Digital Commons Network^™

Addressing Urban Traffic Congestion: A Deep Reinforcement Learning-Based Approach, Tairan Liu

Mineta Transportation Institute

In an innovative venture, the research team embarked on a mission to redefine urban traffic flow by introducing an automated way to manage traffic light timings. This project integrates two critical technologies, Deep Q-Networks (DQN) and Auto-encoders, into reinforcement learning, with the goal of making traffic smoother and reducing the all-too-common road congestion in simulated city environments. Deep Q-Networks (DQN) are a form of reinforcement learning algorithms that learns the best actions to take in various situations through trial and error. Auto-encoders, on the other hand, are tools that help simplify complex data, making it easier for the DQN to …

Building Services Engineering May/June 2025

Building Services Engineering

No abstract provided.

Multifunctional Prospects Of Physical Vapor-Deposited Silver-Based Metal-Dielectric Nanocomposite Thin Films, Mohammad Nur-E-Alam, Boon Kar Yap, Mohammad Khairul Basher, Mohammad Aminul Islam, M. Khalid Hossain, Manzoore Elahi M. Soudagar, Narottam Das, Mikhail Vasiliev, Tiong Sieh Kiong

Research outputs 2022 to 2026

Silver-based metallic thin-film nanostructured materials are extensively utilized in advanced technological applications, including sensors, energy-efficient coatings, antibacterial coatings, and optical filters. Physical vapor deposition has emerged as a significant technique for synthesizing silver (Ag)-based nanocomposites, enabling the modification of structural and optical properties of thin metallic films. This advancement facilitates material development and applications in electronics, catalysis, magnetics, optics, environmental and health sectors, and specialized optical coatings. Research has demonstrated the successful integration of various nanomaterials with Ag matrices, resulting in multifunctional thin-film systems. Ag-based nanocomposite thin films exhibit exceptional electrical conductivity, rendering them suitable for electronic and optoelectronic devices. …

Data-Driven Machine Learning Applications For Predictive Modeling Of Petrochemical And Ecofriendly Systems, Noora Saleh Al Mansoori

Theses

Traditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient …

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.

How Mass Timber Supports Sustainability During Construction: An Industry-Based Analysis, Nicholas T. Mayton

Construction Management

As the construction industry explores new ways to reduce environmental impact, mass timber has emerged as a promising alternative to conventional materials. While much of the existing research on mass timber focuses on life-cycle assessments and carbon comparisons, few studies capture insights from professionals directly involved in building with it. This paper analyzes a series of industry interviews with contractors, designers, and sustainability consultants who have experience working on mass timber projects. The aim is to understand how sustainability plays out during the construction phase—not just in theory, but in practice. Using qualitative methods and word pattern analysis, three core …

Skip The Grid: Delivering Solar Energy To The Navajo Nation, Lucca Giuseppe Pracilio

Construction Management

Skip the Grid (STG) is a program where companies within the construction industry come together to better the quality of life for the people of the Navajo Nation. Through collaboration with Goal Zero, SOLV Energy, NEXTracker, and Heart of America, the fourth Skip the Grid Trip installed 40 solar systems on homes within the Chinle region in northeastern Arizona. Officially finishing on March 27^th, 2025, the trip not only consisted of providing a service for the people of Chinle but also allowed California Polytechnic State University (Cal Poly) students to learn about the history of the Navajo Nation …

2025 Annual Hackathon Redesigning Safety Fall Protection, Cole Lewis

Construction Management

The Verifying Everyone’s Safety Together (VEST) Hackathon is an annual event organized by construction management and social science students at California Polytechnic State University, San Luis Obispo. Its purpose is to redesign a specific piece of personal protective equipment (PPE) to improve usability, comfort, and inclusivity, with a focus on providing equitable safety for all workers. This hackathon centered on fall protection, specifically safety harnesses, which are essential for protecting workers at height but often lack comfort and user-friendliness. Students, faculty, and industry guests gathered to examine current harness designs and develop practical, worker-informed improvements. The event space was outfitted …

Colloidal Quantum Dots: A Path Toward Making Mid-Wave Infrared Sensing A Ubiquitous Technology, Mohammad Mostafa Al Mahfuz

Dissertations

Reducing the size, weight, power consumption, and cost (SWaP-C) of infrared detectors could make infrared sensing more widely accessible. In the critical mid-wavelength infrared (MWIR) spectral range of 3-5 gm, commercially available detectors are limited by the high costs associated with epitaxial growth and hybridization, as well as the need for cryogenic cooling. These factors restrict their use to defense and space applications.

Colloidal quantum dots present a promising material for overcoming these challenges, with wafer-scale monolithic integration and Auger suppression being the key material capabilities to minimize the sensor's SWaP-C. Infrared sensors based on colloidal quantum dots have been …

Childhood Neuroanatomical Markers Of Familial And Nonfamilial Attention-Deficit/Hyperactivity Disorder, Rahman Baboli

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

The Interplay Of Accommodation And Oculomotor Vergence Within Young Adults With Binocularly Normal Vision And Typically-Occurring Convergence Insufficiency, Sebastian Fine

Dissertations

Concerted binocular coordination evoking oculomotor and refractive responses to visual stimuli are essential to daily function. Oculomotor dysfunctions can inhibit binocular responses to visually-near stimuli and have high comorbidities to accommodative dysfunctions. Three visual cues for inward (convergent) and outward (divergent) oculomotor movements, when presented concertedly create natural-viewing conditions: disparity- the binocular difference in light cast onto the fovea due to differing ocular perspectives, blur- the acuity of a visual target which stimulates accommodation, and proximal- the perceived distance of a visual stimuli based on size.

This study aims to quantitatively investigate oculomotor vergence and accommodation performances between individuals with …

Modulation Of Cerebellar Cells By Transcranial Ac Stimulation In Anesthetized Rats, Qi Kang

Dissertations

Noninvasive brain stimulation (NIBS) is increasingly utilized in clinical trials for the treatment of neurological disorders. Each NIBS technique offers distinct advantages. Transcranial electrical stimulation (tES) is easy to apply and requires only simple equipment, while transcranial magnetic stimulation (TMS) can penetrate deeper than tES into the brain and it is more focal. Transcranial focused ultrasound stimulation (FUS) is superior to both in terms of penetration and focal stimulation. This study focuses on the modulation of the cerebellum, traditionally believed to be associated with motor coordination but increasingly recognized for its role in cognition and emotion as well. While tES …

Next-Generation Extended Reality Systems With Real-Time Edge Artificial Intelligence And Mobile Computing, Pedro H. Regalado

Dissertations

Mixed reality (MR) and augmented reality (AR) systems are reshaping digital experiences by seamlessly integrating physical and virtual environments. This dissertation presents a comprehensive framework for next-generation immersive systems, combining advances in real-time data processing, multi-user synchronization, and secure communication. The core contributions are structured around three interconnected systems: MediVerse, TeleAvatar, and MultiAvatarLink, each addressing critical challenges in mobile MR.

MediVerse is a secure and scalable framework for real-time health and performance monitoring, integrating intelligent IoT sensors, wearable technologies, and MR interfaces. It supports multi-camera fusion, adaptive compression, and real-time three-dimensional (3D) point cloud generation, enhancing data accuracy and responsiveness …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

Building Ecologies: Maintaining Land, Working With Wood, Taylor Jais

Masters Theses

This thesis critiques contemporary architecture and building practices for their fundamentally unsustainable and place-less assemblies that rely on extracting finite resources that generate significant emissions while simultaneously neglecting to consider the needs and existence of humans and non-human species with in the built environment. Through experimental material research that diverts “wood” waste streams and integrates land and multispecies care, this work demonstrates how maintenance-based approaches to construction can transform built assemblies into ecological systems. By deconstructing wood to its cellular components and recombining these elements in novel ways, this research reveals possibilities for collaborative, materials-driven, place-based building practices. This thesis …

Linear Mixed Model For The Surface Roughness Prediction In Hard Turning Operation, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Surface machining using hard turning is an intricate operation due to the influence of multiple machining parameters, their non-linear interactions, and the inherent variability introduced by different experimental trials. This study proposes a Linear Mixed Model (LMM) for predicting surface roughness, effectively addressing the challenges in traditional linear models, posed by the influencing factors, non-linearity, and interactions. The LMM incorporates variability from both fixed effects, such as cutting parameters (feed rate, depth of cut, and cutting speed), and random effects arising from tool wear across experimental runs. As a result, it provides a more comprehensive understanding of how these factors …

Price-Signal-Based Control Strategy For Heat Pump Water Heaters In Demand Response Applications, Othman A. Murad

Dissertations and Theses

This work presents a price-signal-based control strategy for residential Heat Pump Water Heaters (HPWHs) aimed at reducing electricity costs and shifting load away from high-price periods. By integrating the California Load Flexibility Research and Development Hub (CalFlexHub) prototype price signals, the Consumer Technology Association (CTA-2045-B) communication protocol, and the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) simulation framework, a rule-based controller was developed to align HPWH operation with dynamic electricity pricing while maintaining hot water availability.

The control strategy was evaluated through two case studies: one using a fixed water draw profile under varying levels of price signal foresight, and another …

Interfacial Phenomena Of Plastics: From Surface Modifications To Environmental Impacts, Kennedy Guillot

LSU Doctoral Dissertations

Plastic is a ubiquitous part of everyday life since its introduction in the previous century. Global production continues to exponentially increase, establishing plastics as one of the most dominant materials in modern manufacturing. Within the scope of academia, plastic is essential due to its low cost and sterility, particularly for minimizing contamination. Beyond the laboratory, plastic use spans numerous industries, and its use and disposal has since raised many environmental concerns. Once valued for its durability, plastics now pollute the environment in landfills and natural ecosystems. In recent decades, the consequences of this pollution have been investigated, yet the full …

Codelympics: An Educational Game, Shreyas Raghunath, Riley Guioguio

Computer Science and Engineering Senior Theses

This thesis addresses the growth of Computer Science and the increasing demand for resources that educate people about programming. The central problem is that existing resources either fall short in engaging the user, or are ineffective at communicating the basics of programming. Through a year of development and testing, we developed an application to serve as a bridge between games and educational content, making programming skills fun and accessible for younger students with no prior experience.

This thesis details the development of Codelympics, an educational game designed to teach the basics of computer programming, including control flow, functions, variables, and …

Axiomatic Aggregation Data, Carl D. Sorensen, Christopher A. Mattson, Michael L. Anderson, Thomas J. Ashworth

ScholarsArchive Data

This data set includes the ideation results of an experiment in ideation effectiveness, along with tools for analyzing the quality of the ideation.

All ideas generated by 15 teams of BYU students are included in the database. The ideas have been placed into an OPED genealogy tree format. The quality of all the ideas has been evaluated. The novelty of one team's ideas has been evaluated.

Instructions for performing the OPED organization, evaluating quality, and evaluating novelty are included in the data workbook.

Software tools to aggregate the individual evaluations into team scores and to display the results of the …

Strongly Confined Bi2se3 Quantum Dots Via Pulsed Laser Ablation In Liquids, Gregory Guisbiers, Rajendra Subedi, Burningham Burningham, Francisco Francisco Ruiz-Zepeda, Qiaohui Zhou, Xin Lu

Faculty Scholarship

Bismuth selenide (Bi2Se3) is a binary compound displaying a strong spin−orbit coupling, resulting in a narrow bulk bandgap material with a gapless metallic surface. By shrinking the size of Bi2Se3 within the strong confinement regime, its optoelectronic properties changed drastically. To achieve this goal, strongly confined Bi2Se3 quantum dots (QDs) were produced by pulsed laser ablation in liquids (PLAL). The laser used for the synthesis was a nanosecond Nd/YAG laser emitting at 1064 nm and pulsing at ∼13 mJ/pulse. The irradiation of the bulk target was performed at 1 kHz in acetone and lasted 5 min. Finally, the Bi2Se3 QDs …

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, …

Classification-Based Point Cloud Denoising And 3d Reconstruction Of Roadways, Chen Denghong, Pang Ning, Nie Wen, Feng Juqiang, Kan Jiliang, Zhang Jinjing

Coal Geology & Exploration

Objective The point cloud denoising and 3D reconstruction of roadways serve as a key step in the digital modeling and analysis of roadways. However, the conventional algorithm based on single filtering fails to effectively remove the noise at varying scales from point clouds. Meanwhile, the existing 3D reconstruction algorithms suffer from low modeling accuracy and high susceptibility to distortion. These necessitate developing methods and technologies to obtain high-quality point cloud data and construct high-accuracy 3D models for roadways. Methods This study proposed an adaptive classification-based point cloud denoising algorithm using neighborhood radius (R), minimum neighborhood point number ( …

Application Of A Uav-Borne Gpr System In The Detection Of Water Bodies And Cavities, Wang Ying, Sun Chenchen, Zhang Lu, Chen Yifei, Lu Song, Zhou Feng

Coal Geology & Exploration

Objective and Methods The unmanned aerial vehicle (UAV)-borne ground-penetrating radar (GPR) system, enjoying the advantages of high resolution and non-contact detection, is applicable to the detection of water accumulation in coal seams and goaves in mines. Therefore, this study proposed a rapid detection method based on a UAV-borne air-coupled GPR system to enhance the exploration efficiency of mining areas and reduce the time and risks of manual explorations. Given the limitations of the test scenarios in actual coal mines, this study investigated the water body of the Fanxiong reservoir in Ezhou City and surrounding drainage culverts for equivalent validation, aiming …

Fluoride Ion Removal From Mine Water Via Nucleation Crystallization Pelleting Process, Zhang Xiyu, Dong Shuning, Wang Hao, Jin Pengkang, Wang Xiaodong, Wang Qiangmin, Zhang Tao

Coal Geology & Exploration

Objective The mine water associated with coal mining tends to be rich in fluoride ions. If discharged directly without effective treatment, such water will cause severe pollution to regional ecology, affecting the quality of water resources and the stability of the ecosystem. Methods This study focuses on the challenging treatment of the fluoride pollution caused by coal mining-associated mine water. To overcome the bottlenecks including low efficiency and weak anti-interference of traditional methods for fluoride removal, this study designed a setup for fluoride removal using the nucleation crystallization pelleting (NCP) processing and proposed a novel fluoride removal method—NCP chemical precipitation. …

Ams-Hd: Acute Mountain Sickness Detection With Hyperdimensional Computing, M. Hassan Najafi, Mehran Shoushtari Moghadam

Faculty Scholarship

Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient …

Ecg-Based Biometric Key Generation Using Principal Component Analysis And Machine Learning, Ahmad Abadleh

Iraqi Journal for Computer Science and Mathematics

Biometric authentication techniques are fast becoming imperative methods for secure identifications in a wide range of applications, while most of the traditional systems are easily spoofed or forged. In this paper a new approach of deriving a unique biometric key from an electrocardiogram signal is presented owing to physiological uniqueness of heart activity. In this respect, by focusing on RR intervals extracted from ECG signals, the PCA is applied in order to reduce its dimensionality and then form a compact and distinctive biometric key. Thereafter, a Random Forest classifier was used in evaluating the effectiveness of features, where a high …

Retracted: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Hadeel M. Saleh, Sahar Hamad Ahmed, Akeel Sh. Mahmoud

Iraqi Journal for Computer Science and Mathematics

Long short-term memory networks can effectively process complex temporal patterns in electrocardiogram data. These sequential models excel at classifying heart disease from the rich signals captured by electrocardiograms. However, traditional algorithms struggle with the intricate waveforms encoded in each heartbeat. Deeper architectures such as LSTM are better equipped to untangle the subtle variations between healthy sinus rhythms and lethal arrhythmias. In this study, an LSTM model was developed to diagnose disease from the PTB dataset. The network was trained using a fusion of deep learning schemes for sequential data. The model underwent several evaluations, from a confusion matrix mapping predictions …