Digital Commons Network^™

Investigation Of Machining (Drilling) Of Bio-Composite Reinforced With Jute Fibers Under Different Machining Conditions, Mohammed Abdul Mujeeb Ansari

Theses

This study investigates the drilling performance of jute fibre reinforced bio-composites under different machining conditions to reduce performance parameters like surface roughness, tool type and delamination damage (entry and exit). Jute composites were fabricated using a process called hand layup. Drilling experiments were carried out using full factorial method focusing on three parameters: Feed Rate (0.05mm/rev, 0.1mm/rev and 0.15mm/rev), Drill bit Type (Titanium Nitride-TiN, High Speed Steel-HSS and High-Speed Steel Cobalt HSS-Co) and Lubrication condition (Dry, Minimum Quantity Lubrication-MQL and Cryogenic-LN2). A total of 27 experimental trials were performed, and the responses were measured for each condition. Statistical analysis was …

In-Context Retrieval For Molecules And Chemical Synthesis Pathways, Abhisek Dey

Theses

Contrastive learning methods require well-defined positive pairs, limiting their applicability to domains where complete, high-fidelity pairings are available. In practice, large-scale scientific corpora --including patents, publications, and web-scale data -- contain vast quantities of contextually relevant but incompletely paired samples that are discarded under standard training paradigms. In this work, we demonstrate that hard negative mining can be leveraged to construct pseudo-positive supervision signals from unpaired or partially paired data, enabling contrastive learning to exploit the full breadth of available corpora without sacrificing representational quality. Using a large-scale chemical drug patent corpus as a testbed, we train a cross-modal contrastive …

Testing The Intermediate Disturbance Hypothesis On Anthropogenic Pressure In Central Alabama, Gavin Terrell

Theses

Biodiversity is fundamental to maintaining ecosystem structure and function globally. Prior to the late 20th century, ecological theory generally predicted that biodiversity would be highest in systems experiencing minimal disturbance. However, Joseph H. Connell challenged this view with the Intermediate Disturbance Hypothesis (IDH), which posits that species diversity peaks at intermediate levels of disturbance and declines under conditions of both low and high disturbance. Although widely cited, the applicability of the IDH to natural systems remains debated, particularly in the context of anthropogenic disturbances and when diversity is measured beyond species richness alone.

This study evaluated the predictions of the …

Branding Through Semiotics, Minimalism, & Branding Psychology: The Eurekafit Brand, Torri Salem

Theses

This paper examines how the combination of minimalism, semiotics, and branding psychology work seamlessly together to create an inclusive brand with an emotionally resonate visual identity system-- a brand that is cohesive and bridges the sub-brands of EurekaFit and Eureka CrossFit. What initially started as a logo redesign of Eureka CrossFit has now expanded into this dual-branding visual system, that communicates approachability, a sense of belonging and fitness, without being intimated, to its audience. This study investigates visual signifiers like color, typography and imagery, through a semiotic analysis and approach. I reduced clutter and visual noise and was able to …

Petals Of The Mind: A Digital Floral Interpretation Of Mental States, Corine D. Arrington

Theses

Petals of the Mind is an applied studio art project that explores the use of symbolic floral imagery to visually represent internal emotional states, specifically depression, anxiety, and ADHD. This project consists of six large format digital posters created in Procreate. Each emotional category is represented by a pair of flowers selected for their symbolic and structural characteristics: Blue Poppy and Black Rose for depression, Tulip and Aster for anxiety, and Honeysuckle and Wildflower Mix for ADHD. These flowers are intentionally deconstructed through layering, fragmentation, and motion cues to mirror common internal experiences such as emotional heaviness, vigilance, cognitive scattering, …

Beyond The Nude: Reimagining Feminist Agency In The Art Of Suzanne Valadon, Sofia Harris

Theses

This thesis examines the work of Suzanne Valadon (1865–1938) through a feminist art historical lens, challenging long-standing interpretations of her female nudes as inherently resistant to the conventions of Western art. Structured as a three-part lecture series, the project situates Valadon within a visual tradition in which women have been overwhelmingly represented by men, arguing that her engagement with the nude remains embedded in the same systems of objectification it is often said to subvert. Lecture One establishes the historical framework by tracing enduring archetypes of femininity, demonstrating how representations of women have functioned as projections of gendered power. Lecture …

Alabama Prairie Reconstruction: Comparing Management Pathways For A Southeastern Landscape, Cecelia R. Geyer

Theses

Southeastern prairies are highly biodiverse yet increasingly threatened by land-use change, fire suppression, and habitat fragmentation. These disturbance-dependent systems rely on periodic fire and grazing to maintain plant diversity and ecosystem function. This study evaluates prairie restoration strategies by testing predictions from the Intermediate Disturbance Hypothesis and the Patch Mosaic Burn Hypothesis. The study site was divided into three treatments: prescribed burned only (B), prescribed burned with mowing (BM) to simulate grazing, and an undisturbed control (C). A standardized mix of native plant seeds was broadcast across all treatments. Ecosystem health and function were evaluated using plant community diversity and …

Maintenance Insights For Power Transformers In Energy Networks, Saleh Hassan Al-Ali

Theses

This thesis examines machine learning approaches for predicting failures in electrical power distribution transformers, with the goal of helping utility operators intervene before outages occur. The dataset covers 16,000 distribution transformers operated by Compa ˜n´ıa Energ ´etica de Occidente (CEO), a Colombian utility serving 42 municipalities in the Cauca Department. Each transformer record includes geographic location, rated power capacity, self-protection features, ceramic insulation criticality levels, removable connector configurations, customer categories, user counts, estimated un-supplied energy, installation types, network topology, and secondary line lengths. Failure event histories were also included, which allowed the problem to be framed as a supervised binary …

Emote - A Modular Action Figure For Childhood Emotional Growth, Terrence Li

Theses

Emotional regulation and communication are one of the most important skills that we can learn. This skill allows us not only to recognize and effectively communicate our feelings to others but also allows us to recognize them in others. Although learning and recognizing these emotions may be a pursuit in which progress varies from person to person, this skill is especially invaluable to young children. Beginning as early as the age of 3, many children begin to show early awareness of their own emotions, such as reacting to discomfort or comfort, or starting to use words for feelings. This learning …

Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal

Theses

Deep neural networks achieve state-of-the-art performance across many domains, yet their deployment in high-stakes settings is constrained by two challenges: opaque decision-making and vulnerability to adversarial manipulation. This thesis investigates explainability and interpretability as principled mechanisms for improving the reliability and trustworthiness of deep learning models. First, we develop new post-hoc explanation methods that improve feature attribution and concept-based explanations. These methods provide faithful decision cues by modeling meaningful feature interactions and extracting faithful coherent concepts, enabling more reliable understanding of why a model predicts a given label. Second, we show that explanation quality is not solely a property of …

Defining Her2 Associated Proteogenomic Features In Breast Cancer And Extending To Gynecologic Cancers, Maya Anand

Theses

HER2 amplification is a well-established driver of breast cancer and serves as the primary basis for clinical classification and treatment selection. However, this framework assumes that HER2-driven tumor biology is defined solely by ERBB2 amplification or overexpression. The goal of this study was to evaluate whether HER2-associated signaling is represented as a pathway-level activation state and whether this framework could help identify tumors with clinically relevant HER2 activity beyond current routine classification methods. HER2-associated transcriptional programs were identified across three independent breast cancer cohorts, resulting in conserved gene sets (P76 and P25). Amplification-independent HER2 activation was assessed using the HER2 …

Leveraging Machine Learning For Traffic Congestion Management In Smart Cities, Omar Alhasai

Theses

Traffic congestion continues to be a major urban issue, leading to traffic delays, higher fuel costs, and air pollution problems. Traffic management systems currently function in reactive mode because their algorithms only operate following congestion development rather than preventing it. Smart cities need predictive systems based on data analytics and machine learning to actively control urban traffic movements because traffic continues to rise as a result of urbanization and population growth. The proposed research designs a machine learning–driven traffic congestion prediction system that uses genuine data obtained from Aarhus, Denmark, and METR-LA, Los Angeles. The study will analyze fundamental traffic …

Wegmans School Of Health And Nutrition’S Culinary Kitchen Cart Manual Development, Neena Bhala

Theses

To facilitate the initiation of culinary medicine at RIT, a manual to guide the use of a mobile kitchen cart was developed and evaluated. This manual was developed to support faculty, staff, and students’ use of a Mobile Kitchen Cart to be able to support culinary medicine and nutrition education activities. The Manual was directed to RIT faculty, staff, and students who have experience with the cart or intend to have future use with the cart. A qualitative evaluation study was conducted with ten participants including RIT faculty (n=2), students (n=4), and staff (n=4). Feedback on the manual was obtained …

A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi

Theses

Large language models (LLMs) have demonstrated strong performance on a range of reasoning tasks, however, their reliability often depends not only on model size or training data, but also on inference-time strategies. However, existing inference-time methods are typically evaluated in isolation and under differing experimental assumptions, making it difficult to draw systematic conclusions about their relative effectiveness. This thesis proposes a controlled empirical study of inference-time scaling strategies for large language models under fixed inference-time compute budgets. The findings reveal that no single strategy dominates uniformly. PRM guided selection with the IBM Granite verifier achieves the highest absolute accuracy across …

Hardware Integrity Checking On An Fpga Through Power Side-Channel Analysis, Ethan Vuong

Theses

FPGAs have seen extensive usage in applications such as cloud-computing, hardware acceleration, mobile devices, and military alike. While the reconfigurability of these devices allow them to be as adaptable as they are fast, it raises concerns of adversaries modifying not mere software, but hardware itself. Moreover, designers face an IP trust issue where they cannot be sure that a third-party IP was not modified in transaction, programming, or even post-programming. Cloud computing centers are hesitant to rent fabric on multi-tenant FPGAs due to the plethora of vulnerabilities and uncertainties that come with allowing users to reconfigure hardware. This thesis aims …

Leveraging Volatile Ecram Dynamics For Short-Term Plasticity In Neuromorphic Circuits, Sean Borkholder

Theses

Short Term Plasticity (STP) is fundamental for information processing and computational efficiency within biological neural systems. STP has previously been implemented at the circuit level using complex designs with high power and area overheads, resulting in designs that are not scalable in neuromorphic systems. Electrochemical random-access memory (ECRAM) devices naturally exhibit STP behavior through volatile ion dynamics, creating transient conductance modulation.  In previous ECRAM implementations, this behavior was seen as an undesirable artifact of device programming when implemented as a Compute in Memory (CIM) device; however, this thesis proposes leveraging the volatile  behavior to instead act as a computational resource. …

Under The Surface: A Scalable Experiential Framework For Accessible Design Archives, Lo Fasano

Theses

Preserving design history is usually done by hiding it away. While institutions like the Vignelli Center for Design Studies house over 750,000 artifacts, the vast majority remain in restricted storage, with minimal space dedicated to displaying process materials alongside canonical final works. Digital archives document portions of these collections, but typically present them as static image galleries, leaving the evolutionary logic of a design, and the human labor behind it, invisible to the public. Under the Surface is a scalable, interactive framework designed to bridge archival preservation and public discovery. By transforming preserved artifacts into real-time digital experiences, the project …

Efficiency Evaluation Of Water Pumping Stations Using Data Envelopment Analysis (Dea), Rowdha Abdullah Alblooshi

Theses

Water pumping stations are a critical component of water transmission systems, ensuring reliable delivery of potable water while maintaining operational requirements and international standards. With the increasing focus on sustainability and energy optimization, improving the efficiency of pumping stations has become a key priority. However, there is a lack of structured benchmarking approaches to evaluate the relative performance of pumping stations across multiple operational factors. This study addresses this gap by applying a Data Envelopment Analysis (DEA) framework to evaluate the efficiency of water pumping stations. A multi-model approach is adopted, including the CCR and BCC models, along with the …

Optimizing Urban Commute Quality Through Traffic Congestion Analysis And Predictive Modeling, Obaid Almansoori

Theses

Urban traffic congestion imposes significant economic, environmental, and social costs on rapidly growing cities worldwide. This research investigates how predictive analytics and machine  learning can be leveraged to classify and forecast traffic congestion severity in real time,  enabling data-driven decision-making for transportation planning, signal optimization, and  congestion management. A real-world traffic monitoring dataset comprising 5,952 observations collected over two months via  computer vision sensors at an urban intersection was analysed under the CRISP-DM frame- work. The  dataset records counts of four vehicle classes including cars, bikes, buses, and trucks at  15-minute intervals, alongside temporal variables such as time of day, …

A Data-Driven Machine-Learning Framework For Intermittent Demand Classification And Forecasting Of Electrical Spare Parts In Dubai’S Water Pumping Stations, Ayesha Khamis

Theses

Irregularity in spare parts demand has been a recurring problem in many critical industries. The same problem is found in Dubai's water pumping stations, where demand is highly intermittent, with long periods of no usage followed by sudden increases. These irregularities are usually caused by maintenance activities or equipment failures. Forecasting such demand is challenging, as irregular patterns can lead to stockouts or overstocking. In this research, a machine learning (ML) forecasting framework is developed to handle intermittent demand for electrical spare parts in Dubai's pumping stations. The framework includes demand classification and prioritization, as well as the application of …

Predicting Teacher Burnout Across Cultures: A Machine Learning Approach Using Talis 2018 Data, Fatma Fraishan Abdulla Hassan Alkhzaimi

Theses

Teacher burnout is a persistent global challenge with significant consequences for educator wellbeing, instructional quality, and school climate. Despite extensive research, most studies rely on small local samples, predefined burnout scales, and limited analytical techniques, leaving gaps in understanding the latent structure of burnout and the factors that predict it across diverse educational systems. This study addresses these gaps by applying a hybrid machine learning framework to the OECD TALIS 2018 teacher dataset (N = 38,081) to discover latent burnout profiles and build predictive models capable of identifying teachers at risk. Unsupervised k-means clustering was used to uncover naturally occurring …

Cognitive Digital Twin Operating System Forwayfinding In Vertical Smart Cities, Basil Adel Ismail Basbous

Theses

Vertically complex urban environments impose elevated spatial cognitive load on pedestrians, a  demand that static wayfinding infrastructure is structurally incapable of addressing. Smart cities  currently lack a formal cognitive navigation operating layer for managing pedestrian movement in  multi-level urban systems. This research introduces and evaluates a Cognitive Digital Twin  Operating System (Cognitive OS) — a city-scale adaptive navigation infrastructure integrating  Digital Twin environmental modelling, AI-driven route optimisation, real-time crowd intelligence,  and spatially embedded adaptive guidance to predict, manage, and reduce spatial cognitive load in  vertically complex environments. The study deploys AI-mediated human behavioral persona simulation as an independent methodological  contribution. …

Cancer Detection System Using Binary Neural Network On Dna Based Architecture, Antar Narayan Chowdhury

Theses

Deoxyribonucleic acid (DNA) is among the most durable chemical storage media, capable of encoding the fundamental instructions for protein synthesis (the central dogma). Each human cell contains a unique DNA sequence characterized by identifiable markers that facilitate pattern recognition. These molecular features offer significant potential for personalized drug development and disease identification. Recent advancements in DNA based research have demonstrated that fundamental arithmetic operations can be executed directly through molecular interactions, bypassing the need for silicon-based computational assistance. These biochemical applications can be further scaled to support Binary Neural Network (BNN) models, which are particularly well-suited for mitigating stochastic noise …

Effect Of Geometry, Cell Size, And Carbon Fiber Reinforcement On The Charpy Impact Strength Of Additively Manufactured Tpms Lattice Structures, Ahmed Yousuf Mohammad Bin Yaroof (Alsuwaidi)

Theses

Scientific research indicates a growing utilization of lightweight, high-performance materials across various disciplines, including engineering, driven by the demands of modern technological advancements. Recent developments within these fields include the creation of advanced lattice structures through additive manufacturing (AM) processes. One such lattice structure is the triply periodic minimal surface (TPMS) structure. TPMS structures possess unique mechanical properties and energy absorption characteristics that distinguish them from conventional AM lattice structures. However, published research to date has largely focused on the quasi-static behavior of TPMS structures, with limited attention given to the impact performance of composite- reinforced TPMS structures. The purpose …

Preparing Future-Ready Graduates For Technological And Workforce Transformations, Ghia El Dirani

Theses

The United Arab Emirates (UAE) is undergoing rapid economic transformation driven by technolog- ical innovation and national strategies such as Vision 2031 and UAE Centennial 2071, positioning STEM (Science, Technology, Engineering, and Mathematics) education as critical to building a knowledge-based economy. However, a persistent gap exists between the competencies developed in STEM higher education programs and the skills demanded by emerging sectors such as artificial intelligence, renewable energy, and advanced manufacturing. While the UAE has introduced pro- gressive education policies and invested in digital infrastructure, most curriculum reforms remain reactive and disconnected from long-term workforce projections. This research applies strategic …

Beyond Anomaly Detection: Classifying Attacker Automation Level From Ssh Honeypot Behavioral Signatures, Ashley Alt

Theses

The proliferation of AI-assisted offensive tools has introduced a new category of cyber attacker that combines the speed of automation with the adaptive reasoning previously associated only with skilled human operators. Despite the richness of behavioral data captured by SSH honeypots, existing analyses treat interaction logs primarily as evidence of malicious activity rather than as a dataset capable of distinguishing between attacker types. This thesis investigates whether human-driven, traditionally automated, and AI-assisted attackers produce distinguishable behavioral signatures within SSH honeypot interactions, and whether machine learning techniques can reliably classify attacker behavior from session-level features. A controlled experimental architecture was developed …

Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo

Theses

Predominantly oral languages (POLs) face a significant "digital divide," as they are often excluded from the benefits of modern natural language processing (NLP) technologies, due to a lack of extensive, readily available machine learning (ML) datasets. We investigate methods to overcome this data scarcity for Bambara, a Manding language, spoken primarily in Mali, with a rich oral tradition but limited digital presence.     The research leverages crowdsourcing and community engagement to build high-quality ML ready dataset resources. Key contributions include methods for automatic speech recognition (ASR) and machine translation (MT) dataset collection and curation and for educational resource creation.      Our findings …

Predicting Luxury Car Sales Using Machine Learning: A Comparative Study Of Linear Regression, K-Nearest Neighbors, And Support Vector Machines, Khalifa Jamal Mohammad Saleh Alblooshi

Theses

This paper explores the use of machine learning to predict the sales of luxury cars in the globe, and BMW as a case study of its sales data in the global market between the period 2010 and 2024. The study focuses on three regression algorithms, which include the Linear Regression, K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) and sees their predictive accuracy, generalisation performance and business applicability. In Python with the help of the Google Colab, an end-to-end analytical pipeline was developed entailing data preprocessing, outlier management, feature engineering, and time-sensitive traintest division. RMSE, MAE, MAPE, and R2 were …

Toward Reliable Computational Social Science: Inconsistency-Aware Methods For Human Annotation And Ai Inference, Sujan Dutta

Theses

As artificial intelligence (AI) becomes increasingly common in computational social science, \textit{inconsistency} has emerged as a key challenge. AI models often contradict themselves when given equivalent inputs, disagree with other models on the same data, and diverge from human judgments in seemingly opaque ways. Human annotators exhibit their own inconsistencies, both within individuals and across groups shaped by differing values and identities. Rather than treating these inconsistencies simply as noise, this dissertation argues that they contain meaningful signals that can be leveraged to improve learning efficiency, strengthen evaluation, and increase the reliability of large-scale social measurement. To study this phenomenon, …

Optimizing Virtual Scrolling Performance In Angular: A Comparative Study Of Cdk And Custom Implementation, Guri Sokoli

Theses

Modern web applications often display large datasets with tens of thousands of items, such as e-commerce catalogs, data tables, and social media feeds. Rendering all items in the Document Object Model (DOM) at once causes browser freezing, high memory use, and slow interfaces. Virtual scrolling solves this problem. It is widely adopted but rarely studied through direct performance comparison. Few empirical studies measure how different implementations behave under varying dataset sizes, devices, or browsers. This research conducts a comparative analysis of Angular CDK Virtual Scroll as an industry-standard baseline and develops an optimized implementation incorporating framework-specific enhancements: OnPush change detection …