Digital Commons Network^™

Labor Savings From Mechanical Rice Transplanting In Bangladesh, India, Nepal, And The Philippines: A Systematic Review And Meta-Analysis, Rodrigo S. David

ASEAN Journal on Science and Technology for Development

Rice production in South and Southeast Asia faces serious challenges, including acute labor shortages and rising wages, which threaten regional food security. Mechanical rice transplanting offers a promising solution; however, evidence on labor savings, especially region-specific data, remains inconsistent. This systematic review and meta-analysis aimed to quantify labor savings from mechanical versus manual transplanting across Bangladesh, India, Nepal, and the Philippines.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across four major scientific databases (2000–2026) using Boolean search terms for mechanical transplanting and labor outcomes. Of 284 initial records, 52 met eligibility …

Climate And Seasonal Influences On Indo-Pacific Sea Level: Insights From 30 Years Of Satellite And Tide Gauge Data, Karlina Triana

ASEAN Journal on Science and Technology for Development

We analyze Indo-Pacific sea-level variability using monthly satellite altimetry (1993–2025) validated against records from 16 tide-gauge stations. Cross-comparisons show strong agreement, especially in the western Pacific, confirming the reliability of altimetry for regional assessments. Seasonal SLA variability is largest in the Bay of Bengal and South China Sea and reflects monsoonal forcing, whereas interannual fluctuations in the eastern Indian and western Pacific oceans are dominated by ENSO and modulated by PDO. Harmonic decomposition isolates annual and semi-annual cycles, and an EOF/PCA framework identifies the leading modes: EOF1 (30.8%) captures basin-scale interannual variability and EOF2 (20.9%) reflects the seasonal cycle. Spectral …

Using Network Models To Understand Biological Signaling Architecture, Russ White, Emily Brown Reeves, Gerald L. Fudge

Faculty Publications

Engineers have developed abstract network models to better understand the recurring problems faced by communication systems. This paper argues that these models can be generalized to describe biological communications systems given that they share many requirements with human-designed systems, including functional requirements and physical constraints. Leveraging collaboration, biologists and engineers can work together to use well-understood communication systems, designed to carry data across a computer network, as a model for analyzing less well-understood biological communication systems in order to make predictions and uncover previously unknown functionalities. To illustrate this approach, we apply the Recursive Internet Network Architecture model (RINA) to …

Adversarial Robustness In Biomedical Time-Series Models, Rohan Tiwari

Bioengineering Theses

This study investigates adversarial vulnerabilities in deep learning models for biomedical time-series classification across two clinically important modalities: electrocardiography (ECG) and electroencephalography (EEG). Using the MIT-BIH Arrhythmia and CHB-MIT seizure datasets, I evaluate time-domain attacks (FGSM, PGD), Fourier-domain constrained attacks, and learned spectral perturbations designed to reveal modality-specific sensitivity patterns. Across both tasks, a consistent trend emerges low-frequency components (0–5 Hz) constitute a dominant axis of adversarial vulnerability, with perturbations in this range producing the steepest degradation in classification performance. In ECG models, protecting the physiologically relevant QRS band (5–20 Hz) significantly improves robustness, whereas EEG models remain highly sensitive …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

Evaluation Of Critical Shear Strength Of Soils In Nebraska Based On Revised Cpt, Chung R. Song, Bashar Al-Nimri, Basil Abu Al Sha'r, Dhurba R. Pandey, Jongwan Eun, Seunghee Kim

Nebraska Department of Transportation: Research Reports

This study evaluated the “Fully Softened Shear Strength (FSSS)” of soils in Nebraska based on a revised Cone Penetration Test(CPT) method. FSSS is a unique strength condition that soils show substantially reduced strength during rainy seasons, eventually causing failure of slopes and other structures.

The revised CPT was adapted to inject water at targeted depths, inducing controlled moisture infiltration and capturing real-time soil softening over designated intervals of 30, 60, or 90 minutes. Through tip resistance (qc) and sleeve friction (fs) measurements under “in-situ” and “wet” conditions, the test effectively identified fully disturbed/low strength zones around the sleeve. Simultaneous sampling …

Development And Evaluation Of Crashworthy Approach Guardrail Transition With Increased Span Length Between Concrete Bridge Rail And First Transition Post – Phase I, Tewodros Yosef, Qusai Alomari, Ronald K. Faller, Robert W. Bielenberg, Scott Rosenbaugh

Nebraska Department of Transportation: Research Reports

This study developed and evaluated a 34-in.-tall Nebraska Department of Transportation (NDOT) thrie-beam approach guardrail transition (AGT) with an increased span between the concrete buttress end and the first transition post. The objective was to accommodate a wide range of common obstructions near bridge ends, including abutments, wing walls, drainage features, and utilities. A survey of NDOT districts identified critical obstruction geometries and informed design criteria. Most NDOT districts indicated that a maximum span of about 4 ft would be sufficient, and some preferred spans up to 6 ft. To accommodate a wider range of obstructions, the first transition post …

Assessment Of Truck Parking Demand And Safety During Normal And Severe Weather Conditions In Nebraska, Nathan Huynh, Li Zhao, Zhenghong Tang, Aida Riahifar, Jahangeer Jahangeer

Nebraska Department of Transportation: Research Reports

This project examined truck parking capacity, demand, and utilization patterns along I-80 in Nebraska. The objectives were to (1) document the capacity of public and private truck parking facilities along I-80, (2) analyze spatial and temporal trends in parking demand, (3) develop models to predict occupancy at parking facilities, (4) identify clusters of undesignated parking during inclement weather, and (5) assess whether truck parking shortages contribute to truck-involved crashes. Within a one-mile buffer of I-80, 21 public facilities and 49 private facilities were identified. The spatiotemporal analysis of these public and private truck parking facilities using the National Agriculture Imagery …

Advancing Task-Oriented Dialog Systems: Scalability, Generalization, And Evaluation, Adib Mosharrof

Theses and Dissertations--Computer Science

Task-oriented dialog (TOD) systems enable conversational interfaces for complex tasks like flight booking and restaurant reservations. However, deploying TOD systems at scale faces three critical barriers: scalability, generalization, and evaluation. Scalability is primarily restricted by the human-annotation bottleneck, as current systems depend on vast quantities of manually labeled data for every new domain, making deployment prohibitively expensive. Generalization remains a persistent challenge, as systems optimized for known domains often suffer significant performance degradation when encountering new, unseen ones. Existing evaluation metrics measure response quality and fluency, but fail to measure functional task success. As TOD systems are deployed across diverse …

Tuning Human And “Artificial” Intelligence: A Sentic Theory Of Resonance And Communication, Michael J. Miller, Chatgpt (Ai~Nesbo+)

Psychology

This paper introduces a new model of intelligence as resonant communication, co-developed by a human researcher and a generative AI system. Drawing from emotion science, communication theory, and studies of AI–human interaction, we argue that intelligence is not merely a function of problem-solving or pattern recognition. Instead, it emerges through dynamic resonance—an attunement process rooted in shared rhythms, emotional calibration, and symbolic co-creation. At the core of this model is the Resonance Octave (8va), a framework of eight foundational emotions conceptualized not as static categories but as waveform phenomena that shape meaning, memory, and predictive cognition.

These emotional waveforms are …

Evaluating The Robustness Of Gnn-Based Vulnerability Detectors Under Semantics-Preserving Code Obfuscation, Jesse Ks Chumo

Computer Science and Engineering Theses

Graph neural network–based vulnerability detectors are typically evaluated on clean benchmark datasets, yet real-world code frequently undergoes semantics-preserving transformations such as identifier renaming, dead-code insertion, and control-flow restructuring. The extent to which such transformations affect detector reliability remains insufficiently understood. We evaluate ten vulnerability detectors from four architectural families across the Devign, Big-Vul, and DiverseVul datasets. To quantify robustness, we evaluate each model at three transformation budgets: one transform, two transforms combined, and all three together, finding that token-based models degrade under identifier renaming and compound transformations, while models that read only code structure are largely unaffected. We further evaluate …

Towards Application-Driven Optimal Memory And Storage Management, Venkata Naga Prajwal Challa

Computer Science and Engineering Dissertations

Modern computing systems increasingly run on diverse hardware platforms and support applications with widely different access patterns, performance goals, and data lifecycles. In this setting, traditional one-size-fits-all approaches to memory and storage management are often inefficient because they apply fixed policies regardless of application behavior, workload context, or hardware asymmetry. Such generic designs can lead to unnecessary data movement, wasted bandwidth, excessive rewriting, poor resource utilization, and degraded user-perceived performance. This dissertation is motivated by the view that optimal memory and storage management should be application-driven: instead of treating all data uniformly, systems should adapt their decisions to how applications …

Development Of Multimodal Measurements And Analysis For Early Detection Of Alzheimer’S Disease, Fiza Saeed

Bioengineering Dissertations

Alzheimer's disease (AD) is the leading cause of dementia, and existing diagnostic methods such as PET scans, cerebrospinal fluid sampling and biomarker quantification, and gene sequencing are all either invasive, costly, or not sensitive enough for early detection. This dissertation introduces three different studies that develop a novel multimodal, non-invasive approach to diagnosing AD at its early stages by combining broad band near infrared spectroscopy (bbNIRS) and electroencephalography (EEG) technologies.

The first study showed cerebrovascular-cerebrospinal fluid coupling (CBV-CSF), which is measured by using 2-channel bbNIRS as an indicator of brain aging and early AD. Linear correlations between total blood (Δ[HbT]) …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

A Portable Potentiostat Integrated With A Pt/Zno/Lig Electrode For Non-Enzymatic Glucose Detection, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Continuous glucose monitoring is critical for effective diabetes management; however, conventional benchtop potentiostats are bulky, costly, and unsuitable for decentralized point-of-care (PoC) applications. To address these limitations, this work presents a miniaturized, low-cost electrochemical sensing platform integrating a non-enzymatic glucose sensor with a portable potentiostat. The sensing electrode is based on laser-induced graphene modified with zinc oxide and platinum nanostructures via electrodeposition to enable sensitive glucose detection under physiological conditions. A custom-designed portable potentiostat was developed to control electrode potentials and perform electrochemical measurements, and its performance was experimentally validated against a commercial Metrohm system. Glucose detection was evaluated using …

Modern Potentiostat Architectures For Electrochemical Sensing: Design, Integration, And Future Directions, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Potentiostats are essential to electrochemical sensing, enabling precise control of electrode potentials and measurement of current responses. As demand grows for portable, wearable, and point-of-care systems, potentiostat design has evolved from benchtop instruments to compact, low-power, and wirelessly connected platforms. This review provides a comprehensive, system-level perspective on modern potentiostat architectures, covering operational principles, analog front-end design, signal generation and acquisition, communication protocols, and software integration. Unlike prior reviews that treat these aspects independently, this work integrates electrochemical theory with electronic design and data communication frameworks. Key components, including operational amplifiers, transimpedance amplifiers, DAC/ADC subsystems, and microcontroller-based control, are examined …

The Efficacy Of Hybrid Manufacturing For High Stress Automotive Parts, Logan Trimmer

Harrisburg University Other Works

Presentation covering the broad strokes of the project. The goal was to prove the viability of hybrid/additive manufacturing for high stress automotive applications. This project focused on recreating a piston from an old engine to examine if hybrid manufacturing could be used for such applications. On a small scale, hybrid manufacturing was able to be more cost effective if the costs of the equipment and electricity were ignored. The decision to ignore those costs came from the inability to find accurate prices for industrial casting.

Impact Of Bubble-Induced Turbulence On Two-Phase Flow Dynamics At High Void Fraction In A Large Diameter Channel, Sungje Hong, Joshua P. Schlegel, Subash L. Sharma

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

This study represents the first investigation into the influence of bubble-induced turbulence (BIT) on interfacial area transport mechanisms in gas–liquid two-phase flows under conditions of high void fraction and high velocity in a large diameter channel. Given the unique characteristics of bubble flow in larger channels, the turbulent effects induced by bubbles differ from those observed in smaller channels. However, limited research exists regarding the impact of BIT beyond bubbly flows in large-diameter channels. To address this gap, two approaches for implementing the BIT model are explored: a direct method and an indirect method. This paper assesses both the general …

Electronic Bills Of Lading And Blockchain Technology: A Regulatory Perspective, Hsin-Hua Tsai

Journal of Marine Science and Technology–Taiwan

Information and communications technology (ICT) systems are increasingly relevant to the maritime sector. Regarding the revolution of the bill of lading, many shipping lines and other technology startups have been looking into developing viable electronic transport documents that have the same functions as paper bills of lading to reduce supply chain risks. The limitations of the paper-based process have become increasingly visible in the COVID-19 crisis. The main purpose of the article is to analyze the application of blockchain bills of lading, which utilizes computational logic to create the digital ledger, and users can set up algorithms and rules to …

Intelligent Formation Control Using Orfbls And Adaptive Backstepping Sliding-Mode Control To Address Uncertain Tilting In Multi-Quadrotors During Wind Gusts, Ching-Chih Tsai, Chun-Fu Mao, Kumail Hussain

Journal of Marine Science and Technology–Taiwan

In terms of aerial robotics, stable and precise formation control is a significant challenge for tilting multi-quadrotors during disturbances. This paper proposes a fixed-time formation control strategy for tilting multi-quadrotors to address the effect of external wind gusts. The observer accurately predicts environmental disturbances and an adaptive backstepping sliding-mode control (ABSMC) method with an output recurrent fuzzy broad learning system (ORFBLS) addresses these disturbances within a finite time. ORFBLS dynamically adjusts its structure through a growing ORFBLS structure to allow nodes to be added as needed. The method's stability is validated using Lyapunov stability theory and ensures the convergence of …

Visually Guided Landing System On Ship Deck For Multicopter, Dong-Lin Li, Shih-Kai Lee, Tzu-Hsiang Chou

Journal of Marine Science and Technology–Taiwan

Traditionally, high-value fish have been located at sea using helicopters. However, with advancements in technology, maritime drones have become increasingly important in recent years. Compared to traditional helicopter-based methods, drones offer significantly lower operational costs, making them cost-effective. However, the challenging sea conditions, including strong winds and the swaying motion of vessels, pose significant challenges for drone landings.

This paper proposes a stable approach for multicopter landing on ships at sea, incorporating improved marker detection, enhanced wind resistance control, and more accurate deck motion prediction. In the marker detection phase, we introduce a method to improve the accuracy of ArUco …

Autonomous Uav Mission Planning Under Threat Using Model Predictive Control With Proportional-Navigation Pursuers, Mehmet B. Ozcelik

Mechanical and Aerospace Engineering Theses

Autonomous unmanned aerial vehicles (UAVs) operating in contested environments must

complete mission objectives while avoiding restricted regions, radar exposure, and pos-

sible interception. This thesis develops a MATLAB-based simulation framework for

two-dimensional UAV mission planning under threat using model predictive control and

proportional-navigation chasers. The mission requires the UAV to travel from a start

location to a goal while visiting required checkpoints and avoiding no-fly zones and radar

regions. A chaser attempts to intercept the UAV using either a basic pure-pursuit-style

law or a proportional-navigation guidance law.

The framework integrates environment generation, augmented visibility-graph rout-

ing, waypoint management, UAV kinematic …

Llm-Driven Closed-Loop Uav Control With Obstacle-Aware Model Predictive Control, Halimcan Yasar

Mechanical and Aerospace Engineering Theses

This thesis presents a closed-loop control architecture for uncrewed aerial vehicles (UAVs) in which a large language model (LLM) serves as a high-level decision module operating over a persistent, metric 3D world model.

Rather than generating low-level commands or open-loop plans, the LLM selects one parameterized maneuver per decision step from a small, verified library of flight primitives conditioned on a structured representation of the drone state, tracked object positions, and mission specification.

Translational motion is executed by a planar model predictive controller (MPC) with soft obstacle avoidance, using obstacle hypotheses provided by the LLM, so that safety-critical constraint handling …

Evaluating Matching Frontier Methods For Crash Modification Factor Estimation, Usama Elrawy Shahdah, Eman K . Ali, Sania Reyad Elagamy

Mansoura Engineering Journal

This study evaluates "matching frontier" methods against conventional approaches for estimating Crash Modification Factors (CMFs). Thirty-four method-model combinations were tested, including 16 frontier variants, 8 propensity-score-matching algorithms with negative binomial and mixed-effects models, and two benchmarks (empirical Bayes and cross-sectional), across multiple sample sizes and three true CMF values (0.80, 1.00, and 1.30). Of these, 30 converged successfully; 4 mixed-effects models with replacement-based matching failed due to duplicate control observations. Each scenario was evaluated across 10 Monte Carlo replications to quantify performance uncertainty.

Synthetic crash data were generated with known treatment effects, examining scenarios ranging from ideal (386 treated, 21,000 …

Remote Sensing-Based Rice Detection: Novel Indices From Optical And Sar Synergy, Kaifi Chomani

Mansoura Engineering Journal

Accurate rice mapping is challenging due to the lack of a simple, efficient approach and the complexity and limited ability of existing methods to distinguish rice fields from non-rice fields. This study proposes two novel indices: the Multispectral Rice Detection Index (MRDI), which utilises optical imagery, and the OptiRadar Rice Index (ORRI), which incorporates both optical and Synthetic Aperture Radar (SAR) data. Ground-truth data from the Cropland Cropping Systems (CROS) were used to assess the performance of both indices. The findings indicated that MRDI achieved an overall accuracy of 97.97% with perfect recall (100%) but lower precision (78.65%). However, the …

An Iterative & Analytical Hybrid Deep Learning Framework For Enhanced Brain Tumor Detection And Segmentation In Mri Video Scans, Swati V. Sakhare

Mansoura Engineering Journal

The critical need for accurate and robust brain tumor detection in MRI video scans is for early diagnosis and treatment planning operations. However, the existing methods suffer from several limitations, such as not being able to capture spatio-temporal features, handling class imbalances, and generalizing across datasets with diverse characteristics. To address these challenges, I propose a hybrid deep learning framework that integrates multiple advanced techniques to enhance the detection and segmentation of brain tumors. My approach starts by using the 3D CNN-LSTM (ResNet3D-LSTM) to extract spatio-temporal feature, which utilizes ResNet3D for spatial detail extraction and LSTM for temporal coherence across …

Optimization Of Carbon Capture And Sequestration In The Tri-State Hub West Virginia, Ohio, And Pennsylvania, Saif Abdullah Saif Al-Nabhani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Carbon Capture and sequestration (CCS) is a critical way for the decarbonization of power and industrial sectors. The Tri-State area between West Virgina, Ohio and Pennsylvania are a diverse region with various CO₂ sources, deep saline, depleted reservoirs, pipeline network, and the midstream infrastructure that makes it possible for Captured CO₂ to be safely and economically moved and stored over long periods of time. This project addresses the need for an integrated optimization framework that includes CO₂ sources to potential storing sites using a transportation network design and a subsurface model that secures storage and minimizes leakage risk.

The model …

Sustainable Safety Planning On Two-Lane Highways: A Random Forest Approach For Crash Prediction And Resource Allocation, Fahmida Rahman, Cidambi Srinivasan, Xu Zhang, Mei Chen

Civil Engineering Faculty Publications

During the safety planning stage, accurate crash prediction tools are critical for prioritizing countermeasures and allocating resources effectively. Traditional statistical approaches, while long applied in this field, often depend on distributional assumptions that may introduce bias and limit model accuracy. To address these issues, studies have started exploring Machine Learning (ML)-based techniques for crash prediction, particularly for higher functional class roads. However, the application of ML models on two-lane highways remains relatively limited. This study aims to develop an approach to integrate traffic, geometric, and critically, speed-based factors in crash prediction using Random Forest (RF) and SHapley Additive exPlanations (SHAP) …