Engineering Commons^™

Session 3a: Karst And Groundwater, Kentucky Water Research Institute

Kentucky Water Resources Annual Symposium

• Examining the Utility of Tryptophan-Like Fluorescence As a Proxy for Fecal Contamination in Karst Basins, Inner Bluegrass Region, Kentucky
• A Predictive Flood Model for Urban Karst Environments
• Improving Best Management Practices for the Siting, Maintenance, and Design of Urban Karst Groundwater Injection Wells

Session 3b: Sediments, Kentucky Water Research Institute

Kentucky Water Resources Annual Symposium

• Sediment Degradation Experiments for a Low Gradient Stream Suggest the Watershed’s Connectivity Regime Exhibits Control on Stream Biogeochemistry
• Does Sediment Organic Matter Content Mediate the Effects of Chironomid Larvae on Benthic Nitrous Oxide Flux
• Drivers of Variability in Annual Sediment Accretion Rates Within Wetland Easements of Western Kentucky

Poster Session, Kentucky Water Research Institute

Kentucky Water Resources Annual Symposium

• Evaluating the Effects of Beaver Dam Removal on the Hydrology, Water Quality, and Bioindicators of a Restored Wetland
• Predictive Sediment Flux Modeling of a Karst Spring Using High Resolution Sensors and Stable Carbon and Nitrogen Isotopes
• Development and Validation of qPCR Assays for Use in eDNA Detection of Four Species of Semiaquatic Salamanders .
• Analyzing Recent Spatio-Temporal Rainfall Erosivity Patterns and Trends Across Kentucky
• Development of eDNA Protocols for Detection of Four Darter (Percidae: Etheostomatinae) Species in Central Kentucky Streams
• Assessing Change in the Seasonality of Stable Isotopes δ18O and δ2H for Meteoric and Terrestrial Waters of Central Kentucky
• Coupling …

Session 2: Water And The Community, Kentucky Water Research Institute

Kentucky Water Resources Annual Symposium

• Development of Human Health Criteria for Surface Waters of the Commonwealth of Kentucky
• Kentucky Workforce Survey Results
• Modeling, Managing, and Mitigating Stormwater Flooding using a Real-time Monitoring Network and Environmental Equity Approach

Session 1a: Watershed Management, Kentucky Water Research Institute

Kentucky Water Resources Annual Symposium

• Motivations and Barriers to the Implementation of Riparian Buffer Zones on Central Kentucky Horse Farms
• Engaging Urban Watershed Stewards in Stormwater Management
• Efficacy Examination of Urban Reforestation Efforts on Streams in Lexington, KY: Macroinvertebrates and Water Quality Assessments
• Over a Decade of Monitoring Water Quality at McConnell Springs – What Have We Learned?
• Glenns Creek Watershed Characterization Efforts

Session 1b: Contaminant Exposure And Treatment, Kentucky Water Research Institute

Kentucky Water Resources Annual Symposium

• Cyanobacteria, Cyanotoxin Synthetase Genes, and Cyanotoxin Occurrence Among Selected Large River Sites in the Lower Ohio River Basin, 2020–21
• Emerging Contaminant Exposure Following WWTP Processes to Surface Waters
• Reactive Membrane Synthesis for Capture and Degradation of Toxic Water Contaminants
• Implications to Tap and Stream Water Chemistry Due to Variations in Sampling Location and Watershed Land Use
• Examining Toxicity of 2D Nanomaterial Phosphorene, Free-Standing and Embedded onto Membranes, and Its Application for Removal of Per-and Polyfluoroalkyl Substances (PFAS)

Proceedings Of 2022 Kentucky Water Resources Annual Symposium, Kentucky Water Resources Research Institute, University Of Kentucky

Kentucky Water Resources Annual Symposium

This symposium was planned and conducted as a part of the state water resources research institute annual program that is supported by Grant/Cooperative Agreement Number G21AP10631-00 from the United States Geological Survey (USGS). The contents of this proceedings document and the views and conclusions presented at the symposium are solely the responsibility of the individual authors and presenters and do not represent the official views of the USGS or of the symposium organizers and sponsors. This publication is produced with the understanding that the United States Government is authorized to reproduce and distribute reprints for government purposes. Mention of trade …

A Roller Coaster For The Mind: Virtual Reality Sickness Modes, Metrics, And Mitigation, Dalton C. Sparks

The Cardinal Edge

Understanding and preventing virtual reality sickness(VRS), or cybersickness, is vital in removing barriers for the technology's adoption. Thus, this article aims to synthesize a variety of academic sources to demonstrate the modes by which VRS occurs, the metrics by which it is judged, and the methods to mitigate it. The predominant theories on the biological origins of VRS are discussed, as well as the individual factors which increase the likelihood of a user developing VRS. Moreover, subjective and physiological measurements of VRS are discussed in addition to the development of a predictive model and conceptual framework. Finally, several methodologies of …

Enrofloxacin And Sulfamethoxazole Sorption On Carbonized Leonardite: Kinetics, Isotherms, Influential Effects, And Antibacterial Activity Toward S. Aureus Atcc 25923, Chanat Chokejaroenrat, Chainarong Sakulthaew, Khomson Satchasataporn, Daniel D. Snow, Tarik E. Ali, Mohammed A. Assiri, Apichon Watcharenwong, Saksit Imman, Nopparat Suriyachai, Torpong Kreetachat

Nebraska Water Center: Faculty Publications

Excessive antibiotic use in veterinary applications has resulted in water contamination and potentially poses a serious threat to aquatic environments and human health. The objective of the current study was to quantify carbonized leonardite (cLND) adsorption capabilities to remove sulfamethoxazole (SMX)- and enrofloxacin (ENR)-contaminated water and to determine the microbial activity of ENR residuals on cLND following adsorption. The cLND samples prepared at 450^oC and 850^oC (cLND450 and cLND550, respectively) were evaluated for structural and physical characteristics and adsorption capabilities based on adsorption kinetics and isotherm studies. The low pyrolysis temperature of cLND resulted in a …

Laboratory Evaluation Of A Novel Self-Healable Polymer Gel For Co2 Leakage Remediation During Co2 Storage And Co2 Flooding, Tao Song, Zhanmiao Zhai, Junchen Liu, Yugandhara Eriyagama, Mohamed Ahdaya, Adel Alotibi, Ze Wang, Thomas P. Schuman, Baojun Bai

Chemistry Faculty Research & Creative Works

For CO₂ storage in subsurface reservoirs, one of the most crucial requirements is the ability to remediate the leakage caused by the natural fractures or newly generated fractures due to the increasing pore pressure associated with CO₂ injection. For CO₂ Enhanced Oil Recovery (EOR), high conductivity features such as fractures and void space conduits can severely restrict the CO₂ sweep efficiency. Polymer gels have been developed to plug the leakage and improve the sweep efficiency. This work evaluated a CO₂ resistant branched self-healable preformed particle gel (CO₂-BRPPG) for CO₂ plugging purpose. This …

Changing Stem To Steam: An Analyzation On The Effects Of Art Integration Up To The University Level, Elizabeth Richmond

Honors Projects

This Honors Project is a combination of an artistic, creative project and a research driven analysis. The focus of this project is to research and understand the effects the arts have on both general students and STEM students in particular, what benefits come from art education and art integration, and if STEM students benefit from art integration. This goal was accomplished through a literature review and annotated bibliography featuring 18 sources. Another goal of the project was to understand where I fit into the research. In my creative portion of the project, I explored and analyzed how the arts affected …

Filters Used For Roundness Error Evaluation: An Experimental Comparison Study, Fatma Elerian

Mansoura Engineering Journal

Filtration is a necessary step in roundness measurement. The type of filter affects value of roundness error. There were many studies that explain the differences between filters theoretically and few of them have studied the differences practically. This study aims at introducing a practical comparison of the following filters (2CR50, 2CR75, 2CRPC50, 2CRPC75, Gaussian and no filter) in order to demonstrate the effect of each on roundness error values. RA-120 ROUNDTEST instrument was used for measuring out of roundness of 25 turned workpieces. Least squares reference circle (LSC), minimum zone reference circle (MZC), maximum inscribed reference circle (MIC) and minimum …

Leveraging Natural Learning Processing To Uncover Themes In Clinical Notes Of Patients Admitted For Heart Failure, Ankita Agarwal, Krishnaprasad Thirunarayan, William Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee

Computer Science and Engineering Faculty Publications

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients …

Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry

Faculty Publications

Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of …

Warming Of The Willamette River, 1850–Present: The Effects Of Climate Change And Direct Human Interventions, Stefan Talke, David Jay, Heida Diefenderfer

Civil and Environmental Engineering Faculty Publications and Presentations

Using archival research methods, we found and combined data from multiple sources to produce a unique, 140 year record of daily water temperature (Tw) in the lower Willamette River, Oregon (1881–1890, 1941–present). Additional daily weather and river flow records from the 1850s onwards are used to develop and validate a statistical regression model of Tw for 1850–2020. The model simulates the time-lagged response of Tw to air temperature and river flow, and is calibrated for three distinct time periods: the late 19th, mid 20th, and early 21st centuries. Results show that Tw has trended upwards at ~1.1 °C …

Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams, Andrew T. Park, Nathaniel Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry

Faculty Publications

Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial …

Parasol: Efficient Parallel Synthesis Of Large Model Spaces, Clay Stevens, Hamid Bagheri

School of Computing: Conference and Workshop Papers

Formal analysis is an invaluable tool for software engineers, yet state-of-the-art formal analysis techniques suffer from well-known limitations in terms of scalability. In particular, some software design domains—such as tradeoff analysis and security analysis—require systematic exploration of potentially huge model spaces, which further exacerbates the problem. Despite this present and urgent challenge, few techniques exist to support the systematic exploration of large model spaces. This paper introduces Parasol, an approach and accompanying tool suite, to improve the scalability of large-scale formal model space exploration. Parasol presents a novel parallel model space synthesis approach, backed with unsupervised learning to automatically derive …

Permafrost Landscape History Shapes Fluvial Chemistry, Ecosystem Carbon Balance, And Potential Trajectories Of Future Change, Scott Zolkos, Suzanne E. Tank, Steven V. Kokelj, Robert G. Striegl, Sarah Shakil, Carolina Voigt, Oliver Sonnentag, William L. Quinton, Edward A.G. Schuur, Donatella Zona, Peter M. Lafleur, Ryan C. Sullivan, Masahito Ueyama, David Billesbach, David Cook, Elyn R. Humphreys, Philip Marsh

Daugherty Water for Food Global Institute: Faculty Publications

Intensifying permafrost thaw alters carbon cycling by mobilizing large amounts of terrestrial substrate into aquatic ecosystems. Yet, few studies have measured aquatic carbon fluxes and constrained drivers of ecosystem carbon balance across heterogeneous Arctic landscapes. Here, we characterized hydrochemical and landscape controls on fluvial carbon cycling, quantified fluvial carbon fluxes, and estimated fluvial contributions to ecosystem carbon balance across 33 watersheds in four ecoregions in the continuous permafrost zone of the western Canadian Arctic: unglaciated uplands, ice-rich moraine, and organic-rich lowlands and till plains. Major ions, stable isotopes, and carbon speciation and fluxes revealed patterns in carbon cycling across ecoregions …

Developing Positive Thermal Coefficient (Ptc) Heaters For Solar Electric Cooking, Katarina Ivana Brekalo, Andrew Shepherd

Physics

Positive Thermal Coefficients, PTCs, are materials that abruptly change in resistance in response to changes in temperature. The purpose of this experiment is to explore the viability of using the switching type ceramic PTC thermistor as a replacement for current resistive heaters. These types of PTCs have a nonlinear change in resistance with increases in temperature. This device will be used as a temperature-controlling heating element intended to power an Insulated Solar Electric Cooker (ISEC). The ISEC is designed to cook meals throughout the day for impacted communities as an alternative cooking method that doesn’t require biofuel as an energy …

An Inexpensive Maximum Power Point Tracking System For An Insulated Solar Electric Cooker Using An Off-The-Shelf Buck Converter, Andrew A. Perez

Physics

A specialized control circuit using an off-the-shelf buck converter is built for an Insulated Solar Electric Cooker (ISEC). Cost and efficient power delivery are the focus. An ISEC is synonymous to a direct load heat resistor, allowing a specific maximum power point tracking (MPPT) algorithm and fewer components. Only a microcontroller, voltage sensor, and digital-to-analog converter are used with the buck converter to maximize the power delivered by a 100W solar panel for the 3.3Ω load.

A Conical-Beam Dual-Band Double Aperture-Coupled Stacked Elliptical Patch Antenna Design For 5g, Feza Turgay Çeli̇k, Kami̇l Karaçuha

Turkish Journal of Electrical Engineering and Computer Sciences

This study investigates a dual-band, aperture coupled and stacked elliptical patch antenna with conical radiation. To obtain such characteristics, the TM21 mode of the radiating elliptical patches is excited by utilizing two special apertures and shorting planes. The proposed antenna operates both at 2.45 GHz and 3.5 GHz. The design aims to be used indoor applications of 5G operating in both free and planned 5G bands, respectively. Therefore, the low-profile antenna element that has a monopole-like radiation pattern is a good candidate for two-dimensional arraying. The design steps and the evolution of the proposed antenna are presented in detail. The …

Segmentation Of Diatoms Using Edge Detection And Deep Learning, Hüseyi̇n Gündüz, Cüneyd Nadi̇r Solak, Serkan Günal

Turkish Journal of Electrical Engineering and Computer Sciences

Diatoms are photosynthesizing algae found in almost every aquatic environment. Detecting the number and diversity of diatoms is very important to analyze water quality appropriately. Accurate segmentation of diatoms is therefore crucial for this detection process. In this study, a new and effective model for the automatic segmentation of diatoms based on image processing and deep learning algorithms is proposed. In the proposed model, edge segments of a given image containing diatoms and nondiatom particles are first obtained. These edge segments are then combined, resulting in closed contours representing diatom candidates. In the final step, the diatom candidates are classified …

Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke

Turkish Journal of Electrical Engineering and Computer Sciences

Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, …

Error Analysis Of Siso And Dual-Branch Communications With Generalized Gaussian Noise Over Ftr Fading Channels, Mehmet Bi̇li̇m

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we present error probability analysis for single-input single-output (SISO) and asymmetric dual-branch networks with additive white generalized Gaussian noise (AWGGN) over millimeter-wave (mmW) fluctuating two-ray (FTR) fading channels. Then, we examine the error probability evaluation of a SISO system with imperfect phase errors over mmW FTR fading channels. The probability density function (PDF) approach is employed for the error probability performance evaluation and the novel PDF of the asymmetric dual-branch system over Nakagami-m/mmW FTR fading channels is obtained. Specifically, closed-form expressions are derived for the error probability of the SISO and asymmetric dual-branch networks. The derived error …

Comparison Of Ml Algorithms To Distinguish Between Human Or Human-Like Targets Using The Hog Features Of Range-Time And Range-Doppler Images In Through-The-Wall Applications, Yunus Emre Acar, İsmai̇l Saritaş, Ercan Yaldiz

Turkish Journal of Electrical Engineering and Computer Sciences

When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the …

Track Density Imaging Using Diffusion Tensor Imaging Data From 1.5 T Mri Scanner, Fatma Betül Köşker, Kazim Zi̇ya Gümüş, Mahmut Tokmakçi, Ni̇yazi̇ Acer, Serkan Şenol, Mehmet Bi̇lgen

Turkish Journal of Electrical Engineering and Computer Sciences

Superresolution track density imaging (TDI) has recently been developed for achieving high resolution track density maps from low-resolution diffusion images acquired at 3 T. But, the utility of the approach is still unclear when applied to diffusion tensor imaging (DTI) data acquired at lower 1.5 T magnetic field strength and thus its advantages or disadvantages awaits for exploration. We implemented an algorithm to generate track density maps of human white matter using streamline tracking and tested its performance with data acquired from two healthy volunteers at 1.5 Tesla. The effects of number of diffusion directions and seed selections on the …

Building A Surrogate Model Of A Perfect Electric Conductor Using Polynomial Chaos Expansion And The Characteristic Mode Analysis, Adem Yilmaz, Hulusi̇ Açikgöz, Alaaldeen Barakat Ahmed Elrouby

Turkish Journal of Electrical Engineering and Computer Sciences

A surrogate model for a perfect electric conductor plate is built by using the polynomial chaos expansion method. The plate is excited via four capacitive coupling elements for which the locations are determined by the analysis of the current distribution for each mode provided by the characteristic mode analysis. A numerical model based on the method of moments is then created to generate a database needed to build the surrogate model. Radiation patterns calculated by the surrogate model are compared with those given by the numerical model. The results show that the surrogate model can mimic the numerical model and …

A New Approach For Congestive Heart Failure And Arrhythmia Classification Using Downsampling Local Binary Patterns With Lstm, Süleyman Akdağ, Fatma Kuncan, Yilmaz Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Electrocardiogram (ECG) is a vital diagnosis approach for the rapid explication and detection of various heart diseases, especially cardiac arrest, sinus rhythms, and heart failure. For this purpose, in this study, a different perspective based on downsampling one-dimensional-local binary pattern (1D-DS-LBP) and long short-term memory (LSTM) is presented for the categorization of Electrocardiogram (ECG) signals. A transformation method named 1DDS-LBP has been presented for Electrocardiogram signals. The 1D-DS-LBP method processes the bigness smallness relationship between neighbors. According to the proposed method, by downsampling the signal, the histograms of 1D local binary patterns (1D-LBP) calculated from the obtained signal groups are …

A Compact Pattern Reconfigurable Antenna Employing Shorted Quarterwave Patch Antennas, Feza Turgay Çeli̇k, Lale Alatan, Hati̇ce Özlem Aydin Çi̇vi̇

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a compact pattern reconfigurable antenna structure is proposed. The proposed antenna is a half-wave square microstrip patch antenna divided into two quarter-wave antenna portions by a conducting wall. This wall forces potential zero at the connection point; therefore, it separates the antenna into two independent quarter-wave portions. Pattern reconfiguration is achieved by separate feeding of the quarter-wave portions. Phase difference between excitations of antenna ports result in variation at the maximum beam direction. Hence, pattern reconfiguration is achieved. Within such a compact antenna, beam steering up to 400 is achieved

A Quantitative Evaluation Of Explainable Ai Methods Using The Depth Of Decision Tree, Nizar Abdulaziz Mahyoub Ahmed, Adi̇l Alpkoçak

Turkish Journal of Electrical Engineering and Computer Sciences

It is necessary to develop an explainable model to clarify how and why a medical model makes a particular decision. Local posthoc explainable AI (XAI) techniques, such as SHAP and LIME, interpret classification system predictions by displaying the most important features and rules underlying any prediction locally. Therefore, in order to compare two or more XAI methods, they must first be evaluated qualitatively or quantitatively. This paper proposes quantitative XAI evaluation metrics that are not based on biased and subjective human judgment. On the other hand, it is dependent on the depth of the decision tree (DT) to automatically and …