Engineering Commons^™

Dynamic Task Planning For Wargaming Based On Large Language Models, Yingang Liu, Ming Ma, Ronghua Zhang

Journal of System Simulation

To address the problems of great difficulty in intelligent decision-making and insufficient dynamism in task planning caused by the complex adversarial environment and strong uncertainty in wargaming tasks, this paper proposed a hierarchical Agent collaborative decision-making framework based on large and small model synergy.Through a multi-level structure, the hierarchical decoupling and dynamic coordination of battlefield tasks were achieved. A memory management module was constructed, and a query optimization mechanism driven by large language models was introduced to dynamically perceive the decision-making process and query intent, completing the semantic reconstruction and context completion of raw queries. A time-driven two-stage task …

Multi-Agent Reinforcement Learning Method For Wargame Simulation Based On Suboptimal Demonstration Guidance, Zicong Zhou, Junjie Zeng, Yue Hu, Zhengqiu Zhu, Quanjun Yin

Journal of System Simulation

To address issues such as fixed behavior patterns and insufficient adaptability in complex adversarial environments exhibited by traditional wargame agent decision-making models, this paper proposes a multi-agent reinforcement learning method based on suboptimal demonstrations (MARLSD). The proposed method integrates reward relabeling with a self-imitation learning mechanism, effectively improving the training efficiency of multi-agent reinforcement learning algorithms in environments with large state-action spaces and sparse rewards, even when only a small number of suboptimal demonstrations are available, while encouraging agents to explore better strategies. Experimental results show that, compared with baselines such as QMIX and MAGAIL, MARLSD significantly improves performance and …

Annotation-Free 6-Dof Grasp Detection Method Integrating Physical And Geometric Priors, Min Shi, Shisheng Guo, Suqin Wang, Zhaoxin Li, Dengming Zhu

Journal of System Simulation

To improve the stability and cross-category generalization capability of grasp pose estimation in complex stacked scenes, an annotation-free 6-DoF grasp detection method integrating physical rules and geometric structure priors was proposed. In the offline stage, a template library of feasible grasp poses was constructed based on multi-physical constraints, without relying on manual grasp annotations. In the network design, the modeling of structural symmetry of objects and spatial overlap relationships was introduced; a geometric guidance mechanism with occlusion perception and exposure modeling capabilities was designed, and robust pose alignment of target objects was achieved by combining keypoint regression. A multi-type stacked …

Robust Two-Stage Mimo-Ofdm Channel Estimation Method Against Sensing Errors, Yi Peng, Jun Wang, Qingqing Yang, Jianming Wang, Hui Li

Journal of System Simulation

To address the challenges of performance degradation, high pilot overhead, and high computational complexity in traditi onal channel estimation methods for integrated sensing and communication (ISAC) assisted MIMO-OFDM systems when radar sensing information contains errors, this paper proposes a robust two-stage sparse channel estimation framework designed to be tolerant of sensing errors. In the first stage, a residual energy weighted simultaneous orthogonal matching pursuit (REW-SOMP) algorithm is designed. Leveraging locally adaptive dictionary expansion and a residual- weighted path selection mechanism, it accurately captures communication-associated paths even under sensing errors. The second stage introduces an adaptive penalty factor alternating direction method …

The Structural Limitations Of Mining Output Growth In Nevada, Mckinley Brown, Emma Kitchen

Undergraduate Economics Working Paper Series

This study compares short and long trends in Nevada to the production output within the mining industry. Nevada produces around 70% of the United States gold and about 15% of the world's critical minerals. From its position as one of the most resource rich mining regions globally to its emergence as a growing economic hub, mining played a central role in Nevada's economy. However, despite the industry’s importance, Nevada still faces inefficiencies in production output due to insufficient attention to key inputs such as labor, infrastructure, technology, and energy resources.

In Silico Molecular Docking Study Of Antidiabetic Bioactive Compounds From Brotowali (Tinospora Cordifolia) Targeting Glut4 In Type Ii Diabetes Mellitus, Gita Euaggelion Tarigan, Surya Dwira

Indonesian Journal of Medical Chemistry and Bioinformatics

Type 2 diabetes mellitus (T2DM) is a global metabolic disorder characterized by insulin resistance and impaired glucose uptake. Despite the availability of pharmacological therapies, limitations such as adverse effects and high costs highlight the need for alternative therapeutic candidates. Tinospora cordifolia has been widely reported to contain bioactive compounds with antidiabetic potential; however, comparative evaluation of their interaction with glucose transporter type 4 (GLUT4) remains limited.

This study aimed to identify the most promising bioactive compounds from Tinospora cordifolia targeting GLUT4 using an in silico molecular docking approach, followed by pharmacokinetic and toxicity (ADMET) prediction. Molecular docking was performed using …

Data Driven Estimation Of Pore Size Using 1d Light Emissions For Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jorge Barron, Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

The quality assurance of the Laser Powder Bed Fusion Process (LPBF) has been extensively investigated over the last decade for in-situ monitoring of metal additive manufacturing. The process inherently generates voids within the bulk of the part, which can detrimentally affect the quality of the printed part. The characterization of these voids by estimating their size and identifying their geometrical features remains a challenge. This study introduces a Machine Learning (ML) based framework for estimating void sizes of varying geometries using layer-wise one-dimensional (1D) average light intensity signal obtained from the optical tomography system during the 3D printing of metallic …

Asymmetric Opinion Formation Of Emotional Excitable Agents, Irene Ferri, Emanuele Cozzo, Aleix Nicolás-Olivé, Albert Díaz-Guilera, Luce Prignano

Northeast Journal of Complex Systems (NEJCS)

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a decreasing function of their emotional arousal, an additional independent variable distinct from the opinion value. Additionally, our framework accounts for agents' ability to broadcast messages, with interactions influencing the timing of each other's message emissions. Our findings underscore the significant role of synchronization in shaping consensus formation. …

Build Digital Annual Survey 2025 And Trends & Comparisons 2022 - 2025, Clare Eriksson, Robert Moore, Bilal Succar

Reports

The Build Digital Annual Survey 2025 provides an overview of digital transformation across Ireland’s construction and built environment sector, drawing on responses from 205 participants across industry, government, and academia. The findings show continued sector engagement with digital transformation, driven by government mandates, project benefits, and partner expectations. Common digital deliverables, such as model-based design, documentation, coordination, and clash detection, are now widely used, while more advanced uses linked to operations, sustainability, interoperability, and AI remain less mature. The report also highlights ongoing challenges, including skills gaps, uneven training provision, limited OpenBIM adoption, and inconsistent implementation of ISO 19650 and …

Characteristics And Implementation Paths Of Goal For Building Energy Powerhouse, Liye Xiao, Jiaofeng Pan, Xiaojiong Wang, Deqiang Sun, Mingliang Qi, Jianlei Mo, Huimin Li, Ting Wang, Jie Yang, Jie Lin, Yuchao Wang, Haiting Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

China is a major energy-consuming country, and ensuring effective energy supply is one of the core tasks for promoting national development and national rejuvenation. To guarantee national energy supply and energy security, and to vigorously develop and utilize clean and low-carbon energy, the Outline of the 15th Five-Year Plan for National Economic and Social Development of the People’s Republic of China clearly states: “We will thoroughly implement the new energy security strategy, accelerate the construction of a clean, low-carbon, safe, and efficient new energy system, and build a strong energy nation. We will promote the safe, reliable, and orderly replacement …

Seizing Strategic High Ground Of Space Computing Power: Global Competition Landscape And China’S Path, Yan Chen, Wenbin Song, Ping Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The deep integration of artificial intelligence and commercial aerospace is accelerating the transformation of space computing power from conceptual exploration to engineering verification, becoming a key direction for building an integrated space-air-ground information infrastructure. This study delves into its strategic value, global landscape, industrial chain bottlenecks, and advancement paths. The research reveals that the core value of space computing power does not lie in replacing ground data centers, but rather in focusing on network coverage blind spots, data transmission limitations, and high-timeliness scenarios, providing a new supply model of “in-orbit computing + space-ground collaboration”. Currently, the world has entered a …

A Predictive-Correlational Study Investigating Flight Students' Perceptions Of The Use Of An Artificial Intelligence Instructor In The Simulated Flight Training Environment, Zachary L. Lamothe

Doctoral Dissertations and Projects

The purpose of this quantitative predictive-correlational study is to investigate how perceived ease of use, perceived usefulness, performance expectancy, and perceived enjoyment impact attitude towards use and the behavioral intention to use an artificial intelligence flight instructor in simulated flight training among aviation students earning an aeronautical degree at a collegiate flight training school in the Mid-Atlantic region. Aviation has benefited from different technological advances, and using artificial intelligence technology for flight training could be another improvement as it becomes increasingly sophisticated. The study adds to the literature by addressing the problem of limited information on the perception of artificial …

From Frames To Strains: Analytically Modeling Inelastic Deformation Under Mapped Single-Site Impacts From High-Speed Footage To Internal State Variable Codes, Joby Milo Anthony

Doctoral Dissertations and Projects

This work adds insight to the physical phenomena of microstructural and stress strengthening of metal components by the inelastic deformation from Surface Mechanical Attrition Treatment (SMAT). Impact behaviors observed by high-speed footage of a Crank-Slider Mechanism (CSM) are examined in the context of analytically moving rigid bodies in spacetime and resolving kinematics upon impact until restitution via Finite Element Analysis (FEA). A Coupled Discrete-Finite Element Model (CDFEM) leverages Bammann plasticity, Horstemeyer damage and void nucleation, growth, and coalescence and Cho recrystallization Internal State Variable (ISV) models to show the localization of plastic strain and onset of recrystallization under any single …

The Impact Of Dispatch Weight Restrictions On Derivative Aircraft Propulsion Technology Evaluation, Timothy T. Takahashi

Faculty Publications

This paper arises from an ARPA-E-sponsored project seeking design opportunities to retrofit existing aircraft with hybrid electric propulsion systems. Engineers typically configure aircraft to fly a given payload over a long range, which is subject to field performance constraints. In practice, operators fly transport aircraft (civilian and military) in a manner where dispatch consciously trades payload and/or range to enable safe operations to and from short runways. This work describes a simple yet novel analytical process suitable for inclusion in conceptual design or technology portfolio trade study evaluations to assess the impacts of weight-restricted dispatch upon usable payloads. We find …

Assessment Of Buildings' Energy-Saving Strategies Resilience To Climate Change: The Case Of Mediterranean Climate, Aya S. Mohamed, Bakr M. Gomaa, Alaa Eldin N. Sarhan

HBRC Journal

The Earth's climate is changing, and projections indicate that global warming will continue throughout this century, leading to increased occurrences of extreme temperatures. This raises critical questions about the performance and resilience of different energy-saving design strategies in buildings under future climatic conditions. To address this, the present study investigates the impact of passive design strategies, including building orientation, window-to-wall ratio, south and east/west shading devices, and thermal insulation on a prototype building's energy performance across four timeframes: 2002, 2020, 2050, and 2080, using validated computer-based thermal simulations. The results indicate that individual strategies vary significantly in their effectiveness, with …

Optimizing Wind Turbine Blades Using Fiber-Reinforced Composites, Rayan A. Akeel

Discovery Day - Daytona Beach

This project explores how fiber-reinforced materials can improve wind turbine blade performance by making them lighter and more durable. It will examine different fiber types, their fabrication methods, and their impact on efficiency and strength.

Explainable Tree-Based Ensemble Models For Diabetes Prediction Using Shap, Maan Y Anad Alsaleem, Omar Shakir Hasan, Yahya Albugg

AUIQ Technical Engineering Science

Due to the generally unqualified nature of prediction data and the difficulty of interpreting predictions, predicting diabetes remains a significant hurdle in the adoption of machine learning within the medical domain. In this study, several tree-based machine learning techniques (LightGBM, XGBoost, CatBoost, and Gradient Boosting) were applied to predict diabetes using the 2015 BRFSS dataset, while two ensemble methods (soft voting and stacking) were employed to improve predictive accuracy. The performance analysis of the individual models and ensemble approaches indicates that CatBoost achieved the highest accuracy among the single classifiers (0.871), with an F1-score of 0.871 and a ROC–AUC of …

An Open-Source Linear Actuated-Quartz Tube Furnace With Programmable Ceramic Heater Movement For Laboratory-Scale Studies Of Combustion And Emission, Casey Coffland, Ryan Bixler, Elliott T. Gall

Mechanical and Materials Engineering Faculty Publications and Presentations

The Linear Actuated Quartz Tube Furnace (LA-QTF) is an instrument engineered to heat and combust materials under controlled conditions, capable of achieving flaming and smoldering states. A ceramic ring furnace is linearly actuated parallel to the length of a quartz tube. The mode of combustion depends on temperature, fuel composition, and oxygen availability; the LA-QTF regulates combustion by controlling the temperature and position of the ring furnace, and airflow within the tube. The LA-QTF can maintain temperatures between 23 °C and 530 °C for extended time periods, with stable temperatures over long-duration (∼120 min) experiments. Flow rate is dependent on …

Visualization And Marker-Less Tracking Of User-Defined Pre-Processed Mri Articulator Data Using Deep Learning, Michael De George

Student Theses

This thesis presents a comprehensive framework for the automated tracking and visualization of articulatory movements based on magnetic resonance imaging (MRI) data. A well-known data analysis tool for markerless pose estimation, known as DeepLabCut, is investigated for this purpose. The performance of this tool is enhanced through the design and implementation of a pre-processor. DeepLabCut is a markerless pose estimation toolbox based on deep learning, which overcomes the issue of making manual annotations frame-by-frame. Limitations from manually marking the MRI images are addressed by implementing transfer learning with convolutional neural networks to achieve accurate, user-defined articulator tracking without markers. Current …

Towards A Methodology For Form Creativity Of Building Envelope To Enhance Thermal Performance (Biomimicry As A Tool For Form Creativity), Amal Ebrahim Ahmed Hassanin, Marwa Atef Abd-Elhady

Mansoura Engineering Journal

It has become necessary to achieve thermal comfort for users in buildings, which enhances human ability to work, create, or rest and enjoy. Since the building envelope serves as the link between the inside and outside of the building, forming the building envelope creatively improves and enhances thermal performance of buildings, thus achieving thermal comfort for users. Nature is the primary teacher and source of creativity for humans. Therefore, Biomimicry was chosen as a tool for creative formation, and the descriptive analytical approach was used by analyzing an example where nature was simulated in the formation of the external envelope …

On The Application Of Machine Learning Techniques For Quality Assurance In An Automobile Paint Shop, Anis Fatima, Shakeel Ahmed, Muhammad Akif Shan

Michigan Tech Publications

The automotive industry depends on high-quality paint coatings for both aesthetic appeal and functional performance. However, surface imperfections such as scratches, paint runs, and orange peel can arise from process and environmental variations. This study employs machine learning (ML) and exploratory data analysis (EDA) to identify key factors that influence surface defect formation in automotive painting. Using historical production data from Lucky Motor Corporation Limited, models based on linear regression, support vector machines (SVM), and random forests were developed and validated under various process conditions. The best-performing model achieved an R² of 0.94, with a mean absolute error (MAE) of …

The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean …

Techno-Economic Assessment And Life Cycle Analysis Of Electrocatalytic Reduction Of Co2 To Ethanol., Omotolani Elizabeth Oduyebo

LSU Master's Theses

This study presents a techno-economic analysis (TEA) and life cycle assessment (LCA) of the electrocatalytic reduction of CO₂ to ethanol, a multi-carbon (C₂) product with significant market value. Prior TEA studies have relied on simplified lump-sum separation cost estimates, and prior LCA studies have rarely examined the combined effect of CO₂ source and electricity supply on carbon intensity gaps that this work addresses through process-simulation-grounded analysis. An Aspen Plus process simulation was developed for an anion-exchange membrane (AEM) electrolyzer system coupled with an extractive distillation separation train using ethylene glycol as the entrainer, achieving 99.9 wt.% ethanol purity …

Optimization And Energy Efficiency Analysis Of An Automatic Feed Mixer With A Rotating Drum Mechanism, Kris Witono, Talifatim Machfuroh, Nurlia Pramita Sari, Lisa Agustriyana, Aini Lostari

Journal of Mechanical Engineering Science and Technology (JMEST)

Energy-efficient feed mixer machines are important for improving the productivity and sustainability of small and medium-scale livestock farms. Previous studies primarily focused on either structural performance or mixing efficiency, with limited studies integrating both aspects. Therefore, this study evaluated an automatic rotating-drum feed mixer by combining Finite Element Method (FEM) analysis and energy modeling. The study used FEM simulations for different materials, namely A36 steel alloy, stainless steel 304, aluminium 6061, and galvanized steel, with thicknesses of 3 mm and 4 mm, as well as different drum systems. The FEM results showed that all evaluated materials met the minimum safety …

Influence Of Gas Atmosphere And Deposition Time On Si/Mno₂ Thin Films Properties Deposited By Magnetron Sputtering For Supercapacitor Application, Tansya Trisnatika Dewi, Boon Tong Goh, Reza Akbar Pahlevi, Ishmah Luthfiyah, Markus Diantoro

Journal of Mechanical Engineering Science and Technology (JMEST)

Thin films of MnO₂ were successfully deposited onto Si substrates using the magnetron sputtering technique in various gas atmospheres and deposition times to study their influence on the structural, morphological, electrical, and electrochemical properties for application as supercapacitor electrodes. Structural analysis using XRD showed that deposition under an Ar+O₂ gas atmosphere increased the crystallite size and crystallinity of the Si/MnO₂ films, while shorter deposition times reduced the crystallite size and lowered the crystallinity. Surface morphology observation using SEM showed that the film deposited in an Ar+O₂ gas atmosphere had smaller, more uniform, evenly distributed, and closely …

Influence Of Fiber Loading And Cu/Sio₂ Fillers On Mechanical And Physical Properties Of Sugarcane Bagasse Epoxy Composites, Ansor Salim Siregar, Indra Indra, Agung Fauzi Hanafi, Sunny Ineza Putri

Journal of Mechanical Engineering Science and Technology (JMEST)

The rising worldwide need for lightweight, high-strength, and eco-friendly materials has driven considerable investigation into natural fiber composites. This research offers an extensive examination of the creation and characterization of a new hybrid composite designed for high-performance sports equipment. The composite structure consists of an epoxy matrix strengthened with sugarcane bagasse fibers and synergistic blend of copper and silicon dioxide hybrid particulate fillers. Three unique composite variations were created, with systematically adjusted weight fractions of sugarcane bagasse fiber (35%, 31%, 27%) and hybrid fillers. A thorough assessment of mechanical and physical properties was carried out following ASTM standards. The results …

Evaluation Of Intracavity Electromagnetic Field Probes And Sources: Implications For Shielding Effectiveness, Joseph Anthony Ferreri

Electrical and Computer Engineering ETDs

To measure the electric field in a reverberant cavity, a small, minimally invasive probe is required. Common solutions include electrically small surface mounted monopole antennas, B-dots, and D-dots. To obtain an accurate field measurement with a particular probe, it is necessary to characterize it to compensate for its ability to convert electric field into voltage which requires a gauge factor known as effective height. The characterization process is straight forward in open space on a ground plane but requires more insight when in situ in a reverberant cavity. This work adapts ground plane probe characterization methods for cavity measurements, facilitating …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …