Engineering Commons^™

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Retraction: A Database Of Teleseismic Shear-Wave Splitting Measurements For The Ordos Block And Adjacent Areas(Seismological Research Letters (2022), 93, 5, (2731–2739), 10.1785/0220210310), Lin Liu, Stephen S. Gao, Kelly H. Liu, Tu Xue, Yan Jia, Sanzhong Li

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

An earlier version of this article (Liu et al., 2022) included private data from the Himalayan-III program which should not have been made public. The authors acknowledge and regret their negligence during the selection of the measurements. The impacted data represent only 2.7% of the total data published in this article, and after review, the authors and the editors agree that this unintentional error has no bearing on the work's scientific conclusions. Therefore, we are retracting the impacted data and have made corrections to remove any Himalayan-III program data from the article's text, figures, and supplemental tables.

Investigating The Thixotropy Of Fresh Struvite Cement-Based Composite: Insights On Mechanisms Of The Pastes’ Thixotropic Behavior, Ugochukwu Ewuzie, Abdulkareem O. Yusuf, Damilola Daramola, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

Struvite (ST) recovered during wastewater treatment has been sparsely applied in cement-based composites. This study systematically evaluated the thixotropic behavior of cement-struvite (CST) pastes, a mix of 5–20 % ST by mass of cement, and proposed the interaction mechanisms leading to the pastes' thixotropy. The hysteresis loop area was used to establish the pastes' thixotropy and investigate its relationship with increased ST content, temperature, and yield stress. In the thixotropic behavior model used, the equilibrium shear stress was employed to examine the CST pastes' irreversible change; the characteristic time of deflocculation was used to study the flocs destruction process; and …

Vertical Hydrokinetic Turbine, Miki Harding, Ben E. Mertz

Rose-Hulman Undergraduate Research Publications

No abstract provided.

Ai Tools In Natural Resources And Environmental Systems For The Classroom, Research, Extension And Industry, Derek M. Heeren, C. T. Agouridis, Debabrata Sahoo, Isaya Kisekka, Trisha L. Moore

Department of Agricultural and Biological Systems Engineering: Presentations and White Papers

This white paper synthesizes a series of panel insights on AI adoption across education, Extension, research and industry contexts within natural resources and environmental systems. It emphasizes that while advanced machine learning and generative AI introduce structural shifts in autonomy, scale, and cognitive augmentation, their value lies in augmenting, not replacing, human expertise. Applied tool examples include decision‑support systems for rural water operators, HAB‑diagnosis apps integrating image classification and natural‑language guidance, an irrigation educational chatbot tailored to user background, and sensor‑driven orchard AI for light interception estimation and data gap filling. A recurring design imperative involves co‑development with end users, …

Redundant Functions Of Mir156-Targeted Squamosa Promoter Binding Protein-Like Transcription Factors In Promoting Cauline Leaf Identity, Darren Manuela, Liren Du, Qi Zhang, Yifei Liao, Tieqiang Hu, Jim P. Fouracre, Mingli Xu

Faculty Publications

No abstract provided.

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Investing Hysteresis In Floodplain Dynamics Of Lakes In The Middle St. Johns River Using Sentinel-1 Sar Imagery, Keenan Hubbard

Doctoral Dissertations and Master's Theses

Floodplain dynamics are often complex, with hysteresis potentially affecting the temporal relationship between flood stage and flood extent during subsequent inundation phases. This study leverages Sentinel-1 synthetic aperture radar (SAR) imagery to map flood extent in the Middle St. Johns River Lake floodplains and examine the presence of hysteresis during flood events. SAR scenes corresponding to river gauge readings were analyzed from the rising and falling limbs of a flood hydrograph. By comparing these flood maps, we assess differences in inundated areas at equivalent water levels during each stage of the flood event. The findings aim to enhance flood monitoring …

Experimental Analysis Of Satellite Operator Training Using Game-Based Virtual Reality Simulation, Lana Laskey

Doctoral Dissertations and Master's Theses

Satellite data plays a vital role in modern global infrastructure by enabling communications, navigation, and weather forecasting. As demand for satellite technology grows, so does the need for highly trained satellite ground operators. Traditional training regimens for satellite operators employ simulation using two-dimensional computer console displays paired with the varied ability of trainees to generate abstract mental imagery of the scenario. However, this development of mental imagery imposes a considerable learning curve and cognitive workload on the trainee, which may negatively impact the user experience and knowledge gained during the training scenario.

This experimental study investigated the effects of game-based …

Aircraft Bird Strike Risk Prediction Using Machine Learning And Analytic Hierarchy Process, Jason Anthony Powell

Doctoral Dissertations and Master's Theses

To address the limitations of Next Generation Radar-based bird strike forecasting, this study modeled 12 spatiotemporal weather features from the National Oceanic and Atmospheric Administration alongside bird strike risk using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), XGBoost regression tree, and Bayesian network algorithms. Five years of bird strike data from four geographically diverse airfields served as the target risk variable, categorized as low, moderate, or severe based on Department of the Air Force risk models. The ensemble model, which combines the LSTM-RNN and XGBoost regression algorithms, yielded the most accurate forecasts, achieving 80% to 93% accuracy across all airfields, …

Graduate School Blog - July 2025 Volume 1, Cynthia Haynes

UofM Grad School Blog

The July 2025 UofM Graduate School Blog – Volume 1 continues the Cost of Graduate School Guide with a deep dive into hidden and variable expenses such as residency-based tuition differences, program-specific fees, and differential tuition. The blog provides practical tips for prospective students on how to ask the right financial questions when comparing programs. It also features a student spotlight on Billy Brooks, a dual MHA/MBA candidate motivated to transform healthcare access and equity. Upcoming events include a Virtual Fall 2025 Open House with Financial Aid and USBS, a Dissertation Writers Retreat, and both in-person and virtual Graduate Student …

Analysis Of Vision Transformers And Domain Adaptation In Long-Range Facial Recognition, Zachary Michael Swanson

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Atmospheric turbulence presents a significant barrier to long-range facial recognition, introducing severe geometric distortions and blur that degrade image quality. This thesis investigates deep learning approaches for mitigating these effects, with a focus on transformer based architectures and domain adaptation strategies.

An in-depth benchmarking study was performed using convolutional neural networks (CNNs) and vision transformers (ViTs) on the Husker BRIAR Research Collection from up to 500m (HBRC-500) face dataset. The results demonstrated that vision transformers, particularly hierarchical vision transformers like the shifted-window (Swin) transformer, outperform CNN-based models at long distances due to their ability to model global spatial relationships and …

Biocomputing Approach To Modeling And Modulating Calcium Signaling, Sehee Sun

School of Computing: Dissertations, Theses, and Student Research

Biocomputing is an emerging field that seeks to perform computational tasks using biological substrates and processes. Unlike conventional computing systems based on silicon hardware, biocomputing leverages the parallelism, energy efficiency, and complex dynamics of living systems. Among various cellular mechanisms, calcium (Ca2+) signaling stands out as a central regulator of diverse biological functions, offering a promising basis for programmable logic and control in living cells.

This thesis introduces a novel framework for modeling and modulating Ca2+ dynamics using biologically inspired Boolean logic circuits. Specifically, we propose the Ca2+ Boolean Logic (CaBL) model, in which Ca2+ fluxes and interactions are abstracted …

Response Of Soybean To Variable Irrigation Levels In Eastern Nebraska Using The Aquacrop Model, Anmol Singh

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

AquaCrop was calibrated and validated for soybean [Glycine max (L.) Merr.] using 19 irrigation treatments from five years using data from field experiments. The model accurately simulated canopy cover (CC), soil water content (SWC), and grain yield, with overall validation nRMSE values of 12%, 7%, and 7%, respectively. The overall validation nRMSE for SWC in individual 30-cm soil layers was comparatively higher, at 19%. The model was subsequently applied for long-term simulations (2010–2024) to estimate soybean yield and water requirements for two dominant soil types, Yutan silty clay loam and Tomek silt loam under three irrigation levels - rainfed, …

Exogenous Ketones Reduce Inflammation And Pathology In The Tgf344-Ad Rat Model, Amaya Rogers, Pheven Yohannes, Juan P. Carcamo, Preetham Gundlapally, Amaya Coker, Macy A. Seijo, Caesar M. Hernandez

Expo Student Presentations

2025 Summer Expo Poster Presentation

• Biological & Life Sciences
• Engineering
• Physical & Applied Sciences

A Uas-Centered Investigation Of Vorticity Characteristics And Cold Pool Structure Across Forward And Left-Flank Boundaries In Supercells, Mark R. De Bruin

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Supercell internal boundaries are the locus of tornadogenesis; thus, understanding the characteristics of these boundaries, particularly in terms of vorticity, is important for identifying the role they play in tornado formation. Insight into the overall characteristics of internal boundaries and their possible role in tornadogenesis have been driven by studies reliant on numerical modeling-based experiments. Observational studies often neglect above-surface conditions or, when these observations are made, lack the spatial resolution to resolve boundary characteristics. During TORUS (Targeted Observation by Radars and UAS of Supercells) 2019 and TORUS-LItE (TORUS Left-flank Intensive Experiment) 2023, uncrewed aircraft systems (UAS) and mobile mesonets …

Feasibility Of Using Recycled Waste Plastic In Concrete Pavements In Nebraska, Amasi Hajahja

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Waste plastic (WP) is a growing environmental concern due to its non-biodegradable nature, large-scale production, and harmful impact on natural resources. With nearly 75% of WP in the United States ending up in landfills, effective recycling strategies are urgently needed. In parallel, the limited availability of natural aggregates highlights the need for alternative materials in concrete construction. This study investigates the feasibility of using recycled waste plastic (RWP) in concrete mixtures, both as aggregates and fibers for rigid pavement applications in Nebraska. Concrete mixtures were prepared by partially replacing natural fine aggregates with recycled plastic aggregates (RPA) at replacement levels …

Advanced Study Of Nickel-Titanium Alloy: Effects Of Point Defects On Mechanical And Thermodynamic Properties, Diego Armando Juarez Rosales

Open Access Theses & Dissertations

High-throughput first-principles calculations of point defects are emerging as a powerful tool to accelerate materials discovery in applications [1]. Substitutional, antisite, and vacancy defects can play an important role in the mechanical and thermal properties of intermetallic alloys [2]. In this present work I compute the thermal and mechanical properties of shape-memory alloy nickel-titaniun (NiTi) in the B19â?? martensitic and B2 austenitic phases from molecular dynamics (MD), using a second nearest neighbor (2NN) modified embedded atom method (MEAM) [3] classical potential in the temperature range from 200K to 600K and composition range from 45 atomic percent to 55 atomic percent …

Modeling Multiple Tasks In Recommendation Systems, Dinh Hieu Do

Dissertations and Theses Collection (Open Access)

Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.

However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

Data Annotations, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Pixel-wise object masks for each polarimetric scene in ASL file format for the Model Desert Terrain Monochromatic DoT data.

Hardware Accelerated Simulation Of Buck Converters Using Physics-Informed Neural Networks, James Clayton Crews

Theses and Dissertations

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly …

Mtu-Llm: Llm-Based Multi-Robot Task Allocation And Path Planning For Heterogeneous Robots In Search And Rescue Operations, Kaushik Kannan, Jungyun Bae

Michigan Tech Publications

Urban Search and Rescue operations after natural disasters involve locating and assisting victims in hazardous environments, which is challenging. Classical Multi-Robot Task Allocation (MRTA) and path planning approaches have been used to deploy heterogeneous robot teams in unsafe areas. However, existing methods often lack focus on workload balance and requirement fulfillment and struggle to generalize across different scenarios. To address these challenges, we propose Multi-robot Task allocation Utilizing LLMs (MTU-LLM), a framework designed to reduce the development time for task allocation and path planning approaches, enabling faster robot deployment. The framework uses an LLM-based “prompt engineering” approach that generates task …

An Exponential Cone Integer Programming And Piece-Wise Linear Approximation Approach For 0-1 Fractional Programming, Hoang Giang Pham, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

We study a class of binary fractional programs commonly encountered in important application domains such as assortment optimization and facility location. These problems are known to be NP-hard to approximate within any constant factor, and existing solution approaches typically rely on mixed-integer linear programming or second-order cone programming reformulations. These methods often utilize linearization techniques (e.g., big-M or McCormick inequalities), which can result in weak continuous relaxations. In this work, we propose a novel approach based on an exponential cone reformulation combined with piecewise linear approximation. This allows the problem to be solved efficiently using standard cutting-plane or branch-and-cut procedures. …

Elevating Next Generation Wireless Devices Towards Contactless Sensing For Healthcare Applications, Aakriti Adhikari

Theses and Dissertations

There is an increasing interest in technologies that can understand and perceive at-home human activities to provide personalized healthcare monitoring, aimed at early detection of disease markers and assisting physicians in making clinical decisions. Existing approaches, such as wearables, require users to wear sensors that can be cumbersome and cause discomfort. Vision based solutions, such as optical cameras, IRs, LiDARs, etc., can be used to design contactless at-home monitoring systems. However, these systems are limited by poor lighting and occlusion, and they are privacy-invasive. Fortunately, high-frequency millimeter-wave wireless devices provide an effective alternative to the existing systems to enable fine-grained …

Multiscale Modelling Of Heat And Mass Transfer In Hemp Wools, Rayan El Sawalhi, Marwan Al Kheir, Louay El Soufi, Hassan Assoum

BAU Journal - Science and Technology

This paper deals with the development of a multi-scale model of coupled heat and mass transfer for hemp wool insulating materials. Natural fibrous insulating materials from renewable resources like hemp offer an attractive environmental option for conventional insulation materials. These materials exhibit excellent thermal performances in terms of thermal conductivity and thermal capacity but are highly sensitive to moisture variations. To fully describe their behavior, we developed a multi-scale approach founded upon three characteristic scales: the heterogeneous fiber scale, the homogenized fiber network scale, and the final product scale. Using the method of volume averaging, we established transfer equations at …

Fedevd: A Federated Road Traffic Event Detector From Social Networks, Ahmad Traboulsi, May Itani, Layal Abu Daher, Ali Haidar

BAU Journal - Science and Technology

The increasing population and the corresponding rise in the number of vehicles, coupled with inadequate public transportation, is increasing the already existing traffic problem. One solution to this problem has been the implementation of Traffic Monitoring Systems (TMS). However, TMS deployment entails significant costs, including (1) the installation of hardware in key areas and (2) the employment of dedicated monitoring personnel. Simultaneously, Online Social Networks (OSNs) have become global platforms with rapidly growing user bases, leading to a surge in user engagement. This widespread participation has transformed social networks into invaluable sources of data for analytics, business intelligence, and decision …

Phytochemical Analysis And Antimicrobial Activity Of Micromeria Barbata Leaf And Stem Extracts Against Pseudomonas Aeruginosa: Insights From Molecular Docking And In Vitro Assays, Shiraz Rawas, Dalia El-Badan, Nawal Al Hakawati

BAU Journal - Science and Technology

The present study provides the characterization of the phytochemical content and the antibacterial activity of ethanolic extracts from the leaves (LM) and stems (SM) of Micromeria barbata (M.barbata) against Pseudomonas aeruginosa . Important functional groups were determined by analyzing the FTIR spectra of LM and SM. The phytochemical profiles were analyzed by GC-MS, and these characterized the chemicals according to retention periods and peak regions. The binding affinities of discovered compounds with the P. aeruginosa LasR and PqsD proteins were evaluated using molecular docking approach. Assays for biofilm formation, MIC, MBC, and agar well diffusion were used to assess the …

Exponential Decay For The Truncated Version Of The Porous-Elastic Model, Dilberto Da Silva Almeida Júnior, Baowei Feng, Luiz Gutemberg Miranda

BAU Journal - Science and Technology

This paper investigates the one-dimensional truncated version of the porous-elastic system that avoids the damaging con sequences associated with the second frequency spectrum. We consider a feedback law acting on the displacement of the elastic solid and establish well-posedness via the Faedo-Galerkin method. Furthermore, we prove an exponential energy decay result that holds independently of any specific relationship between the system’s coefficients, using the energy perturbation method.