23671 research outputs found
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Equitable regional load curtailment framework considering community fairness and line congestion
No full textClick on the DOI link to access this article at the publishers website (may not be free).High impact low frequency events could lead to longlasting power outages and catastrophic grid failures. To limit the impact of such events, as the last resort, system operators might enforce emergency load curtailment. This emergency curtailment focusses mainly on system level load serving capacity i.e., necessary generation with reserve margin. As such the existing load curtailment policies employed by regional transmission operators have been identified to have key limitations as they do not take consumer fairness and system operational reliability impacts into consideration. This work proposes a two-stage framework that considers system operability and customer fairness in the computation of transmission node level load curtailment. In the first stage, the required system level load curtailment is allocated to each node through fairness-based load curtailment share ratio that considers the node level loading, impact to node voltage and equity. The second stage focuses on operational reliability of the curtailment share from stage 1 by reallocating to resolve any line loading violation. The effectiveness of the proposed framework is evaluated using the IEEE RTS 96 test system. The results highlight the importance of factoring both system operational impacts and consumer fairness in formulating emergency load curtailment policies. © 2025 IEEE
Neuroimaging motion artifact simulation
No full textPresented to the 24th Undergraduate Research and Creative Activity Forum (URCAF) held in Woolsey Hall, Wichita State University, April 25, 2025.Data collection of the brain's physiological functions is growing exponentially as technological advances make neuroimaging more feasible to conduct. Currently, Functional Near- Infrared Spectroscopy (fNIRS) is one of the ways to image the brain's functions. fNIRS are a portable and non-invasive method of diffusing near-infrared light throughout the scalp and brain to detect areas of activity occurring in the brain. When a patient is being observed with fNIRS, the patient can only make minor movements to reduce motion artifact levels in the fNIRS signal to ensure liability and repeatability to reduce the creation of motion artifacts in the data collection process. The need for understanding what movements of the head cause motion artifacts to develop is necessary to innovate a device capable of collecting data while the patient is moving. To determine what motion of the brain within the skull causes artifacts, a model to experience different kinds of forces must be made and validated. To model the brain's movement in the skull, MSC Apex and MSC NASTRAN are being used to create a finite element model (FEM) to undergo finite element analysis (FEA). The FEM went through iterations of a single, solid sphere representing the brain to a hollow sphere, the skull, with a smaller sphere inside, the brain, with a layer in between the two to represent cerebrospinal fluid. Creating an FEM of the skull and brain will show how much force the skull can withstand before the brain begins to deform, leading to motion artifacts. The FEM models are on course to be validated by NIAR's Displacement Field Measurement Rig with 3D phantoms. The 3D phantoms will progress methodically from a cube to a sphere to ensure the accurate data between our 3D phantom and literature
Variations in the wake structure of non-elliptical lift distributions near wingtip
No full textThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Non-elliptical lift distributions, particularly bell-shaped distributions with extended spans, have shown potential to disrupt conventional tip vortex roll-up, even eliminating trailing vortices in the near wake. This study investigates the aerodynamic performance and near-wake characteristics of four wing configurations: a baseline untwisted wing, an elliptically loaded wing, and two nonelliptical lift distributions. Force-based experiments and streamwise Particle Image Velocimetry (PIV) were conducted in the University of Dayton Low-Speed Wind Tunnel (UD-LSWT), with results compared to numerical simulations using FlightStream®. The findings reveal that, at moderate CL, wings with non-elliptical lift distributions exhibit a near-planar wake with no discernible trailing vortex signature, while the elliptically loaded wing demonstrates clear trailing vortex roll-up. Additionally, the non-elliptical configurations displayed reduced downwash and thinner wake structures compared to other configurations. These results underscore the distinct wake characteristics of non-elliptical lift distributions, marking a significant deviation from traditional trailing vortex behavior
Defining strength: Prevention educators’ perspective on strong teen dating violence policies and prevention education
No full textThesis (Ph.D.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of PsychologyThe purpose of the present study was to examine the impact of state-level teen dating violence (TDV) policies on school-based TDV prevention education programs. Roughly, thirty-eight states have TDV language in their legislation and prior research has used the language in these policies to determine their strength. Black et al. (2022) identified seven states as having strong policies and this finding was used to recruit participants. Seventeen (n= 17) prevention educators from local gender-based violence (GBV) agencies were interviewed. Interviews were analyzed using two qualitative methods: reflexive thematic analysis and qualitative content analysis. Findings from the reflexive thematic analysis led to three themes: (1) Justifies the Need, (2) Buy-in not Included, and (3) Looking for More. Categories from the qualitative content analysis include: (1) Benefits of Policy and (2) Drawbacks of Policy. Collectively, these findings reveal that policies considered strong by researchers do not equate to good TDV prevention implementation practices. Findings indicate the need to incorporate the practice-based knowledge of prevention educators into policy and TDV prevention programming implementation
Investigating the impact of algorithms and hardware on machine learning models in HPC systems
No full textClick on the DOI link to access this conference paper at the publishers website (may not be free).The development and effectiveness of machine learning (ML) applications rely on the support from underlying computing systems. This project investigates the impact of algorithmic techniques and high-performance computing (HPC) system components on ML performance. Following standardized image data preprocessing, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for class balancing, and the Recursive Feature Elimination with Cross-Validation (RFECV) technique is employed for optimal feature selection. An HPC cluster featuring hundreds of central processing unit (CPU) cores, multiple graphics processing unit (GPU) accelerators, several terabytes of random-access memory (RAM), and running the CentOS Linux distribution is used to investigate the training time and prediction accuracy of various ML models, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). Per fair-share scheduling policy, this study uses up to four CPU cores, two GPU accelerators, and 150 gigabytes of RAM. Simulation results show that the CNN model outperforms the other models. Using the top 50% of balanced features reduces the CNN model's training time significantly, up to 90.39%, with a slight increase in accuracy. Allocating four CPU cores and two GPU accelerators, rather than relying on a single CPU core without GPU support, cut the training time up to 56.18%, while maintaining comparable accuracy. The impact of hardware support on ML models can be extended to investigate how resource allocation affects ML inference time
Annual Commencement Program
No full textCommencement addresses for undergraduate ceremonies:
9 a.m. commencement address by Danielle Johnson. Johnson is the Executive Director of Wichita Habitat for Humanity, the principal owner of Inclusive Growth Strategies, and a Wichita State University alum.
1 p.m. commencement address by Matt All, President and CEO of Blue Cross and Blue Shield of Kansas.
5 p.m. commencement address by Laura Bernstorf, Director of Special Missions Program Management at Textron Aviation and Wichita State University alum.Processional / Pomp & Circumstance -- Opening of Ceremony / (Graduate ceremony): Mathew Muether, Faculty Senate President; (Undergraduate ceremony, 9 a.m.): Tom Wine, Professor and Program Director of Music Education, School of Music, College of Fine Arts; (1 p.m.): Sun Young Lee, Assistant Professor Elementary Education, School of Education, College of Applied Studies; (5 p.m.): Dotty Harpool, Executive Director, Engagement and Prominence/Senior Educator, W. Frank Barton School of Business -- National Anthem / Pulip Han, Music Opera Performance Graduate Student -- Welcome and Introduction of Guests / Monica Lounsbery, Senior Executive Vice President and Provost -- Student Welcome / (Graduate ceremony): Donna Tran, Graduate Student of the Year; (All undergraduate ceremonies): Makenna Roths, Undergraduate Student of the Year -- Alumni Welcome / WSU Foundation -- Greetings from the Kansas Board of Regents / Neelima Parasker, Kansas Board of Regents -- Commencement address / (9 a.m.): Danielle Johnson; (1 p.m.): Matt All, (5 p.m.): Laura Bernstorf -- Conferring of Degrees / Richard Muma, President; Monica Lounsbery; (Graduate): Coleen Pugh, Dean, Graduate School; Jeremy Patterson, Dean, College of Innovation & Design; (9 a.m.): Marie Bukowski, Dean, College of Fine Arts; David M. Eichhorn, Interim Dean, Fairmount College of Liberal Arts and Sciences; Kimberly Engber, Dean, Dorothy and Bill Cohen Honors College; (1 p.m.): Jennifer Friend, Dean, Applied Studies; Gregory Hand, Dean, Health Professions; (5 p.m.): Larisa Genin, Dean, Business; Anthony Muscat, Dean, Engineering -- Alma Mater / Pulip Han -- Hail Wichita / Shocker Sound Machine -- Closing of the ceremonies / (Graduate): Mathew Muether; (9 a.m.): Tom Wine; (1 p.m.): Sun Young Lee; (5 p.m.): Dotty Harpool -- Recessional / Salute to Kansas, John Phillip Sousa, Presented by WSU Symphonic Wind Ensembl
Numerical methods for modeling type IV shock-shock interactions at hypersonic velocities
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Aerospace EngineeringThis study explores hypersonic shock-shock interactions for a double ramp geometry, specifically Edney Type IV interactions. The complexity of shock-shock interactions poses a challenge to researchers trying to accurately model the interaction. Several experiments found the shock-shock interaction behavior is due to the two ramp angles, the Mach number of the freestream flow, and chemical non-equilibrium flow. The successful modeling of the chemical non-equilibrium flow captured the Type IV shock-shock interaction and the resulting high-pressure jet. The use of chemical non-equilibrium processes and Navier Stokes equation techniques requires advanced models which are crucial to understand pressure and heating loads. This study provides a comprehensive insight on Edney Type IV shock-shock interaction, chemical non-equilibrium flow, mesh refinement, and detailed information for the boundary conditions in order to obtain accurate simulations and data for comparison with the experimental data
Employing high-performance PETSc network simulation for business profit analysis
No full textClick on the DOI link to access this conference paper at the publishers website (may not be free).Business entities such as shops can be modeled as nodes in a traffic network, where vehicular flow at nearby intersections influences a shop's profit. To enable profit-based network analysis, this work presents a methodology for integrating geo-located business and traffic data into high-performance network simulations using the Portable Extensible Toolkit for Scientific Computation (PETSc) and its Data Management Network (DMNetwork) libraries, developed by Argonne National Laboratory (ANL) and UChicago Argonne. Since PETSc and DMNetwork do not natively support mapping business entities to network nodes, we create a new mapping strategy based on geographic coordinates (longitude, latitude). The road network is first constructed using OpenStreetMap (OSM) data and Simulation of Urban Mobility (SUMO) tool. Then, shop locations are obtained via the Google Maps Platform (GMP) and LocationIQ (LIQ) application programming interfaces (APIs). Each shop is assigned to its nearest traffic intersection by calculating the shortest geographic distance, effectively linking business data to the underlying traffic network. We also develop a methodology to obtain real-time traffic data and predict future traffic data at the network nodes for profit analysis. While OSM provides the base topology, GMP and LIQ supply detailed business location information, where LIQ proved to be more comprehensive. The enriched datasets are then used for profit analysis in PETSc/DMNetwork simulations. Simulation results show that utilizing eight processors instead of one reduces computation time by more than 73%, demonstrating the benefit of high-performance PETSc simulation for business analysis
Economic evaluation of an electromechanical facility
No full textPublished in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, November 2025. 2025 IEMS Officers: Gamal Weheba (Conference Chair); Hesham Mahgoub (Program Chair); Dalia Mahgoub (Technical Director); Ed Sawan (Publications Editor); Wilfredo Moscoso (Proceedings Editor); Abdulaziz G. Abdulaziz (Associate Editor)This study provides a structured framework through the integration of INCOSE's risk management process with the financial valuation ratios from CFI to evaluate the viability of an electromechanical startup from a financial perspective. This study uses the three-statement financial model to show how risk-informed decision making improves profitability within a quarterly timeline in the first year while simultaneously stabilizing leverage and efficiency ratios. The findings show that combining systems engineering risk management with financial modeling improves cost efficiency, schedule adherence, and economic sustainability, while aligning with KEEN's “create value” principle
