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Episode 35 – Another Broken Egg Café
No full textPresident Rick Muma is joined by proud Shocker alums and co-franchisees of Another Broken Egg Café in Wichita. Learn about Jacob O’Connor and Jon Peterson’s vision and the entrepreneurial spirit we strive to foster at Wichita State.The “Forward Together” podcast celebrates the vision and mission of Wichita State University. In each episode, President Rick Muma will talk with guests from throughout Shocker Nation to highlight the people and priorities that guide WSU on its road to becoming an essential educational, cultural, and economic driver for Kansas and the greater good
Crosslinking interactions of the dioxolenium ion for molecules based on 2-bromo-3-hydroxypropionic acid
No full textPoster and abstract presented at the FYRE in STEM Showcase, 2025.Research project completed at the Department of Chemistry and Biochemistry.Aromatic polymers provide excellent mechanical strength in comparison to their aliphatic counterparts. Unfortunately, these polymers have difficulties with material processing and manufacturing. To mitigate this issue, a thermal crosslinker is used with aeromantic polymers to strengthen and stabilize while avoiding byproducts. This study investigates the crosslinking interactions between methyl-3-acetoxy-2-bromo propionate (Acetoxy-BrH) which undergoes rearrangement to a dioxolenium ion, a more reactive intermediate, and electron rich aromatic compounds such as diphenyl ether(DFE). Since crosslinked polymers demonstrate increased thermal and mechanical stability, differential scanning calorimetry is used to monitor the reaction and determine rearrangement and crosslinking. An endothermic thermal transition of the Acetoxy-BrH can be seen beginning at 86.06 ˚C, which is indicative of rearrangement. Additionally, the brominated dimer undergoes electrophilic aromatic substitution when reacted with DFE. Further reactions between Acetoxy-BrH and other electron rich aromatic compounds will be studied and developed
Bangladocatlas: A multi-class annotated dataset for complex bangla document layout analysis
No full textClick on the DOI link to access this conference paper at the publishers website (may not be free).Optical Character Recognition (OCR) technology is a vital tool for digitizing printed content, enabling efficient data extraction and enhancing document accessibility. Traditional OCR techniques rely on pre-stored templates for fonts or structured documents. Recent advancements in Machine Learning (ML), particularly Convolutional Neural Network (CNN) and transformer-based architectures, have enhanced OCR technologies with human-like intelligence. However, these models often fall short due to limitations in the diversity of document types, layouts, and content in the training datasets, particularly for complex Bangla documents. In this paper, we address the challenge of a limited, diverse dataset by introducing BanglaDocAtlas, a versatile and multi-class annotated dataset specifically designed to advance Bangla document layout analysis. The dataset includes eight distinct classes: paragraph, text, image, title, caption, table, advertisement, and page number, enabling comprehensive OCR applications. State-of-the-art segmentation models, i.e., You Only Look Once (YOLO), and a detection model, e.g., Real-Time DEtection TRansformer (RT-DETR), are trained and evaluated on the BanglaDocAtlas dataset. The results demonstrate that YOLOv9 achieves the highest precision, with values of 0.87 for bounding boxes and 0.79 for masks, while RT-DETR outperforms in recall, with a value of 0.86 for bounding boxes
Time-varying direction-of-arrival estimation exploiting mamba network
No full textClick on the DOI link to access this article at the publishers website (may not be free).Direction-of-arrival (DOA) estimation for moving targets presents a significant challenge in array signal processing. Traditional DOA estimation and tracking methods often encounter limitations due to the infeasibility of acquiring large volumes of stationary data and performing subspace-based processing over many snapshots, and lead to high computational costs. Recently, deep learning techniques have been effectively applied in DOA estimation, owing to their reduced complexity during inference. In this paper, we propose the use of Mamba network as a state-space model-based approach to estimate and track DOAs that vary snapshot-by-snapshot. The proposed network is interpretable and hardware-efficient, making it advantageous for training and real-time inference. © 2025 IEEE.Air Force Research Laboratory, AFRL, (FA9453-22-C-A127); Air Force Research Laboratory, AFRLThis material is based upon work supported by the Air Force Research Laboratory (AFRL) under Contract No. FA9453-22-C-A127. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the AFRL
Department of Dental Hygiene Class of 2007
No full textFirst row (left to right): Maria Giglio, SADHA President 06-07, SADHA Vice President 05-06; Breanna Bates, SADHA Secretary 06-07; Rachael Kaps, SADHA Treasurer 05-07; Rebecca Jones, SADHA Ways and Means Chair 06-07; MariAnn Simpson, SADHA Ways and Means Chair 05-07; Christi Cavness, SADHA Class Representative 05-06; Sarah Lawrence, SADHA Class Representative 05-07; Brittany Mahieu, SADHA Class Representative 06-07Second row (left to right): Fatima Awan, Brenda Barber, Jolynn Beeman, Emily Bentz, Carey Christiansen, Brandy Cleaton, Callie Cole, Andee Dunagan, Courtney FaflickThird row (left to right): Dana Finstad, Valerie Gonzalez, Stephanie Gray, Courtney Harper, Sara Hiskett, Dana Johnson, Megan Knott, Lindsay Mayfield, Nancy MilanFourth row (left to right): Laila Nelsen, Wendi Poctor, Tram Quach, Cara Radford, Laura Schauf, Jen Swearengin, Rachel Turner, Danielle Weilert, Lynette WilkensDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
In-plane characterization of 3K70PW carbon fabric/inf114 system for ls-dyna’s mat 213 material model
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Aerospace EngineeringThe in-plane characterization of 3K70PW fabric/INF 114 resin system for LS-Dyna’s MAT 213 material model is presented in this thesis. Obtaining material properties, flow rule parameters, tabulated stress-strain curves, and damage data for the material system of interest were key aspects of this research. Basic tension, compression, in-plane shear, and 45° off-axis tension/compression tests were conducted in accordance with the respective ASTM test standards. Digital Image Correlation (DIC) using GOM ARAMIS Photogrammetry system was used for strain mapping for the aforementioned test procedures. The influence of changing DIC parameters such as facet sizes and overlap on the obtained data was studied as part of this investigation.
Yield strengths were obtained from the mechanical test data and using a quadratic yield function based on Tsai-Wu’s failure criterion to develop a convex yield surface. These strengths were used to develop stress -vs- plastic strain curves for different loading modes. Finally, the flow rule coefficients that determine the plastic flow potential of the material system according to the non-associate flow rule were computed by minimizing the error between effective stresses from a chosen master curve and those from different loading modes. This study focused on obtaining all the input parameters necessary to model the non-linear behavior of the 3KPW70 material system using the MAT 213 material model
Object locating dataset for human-robot collaboration
No full textPoster and abstract presented at the FYRE in STEM Showcase, 2025.Research project completed at the Department of Mathematics, Statistics and Physics.Humans and robots interact more closely as time marches forward. Whether it is general AI or any other learning module, robots need a database in which new information is processed, learned, and built upon. In particular, the future of human-robot collaboration (HRC) depends on computational understanding of spatial relationships. This study allows for more natural commands during such interactions, like “to the right of” instead of exact coordinates, and improves reasoning for unfamiliar objects. To build a dataset to apply these algorithms, we have chosen 56 household objects that can be sorted into various categories of affordance, the possible actions that can be done to an object for a desired usage outcome. Affordance is helpful when the robot is dealing with unfamiliar objects. We took various images, where each image sets a unique scene of multiple objects to be used as a framework for assigning spatial relationships. The analysis includes three main phases. First, the red-green-blue (RGB) and depth components of these images are used in the Faster Region-based Convolutional Neural Network (RCNN) for object detection, which outputs both objects’ bounding boxes and labels. Secondly, the bounding box coordinates are passed to a connected Bayesian Neural Networks (BNN) to classify the spatial relationships of said objects. Finally, these relations are integrated with a robot through a large language model (LLM) to create fluid communication between the user and the robotic companion. This has applications in the kitchen, offices, and general spaces, where one can ask, “Grab the ___; it is next to the ___” and the robot will understand exactly one’s intention. Using techniques like BNNs and LLMs, we push boundaries of robotic spatial understanding through deep learning
A comparative study of municipal fiscal responses to the COVID-19 and the Great Recession
No full textClick on the DOI link to access this article at the publishers website (may not be free).This research investigates municipal fiscal policy responses to the COVID-19 pandemic, comparing them to those during the Great Recession using a deductive two-stage approach. First, to illustrate the general trends in municipal fiscal policy responses to these two crises, it compares municipal fiscal policy responses for 100 major U.S. cities. Then, through an in-depth investigation of four selected cities, it explores unique fiscal reactions to similar financial and environmental challenges. Providing a current overview of municipal fiscal responses, this study informs policymakers about dynamics in fiscal policy formulation across diverse fiscal environments. © The Author(s) 2025.ARPAAccording to the mayor, the COVID-19 pandemic allowed the city to explore and address underlying issues in its financial structure. It served as an impetus for the city to operate more efficiently and maintain a sustainable staffing level aligned with its revenues. The federal funding from the CARES Act and ARPA played a pivotal role in balancing the budget through expenditure reductions without draining fund balance or reserves. Modesto utilized these federal funds for delayed projects focused on economic vitality, governance and service delivery, and overall quality of life. These funds also helped the accomplishment of Modesto\u2019s pre-pandemic goal of maintaining an unassigned general fund balance of 8% relative to general fund expenditures. By the end of the 2021 to 2022 fiscal year, the city\u2019s unassigned fund balance increased to $6,395,913, constituting 4.4% of the total general fund expenditures
Faculty Senate meeting, March 10, 2025
No full textAgenda: (Approval of Minutes): February 24, 2025 -- (Informal Statements) / Senator N. Thompson -- (President's Report) / Mathew Muether – (Old Business): Continued discussion of 11.10 Poster/Flyer Policy for University Grounds and Facilities, Comparisons and 11.12 Use of University Campus for Free Expression Activitie
Python-based intelligence layer for SWIFT messaging systems using LLMs to predict routing and compliance failures
No full textClick on the DOI link to access this article at the publishers website (may not be free).In recent years, SWIFT message routing efficiency and adherence within the financial domains are of utmost importance for the global transaction systems' integrity. This paper describes an artificial intelligence layer implemented in Python for detecting and anticipating compliance and routing renegade procedures within SWIFT messaging frameworks, powered by Large Language Models (LLMs). We developed a hybrid pipeline that integrates natural language embeddings with semi-structured text fields, utilizing over 4.2 million historical SWIFT messages. The designed system enables the real-time tracking and analysis of regulatory violations in transactions, enhancing the system's ability to minimize false positives and lag while maintaining high levels of interpretability. We apply various architectures of LLMs, such as BERT, GPT-Neo, and a fine-tuned domain-specific variant of GPT, to compare with traditional sequence models. The analysis reveals improvements in predictive performance, with the best model achieving 92.4% accuracy and an AUC of 0.96 in detecting compliance failures. Forecasting routing failure showed a 31% better error rate than the rule-based benchmarks. Furthermore, the insights provided by the LLMs' attention mechanisms concerning compliance decision processes enhance the transparency required for auditability behind some structured message fields, exposing critical messages. This research demonstrates the potential for integrating language models into the financial message routing backbone, particularly in proactive regulatory anomaly detection and compliance risk management. The system provides a foundation for further development, such as learning from evolving compliance benchmarks and operating within federated environments. © 2025 IEEE