Operational Research Commons^™

Simulating The Impact Of Self-Sensing Materials On Aircraft Sortie Generation, Harmoni J. Blackstock

Theses and Dissertations

In conjunction with the Air Force Research Laboratory Materials Lab(AFRL-RX), this study evaluates the potential military value of the prototype material sensing composites on Unmanned Aerial Vehicle (UAV) operations in intelligence, surveillance, reconnaissance (ISR), and close air support (CAS) missions within a contested Indo-Pacific theater. Using a Simio based simulation,UAV performance was assessed under varying combat conditions, focusing on Remote Sensing, deployment strategies, initial lay-downs, and varying loss rates. Re-sults show that UAVs equipped with Remote Sensing technology significantly improved sortie generation and logistical efficiency. Scenario 17 achieved the highest sortie rate(965.5 sorties), outperforming the next-best scenario by 25 sorties. …

Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff

Theses and Dissertations

This research develops a digital twin of the global maritime shipping system to model disruptions in major shipping lanes like the Suez and Panama Canals. By incorporating live ship-tracking data, the model simulates closures, forecasts queue lengths, and determines the best rerouting options. Findings show that canal closures cause large traffic backlogs and increased congestion at alternative chokepoints, while rerouted ships may face higher piracy risks in regions like the Gulf of Guinea and the Strait of Malacca. This tool helps decision-makers respond effectively to maritime disruptions.

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 …

Symbology Detection And Numerical Recognition For T-38 Heads-Up Display Recordings, Ben T. Hepner

Theses and Dissertations

The extraction of symbology and numerical data from the T-38 Heads-Up Display (HUD) enhances post-flight analysis and supports real-time decision-making. This research develops a deep learning pipeline using YOLO-based object detection and Optical Character Recognition (OCR) to analyze HUD video data. Model evaluations showed mAP0.5:0.95 ranging from 0.422 (YOLOv11m, hard test set) to 0.696 (YOLOv8m, medium test set), demonstrating robust symbology detection. Numeric detection performed well (mAP_0.5:0.95 = 0.764), but OCR struggled with glare and resolution limitations, achieving a recognition accuracy of 17.35%. These results validate deep learning for HUD data extraction but highlight the need for improved robustness …

Are Emojis The New Words? A Sentiment Analysis Of Social Media Brand Conversations, Yashodhan Karulkar, Dev T. Vora, Siddharth Vaddepalli, Yash Thakur

Journal of International Technology and Information Management

Emojis have become an increasingly important aspect of consumer-brand interactions in the Indian subcontinent. However, the impact of emoji use on brand image and mental health remains underexplored, particularly in emerging economies like India, where structured research on this topic is limited. To address this gap, the present study analyzes over 4,600 consumer tweets related to 19 prominent brands across eleven industries. Using VADER sentiment analysis, the research develops a metric to assess consumer sentiment and brand engagement in relation to emoji usage. The findings indicate that effective integration of emojis contributes to positive consumer sentiment and enhanced brand engagement. …

The Location Set Covering Disruption Problem, Richard A. Sheldon

Theses and Dissertations

This research models and analyzes a variant of the Location Set Covering Problem (LSCP) in a bilevel, game theoretic setting by posing the LSCP as a non-cooperative attacker-defender Stackelberg game, where facilities are to be emplaced by the defender from a boarder set of potential facility locations to cover a set of demands; however, an attacker removes the possibility of emplacing q specific facility locations with the objective to remove the maximum weighted value demands, and then lexicographically maximize the cost of coverage of remaining demands. A novel methodology leveraging lexicographic programming computed an optimal solution for 98% of all …

Military Entrance Processing Station Location And Capacity Optimization, Micah A. Hurst

Theses and Dissertations

This research optimizes the number, placement, and capacity of Military Entrance Processing Stations (MEPS) to minimize applicant and recruiter travel and improve recruitment efficiency. Using mixed-integer programming, it develops capacitated facility location (CFLP) and maximal covering location (MCLP) models, considering facility capacity, budget, and geographic coverage. Computational testing and scenario evaluations highlight opportunities to reduce travel and balance capacity. For example, the CFLP model adds three new MEPS, reducing annual applicant travel by 1.2 million miles in Florida and Texas and 1.0 million in California, while increasing accessibility within 60 miles of a MEPS. This data-driven approach provides USMEPCOM with …

Utility Of Self-Sensing Damage Technology Through A2/Ad Drone Combat Simulation, Sidhanth Venkatasubramaniam

Theses and Dissertations

Since the introduction of the first unmanned aerial vehicle (UAV), UAVs have consistently improved in capability and versatility. The ability to perform military operations without the risk of losing human life is crucial for the United States military. The trade-off for this versatility is cost, and several ongoing research efforts are being made to improve UAV mission success and the lifespan of UAVs. An area of research that falls under the categories mentioned is self-damage detection. The Air Force Research Laboratories (AFRL) are developing a capability to enable a UAV to assess airframe damage, enabling real-time determination of damage potentially …

Improving Zero Shot Learning By Linking Multi-Label Cnns With Llms, Michael A. Wegner

Theses and Dissertations

Classifying previously unseen objects poses a significant challenge for traditional computer vision algorithms, which rely on extensive labeled training data. Zero-shot reasoning offers a way to overcome this limitation. This research explores a novel method for image recognition using the Animals with Attributes 2 (AWA2) dataset as a proof of concept. A multi-label ResNet50 model predicts core attributes like color, ear shape, or number of limbs. Those attributes then feed into ChatGPT which leverages its extensive knowledge base to classify the animal based on the provided attributes. This novel approach skips the need to train on every possible class. Instead, …

Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros

Theses and Dissertations

Pacific Islands under U.S. jurisdiction are highly vulnerable to natural disasters, yet many lack the infrastructure to effectively respond and recover. Clear communication during and after such events is critical for evacuation, hazard awareness, and first responders’ coordination. This research explores a simulation-based approach using Bluetooth communication to relay messages across Guam, assessing its efficiency through statistical analysis. By examining regional differences and geographic impacts on Bluetooth messaging, the study aims to identify key factors that enhance peer-to-peer communication for timely and effective disaster response.

Practical Estimation Of Action-Generation Mechanisms In Repeated Games, Vladimer Kellachow Iii

Theses and Dissertations

The goal of this research is to gain insight into how players of a game learn their strategy during the course of repeated play. The study employs the Experience Weighted Attraction (EWA) model, developed by Dr. Colin F. Camerer and Dr. Teck-Hua Ho, as the foundational behavioral framework. Using historic observed strategy decisions, the parameter values that define an opponent’s learning process are updated using various inference methods.

Machine Learning Techniques To Detect Anomalies In T-38 Flight Sensor Data, Sydney M. Wekamp

Theses and Dissertations

Accurate sensors are critical for ensuring the safety of aircrew. However, detecting faulty sensors remains a significant challenge for the Test Pilot School at Edwards Air Force Base in California. Current methods rely on either student pilots identifying anomalies or waiting for sensors to fail completely before repairs are made—an approach that lacks reliability and consistency. This research aims to address these shortcomings by implementing machine learning techniques to detect sensor faults proactively. To date, applying machine learning to a dataset of this size, encompassing numerous sensors on the same aircraft, is unprecedented. The project focuses on establishing strong baseline …

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.

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.

Analysis Of An Army Recruiter Allocation Model (Ram), Erik J. Wittendorf

Theses and Dissertations

The United States Army Recruiting Command’s mission to recruit America’s best and brightest volunteers that can deploy, fight, and win requires an effective distribution of its recruiting force to serve as local community ambassadors. This research analyzes an Army recruiter allocation model (RAM) and assesses its underlying assumptions, objective function, and constraints. A detailed study of relative market potential and production rates for up to 1,319 Army recruiting stations and 18,789 ZIP codes enables RAM modification recommendations leveraging evolving recruiting concepts and identifies areas of future work to continue improving the Army’s understanding of the recruiting environment.

Evaluating The Performance Of Conformal Prediction Generated Uncertainty Sets In Robust Optimization, Zion C. Johnson

Theses and Dissertations

Uncertainty is a major challenge in optimization, especially in problems where unpredictable costs impact decision-making. Robust optimization addresses this by modeling uncertainty via uncertainty sets. These sets are then used such that solutions hold under worst-case scenarios, with success depending on the accuracy of the uncertainty sets. This research examines the use of conformal prediction to construct uncertainty sets for RO, an approach that has not been widely explored. We test split and full conformal prediction in a robust optimization minimum cost flow problem, and comparing them to interval-based and normal-based ellipsoidal uncertainty sets. Experiments run across different network structures …

Reinforcement Learning For Aeromedical Evacuation In Nonstationary Combat Environments, Micah J. Kartchner

Theses and Dissertations

This research formulates the medical evacuation (MEDEVAC) dispatching problem as a sequential decision process and investigates the application of reinforcement learning under nonstationary conditions. We model the dynamic arrival rate of MEDEVAC requests using a nonstationary Hawkes process and design a Double Deep Q-Network algorithm that incorporates belief states to anticipate future requests. Through computational experimentation, we analyze the impact of belief formulation on decision quality and system performance. Results indicate that policies incorporating belief states significantly outperform myopic dispatching policies, reducing urgent casualty wait times by up to 49.68% and increasing on-time evacuations by up to 21.91%.

Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph

Theses and Dissertations

Artificial intelligence (AI) grows ever-more important in warfighting. Emerging technologies allow for the use of AI to control aircraft and weapons systems. This research investigates the application of reinforcement learning (RL) through the Proximal Policy Optimization (PPO) algorithm to a two-versus-two (2v2) beyond-visual-range (BVR) air combat maneuvering problem (ACMP). Implemented in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), the methodology frames the engagement as a Markov decision process, wherein an autonomous RL agent learns continuous control decisions—throttle, pitch, roll, and yaw—under a cooperative communication scheme. A multi-phase curriculum-learning approach facilitates the progressive acquisition of flight stability, weapon deployment, …

Incorporating Sustainability In Facility Layout Planning Algorithms And Assessing Hybridization Techniques On An Egyptian Case Study, Islam Atia

Theses and Dissertations

Due to the growing consequences faced as a result of global warming and climate change; humanity has come together to take an inclusive stance to combat this serious phenomena and work towards a more sustainable future. Large amounts of carbon dioxide emissions are a major contributor to global warming, and a vast proportion of this emission come from industrial and commercial facilities. Hence, if industrial facilities are built with a larger focus on carbon footprint, it will yield a significant reduction in global emissions throughout the lifetime of the facility and will constitute a huge milestone in the journey to …

Optimized Hiv/Aids Resource Allocation In Ohio: A Linear Programming Approach, Godfred Ahenkroa Kesse

Data Science and Data Mining

This study employs a linear and integer programming approach to optimize HIV resource allocation in Ohio, aiming to minimize new infections and enhance the impact of limited resources. With the advances in HIV prevention and treatment, Ohio faces challenges in addressing disparities in access to healthcare, particularly among high-risk populations. The proposed model integrates data on infection rates, transmission patterns, demographic factors, and cost-effectiveness to provide a decision-support framework for policymakers. Using epidemiological data and equity constraints, the model prioritizes high-risk regions and populations while ensuring fair resource distribution. Results indicate that increased funding allocations significantly enhance the potential to …

Nonconvex Optimization Methods Under Inexact Information, Dat Ba Tran

Wayne State University Dissertations

This thesis focuses on the design and convergence analysis of algorithms for solving nonconvex optimization problems under inexact first-order information. We introduce Inexact Reduced Gradient (IRG) methods for general smooth functions and Inexact Gradient Descent (IGD) methods for $\mathcal{C}^{1,1}_L$ functions with relative and absolute errors. Additionally, we develop Inexact Proximal Point and Inexact Proximal Gradient methods for weakly convex functions. Our methods improve the performance of standard inexact proximal point methods, inexact proximal gradient methods, and inexact augmented Lagrangian methods by approximately 2.5 to 10 times in terms of iteration complexity for image processing tasks. Moreover, we propose new derivative-free …

Crime Theory Informed Agent-Based Modeling For Crime Prediction And Patrolling Route Optimization, Shohreh Moradi

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Crime reduction remains a global priority, demanding both accurate modeling of criminal dynamics and efficient allocation of scarce policing resources. To address these needs, this study presents a two‐fold framework that (1) simulates street‐level crime patterns using an agent‐based model (ABM) grounded in Routine Activity Theory (RAT), Rational Choice Theory (RCT), and Crime Pattern Theory (CPT), and (2) optimizes patrol routing through a time-dependent, multi‐visit mixed‐integer linear programming (MILP) formulation.

In the first component, we integrate real‐world crime, environmental, and census data to reproduce realistic offender, citizen, and Police behaviors, capturing where and when robbery, burglary, and larceny occur across …

Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …

Introducing Sustainable Development Goals In College Curricula: A Way Forward, Christy Ashley, Jason D. Oliver, Hillary Leonard

Markets, Globalization & Development Review

The commentary proposes a blueprint to help guide curriculum innovations, partnerships, and teaching interventions that incorporate the United Nations sustainable development goals (SDGs) into the curriculum. It suggests the utilization of AACSB’s Societal Impact Canvas with Ancona et al.’s (2007) Leadership Capabilities (Sensemaking, Relating, Visioning, Inventing) to provide a blueprint for mission-aligned SDG integration at a local level. It provides an illustrative example from the University of Rhode Island (USA), where the focus is on the Blue Economy. It aims to provide practical guidance for how a college or university can efficiently organize to gain stakeholder input that helps enhance …

การวิเคราะห์ความสามารถในการขับขี่และพฤติกรรมจากความเหนื่อยล้าโดยใช้สัญญาณภาพ, ภาสวิชญ์ บุญนุช

Chulalongkorn University Theses and Dissertations (Chula ETD)

อาการเหนื่อยล้ายังคงเป็นประเด็นด้านความปลอดภัยที่สำคัญในกลุ่มผู้ขับรถบรรทุก ซึ่งมักจะส่งผลให้ระดับความสามารถในการขับขี่ลดลงและเพิ่มความเสี่ยงต่อการเกิดอุบัติเหตุ การศึกษาในครั้งนี้นำเสนอแนวทางการใช้แบบจำลองการเรียนรู้ของเครื่อง (Machine Learning) โดยมีการรวมข้อมูลเหตุการณ์การเกิดพฤติกรรมของผู้ขับขี่ที่ตรวจจับได้จากระบบ AI Camera และข้อมูลด้านประชากรศาสตร์และลักษณะการทำงาน เพื่อทำนายระดับความสามารถในการขับขี่ที่ลดลงที่มีสาเหตุมาจากอาการเหนื่อยล้า โดยทำการศึกษาจากข้อมูลจำนวน 600 วัน ซึ่งประกอบไปด้วยผู้ขับขี่จำนวน 105 คน โดยแบ่งระดับความสามารถในการขับขี่ออกเป็น 3 ระดับ ได้แก่ ความสามารถปกติ ความสามารถลดลง และความสามารถที่มีความเสี่ยงสูง โดยสังเกตจากพฤติกรรมการตอบสนองขณะขับขี่ การวิเคราะห์จะพิจารณาการรวมข้อมูลด้านประชากรศาสตร์และลักษณะการทำงาน วิธีการพัฒนาเกณฑ์ในการระบุระดับความสามารถในการขับขี่ กรอบเวลาในการสังเกตอาการเหนื่อยล้า และคุณภาพของข้อมูลเชิงพฤติกรรม โดยใช้แบบจำลองการเรียนรู้ของเครื่องภายใต้กรอบเวลาในการสังเกตที่แตกต่างกันก่อนเกิดที่ระดับความสามารถในการขับขี่ลดลง ผลการศึกษาแสดงให้เห็นว่าปัจจัยด้านประชากรศาสตร์และปัจจัยที่แสดงลักษณะการทำงานช่วยเพิ่มความแม่นยำในการทำนาย โดยสำหรับการศึกษานี้ แบบจำลองชนิด LightGBM ให้ผลการทำนายที่ดีที่สุดที่ค่าความแม่นยำ 90% ภายใต้เงื่อนไขเหตุการณ์ที่ผ่านการตรวจสอบและใช้กรอบเวลา 8 นาที ซึ่งสะท้อนให้เห็นถึงประโยชน์ของการใช้กรอบเวลาสั้นในการตรวจจับอาการเหนื่อยล้า การวิเคราะห์ความสำคัญของแต่ละปัจจัย (Feature Importance) พบว่า การหาว การปฏิบัติงานต่อเนื่องเกิน 4 ชม. และการหลับตา เป็นตัวบ่งชี้ทางพฤติกรรมที่สำคัญที่สุดตามลำดับ ในขณะที่อายุ การยกของก่อนขับรถ ช่วงเวลาในการทำงาน ประสบการณ์ในการทำงาน เป็นปัจจัยด้านประชากรศาสตร์และการทำงานที่มีอิทธิพลต่อการลดลงของระดับความสามารถในการขับขี่ ผลการศึกษานี้ชี้ให้เห็นถึงความสำคัญของการรวมข้อมูลเหตุการณ์ที่ผ่านการตรวจสอบเข้ากับลักษณะเฉพาะของผู้ขับขี่ และกรอบเวลาในการสังเกตอาการเหนื่อยล้าที่เหมาะสม เพื่อใช้ในการประเมินระดับความสามารถที่ลดลงที่มีสาเหตุจากอาการเหนื่อยล้าได้อย่างมีประสิทธิภาพในงานขนส่ง

การเปรียบเทียบขนาดของรอยฝ่าเท้าที่ได้จากเครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพาและโฟมพิมพ์เท้า, วรัญญา รัตนสุมาวงศ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ความแม่นยำของเครื่องมือใหม่ที่ถูกพัฒนามาใช้ทดแทนเครื่องมือเดิม เป็นปัจจัยสำคัญต่อความน่าเชื่อถือ ได้มีการพัฒนาเครื่องพิมพ์รอยฝ่าเท้า 3 มิติแบบพกพาเพื่อทดแทนการใช้โฟมพิมพ์เท้า และใช้ iPhone 12 mini เป็นอุปกรณ์สแกนรอยฝ่าเท้าเพื่อทดแทนเครื่อง 3D scanner เพื่อประเมินให้เห็นว่าสามารถนำเครื่องพิมพ์รอยฝ่าเท้า 3 มิติแบบพกพาและ iPhone 12 mini มาใช้ทดแทนได้ จึงได้มีการออกแบบการทดลองแบบ Randomized Complete Block Design (RCBD) โดยศึกษาปัจจัยของประเภทเครื่องมือที่ใช้พิมพ์รอยฝ่าเท้า ได้แก่ โฟมพิมพ์เท้า และ เครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพา และประเภทเครื่องสแกน 3 มิติ ได้แก่ 3D scanner และ iPhone 12 mini การทดสอบได้เก็บข้อมูลเท้า 18 ข้าง จากผู้เข้าร่วมวิจัย 9 คน โดยพิมพ์ลงบนเครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพาและโฟมพิมพ์เท้า และทำซ้ำ 3 ครั้ง รอยฝ่าเท้าที่ได้ถูกสร้างรูปร่างรอยฝ่าเท้าดิจิตอลด้วย iPhone 12 mini และ 3D scanner เพื่อเก็บข้อมูลตัวชี้วัดทั้ง 7 ตัว ได้แก่ ความกว้างรอยฝ่าเท้า ความยาวรอยฝ่าเท้า ความสูงรอยฝ่าเท้าในพื้นที่บริเวณอุ้งเท้า 4 ตำแหน่ง และความสูง Metatarsal head ที่ 3 จากผลการวิเคราะห์ทางสถิติแบบ Randomized Complete Block Design (RCBD) ที่ระดับความเชื่อมั่น 95% พบว่าตัวชี้วัดทั้ง 7 ตัว ของเครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพากับโฟมพิมพ์เท้าไม่มีความแตกต่างกันอย่างมีนัยสำคัญ และการใช้ iPhone 12 กับ 3D scanner ไม่มีความแตกต่างกันอย่างมีนัยสำคัญเช่นกัน ผลการวิจัยสรุปได้ว่า …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

An Operational Field Study: A Comparison Of Piloting Uncrewed Underwater Vehicles And Uncrewed Aircraft Systems, David Thirtyacre, Joseph Cerreta, Pete Miller, Kimberly Luthi, Jolee Thirtyacre

Publications

and operations, the ability to cross-train personnel in both Uncrewed Underwater Vehicles and Small Uncrewed Aircraft System operations has become a focal point for efficiency and workforce optimization. This study presents a comparative analysis of the operational and human factor considerations involved in piloting mini UUV and sUASs, highlighting the key similarities and differences in control methods, environmental influences, navigation, emergency procedures, and situational awareness. A qualitative experimental field study was conducted between July 2024 and October 2024, involving real-world deployments of both systems in maritime and aerial environments. Findings indicated that while UUV and sUAS operators relied on remote …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …