Engineering Commons^™

What Is Passive And Active Release Retrofitting Rainwater Harvesting Systems?, Sarah E. Waickowski, Amy E. Scaroni, Beatriss H. Calhoun

Forestry and Natural Resources

Rainwater harvesting is the collection and temporary storage of rainwater for non-potable applications and stormwater management. Rainwater harvesting systems can be retrofitted with passive or active release to improve how well the systems manage stormwater runoff. This publication contains information that may be useful to municipalities, stormwater professionals, rainwater harvesting system owners and/or managers, homeowner associations, and stormwater educators, among others, on what is passive and active release, including their benefits and where to find more information.

Evaluating The Response Of Ageing Rc Bridge Piers To The 2023 Kahramanmaraş Earthquake Using Bouc-Wen Hysteretic Models, Negin Fayaz, S. Kocakaplan Sezgin, E. Ahmadi, M. M. Kashani, M. Zaker Esteghamati

Civil and Environmental Engineering Student Research

This study examines the seismic response of reinforced concrete (RC) bridge piers subjected to ground motion (GM) records from the 2023 Kahramanmaraş earthquake by comparing the impact of pulse-like versus no-pulse records. A suite of 300 nonlinear Bouc-Wen hysteretic models was developed to represent piers with varying nonlinear behavior. Each Bouc-Wen model was assessed under 24 pulse-like and 28 no-pulse GMs, resulting in 15,600 nonlinear time-history simulations. Subsequently, the relationship between Bouc-Wen parameters (i.e., period and yield displacement) and seismic responses (i.e., maximum displacement, residual displacement, and maximum acceleration) was analyzed. The results show that pulse-like records produce 5 times …

When The Student Surpasses The Teacher: Better Generalization In Ai-Based Seismic Waveform Quality Assessment Using Knowledge Distillation, Ali Namin, F. Jalaeifar, A. Kottke, M. Zaker Esteghamati

Civil and Environmental Engineering Student Research

This paper presents a knowledge distillation (KD)-based approach to reduce computational expenses and improve the generalizability of deep learning-based models for quality assessment of seismic waveforms. Using two different waveform datasets, teacher models are distilled into lightweight student models from two families of time-series-based and image-based models. Models from both student families are trained on a development dataset and evaluated on the testing portion of the development dataset, as well as on an entirely different external testing dataset. Results indicate that KD can provide student models with substantially reduced size (5%-10% of the teacher model's size) and latency while preserving, …

Sensing Negative-Cone Rotational Diffusion Of Dipole-Like Emitters, Yuanxin Qiu, Kaizhi A. Nie, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Fluorescence anisotropy and single-molecule orientation-localization microscopy (SMOLM) are powerful techniques that quantify the rotational diffusion of dipole-like emitters, which is important for sensing molecular interactions and chemical environments at the nanoscale. Numerous theoretical and experimental studies have thoroughly characterized single-molecule rotations even when those rotations are much faster than the detector integration time. Here, we extend the theory of measuring rotational diffusion to situations where a single dipole rotates uniformly everywhere outside of an isotropic cone of a certain size, termed a negative cone. This scenario corresponds to negative fluorescence anisotropy 𝑟 and has been observed in emitters exhibiting strong …

Dual-Function Plasmonic Structure For Logic Operations And Circular Polarization Detection, Marjan Bazian, Mark C. Harrison

Engineering Faculty Articles and Research

Many integrated photonic devices have a high potential for dual applications, making them more versatile components in photonic integrated circuits. In this study, we show that a plasmonic XOR gate structure, originally designed to perform logic operations, can also be used as a circular polarization detector. This multi-purpose capability stems from the phase-encoded input mechanism that controls the output response of the structure. We adapted this mechanism to select the output based on the polarization state of light. The design of this structure, utilizing the subwavelength field confinement of plasmonic waveguides, enables the integration of logic and detection functions into …

Computational Modeling Of Enhanced Nonlinear Optical Response In Plasmonic Waveguide Devices With Epsilon-Near-Zero Films, Kevin T. Le, Mark C. Harrison

Engineering Faculty Articles and Research

Silicon photonics faces fundamental limitations in nonlinear applications due to weak Kerr nonlinearity, substantial two-photon absorption, and coupling challenges with subwavelength structures. This work investigates epsilon-near-zero (ENZ) materials integrated into plasmonic waveguide architectures for enhanced nonlinear photonic devices. ENZ materials exhibit nonlinear refractive indices several orders of magnitude higher than conventional materials near their zero-permittivity wavelength, enabling giant optical effects over deeply sub-wavelength interaction lengths. We present finite element method simulations of plasmonic ENZ waveguides, investigating layer thickness optimization for nonlinear enhancement while minimizing optical losses. Initial modal analysis confirms superior field confinement in hybrid plasmonic-ENZ structures compared to conventional …

Enhancing Wildlife Strike Risk Assessment Through Large Language Model–Driven Data Analysis, Bill Deng Pan

Student Research Symposium (SRS)

Wildlife strikes remain a persistent safety and economic concern across global aviation operations, highlighting the need for advanced analytical methods to improve risk assessment and mitigation. Traditional statistical approaches to wildlife-strike data, while effective for structured variables such as altitude, phase of flight, or aircraft type, often overlook valuable insights embedded in the unstructured narrative components of strike reports. This study proposes the application of Large Language Models (LLMs) for the automated extraction, classification, and interpretation of information within the Federal Aviation Administration (FAA) Wildlife Strike Database. Using natural language processing (NLP) techniques, LLMs will be utilized to identify key …

Development Of Hypergraph Based Deep Neural Framework For Precise Cancer Subtyping And Meta Visualization, Pooja G Ms

Theses and Dissertations

Accurate Cancer Subtyping is a cornerstone of modern oncology essential for effective diagnosis and guiding personalized treatment. Histopathological Images (HIs) which capture the microscopic structure of tissues are widely used for cancer detection and subtyping. Even though deep learning has made significant advances, existing HI based subtyping methods often focus on specific cancer types, lacking a generic framework.

A unified framework that can classify multiple cancers with high specificity is desperately needed. In response to these limitations, this thesis proposes a robust multi-cancer, multi-class subtyping framework called DSHGNet (Depthwise Separable Hypergraph Convolutional Neural Network) which integrates Depthwise Separable Convolutional Neural …

Investigating Confounding Factors In Shear Wave Speed Measurements Of Fibrotic Liver Tissues: A Computer Simulation Study, Emily J. Miller, Yongmei Jin, Jingfeng Jiang

Michigan Tech Publications

Screening patients with liver fibrosis and identifying those at risk of developing advanced liver fibrosis is of clinical interest. Shear wave elastography (SWE), a promising non-invasive screening tool used to distinguish healthy tissue from diseased tissue, measures tissue shear wave speed (SWS) to describe tissue stiffness and identify liver fibrosis. However, considerable variations in the reported results have been found. We propose that the heterogeneity of the liver tissue background, such as the presence of fatty liver tissue and the preferred local orientation of the scarred fibrotic liver tissues embedded into the liver parenchyma, may contribute to the uncertainty in …

Dynamic Deep Prompt Optimization For Defending Against Jailbreak Attacks On Llms, Doniyorkhon Obidov, Honggang Yu, Xiaolong Guo, Kaichen Yang

Michigan Tech Publications

Large Language Models (LLMs) demonstrate impressive capabilities across many applications but remain vulnerable to jailbreak attacks, which elicit harmful or unintended content. While model fine-tuning is an option for safety alignment, it is costly and prone to catastrophic forgetting. Prompt optimization has emerged as a promising alternative, yet existing prompt-based defenses typically rely on static modifications (e.g., fixed prefixes or suffixes) that cannot adapt to diverse and evolving attacks.

We propose Dynamic Deep Prompt Optimization (DDPO), the first jailbreak defense based on deep prompt optimization. DDPO uses the target LLM’s own intermediate layers as feature extractors to dynamically generate defensive …

Research Of Logic Elements Based On Complementary Bipolar Transistors, Nodira Batirdjanovna Alimova, Nilufar Baxtiyorovna Gulyamova

Chemical Technology, Control and Management

Important parameters for any type of inverter – a NOT logic gate – are the power consumption during switching and the supply voltage. The proposed connection of complementary (two different types) bipolar transistors reduces the current consumption and supply voltage by simultaneously using the cutoff and saturation modes of the bipolar transistors. It has been theoretically and experimentally established that an inverter using complementary bipolar transistors operates at low supply voltages, approximately 0.7 V. Power consumption is virtually independent of the inverter's static state. A method for calculating the transfer characteristic of an inverter using complementary bipolar transistors is developed, …

Algorithms Of Stable Adaptive Observation Of A Multidimensional Undefinite Object, Tursunova Sadoqat Abdusalom Qizi

Chemical Technology, Control and Management

This article presents an algorithm for simultaneously estimating the parameters and state coordinates of a multidimensional control object when some of its state variables are not directly measured. The inability to measure all state variables (coordinates) of an object is a well-known drawback of identification schemes. Such conditions require the construction of adaptive state observers. This work demonstrates that when identifying the parameters of a mathematical model for an uncertain multidimensional object, the asymptotic stability of the object and the convergence of its parameters to the model parameters are ensured, provided the input vector is sufficiently informative. The construction of …

Impediments To Transforming The Healthcare Delivery System: Shifting The Paradigm From Provider Centric To Patient Centric, Elizabeth A. Regan, Manasa Devi Chinta

Faculty Publications

Introduction: 

Stated aims for digital healthcare transformation frequently cite goals for better coordinated patient-centric systems. However, despite advances in medical science, digital technologies, health policies, and billions of dollars invested over the past 25 years, most healthcare providers are far from fully realizing the demonstrated benefits of today's digital technologies for improving patient care. Sharing information across healthcare systems remains challenging. Problems with fragmentation, quality, inequities, and rising costs of care delivery persist. A recent study of 1,026 U.S. hospital systems found that only 15.8 percent achieved a digital maturity level needed to provide digitally enabled healthcare services to better …

Infrastructural Resiliency Analysis Concerning Extreme Events, Yasaman Norouzi

Theses and Dissertations

Infrastructural resiliency is critical for maintaining functionality during and after extreme events, ensuring minimal service disruptions and supporting rapid recovery. Key resiliency attributes—robustness, redundancy, resourcefulness, and rapidity—enhance infrastructure adaptability and safety. This study investigates significant factors and methods for improving resiliency, with a focus on equity and reliability. A new resiliency metric, incorporating equity as a core element, is developed using probabilistic approaches. This metric integrates structural vulnerability with accessibility, income, cost, and exposure factors to evaluate community impact on infrastructure access. To achieve this, limit state functions are defined to assess how infrastructural vulnerabilities affect diverse communities. Apart From …

Physical And Chemical Study Of Interaction In The Urea–Valine System For The Creation Of Modified Liquid Nitrogen Fertilizers, Suvonkul Erkhanovich Nurmonov, Saydullo Khamidovich Azimov

Chemical Technology, Control and Management

This study reports the results of a comprehensive physicochemical investigation of the urea–L-valine binary system, conducted to validate its potential as a modified base for liquid nitrogen fertilizers.

By utilizing the methods of isomolar series, visual-polythermal analysis, X-ray phase (XRD) and single-crystal X-ray diffraction (SC-XRD), synchronous thermal analysis (TGA/DTA), and FT-IR spectroscopy, it was determined that the interaction between components in both aqueous solutions and the solid phase results in the formation of molecular associates stabilized by hydrogen bonds, without the formation of new stoichiometric compounds.

The concentration and temperature limits of the system's stability were defined, revealing a reduction …

Analysis Of The Carbonization Process Of Ammoniated Brine In Ammonia-Soda Production, Nadirbek Rustambekovich Yusupbekov, Djalolitdin Paxritdinovich Mukhitdinov, Fotima Faxritdinovna Iskhakova

Chemical Technology, Control and Management

The work provides an analytical review of the current state of modeling and controlling the carbonization process of ammoniated brine. It analyzes modern methods of mathematical, simulation, and intelligent modeling of heat and mass transfer, hydrodynamics, carbonation kinetics, and crystallization. The paper considers the potential of using digital twins, neural network models, fuzzy logic devices, and hybrid models to improve forecasting accuracy and the adaptability of process solutions. Modern carbonation process control strategies are also considered. A detailed analysis is provided of predictive control (MPC), neuro-fuzzy controllers, decentralized and multivariate control systems that ensure stable column operation under disturbances, optimize …

Analytical Method For Investigating Nonlinear Magnetic Circuits Of Measuring Transducers With A Standard Parameter Distribution Structure, Javhar Sulton O'G'Li Fayzullayev

Chemical Technology, Control and Management

The article proposes a new analytical method for investigating magnetic circuits with distributed parameters and nonlinear magnetic coupling. The method is based on introducing into the system of nonlinear differential equations of such circuits the condition that the second derivative of the magnetic flux with respect to the circuit length is equal to zero, as well as on assuming that one of the geometric parameters of the studied magnetic circuit – the value of the air gap between ferromagnetic cores, their thickness, width, or the linear value of the number of turns of the distributed excitation winding – is a …

Pid Control For Lower Limb Exoskeletons: A Framework Evaluation, Javlonbek Rakhmatillaev, Vytautas Bučinskas

Chemical Technology, Control and Management

This research provides a detailed guideline for implementing and evaluating Proportional Integral Derivative (PID) control frameworks in lower limb rehabilitation exoskeleton robotics. It examines the role of control systems within rehabilitation robotics, outlines the principles of PID control, describes exoskeleton architecture, explores applications of PID control, reviews optimization strategies, presents experimental validations, and considers future developments in the field. The proposed control framework incorporates aspects of mechanical design, actuator and sensor selection, and PID-based control algorithms, thereby promoting safe, accurate, and individualized rehabilitation support. Recommendations and effective guidance for future work are also presented.

Inductive Transducers For Measuring Vibrations, S.F. Amirov, A.Kh. Sulliev, A.A. Shoimkulov

Chemical Technology, Control and Management

A new design of an induction transducer has been developed for measuring linear and torsional vibrations in different directions with high sensitivity by constructing an inertial element consisting of four mutually perpendicular sectors and making the masses of two adjacent sectors different from the masses of the other two adjacent sectors. By constructing an inertial element in the form of a sector with two mutually diametric magnetic cores and different masses, and placing it between the horizontal and vertical axes, a design of an induction transducer has been developed that highly sensitively measures linear and torsional vibrations in different directions, …

The Main Errors Of The Ultrasonic Sensor In Measuring Water Flow In Open Channels, Anvar Urolovich Djalilov

Chemical Technology, Control and Management

This article analyzes the use of ultrasonic sensors in measuring water flow and the main errors that may occur in this process. In the conducted scientific research, a time-pulse ultrasonic sensor was tested. The absolute, relative and repeatability errors of the sensor during water flow measurement were studied. The absolute error represents the largest difference between the value recorded by the sensor and the real value, affecting the overall accuracy of the measurement system. This error can vary depending on environmental factors, the design and operating principles of the sensor. During the experiment, the performance of this sensor was …

Synthesis Of An Adaptive Synergistic Fuzzy Discrete Controller For Nonlinear Systems, Isamidin Xakimovich Sidikov, Gulruxsor Murot Qizi Nashvandova, Feruzakhon Botirxon Qizi Sodiqova

Chemical Technology, Control and Management

The paper considers the issues of synthesizing an adaptive fuzzy synergetic controller with discrete time for nonstationary nonlinear dynamic objects. The proposed approach is based on the integrated use of synergetic control principles and fuzzy logic methods, which ensure the formation of a control law for nonlinear dynamic objects that provides asymptotic stability of the control system. Such a hybrid combination makes it possible to guarantee the asymptotic stability of the closed-loop system and to shape the required dynamic behavior of the object over a wide range of operating modes. In addition, this approach provides the ability to adapt to …

Adaptive-Robust Control Of Dynamic Systems Using Generalized Predictive Methods, Shuxrat Tulyaganov

Chemical Technology, Control and Management

This paper considers an approach to adaptive-robust control of dynamic systems using generalized anticipation, where the object is described by a locally linearized model. Known self-tuning algorithms show insufficient stability to inaccurate selection of delay or model order. A generalized predictive control approach is suggested, with simulation outcomes showing superiority over traditional methods like generalized minimum variance control and pole assignment.This sliding horizon algorithm is based on predicting future system output signals several steps ahead, based on assumptions about subsequent control actions. One effective assumption is the presence of a “control horizon,” beyond which control signal increments are assumed to …

Review Of Modern Methods For Identification, Forecasting, And Intelligent Control Of Wastewater Biological Treatment Processes, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon O'G'Li Mannobjonov

Chemical Technology, Control and Management

This article presents a comprehensive review of contemporary approaches to the automation and intelligent control of wastewater biological treatment processes. Particular emphasis is placed on the digitalisation of wastewater treatment plants, ranging from the implementation of automated process control systems (APCS/SCADA-based solutions) to the application of predictive algorithms and the development of digital twins of bioreactors.

Special attention is devoted to mathematical models that underpin the control of bioprocesses. The evolution of the most widely used activated sludge models—ASM1, ASM2d, and ASM3—is examined, as these models describe key processes such as microbial community growth, nitrification, denitrification, and phosphorus removal. It …

Self-Aware Quantum-Inspired Routing Framework For 6g And Quantum Networks, Haitham M. Abdelghany, Mohamed M. Ashour

Mansoura Engineering Journal

This paper introduces a quantum-hybrid-aware routing framework that incorporates the concepts of quantum computing with meta-cognitive learning to improve adaptability, reliability, and awareness in dynamic quantum networks. The proposed system accommodates a new IoT, edge computing and 6G system, where routing schemes should be flexible and robust and have cross-topology functionality. Conventional methods of quantum reinforcement learning can be characterized by a trade-off between exploration and decision stability. To cope with this, the proposed self-aware hybrid architecture is a combination of quantum-enhanced learning and a meta-cognitive awareness layer to allow autonomous agents to monitor performance and adjust routing strategies on …

The Effect Of Polyester And Lycra Loop Lengths On The Recovery Properties Of Single Jersey Knitted Fabrics, Abdelmonem Fouda, Moaaz Eldeeb

Mansoura Engineering Journal

Knitted fabrics are well known for their comfort due to their ability to elongate in the wale direction at the expense of the course direction, and vice versa. However, they lack dimensional stability, especially single jersey knitted fabrics. To address this issue, Lycra threads have recently been incorporated into their production to improve shape recovery after stress is released. Despite this enhancement, the fabric does not fully return to its original shape, highlighting the need to measure this phenomenon accurately. This study aims to firstly: simulate the production of a frame designed to measure the recovery and elongation of knitted …

Influence Of Pulse Width On Energy Deposition And Temperature In Nanosecond-Pulsed Discharges, Christopher B. Reuter, Joshua B. Sinrud, Tanvir I. Farouk, Nicholas S. Dewey, Dmitri Kaganovich

Faculty Publications

Nanosecond-pulsed discharges are a promising method to enhance combustion but can generate significant levels of electromagnetic interference (EMI). Modifying the discharge pulse width is an unexplored option to reduce EMI, but few studies have examined how changing the pulse width affects discharge parameters such as energy and temperature. This study addresses this issue by systematically investigating how the pulse width affects the energy per pulse, breakdown time, rotational temperature, and vibrational temperature in air across different frequencies, flow velocities, and gap distances in a plasma-assisted flow tube. It is observed that the pulse width has a substantial impact on the …

Comment On The Draft Environmental Impact Statement For New Operations For Lake Powell And Lake Mead Post-2026, Bryan Williams, Caitlyn Andrus, Brittany Fager, Matthew Fugal, Parker Hansen, Ashley Jones, Shaun Joseph, Ashley Mcallister, Braden Wilding, David Rosenberg

Civil and Environmental Engineering Student Research

We appreciate the Bureau of Reclamation’s extensive effort in preparing the Post-2026 Draft Environmental Impact Statement (Draft EIS) for operational guidelines for Lake Powell, Lake Mead, and the Colorado River Basin. This commentary will address positives, questions, and concerns from the Draft EIS. We also share recommendations for consideration in the Final EIS.

Design And Evaluation Of A Collaborative Xr Framework With Abstract Building Blocks For Manufacturing System Prototyping, Shaun Macdonald, Bixun Chen, Yuanjie Xia, Rami Ghannam

School of Engineering Technology Faculty Publications

Manufacturing systems are complex installations designed and developed by stakeholders with differing expertise. However, system design primarily features traditional methods, long lead times, and siloed environments, which obstruct comprehension, communication, or collaboration. Prior research has used extended reality (XR) to prototype and visualise specific manufacturing applications; however, flexible system modelling remains complex. To address these issues and lay the foundation for future-facing manufacturing design as part of Industry 4.0, we introduce ARTIFY—a prototype XR framework for conceptual system prototyping. It features abstract building blocks that can be semantically reconfigured to support early-stage modelling of varied manufacturing systems in immersive environments. …

Co-Electrospinning Polyacrylonitrile (Pan) / Polymer Of Intrinsic Microporosity-1 (Pim-1) For Electrochemical-Based Sensor For Pyridine Detection And Absorption, Samar A. Salim

Nanotechnology Research Centre

The electrospinning of the polymer of intrinsic microporosity-1 (PIM-1) poses challenges due to its limited solubility and tendency to form bead-like structures at high concentrations. This work details the synthesis and analysis of electrospun composite fibers made from polyacrylonitrile (PAN) and PIM-1 for electrochemical applications. The data analysis confirmed the successful incorporation of PIM-1 into the PAN matrix. The fiber characteristics were significantly influenced by PIM-1 loading (1–10%), resulting in fiber diameters ranging from 0.8 to 1.7 μm. A 10% concentration of PIM-1 results in the formation of macropores with diameters ranging from 0.5 to 1.7 μm. Optical analysis using …

Hybrid Cnn–Gru-Based Demand–Supply Forecasting To Enhance Sustainability In Renewable-Integrated Smart Grids, Süleyman Emre Eyimaya, Necmi Altin

Faculty Publications

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative …