutilized entities that benefit the least, without adequate access to financial services, such as banking and lending opportunities. This study’s participants consisted of 12–15 African American business owners in central Texas, through a qualitative descriptive exploration of their experiences …

Isolation And Self-Efficacy Among Texas Educators With Disabilities Post-Covid: A Narrative Inquiry, Paul M. Nimon

Electronic Theses and Dissertations

The purpose of this qualitative inquiry was to explore the narratives of 10 current Texas public school educators with disabilities, as defined by the Americans with Disabilities Act, to understand how these narratives shaped their perceptions of burnout in relation to isolation and self-efficacy in the aftermath of the COVID-19 pandemic. The specific problem addressed the systemic, cultural, and interpersonal barriers that contribute to emotional exhaustion and professional marginalization for this population. The paradigm used was action research. The specific research methodology was a qualitative narrative inquiry. Data collection methods included 60-to-90-minute semistructured interviews conducted via Zoom and transcribed for …

History To Future: Evolving Agent With Experience And Thought For Zero-Shot Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Zihan Wang, Guo-Sen Xie, Yang Yang, Jinshan Pan, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve …

Task Complexity Matters: An Empirical Study Of Reasoning In Llms For Sentiment Analysis, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families—including adaptive, conditional, and reinforcement learning-based reasoning architectures—on sentiment analysis datasets of varying granularity (binary, five-class, and 27-class emotion). Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions: (1) Reasoning shows task-complexity dependence—binary classification degrades up to -19.9 F1% points (pp), while 27-class emotion recognition gains up to  +16.0 pp; (2) Distilled reasoning variants underperform base models by 3–18 pp on simpler tasks, …

Cfalr: Collaborative Filtering-Augmented Large Language Model For Personalized Fashion Outfit Recommendation, Yujuan Ding, Junrong Liao, Yunshan Ma, Yi Bin, Wenqi Fan, Tat-Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibility. Collaborative filtering (CF) provides a traditional solution for this, but it struggles with data-sparse scenarios and complex user-item-outfit relationships. Meanwhile, existing template-based approaches are constrained by rigid pre-designed structures. To bridge these research gaps, we introduce CFALR (Collaborative Filtering-Augmented Large Language Model for Recommendation), a novel framework that synergizes collaborative filtering with large language models for personalized outfit recommendation. Specifically, CFALR describes user-outfit interactions in natural language and leverages LLMs to capture fashion semantics while employing CF-enhanced embeddings …

The Latino Higher Education Landscape In Massachusetts, Fatema B. Ahad, Fabián Torres-Ardila, Elisa Castillo, Lorna Rivera, Joseph B. Berger

Gastón Institute Publications

This white paper provides a statistical overview of HSIs in Massachusetts within the state’s evolving demographics and postsecondary education context. It presents historical trends on student enrollment, student outcomes, and faculty and staff composition, highlighting the state of Latino students in the Commonwealth. The analysis offers descriptive data to inform policymakers, practitioners, and institutional leaders. It does not evaluate causal relationships or institutional effectiveness. The institutions included in this report were identified as HSIs and emerging HSIs through full-time equivalent (FTE) analysis for fall 2024 data. This is not an official or comprehensive list of schools eligible for, or that …

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 …

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 …

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 …