Engineering Commons^™

A Comparison Of Unconventional Microwave And Ultrasound-Assisted Extraction Methods Used For Flavonoids, Istiqomah Rahmawati, Daffa Hafiziaulhaq Azizi, Jihan Nafila Wibowo, Muhammad Reza, Boy Arief Fachri, Bekti Palupi, Meta Fitri Rizkiana, Helda Wika Amini, Ifan Ramadana, Felix Arie Setiawan

Makara Journal of Science

Cocoa pods (Theobroma cacao L.) are a rich source of flavonoids, which are natural antioxidants known for their health benefits. This study investigated the use of microwave-assisted extraction (MAE) and ultrasound-assisted ex-traction (UAE) to extract the maximum flavonoids and antioxidants from cocoa pods. MAE and UAE are efficient and sustainable methods for extracting bioactive compounds like flavonoids and antioxidants from cocoa pods, offer-ing faster extraction, reduced solvent use, and better compound preservation compared to conventional methods. These technologies unlock the untapped potential of cocoa pods for applications in food, cosmetics, and pharmaceuti-cals. The effects of extraction time (2–10 min), microwave …

Challenges In Artisanal Small-Scale Gold Mining: Perspectives And Transformations To Sustainability Along Br-163 In Brazil, Carlos Henrique Xavier Araujo, Irfan Ullah, Giorgio De Tomi

Journal of Sustainable Mining

Artisanal Small-scale Gold Mining (ASGM) is a sector beset with unique and complex challenges. Recent literature highlights the growing acknowledgment of the critical need for reforms in how the ASGM industry interacts with communities and the environment. Discussions about sustainable transformations go beyond theoretical and conceptual borders, attempting to understand how local transformative events might reflect global developments. Thus, the goal of this article is to give an analysis from the perspective of the actors participating in artisanal gold mining activities. A survey was carried out along BR-163, which runs from Sinop (Mato Grosso) to Santarém (Pará). Fifty-five (55) interviews …

Effect Of Niobium Dopant On Zno Thin Films Prepared Via The Sol–Gel Spin Coating Method, Kevin Alvin Eswar, Nur Fairuz Rostan, Maryam Mohamad, Rabiatuladawiyah Md Akhir, Rosfayanti Rasmidi, Muliyadi Guliling, Najwa Ezira Azhar, Irmaizatussyehdany Buniyamin, Mohd Firdaus Malek, Mohamad Rusop Mahmood, Husairi Fadzilah Suhaimi, Saifollah Abdullah

Makara Journal of Science

Thin films of zinc oxide (ZnO) and niobium (Nb)-doped ZnO were deposited on a glass substrate using the sol–gel spin coating method. Diethanolamine, isopropyl alcohol, and zinc acetate served as the stabilizers, solvent, and starting ma-terial, respectively. Niobium pentachloride was employed as the dopant source. Energy-dispersive X-ray analysis con-firmed Nb incorporation. Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), and ultravio-let–visible spectroscopy (UV–Vis) analyses characterized the morphology, structure, and optics of the films, respective-ly. Both films, comprising nanoparticles, were visible in FESEM. Nb doping reduced the particle size from 44.2 nm to 35.8 nm. The XRD peaks at 31.23°, …

Artificial Intelligence In Decision-Making: Literature Review, Najm A. Kh. Alhatimi Aleessawi, Leila Djaghrouri

Journal of the Association of Arab Universities for Research in Higher Education مجلة اتحاد الجامعات العربية للبحوث في التعليم العالي

In the fast-changing world of artificial intelligence (AI), the relationship between technology and decision-making has become a central area of study. Over the past five years, numerous papers have been published examining how AI methods are applied to decision-making processes across various industries. This article aims to highlight the key potential of artificial intelligence to enhance decision-making. It does so by systematically reviewing the literature on the role of AI in improving decision-making, particularly studies published between 2020 and 2024. The review consolidates the main findings from articles in renowned databases such as Google Scholar, Scopus, and IEEE Xplore, offering …

Layout-Aware Quantum Circuitry And Algorithmic Extensions To Grover's Algorithm, Ali Al-Bayaty

Dissertations and Theses

Lov K. Grover introduced, in 1996, Grover's algorithm as a quantum search algorithm to find all solutions for quantum oracles representing classical problems. My research observed that the Grover diffusion operator of Grover's algorithm gives wrong solutions when Boolean oracles are designed in some logical structures. Therefore, I invented the new "controlled-diffusion operator" for Boolean oracles as a new approach for Grover's algorithm that always correctly solves the problem of Grover's algorithm.

Another important problem in quantum computing is designing reliable and cost-effective quantum gates using reversible binary logic. In classical logic design, the stage of logic design can be …

Magnetic And Optical Properties Of Airborne Dust Particles Nearby Coal-Fired Power Plants In Upper Silesia, Poland: A Mineralogical, Petrographical And Chemical Approach, Chrysoula Chrysakopoulou, Konstantinos Perleros, Małgorzata Wojtaszek-Kalaitzidi, Lambrini Papadopoulou, Nikolaos Kantiranis, Stavros Kalaitzidis

Journal of Sustainable Mining

Coal mining and exploitation pose certain challenges in terms of environmental management. The objective of this research is the study of airborne dust from Knurow region, Southern Poland, aiming to identify the level and the features of anthropogenic particles, mostly in the form of fly ash. Two samples collected from a domestic gutter system were analysed regarding their mineralogical, chemical and petrographical features, emphasizing the magnetic fraction and the carbonized organic particles. The airborne dust contains 22 wt.% of fossil and fresh organic matter, whereas the major mineralogical phase is magnetite. The magnetic fraction (up to 3 wt.%) appears in …

The "Distributed Ghost" With Independence - A Study On Computational Ability Of Skewed Asynchronous Cellular Automata, Shrey Salvi, Shlok Shelat, Sumit Adak, Souvik Roy

Northeast Journal of Complex Systems (NEJCS)

This paper explores the computational ability of ``distributed ghost" cellular automata (CA) \cite{10.1162/artl_e_00450} after introducing independence in the updating scheme. Traditionally, the CA system dictates all cells to update together following the concept of the global clock. To introduce independence in the system, CA researchers have introduced the notion of fully asynchronous updating scheme with atomicity property where, again, the CA system dictates two neighbouring cells not to update together. In this study, we explore the skewed asynchronous system after breaking the atomicity property. Specifically, we study the computational ability of the proposed skewed asynchronous system in the context of …

Analysis Of Systematic Trade-Offs Between Military And Healthcare Expenditure Alongside Gdp Growth Of Select Asian And Western Exporting Economies In The 21st Century, Rahul Balamurugan, Carlos Gershenson, Preethi Nanjundan, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality …

Prediction Of Unit Haulage Cost In An Underground Mine Using Machine Learning Techniques, Marco Cotrina, Jairo Marquina, Junior Polo

Journal of Sustainable Mining

The primary objective of the research was to apply machine learning techniques to forecast the unit costs of ore hauling in an underground mine. The methodology employed was quantitative, with a non-experimental and descriptive design. Haulage data were collected over a 12-month period. Furthermore, an exploratory data analysis (EDA) was conducted using various models, including ANN-MLP (Artificial Neural Network – Multilayer Perceptron), Random Forests, Extreme Gradient Boosting, Support Vector Regression, Decision Tree, KNN, and Bayesian Regression, to handle the data’s complexity. The data were split into 80% for training, 10% for testing, and 10% for validation. The results indicated that …

Enhancing The Efficiency Of Eaop Degradation Using Ultrasound: A Study With Methylene Blue And Pfoa As Model Pollutants, Jamiu Busayo Ahmed

Master's Theses

No abstract provided.

Solid-State Crystallization Of Zeolites And Their Use In Plastic Upcycling Applications, Yixin Liao

Doctoral Dissertations

Plastics have been an irreplaceable component of modern technology as well as everyday life. They have brought much convenience to us with their characteristics of great malleability, durability, and stability. The versatile and low-cost nature of plastics also enables their wide engagement in many modern industries including automobile, medical, communication as well as aerospace. Polyethylene (“PE”), made from polymerization of ethylene, is one of the most widely used plastics in the world. Being cheap, flexible, and long-lasting, they are extensively used in the packaging industry, especially for plastic bags and other sorts of containers. However, the durability of plastics, on …

Novel Approach To Traveling-Wave-Based Fault Location In Nonhomogeneous Transmission Lines, John Parker Burrell

Master's Theses

Accurate fault location is a critical aspect of power system protection, ensuring grid reliability and minimizing downtime. Traditional traveling-wave-based fault location methods face limitations when applied to nonhomogeneous transmission lines due to the reliance on precise segment velocities and propagation time data. This thesis addresses these challenges by proposing a novel algorithm that leverages historical fault data to estimate segment velocities and refine these estimates as more faults occur. The algorithm was rigorously tested using the digital model of an 11-segment, 65.694 km real overhead transmission line and validated using both simulations and hardware tests using commercially available time-domain protective …

Collaborative Ai: Oer Materials For Exploring Ai As A Partner Rather Than A Tool, David Smith

Open Educational Resources

The Collaborative AI Open Educational Resource (OER) explores how artificial intelligence can act as a creative and analytical collaborator rather than a tool. Centered on the Balanced Blended Space (BBS) framework and the philosophy of the Center for Holistic Integration (CHI), the OER includes curriculum materials, theoretical models, and live research environments. It offers an interesting approach to blending physical, virtual, and conceptual spaces through shared human–AI agency and invites ongoing participation in interdisciplinary meta-projects.

Real-Time Prediction Of Dynamical Systems Using A Hybrid Analog Computer: Network Traffic Modeling, Majd Zuhair Tahat

Doctoral Dissertations

As the number of online users grows exponentially, the number and severity of cyber threats escalate, urgently requiring advancements in real-time network modeling and response. Swiftly predicting and analyzing network traffic is crucial for effective network monitoring and control, preventing cyber breaches, and maintaining healthy network functionality. This research presents a novel approach to real-time modeling based on analyzing evolving properties and patterns in a dynamical network system using a hybrid analog-digital computer. An analog computer was utilized as a co-processor to compute differential equations that model the Transmission Control Protocol (TCP) window size. A comparative analysis was conducted between …

A Human-In-The-Loop Framework For Scalable And Interpretable Event Triaging In Large-Scale Systems, Ibrahim Khaled Al-Agha

Doctoral Dissertations

This dissertation presents a comprehensive and scalable framework for real-time fault detection and event triage in industrial systems, addressing critical challenges such as class imbalance, ambiguous feature boundaries, and the prioritization of complex, high-dimensional event data. The proposed framework integrates advanced methodologies, including micro-batch processing, retrospective divergence-based event detection (DB-RED), association rule mining (ARM), clustering, and Dempster-Shafer Theory (DST) for conflict resolution. Together, these components enable the systematic stratification of events into actionable priority levels, ensuring robust and interpretable decision-making in real-time environments. DB-RED forms the cornerstone of the framework, leveraging KL-divergence and PE-divergence metrics to detect subtle and transient …

Impact Of Stochastic Travel Times On The Military Port Selection Problem: A Stochastic Programming Approach, William M. Titus

Theses and Dissertations

This research models and analyzes the impact of stochastic travel times on port selection during a large-scale mobilization of equipment from continental United States installations to deployment locations using sealift ships. A stochastic mixed-integer programming model is developed to minimize the average arrival time of equipment into theater. The model is solved using Sample Average Approximation. In the first stage, the model selects ports to open and assigns installations, equipment, and ships to open ports. In the second stage, travel times are realized, and equipment is assigned to specific ships that are scheduled to depart. Results show that the marginal …

Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl

Theses and Dissertations

The Army’s recruiting landscape has changed markedly in recent years, raising questions about whether forecasting methods of Army contracts remain robust. This thesis recreates the presented models in Joshua McDonald’s 2015 thesis. It replicates and evaluates the models with updated data (2018–2023) to assess their current validity and compare them to novel alternative approaches, such as simpler regression models or neural networks. While the 2015 model remains a valuable baseline, results suggest that either refining its variables or adopting alternative methods can improve predictive accuracy and interpretability. Ultimately, the United States Army Recruiting Command has many options regarding how it …

Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros

Theses and Dissertations

Classification “flickering,” where the classification of an object changes inconsistently between consecutive video frames, remains a persistent issue in modern object classification algorithms. This problem undermines the reliability of autonomous vision systems and poses significant risks in high-stakes applications such as autonomous vehicles. This thesis explores the use of response surface methodology, a statistical design of experiments technique, to optimize hyperparameters across three object classification pipelines. The first pipeline combines YOLOv8 with SORT to establish a benchmark. The second integrates a Bayesian back-end, while the third employs an exponential smoothing back-end. Hyperparameter tuning was conducted using a two-step process: an …

Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente

Theses and Dissertations

Cyber competition and conflict remain an enduring concern for the Department of Defense (DoD). Positive control of cyberspace is crucial across the vast diversity of military operations and supporting activities. Military members play an important role in cyber prevention, detection, and remediation, but most receive relatively little training outside of the annual Cyber Awareness Challenge. Particular career fields within the DoD may benefit from specialized training in cybersecurity, in particular the civil engineering (CE) community supporting critical infrastructure protection. Prior research has suggested that game-based learning (GBL) can be beneficial for teaching cyber concepts.

Graph Neural Network-Based Uav Coverage Planning For Robust And Efficient 3d Environments, Gal Tsfaty

Theses and Dissertations

This thesis addresses the challenge of generating optimized UAV waypoints for complete coverage of complex 3D environments, utilizing graph-based computational techniques. The proposed framework replaces computationally intensive steps—triangulation and three-coloring—within the Vantage Waypoint Set Generation Algorithm (VWSGA) pipeline with Graph Neural Networks (GNNs). By learning structural patterns, the GNN achieves scalable and robust triangulation and node classification, enabling enhanced coverage planning in irregular geometries. A novel penalty mechanism ensures alignment with graph structure during adjacency prediction. Experimental results demonstrate the effectiveness of GNNs in balancing accuracy, computational efficiency, and adaptability, advancing UAV coverage optimization.

Autonomous Vehicle Path Planning Under Uncertainty, Madison C. Gillan

Theses and Dissertations

Autonomous vehicles are increasingly being deployed for use in high-stakes and uncertain environments where safe and efficient navigation is critical. In these scenarios, traditional path planning approaches, which rely primarily on deterministic models and fixed assumptions, fall short due to the inherent uncertainty of dynamic threats, sensor inaccuracies, and incomplete information. This research addresses these challenges by developing a novel path-planning methodology that combines the Chance-Constrained Rapidly Exploring Random Tree* (CC-RRT*) algorithm with a probabilistic risk assessment heuristic. This method models uncertainty in sensor detection zones, obstacles in the environment, and the Autonomous Vehicle itself, which allows for uncertainty during …

Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick

Theses and Dissertations

The main contributions of this research is to add to the growing library of literature on the use of deep learning algorithms for the spatiotemporal prediction of dangerous atmospheric and hydrologic phenomena. Specifically, we develop novel attention-based and non-attention-based recurrent neural network frameworks to produce short-range sequential forecasts for lightning and tornado occurrences. Additionally, we introduce methods that account for and include error in the model tuning process to generate more reliable models. Furthermore, we have created a lightweight spatiotemporal tornadic prediction dataset that we plan to make publicly available. The first component of this research develops three novel spatiotemporal …

Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case

Theses and Dissertations

United States Air Force (USAF) operations rely on sortie generation, a complex system involving aircraft maintenance, operational planning, munitions, security forces, and aircrew. Failures in any of these areas can jeopardize a mission, and extreme weather events such as lightning, high winds, and snow further complicate operations. This thesis examines the impact of extreme weather on sortie generation, focusing on developing a data-driven discrete-event simulation (DES) to predict generation timelines and identify high-risk areas. The model allows users to adjust key inputs, including the month, number of aircraft, processing times, and personnel/equipment availability. By simulating real-world conditions, the model helps …

Data Lakehouse And Machine Learning Pipeline For Aircraft Fuel Efficiency Experimentation, Skyler G. Kepley

Theses and Dissertations

Fuel efficiency is crucial for the U.S. Air Force, impacting mission success, aircraft performance, and cost savings. This study presents an information system that integrates flight and maintenance data using a data lakehouse. It automates ingestion, enrichment, and predictive modeling, leveraging AutoML for optimization and SHAP for transparency. A case study on C-130J aircraft shows that optimizing D Check cycles can save 11.52 pounds of fuel per flight hour. These findings highlight the effectiveness of data-driven decision-making in aviation, offering a scalable, automated solution for improving fuel efficiency and reducing costs.

Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski

Theses and Dissertations

This study applies advanced Machine Learning (ML) to Flight Data Recorder (FDR) data for fuel consumption predictions. It explores feature engineering, model selection, and Hyper-Parameter Optimization (HPO) across all flight phases. Baseline models like Ordinary Least Squares (OLS) regression, Multi- Layer Perceptrons (MLPs), and decision trees are compared to Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) with Gated Recurrent Unit (GRU) layers, and XGBoost. Results analyze segmentation strategies, tailored features, and model performance. A counterfactual analysis compares ML models to operational fuel predictions, demonstrating their deployment potential. Findings establish a foundation for future ML-driven advancements in aviation fuel optimization.

Class Imbalance: A Landscape Of Classification Models, Joshua L. Edmonds

Theses and Dissertations

Class imbalance poses significant challenges in machine learning classification. This study evaluates the performance of seven models (ANN, k-Means, kNN, LDA, LR, SVM, XGBoost) across multiple imbalance levels (10\%, 5\%, 1 \%, 0.5\%) and investigates the effectiveness of sampling techniques (Undersampling, SMOTE, SMOTE-ENN). ANOVA results confirm that model choice is the most critical factor, with XGBoost and SVM demonstrating superior robustness. SMOTE improves recall but reduces precision, while undersampling generally degrades overall performance. While significant, imbalance levels do not play a critical role in model effectiveness.

Evaluating Atmospheric Water Generation For The Indo-Pacific: Predictive Modeling, Energy Considerations, And Regional Viability, Jose I. De La Serna

Theses and Dissertations

Atmospheric Water Generator (AWG) technology presents a promising solution for extracting and harvesting water from ambient air through condensation methods. This innovative approach offers a viable alternative for water production in regions with limited or unreliable water sources. AWGs operate most effectively in hot and humid environments, typically at temperatures of 80°F and relative humidity levels of 80%. As of 2020, the Department of Defense (DoD) has identified the Indo-Pacific region as a strategic focus for addressing future greatpower competition. Within the framework of Agile Combat Employment, this study evaluates the feasibility and performance of AWG technology at pre-determined locations …

Assessing The Feasibility Of Managed Aquifer Recharge For The United States Air Force, Daniel Hendrix

Theses and Dissertations

Water stress is becoming an increasing global issue, with 4 billion people (50% of the world’s population) experiencing water stress at least one month per year. By 2050, 60% of the world’s population and $70 trillion USD in global gross domestic product will be affected. This research analyzes 78 CONUS USAF installations to determine location-specific water stress and feasible Managed Aquifer Recharge (MAR) solutions. Although thousands of MAR projects have been implemented globally, active-duty USAF installations have yet to contribute to solving this growing issue. Important factors such as required subsurface conditions, physical limitations, design, cost, and regulatory constraints are …

A Reinforcement Learning Approach For Maneuvering And Firing Decisions In Sead Operations, Nathaniel Garcia

Theses and Dissertations

The integration of automated processes in defense continues to expand, enhancing the lethality of military forces. Artificial intelligence accelerates decision-making cycles, removes the constraints of human-operated hardware, and improves coordination by enabling seamless integration across multiple systems. Suppression of Enemy Air Defenses (SEAD) missions are critical to the United States (U.S.) military, as they neutralize hostile air defense systems, ensuring air superiority and enabling safe and effective operations for aircraft in contested environments. Therefore, it is necessary to pair emerging autonomous capabilities with an important mission set in defense. This research investigates the Autonomous Unmanned Air-to-Ground Strike (AUAGS) problem, modeling …

Cloud One Migration Duration And Its Drivers, Grayson T. Hall

Theses and Dissertations

As modern warfare evolves with rapid technological advancements, cloud computing plays a critical role in managing the vast amounts of data required for real-time decision making, as well as enabling seamless organizational access to mission-critical programs and information from around the globe. Recognizing its importance, the Department of Defense (DoD) identified cloud computing as essential for maintaining the military’s technological edge. However, despite cloud computing’s strategic significance, the DoD faces challenges in successfully implementing department-wide cloud computing. In contrast, the Air Force’s cloud computing environment, Cloud One, is fully operational and has already integrated over 145 systems into its platform. …