Digital Commons Network^™

Nebraska Test 2310: John Deere 9rx 540 Single Turbo (Chassis Serial Numbers 1rw9540xxsx828001 And Higher), Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

Nebraska Test 2309: John Deere 9rt 540 Single Turbo (Chassis Serial Numbers 1rw9540xxsx929001 And Higher), Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To …

You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …

Aim5b: Ai Integrated Semantic Framework For 5g And Beyond Network Management, Thanveer Sulthana, Ava Sharif Jourabchi, Venkat Rao Manavarthi, Jayadithya Nalajala, Ankitha Srirama Reddy, Baek Young Choi, Sejun Song

Computer Science Faculty Research & Creative Works

Scalable, interpretable, and intelligent network monitoring and management are critical for 5 G and future networks. This paper introduces Aim5B, an AI-integrated semantic framework for 5 G and beyond network management to address these challenges. Aim5B processes unstructured logs from key 5G core network functions, and transforms them into a knowledge graph aligned with the semantic structure of control-plane events. Leveraging a large language model (LLM), Aim5B enables natural language queries to be translated into Cypher graph queries, facilitating precise log retrieval, event analysis, temporal correlation, and statistical summarization-without relying on static parsing rules or predefined dashboards. Integrated on a …

Harnessing Coherent-Wave Control For Sensing Applications In Scattering Media, Pablo Xavier Jara Palacios

Doctoral Dissertations

"Diffuse electromagnetic waves are widely used for non-invasive sensing and imaging in complex scattering media such as biological tissues. A fundamental limitation of such techniques is the scarcity of detected photons: as the source-detector separation increases to access deeper regions of the medium, the signal strength decays rapidly, leading to poor signal-to-noise ratio and limited sensitivity. This dissertation addresses this challenge through a combination of theory and computation.

We show that coherent control of the incident optical wavefront can compensate for the scarcity of detected photons that limits conventional diffuse optical imaging, typically performed in the near-infrared spectral region. By …

Clique Annealing: Semi-Supervised Community Detection Under Crystallization Kinetics, Ling Cheng, Jiashu Pu, Ruicheng Liang, Qian Shao, Hezhe Qiao, Feida Zhu

Research Collection School Of Computing and Information Systems

Semi-supervised community detection seeks to find a specified community type when only few communities are labeled. Existing "select-then-refine" pipelines often start from mis-aligned cores and rely on Reinforcement-Learning or Generative Adversarial Network, increasing computational cost and limiting scalability. We address these issues with a unified energy framework under crystallization kinetics that jointly models energy, structure, and growth. Based on this perspective, we propose CLique ANNealing (CLANN), which first employs Nucleus Proposer to select candidate clique as community core under four physics-inspired criteria. A learning-free Transitive Annealer then iteratively merges neighboring cliques and repositions the nucleus, enabling spontaneous, scalable community growth. …

Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, combining the strength of large language models (LLMs) and Evolutionary Computation (EC) has shown promising results for addressing optimization problems. It typically involves either iterative next-step solution seeking or directly prompting LLMs to generate critical optimization codes. However, these methods often suffer from low computational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. LLaMoCo features a comprehensive instruction set that includes code-style problem descriptions as input prompts and robust optimization codes from expert EC optimizers as …

Realign: Text-To-Motion Generation Via Step-Aware Reward-Guided Alignment, Wanjiang Weng, Xiaofeng Tan, Junbo Wang, Guo-Sen Xie, Pan Zhou, Hongsong Wang

Research Collection School Of Computing and Information Systems

Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads to semantically inconsistent or low-quality motions. To address this limitation, we propose Reward-guided sampling Alignment (ReAlign), comprising a step-aware reward model to assess alignment quality during the denoising sampling and a reward-guided strategy that directs the diffusion process toward an optimally aligned distribution. This reward model integrates step-aware tokens and combines a …

Predictive Analytics For Optimising Delivery Performance In Supply Chain Management, Vishwanath Sai Krishna Teja

ICT

Supply chain management has always been a challenging area where delivery delays stand as one of its main ongoing issues. Many companies continue to use outdated data collection systems despite the advancements in data collection tools. This study investigates how predictive analytics can help companies to prevent their operational problems from reaching their customers.

In this study, two datasets were used. The first is the Data Co Smart Supply Chain dataset which covers order level operations from 2015 to 2018, and the second is the World Bank Logistics Performance Index, which covers logistics quality across more than 160 countries. After …

Balancing Accuracy And Discovery: A Bias-Aware Evaluation Framework For Music Recommendation, Jose Luis Maidana

ICT

Music recommender systems play an important role in helping users navigate large catalogues, but their reliance on historical interaction data can reinforce popularity bias and limit the visibility of less popular, long-tail items. This research develops and evaluates a bias-aware framework for assessing and mitigating popularity bias in music recommendation using implicit user-item interaction data. The study first quantifies popularity bias through inequality and long-tail metrics, before evaluating two baseline recommender approaches: a non-personalised Most-Popular model and a personalised Matrix Factorisation model. Their performance is assessed using both predictive accuracy measures, including Precision, Recall and NDCG, and beyond-accuracy measures relating …

Pxaa: A Root-Cause Decision-Support System For Airline Passenger Complaints Using Sentiment Analysis And Complaint Classification, Adélia Acacio Da Silva, Stefani Rabelo De Campos Nunes

ICT

PXAA (Passenger Experience Analytics Assistant) is a machine learning and decision-support prototype developed to analyse airline passenger complaints using sentiment analysis and complaint classification techniques. The project follows the CRISP-DM methodology and uses airline passenger reviews collected from AirlineQuality.com through Python web scraping. Natural language processing (NLP) techniques were applied to clean and preprocess the textual data, while logistic Regression was used to classify passenger sentiment into positive, negative and neutral categories.

The project also implemented rule-based complaint classification to identify common complaint themes such as delays, baggage issues, customer service problems, comfort issues, payments/refund complaints and communication failures. These …

Design And Evaluation Of A Supervised Machine Learning-Based Intrusion Detection System., Tarquin Qazi

ICT

This project involves designing and evaluating a supervised machine-learning-based intrusion detection system (IDS) using the CIC-IDS2017 dataset. The project is structured around the cross-industry standard process for data mining (CRISP-DM) framework. The dataset comprises over 2.8 million labelled instances and was cleaned and reduced from 78 to 63 features via redundancy analysis. These data were used to train and tune three supervised classifiers: Logistic Regression algorithm as a linear baseline, a Decision Tree algorithm and a Random Forest algorithm. Hyperparameter optimisation was conducted using RandomizedSearchCV with 3-fold cross-validation, optimising for the F1-macro scoring metric.

While the random forest performed similarly …

Comparative Economic Impact Modelling Of Floods Vs Drought On A Global Scale, Diogo Lemos Aguiar, Paulo Henrique Oliveira Machado

ICT

Climate change is generating unprecedented economic losses through extreme weather events, yet comparative predictive analysis between different hazard types remains limited. This report presents a Machine Learning–Based Comparative Economic Impact Modelling System designed to predict and compare the economic damages caused by floods and droughts on a global scale. The system integrates historical disaster records from the EM-DAT International Disaster Database with country-level socioeconomic indicators from the World Bank's World Development Indicators, applying supervised regression modelling to a unified dataset of 1,301 disaster events across 133 countries from 2000 to 2025. XGBoost and a Multi-Layer Perceptron Neural Network were evaluated …

Predicting Co₂ Emissions From Cattle-Driven Deforestation In The Amazon Rainforest Using Machine Learning, Daniel Ambrosio, Suelen Rocha

ICT

Deforestation has become a major environmental issue worldwide, especially in the Amazon Rainforest, which is known as one of the world’s largest carbon sinks. This region has been significantly impacted by cattle farming, contributing to increased CO₂ emissions and land degradation. As Brazil is one of the world’s largest beef producers and exporters, the environmental impact of livestock production has attracted increasing attention from organisations, governments, and sustainability analysts.

This project aims to analyse the relationship between cattle farming, deforestation, and CO₂ emissions in the Amazon using Machine Learning. Using the CRISP-DM framework, the project explored each stage of the …

The Sub-Lethal Effects Of Trematode Parasites On The Migratory Behavior Of Lesser Scaup (Aythya Affinis), Scott Alan Herman

Graduate Research Theses & Dissertations

The introduction of the invasive freshwater faucet snail (Bithynia tentaculata) and its associated parasites in the Upper Mississippi River and Great Lakes Region provides the opportunity to investigate how anthropogenic stressors impact the life history of lesser scaup (Aythya affinis) using the Mississippi Flyway. To understand the life histories and interactions between lesser scaup and faucet snails we examined historical records, research, and ecology textbooks. Our literature review identified critical knowledge gaps in lesser scaup literature surrounding population dynamics, reproduction, and migration. Specifically, the sub-lethal effects of invasive trematodes on the migratory behavior of lesser scaup in the Upper Mississippi …

Climate Change Effects On Biomass And Greenhouse Gas Emissions Are Ameliorated By Nontoxic Endophytes In Southeastern Usa Transition Zone Tall Fescue Pastures, Rebecca Kay Mcgrail, Jonathan D. Moore, A. Elizabeth Carlisle, Jim A. Nelson, Rebecca L. Mcculley

Plant and Soil Sciences Faculty Publications

Background: The plant–microbe interaction between tall fescue and its endophyte Epichloë coenophiala can affect ecosystem responses to changing rainfall patterns and increasing temperatures. Endophyte-infected (E+) stands often exhibit improved stress tolerance and differing soil greenhouse gas (GHG) emissions compared to endophyte-free (E−) stands. However, it is unknown if mammalian nontoxic endophyte (NTE) strains introduced into some tall fescue cultivars confer differing stress tolerances or reduce soil GHG emissions.

Methods: We quantified the impact of fescue-NTE symbioses in Jesup and Texoma on biomass and GHG emissions in response to increased temperature (+3°C) and altered precipitation (50% lengthened interval between events) using …

Uav-Based Sensor Integration For In-Situ Lake Water Quality Monitoring, Alec Campbell

2026 Research Poster Competition

Current water quality assessment methods rely heavily on manual sampling and laboratory analysis, which are time-consuming and difficult to perform in remote or hazardous environments. In surface water monitoring, spatial and temporal resolution is often constrained by labor, access, and logistical limitations of manual sampling programs. This research aims to develop a drone-based system for real-time, in situ lake water quality monitoring. To address these challenges, the project focuses on integrating multiple water quality sensors, including oxidation-reduction potential (ORP), pH, electrical conductivity (EC), turbidity, and a multispectral spectrometer, into a compact, UAV-compatible design capable of collecting spatially referenced data. Methods: …

Flair- Flow-Level Anomaly Intrustion Recognition, Joseph Dumond

2026 Research Poster Competition

Industrial control systems (ICS) and industrial Internet of Things (IIoT) networks support critical infrastructure such as power generation, water treatment, and manufacturing. As these systems become more connected, they are increasingly exposed to cyber threats. However, many existing intrusion detection approaches rely on detailed packet inspection or supervised machine learning techniques that require labeled attack data and extensive tuning, making them difficult to deploy in real industrial environments.ICS networks typically generate highly regular and predictable communication patterns due to periodic control logic and deterministic device behavior. Because of this structure, abnormal or malicious activity often appears as a disruption to …

P4 Driven Data Plane Analytics For Industrial Control Network Security, Haden Fowler

2026 Research Poster Competition

Industrial control systems manage critical infrastructure such as power grids, water treatment plants, and manufacturing facilities. These systems rely on specialized network protocols to send measurements and commands between sensors, controllers, and operator workstations. Protecting these networks from cyberattacks is essential because a successful intrusion could disrupt services that millions of people depend on daily. Most current security monitoring approaches analyze copies of network traffic after it has already passed through the system. This delay means that malicious commands may reach their targets before any alarm is raised. More importantly, when alerts do occur, operators often lack the evidence needed …

Comparative Evaluation Of Hplc And Capillary Electrophoresis In Intestinal Permeability Of Oseltamivir Phosphate Via Single-Pass Intestinal Perfusion (Spip) Method, Senem Şanli, Gülpembe Halay, Mustafa Sinan Kaynak, Nurullah Şanlı

Arts & Sciences Faculty Publications

Introduction: Oseltamivir phosphate (OP), a widely used antiviral prodrug for influenza, requires accurate Biopharmaceutics Classification System (BCS) classification to inform formulation and bioequivalence decisions. Despite its high solubility, the permeability characteristics of OP remain inadequately defined. Methods: This study evaluated the intestinal permeability of OP using the in situ Single-Pass Intestinal Perfusion (SPIP) model in male Sprague Dawley rats. Two validated analytical methods - High-Performance Liquid Chromatography (HPLC) and Capillary Zone Electrophoresis (CZE) were developed and compared. Metoprolol tartrate served as the high-permeability reference standard, and phenol red was used to monitor water flux. Permeability coefficients (Peff) were calculated after …

Propensity Score Matching Accounting For Longitudinal Trends Before Baseline With Group-Based Trajectory Modeling, Dustin R. Bastaich

Theses and Dissertations

Propensity score matching is used in observational studies to balance baseline attributes between a treatment of interest and a control group. Propensity score matching typically relies on baseline variables, but longitudinal trends in patient characteristics can also influence treatment decisions and subsequent health outcomes. This dissertation extends standard approaches by explicitly incorporating longitudinal trajectories of key variables into the propensity score estimation process.

Trends in a longitudinal variable prior to baseline were characterized using group-based trajectory modeling. A two-step modeling approach was implemented where trajectory groups of a key variable were first estimated and then included as covariates in the …

Evaluating Willow Establishment With Biochar For Remediation Of Mine Waste Soils: Amendment Ph Determines Plant Biomass And Metal Mobility, Madeline R. Webb

Dissertations, Master's Theses and Master's Reports

Heavy metal contaminated mine tailings (“stamp sands”) from historic copper mining inhibit plant establishment and contribute to ongoing metal contamination. Biochar amendments may enhance plant growth and reduce metal mobility in highly permeable mine waste soils, but the efficacies of different application methods or biochar sources and composition are uncertain. We evaluated different biochar sources (neutral or alkaline pH) and application methods (either mixed or layered) for planted willow growth, moisture retention, and metal mobility. A 10-week growth chamber experiment assessed plant performance, heavy metal content and heavy metal mobility in soil leachates and plant tissues. Biochar pH was the …

Hybrid Patchtst And Physics-Based Framework For Predicting Lithium-Ion Battery State Of Health, Pavan Ravuri

Honors Undergraduate Theses

Accurately forecasting the state of health of lithium-ion batteries is critical for improving performance, reliability and lifetime in energy storage applications. Battery capacity degrades into a nonlinear pattern over cycling due to electrochemical processes where neither purely data driven nor physics-based models can capture alone. This study looks at a hybrid framework combining that PatchTST patch-based transformer architecture with physics-based features derived from the solid electrolyte interphase and pseudo two-dimensional models. Physics inspired proxy features were computed from cycling data and concatenated with electrochemical measurements as added input channels before patch segmentation. There are five model configurations that were evaluated …

From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins

Honors Undergraduate Theses

With their deserts, castles, and ghost houses, the environments of Super Mario games are colorful, whimsical, and charming, but why are they so compelling, and what happens when our analysis of these environments extends beyond individual levels to expansive game worlds? Drawing on Cresswell’s theory of place (2014) and recent work on musical place-building in Mario Kart 8 (Heazlewood-Dale, 2024), I propose a spectrum between localized and globalized scale in games. As game environments become increasingly globalized, the music may be similarly altered to account for this shift in scale. Consequently, players may then encounter a broader, less musically congruent …

Evaluating The Comparison Of Different Body Locations For Data Collection Using Galvanic Skin Response Of Forensic Chemistry Students When In Lecture And Laboratory Environments, Elisa Guerrero

Honors Undergraduate Theses

Anxiety is the response that humans can have when exposed to a stimulus. It can be caused by a multitude of factors, such as psychological, biological, or physiological stimuli. Anxiety can be presented as a state or as a trait in a person. Trait anxiety is typically seen as a more stable personality trait, whereas state anxiety is a more intense temporary emotional state. In our study, anxiety was regarded as a more state-like characteristic. A galvanic skin response (GSR) Shimmer3+ device can measure skin electro-conductance changes following stimulation. In a situation where a participant is experiencing anxiety, levels of …

High-Resolution Mapping Of Soil Moisture Variation Using Uas Thermal And Multispectral Imagery, Jackline Amma Timah

Theses and Dissertations

In agricultural landscapes, soil moisture regulates hydrologic partitioning, nutrient transport and water quality, land-atmosphere energy exchange that shapes local climate, and ecosystem resilience. However, traditional monitoring approaches, such as in-situ sensors and satellite imagery, often lack the spatial resolution required to capture fine-scale soil moisture variability. This study evaluated whether unmanned aerial system (UAS)-derived thermal, multispectral, and terrain variables can capture fine-scale spatial variability in volumetric water content (VWC) within an SRB in central Illinois.

High-resolution imagery was collected and paired with 50 field-measured VWC observations. Land surface temperature (LST), vegetation indices (NDVI and NDRE), spectral bands, and slope were …

An Application Of Molecular Dynamics To Hed Plasma Physics: Point And Group Defect Diffusion In Coulomb Crystals, Levi Webb

Theses and Dissertations

Molecular dynamics (MD) simulations are used to explore the microscopic behaviors of complex systems that cannot be easily studied experimentally. In particular, the microphysics of high energy density (HED) astrophysical plasma environments remain inaccessible by modern experiment---even in laser facilities, these plasmas exist only on short timescales that do not lend easily to investigation. The diffusion of point and group defects through crystallized plasmas in the cores of white dwarfs and neutron star crusts, called Coulomb crystals, has potentially macroscopic implications for the evolution of these bodies. Thus, I employ the widely-used MD code LAMMPS to simulate small Coulomb crystal …

Type Ii Diabetes Treatment Comparison Via Compartment Modeling, Abigail M. Collins

Theses and Dissertations

Type II diabetes mellitus affects one in ten adults worldwide, yet the effects of treatment type and adherence level on developing complications and quality of life have not been well characterized at the population level, and mathematical modeling offers a structured way to examine these dynamics. This thesis adapts the Boutayeb et al. (2004) model to incorporate dynamic treatment types and levels of adherence, producing nine scenarios in which complication development rate and complication recovery rate differed, to compare peak complications and quality of life across treatment and adherence conditions. Using a system of ordinary differential equations and compartment modeling, …

A Nuclear Terrorism Scenario: Improvised Nuclear Weapons With 60%-Enriched Uranium, Matt Caplan

Faculty publications – Physics

In this work we show that approximately 60–70 kg of 60%-enriched uranium can be used to build a crude nuclear weapon with a kiloton yield. While too large to fit on a missile, such a weapon could be delivered by a shipping container. This analysis is motivated by the June 2025 Israeli and US attacks on Iran, especially the bombings of the nuclear facilities at Natanz, Fordow and Isfahan. The Iranian stockpile of approximately 440 kg of 60%-enriched uranium is, at the time of writing, inaccessible to IAEA inspectors and stored in secret. The rapid clandestine relocation of this material …