Physical Sciences and Mathematics Commons^™

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Environmental Impact Of Preventative Fire-Retardant Applications On Soil Water Chemistry, Patrick C. Michelsen

Master's Theses

Between 1992 and 2012, 84% of all documented wildfires were started by humans. These fires often start around high-risk locations such as power lines and roadsides where fuel loads are near these potential human-caused ignition sources. Point source ignition sites that are surrounded by shrubs or grasslands are especially important because fires that occur within these biomes are responsible for far more destruction of property than forest fires. One strategy that is effective at mitigating the spread of fires from ignition sites that are within grassland or shrub biomes are preventative fire-retardant products (PFRPs). These PFRPs differ from traditional fire-retardant …

2026 March - Tennessee Climate Snapshot, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

No abstract provided.

2026 March - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

Hi All,

It was a very warm and dry March across Tennessee, with temperatures 10°F+ above average for most parts of the state through the 1st, 2nd, and 4th weeks of March. Based on monthly mean temperatures, it was a top-5 warmest month on record for Memphis, Nashville, Oak Ridge, Knoxville, Chattanooga, and the Tri-Cities, and a top-5 driest month for Nashville. A strong cold front March 16-17 produced the first tornadoes of the year, in Middle Tennessee.   With warm and dry conditions, we've also seen areas classified by the US Drought Monitor as Abnormally Dry (D0) to Extreme Drought …

Identifying Priorities To Guide Extension Water Quality Programming In Utah, Clare Entwistle, Sarah Erwin, Stephanie Vaughn

Outcomes and Impact Quarterly

Utah State University Extension water quality faculty conducted a statewide water quality needs assessment to identify priority contaminants and resource gaps. The results found that elevated nutrient levels and sedimentation were the top concerns where additional resources would be beneficial. In addition, insufficient funding for improvements and low public engagement were identified as key challenges in addressing water quality issues. Results revealed current unmet needs with regional variation to inform Extension resource allocation and programming.

In Vitro Pharmacological Evaluation Of Novel Cmpi Derivatives As Selective Positive Allosteric Modulators Of The Alpha4 Beta2 Nicotinic Acetylcholine Receptor, Josue Gaona, Nataly Sanchez, Wilder Felix, Nathalia Menegasso, Brisa Cea, Ganesh Thakur, Ayman K. Hamouda

School of Medicine Student Publications and Presentations

Nicotinic acetylcholine receptors (nAChRs) contribute to the pathogenesis of neurodegenerative diseases such as Parkinson’s disease (PD), where degeneration of dopaminergic and cholinergic neurons disrupts neurotransmission. The (α4)₃(β2)₂ nAChR subtype is expressed on affected neuronal populations and represents a promising therapeutic target. Positive allosteric modulators (PAMs) may enhance receptor function while preserving endogenous signaling. We evaluated chemical derivatives of CMPI [3-(2-chlorophenyl)-5-(5-methyl-1-(piperidin-4-yl)-1H-pyrazol-4-yl)isoxazol] as selective PAMs of (α4)₃(β2)₂ nAChRs. Two-electrode voltage-clamp recordings were performed in Xenopus laevis oocytes expressing defined receptor stoichiometries to assess potentiation and generate dose–response curves. Cytotoxicity was assessed in HEK cells using the MTT assay. Substitutions at the pyrazole …

Using Ensemble Disagreement To Stabilize Conformal Prediction Under Distribution Shift, Patrick D. Murphy

Master's Theses

Semantic segmentation of eelgrass from drone imagery is crucial for coastal habitat monitoring, restoration, and management, as these habitats continue to see rapid changes due to climate change and human influence. However, the reliability of generalizing a deployed classification model relies on both high-accuracy segmentation as well as robust uncertainty quantification that holds up when conditions change over years or locations. Conformal prediction (CP) is a method that converts a classifier's output into prediction sets with a guaranteed average coverage level for in-distribution data. However, the “vanilla” conformal score can often under-cover in hard or out-of-distribution (OOD) regions under drift. …

Neurosymbolic Counterpoint Generation, Paul D. Jarski

Master's Theses

Recent advancements in generative artificial intelligence have revolutionized music generation, yet research has predominantly focused on raw audio synthesis over music in symbolic form, i.e. a score. This thesis presents the first neurosymbolic model designed to generate imitative Renaissance counterpoint in symbolic (MIDI) format. By leveraging an autoregressive Transformer architecture, this research explores the capacity of deep learning models to manage independent voices and strict stylistic constraints.

We compare multiple data representation strategies with distinct tokenization methods. The proposed model incorporates a symbolic component that enforces fundamental contrapuntal rules. Additionally, this thesis contributes a preprocessed dataset of Renaissance polyphony, in …

A Unified Methodological Framework For Generating Digital Twins Of Multi Class Uncrewed Systems (Uxs), Sai Raghava Pathuri

Shelby Hall Graduate Research Forum Presentations

No abstract provided.

Detecting Bitstream-Level Fpga Trojans With An Snn, Kylie Arnett

Shelby Hall Graduate Research Forum Presentations

Limited research has been conducted on SNNs for FPGA Trojan detection. FPGA design is often handled by manufacturers outside the U.S. FPGA manufactures outsource production to third-party foundries. This multi-step process introduces security vulnerabilities and increases risk of Hardware Trojan insertion.

Key Questions: To what extent can an FPGA be manipulated at the bitstream level to enable or disable encryption algorithms?

Can SNNs accurately detect the presence of Trojans within an FPGA?

A High-Fidelity Multimodal Synthetic Dataset Generation Framework For Off-Road Unstructured Terrain Navigation Training Of Autonomous Robots, Liyana Wijayathunga, Dulitha Dabare, Alexander Rassau, Douglas Chai, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

The success of deep learning methods in a wide range of application areas has inspired many recent developments in the urban and off-road autonomous navigation domain. In particular, techniques for semantic scene understanding, a key aspect of the navigation pipeline, have been researched extensively, resulting in many real-world and synthetic datasets. However, in comparison to urban semantic segmentation datasets, the availability of datasets for off-road environments remains sparse. In this paper, we aim to overcome this challenge by introducing a methodology capable of efficiently generating photorealistic synthetic datasets for off-road environments with support for multiple sensor modalities. The developed approach …

An Explainable Transformer-Based Model For Phishing Email Detection: A Large Language Model Approach, Mohammad Amaz Uddin, Md Mahiuddin, Iqbal H. Sarker

Research outputs 2022 to 2026

Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm. Attackers, often posing as trustworthy entities, exploit technological advancements and sophistication to make the detection and prevention of phishing more challenging. Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape. In this research paper, we present a fine-tuned transformer-based masked language model, RoBERTa (Robustly Optimized BERT Pretraining Approach), for phishing email detection. In the detection process, we employ a phishing email dataset and apply the preprocessing …

Explainable Artificial Intelligence Models For Detecting Suspicious Bank Transactions, Narasimha Kumar Narasapuram, Syed Afaq Ali Shah, Mohd Fairuz Shiratuddin, Ferdous Sohel

Research outputs 2022 to 2026

Detecting financial crime is a complex challenge due to evolving criminal strategies and fragmented detection systems, particularly in the areas of money laundering and fraud. While it is easy to implement, traditional rule-based approaches lack adaptability to new threats, and machine learning models, though more effective, often function as opaque "black boxes," limiting their practical use in regulated domains like banking, where interpretability and accountability are essential. This research presents a novel framework that combines intrinsic and post-hoc XAI techniques to detect suspicious bank transactions. Intrinsic methods provide model-inherent transparency, while post-hoc methods offer behavior-level explanations, enabling robust cross-verification of …

College Of Natural Sciences 2025 Year-End Publication, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Page 2 Dean's Message 
Page 3 Department Highlights 
Page 4 One Day for State 
Page 5 Ice cores reveal volcanic eruptions in 13th century 
Page 5 How do our cells interpret stress 
Page 6-7 Faculty Excellence
Page 8 New chemical biology consortium will accelerate cancer research
Page 9 SDSU to combat crop disease, biofilms in new NSF-back project 
Page 9 Science as Art Competition 
Page 10-11 Student Excellence  
Page 12 SDSU researcher developing natural alternative to synthetic dyes
Page 12 Browning Retired
Page 13 Quantum technologies through NSF-backed project
Page 13 NASA Funds CNS Development of Model
Page 14 GGS …

Pointwise Self-Homeomorphic Generalized Inverse Limits, Ali H. Ali, Faruq A. Mena, Robert Paul Roe

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we find uncountable families of generalized inverse sequences on intervals, where the bonding functions consist of a finite number of line segments, such that the inverse limit spaces of these sequences are pointwise self-homeomorphic continua. We give several examples of pointwise self-homeomorphic continua obtained in this manner including the dendrite D3 and a dendrite containing Dω. The dendrite D3 was obtained previously, by others, as a generalized inverse limit but the bonding function in that example contained infinitely many line segments. We show that the techniques we use on intervals can be extended to inverse limits where …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Two-Group Structural Heterogeneous Syringe-Sharing Network (M2), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model extended a baseline homogeneous model by incorporating structural heterogeneity via a two-group interaction framework. The syringe-sharing population in the model is divided into inner and outer circle groups representing individuals with differing levels of syringe-sharing interaction intensity. While all agents share the same syringe-sharing probability and epidemiological processes remain identical across agents, the number of daily interaction opportunities differs between the two groups. Interactions in the model are generated dynamically using proximity-based sampling at each timestep …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Three-Group Structural Heterogeneous Syringe-Sharing Network (M3), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. While the syringe-sharing rate and all epidemiological processes remain identical across agents, the number of daily interaction opportunities differs by agent grouping, capturing variation in structural position within the syringe-sharing network. Interactions are generated dynamically using proximity-based sampling at each …

The Illusion Of Causality In Llms: A Developmentally Grounded Analysis Of Semantic Scaffolding And Benchmark–Capability Mismatches, Daisuke Akiba

Publications and Research

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal …

Complex Spin Structure In Co-Trimer-Chain Li2Co3Se4O12, Jie Xing, Feng Ye, Daniel Duong, Sai Mu, Max T. Pan, Rongying Jin

Faculty Publications

Complex magnetic materials are extremely attractive for revealing unconventional spin states and novel magnetic excitations. Here, we report the structural, thermodynamic, and magnetic properties of a novel magnetic material Li₂Co₃Se₄O₁₂based on x-ray and neutron diffraction, specific heat, magnetization, and x-ray photoelectron spectroscopy measurements. X-ray and neutron diffraction refinements reveal two Co sites Co (1) and Co (2) even though both are in the octahedral environment. While they are not connected along the b and c directions, these octahedra are edge-shared forming the Co (2) – Co (1) – Co (2) trimer chain …

Ecology And Life History Of Baleen Whales Inform Climate Change Vulnerabilities And Priorities For Future Monitoring, Lesley H. Thorne, Erin L. Meyer-Gutbrod

Faculty Publications

Baleen whales are key consumers in marine habitats that serve as vectors of nutrients, but their populations have been slow to recover from past commercial whaling due to their low reproductive rates and ongoing anthropogenic threats. Climate impacts have become central to the demography and habitat use of baleen whales, and conservation efforts must account for these impacts to be effective. However, knowledge of baleen whale climate responses is lacking, and current survey effort is insufficient to capture changes in migration and habitat use for many species. Due to their unique combination of ecological and life history characteristics, baleen whales …

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host-pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer …

Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu

Research Collection School Of Computing and Information Systems

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization …

Private Set Intersection: A Systematic Review, Yunbo Yang, Defan Zhu, Jianting Ning, Qi Feng, Xiaoguo Li, Yuejia Cheng, Guomin Yang, Kui Ren

Research Collection School Of Computing and Information Systems

Various services, such as search engines, are increasingly deployed in cloud-based and distributed systems. However, data are typically managed by trusted servers, making user privacy and data security critical concerns. Private set intersection (PSI) is a powerful cryptographic primitive that enables multiple parties to compute the intersection of their datasets without revealing private inputs. It has been extensively studied over the past two decades, leading to significant gains in computational and communication efficiency. Yet, in many real-world scenarios, revealing the raw intersection may still leak sensitive information. To address this, numerous PSI variants have been developed to meet different application …

Selecting Without Replacement From A Population Of Bands Of Serially Connected Objects, James E. Marengo, Dominick Banasik, Joseph Voelkel, David L. Farnsworth

Articles

The sampling procedure from a finite population of objects that are serially attached into bands is described and analyzed. One object is randomly selected and removed at a time, which results in that object’s band being broken into two bands or shortened by one object. The main result gives the probability of choosing an object that is part of a band of serially connected objects of any specified size at each stage of the selection process.

Draft 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.

Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study, Benjamin Lee, Wolfgang Garn, Masoud Fakhimi, Nick F. Ryman-Tubb

Research Collection School Of Accountancy

Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics …

Evaluating Carbon Risk And Benefit Under Improved Forest Management Prescriptions In A California Mixed Conifer Stand, Jonathan A. Garcia

Master's Theses

Improved forest management (IFM) prioritizes carbon accumulation through practices such as extended rotations and retention harvesting. These management strategies, increase fuel loads and elevate the potential fire intensity, may offset the benefits of carbon sequestration. Additionally, IFMs use spatially complex silvicultural treatments that may introduce prediction errors into the simulation processes used for long-term projections, and carbon accounting.

We evaluated the extent to which calibration in the Forest Vegetation Simulator (FVS) reduces error in aboveground carbon stock predictions and tested whether modeling approaches and calibration influence the magnitude and direction of basal area increment (BAI) prediction error using generalized mixed-effects …

Law Library Blog (March 2026): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

The Modern Sisyphus Blows Leaves: Attitudes And Impacts Regarding Leaf Litter Removal On A Suburban Campus, A. C. Nony, M. R. Mcclung, M. L. Reid

Journal of the Arkansas Academy of Science

Leaf litter habitat disruption in suburban spaces stems from the prevalence of “lawn culture,” that is, the accepted societal norm that land immediately surrounding humans or meant for human recreational use should be clean, tidy, controlled, and aesthetically pleasing, above other considerations. When leaf litter on residential and recreational lawns is cleared, it removes vital habitat for organisms of several taxa, especially species that rely on litter as a moisture and temperature-controlled substrate for overwintering. We administered an opinion survey to students, staff, and faculty at Hendrix College, a highly residential and suburban campus in Conway, Arkansas, to assess views …