23671 research outputs found
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Department of Dental Hygiene Class of 2023
No full textFirst row (left to right): Bailey Bevan, Class Representative; Cameren Bartlett, Treasurer; Aaron Fulcher, President; Shania Tran, Secretary; Holly Smith, Class RepresentativeSecond row (left to right): Addison Austin, Melissa Barrera, Emily Bradley, Sierra BradshawThird row (left to right): Alexis Coffman, Flor Cantreras, Shelly Dang, Reagen Ebenkamp, Monica Flores-Sanchez, Zoe GardnerFourth row (left to right): Estefania Granado, Jennifer Lee, Bryana Loisranoi, Nancy Martinez, Yareli Mendoza, Tina Nguyen, Aracely Nieto, Jessica Orchard, ShaQuencia Raymond, Monica RegaladoFifth row (left to right): Abby Reiswig, Stephanie Sanchez, Alesia Smith, Brooke Stover, Taryn Tanguay, Brea Townsend, Haley Ward, Brianna WhisenhuntDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Investigation into Florence chert types: Thermal alteration and elemental analysis on Florence variabilities
No full textThesis (M.A.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of AnthropologyA prominent raw material found in the southern Plains region of south-central Kansas, and northwest Oklahoma is Florence chert. This chert is commonly associated with the Ancestral Wichita people. Florence chert was known to be intentionally heat treated by the Ancestral Wichita. Observational analysis has been the primary method in identifying the chert and its chert types. This study investigates the effects of heat treatment on various types of Florence chert using both observational and analytical methods. Experimental results suggest that Ancestral Wichita likely heated chert between 400 °C and 600 °C to optimize flaking properties without compromising material integrity. This study also explores the geochemical and observational distinctions among Florence chert types A, B, and C, emphasizing the utility of pXRF and statistical analyses (PCA, MANOVA, ANOVA, CV) in archaeological sourcing and material classification. Results confirm that these chert types are geochemically distinct, with elements such as Zn, Sr, Rb, Mn, and Ca serving as key discriminators. Elemental analysis via pXRF with principal component and MANOVA testing confirming statistically significant differences among Florence Types. Florence A demonstrated the most internal consistency, while Florence B showed high variability, and Florence C indicated moderate stability. Results highlight the effectiveness of raw material analysis via observational methods that can be further supported by p(XRF) and statistical analysis suggesting future development of a digital reference system to support broader regional sourcing efforts
Integrating motion physics knowledge into deep learning for accurate Parkinson's disease classification
No full textPresented to the 21st Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 11, 2025.Research completed in the School of Computing, College of Engineering.Parkinson’s Disease (PD) is a progressive neurological disorder characterized by a variety of motor and non-motor symptoms, significantly impacting patients’ quality of life. Early and accurate detection of PD is crucial for timely interventions and improved patient outcomes. Recent advancements have highlighted the potential of time-series data collected from Inertial Measurement Unit (IMU) sensors during patient-performed activities as a valuable source for PD detection. Previous studies have employed diverse data collection methodologies, ranging from single-device setups attached to the patient’s waist to multi-sensor configurations attached to various body segments. However, these approaches often face limitations due to missing modalities or complex sensor setups. To address these challenges, this research proposes a novel hybrid framework for PD detection and classification. The designed Physics-Informed Neural Network (PINN) model integrates kinematics formulas with neural networks to approximate kinematic parameters such as relative rotational angles between body segments. Then, the proposed Bayesian Neural Network (BNN) model classifies the severities of PD affected levels. The proposed method also focuses on data collected using IMU sensors strategically placed on the upper body—specifically the lower and upper arms on both sides, as well as the chest—to achieve simplified yet efficient data acquisition. Given the limited availability of clinical datasets, this study incorporates an extensive data augmentation pipeline. Techniques such as noise injection, Fourier-based transformations, time-warping, and scaling are applied to enrich the dataset, improving the generalization and robustness of the neural network models. By leveraging IMU sensor data captured during multiple activity performances, this work developed a scalable and practical solution for PD detection that reduces dependency on complex multi-modal systems. The proposed hybrid framework not only improves classification accuracy but also addresses critical challenges in current research, paving the way for more accessible and reliable PD monitoring solutions.Graduate School, Academic Affairs, University Librarie
Quantum computing for future energy systems
No full textClick on the DOI link to access this article at the publishers website (may not be free).Quantum computing hardware continues to advance rapidly, becoming increasingly accessible to the public. Theoretical evidence suggests that quantum computing exhibits exponential advantages in various domains, including optimization, materials research, and drug discovery, among others. The energy sector is projected to be among the initial industry sectors to experience long-term advantages from quantum computing methods. The future energy systems domain, which includes transportation, low-carbon fuels and decarbonization, sustainable building design, smart grids, and electricity, is examined in this chapter along with possible use-cases of quantum computing technologies. © 2025 selection and editorial matter, Mohammad Hammoudeh, Clinton Firth, Harbaksh Singh, Christoph Capellaro, and Mohamed Al Kuwaiti; individual chapters, the contributors
Department of Dental Hygiene Class of 2024
No full textFirst row (left to right): Arianna Garraway, President; Amairani Salgado, Student Reprensentative; Lindsey Stansbury, Student Reprensentative; Michael Sengvilay, Social Coordinator; Brianna Hensley, Seretary; Jennifer Williams, TreasurerSecond row (left to right): Krissy Alonso, Halle Budke, Amy Bui, Christine Cleary, Alli Crosby, Kylee Crump, Hannah Daily, Quynh Dang, Alyssa Dooms, Kacie FaganThird row (left to right): Dusty Gabler, Makayla Hollis, Peyton Hurla, Seneke James, Ciara Keeler, Kaylee Kuttler, Diane Nguyen, Cadence Pfaff, Anita Phanthavong, McKenna RennFourth row (left to right): Chantel Schuster, Kirsten Shoemaker, Anna Smith, Anna Smoots, Ashley Taylor, Gisell Vazquez, Alejandra Villarreal, Alexix WilsonDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Evaluating horizontal distance measurements in ergonomic risk assessments for lifting tasks
No full textFirst place winner of poster presentations for Applied Sciences at the 24th Annual Undergraduate Research and Creative Activity Forum (URCAF) held in the Woolsey Hall, Wichita State University, April 25, 2025.Back injuries are a significant concern in the manufacturing industry, often resulting from manual lifting and lowering tasks. The external moment, created by the weight of a load and its horizontal distance from the body, is a key risk factor for low back injuries. Two widely used ergonomic assessment methods—the Revised NIOSH Lifting Equation (RNLE) and the Lifting Fatigue Failure Tool (LiFFT)—quantify this risk using different horizontal distance measurements. LiFFT measures from the hip to the hands, while the RNLE measures from the mid-point between the ankles to the hands. This study examines whether these measurements can be used interchangeably to improve efficiency in risk assessments. Using an infrared motion capture system, these horizontal distances were quantified as participants lifted a box from ten different horizontal reach distances and vertical height zone combinations. Statistical analysis revealed statistically significant differences (p<0.05) in moment arm measurements in 7 of the 10 lifting conditions. The 3 lifting conditions that are not statistically significant are at the Knee vertical height. Mean hip-tohands distances (LiFFT approach) were generally greater than ankle-to-hands distances (RNLE approach), with differences ranging from -3.72 to 3.75 inches depending on the lifting zone. These findings indicate that the measurements used in RNLE and LiFFT are not interchangeable, as they provide distinct biomechanical insights that influence lifting risk
Simulation and analysis of workload considerations on dispatch strategies for emergency medical services
No full textPresented to the 21st Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 11, 2025.Research completed in the Department of Industrial, Systems, and Manufacturing Engineering, College of Engineering.Workload within Emergency Medical Services has been studied using different methods. However, few existing dispatching models within EMS consider workload in their decision-making frameworks, and no prior studies have evaluated multiple measures of workload as optimization criteria. This project used a Discrete Event Simulation model to study the impact of different dispatching strategies on response time and three different measures of workload. These workload measures include priority-stratified call volume, call response utilization, and overall utilization models. Using a year's worth of data, the simulation was created representing Sedgwick County. Preliminary results show response time and call volume per medic did not appear to differ significantly between the four dispatching strategies evaluated. This shows that implementing workload-based dispatching does not necessarily worsen operational performance measures, making it a feasible strategy to improve the experience of EMS providers at work.Graduate School, Academic Affairs, University Librarie
Significant progress in stem cell treatment for Alzheimer's disease: A critical review
No full textThis handbook comprehensively explores various facets of stem cells and their secretome in the field of regenerative medicine, covering topics such as the biology, characteristics, and applications of mesenchymal and non-mesenchymal stem cells in diverse medical contexts. It delves into their potential for treatment of radiation injuries, diabetes, aging-related diseases, osteoarthritis, Alzheimer's disease, and oral surgery. Furthermore, it investigates the potential of neuronal stem cells for neural repair, myoblast implants for Duchenne muscular dystrophy, and immunotherapy using non-genetically modified natural killer cells. The book also uncovers the possibilities of botanical leads for stem cell therapy, explores the role of stem cells in promoting healthy aging, and sheds light on their complex interplay with cancer, particularly in gliomas and gynecologic cancers. Additionally, it reviews the use of stem cell-derived insoluble factors, focusing on extracellular vesicles as therapeutic agents in dentistry, cardiovascular diseases, and neurodegenerative disorders. The chapters discuss the potential of engineering mesenchymal stem cells secretome for bone regeneration and treating intracerebral hemorrhage. Towards the end, the book elucidates cell-free scaffolding for tissue engineering, adult stem cells' disease tropism, and the evolving strategies in regenerative medicine. It is intended for researchers, professionals, and academicians in the fields of regenerative medicine, stem cell biology, and medical biotechnology
Department of Dental Hygiene Class of 2008
No full textFirst row (left to right): Rachel Clark, SADHA President 2007-2008; Jennifer Grapengater, SADHA Secretary 2007-2008; Cassie Nutter, SADHA Treasurer 2007-2008; Kami Staib, SADHA Ways and Means; Yoly Arends, SHOR; Barb Wyss, SADHA Class Repesentative 2007-2008; Ashleigh Hermann, SADHA Class Representative 2007-2008; Lisa Penke, SADHA Class Representative 2007-2008Second row (left to right): Mandy Betzen, Toni Evans, Danielle Fells, Michelle Freeman, McKina Frenzl, Stephanie Gonzales, Brooke Horner, Andrea Johnson, Lindsay Kaufman, Anna MasonThird row (left to right): Michelle Mosler, Tracie Moya, Amy Orth, Mychel Pflughoeft, Shannon Pickle, Angie Pickle, Jessica Rush, George Schmidt, Lesa Schrader, Yvonne StarnesFourth row (left to right): Tera Sumpter, Nicole Versch, Thy-Huyen Vu, Shanelle Wernli, Hayley Westerfield, Carry Wright, Sannie Yeung, Romy ZimmermanDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Assessing tongue mobility and strength using PARROT: An oral device
No full textPoster project completed at Wichita State University, College of Innovation and Design and Department of Human Performance StudiesPresented at the 22nd Annual Capitol Graduate Research Summit, Topeka, KS, March 25, 2025.The tongue plays a crucial role in human health, contributing to chewing, swallowing, breathing, speech, and overall well-being. Composed of eight interwoven muscles, it performs complex movements essential for food manipulation, articulation and upper air-way patency. Reduced tongue mobility or strength—caused by weak muscles, poor tone due to aging, obesity, or neurological disorders such as stroke or Parkinson's disease—can lead to conditions like dysphagia, speech impairments, and obstructive sleep apnea. This research aims to develop "PARROT," a wireless wearable mouthpiece device designed to assess tongue function through lingual pressure mapping at various points in the oral cavity with real-time feedback. The device will be used for diagnosing and treating tongue positioning habits and for providing personalized, targeted exercises to improve lingual function. Not restricted by location, PARROT will accommodate the individual needs of both caregivers and patients, whether inside or outside a clinical setting. Incorporating AI and machine learning algorithms, the system will customize training regimens by monitoring progress to enhance therapeutic outcomes. This innovation has the potential to improve tongue functionality and overall health, addressing challenges faced by individuals with lingual dysfunction in the state of Kansas and beyond