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University Staff Senate meeting, June 17, 2025
No full textAgenda: (Call to Order) -- (Discussion and New Business) -- (Old Business) -- (Senate Committee Updates) -- (Campus/University Business Updates & Discussions) -- (Adjourn/Upcoming Meetings and Events/Shoutouts)
Minutes: (Call to Order): Approval of minutes -- (Discussion and New Business): Staff Senate Internal Awards and Recognition -- New Senator Orientation & Senate Retreat – July 15, 2025 -- HR New Employee Orientation -- (Old Business) -- Executive Team Elections -- (Senate Committee Updates): Awards and recognition -- Communications and website -- Elections -- Policy review -- Professional development and service -- Scholarships -- Shocker STRIVE -- Admins Coming Together -- Review moving August meeting -- Designate a specific time for the retreat that will be our formal business meeting in order to accommodate Kansas Open Meetings requirements -- (Campus/University Business Updates & Discussions): Academic forum -- Budget Advisory Committee -- Human Resources (Joint with Faculty Senate) -- Legislative update + KBOR briefing -- Parking appeals + traffic appeals -- President’s meetings -- RSC Board of Directors -- UPS/USS Presidents Council (KBOR) -- Library Committee -- Question regarding the compensation communication from the President -- WuHire Training -- (Adjourn/Upcoming Meetings and Events/Shoutouts): June Senate Meeting: Tuesday, July 15, 2025 -- Check the Events Calendar for upcoming events on campus
Attachments contain Senate meeting minutes with Vietnamese translation
Wichita State Research and Innovation News, June 2025
No full textWichita State's NIAR receives patent for composite part inspection robot -- Wichita Biomedical Campus marks one year since its groundbreaking -- Wichita State, Dassault Systèmes launch innovation center to advance virtual design, automation and additive manufacturing -- Wichita State, Connected Nations, Newby Ventures break ground on new Internet Exchange Point at WSU -- From law enforcement to mentorship: WSU professor is shaping research and student success -- New data literacy curriculum, developed as part of WSU professor's NSF grant, empowers high schoolers to think like scientists -- NIAR fiber patch placement research earns SAMPE technical paper award -- Barton School's Spero program secures funding for year 2 expansion -- Shocker alum creates business pioneering recycling composites, manufacturing materials -- WSU in the news -- Research at Wichita State -- Innovation at Wichita Stat
Computational methods for evaluating and improving the resilience of electric vehicle charging infrastructure
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing EngineeringThe use of electric vehicles (EVs) in the United States is growing at a rapid pace, with some projections estimating that the majority of cars will be electric by the year 2050. This transition aims to mitigate greenhouse gas emissions and provide a more sustainable means of transportation. However, as EV adoption rates rise, it becomes more challenging to support EV drivers with sufficient charging infrastructure that can be counted on even during extreme scenarios, such as natural disasters or road shutdowns. The inacessibility of adequate charging stations during these extreme scenarios can have catastrophic fallout. Therefore, building resilience into EV infrastructure is essential not only for daily operations but also for disaster preparedness and recovery. This requires the development of methodologies to forecast charging demand, identify weakness in current charging infrastructure, and optimize capacity expansion decisions.
This thesis presents proof-of-concept computational tools which can be used for modeling, analyzing, and optimizing the resilience of EV charging infrastructure under both normal and disrupted conditions. Specifically, the study first quantifies the impact of natural disasters on traffic flow utilizing a long-short-term-memory neural network to analyze time series traffic flow data and build a counterfactual prediction for traffic flow at the time of a real-world natural disaster. The same traffic data is used to estimate trip volumes on the road network by building a least squares model to generate a realistic route schedule. The trip generation model provides input to an agent-based simulation developed to evaluate the performance of the existing EV charging network under disruption conditions, including charging station outage, road closure, and cold weather, while capturing the impact on EV users. Key metrics are collected from the simulation and provide a foundation for an objective function to a bi-level optimization model, which identifies optimal placement of additional charging stalls. By integrating data-driven disruption analysis, behavioral simulation, and network optimization, this research offers a computational framework to support resilient EV infrastructure planning
QRS 3D voltage-time integral in narrow QRS complex – Establishing the normal reference range
No full textThis is an open access article under the CC BY license.Background: Vectorcardiographic 3D QRS voltage-time integral (VTIQRS-3D) is a novel marker of ventricular dyssynchrony pertinent for cardiac resynchronization therapy. It may have additional clinical utility but its normal reference ranges have not been established. We sought to define reference ranges for VTIQRS-3D in healthy individuals. Methods: We retrospectively analyzed 12‑lead ECGs of healthy adults (2010–2014) and compared them to patients with cardiomyopathy with reduced ejection fraction (EF) <50 %. Using the Kors matrix, 12‑lead ECGs with QRS duration ≤120 ms were converted to vectorcardiographic X, Y, and Z leads. VTIQRS-3D was calculated as the instantaneous root-mean-square (3D) voltage integrated over the QRS duration. Reference range limits were defined as the 2.5th to 97.5th percentiles respectively for healthy females and males in age groups 18–34, 35–54 and ≥ 55 years. Results: The study included 468 healthy adults (age 44.6 ± 17.0 years; 63.9 % female) and 314 patients with cardiomyopathy (age 62.1 ± 14.0 years; 34.4 % female). VTIQRS-3D was significantly larger in the cardiomyopathy patients compared to the healthy population (48.2 ± 21.4 vs. 38.1 ± 9.3 μVs, p < 0.0001). Increased age and female sex were significant predictors of lower VTIQRS-3D in the healthy population (both p < 0.0001). VTIQRS-3D reference ranges for respective age groups for healthy females were 23.2–55.0, 23.9–56.4 and 19.6–50.9 μVs, and for healthy males were 29.9–57.2, 28.2–56.7 and 21.4–55.9 μVs. Conclusion: VTIQRS-3D is higher in younger individuals and males within healthy adult population but is overall higher in patients with cardiomyopathy with reduced EF. Age and sex need to be accounted for using VTIQRS-3D as a marker for cardiac disease. © 2025National Center for Advancing Translational Sciences, NCATS; University of Kansas Medical Center, KUMC; National Institutes of Health, NIH; University of Kansas, KU, (UL1TR002366); University of Kansas, KUProject support: This work was supported by the Department of Cardiovascular Medicine at The University of Kansas Medical Center, and Clinical and Translational Science Award (CTSA) from National Center for Advancing Translational Sciences (NCATS) awarded to The University of Kansas for Frontiers: University of Kansas Clinical and Translational Science Institute (UL1TR002366). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCATS or National Institutes of Health (NIH)
Faculty Senate meeting, January 13, 2025
No full textAgenda: (Approval of Minutes): November 25, 2024 -- (President's Report) / Mathew Muether -- (Old Business): 5.17 Inclement Weather Policy update -- (New Business): Welcome / Senior Executive Vice President and Provost Monica Lounsbery, Blueprint for Literacy update – Dr. Kim Wilson and Dean Jennifer Friend, Update of KBOR Faculty Award Eligibility to include non-tenure track (NTT)/ Mathew Muether -- (As May Arise): Reminder that Academic Resources Conference is this week / John Jone
What are they looking at: directors’ facial appearances and shareholder voting outcomes
No full textIn this study, we investigate whether directors’ facial appearances are associated with shareholders’ voting behavior. Drawing on research in neuroscience and cognitive psychology and utilizing machine learning technology to measure directors’ trustworthiness, attractiveness, and dominance, we find that individual directors’ trustworthiness and attractiveness are positively associated with director elections. Furthermore, the higher average level of (variation in) directors’ trustworthiness or attractiveness leads to greater (lower) support from shareholders on say-on-pay proposals. The impact of facial appearances on shareholder voting behavior is weaker when shareholders know more about the directors’ track record. Finally, we find that directors’ perceived trustworthiness is positively associated with board meeting frequency, suggesting that impressions of trustworthiness can influence board dynamics and functioning. © 2025 Elsevier Inc.Purdue University, PU; Texas Christian University, TCU; Wichita State UniversityWe thank Yongtae Kim, Volkan Muslu, Karen Nelson, Siew Hong Teoh, Aaron Yoon, and workshop participants at Purdue University, Texas Christian University, and Wichita State University for their helpful comments
Preserving the Artistic Impact, Life History, and Legacy of the Nearly Forgotten Prairie Print Maker, Coy Avon Seward, born in Chase, Kansas.
No full text2025 Library Research Award Graduate WinnerResearch project for ANTH 597B Independent Museum Studies with Rachelle MeineckeThis research highlights the nearly forgotten history of Coy Avon Seward, a Kansas artist and founding member of the Prairie Print Makers, emphasizing his connection to Chase, Kansas, and his artistic impact both within the state and beyond. The study aims to honor and preserve his legacy
Optimizing laser cutting parameters for austenitic stainless steel: Insights from gaussian process regression and sensitivity analysis
No full textClick on the DOI link to access this article at the publishers website (may not be free).Laser cutting is a crucial subtractive manufacturing process employed across various industries, including healthcare, manufacturing, and semiconductors.This study aims to develop a predictive model for laser cutting of austenitic stainless steel using Gaussian Process Regression (GPR) and to rank the importance of cutting parameters through sensitivity analysis.The research focuses on understanding the influence of input parameters (laser speed, gas type, laser power, and laser focus distance) on output variables such as kerf width, heat-affected zones, and surface roughness.The methodology combines GPR, a nonparametric Bayesian approach ideal for small datasets, with variance-based sensitivity analysis.GPR employs radial-based function and Marten kernels for covariance functions, while sensitivity analysis utilizes first-order and total effect indices to evaluate the significance of input factors and their interactions.Preliminary findings indicate that laser power has the most substantial influence (85%) on the cutting process, followed by laser speed (10%) and laser focus distance (<5%).The study utilizes both experimental and computational data, analyzed using statistical and machine learning models implemented in Python.This research contributes to the optimization of laser cutting processes by identifying key parameters and their interactions.The findings have potential applications in improving cutting efficiency and quality across various industrial sectors.Future research could explore the integration of these insights into real-time process control systems and the extension of this methodology to other materials and laser cutting configurations. Copyright © 2024 by ASME
Department of Dental Hygiene Class of 2010
No full textFirst row (left to right): Danielle Lawrence, SADHA President, 2009-2010; Gina Nichols, SADHA Secretary, 2009-2010; Deann Mason, SADHA Treasurer, 2009-2010; Amy Edwards, SADHA Class Representative, 2008-2009; Elizabeth Kristek, SADHA Class Representative, 2008-2009; Rachel Parman, SADHA Class Representative, 2009-2010; Ashley Williams, SADHA Class Representative, 2009-2010Second row (left to right): Virginia Aggson, Rachel Arnett, Jana Bornowsky, Ashley Cochran, Lisa Douglass, Laura Epperson, Breanna Frayne, Anastasia Gifford, Minh Hong Hoang, Kimberly JacobsThird row (left to right): Alyssa Lang, Rashell Lipps, Natalie Melichar, Kathryn Pauls, Makenzie Ravenstein, Jesica Rothwell, Susan Shelite, Hope Shelley, Cassandra Shurtz, Kristen SmithFourth row (left to right): Annie Sorenson, Taylor Titus, Adriana Vela, Alison Ward, Jena Watson, Shenandoah Wright, Denny WyethDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Sensor-based trajectory tracking of anthropomorphic test device in crash testing: A methodology
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Mechanical EngineeringThe evaluation of Anthropomorphic Test Device (ATD) kinematics, particularly head trajectory, presents a significant challenge in aerospace seat development and certification testing. Optical motion tracking is a widely used method for tracking and plotting trajectories during aircraft crash tests. It has however, several inherent limitations. While recent advancements in software have enhanced the accuracy of post-processing, maintaining continuous target visibility remains a challenge.
This study focuses on development of an approach for tracking the head trajectory of an ATD for better understanding of ATD kinematics during crash tests. While sensor-based tracking is a more time and cost-effective method than optical motion tracking, previous studies have indicated that it might at times provide lower accuracy. The primary objectives of this research are to investigate and improve the accuracy of sensor-based tracking, to develop a methodology to capture it, and to compare the results for accurate and better prediction.
The proposed methodology relies on a MATLAB-based algorithm consisting of spatial transformation matrix operations with Euler parameters, numerical methods and integration techniques. It utilizes initial ATD position measurements, angular velocity and acceleration data from the sensors, as inputs. It is capable of generating the 3D trajectory of the head. The potential for use of this methodology to the ATD chest is also investigated. It shows promising advancement in sensor-based tracking, suggesting that with the development of proper MEMS technology, sensor-based tracking could potentially replace optical motion tracking. For further studies, there is a wide possibility of extending the research to other body parts like lower extremities of the body to get holistic understanding of kinematic behavio