Engineering Commons^™

Microplastics Fouling Mitigation In Forward Osmosis Membranes By The Molecular Assembly Of Sulfobetaine Zwitterion, Javad Farahbakhsh, Mitra Golgoli, Mehdi Khiadani, Amir Razmjou, Masoumeh Zargar

Research outputs 2022 to 2026

Forward osmosis (FO) membranes have potential for the efficient water and wastewater treatment applications. However, their development has faced significant challenges due to their fouling propensity. In this study, FO membranes modified with sulfobetaine zwitterions (i.e., [2-(Methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) ammonium hydroxide) were fabricated and used for the first time to address microplastic (MP) fouling issue. Water flux, reverse salt flux (RSF), fouling, and flux recovery were evaluated for the membranes loaded with different quantities of the zwitterions ranging from 0.25 % to 2 %. The developed membranes were tested over 49 h with feed solutions containing polyethylene MPs and bovine serum albumin …

Application Of The Immobilized Low-Activity Waste Glass Corrosion Model To The Static Dissolution Of 24 Statistically-Designed Alkali-Borosilicate Waste Glasses, Sebastien N. Kerisit, James J. Neeway, Charmayne E. Lonergan, Benjamin Parruzot, Jarrod V. Crum, Richard C. Daniel, Gary L. Smith, R. Matthew Asmussen

Materials Science and Engineering Faculty Research & Creative Works

Glass corrosion models that capture the complex mechanisms of the glass-water reaction enable the prediction of nuclear waste glass durability in disposal scenarios. Parameterization of such models is challenging because of the need to capture changes in corrosion behavior with time, reaction conditions, and glass composition. Here, we describe and employ the ILAW (immobilized low-activity waste) glass corrosion model (IGCM) in geochemical simulations of static dissolution tests, at two temperatures (40 °C and 90 °C), for a matrix of 24 enhanced low-activity waste (eLAW) glasses statistically designed to cover a processable composition space defined by 8 major glass components (Al …

Ensemble-Learning Model Based Ultimate Moment Prediction Of Reinforced Concrete Members Strengthened By Uhpc, Woubishet Zewdu Taffese, Yanping Zhu, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Data-driven model development brings new approaches to solve conventional civil engineering problems, which are usually considered and answered by experimental, analytical, and numerical methods. This study aims to develop an ensemble learning model (i.e., XGBoost: eXtreme Gradient Boosting) to predict ultimate moment of reinforced concrete (RC) members strengthened by a newly developed concrete technology – ultrahigh performance concrete (UHPC). The study considered two scenarios, incorporating eighteen and seventeen features, with one feature modification involving the transformation of width and height to the cross-sectional area of RC members. Incorporating three substrate damage levels, two substrate surface treatments preceding UHPC strengthening, and …

Decoding Crystallization Behavior Of Aluminoborosilicate Glasses: From Structural Descriptors To Quantitative Structure – Property Relationship (Qspr) Based Predictive Models, Yingcheng Zhang, Marco Bertani, Alfonso Pedone, Randall E. Youngman, Gregory Tricot, Aditya Kumar, Ashutosh Goel

Materials Science and Engineering Faculty Research & Creative Works

Successful decoding of structural descriptors controlling the crystallization in multicomponent functional glasses can pave the way for the transition from the trial-and-error approach and empirical modeling for glass/glass-ceramic composition design toward more rational and scientifically rigorous Quantitative Structure-Property Relationship (QSPR) based models. However, due to the compositional and structural complexity of multicomponent glasses and the longer time and length scales associated with nucleation, the development and validation of QSPR models are still in it's infancy. The work presented in the article is an attempt to leap forward in this pursuit by combining the strengths of experimental and computational materials science …

Seeding Effects Of Submicron Caal-No3 Ldh Particles On The Hydration And Properties Of Portland Cement And Sulfoaluminate Cement Pastes, Sukanta K. Mondal, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

Layered Double Hydroxide (LDH) Is Reported To Improve The Durability Of Concretes, Primarily Due To Its Ability To Exchange Anionic Species, Including Chloride, Which Is Implicated In Corrosion-Driven Durability Issues. However, There Is No Comprehensive Study Investigating The Effect Of LDH On The Properties Of Different Cement Systems At Both Early And Mature Ages. In This Study, The Early Age And Mature Age Properties Of Portland Cement (OPC) And Calcium Sulfoaluminate (CSA) Cement Pastes Seeded With Submicron-Sized Calcium Aluminum-NO3 LDH (CaAl-NO3 LDH) Were Investigated. The Effects Of The 1–5%mass Dosage Of LDH On The Hydration Of Both Cement Systems Were …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti

Electrical and Computer Engineering Faculty Publications

The exponential emergence of Field-Programmable Gate Arrays (FPGAs) has accelerated research on hardware implementation of Deep Neural Networks (DNNs). Among all DNN processors, domain-specific architectures such as Google’s Tensor Processor Unit (TPU) have outperformed conventional GPUs (Graphics Processing Units) and CPUs (Central Processing Units). However, implementing low-power TPUs in reconfigurable hardware remains a challenge in this field. Voltage scaling, a popular approach for energy savings, can be challenging in FPGAs, as it may lead to timing failures if not implemented appropriately. This work presents an ultra-low-power FPGA implementation of a TPU for edge applications. We divide the systolic array of …

Meso-Scale Seabed Quantification With Geoacoustic Inversion, Tim Sonnemann, Jan Dettmer, Charles W. Holland, Stan E. Dosso

Electrical and Computer Engineering Faculty Publications and Presentations

Abstract Knowledge of sub-seabed geoacoustic properties, for example depth dependent sound speed and porosity, is of importance for a variety of applications. Here, we present a semi-automated geoacoustic inversion method for autonomous underwater vehicle data that objectively adapts model inference to seabed structure. Through parallelized trans-dimensional Bayesian inference, we infer seabed properties along a 12 km survey track on the scale of about 10 cm and 50 m in the vertical and horizontal, respectively. The inferred seabed properties include sound speed, attenuation, density, and porosity as a function of depth from acoustic reflection coefficient data. Parameter uncertainties are quantified, and …

Performance Enhancement Of A Solar-Driven Dcmd System Using An Air-Cooled Condenser And Oil: Experimental And Machine Learning Investigations, Pooria Behnam, Abdellah Shafieian, Masoumeh Zargar, Mehdi Khiadani

Research outputs 2022 to 2026

Solar-driven direct contact membrane distillation systems (DCMD) are disadvantaged by low freshwater productivity and low gain-output-ratio (GOR). Consequently, this study aims to achieve two primary objectives: i) improving the solar DCMD performance, and ii) harnessing machine learning models for precise and straightforward modeling of the solar DCMD system. To achieve these goals, a novel solar DCMD system powered with oil-filled heat pipe evacuated tube collectors (HP-ETCs) and equipped with an air-cooled condenser was used for the first time. The system was evaluated under eight different scenarios covering both its energy and economic performances. The performance prediction of three different machine …

On The Feasibility Of Ultrasonic Full Waveform Evaluation With Changing Testing Conditions For The Quality Control Of Manufacturing Parts, Simon Schmid, Thomas Schumacher, Christian U. Grosse

Civil and Environmental Engineering Faculty Publications and Presentations

Fast volumetric non-destructive testing methods are needed, especially for quality control in manufacturing lines. Ultrasonic testing with full waveform evaluation is a promising method for this. However, changes in coupling conditions or environmental factors can significantly alter the ultrasound signal, sometimes more than actual defects. This study investigates the effect of various factors on the ultrasound signal based on a Monte Carlo study with wavefield simulations. The test specimens comprise aluminium plates with holes of varying sizes and positions. Using both experimental as well as simulated data, the performance of two commonly used comparison metrics, namely the R2 score and …

Processworkflows Cwmf To Iso 19650-2, Emma Hayes, Robert Moore

Tools

Process workflow that presents the Capital Works Management Framework (CWMF) according to ISO 19650-2, Government of Ireland.

doi:10.21427/nhzb-pp57

Utilizing Hybrid Machine Learning Techniques And Gridded Precipitation Data For Advanced Discharge Simulation In Under-Monitored River Basins, Reza Morovati, Ozgur Kisi

Civil and Environmental Engineering Faculty Publications

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran's Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in …

Bioaerosol Size As A Potential Determinant Of Airborne E. Coli Viability Under Ultraviolet Germicidal Irradiation And Ozone Disinfection, Weixing Hao, Yue-Wern Huang, Yang Wang

Biological Sciences Faculty Research & Creative Works

Ultraviolet germicidal irradiation (UVGI) and ozone disinfection are crucial methods for mitigating the airborne transmission of pathogenic microorganisms in high-risk settings, particularly with the emergence of respiratory viral pathogens such as SARS-CoV-2 and avian influenza viruses. This study quantitatively investigates the influence of UVGI and ozone on the viability of E. coli in bioaerosols, with a particular focus on how E. coli viability depends on the size of the bioaerosols, a critical factor that determines deposition patterns within the human respiratory system and the evolution of bioaerosols in indoor environments. This study used a controlled small-scale laboratory chamber where E. …

Risk–Reward Share Allocation Under Different Integrated Project Delivery Relational Structures: A Monte-Carlo Simulation And Cooperative Game Theoretic Solutions Approach, Radwa Eissa, Mohamad Abdul Nabi, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Sharing of risks and rewards is considered to be one of the key benefits and principles of integrated project delivery (IPD). Despite its importance, risk–reward strategies are not implemented widely in IPD construction projects due to the lack of a well-defined basis for establishing adequate allocation plans. This paper fills this knowledge gap. This research followed a multistep methodology. First, the authors calculated the risk control valuations of all potential combinations of coalitions for IPD stakeholders. This was performed using interrelated steps of risk identification and quantification, risk assignment based on associated contractual analysis, establishment of a coordination network, and …

First-Principles Calculations Of Lattice Thermal Conductivity In Tl,3Vse4: Uncertainties From Different Approaches Of Force Constants, Zhi Li, Yi Xia, Chris Wolverton

Mechanical and Materials Engineering Faculty Publications and Presentations

Accurate and reliable first-principles simulations of lattice thermal conductivity (κL) of highly anharmonic crystals have long been challenging in condensed matter and materials physics. With recent theoretical advances, the calculation of κL has evolved into a sophisticated process requiring the consideration of higher levels of refinements, such as high-order phonon-phonon scattering, anharmonic phonon renormalization, and heat transport beyond the phonon gas picture. Interatomic force constants (IFCs), however, as a shared pillar of the above concepts, are sometimes ambiguously implemented in this process, resulting in non-negligible uncertainties among different studies. Here, we revisit the ultralow κL of Tl3VSe4 and make a …

Improvement Of Subgrade California Bearing Ratio (Cbr) Using Recycled Concrete Aggregate And Fly Ash, Safkat Tajwar Ahmed, Mozaher Ul Kabir, Chowdhury Zubayer Bin Zahid, Tahsin Tareque, Seyedali Mirmotalebi

Civil Engineering Faculty Publications and Presentations

The study aims to understand the effect of different admixtures on improving the quality of flexible pavement subgrades. In this paper, recycled concrete aggregates (RCA) and Fly Ash were used as the admixtures in improving the maximum dry density (MDD), swelling potential, and California bearing ratio (CBR) of subgrade soil. The percentages of RCA and fly Ash used were 5%, 10%, and 15%. With the upscaling in fly ash dosage, the optimum moisture content and the California bearing ratio increased. However, the MDD of soil decreased for higher fly ash contents. On the contrary, the optimum moisture content (OMC) of …

Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia

Research Collection School Of Computing and Information Systems

The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …

Rethinking Wind In Kentucky, Lawrence E. Holloway, Aron Patrick, Dan M. Ionel

Power and Energy Institute of Kentucky Faculty Publications

Recent analyses and developments suggest that wind energy could play a role in Kentucky's future power generation mix. This recent change in outlook for Kentucky wind has been driven by three factors: (1) improved wind turbine technologies, (2) improved economics, and (3) recent analyses showing improved grid reliability due to wind's complementarity to solar power generation.

Seismicity Characteristics Of The Gulf Of Aqaba Seismogenic Zone And Their Hazard Implications In Northwestern Saudi Arabia, Ali Abdelfattah, Mohamed Ezzelarab, Hazem Badreldin, Hassan Alzahrani, Saleh Qaysi, Bassam Abuamarah, Neil Lennart Anderson

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The seismogenic characteristics of the Gulf of Aqaba zone have been assessed using the maximum likelihood method to estimate various earthquake recurrence parameters. These parameters encompass the β-value, annual recurrence rate (λ), and maximum probable magnitude (Mmax). This assessment has identified three sub-seismogenic zones, each corresponding to specific structural faults within the Gulf. These zones are associated with the Aragonese, Arnona and Aqaba faults, delineating pull-apart basin structures in the Gulf of Aqaba. An updated earthquake catalogue has been compiled using a unified moment magnitude (Mw) scale to improve the analysis, established by developing two empirical relationships. According to the …

Comprehensive Understanding Of Factors Impacting Competitive Construction Bidding, Muaz O. Ahmed, Islam H. El-Adaway, Aubrie Caldwell

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction competitive bidding has been studied by many researchers; however, their focus was mainly on certain bidding aspects. Thus, despite their value, existing bidding models have limited applicability for various reasons including the non-inclusion of some real-life factors that impact bidding-related decisions. As such, there is a need for identifying a comprehensive list of bidding factors that impact construction bidding-related decisions, studying their associations, and identifying understudied factors to direct future research efforts. This paper fills this knowledge gap. First, the authors conducted a systematic selection of 124 construction bidding-related articles published within the past 40 years. Second, the authors …

Reducing Bias In Cyberbullying Detection With Advanced Llms And Transformer Models, Dahana Moz Ruiz, Annaliese Watson, Anjana Manikandan, Zachary Gordon

Center for Cybersecurity

This paper delved into a comprehensive exploration of the inherent biases present in Large Language Models (LLMs) and various Transformer models, with a focus on their role in identifying and addressing instances of cyberbullying. The objective was to refine and enhance the accuracy and fairness of these models by mitigating the biases deeply ingrained in their structures. This was crucial because language models could inadvertently perpetuate and amplify existing biases present in the data they were trained on.

Inertial Particle Clustering Due To Turbulence In An Air Jet, Bianca Viggiano, Kris Gish, Stephen Solovitz, Raul Bayoan Cal

Mechanical and Materials Engineering Faculty Publications and Presentations

Explosive volcanic eruptions create turbulent plumes of fine ash particles. When these particles collide in the presence of moisture and electrostatic fields they combine into larger aggregates, which can significantly change the atmospheric residence time of the airborne cloud. Previous studies have suggested that turbulence may lead to preferential concentration—also known as clustering—of particles within the flow, increasing the likelihood of collisions and aggregation. Few experimental studies have quantified these processes for volcanic plumes. This behavior was investigated using a particle-laden air jet. By systematically varying the exit speed and the size, density, and concentration of particles, flows were produced …

Small Modular Reactor And Advanced Reactor Feasibility Study Interim Report, Purdue University Administrative Operations, Purdue University School Of Nuclear Engineering, Duke Energy

Administrative Operations Reports

No abstract provided.

A Guide To Fifty Years Of Research At Montana Tech: Part 3-Decontamination Of Ratioactively Contaminated Steel By Melt Refining/Slagging Processing, Larry G. Twidwell, Samuel A. Worcester

Metallurgy

This presentation includes a discussion of the research conducted at Montana Tech in the Department of Metallurgical and Materials Engineering. The discussion is focused on Decontamination of Radioactively Contaminated Steel by Melt Refining/Slagging. This presentation is based on the research of Master of Science graduate students, industrial and academic colleagues, at the Montana College of Mineral Science and Technology (which morphed to Montana Tech [1977], then to Montana Tech of The University of Montana [2000], then to Montana Technological University [2019]). The referenced work of each of the graduate students in this presentation is gratefully acknowledged. The following summary presentation …

Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy

Computer Science Faculty Research & Creative Works

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …

Recent Progress And Challenges Of Implantable Biodegradable Biosensors, Fahmida Alam, Md Ashfaq Ahmed, Ahmed Hasnain Jalal, Ishrak Siddiquee, Rabeya Zinnat Adury, G M Mehedi Hossain, Nezih Pala

Electrical and Computer Engineering Faculty Publications and Presentations

Implantable biosensors have evolved to the cutting-edge technology of personalized health care and provide promise for future directions in precision medicine. This is the reason why these devices stand to revolutionize our approach to health and disease management and offer insights into our bodily functions in ways that have never been possible before. This review article tries to delve into the important developments, new materials, and multifarious applications of these biosensors, along with a frank discussion on the challenges that the devices will face in their clinical deployment. In addition, techniques that have been employed for the improvement of the …

Effect Of Fabrication Parameters On The Ferroelectricity Of Hafnium Zirconium Oxide Films: A Statistical Study, Guillermo A. Salcedo, Ahmad E. Islam, Elizabeth Reichley, Michael Dietz, Christine M. Schubert Kabban, Kevin D. Leedy, Tyson C. Back, Weison Wang, Andrew Green, Timothy S. Wolfe, James M. Sattler

Faculty Publications

Ferroelectricity in hafnium zirconium oxide (Hf_1−xZr_xO₂) and the factors that impact it have been a popular research topic since its discovery in 2011. Although the general trends are known, the interactions between fabrication parameters and their effect on the ferroelectricity of Hf_1−xZr_xO₂ require further investigation. In this paper, we present a statistical study and a model that relates Zr concentration (x), film thickness (t_f), and annealing temperature (T_a) with the remanent polarization (P_r) in tungsten (W)-capped Hf_1−xZr_xO₂. …

Underwater Double Vortex Generation Using 3d Printed Acoustic Lens And Field Multiplexing, Chadi Ellouzi, Ali Zabihi, Farhood Aghdasi, Aidan Kayes, Milton Rivera, Jiaxin Zhong, Amir Miri, Chen Shen

Henry M. Rowan College of Engineering Faculty Scholarship

The generation of acoustic vortex beams has attracted an increasing amount of research attention in recent years, offering a range of functions, including acoustic communication, particle manipulation, and biomedical ultrasound. However, incorporating more vortices and broadening the capacity of these beams and associated devices in three dimensions pose challenges. Traditional methods often necessitate complex transducer arrays and are constrained by conditions such as system complexity and the medium in which they operate. In this paper, a 3D printed acoustic lens capable of generating a double vortex pattern with an optional focusing profile in water was demonstrated. The performance of the …

Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …

Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …