Digital Commons Network^™

Overseas Agent Selection For International Logistics Companies: A Case Study, Hung-Ta Lin, Hua-An Lu

Journal of Marine Science and Technology–Taiwan

International logistics companies (ILCs) have aggressively participated in the supply chain systems of production industries as part of global trade development. They have adopted diversified and multinational models by expanding their overseas services to align with the various characteristics of regional economic patterns. Assigning specific local agents for their forwarding operations has been one of the key strategies for overseas services. Notably, the performance of these selected agents has significantly influenced service quality in their respective regions. In collaboration with the top management of an ILC, this study proposes an evaluation framework comprising 4 criteria and 16 subcriteria for selecting …

Exploring The Red Sea Crisis’S Supply Chain Disruption Impacts On The Shipping Industry, Po-Hsing Tseng, Nick Pilcher

Journal of Marine Science and Technology–Taiwan

Since October 7, 2023, the Houthi militia in Yemen has continuously attacked Israel and its nearby ships in the Red Sea. This crisis has impacted on global ship operations, freight rates, and shipping networks, since this conflict seriously affects the international trade between Europe and Asia and brings many unpredictable butterfly effects (e.g. supply chain disruption, and high transportation costs in shipping and air cargo). This papero explores its impact on the supply chain and suggests potential solutions;, and to consider perceptions on who should be responsible for the crisis, and who will actually shoulder the responsibility for the crisis. …

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

Computational Pangenomics And Machine Learning For Genotype-Phenotype Analysis, Tejaswi Vemuri

UNF Graduate Theses and Dissertations

Phenotypes are the observable characteristics of an individual organism. Predicting quantitative phenotypes from genomic variation remains challenging when causal signals span both local motifs and distal regulatory contexts. Building on Frequented Regions (FRs)—subsequences conserved across genomes and extracted from a pangenome graph generated from a large collection of closely related species—we compare several modeling strategies across 35 Saccharomyces cerevisiae growth phenotypes: Random Forest (RF) on FR counts (called RFCounts), RF on FR sequences, 1D convolutional neural networks (CNN) on FR sequences, Long Short-Term Memory (LSTM) networks on FR sequences, a Genomewide Association Study (GWAS) baseline, and a sequence-based transformer model, …

Toward General Purpose Llms: From Domain Alignment To Multimodal And Multi-Agent Systems, Thang Nguyen

Dartmouth College Master’s Theses

Large Language Models have shown remarkable capabilities, but their generalization across domains, modalities, and collaborative settings remains limited. This work explores how LLMs can be adapted along three dimensions: domain specialization, multimodal processing, and multi-agent reasoning. First, we introduce a reward-guided retrieval mechanism that fine-tunes the retrieval component of a language model using preference-based supervision, improving response relevance in specialized domains. Building on this, we design a multi-agent framework for complex information-seeking tasks, where distinct agents handle query clarification, evidence extraction, and answer synthesis, enabling robust reasoning without additional model training. Extending beyond language, we investigate how LLMs can operate …

Cultivating Plain Language Skills For Engineering Students, Catherine Wong, Cynthia Carlson

Faculty Publications

While engineers are learning the vocabulary of the profession, understandably, they want to practice, and perhaps show their professors that they are proficient. This leads to student writing that is overly complicated and full of jargon. This is not the type of document that would be clear to the public, or even to a professional outside of the narrowly focused field of the particular engineer. After graduation, engineers’ writing becomes exponentially more important. Often approval of projects relies on residents’ or clients’ understanding of engineers’ work. For example, an engineering firm completing an investigation of an important health concern such …

Geospatial Analysis For Urban Resilience: Green Space, Flood Susceptibility, And Wetland Restoration Potential, Jessica Lareaux

Dissertations, Master's Theses and Master's Reports

Through the integration of high-resolution geospatial data, hydrologic modeling, and spatial statistics, this research addresses three interrelated topics that are critical to sustainable, equitable, and climate-resilient urban planning. The overall objective of this dissertation is to provide data-driven and machine learning-based geospatial analysis frameworks that enhance our understanding of environmental inequities, flood exposure, and the potential for wetland restoration, with a focus on urban contexts. The study presented in Chapter Two examines the distribution and accessibility of urban green spaces in Detroit, Michigan, using high-resolution geospatial data and geospatial analysis methods, including geographically weighted regression (GWR) and network-based analyses. This …

Semantic Think-On-Graph (Semtog) : Enhancing Graphrag Through Semantic Community Detection, Sugam Mishra

Electronic Theses and Dissertations

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances large language models (LLMs) by grounding their reasoning in structured knowledge graphs, making them more reliable for multi-hop reasoning and factual QA. A central mechanism in Think-on-Graph systems such as ToG[14] and FastToG[7] is community detection, which groups locally related nodes into compact subgraphs so that LLMs can reason over focused, information-rich neighborhoods instead of traversing the entire graph. However, these methods rely purely on structural connectivity, often scattering semantically related entities across different communities and weakening the evidence provided to the LLM. We propose Semantic Think-on-Graph (SemToG), a semantic-aware extension of FastToG[7] that integrates …

Intelligent Tutoring Systems In The Age Of Llm-Based Agentic Frameworks - Adapting Small On-Device Language Models For Fact-Checking And Student Compliance Detection, Evan Vahid Naderi

Honors Undergraduate Theses

As large language models (LLMs) demonstrate increasingly sophisticated language understanding capabilities, they offer transformative potential for educational technology, particularly in developing more responsive intelligent tutoring systems (ITS). To develop these systems, a synthesis that balances theoretical frameworks with practical, cutting-edge, and deployable technology would be useful. This thesis aims to advance the field of ITSs theoretically by viewing the modern advancements in natural language processing within the context of the foundational frameworks important to the fields of self-regulated learning, ITSs, and cognitive agents. Then, building on prior research on the ITS MetaTutor \citep{Azevedo2022}, this research focuses on how functions of …

Navigating Big Data In Cyber Archaeology: An Updated Four-Field Approach Applied To San Miceli, Jared Wilson

Dissertations

Problem

This dissertation examines the evolving role of digital technologies in archaeological research, focusing on the management and interpretation of extensive digital datasets generated by modern excavations. Drawing on six years of comprehensive fieldwork at San Miceli, Sicily, this study critically evaluates and updates the established four-field methodology proposed by Levy et al. (2012), encompassing acquisition, analysis, dissemination, and curation. While traditional archaeological documentation methods rely heavily on analog techniques, these approaches increasingly fall short due to the rapid expansion and complexity of digital data.

Method

To address this gap, the dissertation refines and expands the current methodological framework by …

Mitigating Information Overload In Aviation Safety: Ai-Driven Hierarchical Tagging And Summarization Of Notam For Pre-Flight Information Bulletin, Rutong Gu, Yefeng Qu, Dongling Chen, Yu Tang, Ailin Zhou

International Journal of Aviation, Aeronautics, and Aerospace

To address the challenges of aviation safety information overload in Pre-flight Information Bulletin (PIB) systems, this study proposes an intelligent classification framework (ERNIE-DPCNN) that integrates knowledge-enhanced semantic representation with a Deep Pyramid Convolutional Network. Traditional systems relying on rule-based filtering mechanisms suffer from inefficiencies in critical information identification and high risks of human misjudgment. The proposed framework achieves breakthroughs through three technical innovations: (1) An aviation domain-adapted ERNIE model is constructed, leveraging phrase-level masking strategies to enhance semantic representation of compound identifiers; (2) A Deep Pyramid Convolutional Network (DPCNN) is designed to extract multi-granularity features via hierarchical convolution-pooling architecture, optimized …

Optimizing Ai Language Models: A Study Of Chatgpt-4 Vs. Chatgpt-4o, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md Fayaz Bin Hossen, Muhammad Rezaur Rahman, Md. Shahadat Jaman

Electrical & Computer Engineering Faculty Publications

This paper presents a comparative analysis of OpenAI's GPT-4 and its optimized variant, GPT-4o, focusing on their architectural differences, performance, and real-world applications. GPT-4, built upon the Transformer architecture, has set new standards in natural language processing (NLP) with its capacity to generate coherent and contextually relevant text across a wide range of tasks. However, its computational demands, requiring substantial hardware resources, make it less accessible for smaller organizations and real-time applications. In contrast, GPT-4o addresses these challenges by incorporating optimizations such as model compression, parameter pruning, and memory-efficient computation, allowing it to deliver similar performance with significantly lower computational …

Snake Optimization Algorithm For Fractional Order Controller In Three-Area Load Frequency Regulation, Shubra Goel, Omveer Singh

ASEAN Journal on Science and Technology for Development

The transition toward renewable energy-dominated power systems have accentuated the intricacies of frequency control, necessitating advanced regulatory mechanisms. This investigation articulates a tri-zonal frequency stabilization approach, employing a hybridized controller that synergizes Fuzzy Fractional-Order PI and Tilt-Integral-Derivative methodologies. The uniqueness of this approach is further amplified by the deployment of the Snake Optimization algorithm for precise parameter tuning. Set within a conventional grid topology integrated with assorted renewable energy sources, the study evaluates the controller’s adaptability and resilience through a series of comprehensive scenario-based analyses.

Similarity May Be Safer: The Effect Of Similarity Between Speech-Based Takeover Request Style And Driver Personality On Self-Driving Takeover Performance, Keer Ma, Jianfeng Wu, Yanxi Lin, Zihan Li, Songyang Guo, Dongfang Jiao, Shihan Yu

Psychology Faculty Publications

In Level 3 automated driving, it is critical that drivers can rapidly and effectively shift from non-driving related tasks (NDRT) back to the driving task. While previous research has examined the modality, timing, and vocal characteristics of takeover requests (TORs), little is known about how the style of speech-based TORs interacts with drivers’ personality traits. This study conducted a driving simulator experiment with 49 participants using a 2 × 2 within-subjects design. Drawing on the dominant-submissive dimension of personality, we examined the similarity of personality tendencies between speech-based TORs and drivers under takeover scenarios of varying urgency (low: road construction; …

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

Application Of Artificial Intelligence Techniques To Improve Leadership Decision Making With Uncertainty, Michael David Parrish

Doctoral Dissertations

"Every good leader is a good manager, but not every good manager is a good leader. The difference between the leader and the manager is critical decision-making. Today’s decision-making environment is characterized as Volatile, Uncertain, Complex, and Ambiguous (VUCA). With the exponential increase in the technical capabilities of systems, the human has become the weakest link in the use of such systems. To remain relevant, good leaders must continuously adapt to new advances in technology and processes.

The research contributions of this work provide several unique and novel solutions for leaders to utilize artificial intelligence tools to improve and optimize …

Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and …

Adaptive Nussbaum Design For Nonholonomic Systems With Asymptotic Stabilization Against False Data Injection, Guilong Liu, Yongliang Yang, Weinan Gao, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article addresses the stabilization challenges of nonholonomic systems under the threat of false data injection (FDI) attacks, which compromise the integrity of state information. A novel adaptive control strategy using Nussbaum-type gains is proposed to ensure the asymptotic stability of the closed-loop system while maintaining signal boundedness. The approach extends conventional Nussbaum designs to handle multiple unknown control directions. It integrates online learning mechanisms to mitigate the impact of FDI attacks. Additionally, adaptive backstepping and fuzzy-logic systems are utilized to approximate and compensate for unknown nonlinear dynamics. The methodology transforms nonholonomic systems into equivalent cascade structures to address inherent …

Investigation Of Under-Frequency Load Shedding Prevention Of Isolated Medium-Sized Power Systems Using Energy Storage, Dilan Indoopa Manamperi

Theses: Doctorates and Masters

Due to the rapid uptake of distributed photovoltaic generation, the visible load to transmission operators in distribution feeders is reducing. Traditional load-shedding schemes are facing challenges due to this low-load situation. The required amount of load reduction cannot be achieved with the same load-shedding scheme as before. In some situations, with reverse power flows, load shedding can further increase the generation reduction. On the other hand, increased Rate of Change of Frequency (RoCoF) due to low inertia can result in false activation of under-frequency load shedding (UFLS) relays. These challenges can cause a serious problem with the power system security. …

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

Personalized Persuasion In The Digital Age: A Data-Driven Approach To Effective Communication, Annye Braca

Doctoral

This thesis investigates the potential of Machine Learning (ML) to personalize persuasive marketing messages. It explores the identification of individuals receptive to specific persuasion techniques based on their psychometric profiles. By developing ML models that incorporate these profiles, the thesis aims to predict the impact of tailored messages and improve the effectiveness of marketing communication.

Machine Learning Applications In Epigenomics And Its Association With Health And Disease, Trevor Doherty

Doctoral

Epigenetic modifications can lead to altered phenotypes without a change in the DNA sequence itself. Disrupted gene expression regulated by epigenetic processes can result in cancers, autoimmune diseases and various other maladies. Machine learning (ML) involves the use of algorithms and models which are trained to learn patterns in data, and has demonstrated remarkable success in solving diverse, complex challenges. Epigenomic studies, such as those that use DNA methylation (DNAm) data, increasingly make use of ML techniques to process extremely high dimensional data obtained from high throughput platforms e.g., DNAm arrays. These datasets suffer from the curse of dimensionality, increased …

Boys Build, Girls Glam: An Examination Of Adult Language Use In Informal Steam Education Settings, Alexandra Horton

Theses and Dissertations

Research on STEAM education has primarily focused on formal education settings such as schools, with more research needed in informal or non-traditional education settings. As children frequently engage with informal and non-traditional education settings, it is important to understand how we can transmit information in these settings to bolster and further the learning process and increase engagement. STEAM fields have been plagued by racial and gender inequities that have led to these groups being marginalized and excluded (Blair et al., 2017; Eaton et al., 2020; Martin, 2018; Martin & Phillips, 2019; Miller et al., 2018; Newall et al., 2018, Sheffield …

Method For Calculating Characteristic Frequency Ab (Rechargeable Batteries), Alisher Ganisher Ugli Bozorov

Technical science and innovation

A method for calculating the characteristic frequency is proposed, based on automatic interpolation of the frequency dependence of the reactance and subsequent calculation of the characteristic frequency as one of the roots of the interpolation function. The developed algorithm allows you to calculate the characteristic frequency from only 4 points of the frequency dependence of the reactance. In this case, the error in calculating the resonant frequency parameter does not exceed ~2 Hz.

Obviously, the values of the coefficients of the equation depend on the number of resistance values X and the corresponding frequencies of the test signal. The indicated …

End-To-End Autonomous Quadcopter Using Reinforcement Learning, Mohamed Marwan Chawa

Theses and Dissertations

This thesis investigates the potential of Reinforcement Learning (RL) for achieving robust and adaptable quadcopter control, focusing on trajectory and attitude stabilization. We compare state-of-the-art RL algorithms, specifically Proximal Policy Optimization (PPO), against traditional Proportional-Integral-Derivative (PID) controllers across three tasks: hovering, slow trajectory following, and fast trajectory following. To enhance realism, we employ a modified PyFlyt simulation environment with a high-fidelity Crazyflie 2.x model, accounting for motor dynamics, noise, wind disturbances, and aerodynamic drag.

The challenge of operating a quadcopter can be divided into two distinct parts: planning a flight path and actually following that path. Our focus is on …

Measuring And Improving Api Usability And Quality: A Comprehensive Framework And Empirical Study, Sultan Alanazy

Computer Science and Engineering Theses and Dissertations

Cloud computing provides on-demand access to flexible computing resources, enabling rapid application deployment without substantial infrastructure investment. Application Programming Interfaces (APIs) play an important role in ensuring the success of cloud applications. The primary users of APIs are the extensive community of application programmers who search, read, and understand APIs before integrating them into their applications or systems. In addition, developers often turn to online API support when seeking help. Problems in such support can result in incorrect API usage and integration problems. There is an urgent need to measure API usability and support issues to identify, characterize, and assess …

Towards A Multimodal Approach For Assessing Adhd Hyperactivity Behaviors, Franceli L. Cibrian, Lauren Min, Vitica Arnold

Engineering Faculty Articles and Research

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition that needs an assessment of inattention, hyperactivity, and impulsiveness symptoms. Particularly in young children, hyperactivity-impulsivity stands out as a primary concern. However, those behaviors may or may not be evidenced when a child is in a small room, one-on-one with a single adult. Therefore, Ambient Intelligence technology that supports data collection in a natural setting, paired with expert human decision-making can potentially improve the quality of assessments. In this paper, we conduct a literature review and analysis to align ADHD assessment criteria with potential sensor technologies to collect …

A Study On Improving The Accuracy And Effectiveness Of Similarity Detection Processes In Text Files Using Nlp Techniques, Noor Abdulmuttaleb Jaafar

Al-Esraa University College Journal for Engineering Sciences

The rapid expansion of the Internet has revolutionized access to information, especially in the area of unstructured data, most of which consists of textual content. While instant access to information brings many advantages, it has also given rise to a prevalent problem – plagiarism. Copying and reusing materials without proper permission poses a significant threat to academic integrity and integrity. Rates of plagiarism, especially in academic and scientific publications, have risen with the advent of the Internet, reaching alarming levels, such as 60% in student projects. This study examines the proposed model that includes computation of similarity using cosine coefficients, …

Secure Healthcare Systems And Big Data: A Bibliometrics Analysis, Rasha Talal Hameed, Saad Ahmed Dheyab, Saba Abdulbaqi Salman, Ahmed Hussein Ali, Omar Abdulwahabe Mohamad

Iraqi Journal for Computer Science and Mathematics

This study conducts a bibliometrics analysis of research on secure healthcare systems and big data, aiming to identify trends, key contributors, and thematic areas within the field. By examining a comprehensive database of academic publications, we highlight the evolution of research from foundational concepts to contemporary innovations in data security and privacy management in healthcare. Key metrics such as publication volume, citation impact, and co-authorship networks are analyzed to uncover the most influential authors and institutions. Additionally, we explore the integration of big data analytics in enhancing healthcare delivery while addressing security challenges. The findings provide valuable insights for researchers …

Documents In The Age Of Non-Human Agency, Bernt Ivar Olsen-Kristiansen Dr, Niels W. Lund

Proceedings from the Document Academy

As documents are inherently tied to humans and their expressions (Lund 2024), facilitating communication across time and space, we might have historically reflected on the implications of inserting a computer or information system between humans. If we envision the human as a handyman engaged in technical engineering, we can comprehend the development of AI, Artificial Intelligence, as part of a much longer history of creating instruments and machines to support human endeavors. These include the printing press, musical instruments, steam engines, typewriters, microphones, and progressing to digital musical instruments that create their own music.

Yet, it remains humans who craft …