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A Review Of The Dry Methods Available For Coal Beneficiation, Nikki Hughes, Marco Le Roux, Quentin Peter Campbell, Fardis Nakhaei

Mining Engineering Faculty Research & Creative Works

Water is a precious global resource that is important in most currently employed coal beneficiation practices. These widely accepted processes deliver consistent and precise separation efficiencies at desired product yields and throughputs. Although favored, the water usage related to wet processing may be impractical and unsustainable in certain regions. Consequently, present-day practices used in coal processing may soon have to adapt, irrespective of any improved technical and economic feasibility offered. Therefore, the development of efficacious dry beneficiation methods has become an appealing research topic. This review assembles information pertaining to the principle and success of commercially available and experimental phase …

Text-To-Model Transformation: Natural Language-Based Model Generation Framework, Aditya Akundi, Joshua Ontiveros, Sergio Luna

Mechanical Engineering Faculty Publications

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model …

Neuro-Symbolic Ai For Deep Analysis Of Social Media Big Data, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit P. Sheth

Faculty Publications

This tutorial introduces a neuro-symbolic AI framework to analyze big data from social media platforms. Integrating human-curated knowledge through symbolic AI with the pattern recognition capabilities of neural networks enhances the adaptability and efficiency of traditional neural network approaches. Knowledge-guided zero-shot learning techniques enable swift adaption to new linguistic contexts and emerging events [6]. Participants will explore how to design, develop, and utilize these models in specific domains, such as public health surveillance, that require dynamic adaptation to new terminologies. This session The tutorial aims to equip attendees with practical skills and a deep understanding of how to apply neuro-symbolic …

Model Predictive Control Of A Grid-Scale Thermal Energy Storage System In Relap5-3d, Jaron Wallace, John Hedengren, Kody Powell, Matthew Memmott

Faculty Publications

This research delves into the integration and control of a Thermal Energy Storage (TES) system with a Small Modular Reactor (SMR), specifically the NuScale VOYGR SMR module in RELAP5-3D. The research methodology centered on modeling the NuScale VOYGR SMR, a light water pressurized water reactor (LWR) with a power output capacity of 77 MWe per module. The reactor and plant details were sourced from NuScale's final safety analysis report and supplemented by information from the NuScale website. The SMR plays a crucial role in energy generation, and to manage and dispatch the produced energy effectively, a robust storage system …

All You Need Is Unary: End-To-End Bit-Stream Processing In Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi

Faculty Scholarship

Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm introduced to achieve energy efficiency with a lightweight and single-pass training model. Hypervectors (HVs) at the heart of the HDC systems play a fundamental role in elevating the accuracy and obtaining the desired performance. Image-based HV encoding requires two types of HVs: Position and Level HVs. State-of-the-art approaches utilize pseudo-random methods for generating these HVs, which might degrade system performance and cause higher power consumption due to poor randomness in HV generation. These conventional methods require iteratively calculating orthogonal Positional HVs for acceptable accuracy. This work proposes a fast, ultra-lightweight, and high-quality …

Common Ground Newsletter Fall 2024, Missouri University Of Science And Technology

Common Ground

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Characterising Stem Ways Of Thinking In Engineering Design (Ed)-Based Tasks, Ravishankar Chatta Subramaniam, Caden L’Fontaine, Amir Bralin, Jason Morphew, Carina M. Rebello, Sanjay Rebello

School of Engineering Education Faculty Publications

Investigating students' thinking in routine classroom tasks, especially in science and engineering, is crucial. Given the rising interest in STEM Ways of Thinking (SWoT), in this exploratory study, we focus on two multi-week Engineering Design tasks within an undergraduate physics laboratory. Given that the term 'ways of thinking' has varied interpretations, we aim to further the discourse by identifying four SWoTs: Design Thinking, Physics Concepts, Mathematical Constructs, and Metacognitive Reflection. Analyzing discussions from 14 student-groups reveals notable differences in how students solve an instructor-assigned challenge given earlier in the semester and a student-generated challenge later in the semester. Students considered …

Cyber Threat Intelligence Sharing In Nigeria, Muhammad Abubakar Nainna, Julian Bass, Lee Speakman

Communications of the IIMA

Cybersecurity challenges are common in Nigeria. Sharing cyber threat intelligence is essential in addressing the extensive challenges posed by cyber threats. It also helps in meeting regulatory compliance. There are a range of impediments that prevent cyber threat intelligence sharing. We hypothesise that we want to maximise this cyber threat intelligence sharing to resist malicious attackers. Therefore, this research investigates factors influencing threat intelligence sharing in Nigeria's cyber security practitioners. To achieve this aim, we conducted research interviews with 14 cyber security practitioners using a semi-structured, open-ended interview guide, which was recorded and transcribed. We analysed the data using an …

Improved Parameters Of Anand Model And Advanced Fea Technique For Low-Cycle Fatigue Life: Finite Element Analysis Models For Pb-Free Solder And Pb Solder In Real Life Ball Gird Ally Package, Hamidreza Nahavandi

Dissertations and Theses

The present study uses the finite element method to evaluate the fatigue life of solder joints of plastic ball grid array packages (PBGA) under thermal-cycling conditions. A Series of precise 3D FE models for solder joints of plastic ball grid array packages is created to improve reliability prediction accuracy. Damage parameters are obtained from the FEA results and used to estimate solder balls’ thermal fatigue life. The Coffin-Manson equation is employed. The predicted thermal fatigue lives are discussed in detail.

Due to marketing and legislative pressures, Pb-free solder is fast becoming a reality in electronic manufacturing. The industry has concluded …

Sustainable Mobility, Shams Tanvir

Mavs Open Press Open Educational Resources - Archive

This textbook and OER material cover tools and basic knowledge required to prepare transportation engineers and planners to contribute towards a carbon-neutral mobility future. The goal of the textbook is to train engineering students on existing tools and policy levers that can be used to incorporate emerging transportation technologies in a sustainable, equitable, and efficient manner.

Fundamentals Of Math, Physics, And Statistics For Future Transportation Professionals, Anurag Pande, Peyton Ratto, Ahmed Farid

Mavs Open Press Open Educational Resources - Archive

This textbook and OER material cover tools and basic knowledge required to master the prerequisite essentials of physics, mathematics, and statistics applied in transportation engineering. The textbook modules are intended for students with undergraduate degrees in planning and other less technical fields who are interested in pursuing transportation careers where background engineering knowledge is required.

Boosting Steam Tolerance And Electrochemical Performance Of An La0.6Sr0.4Co0.2,Fe0.8O3− Δ-Based Air Electrode For Protonic Ceramic Electrochemical Cells, Lei Wu, Jiqiang Sun, Huiyang Qi, Baofeng Tu, Chunyan Xiong, Fanglin Chen, Peng Qiu

Faculty Publications

La_0.6Sr_0.4Co_0.2Fe_0.8O_3−δ (LSCF) is the state-of-the-art air electrode material for solid oxide electrochemical cells using oxide-ion electrolytes, yet its application in proton ceramic electrochemical cells (PCCs) remains limited, mainly attributed to its instability under operating conditions of high temperature and high humidity. To address this issue, coating a PrCoO_3−δ (PCO) catalyst onto the LSCF scaffold has been evaluated in this study. The introduction of the PCO coating not only enhances the LSCF electrode's electrochemical performance but also significantly improves its steam tolerance by preventing direct contact between steam and …

Utilizing Deep Learning In Smart Glass System To Assist The Blind And Visually Impaired, Asmaa A. Hekal, Mohamed S. Sharaf, Ahmed A. Sayed, Ibrahim R. Abdelrahman, Ahmed A. Salem, Ahmed M. Elhussieny, Saeed Y. Kouta, Eman S. Abass

Future Engineering Journal

This paper presents a groundbreaking assistive technology designed to empower visually impaired individuals in their daily lives. With an estimated global population of 2.2 billion facing visual impairments, addressing the challenges they encounter is of paramount importance. The research introduces a comprehensive electronic device integrating advanced computer vision and deep learning techniques. The system incorporates real-time object detection, robust facial recognition, and precise currency denomination identification. Powered by a Raspberry Pi 4 Model B+ and an ESP32-CAM Development Board, the device offers users unparalleled environmental awareness. Utilizing YOLOv4-tiny for object detection and a hybrid face recognition model combining HaarCascades, Histogram …

Methods For Efficient Computation Of Neutron Multiplicity Distributions And Snm Sample Characterization, Jawad Ribhi Moussa

Nuclear Engineering ETDs

This dissertation advances neutron multiplicity counting (NMC) by developing a hierarchy of computational methods that combine deterministic and stochastic techniques with modern machine learning for improved special nuclear material (SNM) characterization. A system state-updating Monte Carlo method is introduced in addition to a dynamic point kinetic model based on a backward Master equation (BME) formulation. These models, along with newly developed distribution reconstruction methods, enable more efficient computation of neutron count distributions in a fixed detector time-gate. NMC limitations associated with finite-size and neutron phase effects are also addressed by extending the BME model to account for time-gated neutron count …

2024 Safety Belt Usage Survey In Kentucky, Erin Lammers-Staats, Derek Young, Kenneth R. Agent

Kentucky Transportation Center Research Report

No abstract provided.

Seal Counting On Our Plages (S.C.O.O.P.), Kaanan Kharwa

Master's Theses

The Vertebrate Integrative Physiology (VIP) lab monitors the population of northern elephant seals at the largest mainland breeding colony, located at Piedras Blancas (San Simeon, CA). As the population expands, more human-seal interactions and conflicts over land use occur. The VIP lab's work informs California State Parks and helps with the management of the rookery. Currently, members of the VIP lab fly a drone over the beaches, capture multiple images, and manually count the seals, which takes around 14 to 21 hours of analysis per survey. Machine learning methods such as Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks …

First-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Neural Ordinary Differential Equations: Mathematical Framework And Illustrative Application To The Nordheim–Fuchs Reactor Safety Model, Dan Gabriel Cacuci

Faculty Publications

This work introduces the mathematical framework of the novel “First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-CASAM-NODE) which yields exact expressions for the first-order sensitivities of NODE decoder responses to the NODE parameters, including encoder initial conditions, while enabling the most efficient computation of these sensitivities. The application of the 1st-CASAM-NODE is illustrated by using the Nordheim–Fuchs reactor dynamics/safety phenomenological model, which is representative of physical systems that would be modeled by NODE while admitting exact analytical solutions for all quantities of interest (hidden states, decoder outputs, sensitivities with respect to all parameters and initial conditions, …

An Exponential Cone Programming Approach For Managing Electric Vehicle Charging, Li Chen, Long He, Yangfang (Helen) Zhou

Research Collection Lee Kong Chian School Of Business

To support the rapid growth in global electric vehicle adoption, public charging of electric vehicles is crucial. We study the problem of an electric vehicle charging service provider, which faces (1) stochastic arrival of customers with distinctive arrival and departure times, and energy requirements as well as (2) a total electricity cost including demand charges, costs related to the highest per-period electricity used in a finite horizon. We formulate its problem of scheduling vehicle charging to minimize the expected total cost as a stochastic program (SP). As this SP is large-scale, we solve it using exponential cone program (ECP) approximations. …

Appointment Scheduling With Delay Tolerance Heterogeneity, Shuming Wang, Jun Li, Marcus Ang, Tsan Sheng Ng

Research Collection Lee Kong Chian School Of Business

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service-time uncertainty. We aim to capture the delay-tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user-tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance Sensitivity, Monotonicity, Convexity and Positive Homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the …

Electromagnetic Theory And Applications, 2nd Edition, Nicholas Madamopoulos, George Kliros

Open Educational Resources

This book intends to provide both the fundamentals of Electromagnetics but also some practical applications of the concepts covered. Having taught electromagnetics for several years, the authors feel that many times the field of electromagnetics comes as “old” and often times students do not appreciate the concepts and their importance in everyday applications. The authors intend to accompany the EM concepts with life applications. Hence, students may see the direct impact of the knowledge they acquire through the study of the field of electromagnetics and better appreciate the field.

Transfer Learning With Spinally Shared Layers, H. M.Dipu Kabir, Subrota Kumar Mondal, Syed Bahauddin Alam, U. Rajendra Acharya

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Transfer-learned models have achieved promising performance in numerous fields. However, high-performing transfer-learned models contain a large number of parameters. In this paper, we propose a transfer learning approach with parameter reduction and potential high performance. Although the high performance depends on the nature of the dataset, we ensure the parameter reduction. In the proposed SpinalNet shared parameters, all intermediate-split-incoming parameters except the first-intermediate-split contain a shared value. Therefore, the SpinalNet shared parameters network contains three parameter groups: (1) first input-split to intermediate-split parameters, (2) shared intermediate-split-incoming parameters, and (3) intermediate-split-to-output-split parameters. The total number of parameters becomes lower than the …

Surrogate-Based Multidisciplinary Optimization For The Takeoff Trajectory Design Of Electric Drones, Samuel Sisk, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the …

A Robust Recurrent Neural Networks-Based Surrogate Model For Thermal History And Melt Pool Characteristics In Directed Energy Deposition, Sung Heng Wu, Usman Tariq, Ranjit Joy, Muhammad Arif Mahmood, Asad Waqar Malik, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based …

Enhancing Ammonium And Bicarbonate Tolerance In Chlorella Sorokiniana Through Ems-Induced Mutagenesis, Wentao Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

This study explores the enhancement of ammonium and bicarbonate tolerance in Chlorella sorokiniana via EMS-induced mutagenesis, aimed at improving the microalgae's resilience and growth under high-stress conditions of elevated NH4+ and HCO3 concentrations. By mutating C. sorokiniana using EMS, we developed mutant strains capable of thriving in conditions that are inhibitory to the wild type strain. The successful adaptation of these strains demonstrates a significant increase in biomass production and tolerance to environmental stressors, highlighting the potential of EMS mutagenesis in industrial algal applications for sustainable biofuel production and environmental remediation.

Enhancing Multi-Agent System Testing With Diversity-Guided Exploration And Adaptive Critical State Exploitation, Xuyan Ma, Yawen Wang, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this …

Genetically Linked Litho- And Organo-Facies In Meter-Scale Cycles Of Lacustrine Deposits Within The Permian Lucaogou Formation In The Bogda Mountains, Northwest China: Implications For Paleoenvironment And Organic Matter Origin, Xin Zhan, Wan Yang, Qiao Feng, Zhao Wen Zhan, Sile Wei, Yujiao Zhang, Hongquan Kang, Tao Cheng, Hongyan Zhao, Wenjie Cai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

A close correlation between lithofacies and organofacies in meter-scale high-order cycles composed of lacustrine sediments enables comparison and refinement of lithofacies-defined cyclostratigraphy. Four lithofacies and four organofacies have been identified in fluctuating profundal high-order cycles in the lower-Permian Lucaogou Formation, southern Bogda Mountains, NW China. The four lithofacies include interbedded and interlaminated coarse siltstone and very fine sandstone, black shale, wackestone and dolostone, and calcareous and dolomitic shales. Four distinctive organofacies have been identified, on the basis of geochemical composition of organic matter and specific biomarker proxies related to organic matter types, rather than to depositional conditions and thermal maturity. …

Antibiotic-Loaded Dendrimer Hydrogels In Periodontal Bone Regeneration: An In Vitro Release Feasibility Study, Nicholas Yesbeck, Da Huang, Caroline Carrico, Parthasarathy Madurantakam, Hu Yang

Chemical and Biochemical Engineering Faculty Research & Creative Works

The prescription of a course of oral antibiotics following bone grafting procedures is a common practice in clinical periodontics to reduce surgical site infections. The goal of this study is to characterize the release profile of antibiotics via local delivery using dendrimer hydrogels (DH) and to analyze the effect of two different particulate bone allografts on the release of the antibiotics in vitro. DH were synthesized from Poly amidoamine (PAMAM) dendrimer G5 and polyethylene glycol diacrylate, and cefazolin was chosen as the antibiotic. The antibiotic-loaded samples were bathed in PBS and incubated at 37 °C; aliquots were taken (1 h, …

A New Air-Assisted Flare Tip Design For Managing Gas Flare Emissions (Cfd Analysis), Ahmed A. Maaroof, Joseph D. Smith, Mohammed H.S. Zangana

Chemical and Biochemical Engineering Faculty Research & Creative Works

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations has instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow the combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well-designed, efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using …

Response Of ¹¹B Enriched Zrb2 Ultra-High Temperature Ceramic To Neutron Irradiation At Elevated Temperatures, Yan Ru Lin, Takaaki Koyanagi, David J. Sprouster, Christian M. Petrie, William Fahrenholtz, Greg Hilmas, Yutai Katoh

Materials Science and Engineering Faculty Research & Creative Works

ZrB₂, an ultra-high temperature ceramic (UHTC) is being considered for use in fusion reactor first-wall structures, yet its response to irradiation remains poorly understood. This study employed scanning/transmission electron microscopy (S/TEM), synchrotron X-ray diffraction (XRD), finite element calculations, and thermal property measurements to thoroughly investigate the neutron-irradiation effects on ¹¹B-enriched ZrB₂. Neutron irradiations were conducted at 220 °C and 620 °C, with a neutron fluence of 2.2 x 1025 neutron/m2 (energy > 0.1 MeV), resulting in 3.9 dpa and 4200 appm He. The study revealed the unusual prevalence of prism loops and a > c anisotropic lattice …

Study Of The Structure Of Zn And Na Borophosphate Glasses Using X-Ray And Neutron Scattering Techniques, Uwe Hoppe, Parker T. Freudenberger, Richard K. Brow, Jozef Bednarčik, Alex C. Hannon

Materials Science and Engineering Faculty Research & Creative Works

The atomic structures of Zn and Na borophosphate glasses were studied using X-ray and neutron scattering techniques. Peaks assigned to the B−O, P−O, and O−O distances confirm that only BO₄ units co-exist with the PO₄ tetrahedra. The Zn−O and Na−O coordination numbers are found to be a little larger than four. The narrowest peaks of the Zn−O first-neighbor distances exist for the glasses along a line connecting the Zn (PO₃)₂ and BPO₄ compositions (50 mol% P₂O₅), which is explained by networks of ZnO₄, BO₄, and PO …