Colin Hisey, Biomedical Engineering, The Ohio State University

Microfluidic Devices for Cancer Diagnostics and Characterization

Microfluidic devices provide a new avenue to interrogate cancer cells and better diagnose and characterize the disease’s progression on an individual patient level. The development of two devices will be presented, one which seeds individual cancer cells onto biomimetic migratory substrates and another which isolates cancer exosomes from serum.

Tamara Lozano, Chemical Engineering, Villanova University

Predictive Catalyst Design by Scaling Relations and Volcano Plots on Alloy Nanoparticle Decorated Graphene

In this work, we perform DFT Calculations to study the effect that different dopants have on the electronic structure of graphene (2D hexagonal carbon sheets) and predict the catalytic activity on different reaction pathways when using graphene as a catalytic support for binary alloy nanoparticles in the oxygen reduction reaction.

Baiping Ren, Chemical and Biomolecular Engineering, The University of Akron

Molecular Design Principle for Generic Amyloid Inhibitors

Given the continuing rise of human life expectancy and the lack of preventive and curative therapeutic approaches to combat Alzheimer disease (AD) and other amyloid diseases, here we combined computational screening, data mining and experimental validations to develop a series of amyloid inhibitors against AD and type 2 diabetes.

Jessica Schiltz, Aerospace and Mechanical Engineering, University of Notre Dame

Additive Manufacturing of Materials for Wear-Resistance Applications

Additive manufacturing presents significant opportunities to process ceramic feedstock, but AM material tribology remains underdeveloped due to challenges associated with producing fully-dense parts. Given that wear performance is critical in the longevity of articulating surfaces, this work evaluates oxide and nitride ceramics processed with various AM technologies.

Sarah Watzman, Mechanical Engineering, The Ohio State University

Berry Curvature-Induced Huge Anomalous Nernst Effect in the Absence of Magnetic Field in the Time- Reversal Symmetry-Breaking Weyl Semimetal YbMnBi2. YbMnBi2

A Weyl semimetal, yields huge conversion between an applied temperature gradient and a perpendicularly measured voltage. This is driven by Berry curvature of the electronic band structure, obviating the conventional need for an externally applied magnetic field. This transverse energy conversion coefficient vastly exceeds that of commercial thermoelectric materials.

Johnson Fadeyi, Industrial and Systems Engineering, Wayne State University

A Framework for Product Modularity Decisions Support for Product Service System Remanufacturing Synergy

Currently, about 80 percent of manufactured products end as waste. This research develops an optimization model that identifies product configurations that enhance product service system remanufacturing business offering. Comparative advantages of the viable alternatives are also determined. The study provides a modular product architecture decision guide that mitigates materials extraction and product disposals.

Lin Lu, Industrial and Systems Engineering, Auburn University

Advancing Safety Surveillance among Manufacturing Workers

Fatigue is a known precursor to negative health outcomes and has significant short/long-term implications. In this talk, we present the results of our recent survey, data-driven fatigue prediction model and knowledge base to illustrate the prevalence, symptoms, main drivers, detection and intervention of worker fatigue in manufacturing.

Hans Ottosson, Mechanical Engineering, Brigham Young University

Handling the Complexity and Uncertainty when Designing for the Resource Poor

Working in global development isn’t for the faint-hearted. It is a complex area filled with uncertainties. How do we know if our products have an impact? We are therefore creating a product development framework for social good with a heavy emphasis on assessing social impact of products and handling uncertainty.

Zahra Sedighi-Maman, Industrial and System Engineering, Auburn University

A Data Driven Framework to Identify the Critical Variables and to Predict the Fatigue in Manufacturing Workers Using Wearable Sensors

Fatigue is an important safety concern in manufacturing. This has motivated researchers to investigate data mining methods to develop a model to detect the fatigue before happening. The goal is to gain hidden and useful information from monitoring the workers by employing data mining techniques, which help to prevent injury occurrence.

Cara Albright, Water Resources Engineering, Villanova University

Urban, Strategic and Resilient: A Hydrologic Analysis of Dynamic Rain Garden Performance in Philadelphia, Pennsylvania

Analysis of continuous monitoring data from urban green infrastructure (GI) systems focused on water cycle component interactions that are used to establish dynamic GI nature. Performance across temporal scales shows that GI routinely capture small storms and large events. Urban GI systems are inherently flexible, contributing to flood management and resilience.

Elyse Stachler, Civil and Environmental Engineering, University of Pittsburgh

Development of crAssphage-Based PCR Methods for Source-Tracking of Human Fecal Pollution in Environmental Waters

Communities rely on environmental water monitoring of fecal pollution to protect the public during recreational activities as well as protecting waters used for drinking sources or food production. Novel viral-based assays were developed as human-specific markers that are easily detected in polluted environmental waters.

Saad Qureshi, Mechanical and Aerospace Engineering, University of Dayton

Saving Lives through Aerial Fire Suppression: From Aerodynamic breakup to a Ground Coverage Predictability Model

The University of Dayton is deriving a model for the United States Department of Agriculture – Forest Service, USDA-FS, to better enable a swift and accurate aerial retardant drop. Precise modeling is critical to controlling wildfires, which ultimately result in protecting lives and property.

Sarah Krug, Electro-Optics and Photonics, University of Dayton

Adaptive Computational Phase Correction of a Partially Coherent Multi-Aperture System

Multi-aperture systems typically require complex hardware to phase the apertures. Increasing relative aperture spacing in the exit pupil allows for computational phasing. Results shown demonstrate increased resolution using this approach to phase multiple apertures.

David Lombardo, Electro-Optics and Photonics, University of Dayton

An Integrated Photonics Biological Sensor Platform for Clinic-On-A-Chip Applications

We are developing a photonic chip on a silicon platform that can sense the presence of molecules using an extremely sensitive optical e_ect known as evanescent_eld sensing. This will allow for robust and inexpensive chemical and biological sensors that can one day be integrated into every day electronics.

Ali Mohamed, Electrical Engineering, University of Dayton

Investigation of Signal and Image Transmission through MVKS Turbulence with and without Chaos

In our research, information is embedded inside an electromagnetic (EM) carrier and imaged via propagation through anisoplanatic phase turbulence using diffraction, MVKS and the Hufnagel-Valley altitude model. The (unmodulated) EM carrier is imaged via standard carrier modulation and also a transparency/lens combination. Furthermore, mitigation of image/signal distortion is explored using an RF chaotic carrier.

Leigh Allin, Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University

Slip Recovery Training Improves Balance Recovery Ability Following Laboratory-Induced Slips

This study evaluated the efficacy of two practical, cost-effective methods for slip recovery training in improving balance recovery after slipping. Both training methods resulted in a higher number of recoveries following unexpected laboratory-induced slips while walking and exhibited improved proactive and reactive control of slipping compared to a control group.

Rachel Baker, Mechanical Engineering, The Ohio State University

Patient Outcomes of Total Knee Replacements: Investigating the Effects of Muscle Forces, Implant Type, and Surgical Technique

Up to 27 percent of patients have functional deficits after total knee replacement (TKR). Determining how muscle forces, implant design and surgical technique together affect functional TKR outcomes provide surgeons and physical therapists with actionable targets for TKR treatment with the end-goal of better quality of life for TKR patients.

Jordan Craig, Bioengineering, University of Kansas

Quantifying Gait Stability Based on Body Segment Coordination Relationships Measured with Wireless Sensors

Persons with multiple sclerosis have a high risk for falls, possibly due to altered coordination between trunk and foot movement variability, which we have shown to be different compared to healthy adults during normal walking. The goal of this project is to determine how relationships between segments relate to stability.

Omid Heidari, Mechanical Engineering, Idaho State University

Upper Limb Kinematic Characterization for Augmented Reality Rehabilitation

This research is part of a project aimed to develop and test a novel augmented reality wearable system for the training of the human arm of post-stroke patients. Fast, accurate and customized modeling and embedding of the arm kinematics is essential for successful perception and training.

Anne Koelewijn, Mechanical Engineering, Cleveland State University

Predictive Simulations of Human Gait and Their Application in Prosthesis Design

Walking can be theoretically predicted by finding a human movement trajectory that requires minimal energy. Currently, predictive simulations cannot predict important features of normal gait. I aim to improve methods to obtain more realistic predictions and to apply predictive simulations to help understand transtibial amputee gait and to improve prostheses.