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For Passion's Sake
No full text2025 Library Research Award Undergraduate WinnerProject for COMM 255 Intro Web Design and Analytic with Jessica NewmanMy essay demonstrates how Ablah Library's resources and staff supported my Sherlock Holmes textual research, combining traditional library methods with modern tools to create a web project making research resources more accessible
Comprehensive real-time insights for state of health prediction: A comprehensive framework for online state of health assessment in commercial lithium-ion batteries
No full textThis is an open access article under the CC BY license.Lithium-ion batteries (LIBs) are widely used for energy storage in various industries due to their high energy density and long lifespan. However, degradation mechanisms may lead to hazardous conditions, such as thermal runaway. Solely relying on capacity changes for state of health (SOH) assessment is insufficient, given the complexity of LIBs. This work introduces comprehensive real-time insights for state of health prediction (CRISP), a novel framework for comprehensive SOH assessment and degradation mechanisms identification. Using data from commercial LIBs, CRISP runs on a low-cost Raspberry Pi with remote monitoring capabilities, generating aged anode half-cell voltages for each reference performance test (RPT) as references for lithium plating assessment. CRISP processed the data of each RPT in ≈2.9 s and outputs multiple physical quantities for SOH evaluation. Results show that LIBs cycled at 100% depth of discharge (DOD) exhibited greater cathode material loss compared to those cycled at narrower DODs. However, no dendritic lithium deposition is detected by evaluating and correlating the physical parameters provided by CRISP. In summary, this study highlights the importance of multi-parameter SOH assessment, demonstrating that single-parameter methods (e.g., capacity-based) fail to capture the full scope of LIBs health. © 2025 The Author(s). ChemElectroChem published by Wiley-VCH GmbH.Wichita State University, WSU; State of Kansas; National Science Foundation, NSF, (OIA‐2148878); National Science Foundation, NSF; Defense University, (N00014‐19‐1‐2275)This material is based upon work supported by the National Science Foundation under Award No. OIA\u20102148878 and matching support from the State of Kansas through the Kansas Board of Regents. The research was conducted using instrumentation funded by the Defense University Research Instrumentation Program (DURIP) grant number N00014\u201019\u20101\u20102275. Financial support from Wichita State University is also acknowledged
Multi-method cooling strategies for photovoltaic systems: a comprehensive review of passive, active, and AI-optimized hybrid techniques
No full textClick on the DOI link to access this article at the publishers website (may not be free).High operating temperatures significantly reduce photovoltaic (PV) system efficiency, lowering power output by up to 20%. This review examines passive, active, and hybrid PV cooling techniques addressing heat management challenges. Passive methods such as radiative cooling and phase change materials reduce PV temperature by up to 20 °C, improving electrical efficiency by 15.5%. Active cooling, including water and air systems, achieves larger temperature drops of up to 55 °C and electrical efficiency gains of up to 22.2%, albeit with higher operational costs. Hybrid approaches combine passive and active methods to optimize performance, balancing complexity, and energy savings. Artificial intelligence (AI)-based control techniques, including Reinforcement Learning, Long Short-Term Memory networks, and Genetic Algorithms, enable real-time optimization by adjusting cooling parameters based on environmental data. These AI methods, validated through experimental and simulation studies, have realized up to 6.7% energy savings and improved system durability by mitigating thermal stress. AI further supports predictive maintenance and adaptive cooling strategy enhancement, advancing smart PV cooling solutions. Despite these advances, challenges related to cost, scalability, and environmental impact persist. This review compares the performance and trade-offs of existing cooling technologies, identifies research gaps, and underscores the potential of integrated AI-driven hybrid systems. These insights contribute to developing next-generation, efficient, and sustainable PV cooling technologies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.PETRONAS—Malaysia, (015LC0-614)The authors of this study profoundly acknowledge the financial assistance received from PETRONAS\u2014Malaysia via grant YUTP\u2014FRG, Cost Center No: 015LC0-614
TL;DR - why gen z can’t even: Understanding post-literacy and communication in the digital age
No full textThesis (M.A.)-- Wichita State University, College of Liberal Arts and Sciences, Elliott School of CommunicationThis thesis explores the evolution of human communication from orality to literacy and into the current digital era of post-literacy. Drawing on the frameworks of media ecology and orality-literacy studies, it analyzes how digital technologies have reshaped not only the way information is transmitted, but how knowledge is processed, retained, and understood. Central to this inquiry is the claim that contemporary media environments have created a form of “secondary orality,” characterized by image- and sound-based communication that often undermines deep, reflective thought.
The historical foundation of the study contextualizes these changes by tracing the cognitive transformations that accompanied prior media shifts, from oral storytelling and early writing systems to the printing press and the rise of print literacy. The thesis then argues that post-literacy - an environment dominated by ephemeral, fragmented, and emotionally charged digital content - presents unique challenges to traditional models of education and communication.
Ultimately, this study offers a nuanced understanding of post-literacy, arguing that the communication crisis attributed to younger generations stems not from intellectual decline but from the disorienting effects of media evolution. It calls for a more intentional, critical approach to education that recognizes the cognitive needs of Gen Z while retaining the value of deep reading, analytical reasoning, and historical literacy
LGBTQ Wichita
No full textThe Center of Wichita and its chairman, Brent Kennedy, a lecturer for the Department of Anthropology, are partners in the project. Since 2010, The Center of Wichita has been a community resource and is proud to preserve the history of the LGBTQ community of the region. Working with a team of researchers and students from Wichita State University, The Center of Wichita hopes that this work inspires future collecting and storytelling.Available in paperback"Located in the middle of the nation's heartland, Wichita, Kansas, has been a regional hub for LGBTQ persons, forming a community that extended well beyond just local residents.
In spite of the area's restrictive laws and conservative attitudes, these people have, since the 1960s, found space among an ever-fluid bar and club scene and a larger network from community centers to rodeos to religious organizations to art and activism groups. It has a history that includes one of the nation's earliest gay rights ordinances as well as pioneering figures in AIDS research. The community has faced discrimination and hostility and the AIDS crisis. Since then, it has celebrated milestones like the legalization of gay marriage and the losses of many of its key leaders. With a legacy that extends from homophile to gender fluid, this story provides a window into how LGBTQ persons in the center of the country have both faced challenges and lived ordinary lives.
Since 2010, The Center of Wichita has been a community resource and is proud to preserve the history of the LGBTQ community of the region. Working with a team of researchers and students from Wichita State University, The Center of Wichita hopes that this work inspires future collecting and storytelling." (Provided by publisher
Nestling condition of a grassland bird is not associated with food availability in restored grasslands; [La condition de nidification d’un oiseau de prairie n’est pas associée à la disponibilité de la nourriture dans les prairies restaurées]
No full textThis is an open access article under the CC BY license.Grassland bird populations have experienced steep declines in recent decades, necessitating better understanding of factors affecting their reproductive success. Grasslands are highly variable environments, and such variation affects the diversity and abundance of arthropods, which constitute the diet for most nestling grassland songbirds. Changes in arthropod abundance might affect parental food provisioning to nestlings and, consequently, nestling condition and survival. During the summers of 2017–2019, we examined the condition of Dickcissel (Spiza americana) nestlings from 288 nests in relation to biomass of arthropod prey across 36 restored grassland sites in Kansas that varied in vegetative management. Orthopteran (principal food for nestling Dickcissels) and total arthropod biomass were not related to cattle grazing or plant diversity in the initial seeding mix. Neither Dickcissel clutch size nor maximum brood size (including Brown-headed Cowbirds, Molothrus ater) were correlated with arthropod biomass measures in all years, which indicated that forage availability varied independently of clutch size. Neither age-corrected mass, mass/tarsus residuals, variation of tarsus length within broods, or plasma triglyceride concentration showed clear relationships with field-level variation in either arthropod biomass measure. This might be due to parental compensation for variable prey abundance. Brood size (including cowbirds) explained some variation in nestling condition with nestlings in larger broods generally exhibiting poorer condition (lower weight vs. structural size) than those in smaller broods. Thus, parents may be more limited in their capacity to feed all nestlings in large broods rather than limited by the availability of food within habitat patches. Consistent with previous hypotheses discounting food limitation to birds nesting in grasslands, our results suggest that Dickcissel nestling condition, known to affect post-fledging survival, might not be affected by spatial variation in food availability, at least in some years. © 2025 by the author(s)
DNA Sequence classification: An advanced Machine Learning framework for accurate splice junction detection
No full textClick on the DOI link to access this article at the publishers website (may not be free).In the context of genomic data analysis, DNA splice junction classification is a critical task for understanding gene expression, as these junctions are sites where introns are removed and exons are joined. Accurate identification of splice junctions is essential for deciphering gene functionality. Traditional methods, such as sequence alignment, are often slow and computationally intensive, especially when processing large-scale DNA datasets. To address this, we developed and evaluated multiple machine learning (ML) and deep learning (DL) models for the accurate classification of splice junctions. Our goal was to enhance classification accuracy, reduce computational costs, and provide a comparative analysis of different modeling approaches to advance research in genomic data analysis. We employed a methodological framework that included traditional ML algorithms, such as Random Forest, Gradient Boosting, Decision Tree, Support Vector Machine (SVM), and XGBoost, as well as contemporary DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The data preprocessing pipeline incorporated one-hot encoding for optimal feature representation. Empirical results demonstrated the superior performance of ensemble learning methods, with Gradient Boosting and XGBoost achieving exceptional classification accuracies of 97.34% and 97.02%, respectively. Among DL models, CNNs outperformed RNNs, achieving 94.51% accuracy compared to 93.89% for RNNs. The results underscore the exceptional performance of tree-based ensemble methods for splice junction classification, highlighting their superior discriminative power and effectiveness in genomic sequence analysis. © 2025 IEEE
4.22 Tenure, Promotion, Professor Incentive Review and Post-Tenure Review Calendar Revision, April 14, 2025
No full textRobust and scalable quantum repeaters using machine learning
No full textThis is an open access article under the CC BY license.Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. In general, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. It is also difficult to manually create and implement the quantum algorithm for the swap circuit as the size of the system increases. Here, we propose a different approach: to use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB 2022a. Training is conducted for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that the scale-up is very effective and relatively easy, and the effects of noise and decoherence are (Formula presented.) as the size of the system increases. © 2025 by the authors