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Ai Tips And Traps, Patrick Barry

Books

Based on a series of popular courses and workshops that Professor Patrick Barry has created for students, professionals, and anyone else interested in taking a skills-based approach to artificial intelligence, this book gives you a chance to engage with important AI concepts, experiment with exploratory AI exercises, and then ultimately develop your own customized list of AI traps to try as well as AI traps to avoid.

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Use Of Generative Ai In Scholarly Research: Challenges And Opportunities, Marco Marabelli, Robert M. Davison, Giovanni Gatti, Ankita Srivastava, Monideepa Tarafdar

Computer Information Systems Faculty Publications

The focus of this panel report is to present evidence of a panel discussion that took place in October 2025 on the use of generative artificial intelligence (GAI) in academic research and publishing. The panel included publishers (Elsevier, Emerald, Gailot Press, MIT Press, Nature/Springer, Wiley) and editors in chief (representing the Information Systems Journal, Journal of the AIS, Journal of Data Science and ICES Journal of Marine Science). Key outcomes of the panel are the following: 1) publishers and editors have some disagreements regarding GAI use; 2) besides disagreements, all recognize that GAI is here to stay, therefore outstanding issues …

Leveraging Machine Learning Techniques To Identify Catastrophic Outliers In Photometric Redshift Estimates, Jason Yoo

Honors Theses

Leveraging the unprecedented data volumes from modern astronomical surveys, this work addresses the critical challenge of identifying catastrophic outliers (COs) in photometric redshift (photo-z) estimates. Reliable redshift estimation is essential for precision cosmology, yet COs (galaxies with severely erroneous photo-z) can introduce significant systematic bias. We develop and evaluate two machine learning approaches for robust CO identification: a multilayer perceptron (MLP) utilizing photo-z point estimates and probability distributions, and a convolutional neural network (CNN) leveraging raw 5-band photometric images. Using datasets emulating LSST conditions, we demonstrate that the MLP model achieves high accuracy and stability across redshift bins, correctly identifying …

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

An Examination Of Ethics When Using Chatgpt, Blake V. Ailes

CCAC Theses and Dissertations

With the general population’s recent and dramatic increase in the frequency of ChatGPT and other similar Artificial Intelligence Generated Content (AIGC) usage throughout various industries, gray areas are becoming more prominent regarding whether ChatGPT is considered to be ethical or unethical in certain situations. Examples of unethical use of ChatGPT include plagiarism, the use of inaccurate information in drawing conclusions, and the creation of malicious code that negatively impacts various companies. Not all of these ethical concerns are necessarily the fault of the user or the AIGC. To date, peer-reviewed research on the ethical usage of ChatGPT is limited, primarily …

Considerations For Assigned Water After Expiration Of The 2007 Guidelines, Kathryn Sorensen, Sarah Porter, Anne Castle, John Fleck, Eric Kuhn, Jack Schmidt, Katherine Tara

The Traveling Wilburys of the Colorado River

As Colorado River supplies and demands reach razor-thin margins, new tools to provide adaptive capacity will play a critical role in sustaining communities across the West. We must reduce our consumption of water, while finding ways to cushion the impact. One of the most innovative tools for doing this, developed over the last two decades, is “Assigned Water” - giving users the ability to store conserved water earmarked for their own future use. Originally developed as “Intentionally Created Surplus” in the 2007 Colorado River Interim Guidelines, Assigned Water has been revised and expanded through U.S. Mexico Treaty Minutes and as …

Deep Learning For Eeg-Based Emotion Recognition With Temporal And Spectral Interpretability, Shruti Rameshbhai Shingala

Selected Full-Text Master Theses 2021-

Electroencephalography (EEG)-based emotion recognition has emerged as a critical component of affective computing and clinical neuroscience. Existing approaches to this problem primarily reduce the multi-dimensional EEG time series to a single averaged feature vector, thereby discarding the temporal structure of the emotional response. The present work addresses three identified gaps in the literature: the absence of temporal interpretability, the uniform use of frequency bands, and the use of single-scale temporal feature extraction. A deep learning architecture, MST-Mamba-Asym, is proposed, comprising four components: Asymmetry Attention, which encodes hemispheric asymmetry by computing signed left–right channel differences, FreqBandAttention, which learns differential weights across …

Artificial Intelligence Methods For Circadian Rhythm Recovery And Disease Identification From High-Dimensional Omics Data, Aram Ansary Ogholbake

Theses and Dissertations--Computer Science

High-throughput transcriptomic and proteomic technologies have enabled opportunities for studying biological processes and disease mechanisms. However, extracting meaningful biological information from these high-dimensional datasets remains challenging due to limited sample sizes, biological heterogeneity, measurement noise, and the absence of biological annotations. In particular, many molecular datasets lack temporal information required for circadian analysis, making the study of circadian rhythms difficult. Moreover, disease diagnosis and biomarker discovery from transcriptomic data often rely on complex machine learning models whose predictions are difficult to interpret and may not generalize well across independent datasets and experimental platforms. These limitations motivate the development of artificial …

Operational Hallucination And Safety Drift In Ai Agents, Shasha Yu, Fiona Carroll, Barry L. Bentley

School of Professional Studies

Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution. While single-turn safety mechanisms are relatively mature, extended interactions reveal structural vulnerabilities where initial alignment degrades over time. This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift, the gradual erosion of declared safety intent leading to constraint-violating actions (e.g., textual refusal followed by reconnaissance and unsafe execution), and Operational Hallucination, persistent repetitive tool calls indicative of flawed state perception (e.g., livelocks even in legitimate tasks). Through controlled multi-turn evaluation on high-stakes ethical dilemmas, malicious requests, and benign …

When Saying "No" Is Not Enough: Cognitive-Action Decoupling And The Illusion Of Safety In Llm Agents, Shasha Yu, Fiona Carroll, Barry L. Bentley

School of Professional Studies

Current safety evaluations of large language models (LLMs) predominantly rely on textual compliance, implicitly assuming that refusal-style responses correspond to safe behavior. This assumption becomes fragile when LLMs are embedded in agentic systems with the ability to execute state-changing actions. In this paper, we present an empirical critique of text-centric safety evaluation through an action-aware study of LLM agents under controlled conditions. Across multiple state-of-the-art models, we observe a recurring cognitive–action decoupling: agents generate policy-aligned refusal language while still producing unsafe tool-mediated action proposals. This produces an illusion of safety, where conversational audits indicate compliance even as operational risk persists. …

Big Tech As Transnational Spyware Regulator, Natalie R. Davidson

Fordham Intellectual Property, Media and Entertainment Law Journal

Spyware has emerged as a potent tool for leaders to shrink dem- ocratic contestation. In response to calls for constraints on the trade in spyware, states have updated the principal multilateral agree- ment on export controls, civil society groups have employed strate- gic litigation, and the European Union has altered its regulation, in each case with the aim of limiting exports where there is a risk of human rights violations. Yet, scandals involving the Israeli company NSO, among others, have made clear that even the updated regula- tory landscape is inadequate. Many actors are currently debating the reasons for existing …

Dynamical Phases Of Higher Dimensional Floquet Cfts, Diptarka Das, Sumit R. Das, Arnab Kundu, Krishnendu Sengupta

Physics and Astronomy Faculty Publications

This paper investigates the dynamical phases of Floquet Conformal Field Theories (CFTs) in space-time dimensions greater than two. Building upon our previous work which introduced quaternionic representations for studying Floquet dynamics in higher-dimensional CFTs, we now explore more general square pulse drive protocols that go beyond a single SU(1,1) subgroup. We demonstrate that, for multi-step drive protocols, the system exhibits distinct dynamical phases characterized by the nature of the eigenvalues of the quaternionic matrix representing time evolution in a single cycle, leading to different stroboscopic responses. Our analysis establishes a fundamental geometric interpretation where these dynamical phases directly correspond to …

Computational Clinical Judgment: Predicting Risk With Large Language Models, Hannah Laqueur, Ryan W. Copus

Faculty Works

For seventy years, research has shown actuarial methods outperform clinical judgment. Yet actuarial approaches have limitations: they generally rely on structured data; cannot exploit rare case-specific details; have limited accuracy where outcome data are scarce or incomplete; and cannot offer case-level justifications. Large language models (LLMs) offer a different approach. Like actuarial methods, they aggregate information algorithmically, but like clinicians, they bring general knowledge and can provide case-level justifications. We prompted seven LLMs to assess rearrest risk from 113 parole hearing transcripts and compared their predictions to a machine learning model trained on 4,000 cases with 91 administrative variables. GPT-5 …

A Comprehensive Statistical And Regional Analysis Of Lng-Powered Marine Vessels On Ghg Mitigation Strategies Considering Existing Bunkering Stations And Gwp Values, Canberk Hazar, Onur Yuksel, Murat Bayraktar

Journal of Marine Science and Technology–Taiwan

This study aims to quantify and analyse emissions from marine vessels that can operate on liquefied natural gas (LNG) but continue to use conventional fuels, largely due to the limited availability of LNG bunkering stations (BSs) over long distances. Four regions have been identified as having high concentrations of LNG-fueled vessels but limited access to operational BSs: the West Coast of the United States of America (USA), South Africa–Good Hope–Madagascar, Northwest Africa, and Brazil. This selection is based on the geographical distribution of these ships and the existing infrastructure. Hourly greenhouse gas (GHG) emissions have been calculated by considering the …

Legal Ethics Of Ai Snake Oil: Navigating The Hype, Harm, And Hope Of Legal Ai, Drew Simshaw

Scholarly Works

This paper argues that the legal ethics of AI extend far beyond competence and hallucinations. It shows how AI often functions as a mirror, exposing deeper ethical questions about institutional incentives, lawyer wellbeing, access to justice, and AI's broader social and environmental impacts.

Cross-Temporal Statistical Approaches For Evaluating Predictor-Outcome Relationships, Jeff Joseph

Dartmouth College Ph.D Dissertations

Central auditory function is linked with cognitive deficits, but few research projects use existing statistical approaches or develop new ones to forecast cognitive deficits using the results of central auditory tests. To address this limitation, we use a series of statistical learning frameworks for predicting a child’s cognitive abilities based on his/her central auditory performances and demographic factors. Two key challenges exist. First, children may start the study at a time when they are unable to perform the central auditory tests or cognitive tests. Second, cognitive performance is age-dependent, particularly in the early formative years of childhood and adolescence. To …

Activated Carbon Prepared From Residual Eucalyptus Wood For The Removal Of Methyl Orange: Equilibrium, Kinetics, Thermodynamics, And Desorption, Mathin Jaikua, Phuthidhorn Thanaphu, Sunisa Ungwiwatkul, Jakkrawut Maitip, Kanyarak Prasertboonyai

Applied Environmental Research

This research utilizes activated carbon derived from residual Eucalyptus wood (EW) for the removal of methyl orange (MO). The residual Eucalyptus wood-based activated carbon (EWAC) was produced through carbonization at 400 °C (EWC) followed by activation with a mass ratio of H3PO4 to EWC of 1:3, which was conducted at 800 °C for 1 h. The characterization of the EWAC involved various analytical techniques, including FTIR, XRD, BET, and CHN analysis. The adsorption parameters, such as pH (range: 3–8), adsorbent dose (range: 0.01–0.5 g per 50 mL of MO solution), contact time (range: 5–720 min), initial MO concentration (range: 5–600 …

Ammonium Adsorption From Aqueous Solutions Using Mnfe2o4-Functionalized Coffee Husk Biochar Nanocomposites : An Evaluation Of Hno3 And Naoh Activation, Thuy-Tien Do, Huu-Tap Van, Thi-Minh-Thu Nguyen

Applied Environmental Research

This research investigated the development of MnFe2O4-based composite adsorbents (MFO@CFA and MFO@CFB) by activating coffee husk-derived biochar with HNO3 and NaOH, followed by synthesis through coprecipitation and hydrothermal techniques. The structural and chemical properties of the nanocomposites were characterized by scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. Batch adsorption experiments were used to assess ammonium removal efficiency across a range of conditions: solution pH (3–8), contact time (5–120 min), adsorbent dosage (0.2–3 g L-1), and initial ammonium concentration (10–60 mg L-1). The findings indicated that compared with MFO@CFB, which was synthesized from NaOH-activated biochar, MFO@ CFA, derived …

Assessing Forest Fragmentation And Edge Expansion In Nan Province, Thailand, Using Landsat Data (2014–2022), Kasidit Rison, Marut Fuangarworn, Chatchawan Chaisuekul

Applied Environmental Research

The increasing pressure from land-use change and agricultural expansion in Nan Province, northern Thailand, has accelerated forest fragmentation and reduced the ecosystem service value (ESV) of the landscape. This study aims to: assess land use and forest fragmentation change in Nan Province between 2014 and 2022, and evaluate the impact of land use change on ESV. Landsat 8 OLI satellite imagery from 2014 and 2022 was used to classify land cover into five types: forest, agriculture, settlements, exposed soil, and others. Forest fragmentation was evaluated using landscape metrics such as patch number, mean patch size, edge length, and core area. …

Environmental Sustainability Performance And Efficiency In Thailand’S Rajabhat Universities : A Cross-Regional Analysis Of The Impact Rankings, Konstantinos Balis

Applied Environmental Research

Higher education institutions (HEIs) are expected to increasingly communicate their contributions to the United Nations’ Sustainable Development Goals (SDGs), with the rise of alternative sustainability rankings serving as prominent reporting instruments for institutional transparency. In Thailand, Rajabhat Universities, a network of government HEIs historically associated with their mission of teacher training and local development, have been actively participating in the Times Higher Education (THE) Impact Rankings, presenting a unique case study to explore cross-regional sustainability efforts in relation to global benchmarks. To evaluate trends, regional disparities, and engagement performance across the sustainability pillars, this study uses a descriptive quantitative design …

Advancing Food Equity Through Explainable Ai (Xai): Identifying Place-Based Factors And Conditions Of Food Security, Leslie Hoglund, Hyoshin Park

Health Behavior, Policy & Management Faculty Publications

Food behaviors, food security, and their association with socioeconomic factors constitute a critical area of study with implications for public health, economic stability, and social equity. Understanding these relationships are essential for developing effective policies and interventions that promote sustainable, healthy food systems and greater food equity. This paper employs explainable artificial intelligence (XAI) methods to identify key features influencing household food behaviors. The insights gained from the XAI analysis are further utilized in conjunction with inverse reinforcement learning (IRL) to examine expert behaviors related to eating habits satisfaction. The XAI results reveal that household health conditions, spending patterns, and …

The Link Fantastic: Detecting And Linking Simulated, Nongravitationally-Accelerating Objects With The Rubin Observatory Pipeline, Ellie White

Theses, Dissertations and Capstones

Rubin Observatory’s Legacy Survey of Space and Time (LSST) is poised to revolutionize our understanding of the Solar System. After only a few months of regular operations, Rubin has already discovered over 11,000 new Solar System bodies, and is projected to discover millions more. With discoveries pouring in, and with the development of moving object detection pipelines for routine observing, one key question is: how effectively can Rubin detect and identify objects with anomalous trajectories? The answer is of interest not only for natural bodies in our Solar System, but also in the search for technosignatures, which could be identified …

Investigating The Viability Of Fully Online Asynchronous Physics Instruction In Formal Education, Clifton W. Massey-Noel

Physics Dissertations

In this dissertation we present results from two consecutive investigations into the fully online asynchronous modality in Physics Education. We first discuss the efficacy of a fully online asynchronous section of Modern Physics held in Fall 2024. The asynchronous section is compared with four prior partially-flipped sections of the same class, two of which were held in-person, and two of which were held synchronously online. Results suggest that all three modalities can be made to be about equally as effective. We also discuss the efficacy of a fully online asynchronous section of Calculus-Based Introductory Mechanics held in Fall 2025. The …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudrish

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

Adaptive Real-Time Fraud Detection Using Online Learning And Explicit Concept-Drift Detection, Purva Govind Tugaonkar

Master's Projects

Real-time credit card fraud detection faces challenges such as extreme class imbalance, delayed feedback, and concept drift in transaction streams. This project implements and evaluates an adaptive streaming fraud detection framework based on three methodologies: (1) online learning with incremental updates, (2) explicit conceptdrift detection using statistical monitoring, and (3) separate models for immediate and delayed supervision, combined with cost-sensitive learning and anomaly detection. The system processes the credit card fraud dataset in a batched streaming fashion, uses multiple online learners and ensembles. Experiments show that online, driftaware models maintain high recall on frauds while controlling false positives under imbalanced …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

Ai Deployment Authorisation: A Global Standard For Machine-Readable Governance Of High-Risk Artificial Intelligence, Daniel Djan Saparning

Student Publications

Modern artificial intelligence (AI) governance lacks a formal, enforceable mechanism for determining whether a given AI system is legally permitted to operate in a specific domain and jurisdiction. Existing approaches-such as model cards, audits, and benchmark evaluations provide descriptive information about model behaviour and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS). This machine-readable, regulator-grade framework evaluates AI systems across five legally and economically grounded dimensions: Risk, Alignment, Externality, Control, and Auditability, derived from safety engineering, alignment theory, algorithmic accountability, and liability economics. ADAS produces …

Metamorphosed Components In Cv Chondrites Camel Donga 003 And Camel Donga 040 As Evidence For Varying Degrees Of Metamorphism Within The Parent Body, Kathryn E. Stone

Honors Theses

The CV carbonaceous chondrites are a group of primitive stony meteorites that record early solar-system evolution processes. They are typically of low metamorphic grade, though some metamorphosed CVs have recently been found. Two CV chondrite breccias, Camel Donga 003 (CD 003) and Camel Donga 040 (CD 040) consist of multiple lithologies. CD 003 consists of three lithologies, one of which – Lithology C – has been metamorphosed to petrologic type ~3.7 (Alevy et al. 2025). CD 040 consists of two lithologies, one of which is metamorphosed to petrologic type 3.7 (Dunn and Alevy, 2024; Dunn et al. unpublished). Both samples …

Fluid Agency In Ai Systems: A Case For Functional Equivalence In Copyright, Patent, And Tort, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern Artificial Intelligence (AI) systems exhibit fluid agency in multi-step workflows: lacking human-like consciousness or culpability, yet they display behavior that is (i) stochastic (probabilistic and path‑dependent), (ii) dynamic (co‑evolving with user interaction), and (iii) adaptive (able to reorient across contexts). These properties generate valuable outputs but collapse attribution, irreducibly entangling human and machine inputs. Doctrines that assume traceable provenance—authorship, inventorship, and liability—fracture under this unmappability, yielding ownership gaps and moral “crumple zones.”This Article argues that only functional equivalence stabilizes doctrine under unmappability: Where provenance is indeterminate, legal frameworks should treat human and AI contributions as equivalent for allocating rights …