Digital Commons Network^™

Integrated Llm-Powered Framework For Real-Time Monitoring In Machining Using Smartphone And Ai Agent Orchestration, Mustafa H. Hadi, Hussam L. Alwan, Atiya Al-Zuheri, Sang-Heon Lee, Yousef Amer

Engineering and Technology Journal

Reliable tool condition monitoring is essential for maintaining machining quality, reducing production downtime, and preventing unexpected tool failures. Conventional tool condition monitoring systems can accurately detect tool wear but typically provide limited support for automated diagnosis and maintenance decision-making. To address this limitation, this paper proposes an economical AI agent-driven maintenance framework for milling operations that integrates smartphone-based vibration sensing, one-dimensional Convolutional Neural Networks (1D-CNNs), and Large Language Model (LLM)-powered AI agents. Vibration signals acquired using a smartphone-embedded accelerometer are preprocessed and analyzed using a 1D-CNN to classify tool conditions into fresh and worn states. The classification results are then …

Macro-Scale Ultra-Vacuum Containment Vaults (Uvcv) And Detached Robotic Logistics Logs For The Absolute Elimination Of Interfacial Oxidative Degradation, Christopher L. Eckes

Defensive Publications Series

This specification formalizes the macroscopic architectural layout, atmospheric seal metrics, and automated robot-to-machine routing protocols required to establish an external environmental guardrail for high-throughput Elemental Transmutation Replicator (ETR) industrial nodes. Prior art (Paper 128) details the micro-scale electro-hydrodynamic lamination of edible protective polymer skins over hyper-accelerated macro-nutrient streams. However, mechanical nozzle failures or unexpected microfluidic backpressure cascades introduce an operational vulnerability: transient exposure to ambient atmospheric oxygen (O₂) and water vapor (H₂O) can cause instant material degradation and structural collapse during high-velocity extrusion passes.

This disclosure resolves this structural vulnerability by placing the entire replication assembly inside a sealed, structural …

Dynamic Harmonic Resampling And Ternary Fluid-Canvas Operating Networks For The Elimination Of Volumetric Washout In High-Throughput System Interfaces, Christopher L. Eckes

Defensive Publications Series

This specification establishes the software-hardware architecture, fluid-canvas compilers, and low-level dynamic phase-alignment algorithms required to eliminate the downstream Acoustic Wave-Saturating Washout bottleneck within the Phase 2 Macro-Texture Structure Matrix of the Elemental Transmutation Replicator (ETR) framework. Prior art (TDC #11772, Paper 126) breaks the thermal limits of Phase 1 Synthesis, yielding a continuous, hyper-accelerated volumetric stream of unorganized liquid protein and lipid emulsions. Under static acoustic conditions, this accelerated flow rate sweeps away stationary ultrasonic nodes, preventing successful macro-texture cross-linking and fiber folding.

This disclosure resolves this fluid-dynamic constraint by deploying a Distributed Ternary Fluid-Canvas Operating Network governed by Module …

System Architecture For Ai-Synchronized Phononic Suppression Matrices And Solitonic Disruption Of The Exothermic Bonding Barrier In Atomic Layer Deposition Cores, Christopher L. Eckes

Defensive Publications Series

This specification formalizes the architectural integration, hardware layouts, and predictive real-time control algorithms required to transition the Phase 1 Ultra-Vacuum Synthesis Bed of the Elemental Transmutation Replicator (ETR) framework (Prior Art: TDC #11772) from an assembly-throttled safety regime to a hyper-accelerated, continuous-flow manufacturing matrix. Prior art (CART-CE #12157) establishes that instantaneous molecular synthesis yields extreme, localized exothermic enthalpy spikes (ΔH°f) that overwhelm the passive thermal conductivity (κ) of Silicon Carbide (SiC) dampening hulls, triggering automated laser throttling and dropping system velocity to protect structural interfaces.

This disclosure resolves this structural material bottleneck by implementing an AI-Governed Active Phononic Dampening Field. …

Techniques For Handling Unstable Agents In Agent-Based Networks, Ashwini S Kulkarni, Sushmitha Vatsal, Priyanka Bansal, Vivek Kumar Singh, Ghulam Ghaus Arshi

Defensive Publications Series

Proposed herein are a system and techniques that facilitate stability-aware metadata synchronization in agent-based networks. The system includes a closed-loop adaptive stability orchestration layer that utilizes a time-sensitive exponential-decay mechanism and historical recovery telemetry to dynamically tune suppression and reuse thresholds, thereby reducing control-plane churn and cascading failures. Rather than relying on binary health checks, the system uses a time-weighted stability score to support granular, policy-driven agent lifecycle management.

A Comparative Analysis Of Clinical Quality And Readability Of Neuropathic Pain Patient Information Leaflets: Türkiye Versus The United States, Gonul Sari, Argun Pire

Turkish Journal of Medical Sciences

Background/aim: Neuropathic pain management requires high patient adherence and complex dose-titration regimens, making the readability of patient information leaflets (PILs) a critical patient safety factor. This study aimed to compare the clinical content quality and linguistic readability of PILs for neuropathic pain medications between Türkiye (TİTCK) and the United States (DailyMed).

Materials and methods: A total of 11 pairs of innovator medications were analyzed. Clinical content was assessed using the 15-item Keystone Clinical Content Checklist (KCCC). Readability was assessed using the Ateşman index for Turkish texts and the Flesch Reading Ease Score (FRES) for English texts. Information quality and reliability …

Beyond Individual Willingness: Family Veto, Legal Knowledge, And The Dynamics Of Organ Donation In Türkiye ​, Hasan Gi̇ray Ankara, Hakan Değerli̇, Semi̇h Baş

Turkish Journal of Medical Sciences

Background/aim: The aim was to understand the factors underlying the dynamics of organ donation behavior by focusing on three different aspects. Three models were tested in this regard, namely (i) individual willingness for the donation of one’s own organs, (ii) willingness to approve organ donation for close relatives, and (iii) a family veto gap.

Materials and methods: A national-level adult sample from the Eurobarometer 72.3 survey (collected in 2009) conducted in Türkiye was utilized. Standard and penalized logistic regression models were employed to conduct the analyses.

Results: The results show that discussing organ donation with family members and being aware …

Robust Soil Moisture Content Detection Based On Data Augmentation Under Different Testing Conditions, Yang Li, Chenghao Liu, Jing Nie, Jingbin Li

Turkish Journal of Agriculture and Forestry

Accurate and cost-effective monitoring of soil moisture content is essential for the development of precision agriculture and the optimization of water resource management. While low-frequency acoustic signals are used to detect soil moisture content, models trained with controlled laboratory acoustic data often exhibit significant performance degradation with field data due to their sensitivity to environmental noise, soil heterogeneity, and structural variations. This study proposes a novel framework to enhance model robustness and generalization by integrating a WGAN-GP network data augmentation strategy into the soil moisture content workflow. This framework is designed to generate high-quality and diverse data on different soil …

Long-Term Effects Of Harvesting Disturbance And Site Preparation On Soil Properties And Tree Growth In A Loblolly Pine (Pinus Taeda) Forest, Kyle M. Caccamesi

LSU Master's Theses

Productivity decline between rotations has long been a concern in intensively managed forest plantations. Research done over the last century has provided greater clarity on why this phenomenon occurs. The objective of this research was to observe long-term soil and tree growth characteristics that are likely to be influenced by operational harvesting activities. The experiment was conducted in Washington Parish, Louisiana on a site dominated by Ruston soil series, a well-drained, fine loamy, siliceous and thermic typic Paleudult. A randomized complete block design with 2 x 2 x 2 factorial treatments was used. The treatment factors were harvesting method, fertilization, …

Scattering Phase Shift In Quantum Mechanics On Quantum Computers: Non-Hermitian Systems And Imaginary-Time Simulations, Peng Guo, Paul Levan, Frank Lee, Yong Zhao

Research & Publications

To overcome the fast oscillatory behavior of correlation functions for extracting scattering phase shift in real-time quantum simulations encountered in the work of Guo et al. [Phys. Rev. D 113, 054512 (2026)], we propose and test two solutions in the present work. One is to simulate Hermitian systems in imaginary time, and the other is to simulate non-Hermitian systems in real time. We demonstrate that both approaches lead to the problem of nonunitary quantum evolution that can be solved by combining two quantum algorithms: block encoding and Hadamard test. The combined quantum algorithm does not require midcircuit …

Ai And The Danger Of Ontological Confusion, Derek Schuurman

University Faculty Publications and Creative Works

There is much in Jonathan Barlow’s piece that resonates with me. I appreciated how he cites some seminal thinkers like Neil Postman, Jacques Ellul, E.F. Schumacher, and Ivan Illich as he frames his argument. In particular, I think Barlow is insightful in identifying the pitfall of “commensurability” when comparing humans and AI in terms of performance. I am reminded of the words of the famous computer scientist Edsgar Dijkstra, who suggested that the question as to whether machines can think is about as relevant as the question of whether “submarines can swim.”

Rethinking Education For Sustainable Development: The Role Of Blended Learning Within Skill-Based Higher Education, Nessrin Shaya, Sandra Baroudi, Serena Aoun, Rawan Abukhait

All Works

Purpose – Drawing on Social Cognitive Career Theory, this study positions blended learning not merely as a technological enhancement but as a strategic pedagogical mechanism for operationalizing sustainable development. It examines how blended learning within interdisciplinary and skill-based higher education environments cultivate employability skills that support sustainable educational practices. Design/methodology/approach – The study adopts a survey design. Where data were collected from 782 undergraduate students across five universities in UAE. Two institutions offered blended, interdisciplinary and skill-based courses, while three delivered the same courses through traditional face-to-face formats. The structural model was examined using structural equation modeling (SEM) with bootstrapping …

Genetic Diversity And Population Structure Of Carob Germplasm From Lebanon, Morocco, And Spain Reveal Potential For Innovative Breeding Programs, Mohamad Ali El Chami, Guillermo Palacios-Rodriguez, Rafael Maria Navarro-Cerrillo, Lamis Chalak, Maria Dolores-Rey

Turkish Journal of Agriculture and Forestry

The carob tree (Ceratonia siliqua L.) is a drought-tolerant species native to the Mediterranean Basin. It has been cultivated for centuries for its highly nutritious, edible pods. Genetic diversity is one of the key requirements for the effective management and utilization of plant genetic resources. In this study, we evaluated the genetic diversity and population structure of 169 seminatural carob individuals from Lebanon, Spain, and Morocco using nine expressed sequence tag-simple sequence repeat (EST-SSR) markers. A total of eight polymorphic EST-SSR loci produced 43 alleles, with Cesi_187 and Cesi_1187 identified as the most informative markers. Results of AMOVA and SAMOVA …

Assessment Of Pareto Optimal Projection Search Algorithm For Next Generation Radiotherapy Planning In Prostate Cancer Treatment, Khanh Pham

Masters Projects

Purpose: Automated treatment planning systems (ATPS) are critical for improving plan consistency and efficiency. This study evaluated the implementation and performance of the Pareto optimal projection search algorithm (POPSA), a proprietary rule-based algorithm (RBA) in prostate site. The RBA's performance was benchmarked against established knowledge-based planning (KBP) and deep learning (DL) dose prediction tools.

Methods:  A retrospective comparative study used a cohort of N=10 previously treated patients, consisting of 10 prostate with lymph nodes cases. Three plans were generated per patient: manual reference (RA) plan, KBP plan, and RBA script-only plan. Performance was quantified by target volume coverage, organ at …

Topology-Aware Graph Neural Network With Spatiotemporal Encoding And Contrastive Learning For Scalable Dynamic Network Representation, Wassan S. Hayale, Rasha S. Ali, Raghda Abd Ul Rab Abd Ul Hasan, Lateef Abd Zaid Qudr, Safwan Nadwe, Nitin S. Solke, Ravi Sekhar, Pritesh Shah, Reyad Omran Essa

Mesopotamian Journal of Big Data

Recently, Dynamic Graph Neural Networks (DGNNs) have become a potent candidate to model time-changing graphs, although other solutions, including Temporal Graph Networks (TGN), Evolving Graph Convolutional Networks (EvolveGCN), and Dynamic Self-Attention Networks (DySAT), still have significant barriers to scalability, computational efficiency, and temporal embedding drift. To overcome these drawbacks, this paper presents a Topology-Aware Graph Neural Network (TAGNN) model capable of incorporating graph-topology encoding and the spatiotemporal memory mechanisms into the learning process of dynamic node representation. The proposed framework includes three key components: (i) returns global and local structural data such as graph Laplacian-based feature transformations and position encodings, …

2026 August 13 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Trace Elements In Complete Feeds Used In Intensive Rearing Of Cattle, Poultry, And Pigs: Occurrence And Predicted Transferred Concentrations In Animal-Derived Food, Elizabeta Dimitrieska Stojkovikj, Vangelica Enimiteva, Aleksandra Angjeleska, Risto Uzunov

Turkish Journal of Veterinary & Animal Sciences

This study investigated the concentrations of essential (Zn, Cu, Mn, Co, and Mo) and toxic (Pb, Cd, As, and Hg) trace elements in complete feeds used for the intensive rearing of cattle, poultry, and pigs in North Macedonia and predicted their transfer into animal-derived foods. A total of 107 feed samples were collected from 40 farms between May 2023 and April 2024 and analyzed using inductively coupled plasma mass spectrometry. The results revealed that while most essential elements were within the regulatory limits, Co exceeded the maximum level in two cattle feed samples. Zn, Cu, and Mn levels were generally …

Machine Learning Applications To Physical Processes, William Charles

Arts & Sciences Graduate Student Theses and Dissertations

This thesis demonstrates how machine learning techniques can solve computationally challenging problems across diverse areas of physics, from high-energy astrophysics to condensed matter systems, accelerating traditional computation. My first contribution addresses the computational expense of Monte Carlo calculations for radiative processes in relativistic plasmas. I develop a neural network sampling method that enables fast sampling from an arbitrary probability density, and demonstrate the method on inverse Compton scattering, achieving a speedup of up to an order of magnitude beyond standard methods. My second contribution addresses the structure and radiation of neutron star magnetospheres. I use physics-informed neural networks to model …

X-Ray Emission From V1674 Her (Nova Her 2021) And Characterization Of Cebr3 Detectors For Gamma-Ray Spectrometry, Tekeba Olbemo

Arts & Sciences Graduate Student Theses and Dissertations

Novae are thermonuclear explosions on the surface of the white dwarf in a close binary system. They are multi-wavelength transients emitting across the electromagnetic spectrum from radio to gamma-rays. This thesis primarily focuses on the X-ray emission from one particular nova, V1674 Her. V1674 Her (Nova Her 2021) is known for its ultra-fast decline time of ��2 ∼ 1 day. This under normal circumstances implies massive white dwarf potentially approaching the Chandrasekhar limit. We test this for V1674 Her by measuring its mass via X-ray spectroscopy method. The method calculates X-ray emission from physically motivated model of post-shock accretion column …

Venezuelan Students Sent To Elbert Covell College By Government, University Of The Pacific News Bureau

Elbert Covell College

No abstract provided.

Anuario De Graduados De Elbert Covell College 1965-1984, Elbert Covell College

Elbert Covell College

No abstract provided.

Congressional Record: Elbert Covell College-Splendid Progress By University Of The Pacific, Congressional Record

Elbert Covell College

No abstract provided.

Ai And The Environment: Solutions For Advancing Technology Safely, Kaitlynn Baker

Discovery Day - Daytona Beach

Since 2022, the world of Artificial Intelligence (AI) has boomed. AI went from a special and rare entity to a commonly used resource available to all through web sites, and phone apps. AI has benefitted everyday activities by making office, class, and personal tasks easier through grammar help, informational citations, and as someone to bounce ideas off of. Additionally, many companies have begun utilizing AI to improve customer service and experience, and train workers more efficiently, therefore, saving thousands of dollars. Despite the benefits humans reap from its use, AI has been harming our environment at growing rates. Data centers …

Flight Testing And System Identification Of An Experimental Cessna 182, Mariano Chavez Rangel

Discovery Day - Daytona Beach

Flight testing and system identification are essential for accurately characterizing aircraft dynamics and supporting the development of reliable flight control systems. This work presents the use of an experimental Cessna 182 as a full-scale platform for flight testing and system identification, conducted by the Eagle Flight Research Center. The objective is to generate high-fidelity flight data to estimate aerodynamic and dynamic coefficients and establish a baseline model for comparison with a sub-scale aircraft incorporating Integrated High-Lift Propulsor (IHLP) technology. The experimental aircraft is equipped with a comprehensive onboard instrumentation suite designed to capture synchronized measurements of air data, aircraft motion, …

Data-Driven Learning Algorithms To Predict Spacecraft Trajectories In The Dro Family, Sarath Murarisetty, Hansaka Aluvihare Aluvihare, Oshani Jayawardane, Annika Anderson

Discovery Day - Daytona Beach

Generating precise, accurate, and efficient trajectories in the Earth-Moon circular restricted three-body problem (CR3BP) is crucial for long-term lunar missions, yet it remains challenging. A primary reason for this is that the CR3BP is an extremely nonlinear and chaotic system. Fortunately, neural networks present a promising approach for addressing such complex nonlinear challenges. In “Data-driven Learning Algorithms to Predict Spacecraft Trajectories in the DRO Family,” this work addresses the challenge of solving a nonlinear system within the CR3BP framework to determine the trajectories of spacecraft within the Distant Retrograde Orbit (DRO) family using neural networks (NNs). For a comprehensive comparison …

Human-Centered Modeling Of Traffic As A Complex System, Poorendra P. Ramlall

Discovery Day - Daytona Beach

Traffic systems are driven not only by motion, but by interaction: vehicles influence one another, drivers continuously adapt to surrounding behaviour, and cognitive processes shape decisions that can propagate through the flow of traffic. Understanding these layered interactions is essential for improving traffic safety and for designing the next generation of intelligent, connected, and automated transportation systems. This PhD research develops a multiscale, data-driven framework for identifying, modelling, and ultimately interpreting interaction structure in traffic systems. The work first established an information-theoretic basis for this problem, demonstrating how information flow can uncover directional relationships in traffic dynamics and help infer …

Assessing Attachment To Ai: Understanding The Theoretical Correlations And Consequences, Brianna Broderick

Discovery Day - Daytona Beach

Current research on Artificial Intelligence (AI) focuses on its capabilities and our understanding of it as an instrumental tool (i.e., utility completing tasks). However, as its ability to replicate natural language improves through both text and voice, an ever-growing number of users have turned to AI for emotional companionship. Concern grows as prior research on technology dependency suggests AI bonding may lead to less interaction with others and, in extreme circumstances, has already led to cases of suicide and divorce. Kasturiaratna & Hartanto (2025) developed the AI Attachment (AIA) scale, consisting of three factors, which include: emotional closeness (i.e., personal …

Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz

Discovery Day - Daytona Beach

This project explores advanced image analysis techniques to assess the microstructure of highly strained austenitic stainless steel. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract precise measurements for grain size from scanning electron microscopy (SEM) images. These metrics will be examined against the computed grain sizes of the sample from electron backscatter diffraction measurements. By automating the extraction of grain size measurements from SEM images, this study contributes to steamlining the quality assurance/ quality control of industrially processed materials.

Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth

Discovery Day - Daytona Beach

Understanding the complex causal relationships underlying aviation accidents is critical for improving safety and preventing future incidents. However, much of this information exists in unstructured narrative reports, making large-scale analysis difficult. This project aims to automatically extract and model causal chains from National Transportation Safety Board (NTSB) accident narratives using a combination of traditional natural language processing (NLP) techniques, transformer-based architectures, and graph-based knowledge representation. Traditional NLP methods, including named entity recognition, dependency parsing, and rule-based pattern matching, will be used to identify structured cause–effect relationships. These approaches will be compared with transformer-based models, including a lightweight encoder for classification …

Numerical Modeling Of A Secondary Breakup In The Veritas Asteroid Family, Jarrett Dieterle

Discovery Day - Daytona Beach

The Veritas asteroid family, located in the outer main belt, is believed to have formed from the catastrophic breakup of a parent body approximately 8.3 million years ago (e.g., Nesvorný et al., 2003). Larger fragments remained in the main belt, while smaller particles evolved inward under radiation forces, forming a toroidal dust structure observable in infrared data as paired bands. Previous studies (e.g., Dermott et al., 2001) have shown that these bands can be linked to their parent families and modeled from their initial disruptions. We propose that the 10° dust bands associated with Veritas may record evidence of a …