Digital Commons Network^™

Making Words Count: Computational Linguistics In Management Research, Joseph J. Simpson, Richard A. Hunt, David M. Townsend, Judy Rady, Elham Asgari, Mohammad Rady, Daniel J. Beal

International Business and Entrepreneurship Faculty Publications

In recent years, a rapidly growing number of management scholars are using computational linguistics (CL) to analyze vast corpora of naturally occurring organizational language. Yet the rapid adoption of CL methods has outpaced the development of shared standards for linking theoretical constructs, linguistic data, and computational measurement. We synthesize 353 articles published in leading management journals for 2013–2025, identifying four recurring challenges shaping the credibility of CL-based research: misalignment between constructs and textual corpora, unnecessary model complexity, the opacity of large language model workflows, and insufficient transparency in reporting. Building on these patterns, we develop four integrative design principles—construct-language fit, …

Metagenomic Polymorphic Toxin Effector And Immunity Profiling Predicts Microbiome Development And Disease-Related Dysbiosis, Hunter W. Schroer, Francesco Beghini, Juan Antonio Raygoza Garay, Nicholas A. Christakis, Dustin E. Bosch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease …

Quantitative Assessment Of Roasted Coffee Freshness Over Time Using Multi-Parameter Analysis, Maya Fetzer

Honors Theses

Roasted coffee undergoes continuous chemical and physical degradation after roasting, yet industry freshness standards remain empirically defined rather than grounded in measurable parameters. This study developed a multi-parameter framework for quantifying roasted coffee freshness by integrating gas-phase and solid-phase analysis, backed by human sensory evaluation, across three roast levels — light (City+), medium (Full City), and dark (Vienna) — over a four-month storage period. CO2 off-gassing was monitored continuously for 30 day, gas chromatography-mass spectrometry (GC-MS) tracked volatile compound changes in headspace gas, and Fourier transform infrared-diffuse reflectance spectroscopy (FTIR-DRIFTS) quantified oxidation-associated functional group changes, and microindentation assessed bean stiffness. …

Effects Of Metal-Modified Catalysts On The Desorption Performance Of Mixed Amine Solutions And Machine Learning Prediction, Xunxuan Heng, Zhenzhen Zhang, Longhua Zhu, Li Yang, Shugang Xie, Zeyu Wang, Dongtai Han, Fang Liu, Kunlei Liu

Mechanical Engineering Faculty Publications

The high energy penalty associated with solvent regeneration is still a major bottleneck in amine-based CO₂ capture. In this work, the effects of five solid acid catalysts on the desorption performance of a mixed-amine solvent were compared, and the HY catalyst with superior desorption behavior was selected and further modified with four transition metals (Co, Mn, Gr and Ce) to enhance its catalytic activity. The findings indicate that the CO₂ desorption capacity and maximum desorption rate of the Co-modified HY catalyst reach 48.96 mmol and 0.02211 mmol/s, corresponding to increases of 36.80% and 35.39% relative to the blank …

Analytics Framework For Cyberattack Risk, Severity, And Timing Prediction Using Survival - Aware Sequence Modeling, Mariia Voronina

Theses and Dissertations

This study aimed to develop an analytics framework for cyberattack risk, severity, and timing prediction using survival-aware sequence modeling. The primary objective was to analyze the temporal dynamics of cyberattacks by jointly considering attack type, severity, risk, and time-to-attack, and to evaluate whether these components can be effectively modeled within a probabilistic framework. The analysis was conducted using a synthetically generated cyberattack dataset designed for cybersecurity research purposes. The dataset included multiple attack categories, such as DDoS, Intrusion, Malware, severity levels, and engineered risk measures. Python was used as the primary programming language for data analysis and model development. The …

Machine-Learning- And Iot-Based Approach For Predicting Water Quality Using Data Classification And Explainable Ai Technique, M. Samir Abou El-Seoud, Omar H. Karam 2, Hosam El-Sofany 3

Computer Science

Maintaining water quality is crucial for both public health and environmental sustainability. This study proposes a machine-learning- (ML-) and Internet-of-Things- (IoT-) based approach to water quality prediction that utilizes explainable artificial intelligence (XAI) and data classification techniques. The proposed approach integrates IoT devices to enhance real-time data collection, facilitating continuous monitoring and early anomaly detection. Ten different ML classifiers—Decision Trees, K-Nearest Neighbors, XGBoost, Naïve Bayes, Logistic Regression, AdaBoost, Random Forests, Support Vector Machines, Voting, and Multi-Layer Perceptron—were evaluated to find out which one works best for predicting water quality. We used three distinct approaches for feature selection: analysis of variance …

Machine Learning For Recession Prediction, Ethan Reusser

Williams Honors College, Honors Research Projects

Macroeconomic predictions present challenges in machine learning due to the rarity of economic recessions, the constantly-changing matter of global markets, and severe class imbalance in historical data. This project focuses on predicting the onset of United States economic recessions within a 12-month window using Python and Jupyter Notebook. A machine learning pipeline was developed utilizing multiple models: Logistic Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied strictly to training data, paired with Platt scaling for calibration on thresholds. The resulting models were evaluated in the 2005 …

Self-Lubricating Nano-Coatings For Tribological Applications: Materials, Mechanisms, And Ai-Driven Design Approaches, Viral Panara, Vikas Panchal, Satayu Travadi, Harmish Bhatt, Madhav Oza

Mansoura Engineering Journal

Self-lubricating coatings with nanostructured structures are becoming crucial in order to decrease friction and wear in critical tribological systems like precision tools, biomedical implants, aerospace actuators, and engines. In this review, the most representative nano-engineered solid lubricants such as transition metal dichalcogenides, diamond-like carbon, hexagonal boron nitride, graphene, MXenes and hybrid multilayer architectures are discussed. The focus of the discussion is on the most important tribological mechanisms like lamellar sliding, oxidative adaptation, tribofilm formation, transfer-film development, and nano-asperity contact evolution. It also summarizes the most significant deposition techniques, such as cathodic arc physical vapor deposition, chemical vapor deposition, plasma-based deposition, …

A Hierarchical, Data-Driven Analytical Paradigm For Strategic Planning, Operational Decision-Making, And Resilience Enhancement In United States Electric Power Systems Under Extreme Demand Conditions, Anyama Hanson Tettey

Dissertations

Historical evidence indicates that the United States electric power grid continues to face persistent criticism for reliability challenges during peak demand conditions. Extreme ambient temperatures are a primary driver of such peaks, rendering the grid most vulnerable precisely when electricity demand is highest. These demand surges are largely driven by coincident operation of heating, ventilation, and air-conditioning systems across customer classes, a trend expected to intensify as sectors such as transportation increasingly rely on electrification. Efforts to mitigate demand related unreliability have traditionally focused on system-level demand forecasting and planning, typically at the scale of cities or entire utilities. However, …

Beyond The Binary: Navigating Ai’S Role In Palliative Ethics-A Review, Reebu Sara Varghese, Tijo Cherian

ASEAN Journal on Science and Technology for Development

Palliative care is being influenced by artificial intelligence, especially when it comes to data-related aspects. In this context, care can be enhanced in terms of the quality of its results and the level of its efficiency, particularly with the help of technological tools such as artificial intelligence, which is capable of managing different types of information in the field of health care. Such characteristics have the potential to improve the quality of patient care while at the same time reducing the workload of healthcare professionals in palliative care. However, there are considerable ethical issues that need to be addressed with …

Strain-Induced Nonvolatile Domain Switching And Tunable Elastic Modulus In Ba1-Xsrxtio3 Membrane By Phase-Field Simulation., Laveeza Ahmad

Material Science and Engineering Dissertations

Ferroelectrics underpin a broad spectrum of technological applications due to its switchable ferroelectric polarization and the associated electro-mechanical responses under electrical, optical, thermal, and mechanical stimuli. Recent advancement in membrane technology offers new opportunities to tune ferroelectric polarizations via mechanical strains at relatively large magnitude and scale. However, its influence on the tunability of mechanical responses of the membrane remains underexplored. Herein, we developed a phase-field model for free-standing Ba_1-xSr_xTiO₃ ferroelectric membranes with stress-free boundary conditions on top/bottom surfaces and achieved strain-induced nonvolatile ferroelectric domain switching in the membrane. It is discovered that a …

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

Assessment Of Carbon Capture Potential In Rubber Plantations Via Landsat 9 Imagery Analysis, Ade Fitria, Rahmawaty Rahmawaty, Razali Razali, Jamin Saputra, Seca Gandaseca

Baghdad Science Journal

Indonesia is a significant greenhouse gas (GHG) emitter, contributing 12.3% of carbon dioxide (CO₂) of total emissions. Carbon dioxide (CO₂), a major GHG, is increasing in the Earth’s atmosphere. The CO₂ absorption can be increased through rubber plantations because rubber plants, such as forest plants, can process CO₂ as a carbon source for photosynthesis. This research aims to analyze carbon uptake in relation to tree density, biomass of rubber vegetation, and soil organic carbon (SOC) content, as well as to map the distribution of carbon potential using remote sensing. This research was conducted in …

An Integrated Data-Driven Framework For Arctic Shipping: Analyzing Vessel Speed, Environmental And Ecological Factors Through Innovative Statistical Spatio-Temporal Methods, Inverse Optimization And Machine Learning, Mauli Pant

Theses and Dissertations

This dissertation develops an integrated data-driven framework to analyze vessel navigation and ecological risk in the United States Arctic from 2010 to 2019. As environmental change and maritime activity increase in the region, understanding how vessels respond to dynamic conditions and how those responses interact with marine ecosystems has become increasingly important. A central theme of this dissertation is the treatment of vessel speed as both an observed outcome and a decision variable reflecting trade- offs among operational, environmental, and ecological factors. The first chapter develops a predictive framework for vessel speed over ground (SOG) using Gaussian Process Boosting (GPBoost), …

Measuring The Efficiency Of The Arts And Culture Sector Relative To Gdp: A State-Level Analysis Of The United States, Ameerkhan Jaffar Khan Khader Khan

Selected Full-Text Master Theses 2021-

Arts and cultural production contributed $1.17 trillion to United States gross domestic product in 2023, 4.2% of the national total, but that contribution is spread very unevenly across states, and the official statistics describe how large the sector is rather than how efficiently it operates (Bureau of Economic Analysis, 2024). This study asks how efficiently each state convert growth in arts and culture employment into growth in value added, how many inputs the efficiency model can carry before it stops distinguishing 51 observations, and whether observable state characteristics explain the differences found. The analysis uses 2023 state-level data for all …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …

Machine-Learning-Guided Design Of A Biomedical High-Entropy Alloy For Additive Manufacturing: Cast-State Benchmark And Preliminary Lpbf Feasibility Assessment, Deyu Jiang, Lai Chang Zhang, Kuaishe Wang, Wen Wang, Chenyuan Zhu, Yuanfei Fu, Kai Wang, Wei Zhai, Ching Chiuan Yen, Weijie Lu, Di Zhang, Liqiang Wang

Research outputs 2022 to 2026

Additive manufacturing of biomedical high-entropy alloys (BioHEAs) demands a combination of low elastic modulus, high strength, and damage tolerance, yet composition discovery remains largely empirical. Here, we establish a machine-learning framework that couples virtual screening with physical prototyping to link composition, deformation mechanism, and properties. Ensemble models for strength, elongation, and modulus were applied to screen Ti–Zr–Nb–Ta–Mo-centered quinary-to-septenary spaces (∼15 million compositions), revealing discrete performance islands anchored by a Ti–Zr backbone. A Zr-rich BCC alloy (Zr₃₉.₃Ti₁₉.₅Nb₁₇.₉Ta₁₆.₈Mo₆.₅) was identified and validated. In the as-cast state, it delivers ∼1.0 GPa yield strength, 22.3% elongation, and an 88 GPa elastic modulus; ductility originates …

Using Real-World Observational Data To Stratify Risk And Predict Outcomes In Chronic Liver Disease, Kristopher Bennett

Peninsula Medical School Theses

Background: Chronic liver disease (CLD) is increasing globally, driven by metabolic and alcohol-related aetiologies. Late diagnosis is common due to a prolonged, subclinical fibrotic phase preceding advanced disease (ACLD). Current pathways and cross-sectional fibrosis tests have poor sensitivity in low-prevalence settings and limited population-scale availability. Predicting future ACLD in primary care offers opportunities for earlier diagnosis and prevention, but few suitable clinical prediction models (CPMs) exist.Aim: To review existing general-population liver prediction models and develop CPMs for incident ACLD in unselected primary care populations using routinely collected data.Methods: A systematic review and PROBAST+AI appraisal of fourteen CPMs (median …

Preserving Platform Innovation With A Narrow Exception: Section 230(C)(1) Immunity For Algorithmic Recommendations, John "Jack" Di Masi

American University Business Law Review

This Comment examines Section 230(c)(1)’s immunity for online platforms that use machine-learning algorithms to recommend third party content and proposes a narrow exception for truly “bad-actor” cases. It reviews Section 230’s text, as well as its early cases, showing that Congress intended broad protection for editorial functions such as content organization. It then traces how recommendation algorithms evolved and how courts treat algorithmic sorting as a neutral tool. This Comment addresses conflicting interpretations and defends broad immunity for routine curation. This Comment argues that immunity should be stripped only after a balancing test under a three-factor “bad-actor” framework—one that evaluates …

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …

Automation And Autonomy In The Ivf Laboratory: Concepts And Implications For Embryologists, Jacques Cohen, Gerardo Mendizabal-Ruiz, Giles Anthony Palmer, Giuseppe Silvestri, Mina Alikani

EVMS School of Health Professions Faculty Publications

Automation is regarded as the next phase in the evolution of laboratory IVF. Despite technological advances, most laboratory procedures remain operator-dependent, contributing to variability in performance and outcomes. While automation has improved reproducibility and efficiency in many areas of medicine, its adoption in IVF has been slow. This review examines how automation may be integrated into IVF laboratories through a set of conceptual distinctions. Automation refers to the execution of procedural steps by machine-controlled systems, whereas autonomy describes the degree to which such systems can operate without human intervention. This review distinguishes between static automation, which maintains or monitors the …

Real-Time Defect Detection In Ceramic Additive Manufacturing Using Deep Convolutional Neural Networks, Dylan Doan, Amir Armani, Marcia Golmohamadi

Faculty Research, Scholarly, and Creative Activity

Although ceramic additive manufacturing technologies have greatly advanced over the past decade, they are still far from perfect. Even small printing defects can have a severe impact on the mechanical properties of additively manufactured ceramic parts due to their low fracture toughness. The objective of this study was to detect these defects during the printing process for a new ceramic additive manufacturing technique, called ceramic on-demand extrusion (CODE). Images were collected, pre-processed, augmented, and separated based on their distinctive classes (i.e., type of defects). Deep convolutional neural networks were developed to identify multiple types of defects in real-time.

Drift Aware Continual Finetuning Of Encoder-Only Text Classifiers Under Temporal Distribution Shift, Simran Kc

Theses

The performance of encoder-only text classifiers degrades silently as their input distribution drifts over time. This thesis presents a closed-loop pipeline for drift-aware continual fine-tuning that combines a sample size invariant drift severity metric, an automated retraining loop on the most drifted samples, and a validation gate that rejects unsafe updates before deployment. We evaluate the pipeline on two datasets, Amazon Electronics Reviews and HuffPost News Category, and observe significant performance improvements over the initially trained model, along with a substantial reduction in compute and labelling costs compared to fresh-per-year and cumulative retraining baselines.

Stem-Fit And Soil-Fit: Integrated Plant And Soil Nitrogen-Hormone Sensing With Machine Learning-Based Forecasting For Next-Generation Precision Agriculture, Nafize I. Hossain, Mohammad Solaiman, A.K.M. Ahsanul Habib, Md Al Mahmud Hossain Al Hadi, Shawana Tabassum

Electrical Engineering Faculty Publications and Presentations

Inefficient fertilizer application in agriculture leads to reduced crop productivity, nutrient losses, and reduced crop resilience, highlighting the urgent need for real-time monitoring of plant–soil nutrient and stress dynamics. This research aims to develop and validate a multiplexed sensing platform for real-time, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation. The proposed sensor suite integrates 3D-printed modules for continuous monitoring of nitrate, ammonium, and pH in both soil and plant sap, along with salicylic acid (SA), indole-3-acetic acid (IAA), methyl jasmonate (MeJA), and ethylene (ET) in plant sap. The sensors, …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Machine Learning For Wearable Sensor-Based Human Movement Rehabilitation: A Five-Year Systematic Review, Yassine Benachour, Farid Flitti, Lina Maloukh, Aicha Beya Far, Elhocine Boutellaa, Mohamed Bentoumi, Marwa Chendeb El Rai, Nour Aburaed, Khaled Ali, Moez Rehman, Sultan Mosleh, Rania Dghaim, Sadok Bouamama

All Works

Wearable-sensor-based human movement analysis is an increasingly important component of digital health and rehabilitation, enabling objective monitoring and data-driven personalization of therapy. In parallel, machine learning (ML) methods have rapidly expanded for interpreting multimodal movement signals, yet the evidence base remains heterogeneous and difficult to benchmark. This PRISMA-guided systematic review synthesizes recent ML approaches for wearable human motion analysis in rehabilitation-oriented health applications. We searched IEEE Xplore, PubMed, and Scopus for English-language studies published from 2021 to 2025 and extracted information on sensor modalities, ML task formulations and model families, dataset characteristics, validation protocols, and reported performance metrics, together with …

Stall Detection In Hydraulic Excavator Operations Using Heuristics And Machine Learning: A Case Study, Mateo Fernando Montenegro Defaz, Kwame Awuah-Offei, Yixiang Gao

Mining Engineering Faculty Research & Creative Works

This work aims to develop a reliable algorithm for stall detection during excavator digging by analyzing key operational variables such as velocity and angular displacements from machine monitoring data. The work develops and validates a heuristic algorithm to detect stalling events and trains a support vector machine classification algorithm to distinguish between "normal" digging cycles and cycles with stalling. This work is a novel attempt at using a classification algorithm to categorize digging cycles into normal and those with stalling events based on machine monitoring data alone. The developed classification algorithm achieved a sensitivity of 100%, indicating it correctly identified …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

A Survey Of Ai Methods For Geometry Preparation And Mesh Generation In Engineering Simulation, Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Franck Ledoux, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich, Yongjie Jessica Zhang

Computer Science Faculty Publications

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons …