Digital Commons Network^™

Evaluation And Research Progress Of Co2 Sequestration In Deep Coal Seams, Xiong Yucan, Xu Shi’Ang, Zhang Pingsong, Lu Haifeng, Jia Yunzhong, Ge Binbin, Li Baiqiang, Liu Chang, Lei Jian, Fu Wenyu

Coal Geology & Exploration

Background With increasing pressure for global carbon emission reduction, geologic CO₂ sequestration has emerged as a critical technical approach to achieving the goals of peak carbon dioxide emissions and carbon neutrality. Owing to their high CO₂ adsorption capacity and favorable sealing conditions, deep coal seams are considered one of the most promising media for CO₂ sequestration, following saline aquifers and depleted hydrocarbon reservoirs. Therefore, the exploitation and utilization of deep coal seams hold significant strategic importance for the green, low-carbon transformation of the coal industry. Methods Based on literature analysis and a review of outcomes from cutting-edge …

Dynamic Prediction On Goaf Surface Subsidence For The Last Two Periods Based On The Erf Time Function, Ren Lianwei, Li Shihui, Wang Wenchang, Li Zhenhua, Chen Shaojie, Dun Zhilin

Coal Geology & Exploration

Background The surface subsidence induced by coal seam mining represents a continuous dynamic evolution process. Constructing a full-time perdition model for surface subsidence in goafs holds significant theoretical and practical values for the rational utilization of land resources, the safety guarantee of engineering construction, and the transformation and development of resource-based cities. Methods To accurately predict the surface subsidence in goafs during its decline and residual periods, this study proposed a prediction model based on the error function (Erf). First, the evolution characteristics of Erf were analyzed, verifying that the subsidence, as well as its velocity and acceleration, predicted using …

Exploring Water Migration Patterns In Reconstructed Soil Layers For Waste Dumps Of Open-Pit Coal Mines Based On Ct Scanning, Fan Tingyu, Zhang Xinghui, Lu Akang, Yang Changde, Wang Shun, Wang Xingming, Sun Xueliang, Ji Quancai

Coal Geology & Exploration

Objective In the arid regions of western China, the ecological restoration of waste dumps within open-pit coal mines plays a key role in green mine construction, while creating a suitable soil profile structure serves as a prerequisite for the ecological restoration.Methods This study investigated the reconstructed soils with a three-layer sponge-like structure consisting of the topsoil, sandy-soil, and mudstone layers for the waste dump of the Hongshaquan open-pit coal mine in Xinjiang. Using computed tomography (CT) scanning, a three-dimensional (3D) pore network model (PNM), and water migration simulation, this study systematically analyzed the pore structures and water migration characteristics …

Performance Optimization Of Superfine Cement Through Composite Modification Using Coal-Based Solid Waste And Modifiers, Kang Zhipeng, Luo Yong, Xiao Diancai, Ren Shuai, Cai Zhiliang, Gao Xiang, Zhang Kunzhe

Coal Geology & Exploration

Objective Superfine cement-based grouting materials generally suffer from limitations including high bleeding rates, unfavourable rheological properties, poor stability, and strength retrogression. These issues can be mitigated by incorporating modifiers, which allows for the reutilization of coal-based solid waste while enhancing the performance of composite, superfine cement-based grouting materials. Methods Superfine cement-based grouting materials with different mixing ratios were prepared by using superfine cement as the basic material, fly ash (FA) and mineral fines (MFs) as primary admixtures, and modified bentonite, methyl cellulose ether (MCE), U-type expansive agent (UEA), and magnesium oxide (MgO) as modifiers. These grouting materials and pure superfine …

Mine Data Fusion Technology Based On Multi-Feature Joint Decision Making, Han Shanshan, Zhang Jifeng, Fan Tao, Liu Qiang, Song Xiaojiao, Qi Zhipeng, Zheng Hangzhou

Coal Geology & Exploration

Background In the field of geological exploration of coal mines, methods such as in-seam seismic exploration, the transient electromagnetic method, and audio-frequency electrical penetration offer unique technical advantages. However, each of these methods can only reflect a single physical property of a medium, suffering from limitations including the one-sidedness of information and the multiple solutions of interpretations, thus failing to adapt to the new situations of current coal mine exploration. Multi-source data fusion promotes a technological development by leaps from one-sided detection to comprehensive analysis through mechanisms of information complementation, feature enhancement, and field-source integration. Therefore, this technology can enhance …

Oil-Based Mud Electrical Imaging Logging Responses Of Carbonate Strata, Wu Yuyu, Lai Qiang, Pan Feng, Zhao Zuo’An, Wang Xiaoxing, Wang Yue, Cheng Lu, Wang Zeyu, Yin Rong

Coal Geology & Exploration

Objectives This study aims to characterize the oil-based mud (OBM) electrical imaging logging responses of carbonate strata, highlighting the response characteristics of dissolution vugs, fractures, stratigraphical structures, and special minerals. Methods Tests were conducted on logging data acquisition of a drilled well within the Dengying Formation in the Sichuan Basin before and after OBM was replaced with water-based mud (WBM). Based on the fine-scale core-to-log (CTL) calibration, this study compared the WBM and OBM electrical imaging logging responses of different geological and pore structures. Furthermore, the characteristics of OBM electrical imaging logging responses were further discussed from the measurement principles …

A Cutting Trajectory Planning Technology Driven By Coal Seam Data In A Comprehensive Mining Face, Guo Yifeng, Xue Xusheng, Ma Hongwei, Zhang Guangming, Su Hao, Qi Ailing, Nie Zhen, Ma Kexiang, Wu Xuefei

Coal Geology & Exploration

Objective Currently, the insufficient full-element digitization and the prevalence of “data silos” in fully mechanized mining faces (FMMF) result in poor cutting precision and operational instability of shearers under complex undulating and abrupt geological conditions. These factors pose significant challenges to high-precision proactive trajectory planning. Methods Guided by the academic philosophy that “mining is data acquisition”, this paper proposes a coal seam data-driven cutting trajectory planning method for FMMFs. It elaborates on three core technologies: high-precision digital coal seam modeling, generative model updating and digital twin construction, and data-driven trajectory planning.To address low modeling precision, an intelligent interpolation-based digital coal …

Breathing-Controlled Electrical Stimulation (Breestim) Selectively Modulates Affective And Cognitive Components Of Pain-An Eeg Study, Ahmad Z Rao, Michael Houston, Hao Meng, Shengai Li, Yingchun Zhang, Sheng Li

Faculty, Staff and Student Publications

Breathing-controlled electrical stimulation (BreEStim) is an innovative neuromodulation intervention that synchronizes deep voluntary breathing with peripheral electrical stimulation. Prior studies have shown its analgesic effects in healthy adults and spinal cord injury patients with neuropathic pain. The present study used EEG to examine BreEStim's neural effects on sensory, affective, and cognitive components of pain. Fourteen healthy participants (7 M, 7 F) completed 30 min of BreEStim and conventional electrical stimulation (EStim) interventions in a randomized, crossover within-subject design. Electrical pain thresholds (EPT) and EEG were recorded pre- and post-intervention. Event-related potentials (ERPs) at pre-EPT-level stimuli before and immediately after each …

From Input To Narrative: Authoring Processes In Ai-Generated Discourse, Fahad Ahmed Otaif

Journal of Arabic Sciences and Humanities

With the growing use of generative AI bots in different disciplines and professions, AI bots have been increasingly adopted to compose, revise, translate, and author content, yet without much research evaluating their quality, particularly when it comes to the processes and features of AI-authored discourse. The current study investigates, based on corpus-based evidence, the extent to which one-theme AI-generated sentences can be reorganised/turned into meaningful discourse through four AI bots (ChatGPT, Gemini, Copilot, and DeepSeek), i.e. by which discourse processes and in what coherent and cohesive patterns. These four AI-bots were fed with well-designed prompts instructing them to produce both …

Oakwood "Future Pathways" Legacy Project: Career Exploration & Curriculum Library, Adrian Rodriguez-Mandujano, Victoria Garibay, Bassam Hanania, Alana Yuzon, Paul Consuelo-Valencia

Service Learning Showcase

The main objective of our SPA project was to develop multi-subject lesson plans that better support fifth- and sixth-grade students in Oakwood Elementary’s Bridge Program as they explore future career pathways. This project included lesson plans representing a variety of academic disciplines, including Pharmacy, Engineering, Criminal Justice, and Music.

Each lesson incorporated an interactive, career-focused activity such as Mock Drug Counting (Pharmacy), a Musical Career Showcase (Music), Creating a Claw (Engineering), and Who Did It? (Criminal Justice). These activities were designed to help students gain a better understanding of different subjects and professions while actively engaging in hands-on learning.

In …

Foraging Load Size And Activity In Leaf-Cutter Ants In Rural And Urban Ecuador, Natalie Tollison, Anna Starman

Science Seminar

Leaf cutter ants are important ecosystem engineers in Neotropical forests, moving a significant amount of nutrients from the forest canopy to the soil. We were interested in understanding how time (day vs. night), weather, and level of human disturbance would impact foraging activity in leaf-cutter ants (Atta spp.). To investigate, we sampled activity levels and leaf size along leaf-cutter ant trails in Mera (urban, N=17) and Tamandua (forest, N=27). During the daytime, ants in Mera carried significantly more leaves that were larger in size as well as more total items than did ants in Tamandua. Factors such as the level …

Building A Course-Specific Genai Chatbot: A Comprehensive Tutorial And Comparison, Elias Gil, David Han, Joseph Mendez

TxDLA Journal of Digital Learning

This practitioner article presents a step-by-step guide for designing and embedding a course-specific generative AI chatbot using the Botpress platform. Aimed at digital learning practitioners and instructional designers, the article demonstrates how curated knowledge bases, carefully crafted prompts, and seamless LMS integration can create AI-powered teaching assistants that support student engagement and learning. Drawing on recent scholarship, the work highlights opportunities and challenges in leveraging generative AI for higher education, including accuracy, trust, and ethical considerations. A comparative analysis of alternative platforms such as ChatGPT and Google Dialogflow, further contextualizes Botpress within the broader landscape of AI-enabled educational tools. By …

Targeting Chemical Pollutants Through Assimilation Of Hydrophobic Deep Eutectic Solvents With Metal-Organic Frameworks, Carbon Nanotubes, And Activated Carbon, Tanner William Baughman

ONU Student Research Colloquium

Water contamination by heavy metals has a harmful human threat to the environment and to human health. This is derived from their bioaccumulation and toxicity characteristics. The study directly looks at the assimilated of lignin-derived hydrophobic deep eutectic solvents (HDES) with 3 different class of materials: Metal-Organic Frameworks (MOFs), Carbon Nanotubes (CNTs), and Activated Carbon (AC). Four different HDES mixtures were assimilated into these porous materials through impregnation. Composites were evaluated by characteristic peaks through Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR). The morphology of the samples was assessed as well through scanning electron microscopy (SEM). The colloidal stability …

Decentralized Multilateral Netting Engine For High Frequency Autonomous-Agent Transactions, Harshwardhan Tripathi Mr., Harsh Agrawal Mr.

Defensive Publications Series

The present disclosure relates to a method and a system for decentralized multilateral netting of autonomous agent transactions. The method comprises receiving a signed IOU from a plurality of autonomous agent devices, where the IOU represents an obligation from a first agent to a second agent. The method further comprises publishing the validated IOU to an event stream upon validating a digital signature associated with the IOU. The method further comprises constructing a directed weighted graph based on validated IOUs. Further, the method comprises executing one or more cycle detection algorithms on the directed weighted graph to identify circular obligations. …

Five-Tunnel Inclined-Solar-Chimney Atmospheric Water Generator (5t-Isc-Awg), Vesa Olavi Lius

Defensive Publications Series

This defensive publication discloses a passive, megalitre-class atmospheric water generator (AWG) architecture in which humid coastal air is drawn through an array of five parallel bedrock tunnels, condensed on the cool rock walls, and discharged through a mountain-slope-integrated inclined solar chimney. The rock itself is used as the primary condenser, the bedrock tunnels are dimensioned at the diameter that matches standard utility-scale tunnel-boring-machine (TBM) production, and the inclined solar chimney is routed along the natural face of the inland ridge so that the bedrock carries all structural loads and the chimney's own upper surface acts as a combined solar collector. …

Llm Agent To Interpret Categorical Quality Feedback, Jacob Cunningham, Alan Yang, Ira Kemelmacher-Shlizerman, Chris Lee

Defensive Publications Series

Conventional supervised fine-tuning for specialized image generation models can be inefficient, potentially relying on manual, intuition-driven experimentation that may slow iteration and have difficulty systematically incorporating detailed quality feedback. An automated system can use a large language model (LLM) agent that may operate in a closed-loop optimization process. The agent can receive a high-level goal and structured metadata describing available datasets and tuning parameters to generate an executable training configuration. The system may also interpret detailed, categorical quality assurance reports that identify specific failure modes, for example, "shape change." By reasoning about the causes of these failures, the LLM can …

Orchestrator-Worker Llm Architecture For Mitigating Prompt Injection Via Separated Privileges, Jason Mayes

Defensive Publications Series

Agentic systems powered by a large language model (LLM) can be vulnerable to prompt injection attacks, where malicious instructions hidden in untrusted data may cause the system to perform unauthorized actions. An orchestrator-worker architecture can be implemented to mitigate this risk by separating system privileges. The design can feature an orchestrator component, comprising software logic and a privileged master LLM, that manages user commands and powerful tools, alongside one or more unprivileged, sandboxed worker LLMs that process untrusted data with a restricted toolset. When a worker LLM returns data, the orchestrator LLM can be configured to encapsulate the output in …

Test-Driven Generation Of Validated Llm Prompts Via Iterative Refinement, Artur Hibner

Defensive Publications Series

A system can assist large language model (LLM) outputs in adhering to specific coding styles, potentially reducing a need for manual prompt engineering or model retraining. This disclosure describes a system for automated, test-driven generation of prompts. The method may use a reference artifact, such as a source code commit, as ground-truth data. An LLM can be tasked to generate an initial prompt intended to reproduce the reference. The system can then enter an iterative loop of executing the prompt, programmatically comparing the output against the reference, and using any resulting differences to instruct the LLM to refine the prompt. …

Biologically Inspired Segmented Electric Traction Motor With Load-Adaptive Stator Recruitment And Thermal-Aware Control, Justin Miles Jorgensen

Defensive Publications Series

Abstract

This disclosure describes an electromagnetic traction motor architecture that mimics the distributed, modular torque production of biological motors (e.g., flagellar motors). The system utilizes a stator divided into independently energizable sectors (circumferential or axial). A supervisory controller dynamically “recruits” or “de-recruits” these sectors based on real-time torque demand, efficiency maps, and thermal headroom. This architecture enables improved part-load efficiency, 800V+ operation with reduced I²R losses, and “limp-home” fault tolerance by isolating degraded sectors without shutting down the entire machine.

Key Technical Keywords for Search Indexing

Electric Traction Motor; Segmented Stator Architecture; Load-Adaptive Stator Recruitment; Multiphase Inverter (6-phase / 9-phase); …

Real-Time Large Language Model Refinement Via Machine Learning And Human Learning Architecture, Saloni Priya

Defensive Publications Series

Large language models face challenges in maintaining accuracy and relevance due to the emergence of new information and the limitations of offline training data. Existing methods, such as reinforcement learning from human feedback or standard retrieval augmented generation, often lack real-time correction capabilities or rely on static data sources.

A method is disclosed for real-time model refinement using a combined machine learning and human learning architecture. Vetted domain experts are integrated into a live feedback loop to provide grounded data for model outputs. A consensus-based review system is utilized to validate corrections through a required number of expert approvals. Contributions …

Adjusting Large Language Model Context Via User-Initiated Thread Renaming And Organization, Victor Carbune, Florian Hartmann

Defensive Publications Series

Large language model inference context often becomes inefficient or contains irrelevant information as conversational threads grow in length. This disclosure describes methods for adjusting inference context based on user-initiated organizational signals, such as renaming threads or grouping them into projects. When a thread is renamed, the conversation is segmented into topics and re-ranked to prune segments that do not align with the new title. Conversely, when threads are organized into a project or given similar names, relevant context from related threads is dynamically loaded to augment the current session. User feedback signals, such as manual ratings or interface interactions, are …

Eco Cell Ir Drop Signoff Using Machine Learning, Praphulla Pandey, Vaibhav Gupta

Defensive Publications Series

Traditional Engineering Change Order (ECO) cell verification may require a full‑chip Electro‑Migration and IR (EMIR) analysis to confirm that newly integrated cells do not induce excessive IR drop failures. For large‑scale integrated circuits, this comprehensive simulation creates significant bottlenecks due to high turnaround times and intensive storage requirements. A machine learning‑based methodology is disclosed to predict dynamic IR drop specifically for ECO instances. A partition‑specific model is trained using data from a preceding stable EMIR execution. Physical and electrical parameters, including resistance, slew, load, and peak current, are extracted for new ECO cells and processed by the trained model to …

On‑Chip Current Sensor For Resistance Calibration Of High‑Speed Input/Output Peripherals, Karthikeyan Subramanian, Kasi Chunduri

Defensive Publications Series

Resistance calibration in high‑speed input/output (HSIO) physical layers typically uses dedicated automatic test equipment (ATE) channels to force current and measure voltage at an external reference pin. This traditional approach increases testing overhead and limits the capacity for concurrent multi‑site device testing. This disclosure describes a method and system for performing resistance calibration using an on‑chip current sensor. The sensor’s differential voltage sensing pins are connected to the internal resistance network. A mixed‑signal transconductor converts the detected voltage drop into a current, which is then integrated, sampled, and digitized. Internal resistance is calculated from these measurements using Ohm’s Law, and …

Reducing Test Time Through Parallel Execution Of Analog And Digital Bist Sequences, Karthikeyan Subramanian, Kasi Chunduri, Achin Grover, Gurumoorthy, Suman Jayachandra

Defensive Publications Series

Traditional test methodologies for semiconductor devices typically execute Analog and Memory Built‑In Self‑Test (MBIST) sequences in a standalone, serial fashion. This sequential approach involves separate initialization and execution phases for each test block, which contributes to total tester time. Specifically, standalone sequences for components (e.g., Phase Locked Loops (PLL), Digital to Analog Converters (DAC), memory blocks) run independently through Joint Test Action Group (JTAG) or external interfaces. The disclosed method utilizes a concurrency algorithm based on the IEEE 1687 IJTAG standard and Procedural Description Language (PDL) to interleave these test sequences. Digital and analog initialization phases may be combined, followed …

Cost Analysis Formulation For Pv–Evcs–Dstatcom Integrated Distribution Systems Using Enhanced Hunter–Prey Optimization, Pappu Soundarya Lahari, Varaprasad Janamala

Mansoura Engineering Journal

With the rapid and significant growth of Electric Vehicles (EVs) and Distributed Generation (DGs), such as Photovoltaic (PV) deployment, in the distribution system, it is essential to address the challenges of rising power losses, voltage fluctuations, and reactive power imbalances. Optimal DSTATCOM placement in PV-EV integrated systems to mitigate power quality issues is an effective solution. It is thus necessary to evaluate the economic considerations of such heavily compensated networks. This study proposes Enhanced Hunter-Prey Optimization (EHPO) to assess the net savings of the PV-EVCS-DSTATCOM integrated IEEE-33,69, and 118 bus distribution networks in both single and multi-objective modes. The multi-objective …

Psilocybin And The Related Tryptamine, Norbaeocystin, Exert Acute Anxiogenic Effects That Depend On Sex, Marisa J. Savini, Kylie M. King, Noelle M. Molnar, Evelynn J. Schneider, Savannah E. Straub, Joe Parker, Lillianna P. Puppel, Aleece K. Al-Olimat, Allison M. Bach, Andrew Jones, Phillip R. Zoladz

ONU Student Research Colloquium

Single treatments with psychedelics have demonstrated rapid, long-lasting antidepressant effects. However, psychedelics often produce unfavorable psychoactive effects that could worsen existing psychological distress. Thus, we compared the effects of psilocybin and the related tryptamine, norbaeocystin, on anxiety-like behavior in male and female rats. In Experiment 1, rats were treated with 1 mg/kg psilocybin (PSIL), 1 mg/kg norbaeocystin (NOR), or vehicle 30 min before a 5-min trial on an elevated plus maze (EPM) or in an open field test (OFT). On the EPM, PSIL led to decreased open arm time and fewer arm entries in males and females. NOR led to …

Bypassing Distributed Access Control For Subsystem‑Level Debugging, Rohit Sinha, Sumit Jha, Mayank Tutwani

Defensive Publications Series

Distributed access control in modern heterogeneous architectures often leads to debugging complexities during software development. Security enforcement logic is typically embedded across multiple initiators and targets, creating a wide search space when failures occur. It is often difficult to distinguish between functional software bugs and access control misconfigurations, as both may result in similar system behavior or silent failures. A system and method are disclosed for the selective relaxation of access control policies through a hardware‑assisted debug controller. The debug controller may provide granular enable signals to individual subsystems. Each subsystem may include a control status register that, when qualified …

Codex Oracle Architecture (Coa): Signal‑Driven, Meta‑Orchestrated Multi‑Agent Coordination For Autonomous Code Generation, Johny Manuel-Devadoss

Defensive Publications Series

This disclosure describes a novel orchestration architecture for multi‑agent code generation systems, referred to as the Codex Oracle Architecture (COA). COA introduces a signal‑driven coordination layer, a meta‑orchestrator (“Oracle Layer”), and a structured conflict‑resolution mechanism that enables large‑scale, adaptive, and emergent collaboration among heterogeneous AI agents. Unlike existing multi‑agent frameworks that rely on static workflows, deterministic message passing, or hierarchical control, COA enables dynamic agent activation, probabilistic decision making, and context‑aware collaboration. The architecture is designed to support autonomous code generation, refactoring, debugging, and long‑term software evolution in complex environments.

Human-In-The-Loop Workflow For Ai-Assisted Preliminary Bug Investigation, Mira Leung

Defensive Publications Series

A disclosed technology can address challenges in managing new software bugs when engineering capacity is limited, a situation which can be impacted by time-consuming initial investigations. The technology can provide a structured, human-in-the-loop workflow where a bug reporter may use an artificial intelligence (AI) assistant, such as a large language model, to perform a preliminary analysis at the point of bug discovery. This process, which can be guided by structured prompts, may generate an enriched bug report containing analysis from the AI, including, for example, potential root causes, code pointers, and ownership suggestions. By shifting a portion of the initial …

Ai-Driven Pipeline For Automated Prompt Refinement Using Evaluation Feedback, Cristina Elena Budurean, Quinn Halpin, Ibrahim Gunay

Defensive Publications Series

Conventional methods for improving generative artificial intelligence (AI) prompts can rely on manual analysis of evaluation feedback, a process that may be time-consuming, subjective, and may not scale effectively. A data-driven pipeline is described that can facilitate automated prompt refinement. The system may operate in a feedback loop, ingesting evaluation data, for example, rater scores and qualitative comments. The system can then use a large language model to perform a multi-stage analysis that may include sanitizing data, diagnosing response failures, extracting successful patterns, and resolving potentially contradictory feedback to generate a revised prompt. This approach can support a more scalable, …