Operations Research, Systems Engineering and Industrial Engineering Commons^™

Thermal Management For Optimal Performance Of Polymer Electrolyte Membrane Unitized Regenerative Fuel Cells, Mythy Tran, Ayodeji Demuren

Mechanical & Aerospace Engineering Faculty Publications

Hydrogen is an excellent carrier for energy storage and can be produced from various green and renewable sources. However, the cost of producing hydrogen and converting it to useful energy is much higher than fossil fuel and traditional energy generation and storage systems. Unitized regenerative fuel cells (URFC) maximize utilization of high-cost cells and their components, thus, lowering system capital cost. Improving the URFC efficiency is an effective way to lower its operating cost. This study evaluates utilization of waste heat during operation and recovery strategy to improve system efficiency of Proton Exchange Membrane (PEM) URFC. A COMSOL Multiphysics 3-D …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

A Systematic Literature Review On Resilient Digital Transformation, Examining How Organizations Sustain Digital Capabilities, Thira Chavarnakul, Li Da Xu, Zhuming Bi, Achyut Shankar, Gaurav Dhiman, Wattana Viriyasitavat, Danupol Hoonsopon

Information Technology & Decision Sciences Faculty Publications

In an era marked by relentless technological shifts and market volatility, digital transformation (DT) alone is insufficient. Organizations must develop Resilient Digital Transformation (RDT)—the organizational capabilities required to sustain DT over a medium-term horizon—to navigate these challenges effectively. This study primarily aims to propose a guideline for fostering RDT. Drawing on the PRISMA guidelines and a systematic review of 77 peer-reviewed papers, this study identifies and synthesizes key targets and drivers across three core pillars: Technology, Organization, and External Environment. These elements collectively foster organizational resilience. Specifically, this study highlights how adaptability, innovation, and scalability form the technological underpinnings of …

Development And Validation Of A Subject-Specific Integrated Finite Element Musculoskeletal Model Of Human Trunk With Ergonomic And Clinical Applications, Farshid Ghezelbash, Amir Hossein Eskandari, Amir Jafari Bidhendi, Aboulfazl Shirazi-Adl, Christian Lariviere

Études primaires

Biomechanical modeling of the human trunk is crucial for understanding spinal mechanics and its role in ergonomics and clinical interventions. Traditional models have been limited by only considering the passive structures of the spine in finite element (FE) models or incorporating active muscular components in multi-body musculoskeletal (MS) models with an oversimplified spine. To address those limitations, we developed a subject-specific coupled FE-MS model of the trunk and explored its applications in ergonomics and surgical interventions. A parametric detailed FE model was constructed, integrated with a muscle architecture, and individualized based on existing datasets. Our comprehensive validation encompassed tissue-level responses, …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

A Comprehensive Academic And Industrial Survey Of Blockchain Technology For The Energy Sector Using Fuzzy Einstein Decision-Making, Umit Cali, Annabelle Lee, Barry Hayes, Claudio Lima, D. Jonathan Sebastian-Cardenas, David Flynn, Emre Kantar, Farrokh Rahimi, Kaung Si Thu, Marco Pasetti, Marthe Fogstad Dynge, Merlinda Andoni, Muhammet Deveci, Murat Kuzlu, Raquel Alanso, Kim-Kwang Raymond Choo, Sambeet Mishra, Shammya Shananda Saha, Sonam Norbu, Srinikhil Gourisetti, Ugur Halden, Vahid Hosseinezhad, Valentin Robu

Engineering Technology Faculty Publications

The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in …

5g-Enabled Iot Gateway For Educational Purposes, Murat Kuzlu

Engineering Technology Faculty Publications

The project aims to develop and implement a 5G-IoT gateway for efficient management and interconnection of IoT devices through advanced sensing and communication technologies. Sensing technologies encompass modern approaches for detecting and measuring physical properties with high accuracy across manufacturing, smart grids, healthcare, smart cities, and other domains. Communication technologies represent the latest developments in high-speed data transmission, offering enhanced reliability and network capacity. This 5G-IoT gateway functions as an educational platform, providing students and educators with hands-on experience in emerging technologies. The system creates opportunities for practical learning and research in telecommunication technologies by enabling direct engagement with 5G …

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

Cognitive Fatigue Detection Using Photoplethysmography (Ppg) And Reaction Time Data, Anhar Sami Mohammed, Prajoy Podder, Maciej Jan Zawodniok, Cihan Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a framework for real-time cognitive fatigue detection among shift workers using an integrated approach that combines photoplethysmography (PPG) data and reaction time analysis with advanced deep learning models, including Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FNNs). The system leverages heart rate variability (HRV) and reaction time data to identify fatigue indicators. The results demonstrate significant performance, with the first FNN model achieving a test accuracy of 98.94% and a loss of 0.2928, while the second FNN model achieved the same accuracy with a slightly higher loss of 0.3089. The LSTM model, designed for sequential …

Vilp: Imitation Learning With Latent Video Planning, Zhengtong Xu, Qiang Qiu, Yu She

School of Industrial Engineering Faculty Publications

In the era of generative AI, integrating video generation models into robotics opens new possibilities for the general-purpose robot agent. This letter introduces imitation learning with latent video planning (VILP). We propose a latent video diffusion model to generate predictive robot videos that adhere to temporal consistency to a good degree. Our method is able to generate highly time-aligned videos from multiple views, which is crucial for robot policy learning. Our video generation model is highly time-efficient. For example, it can generate videos from two distinct perspectives, each consisting of six frames with a resolution of 96 × 160 pixels, …

Unit: Data Efficient Tactile Representation With Generalization To Unseen Objects, Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip Glen Crandall, Wan Shou, Dongyi Wang, Yu She

School of Industrial Engineering Faculty Publications

UniT is an approach to tactile representation learning, using VQGAN to learn a compact latent space and serve as the tactile representation. It uses tactile images obtained from a single simple object to train the representation with generalizability. This tactile representation can be zero-shot transferred to various downstream tasks, including perception tasks and manipulation policy learning. Our benchmarkings on in-hand 3D pose and 6D pose estimation tasks and a tactile classification task show that UniT outperforms existing visual and tactile representation learning methods. Additionally, UniT's effectiveness in policy learning is demonstrated across three real-world tasks involving diverse manipulated objects and …

Vibtac: A High-Resolution High-Bandwidth Tactile Sensing Finger For Multi-Modal Perception In Robotic Manipulation, Sheeraz Athar, Xinwei Zhang, Jun Ueda, Ye Zhao, Yu She

School of Industrial Engineering Faculty Publications

Tactile sensing is pivotal for enhancing robot manipulation abilities by providing crucial feedback for localized information. However, existing sensors often lack the necessary resolution and bandwidth required for intricate tasks. To address this gap, we introduce VibTac, a novel multi-modal tactile sensing finger designed to offer high-resolution and high-bandwidth tactile sensing simultaneously. VibTac seamlessly integrates vision-based and vibration-based tactile sensing modes to achieve high-resolution and high-bandwidth tactile sensing respectively, leveraging a streamlined human-inspired design for versatility in tasks. This paper outlines the key design elements of VibTac and its fabrication methods, highlighting the significance of the Elastomer Gel Pad (EGP) …

Quantifying The Transfer Effectiveness Of An Artificial Intelligence-Based Simulator Pre-Training Program For Student Pilots, Ryan Guthridge

Journal of Aviation/Aerospace Education & Research

Since the airline pilot shortage was initially studied in 2016, the pilot hiring model has been significantly impacted, with airlines hiring qualified pilots at unprecedented rates. The COVID-19 pandemic has slowed this hiring rate, however it is expected that airline hiring will soon increase to a rate higher than initially expected (Bureau of Transportation Statistics, 2022). With this dynamic, certified flight instructors are often the most qualified recruits for airlines, due to the number of hours and experience they have gained in the flight training organization. In turn, certified flight instructors are in short supply for flight training organizations worldwide. …

Empowering Precision Forecasting: Self-Supervised Lstm For Hourly Pressure And Temperature Prediction, Anand Shankar, Deepak K. Singh, Mantosh Kumar, Pankaj Kumar, Pradhan Parth Sarthi

Journal of Aviation/Aerospace Education & Research

The most important parts of any flight are landing and takeoff, and an aircraft's takeoff configuration must balance the regulated takeoff weight, runway length, and weather conditions to ensure a safe departure and arrival. In addition to runway length, wind, temperature, pressure, and visibility determine the total allowed takeoff weight and the economic viability of any trip. Thus, any meteorological office involved in flight planning and operation at any airport must accurately assess these factors, known as takeoff data. This research paper suggests multivariate self-supervised LSTM-based models to accurately predict the temperature and pressure (MSLP) of the takeoff data. The …

The State Of Uas Operations At Airports, A Perspective From Airport Managers, Damon Lercel, Sarah M. Hubbard

Journal of Aviation/Aerospace Education & Research

As the number of Uncrewed Aircraft Systems (UAS) operating in our National Airspace System (NAS) increases, so do UAS operations near or at an airport. The accelerating technology in Advanced Air Mobility (AAM) and related business opportunities will only further increase UAS operations at airports. This continued growth in new UAS technologies and applications introduces new hazards and risks to the airport environment. This proliferation of UAS highlights the importance of airports developing a robust Safety Management System (SMS) that includes specific UAS risk mitigations. There is currently little empirical data regarding UAS traffic around airports and there is no …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

Pla Polymer Binder In Core Production - Influence On Final Casting Dimensions, Artur Soroczyński, Krzysztof Rechowicz

Virginia Digital Maritime Center (VDMC) Faculty Publications

The foundry industry is seeking an ecological alternative to synthetic molding resins. This study evaluates the technological properties of core sands bonded with biodegradable polylactide (PLA). Cores prepared on a 2% quartz sand matrix were subjected to casting processes using two alloys with extremely different pouring temperatures: gray cast iron (approx. 1200 °C) and AK11 silumin (approx. 710 °C). The research methodology included macroscopic assessment, dimensional analysis using 3D scanning (GOM Inspect), and qualitative knock-out assessment supported by numerical temperature field simulation. The results showed that the high crystallization temperature of cast iron leads to complete thermal degradation of the …

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, …

Pedagogy In The Age Of Ai: Exploring Generative Ai For Higher Education, Alison Munsch Phd

Journal of International Technology and Information Management

Generative Artificial Intelligence (AI) presents transformative opportunities for higher education, enabling personalized learning, enhanced student engagement, and efficient pedagogical practices. This tutorial-style article guides educators in integrating generative AI into their classrooms through hands-on activities, practical strategies, and reflective exercises. It explores the capabilities of AI tools such as ChatGPT, their applications across disciplines, and the ethical considerations for their use. By cultivating critical thinking and fostering student readiness for AI-driven futures, this article underscores the transformative potential of generative AI in higher education with an emphasis on the academic areas of business analytics, information systems, and computer science.

Surviving And Thriving In The Hybrid Cloud: A Review Of The Current Cloud Computing Landscape, Peter Munsch, Alison Munsch

Journal of International Technology and Information Management

Background and Purpose

Both academic and industry institutions have increasingly migrated essential services to public cloud providers (e.g., Microsoft, AWS, Google) with mixed outcomes. Some industry leaders attempted to fully replace their on-premises data centers with public cloud services, a move not advised without thorough performance and cost analyses (Potel, 2023). Despite some organizations pulling back from the “Cloud First” strategy, the public cloud services market continued to grow, with revenue increasing by approximately 20% year-over-year since 2020 and surpassing half a trillion dollars in 2022 (IDC Worldwide Semiannual Public Cloud Services Tracker, 2H 2022). Cloud technologists suggested that hybrid …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Ease Of Product Disassembly Through A Systematic Structured Time-Based Design For Disassembly Methodology, Emeka S. Igwe

College of Graduate Studies: Theses & Dissertations

This research introduces a systematic time-based design for disassembly (DfD) framework aimed at optimizing product disassembly by addressing important features like liaisons between components in product, component accessibility and the overall modularity of the product. This study specifically covers electromechanical and mechatronic systems in both household and industrial setup, identifying their disassembly challenges and high value pointers for improvement. The methodology involves using a design for disassembly framework called LeanDfD in carrying out a holistic disassembly process and evaluating quantitative metrics like disassembly time and complexity and suggesting further redesign strategies to minimize disassembly time and cost. Adopting this systematic …

Leveraging Physiological Signal Activity And Self-Report Data To Assess Students’ Trust In “My Friendly Mind” App And Its Impact On Their Mental Health Knowledge: A Mixed-Method Phase 1 Clinical Trial Focusing On Depression And Attention Deficit Hyperactivity Disorder From Human Factors Standpoint., Yeganeh Shahsavar

Graduate Theses, Dissertations, and Problem Reports (ETD)

Mental health issues have become a significant global public health concern, especially among younger generations. The growing number of mental health challenges, combined with limited access to quality care, makes the problem even worse. Studies reveal that over 70% of individuals worldwide in need of mental health services do not receive appropriate care. Digital health technologies have the potential to enhance mental health services by making them more accessible and affordable. Despite the increasing popularity of mental health mobile applications (mHealth), there remains a lack of robust evidence of their effectiveness and the level of user trust, particularly in areas …

Reliability Of An Extended Version Of The 3m™ Eargage Tool To Assess Earcanal Size And Assist Earplug Selection, Bastien Poissenot-Arrigoni, Laurence Martin, Alessia Negrini, Djamal Berbiche, Olivier Doutres, Franck Sgard

Études primaires

Objective: Evaluate the ability of an extended version of the 3 MTM Eargage to estimate the earcanal size and assess the likelihood that a particular earplug can fit an individual’s earcanal, ultimately serving as a tool for selecting earplugs in the field. Design: Earcanal morphology, assessed through earcanal earmolds scans, is compared to earcanal size assessed with the extended eargage (EE) via box plots and Pearson linear correlations coefficients. Relations between attenuation measured on participants (for 6 different earplugs) and their earcanal size assessed with the EE are established via comparison tests. Study sample: 121 participants exposed to occupational noise …

Synthetic Data–Driven Early Prediction Framework For Acute Kidney Injury In Patients Receiving Vancomycin And Ceftazidime/Avibactam, Maryam Ramazani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: The nephrotoxic risks of combining ceftazidime/avibactam (AVI) with vancomycin (VAN) remain underexplored, despite both agents independently being linked to acute kidney injury (AKI). This study assessed the risk of AKI associated with concurrent VAN and ceftazidime/avibactam (VAN-AVI) therapy and developed synthetic data models to enable early prediction of AKI.

Methods: We conducted a retrospective analysis using electronic health record data from hospitalized adults between 2015 and 2022. The incidence of AKI was compared among patients receiving VAN-AVI or VAN in combination with piperacillin/tazobactam (VAN-TPZ) versus VAN monotherapy. AKI was defined as a composite of de novo and recurrent AKI …

Framework For Development Environment Selection In Digital Twin Applications, Carlos Dodero Fernandez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Digital Twin (DT) technology, a cornerstone of Industry 4.0, facilitates real-time synchronization between virtual models and physical manufacturing systems, enhancing operational efficiency and decision-making. However, its widespread adoption is hindered by the absence of standardized methods for selecting Development Environments (DEs) for DTs, compounded by challenges in cost, interoperability, and connectivity with Industrial Internet of Things (IIoT) protocols. This thesis proposes a Systematic Selection Framework to address this gap, offering a structured methodology to evaluate DEs based-on visualization quality, scalability, interoperability, and cost-effectiveness for manufacturing applications. The framework categorizes and compares sixteen DEs into Game Engines, Robotics Engines, and Simulation …

A Strategic Infrastructure Improvement Framework For Intermodal Transportation Networks, Ayoub Abusalih

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this research, we propose a novel approach to design infrastructure networks for intermodal freight transportation systems, which incorporates railways, highways, and inland waterways (IWW). The objective of our study is to identify the optimal set of hubs to be built and operated over an extended time, based on the projected domestic cargo demand. Unlike traditional hub location models, our approach introduces hybrid hubs, where hybrid transportation modes are integrated to facilitate cargo handling. This innovative integration enables more efficient intermodal connections, leading to tangible reductions in operating costs, and carbon emissions. Specifically, we propose a mixed integer programming model …

Leveraging Synthetic Data For Efficient Training Of Ai Models For Real-World Object Detection, Reinaldo A. Moraga

Graduate Research Theses & Dissertations

Modern computer vision (CV) systems largely depend on real-world data for training, which is costly in terms of time, materials, and resources. As industries push toward automation and Artificial Intelligence (AI) -driven solutions, the need for enabling more efficient model training is growing. The primary aim of this work is to explore a framework tailored for industrial applications that uses synthetic images generated from 3D models to train a CV model capable of real-world object detection. This approach seeks to reduce the time, cost, and resources typically required for training AI models with real-world data. This work presents a method …

Order Acceptance And Detailed Scheduling In A Make-To-Order Job Shop With Discrete And Batch-Processing Machines, Dheeban Kumar Srinivasan Sampathi

Graduate Research Theses & Dissertations

In today's ever-evolving production landscape, characterized by a growing demand for personalized products to enhance consumer satisfaction, the strategy of pursuing high-mix, low-volume manufacturing has gained importance. More than ever, manufacturers are adopting the Make-To-Order (MTO) approach, aiming to balance efficient cost management by meeting strict customer deadlines. This research explores the complex state of job shop scheduling, a significant challenge faced by manufacturing entities trying to optimize production time and costs while making the best use of their machinery and resources. The primary concern is the dynamic relationship between order acceptance and scheduling within a job shop environment, which …