Distributed Vibration Sensing For Identification Of Loose Connectors In Coaxial Data Transmission Lines, Saidanvar Esanjonovich Valiev, Anthony C. Okafor, Jeremiah J. Rittenhouse, Jie Huang, Daniel S. Stutts

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This study investigates the effect of vibration-induced loose connections on signal reflection (S11) for loose connection identification in aerospace coaxial cables using distributed sensing approach, which is effective in filtering the noise and identifying minor discontinuities. In this approach, a sliding gated window is applied to S11 signal, a fast Fourier transform is performed over the gated windows, cross-correlation is computed between the baseline and vibration-affected signals, and the standard deviation is mapped along the cable length. Sinewave signals from 9 kHz to 5 GHz were swept through cables with vibrating connectors under three conditions: fully tightened, loosened by 180°, …

Nonlinear Control Of A Ciws-Style 2-Dof Turret, Ryan Baur, Ethan Wang, Nilay Kant

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper develops and compares multiple control strategies for a two-degree-of-freedom CIWS (Close-In Weapon System)-style turret tracking rapidly maneuvering airborne targets. A simplified nonlinear manipulator model with realistic actuator torque limits is used as the plant. Five controllers are implemented: a baseline PID (Proportional-Integral-Derivative) controller, a feedforward PID, a Kalman-filter-assisted PID, and two feedback-linearized designs using PID and LQR (Linear-Quadratic-Regulator)-based surrogate dynamics. Controller performance is evaluated on increasingly aggressive three-dimensional target trajectories under varying sensor noise. Results show that PID-family controllers achieve competitive tracking accuracy while remaining torque-efficient and largely unsaturated. Feedback-linearized controllers improve tracking accuracy only when sufficient actuator …

Tomo-Piv Study Of A Parallel Two-Dimensional Jet In Supersonic Flow, Josiah Mcdermott, Connor Bell, Davide Vigano

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Stabilizing combustion in supersonic flows is a formidable challenge due to the small time scales afforded for air-fuel mixing. Numerous studies in this area have demonstrated the potential of strut-style platforms for fuel injection and mixing enhancement, which remains an active area of research. In the Aerodynamics Research Laboratory at Missouri S&T, a strut-style injector system has recently been installed. In this study, we characterize the flow structures behind this platform in a range of injection pressures and corresponding mass flux ratios. The wake generated by a strut and injection plume, even absent combustion or mixing enhancement geometry, has an …

2-Point Focused Laser Differential Interferometry Measurements Of A Parallel Jet In Supersonic Flow, Joshua Gary, Joseph Villarreal, Davide Vigano

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Turbulence in compressible flows plays a central role in applications such as air-fuel mixing in supersonic combustors and is significantly more complex than incompressible turbulence due to the presence of fluctuating thermodynamic quantities. As such, models like the Strong Reynolds Analogy (SRA) are used to relate these quantities. However, SRA validity has been examined primarily in boundary-layer flows. In this work, a newly developed Two-Point Focused Laser Differential Interferometry (2-FLDI) system is implemented in a two-dimensional parallel supersonic jet. The diagnostic is described in detail, including optical alignment procedures, calibration methods, and data analysis techniques. Measurements acquired at multiple streamwise …

Evaluating The Effectiveness Of Hard Coastal Protection Structures For Long-Term Erosion Mitigation, Case Study: Eastern Rosetta Shoreline, Nile Delta, Egypt, Basma Sayed, Ayman Sabry Koraim, Tarek Hemdan Nassrallah, Ahmed Abouelfetouh Abdelaziz, Nada Mansour, Fahmy Salah Fahmy Abdelhaleem

Mansoura Engineering Journal

Coastal erosion threatens densely populated shorelines worldwide. At the Rosetta Promontory (Nile Delta), shoreline retreat has accelerated since the construction of the Aswan High Dam due to reduced fluvial sediment supply. This case study numerically evaluates shoreline response to six hard-protection scenarios along the eastern promontory, combining groins and detached breakwaters, over a 20-year forecast (2022–2042). Offshore wave conditions were transformed to nearshore hydrodynamics using MIKE21 Spectral Wave, and long-term shoreline evolution was simulated with DHI-LITPACK. The numerical models were forced using a 40-year wave dataset derived from the ECMWF ERA5 reanalysis. Results indicate that the configuration with eight detached …

Finite Element Analysis Of Multiscale Fgm Plate Using Modified Third Order Shear Deformation Theory, Ravi Kumar, Ajay Kumar

Mansoura Engineering Journal

Present paper aimed to examine the static and dynamic response of a carbon nanotube (CNT) infused multiscale functionally graded (FGM) rectangular plate structure. A third-order shear deformation theory (TSDT) based on Reddy’s formulation with enforcing negligible transverse shear stresses at the outermost surface of the plate, is used. A nine-noded isoparametric element with 13 displacement variables per node is numerically investigated for finite element analysis. Using the Halpin-Tsai and homogeneous equation the elastic behaviour of the final material is obtained. The current result is first checked against a previously published result to validate the present mathematical model. The finite element …

Control Of Steel Corrosion Using Natural Corrosion Inhibitor, Rasha A. Nasef, O. Hamed, M. M. El-Halwany, M. H. Mahmoud

Mansoura Engineering Journal

This study investigates corrosion control of AISI 430 stainless steel in 1M HCl medium using an eco-friendly inhibitor extracted from pomegranate peel (PPE). The inhibition efficiency was evaluated at different PPE concentrations, with the highest applied concentration of 13g/L. Corrosion behavior was evaluated through weight-loss measurements and potentiodynamic polarization techniques conducted at room temperature.

The experimental results showed that corrosion resistance improved as the inhibitor  concentration increased. Lower corrosion rates were observed in the presence of PPE, along with reduced anodic and cathodic activities, indicating that the extract functions as a mixed-type inhibitor. Surface analysis confirmed the formation of …

Data Collection Algorithm Based On Regression Analysis, Aiming To Minimize Data Processing Delays In 6g-Enabled Iot Networks, Archana Rajendra Mane, Sachin Vasant Chaudhari

Mansoura Engineering Journal

Researchers are currently investigating 6G wireless communication technologies because they can offer ultra-low latency, high data rates, and smarter network management than 5G. IoT services have spurred the development of cutting-edge technologies such as quantum communications, terahertz (THz), and artificial intelligence (AI), all of which are expected to be included into the subsequent generation of 6G networks. This paper presents a regression-based data collection algorithm for 6G-enabled IoT networks that dynamically prioritizes data through Random Forest regression. With a Mean Absolute Error (MAE) of 0.62 ms and a Root Mean Squared Error (RMSE) of 0.83 ms, the proposed method can …

Effect Of Load Variations On Power Quality And Ev Battery Performance In Vehicle-To-Grid System, Jitender Kaushal, Rahul Paswan

Mansoura Engineering Journal

Vehicle-to-Grid (V2G) technology enables the exchange of electricity between electric vehicles (EVs) and the utility grid, helping improve the integration of renewable energy sources and the grid's flexibility. Load-dependent power quality disturbances can affect the performance and reliability of V2G systems, which in turn can negatively impact system and battery operation. This study investigates the effect of resistive, inductive and nonlinear loads on the electrical performance and battery behaviour of a bidirectional V2G system. A complete MATLAB/Simulink model was developed to investigate the grid voltage, current, frequency, THD and the battery voltage, current and state of charge. The model uses …

A Comparative Review Of Roller Compacted And Conventional Concrete: Performance, Economics, And Environmental Sustainability, Masood Abu-Bakr, Hersh F Mahmood, Soran Abdrahman Ahmad

Mansoura Engineering Journal

Roller Compacted Concrete (RCC), distinguished by its zero-slump consistency and high density achieved through heavy roller compaction, has gained recognition as an effective alternative to conventional concrete in applications such as pavements, industrial flooring, and dam construction. This paper reviews the evolution, material composition, and common uses of RCC in comparison with Conventional Concrete (CC), emphasizing the advantages and limitations of each. Key performance aspects, including mechanical strength, durability, workability, construction efficiency, and maintenance demands, are examined. The findings indicate that RCC can reduce construction time by up to 25% and lower maintenance costs by approximately 30–40%. Additionally, life-cycle cost …

Revisiting The Life Cycle Of Margalefidinium Polykrikoides Group Iii, Eduardo Pérez-Vega, Kenneth N. Mertens, Pjotr Meyvisch, Margaret R. Mulholland

OES Faculty Publications

Dinoflagellates produce cysts as a strategy to withstand environmental stressors, with nutrient depletion generally considered a key trigger for cyst production. Resting cysts are thick-walled, typically composed of one to several layers, and characterized by a prolonged dormancy period. In contrast, pellicle cysts possess a thin, single wall and exhibit no dormancy or a markedly shorter dormancy than resting cysts of the same species. Margalefidinium polykrikoides produces pellicle and resting cysts, whereas its congener, M. fulvescens, has been shown to produce pumpkin-like structures. Using phase-contrast microscopy, time-lapse microscopy, FlowCam, and attenuated total reflection Fourier transform infrared microspectroscopy (ATR μ-FTIR), …

Pillpetz: A Smarter Way To Encourage Medication Adherence In Children, John H. Begley

CMC Senior Theses

Medication adherence is usually framed as a problem of patient behavior, but this thesis argues that it is equally a problem of design. Children who take daily medication face barriers that adults often do not: developing executive function, dependence on caregivers, shifting school and home routines, privacy concerns, and stigma around being perceived as different. The standard prescription bottle, by contrast, was designed primarily for dispensing efficiency, safety, and accidental ingestion prevention—not for sustained daily use by a developing child.

This thesis proposes PillPetz, a smart pill case and digital companion that uses routine, play, dose confirmation, and caregiver-connected support …

Construction-Phase Leed Compliance In Egypt: Development Of A Rule-Based Expert System For Contractor Decision Support, Mohamed R. Ahmed, Atef A. Ragab, Sameh Y. Mahfouz

Mansoura Engineering Journal

This study develops a rule-based expert system implemented in Python to support contractors in achieving LEED BD+C: New Construction v4 certification during the construction phase in Egypt. The research identifies and validates construction-phase obstacles affecting LEED compliance through a structured questionnaire targeting building contractors in Egypt. The questionnaire process was conducted in two stages: a pilot survey was used to screen and refine the initially identified obstacles, while the main survey was used to validate the practical relevance of the retained obstacles within the Egyptian construction context. In parallel, the study analyzes construction-phase prerequisites and credits relevant to contractors through …

Explainable Multi-Horizon Wind Power Forecasting Via Aquila-Optimized Machine Learning Models, Mostafa A. Abdelnaby, Nahla B. Abdel-Hamid, Eman M. El-Gendy, Mahmoud M. Saafan

Mansoura Engineering Journal

The inherently unpredictable nature of wind energy necessitates the development of sophisticated forecasting models to ensure grid stability and optimal distribution. In this research, we propose a novel and systematic approach to wind power forecasting (WPF) across diverse timescales. This approach leverages the power of various machine learning (ML) models, metaheuristic hyperparameter optimization, and utilizes explainable artificial intelligence (XAI). The developed methodology is based on the Aquila optimizer (AO), capable of automatically adjusting different ML models for four different time periods (30 minutes, 6 hours, 24 hours, and 36 hours) on the data collected from the Gabal El-Zayt wind power …

Effect Of Two Cut Off Rows On Seepage Underneath Hy-Draulic Structures Using Weak Form Differential Quadra-Ture Element Method: A Theoretical Approach, Esraa Ahmed, El-Masry A. A, Hossam A.A. Abdel Gawad, Ahmed Elhamrawy

Mansoura Engineering Journal

Investigating confined seepage underneath hydraulic structures is crucial to ensure the safety of these structures. The Weak Form Quadrature Element Method (WFQEM) is the basis of this paper, which explores the behavior of confined flow. The seepage flow governing equation is solved numerically to estimate the uplift pressure underneath the hydraulic structures and the exit gradient.The Gauss–Lobatto–Legendre (GLL) type is used as nodal and integration points. Several models were solved to validate the applied numerical method. The obtained results are compared with theoretical and preceding numerical solutions mentioned in the literature. The system of equations generated from applying the WFQEM …

A Hybrid Geospatial And Remote Sensing Methodology For Drought Vulnerability Assessment In Semi-Arid Ecosystems, Kaifi Fakhir Chomani

Mansoura Engineering Journal

The Kurdistan Region of Iraq (KRI) faced significant drought challenges due to global and environmental changes, necessitating drought assessments. Advanced techniques of remote sensing, Geographic Information Systems (GIS), and Analytic Hierarchy Process (AHP) were combined in this research to perform drought vulnerability zonation for KRI. Average annual rainfall, Average number of rainy days, Average annual temperature, slope, elevation, normalised difference water index (NDWI), normalised difference vegetation index (NDVI), land surface temperature (LST), and temperature condition index (TCI) were selected as contributing parameters for drought vulnerability assessments. The considered parameters were weighted using pairwise comparison, and thematic maps were created to …

Generating Concentrated Ring Polymers Through Supercoiled And Open Circle Pdna Isoform Separation, Kelsey E. Hodsdon

Honors Theses and Capstones

DNA can serve as a model polymer due to its high uniformity compared to synthetic polymers. Plasmid DNA (pDNA) in particular offers a well-defined, low-dispersity model for studying topologically uniform systems of a cyclic nature. pDNA has unique isoforms known as open circle (OC) and supercoiled (SC). Using fast protein liquid chromatography (FPLC), specifically anion exchange chromatography (AEX), these OC and SC isoforms can be separated in order to access their unique material properties. Our lab was able to yield the pure SC isoform. However, OC isoform separation has been more challenging. Various difficulties arose during this process, including imaging …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Utilizing Digital Twins To Model And Optimize Hydraulic Excavator Operator Performance Through Arena Simulation, Mateo Fernando Montenegro Defaz, Kwame Awuah-Offei

Mining Engineering Faculty Research & Creative Works

This study aims to provide a digital twin tool for mining and construction companies to evaluate the effect of operator behaviors on the truck-loading process, focusing on swing and bucket rotation. While equipment performance estimators are available, there are few tools to assess the influence of operators. The aim is to create a discrete event simulation (DES) model in Arena® to evaluate how variations in swing and bucket angles affect hydraulic excavator production rates. We validated the DES model using real-world data from a case study to assess the differences in behavior among four operators. Results showed that Operator 1 …

Stall Detection In Hydraulic Excavator Operations Using Heuristics And Machine Learning: A Case Study, Mateo Fernando Montenegro Defaz, Kwame Awuah-Offei, Yixiang Gao

Mining Engineering Faculty Research & Creative Works

This work aims to develop a reliable algorithm for stall detection during excavator digging by analyzing key operational variables such as velocity and angular displacements from machine monitoring data. The work develops and validates a heuristic algorithm to detect stalling events and trains a support vector machine classification algorithm to distinguish between "normal" digging cycles and cycles with stalling. This work is a novel attempt at using a classification algorithm to categorize digging cycles into normal and those with stalling events based on machine monitoring data alone. The developed classification algorithm achieved a sensitivity of 100%, indicating it correctly identified …

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO₂, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO₂-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and ¹H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

Analysis Of Pull-Out Tests For Resin-Grouted Rib Bolts, Khaled Mohamed, Yuting Xue, Dogukan Guner, Alper Kirmaci, Taghi Sherizadeh

Mining Engineering Faculty Research & Creative Works

Resin-grouted rib bolts serve as a crucial means of stabilizing spalling of coal ribs in underground coal mines. A comprehensive investigation into their efficacy was undertaken by a collaborative effort between the National Institute for Occupational Safety and Health (NIOSH) and Missouri University of Science and Technology (MST). This study involved pull-out tests of rib bolts in multiple locations, including six coal mines and the NIOSH research mine. A total of seventy-three (73) pull-out tests were conducted for rib bolts installed at different rotation speeds and a wide range of anchorage lengths, ranging from a short encapsulation length of 0.305 …

Sustmine: A Framework For Integrating Sustainable Development Dimensions Into Strategic Mine Planning, Hussam Altalhi, Kwame Awuah-Offei, Gabriel Nicolosi, Abdullah Al Moinee, Nasib Al Habib

Mining Engineering Faculty Research & Creative Works

Strategic mine planning involves optimizing resource extraction to achieve economic, environmental, and social objectives. However, only a few frameworks offer a unified approach that simultaneously employs systematic indicator selection and weightless trade-off evaluation. This study addresses these issues by proposing a comprehensive framework for integrating sustainability into strategic mine planning, named SustMine. SustMine introduces two key advancements: (1) a rigorous, systematic, criteria-based process for selecting site-specific sustainable development indicators (SDIs), and (2) a weightless evaluation strategy that utilizes Pareto front analysis to construct composite indices. This allows decision-makers to identify non-dominated (optimally balanced) alternatives without relying on arbitrary or subjective …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Radial And Carotid Arterial Pulse Signals For Assessing Cardiovascular Function At Rest And During Post-Exercise Recovery In A Heart Transplant Patient: A Case Study, Md Mahfuzur Rahman, Mamun Hasan, Jennifer F. May, John M. Herre, Leryn Reynolds, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Aim: This study investigates the feasibility of using radial and carotid arterial pulse signals to assess cardiovascular (CV) function at rest and during post-exercise recovery in a heart transplant (HTx) patient. Method: Two micro-fabricated tactile sensors were used to simultaneously acquire arterial pulse signals at the radial artery (RA) and carotid artery (CA). Measurements were taken at rest and at multiple time points post-exercise on three subjects: an HTx patient, a percutaneous coronary intervention (PCI; coronary stent) patient and a healthy control. An SDOF-TF-based time-frequency analysis algorithm was applied to extract a comprehensive set of CV parameters, including heart rate …

Adaptive Machine Learning Framework For Microstructural Optimization And Mechanical Performance Prediction In Steels, Henry Adekola Haffner

Doctoral Dissertations

Evolution of microstructures, such as polygonal ferrite, acicular ferrite, bainite, and martensite, plays a pivotal role in determining the final microstructural and mechanical properties of steel products. Given the established inter-relationship between processing parameters, microstructure, properties, and performance, precise control of phase transformation is essential to achieve pre-determined properties. To understand transformation routes in different steel grades, time-temperature-transformation (TTT) and continuous-cooling-transformation (CCT) diagrams are necessary and can be described using the Johnson-Mehl-Avrami-Kolmogorov equation and Scheil’s additivity rule. This study presents a comprehensive computational framework for predicting and optimizing microstructure and mechanical properties in advanced high-strength steels (AHSS) using adaptive machine …

Enhancing The Performance Of Disk-Based Key-Value Stores: From Learned Index Acceleration To I/O-Efficient Hybrid Caching, Sujit Maharjan

Computer Science and Engineering Dissertations - Archive

The exponential growth of data in modern computing environments has rendered the efficient extraction of information from massive datasets a critical systemic requirement. Key-value (KV) storage systems serve as the backbone for these operations; however, their performance is consistently bottlenecked by two primary functional requirements: identifying the data's location and managing the physical cost of accessing the storage device. Data locations are typically identified via an index, while disk I/O is minimized through caching. This dissertation presents LearnedStore, TurboIndex, and ReadBooster, which break these performance bottlenecks by introducing architectural modifications to the index and cache. LearnedStore accelerates operations by adapting …

Adaptive Synchronization In Digital Twin–Enabled Iot Systems: A Unified Framework For Energy, Fidelity, And Latency Trade-Offs, Uzma Zehra

Computer Science and Engineering Theses

Digital twin technology has emerged as a foundational paradigm for enabling real-time monitoring, analysis, and control in Internet of Things (IoT) systems by maintaining virtual representations of physical processes. Its effectiveness, however, critically depends on timely and accurate synchronization between distributed sensing devices and their corresponding digital counterparts. Frequent synchronization improves reconstruction fidelity and system responsiveness but incurs significant communication energy consumption and network latency. In contrast, infrequent synchronization conserves communication resources but can lead to stale or inaccurate digital twin states, particularly in environments with rapidly changing dynamics. These opposing effects give rise to a fundamental trade-off among energy …

Performance Evaluation Of Modified Moisture Barrier For Subgrade Stabilization Of Pavements On Expansive Soils, Md Tamim Shahriar

Civil Engineering Theses

Expansive soils inflict an estimated nine to fifteen billion dollars in annual damage to infrastructure across the United States, surpassing the combined losses from earthquakes, floods, hurricanes, and tornadoes (Nelson and Miller, 1997; Jones and Jefferson, 2012). In Texas, eighteen of twenty-five TxDOT districts contend with pavement failure rooted in moisture-driven volume change of subgrade clay, consuming roughly twenty-five percent of the agency's annual budget for maintenance and repair (Sebesta, 2002; Wanyan et al., 2010). Conventional remediation approaches, including soil replacement, chemical stabilization with lime or cement, and prewetting, are either prohibitively expensive, unsuitable for high-sulfate soils, or confined to …

Generative Imaging For Computational Pathology, Md Jillur Rahman Saurav

Computer Science and Engineering Dissertations

Hematoxylin and eosin (H&E) staining remains central to cancer diagnosis, providing morphological information essential for pathological assessment. Immunohistochemistry (IHC) and newer multiplexed imaging technologies complement H&E by revealing molecular information critical for accurate tumor subtyping and treatment decisions. In practice, however, H&E and IHC are obtained from different consecutive sections that are not spatially aligned, comprehensive multiplexed panels are expensive and tissue-consumptive, and not all stains are available at every clinical site, limiting comprehensive molecular profiling and the full diagnostic potential of these technologies in clinical practice. This dissertation addresses these gaps through three complementary generative deep learning studies in …