Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host-pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer …

Bridging 2d And 3d Computational Modeling Of Vacuum Arc Remelting: Capturing Rotating Arc Dynamics In Axisymmetric Simulations, Zilong Zhang, Elaheh Dorari, Ramesh Minisandram, Shakarjee Krishnamoorthi, Lang Yuan

Faculty Publications

Computational modeling of the Vacuum Arc Remelting (VAR) process has been developed to provide a deeper physical and metallurgical understanding and assist in the manufacture of defect-free ingots. While fully 3D, time-resolved arc models capture arc–melt interactions with high fidelity, their high computational cost makes 2D steady arc models still the preferred option in industrial applications. In this study, a multi-physics VAR model, which accounts for magnetohydrodynamics, heat transfer, fluid dynamics, and melting/solidification, was established in ANSYS Fluent for Alloy 718 in 3D with a rotating arc. It was validated against measured melt pool morphology. A new 2D axisymmetric oscillating …

A Unified Methodological Framework For Generating Digital Twins Of Multi Class Uncrewed Systems (Uxs), Sai Raghava Pathuri

Shelby Hall Graduate Research Forum Presentations

No abstract provided.

Ai Agent For Healthcare Education, Sudhanshu Tarale

Shelby Hall Graduate Research Forum Presentations

No abstract provided.

Dataset For Equity And Justice In Water Pricing And The Effects On Water Distribution System, Brian Barkdoll

Michigan Tech Research Data

“Equity and Justice in Water Pricing and the Effects on Water Distribution System Management” Equity and justice regarding access to potable drinking water are paramount. Municipal Water Distribution Systems supply potable water to users and are essential to public health. They comprise a complicated set of source(s), pump(s), tank(s), and pipes. The price of water to users can affect the amount of usage. To find the optimal price of water to maximize utility revenue and allow changes to the system components for capital and maintenance expenditures, network modeling using the network solver fixed was performed for 15 real MDWSs.  A …

Mud Sound Speed Profile Constraints From Sub-Bottom Arrival Times, Charles W. Holland

Electrical and Computer Engineering Faculty Publications and Presentations

Arrival times of sub-bottom reflected paths together with co-located direct measurements of the angle of intromission provide narrow constraints on the sediment sound speed profile. Measurements of these two quantities at the central New England Mud Patch in March 2017 indicate that the average sound speed gradient in the upper 3m and upper 9m of mud is 6 s1. Gradients of 5 s1 and 7 s1 over the upper 9m are too small and large, respectively. Below 9m, the sandmud transition interval has a gradient of 200 s1. VC

Addressing System Strength And Reliability Concerns In Renewable Energy-Based Weak Grids Using Synchronous Condensers Determined By Hybrid Gru-Classical Optimization Method, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum, Thair Mahmoud

Research outputs 2022 to 2026

Owing to the higher-integration of renewable energy generators (REGs), conventional coal-based synchronous generators are being decommissioned from generation fleets, resulting in system strength and reliability concerns. Along with the increasing load demand, deficiency of system strength can be a huge risk to system stability and can eventually lead to blackouts by disconnecting REGs from grid systems. In the literature, researchers and power engineers have proposed to deploy synchronous condensers (SynCons) as a mitigation strategy to address the system strength and reliability challenges. SynCons are, however, expensive and require investigation for higher reliability results before installation. To address the concerns, SynCons’ …

Autonomous Vehicle Adoption Behavior And Safety Concern: A Study Of Public Perception, Fatemeh Nazari, Mohamadhossein Noruzoliaee, Abolfazl (Kouros) Mohammadian

Civil Engineering Faculty Publications

Realizing the economic and societal benefits of autonomous vehicles (AVs) hinges on widespread public acceptance. However, existing research offers limited insights into two key behavioral factors shaping AV acceptance, namely, perceived AV safety concern and travel behavior, the latter reflecting how heterogenous mobility patterns influence the AV acceptance. These factors are often treated as exogenous, limiting insight into their true behavioral interdependencies with AV acceptance and their distinct behavioral roots. This study addresses these gaps by introducing a recursive trivariate econometric model that jointly estimates AV acceptance, perceived safety concern, and current travel behavior (proxied by annual vehicle-miles traveled or …

Evaluating The Impact Of Aggregate Size And Reinforcement On Alkali-Silica Reaction In Concrete Through Nondestructive Testing Techniques, Li Ai, David Bianco, Vafa Soltangharaei, Rafal Anay, Mahmoud Bayat, Paul Ziehl

Civil Engineering Faculty Publications

This research investigates different nondestructive evaluation (NDE) methods to assess concrete under alkali-silica reaction (ASR) development. Four methods including acoustic emission (AE), ultrasonic pulse velocity (UPV), crack width measurement, and strain measurement were applied to reactive and control specimens under accelerated ASR conditioning. The innovation lies in using NDE methods to monitor concrete with varying aggregate sizes, quantifying method sensitivity through measured indices, and highlighting the effectiveness of each method to capture ASR development. The results indicate that the unconfined reactive fine-aggregate sample exhibited isotropic expansion, while coarse-aggregate specimens showed around 50 % greater longitudinal expansion and AE cumulative signal …

Explainable Machine Learning And Life Cycle Assessment For Sustainable Design Of Fiber-Reinforced Asphalt Concrete, Xiao Tan, Jianglei Xing, Soroush Mahjoubi, Pengwei Guo, Ziyao Wei, Yuan Wang, Jie Ren, Li Ai, Weina Meng, Yi Bao

Civil Engineering Faculty Publications

Conventional asphalt concrete has a limited lifespan due to cracking, deformation, and environmental degradation, driving the development of fiber-reinforced asphalt concrete (FRAC). However, key gaps remain in current data-driven FRAC studies due to small and homogeneous datasets, “black-box” machine learning models, and trade-offs between mechanical-sustainable performance, failing to provide a transparent understanding of features governing FRAC behaviors. This paper proposes a framework integrating explainable artificial intelligence and life cycle assessment (LCA) to advance mechanical and sustainable design of FRAC. A dataset of 2490 laboratory samples covers 15 input features and 3 mechanical outputs. Eight machine learning models, along with a …

Joint Capacity Allocation And Job Assignment Under Uncertainty, Peng Wang, Yun Fong Lim, Gar Goei Loke

Research Collection Lee Kong Chian School Of Business

We study a multi-period joint capacity allocation and job assignment problem. The goal is to simultaneously allocate resources across J different supply nodes and assign jobs from I different demand origins to these J supply nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We consider three features: (i) supply is replenishable after some random time, (ii) demand is random, and (iii) demand can wait and needs not be fully fulfilled immediately. Such problems emerge in many service management settings such as fleet re-positioning for car-sharing, and patient management in healthcare. We …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

Ƒ(Cell): Software For Reproducible Analysis Of Optoretinograms, Robert F. Cooper, Mina Gaffney, Brea D. Brennan, Niko Rios

Biomedical Engineering Faculty Research and Publications

Purpose: There has been a marked increase in use of a noninvasive functional imaging technique called optoretinography (ORG). As more groups use ORGs, it is crucial to have a consistent methodology, and understand what analysis parameters influence repeatability. In this work, we present an open-source software library called ƒ(Cell) designed to facilitate reproducible and repeatable analyses of ORG data.

Methods: We designed ƒ(Cell) as a Python software library that can co-register and analyze ORG datasets, while also enabling process auditing. To validate the software, we used our previously obtained normative optoretinography datasets as well as datasets from six …

Fixel-Based Analysis Of Pretreatment Mri Identifies White Matter Abnormalities In Pediatric Anti-Nmdar Encephalitis, Daniel Ackom, Janine Taitt-Tap, Scott A. Beardsley, Ricardo Vega, Alyssa Jobe, Andrew J. D. Crow, Brian Schmit, Lileth Mondok, Pradeep Javarayee

Biomedical Engineering Faculty Research and Publications

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an autoimmune disorder in which conventional MRI often appears normal, leading to clinical–radiologic dissociation and hindering early diagnosis and monitoring. We retrospectively studied five pediatric patients with anti-NMDAR encephalitis and compared their pretreatment diffusion MRI to age- and sex-matched controls. Using fixel-based analysis (FBA), we quantified tract-specific white matter abnormalities at the individual level. All patients showed significantly reduced fiber density and cross-section, with patterns ranging from focal to widespread involvement. In two patients, FBA abnormalities corresponded to seizure lateralization on EEG despite normal MRI, emphasizing FBA's added value in detecting seizure-concordant injury. One patient, …

Improving Interlimb Coordination And Paretic Limb Use After Stroke Using A Novel Robotic Split-Crank Pedaling Device: A Cross-Sectional Study, Tom S. Ruopp, Brian Schmit, Sheila Schindler-Ivens

Biomedical Engineering Faculty Research and Publications

Background. Many stroke survivors cannot walk effectively, even after rehabilitation. Causes include impaired muscle activation, poor interlimb coordination, and limited restorative interventions. To address this, we developed CUped (pronounced “cupid”), a motorized split-crank pedaling device designed to compel use of the paretic limb and retrain interlimb coordination. We examined its within-session effects, comparing three proportional control schemes—assist (A), resist (R), and assist plus resist (A + R)—to identify which best promotes recovery-related movement.

Methods. Nineteen individuals with stroke and eleven controls pedaled in 5-min bouts, one per control scheme. Each bout included pre-test, exposure, and post-test periods. Participants were instructed …

Orthogonal Time Frequency Space Modulation For Underwater Acoustic Communication Systems: A Review, Bevek Subba, Quoc Viet Phung, Stefan Lachowicz, Walid K. Hasan, Muhammad Haziq, Daryoush Habibi, Iftekhar Ahmad

Research outputs 2022 to 2026

Underwater Acoustic Communication (UAC) has garnered significant attention due to its applications in marine science, defence, and exploration. With the vast majority of the Earth's surface covered by water, there is a growing demand for reliable and effective communication techniques in underwater environments. However, the underwater acoustic channel is the most challenging channel due to its harsh characteristics, which significantly hinder the reliability and performance of UAC systems. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution for next-generation UAC systems to address high Doppler and high mobility scenarios. It has demonstrated tolerance to fast time-varying channel …

Magnetically Driven Engineering Of Magnetic Hydroxyapatite/Ca Ro Membranes: Enhancing Surface Physicochemical Properties For High-Performance Water Desalination, Fariba Oulad, Ali Akbar Zinatizadeh, Sirus Zinadini, Amir Razmjou, Sara Eavani

Research outputs 2022 to 2026

This research focuses on the development of innovative hydrophilic reverse osmosis (RO) hydroxyapatite/cellulose acetate (HAP/CA) and magnetic-hydroxyapatite (M-HAP)/CA membranes, utilizing a phase-inversion technique with varying nanoparticles (NPs) concentration. The positioning of magneto-responsive M-HAP NPs within the advanced M-HAP/CA membranes was adeptly controlled using magnetic guidance during immersion- precipitation process in a coagulation bath. This precise control enabled a densely organized arrangement of M-HAP NPs, shifting from the membrane's core to its upper separation layer. Consequently, the barrier properties, mechanical integrity, and surface characteristics of the advanced M-HAP/CA membranes saw significant enhancement due to the uniform distribution of hydrophilic porous NPs …

Numerical Investigation On Enhancing The Performance Of A Parabolic Trough Collector Using A Threaded Absorber Tube, Shayan Pourhemmati, Abdellah M. Shafieian, Hussein A. Mohammed, Barun Kumar Das, Majid Tolouei-Rad

Research outputs 2022 to 2026

The growing global demand for energy and heightened environmental concerns have elevated the importance of solar energy harvesting in recent years. Parabolic trough collectors (PTCs) are widely utilized for capturing direct solar radiation; however, conventional PTC efficiency is limited by low convection heat transfer rate in the absorber section, which is then delivered to heat transfer fluid (HTF). This study numerically investigates the use of a threaded tube as a fluid flow disruption technique to enhance heat transfer rate within the absorber, with Reynolds numbers ranging from 4000 to 10,000. Three key geometric parameters of the thread: pitch (300, 150 …

State Of Charge Estimation Of Ev Secondary Battery Pack Using Hybrid Hedge Feedforward Feedback-Based Gated Recurrent Unit To Extend Lifespan, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum, Paul Moses

Research outputs 2022 to 2026

Accurate estimation of state of charge (SoC) and maintaining balanced charge levels across secondary battery cells are crucial in battery management systems (BMSs) to extend battery life while improving the performance and thermal stability of Li-ion batteries (LIBs) in electric vehicles (EVs). However, there are still underexplored challenges associated with circulating currents in electrochemical cells during continuous operation which can overheat battery packs, reducing their life span or result in dangerous thermal runaways. This paper investigates SoC estimation using various real-world charging and discharging profiles, along with charge-balancing strategies to enhance the longevity of parallel-connected Li-ion battery cells. A newly …

Evaluation Of Sanafoam Vaporooter Ii Stress On Activated Sludge Performance For Carbonaceous Removal And Nitrification, Ensiyeh Taheri, Ali Fatehizadeh, John Nazimek, Mustafa Nabi, Wayne Bagg, Amir Razmjou, Paul Nolan, Ratish Permala, Mehdi Khiadani

Research outputs 2022 to 2026

Root intrusion control in sewer pipelines often relies on chemical herbicides, yet their unintended impacts on downstream biological treatment remain underexplored. This study investigated the effects of Sanafoam Vaporooter II (SVII) on carbon removal and nitrification in activated sludge reactors (ASRs) under long-term operation. Two parallel bench-scale ASRs were installed and operated at Subiaco municipal wastewater treatment plant in Perth, Western Australia: one was exposed to incremental SVII concentrations (1–3000 mg/L), while the other served as an unamended control. Stoichiometric analysis revealed stable organic matter removal across all SVII doses. In contrast, nitrification decreased progressively at concentrations exceeding 128 mg/L. …

Green And Charge-Tuned Β-Cyclodextrin Polymers For Efficient Removal Of Pfas And Anionic Dyes From Water, Samira Sadeghi, Ahmad Najafidoust, Mark Mullett, Shayan Karimi, Masoumeh Zargar

Research outputs 2022 to 2026

Water contamination by per- and polyfluoroalkyl substances (PFAS) poses a major environmental challenge due to their persistence. This study evaluates positively charged, green-synthesized β-cyclodextrin polymers (β-CDP⁺) for the removal of perfluorobutanoic acid (PFBA) and perfluorooctanoic acid (PFOA) as short- and long-chain PFAS, respectively, from water. Methyl orange (MO) and acid red 1 (AR1), as anionic dyes, were used as proxies to optimize PFAS adsorption conditions. β-CDP⁺ exhibited high removal efficiencies (>90%) for all pollutants within 15–50 mg/L, reaching equilibrium within 30 min. Maximum adsorption capacities (Qm) were achieved for MO (335 mg/g), AR1 (384 mg/g), PFOA …

Design, Testing, And Safety Performance Of Movable Guardrail Systems: A Prisma-Based Systematic Review, Navid Hashemi Taba, Ahdieh Sadat Khatavakhotan, Majid Tolouei-Rad

Research outputs 2022 to 2026

Movable guardrail systems are increasingly used in work zones, reversible lanes, and temporary traffic operations; however, evidence on their crashworthiness, material performance, and operational reliability remains dispersed across multiple design typologies and regulatory frameworks. This PRISMA-compliant systematic review synthesizes 78 studies involving full-scale crash tests, validated finite-element simulations, field performance evaluations, and compliance evaluations under MASH, EN 1317, NCHRP 350, and AS/NZS 3845.1. The findings indicate that modular rigid barriers reliably achieve TL-3/TL-4 performance when joint alignment and foundation conditions are properly controlled; semi-rigid steel systems provide a practical balance between containment capacity and redeployability, but remain sensitive to post …

Shear Stability Enhancement In Fracture Plugging Zones: Unveiling Failure Mechanisms And Adhesive Benefits Through Photoelastic Analysis, Haoran Jing, Yili Kang, Chengyuan Xu, Lei Liu, Xiaopeng Yan, Zhenjiang You

Research outputs 2022 to 2026

Addressing lost circulation during drilling operations typically involves the utilization of lost circulation materials (LCMs) to create a fracture plugging zone (FPZ), which effectively decouples the wellbore fluid column pressure (Pw) from the formation pressure (Pf). This FPZ, comprised of aggregated granular LCM particles, frequently succumbs to shear stress, with shear failure being the predominant mode of structural failure, leading to ongoing fluid losses. To illuminate the failure mechanisms of the FPZ under conditions of escalating drilling differential pressure (Pw - Pf), we utilized photoelastic experiments to capture and visualize the evolving mesoscopic mechanical structure of the FPZ. Photoelastic images …

Machine Learning-Based Upscaling Of Rock Permeability From Pore Scale To Core Scale: Effect Of Training Dataset Size And Sub-Core Volumes, Yaotian Guo, Fei Jiang, Takeshi Tsuji, Yoshitake Kato, Mai Shimokawara, Lionel Esteban, Mojtaba Seyyedi, Marina Pervukhina, Maxim Lebedev, Ryuta Kitamura

Research outputs 2022 to 2026

Permeability characterizes the capacity of porous formations to conduct fluids, thereby governing the performance of carbon capture, utilization, and storage (CCUS), hydrocarbon extraction, and subsurface energy storage. A reliable assessment of rock permeability is therefore essential for these applications. Direct estimation of permeability from low-resolution CT images of large rock samples offers a rapid approach to obtain permeability data. However, the limited resolution fails to capture detailed pore-scale structural features, resulting in low prediction accuracy. To address this limitation, we propose a convolutional neural network (CNN)-based upscaling method that integrates high-precision pore-scale permeability information into core-scale, low-resolution CT images. In …

Emerging Design Paradigms And Microstructural Innovations In Refractory High-Entropy Alloys: A Critical Review, Deyu A. Jiang, Lai Chang Zhang, Kuaishe Wang, Mahmoud Ebrahimi, Wen Wang, Liqiang Wang, Weijie Lu, Di Zhang

Research outputs 2022 to 2026

Refractory high-entropy alloys (RHEAs) are being developed to meet mechanical, thermal and chemical requirements that exceed what current super-alloys can withstand. This review explains how composition design, processing routes and the resulting microstructures now combine to realize that potential. We first link phase selection in BCC-, FCC- and dual-phase RHEAs to atomic-size mismatch, mixing enthalpy and valence-electron concentration, and compare manufacturing paths ranging from arc melting to powder metallurgy, additive manufacturing and vapor deposition, showing how each reshapes grain structure and defect chemistry to improve high-temperature strength, corrosion resistance and irradiation tolerance. Computation-led tools—density-functional theory, calculation of phase diagrams and …

Informing Consumer Product Sound Quality Analysis Using Generative Adversarial Networks, Daniel Jeffery Lesko, Vinh Nguyen

Michigan Tech Publications

Sound quality attributes have been proven important predictors of customer satisfaction with consumer goods and appliances. The results of several studies indicate that level-based, tonal, and temporal aspects of sound influence the perceived quality of consumer products. The current state-of-the-art of inferring consumer satisfaction with sound attributes is based on the jury test methodology. However, product engineers often find the models generated from these studies incomplete, resulting in products that fail to meet consumer expectations. Therefore, this study aims to utilize generative data-driven approaches to create a range of acceptable sound quality attributes given consumer satisfaction requirements. A baseline sound …

Evaluating Regularized Logistic Regression And K-Nn On Mnist Under Increasing Random Missingness, Daniel Markwei

Data Science and Data Mining

This paper investigates the effect of random missingness on the performance of regularized multinomial logistic regression and the k-nearest neighbors (k-NN) classifier for handwritten digit recognition on the MNIST dataset. In particular, we study L1-regularized (LASSO) logistic regression and L2-regularized (Ridge) logistic regression alongside k-NN. Varying percentages of random missingness were introduced into the original dataset, and each model was evaluated in terms of its classification performance. The results show that random missingness degrades the performance of all three classifiers. Overall, k-NN consistently achieves higher accuracy than both L1- and L2-regularized logistic regression across all missingness levels; however, its performance …

A Pathfinder Lunar Construction Mission Concept Using Regolith Filled Bags, Cameron S. Dickinson, Fu Nan Shi, Ketan Vasudeva, Rudranarayan M. Mukherjee, Joshua Blanchard, Steve Dubrule, Paul Van Susante, Et Al.

Michigan Tech Publications

Two challenges that have a permanent presence on the Moon are solar and cosmic radiation, as well as the large surface temperature variation between lunar day and night. To address these problems, we propose a lunar pathfinder mission concept that uses robotic systems to investigate whether regolith-filled bags can be used as a versatile construction medium for lunar surface structures and sensors to obtain data on the lunar regolith. The primary objectives of this mission are as follows: evaluation of the surface and subsurface regolith as fill material, lunar excavation using a robotic manipulator equipped with a bucket scoop, bag …

Renewable Microgrid Frequency Regulation Using Active Disturbance Rejection Control And Elephant Herding Optimization, Ehab Bayoumi

Mechanical Engineering

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Renewable Microgrid Frequency Regulation Using Active Disturbance Rejection Control and Elephant Herding Optimization