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Exploration of groundwater knowledge and private well programs across the United States
No full textGroundwater serves one third of the United States (US) population as their main source of drinking water. Groundwater accessed from private wells serves about 15% of the population. Private well water is not regulated by the federal government, and less than half of states regulate private well water quality. Thus, the responsibility of management falls on the private well owner. Management involves many facets such as knowledge about well type, well depth, local hydrogeology, local landcover and land use, and how and when to test water quality. These factors contribute to water quality and may be difficult to find information about, furthermore implementing management may be arduous. Well stewardship, a form of management, is a way for well owners to learn about their wells and well water. Well stewardship includes annual testing of water quality and can be facilitated in many ways. One way stewardship is encouraged is through outreach, like private well programs (PWPs). PWPs utilize educational materials like online resources and handbooks as well as educational events like workshops and webinars lead by well water professionals to aid well owners in making management decisions. Previous researchers have evaluated barriers to well stewardship and effective methods for outreach PWPs to promote stewardship. However, there is no central resource about PWPs across the US. Therefore, the first objective of this study was to create an inventory and webmap of PWPs and resources across the US, and to identify areas that may need resources. Methods similar to a literature review were utilized, and search terms were generated and conducted on all 50 states and data was collected about programs and resources. In addition, few studies have used a mixed methods approach to understand how PWPs affect knowledge about groundwater. Without knowledge about groundwater and stewardship, well owners may not be aware of potential risks and ways to prevent them. Thus, the second objective of this study was to deploy the Groundwater Concept Inventory (GWCI) to a well owner population to explore the differences in knowledge between well owners who participated in PWPs and those who did not. Statistical analysis revealed a significant difference between respondent’s groundwater knowledge. Then, program coordinators of PWPs were interviewed to elucidate how programs engage with well owners using thematic coding.
Our results show that 64% of states had an established PWP and that 72% of PWPs are housed in Cooperative Extension. Results also found that 18% of states had no programs or resources (https://aub.ie/pwpinventory). This could be due to a small well owner population in the state or regulation in the state. The second objective results shows that private well owners that participated with a PWP had more groundwater knowledge than well owners that had not participated (t-Test = 2.18; p = 0.038), leading us to deduce that PWPs do result in more knowledge. Results from thematic analysis of interviews found four themes: Program Establishment, Program Purpose, Engagement, and Testing. The most common advice from interviewees was for well owners to test their well water, which PWPs can help with. The findings from these studies can be useful to program coordinators to connect with other states and develop programs in states without PWP. In addition, we encourage funding, access, and awareness of PWPs
Additive Nanomanufacturing of Multifunctional and Multimaterial Devices
No full textThere is always great interest in finding new advanced manufacturing techniques to pave the way for the realization of future flexible and wearable electronics. Direct printing of functional materials, structures, and devices on various platforms, such as flexible to rigid substrates, is of interest for applications ranging from electronics to energy and sensing to biomedical devices. Current additive manufacturing (AM) at microscale processes is either limited by the available sources of functional materials or requires precisely designed inks. In addition, surfactants/additives in inks add further printing complexity and contamination issues to the process. Here, we report a novel laser-based additive nanomanufacturing (ANM) approach capable of in-situ and on-demand generations of nanoparticles that can serve as nanoscale building blocks for real-time sintering and printing of various multifunctional materials and patterns at atmospheric pressure and temperature. We show the ability to print different materials, including titanium dioxide (TiO2), barium titanate (BTO), and indium tin oxide (ITO), on various rigid and flexible platforms such as silicon dioxide (SiO2), paper, polydimethylsiloxane (PDMS), and polyethylene terephthalate (PET) substrates. This nonequilibrium process involves a pulsed laser to ablate targets and in-situ formation of pure amorphous nanoparticles at atmospheric pressure and temperature. These amorphous nanoparticles are then guided through a nozzle via an inert carrier gas onto the surface of the substrate, where they are sintered/crystallized in real time. We further show the process-structure relationship of the printed materials from nano to microscale.
We demonstrated the dry printing and additive nanomanufacturing of flexible hybrid electronics and sensors on flexible polyimide and PET substrates. The electrical and mechanical characterization of the printed lines are studied, different flexible hybrid electronics designs are printed, and the performance of the devices is tested.
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In order to print lateral and vertical hybrid structures and devices, we adjusted and used the ANM printer’s multimaterial additive nanomanufacturing (M-ANM) function. Numerous multimaterial devices were produced and tested, including hybrid silver/aluminum oxide (Ag/Al2O3) circuits and silver/zinc oxide (Ag/ZnO) photodetectors.
Since copper (Cu) is the 25th most prevalent metal in the world, it is less expensive than silver (Ag). Because of its enormous potential, printing Cu has gained increased attention. The ANM method allowed us to achieve the resistivity of 12 μΩ.cm. A 5-month measurement of the printed Cu's resistance revealed very little fluctuation, indicating the long-term durability of the printed Cu. The ASTM adhesion test's 5B classification verified the good adhesion on the substrates.
Among the plethora of semiconductor materials, Zinc Oxide (ZnO) emerges as a compelling candidate for Schottky diode fabrication, propelled by its unique properties and promising prospects in electronic devices. ZnO, with its wide bandgap, high electron mobility, and intrinsic stability, presents an intriguing avenue for enhancing device performance and functionality. Its compatibility with various deposition techniques further accentuates its appeal, enabling precise control over device architecture and characteristics. The LASED process used in this study is a dry multi-material printing technology that enables the on-demand generation of nanoparticles from solid sputtering targets and subsequently laser-sinters them in real time, creating 2D patterns. The Schottky diode fabrication strategy, Raman, XRD and current-voltage (I-V) analysis are presented
Engineering Functional Materials from Cellulose Nanocrystals by Exploring Their Structure and Property Relationships
No full textThe objective of this research was to utilize cellulose nanocrystals (CNC), a biobased nanomaterial, to understand their fundamental dispersion behavior and to develop CNC based films with high transparency, flexibility, and good optical qualities, which can be used in film packaging, coatings in the electronics industry, or as piezoelectric and optical sensors.
First, the research aimed at exploring the impact of electrolytes on the orientation of cellulose nanocrystals (CNC) in a mechanically shear-cast thin solid films prepared from CNC aqueous gels. Alignment in CNC films are the result of both the ease of achieving shear-induced alignment in the dispersion and the ability to retain that alignment during drying. Changes in the aqueous CNC gels’ rheological properties with electrolyte addition were correlated to the orientation and optical properties of dried CNC films. Film alignment was qualitatively assessed using cross-polarized optical microscopy and quantified by order parameters computed by UV-Vis transmission spectroscopy. Electrolyte addition resulted in an increased alignment in dried CNC films. For pure CNC, the film order parameters remained constant at approximately 0.3 for shear rates from 20 s-1 to 100 s-1. However, higher order parameters were achieved in the presence of electrolytes. Notably, an order parameter of 0.88 was achieved at a shear rate of only 20 s-1. In addition, films produced from dispersions containing electrolytes exhibited improved clarity and haze as well. The results of this work highlight that electrolyte addition can enable higher order parameters at lower shear rates and facilitate the development of aligned CNC films for applications such as polarizers, clear coatings, and piezoelectric materials.
CNCs produced from sulfuric acid hydrolysis contain an anionic sulfate ester group, which play a crucial role in the structural orientation. Thus, its effect on self-assembly was investigated in the CNC self-assembled solid film. NaOH treatment was performed on commercially purchased sulfated CNC to partially desulfate the CNC, and the self-assembled film was produced by pouring it into a Petri dish and allowing to evaporate in ambient conditions. Comparison between pure CNC and NaOH-treated CNC revealed differences in structural orientation of self-assembled solid films. Complete absence of chiral nematic organization was observed after reaction and the formation of the nematic structure was identified through SEM, cross-polarized microscopy, and UV-Vis spectroscopy. The optical properties of the films were thoroughly investigated and exhibited lowered haze and high clarity and sharpness. Lastly, polyvinyl alcohol (PVA) was added to the CNC to observe the effect of PVA on the optical quality of the film. Overall, a transparent and flexible composite film was developed, which could be utilized as a promising candidate in the film packaging application, especially in the electronic industry.
Moreover, a facile method, the freeze-thaw technique, was introduced where, without any external force or chemical reaction, a completely transparent CNC film can be produced by locking the CNC in an oriented manner. To date, self-assembled transparent CNC solid film can be obtained through chemical doping. It was found that the freeze-thaw method completely eliminated the chiral nematic structure, resulting in transparent films without structural color. Detailed internal structure characterization using SEM, XRD, and UV-Vis spectroscopy coupled with optical property analysis revealed a remarkable improvement in the clarity and sharpness of freeze-thaw based CNC films without compromising transmittance and haze, showing the promise of freeze-thaw based CNC films in packaging and coating applications.
Lastly, the study focused on utilizing cellulose nanocrystal (CNC) – polyvinyl alcohol (PVA) composites as optical sensors to detect high humidity conditions and determine water concentration in ethanol. The chiral nematic structure of CNC was used to prepare a colorimetric sensor. Upon the moisture absorption, the composites demonstrate a visual color change. A CNC-PVA sensor was developed, which can detect high humidity with 2 hours of exposure time. 2,2,6,6-tetramethylpiperidin-1-piperidinyloxy oxidized CNC (TEMPO-CNC) having carboxylic functionality was also used to prepare CNC-PVA composite films in order to compare the effect of functional groups on moisture sensitivity. Finally, we demonstrated a facile method for the utilization of the composite as an optical sensor to detect water concentration in ethanol efficiently, which can have applications in polar organic solvent dehydration
Electronics, Pneumatics and Computer Vision for Advancing Droplet-based Microfluidic Automation
No full textThis dissertation explores the subdomain of droplet-based microfluidics flow control, a field of study to precisely manipulate multiphase flow segmentation at the microscale. The focal point of this research is to advance the automation of droplet-based microfluidic systems through the synergistic integration of electronics, pneumatics, and computer vision. By addressing the limita-tions inherent in existing methodologies, this work lays the groundwork for the development of innovative applications.
Chapter 1 introduces droplet microfluidics, highlighting its development from stable emulsions to a versatile tool in scientific research. The chapter reviews key advancements in droplet manipu-lation and analysis, enabling high-throughput screening and precise control. It discusses passive and active droplet control and the integration of optical detection methods. Innovations in fluidic control and unique droplet junction designs have been addressed. Despite these advancements, challenges remain integrating solid-phase techniques and enhancing the accuracy and efficiency of droplet-based systems for broader use. Identifying the key challenges, this chapter provides the outline for the rest of the dissertation where different power-based control systems have been explored in the context of droplet microfluidics.
Chapter 2 focuses on the design and fabrication of microfluidic devices using direct 3D printing and polydimethylsiloxane (PDMS) with 3D-printed templates. The reproducibility of both meth-ods has been validated through the development of various custom microenvironments. The chapter provides a detailed discussion on the curing chemistry of PDMS on 3D templates, fol-lowed by the development, characterization, and application of several microfluidic flow control systems for single and multiple, laminar, and segmented flows.
Chapter 3 explores the automation of microfluidic droplet generation and manipulation through computer programming, utilizing LabVIEW and Arduino to orchestrate a ‘normally closed’ valve system. A multi-step microfluidic automation has been proposed, designed, and validated on the system developed in the previous chapter. This chapter also introduces a serial communication-based valve control system capable of executing multiple operations simultaneously and a high-resolution pressure sensor for detecting rapid pressure changes, to be integrated for device func-tionality and control.
Chapter 4 transitions to a pneumatic automation approach, employing in-house designed, 3D-printed and assembled logic gates for droplet-based manipulation. Characterization of the fun-damental pneumatic logic gates is followed by their applications in controlling single and multiple phase aqueous in oil droplet generation. After demonstrating how the entire droplet generation process can be completely automated without electronic power, the development of a pneumatic multiplexer illustrates a significant move towards minimizing electronic dependency, showcasing the feasibility of a fully pneumatic-controlled microfluidic system.
Chapter 5 introduces computer vision – integrated with serial communication - as a transforma-tive tool for real-time feedback and control of droplet generation. By analyzing microscopic data through Python programming, the system dynamically adjusts valve actuation, optimizing droplet formation and demonstrating a leap towards precision and efficiency in microfluidic automation.
In Chapter 6, the dissertation concludes by contemplating future research directions, emphasiz-ing the potential of these technologies to foster fully autonomous, electronic-less microfluidic devices for a wide array of applications
An Examination of the Emotional Intelligence and Leadership Behaviors of Alabama FFA Officers
Get PDFLeadership has been referenced as, “One of the most observed and least understood phenomena on earth” (Burns, 1978/1995). Although numerous leadership theories occupy the literature and seemingly limitless research on the construct exists, the question still remains – what characteristics, skills, or behaviors constitute an effective leader? This study sought to contribute to the field of leadership research by examining the leadership behaviors and emotional intelligence of a group of adolescent leaders. If these student leaders were determined to possess above-average emotional intelligence, then it might open the door for future research regarding the role that emotional intelligence plays in the success of adolescents serving in positions of leadership.
Numerous studies have examined the connection between the emotional intelligence and leadership ability of college and career adults; however, this relationship has not been as well-researched among the adolescent population. This study endeavored to add to the existing literature by providing insight into the emotional intelligence and leadership behaviors of students who had completed a year of service in a leadership role as an FFA officer. The population consisted of students who had served as an officer at either the chapter, district, or state level in the state of Alabama during the 2023-2024 school year.
Through the utilization of two quantitative survey instruments, the Multifactor Leadership Questionnaire (MLQ) and the Bar-On Emotional Quotient Inventory: Youth Version Short Form (Bar-On EQ:iYV(S)), the officers’ emotional intelligence and perceived leadership behaviors were examined. The collected data determined that these student leaders more frequently possessed transformational leadership behaviors as well as the upper echelon of transactional leadership, and the officers’ mean scores on each of these behaviors aligned with the U.S. norms. However, the emotional intelligence of the officers was above the national average in all but one of the Bar-On EQ:iYV(S) scales. Further research is required to determine if these elevated emotional intelligence scores are indicative of only this group of student leaders or if the connection between leadership behaviors and emotional intelligence is applicable to the adolescent population
Quantifying Effectiveness of Red Flashing LEDs around WRONG-WAY Signs in Deterring Wrong-way Driving: A Before and After Study
Get PDFWrong-way driving (WWD) crashes are a significant safety concern due to their high fatality rates. Traditional countermeasures like static WRONG WAY (WW) signs have shown limited effectiveness. This study evaluates the additional impact of red flashing LEDs around WW sign borders on deterring WWD incidents at a university campus area. Using a Wrong Way Alert System with radar detectors, cameras, and flashing LED-equipped signs, 416 WWD incidents were analyzed over 29 months, comparing periods with LEDs activated and deactivated.
The Two-Proportion Z-Test showed a 20% higher driver turnaround rate with LEDs activated (p<0.001). A Random Forest model identified LED status as the most influential factor, followed by time of day, day of week, and academic calendar period. Results highlight the LED’s enhanced effectiveness during specific contexts. Recommendations include AI-driven enhancements and pavement marking implementation to reduce system false positives and prevent drivers from entering the wrong direction
PianoMentor: A Technological Framework to Teach and Practice Piano/Keyboard Online via Machine Learning with an Embedded Practice Lesson Generator
No full textIn this dissertation, we aim to create an advanced artificial system that enhances its ability to perceive, coordinate, and analyze music scores generated from human performances in real time and revolutionizes online music technique learning. By harnessing cutting-edge technology, this system aims to improve existing music practice methods and foster a new era of self-directed learning among students, thereby significantly impacting music education.
Artificial Intelligence (AI) and Human-Computer Interaction (HCI) have significantly advanced computer music systems, enabling them to collaborate with humans across various applications. This dissertation employs various techniques, particularly machine learning and deep learning algorithms, to offer music students and educators a more intuitive and effective experience. The research focuses on three main aspects of human-computer collaborative music practice:
1- Automating music transcription to extract pitch and timing information.
2- Generating real-time performance analysis reports as annotated music scores.
3- Developing practice exercises based on students' performances while adapting different practice strategies.
We utilize a framewise pitch detection model based on pitch onset predictions to achieve these goals, allowing a new note to start only when the onset detector confirms its existence. This integrated approach to improving onsets and offsets aligns better with human musical perception.
Additionally, we propose a system that facilitates the visualization and comparison of MIDI files, addressing the abstract nature of MIDI data. Depending on user groups and tasks, we present various visualizations, including card lists for viewing multiple MIDI files, heatmaps for note distribution, a Note Histogram for note occurrence counts, pitch-time charts, adapted MatrixWave visualizations for note sequences, and diagram designs for visualizing similarities between pattern representations of sequences. This system is implemented as a browser application and evaluated using a usage scenario, demonstrating its effectiveness for the specified user tasks and highlighting some limitations.
Finally, we explore the potential of large-scale language models (LLMs) like ChatGPT, ChatMusician, and MusicLang in generating relevant practice exercises based on students' performance. Unlike traditional methods requiring deep musical and statistical knowledge, LLMs enable users to describe their musical desires directly. We evaluate LLMs' composing abilities based on their alignment with user input and the overall quality of their compositions, considering factors like repetitiveness and scale diversity. This research aims to underscore LLMs' strengths, applications, and limitations in music, paving the way for their expanded role in computer-assisted composition and the broader music industry
Spray evaporation on enhanced tube bundles with LGWP refrigerant-miscible oil mixtures
No full textGreenhouse gas emissions stemming from refrigeration systems can be categorized into direct and indirect emissions. Indirect emissions arise as byproducts of electricity generation at power plants, while direct emissions occur when refrigerants are released into the atmosphere during refrigerant production, operation, and end-of-life disposal refrigerant in the equipment. To mitigate these emissions, researchers have proposed adopting refrigerants with low global warming potential (LGWP) and reducing the system charge yield, thus minimizing the direct contribution to emissions. The refrigerant R1234ze(E) (HFO), based on Hydrofluoroolefins, has emerged as a promising low global warming potential (LGWP) candidate for replacing the hydrofluorocarbon-based refrigerant R134a (HFC). Several experimental studies have been carried out to investigate the pool boiling heat transfer characteristics of R1234ze(E) and its comparison with other refrigerants. In commercial and industrial applications, chiller systems often employ flooded-type shell and tube heat exchangers, necessitating a substantial refrigerant charge to ensure efficient operation. In contrast, spray or falling film evaporators are seen as highly promising alternatives to flooded-type heat exchangers, as they demand a significantly smaller refrigerant volume. This attribute is particularly advantageous for mitigating direct emissions from chiller systems. In addition to the minimized refrigerant requirement, falling film evaporators also showcase the potential for elevated heat transfer coefficient (HTC) due to the substantial involvement of both nucleate boiling and convection. This differs from the primarily pool boiling mechanism characteristic of flooded type evaporators. Thus, using low-GWP refrigerants with spray evaporators in refrigeration systems mitigates the adverse effect of current cooling fluids on global warming.
Oil is often used in vapor compression systems for lubricating the moving parts in the compressor. If not removed completely, the oil will flow through the heat exchangers and other components of the system. In heat exchangers, this will affect the flow pattern and both the heat transfer and pressure drop depending on the concentration. In the existing literature, there is a lack of studies on spray evaporation of refrigerant/oil mixture on tube bundles. Even in studies focusing on single tube falling film evaporation, the presence of foam and the formation of dry patches have been reported. With tube bundles, the occurrence of foaming and the bundle effect complicates the heat transfer process, highlighting the importance of addressing these issues for the design of improved spray evaporators. Given the growing interest in low-GWP refrigerants and the significant impact of lubricating oil, this study specifically examines how lubricating oil affects the heat transfer performance of low-GWP refrigerants during spray evaporation on enhanced tube bundles
Using Unoccupied Aircraft System (UAS) to Assess Crop Damage by Wild Pigs in Alabama
Get PDFAgriculture is essential for human sustenance and global economies, cultures, and
societies. However, wildlife damage to crops can significantly diminish productivity,
necessitating effective mitigation strategies. Among the most destructive species are wild pigs
(Sus scrofa), renowned for their severe impact on row crop damage through consumption,
rooting, and trampling. In our study, we assessed the extent of wild pig damage to row crop
fields in southern Alabama, USA. We utilized aerial imagery collected via unoccupied aircraft
systems (UAS) and developed detection models using deep learning algorithms to quantify
damage. Additionally, we evaluated the economic ramifications of wild pig damage on row crops
and analyzed surrounding landscape elements as potential predictors of field predation by wild
pigs. We successfully developed detection models with over 90% accuracy for corn and peanut
crops. However, our attempts to develop a similar model for cotton proved infeasible due to
flying at too high of an altitude, resulting in a ground sampling distance (GSD) with a resolution
that was too large. Corn experienced more frequent damage compared to peanuts and the average
amount of damage was greater for damaged corn fields (0.12 ha, 6.28% overall field damage)
than damaged peanut fields (0.08 ha, 0.38% overall field damage). However, the cumulative
losses were greater for peanut (n = 23 fields, 16.13/ha across damaged
fields) than corn (n = 6 fields, 49.21/ha). Furthermore, crop type, distance to water,
and landscape patch density were significant contributors to the likelihood of wild pig-induced
damage. Our findings offer valuable insights for policymakers, landowners, and wildlife
managers striving to combat the challenges posed by wild pig predation in agricultural
landscapes. By integrating ecological understanding with practical management strategies, we
can effectively address the adverse impacts of wild pig predation and sustain agricultural
productivity
On the development of a Neural Radiance Field technique for tomographic reconstruction applied to flow diagnostics
No full textIn this work, a novel neural implicit representation tomography algorithm based on Neural Radiance Fields is developed and demonstrated for 3D flow diagnostics. Neural Radiance Fields (NeRF) originate from the computer vision community that uses a machine learning approach to approximate a scene of interest as a continuous function using a neural network. The NeRF machine learning concept provides some advantages over traditional tomography methods, including i) a continuous approximation of the volume that removes the inherent limitation on volume resolution that is present in discretized representations, ii) a reduction in memory requirements for the volume prediction, and iii) an adaptable tomography framework that can include additional inputs, outputs, imaging models, and constraints. The method developed in this work, FluidNeRF, predicts the intensity per unit volume as a continuous function of 3D spatial (static) or 4D spatial-temporal (time-resolved) coordinates. FluidNeRF trains similarly to other algebraic reconstruction techniques, where the volume approximation is updated by comparing predicted and captured images of the volume. The image rendering technique of FluidNeRF employs an emission-based imaging model. Static and time-resolved FluidNeRF was evaluated using both i) a DNS-generated turbulent mixing jet and ii) an experimental dataset of a low-speed, smoke-entrained jet flow. The synthetic datasets systematically investigated the hyperparameters, camera configuration, and image noise regarding reconstruction quality. Static FluidNeRF was also compared to a traditional ART-based tomography model. The results show that i) FluidNeRF is a viable technique for tomography of flow diagnostics, ii) FluidNeRF produces comparable or superior reconstruction accuracy and is more robust to noise than traditional tomography methods, and iii) the method can scale to larger problems. Additionally, the results proved the FluidNeRF can be expanded to time-resolved reconstructions, which further compresses the volume representation and implicitly constrains the problem in time