2020 WSU Annual CGRS Abstracts

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    Sustainable freshwater harvesting from atmosphere through electrospun superhydrophobic polyacrylonitrile nanocomposite fibers
    (Wichita State University, 2020-02-26) Uddin, M. Nizam; Asmatulu, Ramazan
    The scarcity of pure drinking water has been one of the major humanitarian challenges in the globe. The world population growth, urbanization, depleting water resources and global climate change have intensified this crisis especially in arid and semi-arid regions. The concern is drastically increasing and therefore scientists and engineers are challenged with urgently developing viable solutions for this problem. The development of a sustainable, cost-effective, reliable and efficient water collection materials and methods for continuous freshwater production is crucial for many regions of the world. In this work, polyacrylonitrile (PAN) and Poly (methyl methacrylate) (PMMA) with various proportions of titanium dioxide (TiO2) nanoparticles and aluminum (Al) microparticles were spun into superhydrophobic nanocomposite fibers using electrospinning technique followed by stabilization and carbonization to remove all non-carbonaceous material from the fibers and use for harvesting fog from the atmosphere. The fiber morphology, surface hydrophobicity and fog harvesting capacity of the nanocomposite fibers were investigated. Test results reveal that the carbonized nanocomposite fibers exhibit superhydrophobic characteristics with a water contact angle of 154.8° with efficient fog harvesting ability of 621 mg/cm2-hr. The nanotechnology-based water collection systems are unique because of the multifunctional properties of the nano-membranes. Kansas is in short supply of freshwater and this technology will help sustainable economic growth in the region. The produced water can be used for drinking, agriculture, gardening, medical, industrial, and other purposes.
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    Intrusion detection for 3D-Printers: An electrical power analysis approach
    (Wichita State University, 2020-02-26) Rott, Michael; Salinas Monroy, Sergio A.
    Manufacturing is one of the largest economic development drivers in the state of Kansas, accounting for more than $25 billion of annual economic output. In recent years, Kansas manufacturers have adopted Industry 4.0 technologies to improve their efficiency and productivity. Unfortunately, by making machine data available over computer networks, these technologies increase the risk of cyberattacks that inject defects into the manufactured objects, and thus can result in financial losses, loss of reputation, and, in safety critical applications such as aerospace, injury and loss of human lives. In this work, we focus on cyberattacks against additive manufacturing, also called 3D-printing. We propose a novel intrusion detection approach that can detect defect injection attacks and it is based on analyzing the 3D-printer's power consumption. Existing intrusion detection techniques are designed for IT systems and ignore attacks that compromise the electronic and physical components of 3D-printers. In contrast, our approach uses the 3D-printers' power consumption to detect malicious intruders that inject defects into the produced object. To analyze the 3D-printer's power consumption, we use a deep learning approach called a multi-layer neural network (NN). The main idea of the NN is to analyze previous power consumption measurements to predict future measurements. If the observed measurement differs from the predicted by more than a specified threshold, then it is likely that an intruder is maliciously manipulating the 3D-printer. Our results show that we can classify 3D prints as benign or malicious with an accuracy of 91.25%, allowing accurate detection of several tested defects.
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    Developing procurement strategy by applying classification algorithms for effective supplier assessment
    (Wichita State University, 2020-02-26) Harikrishnakumar, Ramkumar; Krishnan, Krishna K.; Nannapaneni, Saideep
    The manufacturing sector ranks one of the top spots in the 2018 Kansas economy and this projection is predicted to continue with 0.5% growth in 2019. Within the manufacturing sector, aerospace is ranked fourth in Kansas with over 30,000 workers. Almost 44% of Kansans work for small businesses (less than 50 employees) and this percentage is expected to increase with the effective assessment of the suppliers in the supply chain network of the business in Kansas. This research aims to provide a comprehensive and robust assessment process for suppliers. Therefore, we propose the use of supervised machine learning algorithms to classify various suppliers into four categories: excellent, good, satisfactory, and unsatisfactory. In this research, supervised learning (classification) algorithms are applied to a supplier assessment problem where a model is trained based on the previous historical data and then tested on the new unseen data set. This method will provide an efficient way for supplier assessment that is more effective in terms of accuracy and time when compared to the multi-criteria decision-making approach. Classification algorithms such as support vector machines (with linear, polynomial and radial basis kernels), logistic regression, k-nearest neighbors, and naïve Bayes methods are used to train the model and their performance is assessed against a test data. Finally, the performance measures from all the classification methods are used to assess the best supplier in any business in Kansas.
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    Hydraulic fracturing noise and health concerns for Kansans
    (Wichita State University, 2020-02-26) Fuksa, Alicia J.; Richburg, Cynthia M.
    In 2016, Kansas had over 93,000 operational oil and gas wells. According to the Kansas Geological Survey, approximately 244,000 oil and gas wells were drilled in Kansas over a period of 64 years. As recently as 2015, residents of Harper and Sumner counties have voiced concerns over the wastewater content, as well as seismic activity produced by this industry. Air and water impacts have been studied throughout the US, but rarely is the noise produced around such sites investigated. The purpose of this study was to (1) measure sound pressure levels in neighborhoods adjoining hydraulic fracturing ("fracking") well pads and compressors and (2) collect survey responses from residents to determine if the fracking noise could potentially cause hearing loss, sleep disturbances, and/or overall health impacts. The surveys and sound level readings have the potential to provide evidence that the health effects from fracking noise are like those from other noise sources (e.g., highway, airport, railroad, etc.). Although these investigators have measured sound levels and obtained surveys from rural areas of Pennsylvania and Oklahoma, the findings have the potential to provide investors and perspective home owners within Kansas information concerning health and wellness as they relate to fracking noise. The varied levels of noise caused by truck traffic, drilling, and compressors, along with reported seismic activity and subsonic sounds produced by the drilling can create levels of anxiety and sleep disruption that has the potential to affect Kansans' health.
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    Relationship between psychosocial health and self-reported oral health among senior center participants in Wichita, Kansas
    (Wichita State University, 2020-02-26) Kanade, Ashwini; Parham, Douglas F.; Chesser, Amy K.
    Poor mental health days are defined as, "mean number of days in the past 30 days adults reported their mental health was not good." According to America's Mental Health Rankings, the poor mental health days for women above 65+ years old in Kansas is greater (4.3) when compared to men (3.0). Oral health impacts mental health and thus, affects the health of Kansas residents. The definition of health has moved past free of disease and healthy mouth is an essential component of whole-person approach to health care. The relations between oral health and overall health of an individual are explicit and crucial. Poor oral health impacts nutrition, self-image, social interactions, mental and physical health, and health-related quality of life. The purpose of this study was to assess gender differences in psycho-social consequences of oral health at senior centers in Wichita, KS. The GOHAI (Geriatric Oral Health Assessment Index) survey was conducted and socio-demographic information was collected. Results of this survey showed higher impact of oral health on women as compared to men. Psychosocial well-being is an important component of mental health and this study assists to signify the mental health problems due to oral health. According to the Kansas Department for Aging and Disability Services (KDADS) report (2019), the behavioral health system is in crisis and the behavioral health problems include psychological distress. By improving oral health related quality of life, the psychological well-being and mental health among older adults can be enhanced.