Digital Commons Network^™

Enhancing Large Language Models For Legal Question Answering : A Case Study On The Land And Building Tax Act In Thailand, Nattapat Tantapong

Chulalongkorn University Theses and Dissertations (Chula ETD)

Thailand's Land and Buildings Tax Act requires interpreting multiple legal instruments. However, general-purpose large language models often produce answers that lack both accuracy and verifiable legal citations, which are essential for legal reasoning. This study investigates whether curriculum-structured fine-tuning enables small Thai-aligned models (8B parameters) to perform comparably to large commercial models. The research constructs a domain-specific corpus of 8,410 Q&A pairs with 30,612 hard-negative triplets, then evaluates four curriculum designs and four adapter ranks using retrieval completeness and answer quality metrics. The experimental results reveal two key findings. First, hard-negative retrieval training improves Multi-HitRate@5 by 2.6% and Multi-MRR@5 by …

Diffusion Model-Based Synthetic Image Augmentation For Enhancing Vehicle Detection Model Performance, Pawaris Parnphotong

Chulalongkorn University Theses and Dissertations (Chula ETD)

A critical challenge for modern object detection models is the significant performance degradation encountered in adverse weather or poor lighting conditions. This issue primarily stems from the imbalance of training datasets, which are predominantly composed of normal daytime scenes, resulting in reduced robustness when models face challenging scenarios. Although traditional data augmentation techniques exist, they are limited to pixel-level adjustments and inherently restricted in generating data diversity. Consequently, this research proposes an approach utilizing diffusion models to generate realistic synthetic images that simulate new atmospheric and environmental conditions. To maximize the efficiency of synthetic data generation, we investigated a crucial …

Large Language Model-Based Framework For Mental Health Support Conversation In Thai, Radchaneeporn Changpun

Chulalongkorn University Theses and Dissertations (Chula ETD)

As mental health support needs continue to rise in Thailand, access to professional care remains constrained. LLM-based chatbots present a scalable alternative, though most current systems are limited to single-turn, solution-focused interactions. This thesis introduces a Large Language Model-Based Framework for Mental Health Support Conversations in Thai, organizing dialogues around five fundamental mental health counseling stages: rapport building, problem identification, goal setting, working, and termination. Drawing from Person-Centered Therapy (PCT) and Acceptance and Commitment Therapy (ACT). The framework was assessed against three single-agent baseline models using LLM-simulated users, with positive user reaction rates evaluated by LLMs. Results demonstrated a 79.01% …

Music Generation Using Large Language Models : A Case Study On Traditional Thai Music, Sutusta Kunasorn

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study presents a framework for music generation using Large Language Models (LLMs), focusing on the creation of symbolic compositions in the style of traditional Thai music. The framework integrates prompting techniques, retrieval-augmented generation (RAG), and multi-agent collaboration to guide the model in producing melodies that reflect Thai musical characteristics. ABC notation is used as the primary representation, allowing LLMs to process musical structure in a text-based format. The system incorporates curated Thai musical examples and theoretical knowledge to support culturally informed generation, while multiple agents work together to compose, evaluate, and refine musical ideas using rubric-based criteria adapted from …

A Hybrid Hardware Modeling, Simulation, And Generation Framework : A Case Study Of Risc-V Out-Of-Order Superscalar Cpu Design, Tanawin Devaveja

Chulalongkorn University Theses and Dissertations (Chula ETD)

We present Kathryn, a novel framework that simplifies hardware design through a hybrid design flow and an integrated hybrid simulation approach. As specialized hardware demand grows, managing design complexity, precise control flow, and parallelism control remains challenging. Kathryn alleviates these issues by abstracting control logic and still maintaining designers’ cycle-accurate control ability. Moreover, Kathryn provides hardware-aggregation features, helping designers manage complex hardware component structures. As a result, in a simple RISC-V design, the model written in Kathryn shows a 2.6x reduction in lines of code compared to those of Verilog. [1] Moreover, in an Out-of-Order design, the model on Kathryn …

Advancing Voice Spoofing Detection In Thai : A Comprehensive Dataset And Performance Analysis On Speaking Styles And Channel Effects, Ticho Urai

Chulalongkorn University Theses and Dissertations (Chula ETD)

Voice authentication is increasingly used in applications such as banking and call center verification, but it faces serious risks from spoofed voices. Recent advances in text-to-speech (TTS) and voice cloning make it possible to generate highly natural fake speech, creating an urgent need for robust anti-spoofing systems. While most prior work focuses on English, little research addresses the Thai language. To fill this gap, we present the Chula Spoofed Speech (CSS) dataset, a large-scale Thai corpus containing 1.3M utterances of both bona fide and synthetic speech. The synthetic samples are generated using five state-of-the-art TTS systems from the same utterances …

Evaluation Of User Experience And Usability Of Organization Super App, Ananya Kumhan

Chulalongkorn University Theses and Dissertations (Chula ETD)

The digital transformation of organizations in Thailand has accelerated the development of internal systems that streamline workflows and improve accessibility to organizational information. Financial institutions, which operate with multiple fragmented systems, have increasingly adopted Enterprise SuperApps as a centralized platform that combines internal services such as corporate news, leave requests, employee directories, resource information, and meeting room reservations into a single application. However, usage statistics in several organizations indicate that continuous adoption remains relatively low. Many employees rely on alternative channels or use only selected features, reflecting challenges in user experience (UX) and usability. This study aims to evaluate the …

Blocfog : Enhanced Transactional Data Encryption Via Fog Computing And Blockchain, Leon Wirz

Chulalongkorn University Theses and Dissertations (Chula ETD)

Protecting Personally Identifiable Information (PII) in financial transactions presents ongoing challenges, particularly in achieving a balance between strong security, fine-grained access control, and system efficiency. Existing approaches often rely on complex cryptographic operations that strain end-user devices or lack the flexibility needed for decentralized environments. Although blockchain provides advantages such as auditability and tamper resistance, its use is often limited by high computational costs and latency. This research proposes an efficient and scalable access control framework that integrates Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with a lightweight fog computing layer. The system is designed to offload heavy cryptographic tasks from end users …

Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma

Chulalongkorn University Theses and Dissertations (Chula ETD)

Hate speech detection is an essential technique in the online environment, especially on social media platforms. This technique helps to create a safer space and reduce the risk of real-world harm. In Chinese, this task is particularly challenging because of unique linguistic structures and the frequent use of indirect expressions, sarcasm, homophones, character variants, and abbreviations. This study investigates how to improve Chinese hate speech detection by combining BERT with FastText and BERT with BiLSTM. There are six model variants that are configured: frozen BERT and fine-tuned BERT, each further extended with either FastText sentence embeddings or a clause-level BiLSTM. …

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

Applying Machine Learning And Optimization Algorithms To Perform Feature Selection, Shizhao Yu

Theses and Dissertations (Comprehensive)

The objective of feature selection in the realms of machine learning and data mining is integral, serving as an efficient mechanism to eradicate redundant or irrelevant features, and subsequently augmenting the performance of predictive models. In the contemporary landscape of big data, with the escalating dimensionality of datasets, the efficacy of traditional feature selection methodologies is compromised, due to their computational complexity and ineptitude in addressing the curse of dimensionality. This thesis posits a pioneering feature selection framework that amalgamates machine learning with advanced optimization algorithms. The methodology employs a Support Vector Machine (SVM), in conjunction with a cutting-edge metaheuristic …

Reinforcement Learning For Scheduling And Routing: Electric Vehicles Charging And Patrol Scheduling, Majid Ghasemi

Theses and Dissertations (Comprehensive)

This thesis offers a comprehensive exploration of Reinforcement Learning (RL), beginning with fundamental theoretical constructs, Markov Decision Processes, Dynamic Programming, Monte Carlo, and Temporal Difference methods, and extending into state-of-the-art deep RL approaches such as Deep Q-Networks (DQN) and policy-gradient algorithms. Through analytical experiments in controlled environments, the thesis demonstrates how distinct algorithmic choices (e.g., exploration techniques, eligibility traces, or network architectures) influence convergence and stability. These foundational insights pave the way for two in-depth case studies, which apply RL techniques to critical, real-world scheduling and routing challenges.

The first case study tackles the Electric Vehicle (EV) routing and charging …

Multi-Lingual And Cross-Domain Frontiers In Machine-Generated Content Detection, Gurunameh Singh Chhatwal

Theses and Dissertations (Comprehensive)

The rapid advancement of generative artificial intelligence, particularly Large Language Models (LLMs) such as GPT-4 and their multilingual capabilities, has significantly blurred the distinction between human-authored and machine-generated content. This technological evolution introduces critical challenges concerning the detection and attribution of textual authenticity and authorship, exacerbating societal issues like misinformation proliferation and compromising academic and professional integrity. Traditional detection methodologies, predominantly monolingual and heuristic-based, have demonstrated inadequate generalizability and efficacy against the sophisticated, multilingual capabilities of contemporary generative models.

This thesis addresses two major problems arising from these advancements. Firstly, it introduces novel multilingual detection methodologies explicitly designed to differentiate …

A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do

Graduate Theses/Dissertations

Ontology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed …

Sparse Transformer For Anomaly Detection In Mobile Crowdsensing, Sanjeev Shrestha

Graduate Theses/Dissertations

Mobile Crowdsensing (MCS) is a sensing paradigm that leverages mobile devices to conduct a large-scale data collection. However, due to its openness and mobility nature, it is highly vulnerable to security issues such as injection attacks of malicious workers and fake tasks that can severely affect the platform’s normal functioning. To address this problem, the arrival of workers and task submission process is represented as a multivariate time series, and a two-stage framework is proposed. In the first step, we propose a novel transformer-based model, DozerAnomaly, that can efficiently detect anomalies in multivariate time series. We integrated a sparse attention …

Thyroid Carcinoma Recurrence Prediction Using Artificial Intelligence On Prognosis Parameter, Supawat Suntornlimsiri

Chulalongkorn University Theses and Dissertations (Chula ETD)

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid malignancy and is increasingly diagnosed worldwide. Although typically localised, PTC exhibits a notable risk of bilateral involvement, with contralateral disease occurring in up to 44 percent of cases. While completion thyroidectomy is recommended in selected high-risk scenarios, it carries potential complications, making accurate prediction of contralateral involvement essential. This study investigates the utility of classical statistical methods and machine learning (ML) algorithms—Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—in predicting contralateral PTC using a retrospective dataset of 122 lobectomy patients. ML models, especially RF with …

ปัจจัยที่ส่งผลต่อความตั้งใจใช้งานโมไบล์แบงก์กิ้งอย่างต่อเนื่อง, รตน รัตนพรเจริญ

Chulalongkorn University Theses and Dissertations (Chula ETD)

การใช้บริการโมไบล์แบงก์กิ้ง ในประเทศไทยมีอัตราเติบโตอย่างต่อเนื่องภายใต้การสนับสนุนจากภาครัฐ แต่ในช่วงหลายปีที่ผ่านมากลับพบว่าผู้ใช้จำนวนมากยังคงใช้งานเพียงบางฟังก์ชันพื้นฐาน เช่น การโอน-จ่าย-รับเงิน เท่านั้น (ธนาคารแห่งประเทศไทย, 2567) และประสบการณ์กับมิจฉาชีพและความเชื่อมั่นในระบบก็ยังคงเป็นประเด็นสำคัญที่สะท้อนให้เห็นถึงพฤติกรรมหลังการยอมรับและใช้งานโมไบล์แบงกิ้งกับการเปิดใจใช้บริการการเงินดิจิทัลอื่น ๆ ของผู้ใช้บริการบริโภค ซึ่งยังขาดการศึกษาในเชิงลึกโดยเฉพาะด้านปัจจัยที่ส่งผลต่อความตั้งใจใช้งานอย่างต่อเนื่อง และการเปิดรับฟังก์ชันใหม่ทั้งจากผู้ให้บริการโมไบล์แบงกิ้งและผู้ให้บริการอื่น ๆ ภายใต้ บริบทของการรับรู้ประโยชน์, และคุณภาพการบริการงานวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาปัจจัยที่ส่งผลต่อความตั้งใจใช้งานโมไบล์แบงก์กิ้งอย่างต่อเนื่อง ความตั้งใจใช้ฟังก์ชันที่ไม่เคยใช้งานและความตั้งใจใช้งานร่วมกับแอปพลิเคชันทางการเงินอื่น โดยอิงกรอบแนวคิดจากทฤษฎีความตั้งใจใช้งานต่อเนื่อง (Continuance Intention Model) รวมถึงการวิเคราะห์ผลจากประสบการณ์กับมิจฉาชีพต่างกัน กลุ่มตัวอย่างทั้งหมดจำนวน 434 คนถูกรวบรวมโดยการสุ่มแบบเจาะจง (Purposive Sampling) จากผู้ใช้โมไบล์แบงก์กิ้งในกรุงเทพมหานครและปริมณฑล ข้อมูลถูกวิเคราะห์ด้วยสถิติเชิงพรรณนาและการถดถอยเชิงพหุ (Multiple Regression Analysis)โดยผลการศึกษาพบว่า ปัจจัยด้านการรับรู้ประโยชน์ ความพึงพอใจ และความเชื่อมั่นในผู้ให้บริการ ส่งผลเชิงบวกต่อความตั้งใจในการใช้งานต่อเนื่องและการเปิดรับฟังก์ชันใหม่ทั้งจากธนาคารและผู้ให้บริการภายนอก ในขณะที่ประสบการณ์กับมิจฉาชีพมีแนวโน้มส่งผลให้ความตั้งใจใช้งาน แต่ไม่ส่งผลต่อการเปิดรับฟังก์ชันใหม่ การศึกษาปัจจัยด้านคุณภาพสารสนเทศทั้งสามมิติพบผลกระทบเชิงบวกต่อความพึงพอใจของผู้ใช้ และการศึกษาด้านความเชื่อมั่นพบผลกระทบเชิงบวกจากความสามารถ ความหวังดี และความซื่อสัตย์ของผู้ให้บริการ ผลการวิจัยนี้สามารถนำไปใช้พัฒนาแนวทางในการส่งเสริมการใช้งานฟังก์ชันของแอปพลิเคชันทางการเงินให้ครอบคลุมยิ่งขึ้น ทั้งในมิติการออกแบบระบบและผลิตภัณฑ์ทางการเงิน การสร้างความเชื่อมั่น และการออกนโยบายจัดการความเสี่ยงจากมิจฉาชีพ อีกทั้งยังสามารถขยายผลเชิงวิชาการในด้านการประยุกต์ใช้โมเดลพฤติกรรมผู้บริโภคในบริบทเทคโนโลยีทางการเงิน ซึ่งยังเป็นพื้นที่วิจัยที่เปิดกว้างและท้าทายในประเทศไทย

Neural-Network Based K-Value Prediction In Clustering Problems Without Distance Computation, Rohhan Rabari

Chulalongkorn University Theses and Dissertations (Chula ETD)

Clustering remains a pivotal component of unsupervised learning, central to tasks such as data exploration and pattern discovery. However, most conventional clustering algorithms are parametric in nature, requiring one or more parameters to be specified in advance—most notably the number of clusters (k). These predefined parameters can drastically alter the outcome of clustering, leading to unstable or misleading results, particularly when the true structure of the data is unknown. This thesis introduces a novel framework that transforms raw input into a latent vector representation, enabling a neural network to automatically predict the optimal number of clusters without any prior parameter …

Robust Palm Print For Mobile Authentication System, Son Nguyen

Chulalongkorn University Theses and Dissertations (Chula ETD)

Smartphones are gateways to financial, health, and personal data; consequently, mobile authentication must be accurate, fast, privacy-preserving, and scalable. This thesis presents an end-to-end palmprint authentication framework that addresses a practical trilemma: label-efficient learning, on-device efficiency, and cloud-scale identification. We pretrain a ResNet-18 encoder with self-supervised contrastive learning on unlabeled palm images, distill its representation to a lightweight MobileNetV3 student for real-time inference on phones, and support both 1:1 on-device verification and 1:N cloud identification using FAISS/HNSW. On public datasets, the system attains 99.2% accuracy, a 0.15% equal-error rate (EER), and ~87 ms end-to-end latency on iPhone-class hardware. FAISS scales …

Scam Slayer : A Gamification For Creating A Cyber Scam Awareness, Xin Lyu

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study explores how gamified learning can enhance the ability to recognize and prevent online fraud. The research team developed a role-playing educational game, Scam Slayer, using Ren’Py, integrating five common fraud scenarios and an Anti-Fraud Assistant to create an immersive and reflective learning experience. Analysis of a pre-game survey with 400 participants and a post-game survey with 40 players shows significant improvements in fraud recognition and alertness, with no demographic differences in learning outcomes. Participants also reported high satisfaction with the game’s narrative and educational value, suggesting that gamified learning can serve as an effective complement to traditional online …

แบบจำลองเชิงรูปนัยของกระบวนการจัดการโครงการในมาตรฐาน Iso/Iec29110 โดยใช้คัลเลอร์เพทริเน็ต, วรันณ์ธร ศิริกระจาย

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอแนวทางการตรวจสอบความถูกต้องเชิงรูปแบบ (Formal Verification) ร่วมกับกรอบการประเมินผลผ่านระบบเว็บแอปพลิเคชันสำหรับองค์กรขนาดเล็กประเมินตนเอง เพื่อสนับสนุนการปฏิบัติตามกระบวนการบริหารโครงการ (Project Management: PM) ของมาตรฐาน ISO/IEC 29110 สำหรับองค์กรขนาดเล็กมาก (Very Small Entities: VSEs) โดยใช้แบบจำลอง Colored Petri Nets (CPNs) ในการนิยามและจำลองกระบวนการหลักของ PM ได้แก่ การวางแผน การดำเนินโครงการ การควบคุม และการปิดโครงการ พร้อมทั้งตรวจสอบคุณสมบัติที่สำคัญของกระบวนการเหล่านี้อย่างเป็นระบบและระบบเว็บแอปพลิเคชันถูกพัฒนาขึ้นเพื่ออำนวยความสะดวกในการเก็บรวบรวมข้อมูล เรียกใช้การจำลอง และนำเสนอผลการประเมิน ข้อมูลที่ได้รับจากผู้ใช้จะถูกแปลงให้อยู่ในรูปของโทเคน เพื่อนำไปประเมินผ่านแบบจำลอง CPN และเชื่อมโยงผลลัพธ์กับระดับการปฏิบัติตามมาตรฐานในแต่ละกระบวนการของ ISO/IEC 29110 PM การทดลองใช้งานระบบยืนยันถึงความถูกต้อง ความสามารถในการตรวจสอบย้อนกลับ และประสิทธิภาพในการดำเนินงานของระบบที่นำเสนอ การผสานรวมระหว่างแบบจำลองเชิงรูปแบบกับระบบอัตโนมัติผ่านเว็บแอปพลิเคชันนี้ เป็นทางเลือกที่มีความยืดหยุ่นและเชื่อถือได้ โดยช่วยเสริมสร้างความเข้มงวดในการตรวจสอบ การประเมินกระบวนการอย่างเป็นระบบ และรองรับการนำไปใช้งานจริงผ่านส่วนติดต่อผู้ใช้ที่เข้าใจง่ายและเหมาะสมกับองค์กรพัฒนาซอฟต์แวร์ขนาดเล็กก่อนรับการตรวจสอบจากผู้ตรวจสอบ

Machine Learning Models For Pancreatic Cancer Survival Prediction: A Multi-Model Analysis Across Stages And Treatments Using The Surveillance, Epidemiology, And End Results (Seer) Database, Aditya Chakraborty, Mohan D. Pant

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pancreatic cancer is among the most lethal malignancies, with poor prognosis and limited survival despite treatment advances. Accurate survival modeling is critical for prognostication and clinical decision-making. This study had three primary aims: (1) to determine the best-fitting survival distribution among patients diagnosed and deceased from pancreatic cancer across stages and treatment types; (2) to construct and compare predictive risk classification models; and (3) to evaluate survival probabilities using parametric, semi-parametric, non-parametric, machine learning, and deep learning methods for Stage IV patients receiving both chemotherapy and radiation. Methods: Using data from the SEER database, parametric models (Generalized Extreme Value, …

Fostering Critically Conscious Lesson Planning In A Generative Artificial Intelligence Era, Derek Riddle, Paula Cristina Azevedo, Catharyn Shelton, Jaime Colwell, Jori Beck

Teaching & Learning Faculty Publications

Teacher candidates (TCs) use digital resources and social media to plan and develop learning material, and with publically accessible generative artificial intelligence (GAI), TCs are able to generate lesson plans within seconds rather than hours or days. While there is research on how to support TCs' evaluation of reliable digital media, there is little known on how to prepare TCs for GAI content. Using the complementary frameworks of Freire’s (1970) critical consciousness and Jonnasen’s (1991) theory of constructivism, this in-progress design based research seeks to develop an adaptable framework that addresses the evolving nature of technology, specifically GAI, and the …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Mihaela Hnatiuc, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020-2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the …

Eulerian Smoke Simulation With Multiple Fields, Diyang Zhang

Dartmouth College Master’s Theses

Fluid simulation is a cornerstone of computer graphics, enabling the realistic depiction of dynamic phenomena such as smoke, fire, and other gaseous behaviours. This thesis focuses on advancing Eulerian smoke simulation techniques, with a particular emphasis on grid-based simulations that capture intricate vortical structures and fine visual details.

We propose several detail-preserving frameworks that incorporate various scalar and vector fields within the simulation pipeline, including velocity, impulse, and Lamb vectors, along with their decompositions and transformed representations. By mathematically analyzing the properties of impulse, we derive its scalar fields decomposition (ImpSFD), which introduces an alternative numerical interpretation, and Vortex-Particles in …

Motion Planning For A Flexible Modular Raft Robot, Chun-Yi She

Dartmouth College Master’s Theses

This thesis presents a hierarchical motion planning framework for SoftRafts, a modular and deformable aquatic robot capable of performing locomotion and manipulation tasks on water surfaces. SoftRafts consist of soft and rigid components that enable structural reconfiguration, offering adaptability in unstructured aquatic environments.

To address the complexity of planning in high-dimensional, deformable systems, the proposed method uses a bounding-shape abstraction, specifically, enclosing circles and rectangular bounding boxes to simplify motion planning. These enclosures abstract the robot's overall shape, reducing the high-dimensional planning problem into a lower-dimensional problem. A global planner uses a probabilistic roadmap (PRM) to compute a collision-free path …

Action This Day: The Mathematics And Machinations That Bested The German Enigma, Jonah Weinbaum

Dartmouth College Master’s Theses

This thesis presents a comprehensive and chronological overview of cryptographic techniques designed to break Enigma, beginning in 1932 and culminating in the creation of the Turing-Welchman Bombe. We discuss the mathematical theory and electromechanical implements used to decode one of history's greatest ciphers.

Reexamining the Bombe through the lens of modern group theory, we critique Alan Turing's estimation of the number of "stops" that the Bombe produces for various plaintext-ciphertext pairing structures. To address its limitations, we introduce a new framework for estimating the number of stops by extending John Dixon's theorem concerning the probability that uniformly distributed elements of …

A Global Application Programming Interface-Enabled Earthquake Ground Motion Relational Database For Engineering Applications, Tristan E. Buckreis, Chukwuebuka C. Nweke, Pengfei Wang, Scott J. Brandenberg, Maria E. Ramos-Sepúlveda, Rashid Shams, Shako Mohammed, Renmin Pretell, Silvia Mazzoni, Paolo Zimmaro, Jonathan P. Steward

Civil & Environmental Engineering Faculty Publications

We present a application programming interface (API)-enabled relational database of global earthquake ground motion intensity measures, associated metadata, and processed time-series data. Raw ground motion records were processed by the authors using either manual or semi-automated processing procedures, and every processed record has passed a quality review by a trained analyst. Computed intensity measures include peak acceleration and velocity, pseudo-spectral acceleration response spectra, cumulative absolute velocity, Arias Intensity, and Fourier amplitude spectra. The processed time-series data, associated metadata, and ground motion intensity measures were organized into a web-served relational database consisting of 32 tables connected by primary/foreign key pairs. Ground …