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Spatial Variability of Turfgrass Stress Responses in Golf Course Fairways using Drone Imagery
Get PDFAdvancements in technology have enhanced the ability of land managers to evaluate plant stress using vegetation indices. Vegetation indices are a valuable tool in evaluating plant systems for stress that may include water deficit, nutrient deficiency, pest development, or abiotic stress. Managers then scout areas of high stress recognition to identify the underlying cause of the stress. These techniques have not been thoroughly evaluated for potential application in turfgrass systems. The integration of remote sensing technology in precision turfgrass management (PTM) offers new avenues for monitoring and managing drought stress in golf course fairways. The objective of this study was to utilize small unmanned aerial systems (sUAS) equipped with multispectral cameras to capture normalized difference vegetation index (NDVI), enabling the detection of drought-stressed areas in fairway turfgrass. Two unique near infrared (NIR) bands (850 and 970 nm) were used in NDVI determination with 850 nm being a standard NIR band and 970 nm being more specifically targeted at plant water stress. The 970 nm band resulted in higher NDVI calculated across nearly all flights at two golf courses. There were significant differences in NDVI observed across flight dates. Moreover, 10-meter parcels of each golf course fairway notated areas of lower or higher plant stress. High stress zones within each fairway can then be evaluated more closely with the golf course management team to diagnose the primary stress and determine corrective actions to improve plant health. These results are critically important because they demonstrate techniques to quickly discern areas of plant stress and limit the need for comparable management inputs over the entire fairway. This alone can have a significant impact on golf course management because targeted solutions will require less time, resources, and labor to apply. Furthermore, reducing inputs while still maintaining consistent plant health and playability standards enhances sustainability of golf course management
Enhanced Oil Field Data-Wrangling using Machine Learning
No full textWithin every aspect of the petroleum industry, time is equivalent to money. Using time wisely is critical to the success of a good operation, whether discovering new oil reserves, determining the best method for reworking a poorly producing site, or simply tracking and analyzing existing production over an entire field. Technology changes rapidly, and understanding the associated information must keep up.
Data has become the new oil of this millennium, where advancements in artificial intelligence and machine learning have taken over almost every aspect. With the capability of new analytical methods came the drastic increase in the quantity of data that has been gathered. The oil and gas industry generates vast amounts of data daily, ranging from sensor readings and geospatial data to operational metrics collected from drilling sites, refineries, and pipelines. A new data paradigm, initially coined as “big data,” quickly grew in scope until previously acceptable data handling methods could no longer keep up. The time needed to process data can now require weeks, months, or even years in some cases when this processed data is usually required immediately. In an industry where precise and timely insights are crucial, data quality issues can result in costly mistakes, suboptimal resource allocation, and even safety risks.
This research covers Data Quality and why proper data handling is necessary for correct judgment. It also quantifies ways to quickly improve data by automating anomaly detection and correcting missing data through imputation. While still not as skillful as a team tasked with sifting and cleaning data, automation of the data-cleaning process allows for quick and timely judgment with far more complete information than would usually be available. While the availability of oilfield data has improved over the years, the next step is to keep it properly “fit for use” by those who need it
Leveraging Teacher Collective Efficacy in Professional Learning Communities to Increase Student Achievement of Special Education Students
No full textThis action research study investigates the impact of design-based interventions: Professional Learning Communities (PLC), targeted and specific professional development and data-driven instructional plans have on increasing Collective Teacher Efficacy (CTE) for special education students in an early childhood campus. This study was driven by the following research questions: In what ways can targeted professional development in a growth mindset, engaging in meaningful collaborative Professional Learning Communities (PLC), and data-driven instructional plans impact teachers’ beliefs about their ability to improve the achievement of special education students? What are the lived experiences of teachers building Collective Teacher Efficacy (CTE) through Professional Learning Communities (PLC) engaging in professional development focused on a growth mindset, collective teacher efficacy, and data-driven instructional practices for Special Education students?
Specific and supported professional development on growth mindset, collaborative PLCs that focus on identifying students' needs, and data-driven instructional plans to meet the needs of Special Education (SE) students are critical to strengthening Collective Teacher Efficacy (CTE). Focusing on the identified change drivers and using the multiple sources of CTE, the participants in the study identified and fostered a more profound belief in their abilities to be changemakers in PLCs and support colleagues in building CTE for special education students.
The long-term goal of this study and the interventions designed is to close the gaps in academic growth for special education students by changing adult beliefs and practices for special education students that, in turn, cultivate Collective Teacher Efficacy
Themes of Toxic Masculinity in Mass Shooter Manifestos: A Content Analysis
No full textThis study uses content analysis methodology to examine themes of toxic masculinity in a sample of manifestos written by individuals who have committed mass shootings in the United States. Two research questions will be examined: 1. Do manifestos of mass shooters contain elements of toxic masculinity? And 2. What do these perpetrators perceive to be influences of their crimes? Manifestos gathered from online databases will be coded and analyzed. Implications for research, practice, and limitations to the project will be discussed
Soil Moisture Prediction Using Machine Learning Techniques
No full textIn the Texas High Plains, water is mainly obtained through groundwater pumping from the Ogallala Aquifer. However, this source of water is being depleted faster than it can be replenished creating a groundwater shortage. The main draw from the Ogallala Aquifer in the Texas High Plains region is for irrigation. This research aims to begin creating a series of models with the end goal of producing a tool producers can use to predict when irrigation is necessary or not using an economic threshold for irrigation. By using a soil moisture predictive model in a tool producers can use to determine irrigation necessity, the goal is to allow producers to become more profitable while focusing on water use and allocate their resources more effectively. The soil moisture predictions will allow the final tool to determine whether irrigation is needed to help maintain the growth of the crop or if it is not timely and will cost more than the producer will profit from irrigation use.
Initial outcome of the model created in this research would be to create a baseline for the irrigation threshold under West Texas environments, thus, allowing for irrigation use to be optimized on farms based on profitability. The first step in building this tool is to utilize machine learning techniques to develop models using data on weather, soil characteristics, and existing stocks of soil moisture that best predict future soil moisture. The value of additional data is analyzed to determine best predictions for use in optimization and management decisions. In the long term, the developed model will be further tested using different management systems on various fields to better deliver a robust and flexible model for the enhancement of regional water use efficiency
3D Printing of Synthetic Organs: Mechanical Testing and Manufacturing Characterization of Blood Vessels
No full textThe field of biomedical engineering has seen transformative advancements with the introduction of 3D printing technology, particularly in the fabrication of synthetic organs. This study focuses on the mechanical testing of 3D-printed synthetic organs and material characterization to assess their performance in medical applications. Through using printing design, materials and techniques, synthetic organs can be produced with high precision, mimicking the complex structures and mechanical properties of natural tissues. This research investigates the mechanical property such as tensile strength, of different types of synthetic biomaterials such as resin, TPU materials and the printing accuracy of the blood vessel. The samples were prepared with dumbbell shape and subjected to uniaxial tensile tests according to the ASTM D412 standard. The data obtained from the tensile test is further analyzed using the Curve Fitter 2022 tool, the curve is plotted using the Uniaxial tensile test data, and the hyper-elastic model is applied to the study. Hyper-elastic models are used to describe the non-linear stress-strain behavior of these materials. Two prominent hyper-elastic models were used: the Yeoh 3rd order model and the Ogden 3rd order model and the constants of the model were calculated. Both models offer detailed mathematical frameworks for predicting the mechanical response of materials under large deformations, although the TPU material has a better elongation property than resin, making it good for applications requiring elasticity and resilience. The data helps in understanding how both materials behave under different stress and strain conditions at the same room temperature, which is important for designing flexible biomaterials that can withstand stress. However, it was observed that Yeoh model was better fitted for the test data compared to the Ogden model. The findings aim to enhance the understanding of material behavior under physical conditions, thereby informing the design and development of reliable synthetic organs. The results of this work are to contribute significantly to the fields of tissue engineering and regenerative medicine, ultimately improving patient outcomes in organ transplantation and repair
Rural Crossroads: The Influence of Rurality on Education Policy Decisions and Subsequent STEM Outcomes
No full textThis dissertation considers educational policy implementation in rural Texas schools across a variety of policy types and STEM outcomes. Rural schools have unique limitations to their resources, setting them apart from their suburban or urban counterparts. Multiple initiatives can stretch these resources thin and require tough decisions that impact both the students and their communities. Using quantitative causal methods and descriptive analysis of state-wide data from the University of Houston’s Education Research Center (UH-ERC), rural implementation of the policies: Foundation High School Program (FHSP) established by state House Bill 5, bond issues for capital improvements, and Computer Science for All are examined and evaluated. The longitudinal data used in this study comes from the Texas Education Agency (TEA) and the Texas Higher Education Coordinating Board (THECB), which are used to consider both short- and long-term student outcomes post-policy implementation.
The first study takes a deeper look at how the term rural is defined and how that definition can influence findings. Three differing classification systems for rurality are compared, first by their definition and student demographics and then through the lens of evaluating the implementation of FHSP. Findings suggest there are key differences in the results linked to how the term rural is defined, highlighting the importance of using a shared and clear definition when considering rural policy outcomes.
The second study considers bond issues for capital improvements in rural schools and their links to STEM outcomes both in school and after graduation. While renovations and capital improvements are often much needed, they can divert administrative focus. Understanding the long-term implications of these efforts is important for informing decision-making on how time and resources are spent. This causal analysis relies on a regression discontinuity design (RDD) comparing those districts that narrowly pass a bond issue and those that narrowly do not. Findings show that while there are more immediate benefits to test scores in mathematics, there is little benefit to long-term outcomes, including teacher retention, high school course-taking, certification, or degree obtainment.
The third study considers long-term outcomes for rural students who engage in computer science (CS) in high school. Using a latent class analysis (LCA) with propensity score matching (PSM), this causal analysis first identifies clusters of course-taking for CS students and then focuses on the class of students who engage in skills-based courses. The PSM is used to compare the outcomes for these students to those who did not engage in a skills-based pathway. Since 50% percent of rural Texas schools do not offer any CS courses, by running a second level of evaluation with only these students, a measure of unobserved variable bias can be determined and subtracted from the model to determine causal links between course-taking and post-secondary outcomes. Findings show those students who are likely to engage in a skills-based set of courses are six percentage points more likely to pursue a STEM degree in college
Visual, Auditory, and Tactile Vigilance: The Effect of Sensory Modality on Vigilance Performance and Cerebral Hemodynamics
No full textVigilance tasks require an individual to sustain attention for an extended period of time in order to monitor for sporadic and unpredictable signals. A typical consequence of performing a vigilance task is a decline in performance throughout time on a task, known as the vigilance decrement. The vigilance decrement is a major concern as maintaining vigilance performance is essential to safety and system performance in a variety of operational settings. Vigilance tasks exist in operational settings as either visual, auditory, or tactile tasks. However, past research has been unable to determine conclusively whether sensory modality affects vigilance performance. Some research demonstrates that vigilance performance levels differ between modalities, which suggests that different modality-specific neurocognitive mechanisms may support vigilance performance in each sensory modality. By contrast, other research has shown that performance is equivalent across sensory modalities, which supports the view that the same supramodal neurocognitive mechanisms are used for vigilance, regardless of task modality. The divergent findings in past research may have been due to variable and inadequate crossmodal matching procedures. Specifically, it is possible that the studies that observed differences between modalities failed to control for variables that could confound modality comparisons (e.g., stimulus discriminability, stimulus intensity). Support for this concern comes from studies that matched modalities for possible confounding differences; between-modality performance differences were reduced or eliminated. Unfortunately, the crossmodal matching methods used in past research may not have effectively eliminated confounds. There is some direct evidence of failures of crossmodal matching efforts, and studies using the same crossmodal matching methods have provided very different conclusions about whether modality affects vigilance performance. The aim of this dissertation was to improve upon the crossmodal matching methods in previous research to determine whether mean vigilance performance levels and the severity of the vigilance decrement are similar or different between visual, auditory, and tactile modalities. Measurement of cerebral hemodynamics was used to further inform whether the neurocognitive mechanisms governing vigilance performance are supramodal or modality-specific. The findings of the current study suggest that methods used to equate sensory modalities addressed issues that may have led to the controversy in findings of previous vigilance research. Performance and cerebral blood flow velocity (CBFV) results were all equivalent across the crossmodally-equated visual, auditory, and tactile vigilance tasks, suggesting that mainly supramodal neurocognitive mechanisms govern vigilance performance. These findings suggest that vigilance tasks may be designed in different sensory modalities without concern for differential effects on performance between modalities. However, future research should investigate the extent to which modality-specific differences in variables that were addressed in this dissertation (e.g., stimulus discriminability, stimulus intensity) may still affect performance in operational settings. These findings also suggest that tracking the neurophysiological dynamics associated with vigilance using CBFV may be possible regardless of sensory modality. Future research should investigate the effectiveness of other neuroimaging methods in tracking the neurophysiological dynamics associated with vigilance across sensory modalities