Engineering Commons^™

Optimal Network Reconfiguration Based On Discrete Metaheuristic Techniques For Reduction Of Power Loss And Carbon Emission In Distribution Networks, Asad Ali, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hussain Shareef, Hasmaini Mohamad, Munir Azam Muhammad

Turkish Journal of Electrical Engineering and Computer Sciences

Power distribution systems play a crucial role in transmitting electrical power from generation sources to end users. During transmission, significant power losses occur in the form of heat as the current flowing along the lines/cables has resistance. To minimize power losses, distribution network reconfiguration (DNR) has been widely adopted. This paper proposes optimal DNR based on metaheuristic techniques with discrete mutation feature targeting active power loss reduction, which subsequently lowers carbon emissions and operational costs. Through the discrete mutation feature, computational time to find optimal solution has been reduced significantly with fewer iterations compared to conventional mutation techniques. The proposed …

Impact Of Glass Powder As A Sustainable Material On The Workability, Strength, And Durability Properties In High-Performance Basalt Fiber Reinforced Concrete: A Statistical Analysis, Ahmed Fathi Mohamed Salih

Journal of Sustainable Construction Materials and Technologies

The global construction sector is increasingly challenged to balance environmental sustainability with the growing demand for durable, high-performance materials. As cement production continues to be a major source of anthropogenic CO2 emissions, the incorporation of alternative, low-impact binders has become a key strategy for reducing the environmental footprint of concrete. In this context, glass powder (GP)—a finely ground industrial by-product rich in amorphous silica—has emerged as a promising partial replacement for Portland cement. Its pozzolanic reactivity contributes to improved hydration and matrix densification, while its use also supports circular economy principles by diverting waste glass from landfills. At the same …

Developing And Delphi-Validating A Phase-Based Sustainable Bsc Framework For Contractors' Sustainable Productivity Management In Developing Countries, Truong Van Luu, Le Minh Long Nguyen

HBRC Journal

This study develops and validates, using the Delphi method, a conceptual decision-support framework for construction contractors' sustainable productivity management (CSPM) in developing-country settings. Guided by KAMET rules, semi-structured interviews were conducted with 15 experts to elicit, refine, and reach consensus on a set of productivity management attributes that construction contractors can realistically apply. The outcome is 29 feasible productivity management attributes, organized under the four Sustainable Balanced Scorecard (SBSC) perspectives and mapped onto five management phases (planning, organizing, staffing, leading/coordination, and controlling). Rather than predicting or demonstrating productivity gains, the framework provides a practical roadmap for diagnosing capability gaps, prioritizing …

Heuristic Particle Swarm Optimization Method For The Stability Analysis Of Geosynthetics-Reinforced Slopes, Hoda Mostafa, Ibrahim Mashhour

HBRC Journal

Slope stability is considered one of the crucial topics in geotechnical engineering. Accurate soil slopes stability analyses are essential as slope failures could cause catastrophic environmental and human disasters. Geosynthetics are widely used as stabilizing elements in slope stability analyses. Incorporating geosynthetic reinforcement generally improves the global factor of safety against slope failure. However, in practice, analyses must not only focus on the global slope stability but should also account for any sliding along geosynthetic interfaces, since geosynthetics are considered as week layers within the reinforced soil mass and act as potential slip interfaces that should be checked for stability. …

Symmetry And Scale: The Precise Engineering Of Chitosan-Based Polyhedral For The Delivery Of Therapeutic Materials At The Nanoscale, Ahmed. J. Jasim, Noor Malik Saadoon

AUIQ Technical Engineering Science

Preparation of chitosan-based drug delivery carriers requires converting linear polysaccharide chains into individual 3D objects. By building on the natural structural properties of chitin-based polymers, in addition to control the geometry of organically derived raw material from feedstock to submicron polyhedral or spherical dimensions. This geometric change is governed by a surface area-to-volume ratio that maximizes the exposure of the therapeutic payload to the external environment. The approach centres on the development of such carriers using ion-complexation and emulsification methodologies, which involve electrostatic cross-linking of ion-binding pairs to define an impenetrable boundary separating a liquid from a solid phase. Critical …

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Etv2 Mediated Differentiation Of Human Pluripotent Stem Cells Results In Functional Endothelial Cells For Engineering Advanced Vascularized Microphysiological Models, Shun Zhang, Zhengpeng Wan, Lei Wang, Caihong Wu, Junkai Zhang, Sarah Spitz, Xun Wang, Marie A. Floryan, Mark F. Coughlin, Francesca M. Pramotton, Liling Xu, Ron Weiss, Roger D. Kamm

Michigan Tech Publications

Patient-specific microphysiological models have become a valuable tool for broad applications, revolutionizing biomedical research. However, limitations persist, with functional vasculature being a significant challenge. With the discovery of ETV2's determinant role in specifying EC lineages during differentiation, researchers have adopted techniques involving ETV2 overexpression to produce h-iECs more efficiently and consistently. Here, we generated multiple h-iPSC lines with inducible ETV2 expression, and subsequently differentiated them into h-iECs, which were validated functionally and by key endothelial markers and RNA-seq analysis. These cells are capable of reproducibly self-organizing into stable microvascular networks (MVNs) in a microfluidic chip, forming lumenized and functional vessels …

Electrochemical Deactivation Of High-Strength, Catechol-Based Adhesives Incorporated With Anhydrous Proton And Electron Conducting Elements, Han Peng, Zhongtian Zhang, Vedika Khare, Abhilash Arjan Das, Fatemeh Razaviamri, Kan Wang, Bruce P Lee

Michigan Tech Publications

Catechol offers switchable adhesion in response to electrochemical redox reaction. However, electrochemistry requires water for effective proton transport, but water weakens adhesive performance. Here, we incorporate proton and electron conducting elements (sulfonic acid-containing monomer and multiwalled carbon nanotube, respectively) into a water-free catechol-based adhesive to create a high-strength adhesive that is also susceptible to electrochemical control. These additions increase the proton and electrical conductivity by over 100-fold. The adhesive also exhibits elevated lap shear adhesion strength (4.6 MPa) to metal substrates and outperforms a commercial epoxy glue. Under mild electrical stimulation (9 V), the adhesive strength decreases by over 90%. …

An Exploration Of Aviation Safety And Security Culture In Nigeria And The United Arab Emirates (Uae), Nicholas Degarmo, Kathrine Lopez, Hana Marz, Cedric Leon, Michael Chrisman

Student Research Symposium (SRS)

Aviation safety and security standards are fundamental for enabling the reliability and sustainability of aviation operations. Aviation safety and security are influenced by organizational and institutional standards, as well as the application of human factors principles. This paper aims to explore the regional variances of aviation safety and security culture within Nigeria and the United Arab Emirates (UAE). Both regions have taken great steps to align with international aviation standards, offering insight into how different regions shape their regulatory environments, organizational culture, and how cultural factors affect safety and security practices. While Nigeria continues to face challenges such as inconsistent …

Interfacial Chemistry Involved In Selective Separation Of Nmc/Lmo And Lco/Lmo Binary Cathode Materials By Froth Flotation Using Oleic Acid, Richard Kofi Oboh, Kaiwu Huang, Seoung Bum Son, Lei Pan

Michigan Tech Publications

The variability in cathode compositions within recycled lithium-ion battery (LIB) feedstocks poses a significant challenge to efficient downstream refining processes. This study demonstrates the feasibility of using froth flotation with oleic acid as a collector to selectively separate lithium nickel-manganese-cobalt oxide (NMC) and lithium cobalt oxide (LCO) from lithium manganese oxide (LMO) materials. Laboratory-scale flotation tests achieved an 80% separation efficiency in a single stage, producing a froth product with >90% purity of NMC/LCO at approximately 90% yield. Concurrently, the LMO materials were enriched in the sink product with ∼90% purity and ∼90% yield. This approach was further validated using …

Comparison Of The Impact Of Probabilistic Versus Deterministic Ground Motions On Structural Responses Of A Steel Moment Building In Salt Lake City, Utah, O. Murphy, M. Zaker Esteghamati, B. R. Cox

Civil and Environmental Engineering Student Research

Earthquakes on the Wasatch Fault pose a significant hazard to Utah’s people and built environment. Code-based seismic design of civil infrastructure in Utah is governed by ground motions determined from regional probabilistic seismic hazard analysis (PSHA). However, with a large earthquake overdue along several segments of the Wasatch Fault, there is growing concern among Utah engineers that PSHA-based ground motions are significantly lower than those from a deterministic scenario. This study compares the structural responses of a four-story steel moment frame in Salt Lake City, Utah, under two ground-motion sets derived from risk-targeted probabilistic and deterministic seismic-hazard analyses. The preliminary …

An Ambient Acoustic Ice-Fracturing Dataset Taken In Shallow Freshwater, John Case, Andrew Barnard, Daniel Brown

Michigan Tech Publications

This paper describes an acoustic dataset collected on a frozen shallow freshwater lake between February and March of 2024. This collection took place over one full week on Portage Lake in the Upper Peninsula of Michigan, USA. The first sub-dataset consists of ambient ice and environmental noises collected by an array of hydrophones, microphones and geophones placed below, above and on the ice respectively. The second sub-dataset consists of instrumented force hammer impacts at a series of locations on the the ice with the corresponding response at each acoustic sensor. All acoustic data were recorded at a sample rate f …

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

Predictive Analysis Of Greenhouse Gas Emissions From Electric Vehicle Charging In The United States, Mahyar Amirgholy, Faysal A. Chowdhoury, Chenyu Wang, S Nikhila Kanigiri

Faculty Articles

Electric vehicles (EVs) emit substantially fewer air pollutants than conventional internal combustion engine vehicles. However, the continuous increase in electricity demand from the power grid for EV charging, resulting from the growing adoption and total vehicle miles traveled, leads to higher greenhouse gas emissions from the power sector. This study presents a predictive analysis of energy sector greenhouse gas emissions from EV charging at the regional level across the United States under various projection scenarios of technology costs, fuel prices, demand growth, and electricity sector policies. The predictive modeling of greenhouse gas emissions from EV charging is performed using a …

A Transcendental Phenomenological Study Of Cognitive Overload In Manufacturing Engineers During Online Training In An Industrial Environment, Sarah J. May

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to explore experiences of cognitive overload among engineers in manufacturing during digital training in industrial environments. Engineers in manufacturing manage substantial physical and cognitive demands in order to maintain safe and efficient industrial environments. In addition to these responsibilities, engineers are often expected to engage in digital training within active work settings, frequently without physical or cognitive separation from job-related tasks. A qualitative research design was employed, with data collected from 10 participants and analyzed through triangulation and thematic coding. Data collection methods included a qualitative questionnaire, individual interviews, and a letter-writing …

Comparative Assessment Of Uav-Based Tseb And Field-Calibrated Aquacrop For Evapotranspiration On The Arid Coast Of Peru, Roxana Peña-Amaro, José Huanuqueño-Murillo, Lia Ramos-Fernández, Abel Ramos-Ayala, David Quispe-Tito, Lena Cruz-Villacorta, Elizabeth Heros-Aguilar, Edwin Pino-Vargas, Alfonso Torres-Rua

Civil and Environmental Engineering Faculty Publications

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ET_lys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature …

Predicting Tart Cherry Stem Water Potential Using Uav Multispectral Imagery And Environmental Data Via Symbolic Regression, Anderson L. S. Safre, Alfonso Torres-Rua, Kurt Wedegaertner, Brent Black, Brennan Bean, Burdette Barker, Matt Yost

Civil and Environmental Engineering Faculty Publications

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Redesigning Online Graduate Orientation To Foster Academic Resilience And Prevent Underperformance, Stella Michael-Makri, David E. Rodriguez

Journal of Academic Underperformance

Graduate students in fully online programs often begin their academic journey without adequate preparation for the emotional, structural, and cultural challenges of graduate-level work. For students who are first-generation, racially marginalized, international, or returning to education after time away, this lack of scaffolding can lead to early disengagement, underperformance, or attrition. Orientation, often treated as a checklist of logistical tasks, represents a missed opportunity for meaningful academic intervention. This manuscript proposes a five-module conceptual model for online graduate orientation designed to proactively support online graduate students in the domains of emotional regulation and academic identity, time management and executive functioning, …

Developing Low-Cost Gnss Remote Sensing Hardware To Measure Precipitable Water Vapor For Flash Flood Nowcasting In Southern Appalachia, Austin Gleydura

Student Research Symposium (SRS)

Flash flood prediction in Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at extended range and signal blockage by mountains, while satellite instruments like MODIS lack sufficient spatio-temporal resolution for sub-kilometer measurements critical to flash flood nowcasting. GNSS-Meteorology offers an established alternative for measuring precipitable water vapor (PWV) and is currently integrated into several numerical weather models. Recent research demonstrates that GNSS-derived PWV products can be used to accurately predict rainfall intensity and timing …

Mitigating Uas Airspace Risks Through Policy Innovation, Christopher Daniel Sidor

Student Research Symposium (SRS)

Uncrewed Aircraft Systems (UAS), commonly known as drones, have become an everyday part of our lives. Once a technology reserved for the defense industry, UAS are now widely available and affordable in the commercial market. These systems have been used for intelligence, surveillance, and reconnaissance (ISR) missions, route mapping, and kinetic deployment of munitions. In modern warfare, drones have been at the forefront, leveraging new tactics, techniques, and procedures to enhance lethality and destruction. The integration of fiber-optic (FO) connected drones, first-person view (FPV) technology, and 3D printed munition-dropping devices in particular demonstrates a dire need for legislative intervention. These …

The Wildlife Intelligence For Aviation Safety (Wild-Ai), Bao Khoa Tran

Student Research Symposium (SRS)

Wildlife strikes pose a significant safety risk and economic burden to the aviation industry. Over 291,000 strikes have been reported in the U.S. since 1990, causing an estimated $248 million in annual losses. The Wild-AI (Wildlife Intelligence for Aviation Safety) project leverages cutting-edge technologies, including large language models (LLMs), machine learning (ML), explainable AI (XAI), and Unmanned Air Vehicles (UAV) imagery, to enhance wildlife hazard management at airports. Wild-AI transforms traditional Wildlife Hazard Assessments (WHAs) by integrating advanced data analytics and UAV monitoring. By utilizing historical wildlife-strike data and ML algorithms, we will identify key predictors of damaging strikes, such …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Sun, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO₄)₂ and Zn(OTf)₂, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Wu, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO4)2 and Zn(OTf)2, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The presence of carboxyl groups in …

Translating Graduate Level Engineering Methods For K12 Students, Ahmad Bshennaty, Joey Marano, Hoda Hatoum

Michigan Tech Publications

Purpose

Advanced engineering tools such as particle image velocimetry (PIV) and computational fluid dynamics (CFD) are rarely introduced in K12 education despite their relevance in modeling engineering systems. This study evaluates whether K12 students can meaningfully engage with these traditional graduate-level methods when applied to cardiovascular flows and delivered through structured modules.

Methods

A 5-day full-time summer class was designed and enrolled 13 secondary students (grades 9–11) to introduce them to 3D printing, coding, PIV, medical image segmentation, and CFD modeling. Students participated in both experimental and computational activities and completed two evaluation forms assessing learning outcomes, instructional methods, challenges, …

Reinforcement Learning For Imbalanced Data In Robotic Anomaly Detection Within Autonomous Manufacturing, Salma Messaoudi, Ahmed Bendaouia, El Hassan Abdelwahed, Mohammed Ameksa, Hajar Mousannif, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Ensuring reliable anomaly detection in industrial robots is critical for safe and autonomous manufacturing operations. However, it remains challenging due to temporal dependencies and class imbalance in sensor data. This study presents a reinforcement learning approach using Deep Q-Network (DQN) enhanced with Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) for robust anomaly detection in robotic systems. The proposed framework integrates an LSTM into the DQN policy to capture temporal patterns. It also introduces a novel GBM-based reward mechanism that mitigates class imbalance by applying SMOTE (Synthetic Minority Over-sampling Technique) after removing temporal dependencies. Experimental results demonstrate that this …

Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain

Faculty Publications

Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …

Scheduling As An Organizational Capability, Felix Delmonte

Mechanical and Civil Engineering Faculty Publications

This study synthesizes evidence on schedule governance as an organizational capability integrating Lean practices, BIM, Digital Twin, and AI to convert scheduling into continuous, data driven decision making that links front line observations to executive risk appetite and funding choices, showing how 4D/5D BIM, clash detection, automated material takeoff, and IoT enabled twins shorten planning horizons, reduce design errors by up to 80%, improve timeline adherence by 20%–30%, and, when AI is applied, further cut delays by about 18% and procurement costs by approximately $2.5 million; a four layer Integrated Lean–Digital Framework is proposed to align strategic intent, collaborative digital …