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Developments in aircraft manufacturing: The role of flame-resistant electrospun nanofibers in upgrading structural integrity
No full textPoster and abstract presented at the FYRE in STEM Showcase, 2025.Research project completed at the Department of Mechanical Engineering.As the number of commercial flying hours continues to grow, stronger safety measures are needed to ensure the protection of crew, passengers, and estimable property. Therefore, upgrading the materials used within the inside of the aircraft, as well as the outside is vital to increasing the well-being of those occupying the aircraft. Incorporating flame-resistant electrospun polymeric nanofibers in the manufacturing process of aircraft structural parts is one of the best methods to help reduce their flammability due to the polymers used in the electrospun solutions. The historical data on plane crashes reveals that when a fire occurs, loss of life is significantly increased due to the flames and dangerous chemicals in the cabin furnishings that are released when burned. Hence, by addressing flammability using the flame retardant electrospun fibers inside the aircraft, such loss of life and property can be reduced. This research aims to develop flame retardant electrospun nanofibers that can be incorporated into aircraft interiors which will act as insulation materials. Primarily, this study will focus on identifying the relevant mixture of materials to produce a nanofiber mat and examine the mechanical and flame properties of the fiber mat produced. Furthermore, the research mentors will aim to educate a freshman undergraduate student on the use of flame retardant nanofibers for safety in aircraft interiors and exteriors. While this initial study aims to present a new safety measure for aircraft regarding loss of life and property to fire, future assessment and work must be done to further develop pragmatic designs for commerical aircraft
Solar energy to improve electric service reliability during winter weather
No full textPoster project completed at Wichita State University, Department of Electrical and Computer EngineeringPresented at the 22nd Annual Capitol Graduate Research Summit, Topeka, KS, March 25, 2025.22nd Capitol Graduate Research Summit (CGRS) -- University AwardIn February 2021, fuel shortages and extremely cold weather led to widespread power outages across Kansas. If solar energy had been available during this event, the outages could have been avoided. This research analyzes actual data from the February 2021 outages to show how households at different income levels could be impacted by solar generation during energy shortages. This includes evaluating how their energy bill would be affected, and quantifying the benefit they would obtain from uninterrupted electrical service. The results show that solar energy would significantly benefit an average Kansas household during energy shortages, and that solar energy would have greater benefits for households with lower incomes
Fabrication of membranes using graphene oxide nanosheets: Advances, challenges, and future directions
No full textClick on the DOI link to access this article at the publishers website (may not be free).Graphene oxide (GO) nanosheets have emerged as a highly promising material for membrane fabrication due to their unique physicochemical properties. The exceptional mechanical strength, tunable surface chemistry, and high aspect ratio of GO nanosheets enable the design of membranes with superior separation performance. This mini-review provides an overview of recent advances in the fabrication of GO-based membranes, with a focus on various fabrication techniques, membrane structures, and their applications in water purification, gas separation, and other areas. The challenges associated with GO membrane fabrication, including scalability, long-term stability, and fouling, are discussed. Finally, future directions for research in this field are outlined, emphasizing the need for innovative approaches to overcome current limitations and to unlock the full potential of GO-based membranes in industrial applications. © TheMinerals, Metals & Materials Society 202
A soft actor-critic approach for energy-conscious flexible job shop scheduling incoporating machine usage constraints and job release times
No full textPublished in SOAR: Shocker Open Access Repository by the Wichita State University Libraries Technical Services, October 2025.According to the International Energy Agency, manufacturing accounts for 30% of global energy consumption, making job shop scheduling a critical lever for reducing energy consumption. In the flexible job-shop scheduling problem (FJSP), each job consists of a sequence of operations, each of which can be processed on one of several eligible machines, and the scheduler must decide both which machine to assign to each operation and the order in which operations are processed on each machine. The Energy-Conscious Flexible Job-Shop Scheduling Problem with Release Times and Machine Usage constraints (EC-FJSP-RTMU) extends the traditional FJSP by (1) embedding a detailed multi-component energy model that quantifies the energy consumption of processing, setup, idle time, transportation, machine startup, and common facility operations, and (2) explicitly enforcing job release times and machine-usage constraints. We address this enriched scheduling problem using the Soft Actor-Critic (SAC) reinforcement learning algorithm, which learns policies to minimize total energy consumption subject to all timing and usage constraints. The SAC agent is trained in a workshop simulation featuring parallel-machine slots, shutdown policies and machine warmup/cool-down thermal dynamics. Benchmark experiments on eighteen instances show that the SAC agent consistently produces constraint-compliant feasible schedules in under 0.42 seconds
The effect of pilot mental health on passenger willingness to fly
No full textPublished in SOAR: Shocker Open Access Repository by the Wichita State University Libraries Technical Services, October 2025.This study explored how passengers’ willingness to fly is affected by the mental health condition of pilots and their various coping mechanisms. Data were collected from 76 participants at a university in the Southeastern United States via a Qualtrics questionnaire. Participants received four scenarios regarding pilot mental health: no history of mental health issues, history of mental health issues with medication as a coping mechanism, history of mental health issues with therapy as a coping mechanism, and history of mental health issues with no coping mechanism. Participants rated their willingness to fly for each scenario. The data were analyzed through a one-way, repeated measures ANOVA. Results indicated that participants’ most preferred scenario was no history of mental health issues. The least preferred scenario was the pilot with no coping mechanism. A significant difference in passenger willingness to fly was found between all pairs of pilot mental health scenarios except therapy compared to medication. The findings emphasized the impact of pilot mental health condition on passenger preferences and provided insight into public perception of pilot mental health
SASLS: Semantic analysis of sentiment in social networks using Lexicon-Based methodology and Semi-Supervised sentiment annotation
No full textThis is an open access article under the CC BY license.Sentiment analysis has emerged as a prominent topic of research within the domain of natural language processing. The advancement of sentiment analysis techniques, particularly those based on dictionaries, has facilitated deeper insights into the sentiments expressed within textual data. Sentiment analysis uses a set of computational operations to identify the sentiment expressed in segment of words. By employing dictionary-based sentiment analysis techniques, researchers can automatically ascertain the polarity (positive, negative, or neutral) of textual content. This study presents a novel strategy for Semantic Analysis of Sentiment in social networks, which combines Lexicon-based methodology with semi-Supervised learning in order to improve sentiment analysis performance (SASLS). SASLS is to detect the polarity of words from twitter using personalized feature selection-based clustering and dictionary-based techniques. Our strategy can well deal with two common challenges in this problem, including the omnipresence of domain-specific vocabulary and the lack of labeled data in different domains. The proposed strategy has been evaluated on several datasets with different scales. Numerical findings show that SASLS significantly outperforms traditional supervised, unsupervised, semi-supervised, and deep learning approaches. Specifically, SASLS provides more than 2.5% more optimal Macro-F1 compared to the best existing state-of-the-art method. These results show that SASLS has good potential for semantic analysis of sentiments in social networks. © 2025 The Author(s
Advanced Education Program in General Dentistry graduates 2010-2011
No full textSchool composite: students included in composite: Amber Royal, Christina Jacob, Genna Patel, Maya Nunley.Digitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only. Contact [email protected] if you have any questions
Modern coastal ecosystems of the American Southeast are shaped by deep-time human-environment interactions
No full textThis is an open access article under the CC BY license.Coastal and estuarine ecosystems are particularly sensitive to climate change, placing them at the forefront of challenges to mediate impacts of a warming atmosphere, rising sea-levels, and increasingly frequent extreme weather events. To model potential loss, predict and prepare for future regime shifts, or to build effective conservation policies, it is important to understand the long-term socioecological processes that structure modern ecosystems. We highlight how modern ecological baselines along the Georgia coast of eastern North America are shaped by 5000 years of Indigenous and Euro-American land use. We demonstrate the extent and intensity of manifestations of past land use on modern landscapes, especially by way of quantifying the scale of shell deposition by Indigenous communities and the landscape infrastructure of Euro-American plantations. Through both intentional and unintentional impacts, modern estuarine ecosystems globally are products of these engagements, alterations, and creative transformations that we refer to as deep-time legacy drivers. © The Author(s) 2025.Pennsylvania State University, PSU; University of Georgia Marine Institute; Ossabaw Island Foundation; Georgia Department Of Natural Resources, DNR; National Science Foundation, NSF, (OCE-1832178, 1748276); National Science Foundation, NSF; NSF-DDRI, (1643072)Special thanks to all who have made many of the projects cited in this work possible over the years, the Muscogee Nation for their input and allowing us to conduct work on their ancestral lands. Funding for different parts of studies used in this manuscript was provided by numerous different agencies and institutions, including the Ossabaw Island Foundation, the University of Georgia Marine Institute, the Georgia Department of Natural Resources, the Pennsylvania State University. This research was supported, in large part, in association with the Georgia Coastal Ecosystems LTER project, National Science Foundation grants to VDT (NSF Grants OCE-1832178, 1748276), and by an NSF-DDRI grant to BR (NSF Grant #1643072). Permissions for these funded projects were granted by the Georgia Department of Natural Resources, but no new permissions were required for the synthetic work presented here
Phase change materials in solar energy storage: Recent progress, environmental impact, challenges, and perspectives
No full textClick on the DOI link to access this article at the publishers website (may not be free).The escalating global energy demand, coupled with the urgent need to combat climate change, underscores the necessity for effective and sustainable energy storage solutions. Phase change materials (PCMs) have emerged as a viable technology for thermal energy storage, particularly in solar energy applications, due to their ability to efficiently store and release thermal energy during phase transitions while maintaining a near-constant temperature. This paper addresses the limitations of traditional thermal energy storage systems and explores the advancements in PCM integration within various solar energy systems. We discuss innovative methods to enhance heat transfer rates and thermal conductivity, including modifications of extended surfaces, heat pipes, cascading PCMs, encapsulation techniques, and the incorporation of nanoparticles. These enhancements can improve system performance by up to 73 %, with nanoparticle dispersion identified as the most economically viable solution. Additionally, we provide a comprehensive overview of the implementation of the artificial intelligence approach in optimizing PCM-based thermal energy storage systems, emphasizing the effectiveness of ensemble learning frameworks for accurate modeling. The review also highlights the development of nano-PCMs, which demonstrate significant improvements—25.6 % in charging and 23.9 % in discharging rates—compared to conventional PCMs. Furthermore, we analyze the economic and environmental implications of PCM-based systems, focusing on critical issues such as carbon emissions, waste minimization, biodegradability, and alignment with circular economy principles. Finally, we discuss the major challenges and future research directions necessary for advancing PCM-based thermal energy storage systems. It is hoped that this article will update readers and experts working in this area on the recent advancements in PCM-based TES systems and provide an in-depth understanding of ML potentials in revolutionizing PCM-based solar energy storage systems. © 2025 Elsevier Lt