TTU Published Journals @ Volpe Library
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*WINNER* Further Optimization of an Ultracold Neutron Spin Dynamics Simulation Code
No full textThe UCNτ experiment is designed to measure an ultracold neutron’s (UCN’s) mean lifetime when trapped by a magnetic field before undergoing beta-decay. One value needed to determine this lifetime to high precision is a UCN’s depolarization rate. This value is difficult to measure empirically because it’s very small, but simulations may be used to estimate it. One simulation we are developing can be validated by comparing to actual trap lifetimes measured at different holding fields. Due to the amount of required computation, on a 10-core computer running Ubuntu 16.04 this code initially took about 36 hours to simulate 100 disjoint UCNs; but we really want to simulate in batches of millions of UCNs to get good statistics, which would take over 40 years to complete on the same hardware when scaled up. Optimization of the simulation code was required for better time efficiency. This code was fully converted to C++ from Python after trying optimization methods in Python and before using basic parallel programming methods, resulting in code 169 times faster than its Python equivalent. Interesting issues encountered during the conversion process, optimization methods used, and some parallel programming methods used to further enhance efficiency will be presented
*WINNER* The relation of microbial biomass carbon with denitrification and nutrient retention in restored floodplain wetlands
No full textRestoration activities have been implemented in the lower Mississippi River basin through the Wetlands Reserve Program (WRP) to enhance essential functions by restoring wetlands. Soil can be a zone of significant nutrient retention in wetlands by providing an environment for microbial development and nutrient processing. The objective of this study was to assess soil microbe-based ecosystem function in three restored riparian wetlands in Western Kentucky, focusing on the relationship between microbial biomass and nutrient removal. We hypothesized that nutrient retention, and specifically denitrification is directly related to microbial biomass carbon (MBC). At each easement, thirty sediment cores were collected and used to measure a suite of soil processes and properties, including soil nitrogen (N) and phosphorus (P) uptake rates, denitrification potential, soil moisture, and MBC. MBC was significantly higher in undisturbed older habitat, followed by remnant forest, and lowest in the shallow water areas. Denitrification potential, and N and P uptake were strongly correlated with MBC and soil moisture. Our results suggest that increasing total soil microorganism biomass is an indicator for monitoring improvements in ecological functions in restored wetlands, with potentially important long-term consequences for denitrification and nutrient retention
A Case Study in Digit Recognition
No full textDigit Recognition is a computer vision technique to predict the numerical value of digits in a dataset. The issue is that handwritten digits are not always the same size and that these digits are not always written the same way from person to person. The goal of this project is to take an image of a handwritten image single digit and determine what it is. The objective is to build an efficient model with an accuracy of at least 90% through testing and training on a Kaggle dataset. We will explore various machine learning algorithms such as SVM and K nearest neighbors as to whether they improve the efficiency of recognizing the hand-drawn digits. We present our findings through various metrics and graphical representations
*WINNER* Sentiment Analysis Using Google's Word2Vec Machine Learning Method
No full textNatural language processing is an important research area of Artificial Intelligence. By competing in the “Bag of Words meets Bag of Popcorn” Kaggle challenge, we plan to produce a machine learning model that has been trained to analyze movie reviews and understand the meaning and semantic relationship among words to determine the sentiment behind the reviews. Our model should be able to distinguish between positive and negative reviews using Google’s Word2Vec, a deep-learning inspired method that focuses on the meaning of words. After our model has been trained using two different data sets (one containing reviews with a positive or negative sentiment label, the other containing reviews without sentiment labels), we will be able to test our model and determine its accuracy. After analyzing our model, we will produce graphical representations of our analysis and collected data. For analysis and goal evaluation, we will be computing a classification accuracy score and plotting an ROC curve. The classification accuracy score should be above 50% and by visually inspecting the ROC curve, the line should be above the line of no-discrimination for the model to be considered successful. We will also generate a report of outcomes wherein we will assess and evaluate the model’s performance and accuracy and provide insight into any changes or improvements that could be made in the future to improve upon the performance of the model
Trog Sink and its Hydrologic Effects on Head Waters of the East Blackburn Fork River
Get PDFA hydrologic model was developed to predict runoff in an urban watershed in Cookeville, TN. In the research area there resides a massive sinkhole responsible for storing and transmitting storm water to the East Blackburn Fork River. The sinkhole is hypothesized to store excess rain water, and release it at a steady rate. Maintaining a higher baseflow discharge well after storms have passed over the watershed. A rain gauge and two stream gauges were deployed to record water level in the sinkhole and at a spring known to be its outlet. ArcGIS Pro software was used to calculate the watershed area and interpret the terrain of the watershed. The hydrologic model HEC-HMS (Army Corps of Engineers) was used to model runoff from a rain event that happened on December 5, 2020. Results showed a normal hydrograph with peak rainfall and a fairly quick return to baseflow estimated at hours compared to the time recorded in field data. Field data showed Trog sink retaining a large volume of water about 8.5ft in height at its maximum, and not allowing the spring to return to base flow for roughly thirteen days. Further research and modeling are hypothesized to display Trog sinks actual retention pattern in a hydrograph and become more synonymous with the field data during the rain events
A Survey Overviewing Technological Aspects of Wastewater Treatment Facilities in the State of Tennessee
Get PDFWastewater is produced from several industrial and anthropogenic activities and includes the sources such as showers, sinks, washing machines, dishwasher, toilet, etc. It contains microbes, pathogens, and several other organic and inorganic substances that are harmful to the environment and that must be removed before the water can safely be returned to natural streams. This sewage is pumped to the cleaning facilities through the drainage system. The treatment facilities called wastewater treatment plants (WWTPs) are operated in the cities at different capacities suitable to handle the water volume. Although all these facilities display a basically similar treatment process, there exists a few variations depending upon the capacities, location, cost of operation, population it serves, and type of contaminants required to remove. In this research, a comprehensive report illustrating the different aspects of WWTPs in the State of Tennessee will be drafted. This will include evaluating and summarizing the similarities and variations among the different WWTPs: For example, some facilities implement chlorination methods in tertiary treatment unit; others, UV-based methods, and more advanced cases use UV photocatalysis, etc. The research will also include a general discussion about the pros and cons of these similarities and differences and recommend some potentially novel technologies. These may be susceptible of upscaling and adaptable to treat sewage more effectively and less costly. We believe that the outcome of this research will be useful information for potentially improving sewage treatment across the State of Tennessee of the current scenarios and provides a future direction
Unipolar Resonant Capacitive Power Transfer For Surface-Powered Cyber-Physical Systems
Get PDFSensors are a critical part of many Cyber-Physical Systems (CPS), and providing power to them is becoming increasingly challenging due to routing constraints. Wireless Power Transfer (WPT) can offer solutions that reduce the routing constraints, but many wireless power transfer approaches are complex, not scalable, or are not feasible for continuous power delivery to multiple sensors. We propose an efficient, inexpensive, simple, and scalable method for continuously providing power to sensor nodes using Quasi-Wireless Capacitive (QWiC) power transfer. QWiC power transfer allows for power to be transferred capacitively or over a conductive surface, simplifying routing constraints and transmitter complexity by removing the need for a dedicated transmitter. In this experiment, we demonstrate surface powered sensor nodes with a peak efficiency over 68% and multiple sensor nodes being powered over a single surface
Using Machine Learning Techniques to Predict the Dimensional Changes of Low-cost Metal Material Extrusion Fabricated Parts
No full textAdditive manufacturing (AM) is a widely used layer-by-layer manufacturing process. However, it is limited by material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is the most widely used AM technologies. Thus, it is adopted in this research. Low-cost metal ME is a new AM technology used to fabricate metal composite parts using sintering metal infused filament material. Since the materials and the process are relatively new, there is a need to investigate the dimensional accuracy of low-cost metal ME fabricated parts for real-world applications. Each step of the manufacturing process such as 3D printing of the samples and the sintering will affect the dimensional accuracy significantly. By using several machine learning (ML) algorithms, a comprehensive analysis of dimensional changes of metal samples fabricated by low-cost metal ME process is developed in this research. ML methods can assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. In this study, single linear regression, linear regression with interactions and neural networks were utilized to assess and predict the dimensional changes of components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy among the other ML methods. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process
Seeing the Value in Der Strewwelpeter
No full textDer Struwwelpeter is a staple of German children’s literature written by Dr. Heinrich Hoffman in the mid 19th century. The book contains ten illustrated stories written in verse meant to teach children moral lessons, often ending with them suffering for their indiscretions. In the stories, children burn to death, waste away from malnutrition, and, most infamously, have their thumbs cut off by a tailor because of bad or unacceptable behavior. Regardless of the book’s enduring popularity, the question of whether it succeeds as a pedagogical narrative continues to be asked due to the graphic content described within. While Der Struwwelpeter itself is not appropriate for the 21st century, its structure as a narrative can be valuable in the modern day. Despite the controversy surrounding it, Der Struwwelpeter served as a successful and effective educational text in its time, and it still has something to offer now
Child Life Specialists provide effective interventions throughout a child's hospital stay
No full textCan working with a Certified Child Life Specialist help a child throughout their hospital stay? Certified Child Life Specialists (CCLS) are healthcare workers who provide developmentally appropriate interventions such as play, procedural preparation, and effective teaching that help children to reduce the fears and anxieties that the hospital may bring. Many children will experience a hospital visit at least once throughout their lifetime. Often a child will need medical attention for chronic illnesses, acute injuries, broken bones, etc. When a child is admitted into the hospital for any reason they may find themselves working with a CCLS. Children may view the hospital as a scary, unfamiliar place which can cause them to be overcome by anxiety. However, CCLS can provide specific resources such as play, education techniques, games, and more to help patients and their families cope with their hospitalization. The goal of this literature synthesis is to examine how CCLS helps children and families overcome their hospital stay by providing age-appropriate interventions to help minimize the negative effects of hospitalization. Reviewing existing research articles will provide an understanding of what services Certified Child Life Specialists provide along with an understanding of the effectiveness of having a CCLS present throughout the child and family’s hospital stay. Throughout literature, there is evidence of positive effects a child life specialist can have on a child’s hospitalization. Research trends indicate that CCLS can help families’ satisfaction throughout their hospital stay and provide effective interventions such as procedure preparation and medical play