Digital Commons Network^™

Ideal Query Expansion Using Reinforcement Learning, Madhuchchhanda Das

Master’s Dissertations

Information retrieval (IR) systems often struggle with short, ambiguous, or underspecified queries, leading to suboptimal document retrieval. Traditional query reformulation methods, such as those based on the Rocchio algorithm, rely on heuristic term selection and relevance feedback but typically apply fixed or manually tuned weights to expanded terms. This limits their adaptability and generalization across diverse query-document contexts. In this thesis, we propose a novel reinforcement learning (RL)-based framework to dynamically optimize term weighting in reformulated queries. We model the problem as a Markov Decision Process (MDP), where each state represents a query as a vector of term weights. An …

Addressing Class Imbalance Problems To Improve Animal Detection Through Aerial Image Data, Suryang Koushal

Master’s Dissertations

Monitoring animal populations in wildlife reserves is essential for conservation, especially for endangered species, but manual censuses are costly, risky, and logistically challenging due to vast, inaccessible terrains. Unmanned Aerial Vehicles (UAVs) with digital cameras provide a safer, scalable solution for collecting aerial imagery to estimate animal populations. However, semi-automated processing of these images faces significant challenges due to class imbalance in datasets, including foreground-background disparities, where background terrain dominates over sparse animal instances, and inter-class imbalances from uneven species representation and varied visual appearances (e.g., species, sizes, fur patterns) against diverse backgrounds like deserts or forests. These imbalances hinder …

Linear Systems Over Pura Vida Neutrosophic Algebra, Rayyanu Abdullahi Muhammad, Abdulhadi Aminu

Neutrosophic Systems with Applications

Neutrosophic numbers offers a strong foundation for representing uncertainty, indeterminacy, and imprecision within mathematical systems. Pura Vida Neutrosophic Algebra (PVNA) expands upon max-plus algebra (also known as tropical algebra or path algebra) using neutrosophic numbers. In this study, we propose a novel extension of the Pura Vida Neutrosophic Algebra (PVNA) by formulating and analyzing linear systems within this algebraic context–an area that, to the best of our knowledge, has not been previously examined. Specifically, we introduce the concept of Neutrosophic Max-Plus Linear Systems, develop an algebraic methodology for their representation, and establish the necessary and sufficient conditions for the existence …

A Climatological Analysis Of Drought And Flood In California, Kristen Faith Coston

Masters Theses

This research is investigating the climate states that produce droughts and floods and seeks to explain how a sudden shift from persistent drought to drought-alleviating flood is possible. Objectives include investigating how drought and flood in California are influenced by temperature and climate patterns, whether there are any correlation between climate/ocean indices and the Standardized Precipitation Index (SPI), and whether Atmospheric Rivers (ARS) are influenced by certain atmospheric/oceanic states.

All data is collected for 41 years, between 1983 and 2024. The SPI dataset is obtained by coding to filter for the mean value of all pixels in Sacramento County. Monthly …

Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning

Research Collection School Of Computing and Information Systems

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …

Impact Of Seed Moisture And Temperature On Hemp Seed Germination, Paul Cockson, Andrea Webb, Natalia Martinez-Ochoa, Lindsey Moffitt, Robert Pearce, Manohar Chakrabarti

School of Integrative Biological & Chemical Sciences Faculty Publications

Germination rates of commercial lots of hemp have been highly variable, resulting in poor stand establishment. Germination rates in some seed lots have decreased by 50% after only 1 year of storage. The objective of this trial was to investigate the impact of seed storage conditions on seed germination over time. Industrial hemp (IH) seeds (cv. NWG2730) were harvested from the field. The seeds were cleaned, sorted, and dried to specific moisture contents (MC) 6%, 8%, 10%, and 14%. Seeds were subdivided, placed in hermetically sealed packets, and stored at temperatures of −20°C, 4°C, 10°C, or 21°C for 3, 6, …

A Digital Dive: Redesigning The Cabrillo High School Aquarium Website, Jacob V. Cacho

Graphic Communication

Tucked away on the Central Coast in Lompoc, you’ll find the Cabrillo High School (CHS) Aquarium. Started in 1986, the CHS Aquarium is the only high school aquarium of its kind in the nation run entirely by high school students. This 10,000+ square foot aquarium serves an underserved community at a Title I school, where students manage all aspects of animal care, nutrition, breeding, educational curriculum development, and visitor tours.

This program is truly one-of-a-kind and deserves the spotlight for just how unique it is. As a CHS graduate, I felt the current website lacked in many areas and could …

Radiation-Induced Cardiotoxicity In Hypertensive Salt-Sensitive Rats: A Feasibility Study, Dayeong An, Alison Kriegel, Suresh N. Kumar, Heather A. Himburg, Brian Fish, S. Klawikowski, Daniel B. Rowe, Marek Lenarczyk, John Baker, El Sayed H. Ibrahim

Mathematical and Statistical Science Faculty Research and Publications

Radiation therapy (RT) plays a vital role in managing thoracic cancers, though it can lead to adverse effects, including significant cardiotoxicity. Understanding the risk factors like hypertension in RT is important for patient prognosis and management. A Dahl salt-sensitive (SS) female rat model was used to study hypertension effect on RT-induced cardiotoxicity. Rats were fed a high-salt diet to induce hypertension and then divided into RT and sham groups. The RT group received 24 Gy of whole-heart irradiation. Cardiac function was evaluated using MRI and blood pressure measurements at baseline, 8 weeks and 12 weeks post-RT. Histological examination was performed …

Minifying Deep Denoising Networks With Knowledge Distillation, Antonio L. Rozzi

Master's Theses

Hearing loss is a prevalent condition, affecting hundreds of millions globally, with a higher incidence among older adults. While hearing aids are the standard treatment, the majority of those who could benefit from hearing aids choose not to wear them, attributing this decision in large part to their inability to perform well in conversations in large groups and in noisy situations. To date, no denoising systems on commercial hearing aids are able to improve speech intelligibility. Recent advances in artificial intelligence research have shown that large deep-learning models can in fact improve speech intelligibility by removing background noise from audio. …

Myceli-Yum: Elucidating Structure-Property Relationships For Polymer Degradation By Mycelial Digestion, Jordan Scott Ford

Master's Theses

Since the industrial entrance of polymer plastic materials, plastic has become ubiquitous in both everyday use and waste. Due to inefficiencies and knowledge gaps, current recycling methods are not able to account for the high scale of plastic waste, resulting in the bulk of this waste being landfilled, mishandled, and deposited in the environment. Mycelium, the microorganism responsible for fruiting mushroom bodies and mold growth, holds potential to reduce plastic waste and can potentially be utilized as a method of industrial recycling. Following a drug-design approach, the active site of mycelial enzymes responsible for natural biopolymer degradation have been assessed …

Roadmap For Mainstreaming Integrated Pest Management (Ipm) Into A Climate Smart One-Health (Cs-Oh) Framework, Henri E.Z. Tonnang, Ghislain T. Tepa-Yotto, Bonoukpoè Mawuko Sokame, Jeannette K. Winsou, Manuele Tamò, Rousseau F. Djouaka

All Peer-Reviewed Publications

Climate change presents significant challenges to agricultural sustainability, particularly in integrated pest management (IPM). To address these challenges, we propose a holistic and interdisciplinary conceptual framework, climate-smart One-Health (CS-OH), which integrates ecological, environmental, and socio-economic factors, considering human, animal, soil, and water health within the environment. This paper introduces a roadmap for Climate-Smart OH IPM, combining One-Health (OH) principles with climate-smart agriculture to promote sustainable pest management amid climate change. The roadmap utilizes Systems Thinking (ST) & System Dynamics (SD) methodologies to comprehend complex interactions in climate-affected agricultural systems. Additionally, we provide a step-by-step implementation of Digital Twin (DT) IPM, …

Status And Distribution Of Diseases Caused By Phytoplasmas In Africa, Shakiru Adewale Kazeem, Agnieszka Zwolińska, Joseph Mulema, Akindele Oluwole Ogunfunmilayo, Shina Salihu, Joy Oluchi Nwogwugwu, Inusa Jacob Ajene, Justina Folasayo Ogunsola, Adedapo Olutola Adediji, Olubusola Fehintola Oduwaye, Kouamé Daniel Kra, Mustafa Ojonuba Jibrin, Wei Wei

All Peer-Reviewed Publications

Phytoplasma (“Candidatus Phytoplasma” species) diseases have been reported globally to severely limit the productivity of a wide range of economically important crops and wild plants causing different yellows-type diseases. With new molecular detection techniques, several unknown and known diseases with uncertain etiologies or attributed to other pathogens have been identified as being caused by Phytoplasmas. In Africa, Phytoplasmas have been reported in association with diseases in a broad range of host plant species. However, the few reports of Phytoplasma occurrence in Africa have not been collated together to determine the status in different countries of the continent. Thus, this paper …

Probing Shielding Tensor Components Of Amino Acids Using Nuclear Magnetic Resonance, Shiva Agarwal

Dissertations

Chirality is fundamental to terrestrial life. While most amino acids exist as nonsuperimposable mirror images, amino acids in terrestrial life are homochiral, with the L-enantiomer being ubiquitous. The detection of an excess of L-amino acids in carbonaceous meteorites suggests that extraterrestrial processes may have contributed to this enantiomeric excess (ee). One proposed mechanism, the magnetochiral model, provides a potential explanation for this phenomenon in stellar environments characterized by strong magnetic and electric fields and the presence of relativistic leptons. According to this model, subtle differences in the electronic environments of chiral amino acids under such conditions …

A Multimodal Fusion Model Leveraging Mlp Mixer And Handcrafted Features-Based Deep Learning Networks For Facial Palsy Detection, Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Research Collection School Of Computing and Information Systems

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessments by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes an MLP mixer-based model to process unstructured data (i.e. RGB images or images with facial line segments) and a feed-forward neural network to process structured data (i.e. facial landmark coordinates, features of facial expressions, or handcrafted features) for detecting facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of …

A Neutrosophic And Q-Rung Orthopair Fuzzy Sets Approach For Desertification Susceptibility Mapping: A Case Study In Matrouh, Egypt, Nabil M. Abdelaziz, Khalid A. Eldrandaly, Amira M. Fawzy, Gehan A. Fouad, Safa Al-Saeed

Neutrosophic Systems with Applications

This study introduces an innovative approach to desertification susceptibility mapping by integrating q-rung orthopair fuzzy sets (Q-ROFS) with a neutrosophic environment. Conducted in Matrouh, Egypt, the research quantifies desertification risk through advanced modeling techniques that address uncertainty and non-linearity in environmental data. The Q-ROFS framework enhances risk prediction by capturing complex relationships among desertification indicators. Neutrosophic logic, meanwhile, effectively addresses imprecision and ambiguity. The resulting susceptibility map clearly distinguishes between vulnerable and non-vulnerable regions, offering valuable guidance for policymakers and planners. The analysis revealed that approximately 79.98% of the study area falls under moderate susceptibility, 14.27% under high susceptibility, and …

A Proposed Mathematical Framework For Fuzzy It Service Management (F-Itsm) And Neutrosophic It Service Management (N-Itsm), Takaaki Fujita

Neutrosophic Systems with Applications

Fuzzy sets, rough sets, hyperrough sets, intuitionistic fuzzy sets, neutrosophic sets, plithogenic sets , and other frameworks for handling uncertainty are under active research every day. These concepts can model a wide range of real-world phenomena and are frequently investigated to facilitate more efficient decision-making. IT Service Management is a systematic approach to designing, delivering, managing, and improving IT services in alignment with organizational objectives. In this paper, we explore the Mathematical Frameworks for Fuzzy IT Service Management (F-ITSM) and Neutrosophic IT Service Management (N-ITSM), which combine these uncertainty-based ideas with IT Service Management practices.

Evaluating Disaster Relief In Supply Chains Using A Neutrosophic Mcdm Approach, Nada A. Nabeeh

Neutrosophic Systems with Applications

Disaster-prone regions and affected areas encounter persistent challenges in maintaining supply chain continuity due to environmental uncertainties and infrastructure disruptions. Effective supply chain disaster management (SCDM) is essential for relief disaster disruptions, specifically in upstream processes and functions within the humanitarian supply chain. The integration of advanced technologies like the metaverse and Multiple-Criteria Decision-Making (MCDM) methods supports strategic planning and enhances resilience. This study presents a multi-criteria decision-making (MCDM) proposed approach for disaster relief evaluation in supply chain management. The proposed model integrates Interval-Valued Neutrosophic Numbers (IVNNs) to manage uncertainty and ambiguity inherent in disaster various criteria which are often …

A Critical Evaluation Of The Criticisms Against Neutrosophic Statistical Methods, Muhammad Aslam, Abdulrahman Alaita, Florentin Smarandache

Neutrosophic Systems with Applications

Neutrosophic statistical analysis has gained attention for incorporating the degree of indeterminacy when analyzing imprecise and interval data under uncertainty–-an aspect often overlooked by classical statistics, fuzzy statistical analysis, and interval statistics. Recently, critical discussions have emerged regarding the use and applications of neutrosophic statistics, with some questioning its usefulness and validity. In this paper, we present a critical assessment of the existing literature, focusing on areas where misunderstandings and misinterpretations of neutrosophic statistical methods have occurred. We also examine flawed comparisons made between the results of neutrosophic statistics and interval statistics. Furthermore, substantial issues have been identified in the …

Biochar Suppresses Growth, Pupation And Eclosion Success Of A Specialist (Manduca Sexta) And A Generalist (Spodoptera Frugiperda) Insect Herbivore, Nischal Wagle, Soumya Unnikrishnan, Satinderpal Kaur, Engil Isadora Pujol Pereira, Rupesh R. Kariyat

School of Earth, Environmental, & Marine Sciences Faculty Publications

Biochar is a charcoal-like substance made by the pyrolysis of organic material from agricultural and forestry waste. While biochar is well documented for altering soil physicochemical conditions, few studies have investigated its possible effects on the management of arthropod pests. Tobacco hornworm (Manduca sexta) and fall armyworm (Spodoptera frugiperda, FAW) are specialist and generalist insect herbivores respectively, that can cause significant defoliation in natural and agricultural ecosystems. In this study, we examined whether walnut shell biochar can affect growth and development of these herbivores. Specifically, we investigated how biochar influences parameters such as mass gain, length …

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

Field Journal: The Excluded Middle, Carrie E. Kouts

Masters Theses

What does it look like to engage with organisms and landscapes at the periphery of anthropocentric value structures? How does one break the internalized myth that the “built” environment is excluded from the natural world? When does a hyper-mobile and hyper-commodified society confront the exponential crisis of animal death? Within this series of journal entries, field notes, collection observations, weird prose, and sensory musings, one will find questions on the nature of being human and the complex narratives of care we encounter in a world shared with more-than-humans. Each handwritten vignette and photograph from daily life weaves a non-linear and …

Artificial Intelligence And Astronomy: Lab Manual For Generative Ai-Based Learning Activities, Vasiliy Znamenskiy

Open Educational Resources

This laboratory manual introduces an innovative approach to teaching astronomy by integrating generative artificial intelligence (AI) tools into hands-on educational activities. Aimed at undergraduate and general education students, the manual guides learners through interactive exercises that involve evaluating AI-generated text responses, creating scientifically inspired images, and producing short educational videos about astronomical phenomena. By engaging with platforms such as ChatGPT, Gemini, DALL·E, and InVideo, students develop critical thinking skills, enhance their digital literacy, and deepen their understanding of space science. The method emphasizes inquiry-based learning, creativity, and scientific communication, preparing students to become thoughtful users of AI in academic and …

Plm-Dbps: Enhancing Plant Dna-Binding Protein Prediction By Integrating Sequence-Based And Structure-Aware Protein Language Models, Suresh Pokharel, Kepha Barasa, Pawel Pratyush, Dukka B. Kc

Michigan Tech Publications

DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several …

Artificial Intelligence Use In Medical Education: Best Practices And Future Directions, Rasheed A. M. Thompson, Yash B. Shah, Francisco Aguirre, Courtney Stewart, Costas D. Lallas, Mihir S. Shah

Department of Urology Faculty Papers

PURPOSEOF REVIEW: This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS: AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in …

Agent-Based Modeling: Introduction And Actuarial Applications, Rick Gorvett

Mathematics and Economics Faculty Working Papers

Agent-based modeling (ABM) has become an important and valued approach to modeling complex systems. In this paper, I advocate for actuaries to recognize the complex systems-nature of socioeconomic and risk processes and for ABM models to become a regular resource in our actuarial toolkits. These models allow for the observation of potential macro-behavior emerging from the underlying agent-level micro-activity and characteristics. Therefore, ABM models can provide significant insight into the quantification of risk and the identification of optimal strategies. This paper is an introduction and guide to ABM models, and it includes several case studies to illustrate their utility.

Microplastic Production And Toxicity Of Marine Ropes On Nantucket Island, Massachusetts, Amelia Lawson

Graduate Masters Theses

Marine environments have been affected greatly over the last 50 years by anthropogenic marine waste, particularly plastic. Plastic waste in the oceans comes from many sectors, such as textiles, shipping, and the fishing industry. As plastic litter degrades, it produces microplastics, including five major forms: foam, films, fragments, beads, and fibers. Fibers are the most prevalent microplastic type and are often formed from marine ropes, which are frequently discarded or lost at sea. These ropes contribute to entanglement, ingestion hazards, and microplastic pollution. While the shedding of microplastic fibers from ropes during active use has been studied, much less is …

Are Cycles Of Neural Activity The Algorithm Of The Brain?, Edwin Omondi Onyango

Computer Science Senior Theses

We propose that precisely timed neural activity cycles can serve as structural primitives for memory and computation in a system that exhibits associative learning like the brain. Inspired by biologically grounded mechanisms such as calcium-dependent plasticity, spike-timing-dependent learning, and phase-sensitive excitability, we construct a spiking neural network model in which repeated temporal coincidences drive the formation of self-sustaining activity loops. These cycles, once formed, persist as dynamic memory traces: not stored as static weights, but as reverberating patterns that replay in time when these loops are restarted. We show that noise alone fails to induce stable structure, but even sparse, …

The Nitrogen-Vacancy-Nitrogen Color Center: A Ubiquitous Visible And Near-Infrared-Ii Quantum Emitter In Nitrogen-Doped Diamond, Brett C. Johnson, Mitchell O. De Vries, Alexander J. Healey, Marco Capelli, Anjay Manian, Giannis Thalassinos, Amanda N. Abraham, Harini Hapuarachchi, Tingpeng Luo, Vadym N. Mochalin, Jan Jeske, Jared H. Cole, Salvy Russo

Chemistry Faculty Research & Creative Works

Photoluminescent defects in diamond, such as the nitrogen-vacancy (NV) color center, are at the forefront of emerging optical quantum technologies. Most emit in the visible and near-infrared spectral region below 1000 nm (NIR-I), limiting their applications in photonics, fiber communications, and biology. Here, we show that the nitrogen-vacancy-nitrogen (N2V) center, which emits in the visible and near-infrared-II (NIR-II, 1000-1700 nm), is ubiquitous in as-synthesized and processed nitrogen-doped diamond, ranging from bulk samples to nanoparticles. We demonstrate that N2V is also present in commercially available state-of-the-art NV diamond sensing chips made via chemical vapor deposition (CVD). In high-pressure high-temperature (HPHT) diamonds, …

Wildlife Road Mortalities During Covid-19 Pandemic-Related Lockdown In South Texas: A Comparative Survey, Bradley Evan Beer, Kevin W. Ryer, Md Saydur Rahman, John H. Young Jr., Richard J. Kline

School of Earth, Environmental, & Marine Sciences Faculty Publications

Mortalities of wildlife caused by collisions with vehicles along roads are increasing in prevalence, threatening the existence of various species and populations. The COVID-19 pandemic-related lockdown provided an opportunity to gain a better understanding of how wildlife vehicle mortality occurrences change in response to anthropogenic variables and how varying survey methods influence obtaining mortality data. In this study, data were collected in three observation periods: pre-lockdown (PreL), during lockdown (DL), and post-lockdown (PostL) in south Texas. There were 194 wildlife mortalities recorded during weeks 4–27 of 2020. Results of this study showed that road mortality survey counts did not change …

Detection And Mitigation Of Out-Of-Band Channel Wormhole Attack In Wireless Network Using Propagation Delay, Harry May

Doctoral Dissertations

Wireless networks, susceptible to a range of attacks due to their simplicity and ease of evasion, face a significant threat from control data attacks, notably the elusive wormhole attack. Detecting and mitigating such attacks poses challenges, particularly in the absence of a digital signature. This dissertation introduces an innovative approach that utilizes the propagation delay associated with malicious nodes’ timing characteristics for detection, employing the Ad-hoc On-Demand Distance Vector (AODV) algorithm as its foundation. The inherent propagation delay in the AODV protocol is calculated for each node link along the entire communication path, offering a distinctive timing method that provides …