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Iron Coated Sand: A Sustainable Substrate Amendment for Nutrient Management and Growth of Floriculture Crops
Get PDFAcid mine drainage and phosphorus pollution are two of the most pervasive forms of water contamination in Appalachia and globally. The horticultural industry is a contributor to nutrient pollution via leaching from greenhouses, necessitating the adoption of more sustainable practices for container production in controlled environments. In this thesis, an acid mine drainage-based iron-coated sand product was assessed on its effectiveness for phosphate adsorption when used as a sustainable substrate amendment for soilless container production of flowering ornamentals. The first study centered around the amount of iron-coated sand to use for production of petunias, pansies, and chrysanthemums. Iron-coated sand at all rates of inclusion (10, 20, 30, and 40% by volume) reduced phosphorus in leachate compared to commercial peat-based potting mix for all three crops. However, high rates of coated sand (\u3e20%) in media had negative effects on growth and performance of all three crops, especially when P-containing fertilizer was applied. When no P was applied, low percentages of coated sand in soilless substrate resulted in similar growth to the control. Based on these results, 20% coated sand in commercial potting mix was used to grow chrysanthemums to test the effects of different rates of P in water-soluble fertilizer on plant growth and phosphate concentration in leachate. Similar to the first study, coated sand reduced phosphate concentration in leachate compared to the potting mix control for each rate of fertilizer. However, as the rate of P in fertilizer increased, the amount of phosphate in leachate also increased, though this was still less in the coated sand treatment. The third study focused on the behavior of the coated sand over time in production and post-production conditions as well as plant uptake of media nutrients. Petunias and chrysanthemums both had minimal differences in growth and performance between the coated sand treatment, a plain sand treatment, and the commercial media control in both production and post-production stages. Once again, plants grown in coated sand exhibited reduced phosphate content in leachate compared to the control throughout production, though this difference eventually become insignificant for the latter half of the post-production stage for both crops. Petunias had very few differences in nutrient content of leaves and roots between treatments at the end of production and throughout post-production. Chrysanthemums, on the other hand, demonstrated several differences, particularly in mature leaf and root tissues. Chrysanthemums grown in coated sand treatments took up much higher amounts of Fe, Mn, Mg, Zn, and Cu than the plain sand treatment or base media control, though these metals are commonly found in acid mine drainage, which may have attributed to this. However, chrysanthemums grown in the plain sand treatment had the highest amount of phosphorus in mature leaves and roots at the end of the post-production stage. The results of these three studies helped elucidate the behavior and efficacy of this novel acid mine drainage-based iron-coated sand product, and it holds promise as a sustainable substrate amendment that effectively adsorbs phosphate and prevents it from leaching, provides similar and occasionally improved yield and quality results in floriculture performance, and offers a panoply of nutrients for plant roots to utilize. By using a major water contaminant to prevent further pollution, this iron-coated sand has enormous potential for securing a more sustainable phosphorus management technique in containerized soilless floriculture production
Development of Dynamic Mass-Energy-Thermodynamics Constrained Hybrid Neural Network Models for Process Systems Applications
Get PDFFirst-principles models can provide very good predictions even for cases when there are no data at all, or data are limited in certain range of operating conditions, or for cases where data collection is infeasible. However, the development of accurate first-principles models for complex nonlinear dynamic systems can be time consuming, computationally expensive, and may be infeasible for certain systems due to lack of sufficient knowledge (information). It is also challenging to adapt first-principles models for time-varying systems. Furthermore, it can be difficult, if not impossible, to develop accurate models for some complex phenomena that are poorly understood. On the contrary, black-box or data-driven models are relatively easier to develop, simulate and adapt online. Over the last few decades, numerous advantages of using data science and artificial intelligence (AI) / machine learning (ML) approaches in the field of chemical engineering have been exploited and analyzed. Neural networks (NNs) have been identified as one of the many different artificial intelligence approaches that exhibit a strong potential to modeling highly complex nonlinear dynamic systems.
This work is primarily focused on the development, training and applications of novel deterministic and stochastic dynamic physics-constrained hybrid neural network structures and hybrid first principles-NN models for data-driven modeling of complex nonlinear dynamic chemical process systems. The NN models developed and proposed as part of this research can be either implemented independently in a fully data-driven approach or be synergistically hybridized with other first-principles (physics-based) models for exploiting their key strengths. Most of the AI/ML techniques, especially neural networks, have evolved since the last few decades for a diverse range of chemical engineering applications such as data classification, fault detection and diagnosis, chemical process design, monitoring, and control. Although a plethora of different types of deep and shallow NN models have been successfully implemented for modeling various complex nonlinear transient chemical processes, the successful development of such techniques require large data sets which may not often be available for modeling various chemical engineering systems. This work develops novel architectures and training algorithms for hybrid all-nonlinear series and parallel static-dynamic NN models for applications to chemical systems. The sequential decomposition-based training algorithms thus formulated exploit the model structure, leading to independent and separate training of static and dynamic networks by different parameter estimation algorithms while still achieving optimal convergence by solving an outer layer optimization. The all-nonlinear series and parallel networks have been shown to significantly outperform typical deep recurrent neural networks, with the proposed sequential training algorithms leading to 50-100 times faster computation than simultaneous training algorithms.
One of the more recent advancements in the field of process modeling using NNs points to the augmentation of various physics conservation laws pertaining to a system while constructing optimal network models during both training and simulation. The primary motivation of such approaches stems from the fact that the measurement / experimental data available for training may not necessarily satisfy mass and energy balance equations and/or other thermodynamics / physics based constraints. If such conservation laws are not considered during machine learning, model predictions can violate system physics and hence are not meaningful. However, in almost all existing literature on physics-informed neural networks (PINNs), the physics conservation equations have been augmented in the loss function as additional penalty terms, thus serving as soft constraints without ensuring an ‘exact’ satisfaction of the conservation laws. This work focuses on the development of algorithms for exactly satisfying physics conservation laws such as mass and energy balances as well as thermodynamics constraints during both training and forward problems using noisy transient data. The mass-energy-thermodynamics constrained neural network models developed in this research are found to be very accurate capturing the system truth with minimum bias for all examples that are evaluated, even when the training data are corrupted with uncertainties.
Another alternative approach that is commonly practiced for including mechanistic (first-principles) information along with data-driven models considers the synergistic integration of first-principles (FP) and AI models leading to the construction of hybrid first-principles artificial intelligence (FP+AI) models. This work also discusses novel approaches developed for coupling FP and AI models in series, parallel, and integrated configurations, along with algorithmic capabilities to ensure that the resulting optimal models do not violate system physics even after hybridization. Additionally, the proposed hybrid first-principles machine learning approaches, when applied to commercial power plant data, demonstrate \u3e15% improved predictive accuracy compared to the corresponding standalone FP models. For modeling uncertain systems, probabilistic neural networks can be highly useful. However, synthesis of optimal hybrid networks and parameter estimation for probabilistic neural network models of dynamic uncertain systems without suffering from the ‘curse of dimensionality’ is considerably challenging. It is also challenging to satisfy physics constraints when probabilistic NNs are used. The final set of model architectures and algorithms developed in this research are aimed at optimal integration of stochastic and conventional NNs as well as efficient formulation of training algorithms for hybrid series and parallel constrained stochastic-deterministic network models. The corresponding approaches proposed in this work for construction of optimal parsimonious hybrid stochastic networks exhibit considerably superior results compared to the state-of-the-art approaches when evaluated for large-scale complex nonlinear process systems. All proposed data-driven / hybrid models and algorithms are evaluated and validated for modeling various complex nonlinear transient noisy chemical process systems
Standardizing Nasogastric Tube Securement: A Quality Improvement Initiative
Get PDFProblem: At a large academic medical center located in the northeastern United States, nasogastric tubes (NGT) were traditionally secured with tape. The institutional policy was nonspecific with no written procedure. The inconsistent use of nasal bridle securement devices (NBSD) and predominant use of tape has the potential to lead to a high rate of NGT dislodgement.
Background: NGT complication and dislodgement rates range from 2%-36% (Gimenes et al., 2019). Stabler et al. (2018) report 29%-63% NGT dislodgement rates in critically ill patients. Patients with intracranial hemorrhage dislodge NGTs in 25% of patients (Rahman et al., 2022)
Available Knowledge: Based on literature, NBSD use reduces NGT dislodgement rates. NGT dislodgement results in medication and nutrition delays, higher healthcare costs, and increased length of hospital stay.
Project Aims: The overarching purpose of this project was to increase the use of NBSD.
Purpose: Evaluate the use of NGT securement use of tape versus NBSD and associated dislodgement rate. Revise and update the organizational NGT securement policy and develop a NBSD insertion procedure. Update electronic health record (EHR) to include a linked NBSD order with NGT insertion order. Train 50% of nurses on each participating unit on NBSD insertion.
Plan for Implementation: Lewin’s change theory guided the project.
Plan for Evaluation: An evaluation plan to clarify the project objectives and goals. Process, outcome, and balancing measures were implemented, collected and observed.
Results: The policy was updated and NBSD procedure was created with associated EHR orders. There was a small increase in the utilization of NBSD, while usage of tape remained
consistent.Although nurses received education, many still experienced discomfort while inserting NBSDs.
Conclusion: There was a reduction in number of NGT dislodgement utilizing NBSD on comparison to the consistent use of tape with NGT. Consequently, the utilization of NBSD has the potential to reduce healthcare expenses, shorten the duration of hospital stays, and minimize complications associated with NGT dislodgement
An Evaluation of a Human-Operant Effort Manipulation and Effects of Effort Disparity on Renewal
Get PDFThe relative effort of target and alternative responses during treatments using differential reinforcement of alternative behavior may impact the likelihood that a previously reduced target response will reemerge following a context change (i.e., “renewal”). The purpose of this study was to evaluate the role of an effort disparity between target and alternative responses in a human-operant arrangement. Eighteen college students clicked on one (Experiment 1) or two (Experiment 2) circles moving on a computer screen for points. In Experiment 1, the speed of the circle was manipulated as an index of effort such that three circle speeds (i.e., 50, 100, 200 mm/s) were used across conditions. Nearly all participants engaged in differential response rates, depending on the speed of the available circle. Criterion response rates (clicks on the target circle) were highest when the speed was slow. Subcriterion response rates (clicks on the background of the computer screen) were inversely related to the speed of the circle. In Experiment 2, a three-phase renewal arrangement was executed across three experimental conditions in which the target response was either the same, easier, or more difficult than the alternative response. The effects of the relative effort of the target response to the alternative response on the occurrence and magnitude of renewal were mixed across participants. The clinical and conceptual relevance regarding the relative effort of target and alternative responses will be discussed
Something from Nothing: Reimaginings, a Resource Guide for the Horn Sonata by Frank Gulino
Get PDFSince publishing his first work in 2006, American composer Frank Gulino has been the driving force behind some seventy new pieces of music. With music commissioned and performed by top professionals around the world, Gulino’s star has risen quickly backed by his prowess for writing music for brass instruments in solo works and chamber music. The subject of this document is a sonata for horn and piano entitled Reimaginings, a work commissioned by the author in 2020. This document presents a biographical sketch of the composer, an interpretation for performing the work, and suggestions for learning key skills needed to play it successfully. The biographical sketch is based on three interviews conducted with Gulino between the months of August 2021 and August 2022. Transcripts of the interviews are included. In these conversations, readers will find a unique, intimate perspective as the composer shares his thoughts on the many aspects of his life as a composer, bass trombonist, and attorney. The performance guide outlines the interpretation of Reimaginings developed for the premiere at the 2021 Northeast Horn Workshop at West Virginia University. Each suggestion has been carefully considered and the detailed plan should serve as an excellent starting point for a player preparing the piece. The pedagogical guide addresses several key skills needed for a student to successfully navigate a performance of the work and strategies for teachers leading them through it
Scenic Design for Molière’s The Misanthrope
No full textThis document is an account of the design process from the initial reading of the script, design, and production meetings with the director and the production team, through to the execution of the design for The Misanthrope by Molière. This production was produced by West Virginia University’s School of Theatre & Dance during the Fall 2023 semester, presented in the Gladys G. Davis Theater at the Canady Creative Arts Center in Morgantown, WV
Women in Extension Persevering in Leadership Roles
Get PDFOver the last several decades, the rates at which women have been pursuing higher levels of education have steadily increased. However, there is a discrepancy between the number of women graduating, and the amount entering the workforce, specifically in leadership roles. The purpose of this study was to share a current profile of women in the West Virginia Cooperative Extension Service by allowing them to describe their career journeys, supports and barriers they may or may not have faced, and mentoring experiences. By using a non-experimental quantitative methodology, a population of male and female Extension Agents and Specialists were surveyed from West Virginia. The Four Domains of Leadership and Gender Framework was used to categorize and analyze responses. While some women in the West Virginia Extension Service noted that they did not perceive challenges due to their gender, others indicated differently. Respondents noted an incomplete mentor program, struggles obtaining a work/life balance, sexist leaders and competitive female co-workers. Opportunities to improve the mentor program, promoting networking and educational opportunities in leadership, and assisting women in balancing their career and personal lives may be the key to keeping women from facing burn out. Continued research on imposter syndrome, gender wage gap, and global/cross cultural leadership in the Extension Service is recommended
Selected Chinese Art Songs from the 20th and 21st Centuries
Get PDFThis paper presents Chinese art songs from the early 20th century to today and highlights selected composers from each of the five traditional eras of recent Chinese social history. It is intended as an introductory guide for singers and collaborative pianists who are interested in Chinese art songs and the development of the genre in China. Selected composers include Youmei Xiao (1884-1940), Yuanren Zhao (1892-1982), Er Nie (1912-1935), Hanhui Zhang (1902-1946), Xinghai Xian (1905-1945), Deyi Shang (1932-2020), Zhongrong Luo (1924-2021), Zaiyi Lu (b. 1943), Jianfen Gu (b.1935), and Cong Liu (b.1956). The paper provides brief biographical information for each composer and lists their selected art songs and a discography of available recordings
Advances in machine learning aided seismic interpretation and inversion for subsurface characterization
No full textGeophysical data are used to “remotely sense” the subsurface to illuminate structures, stratigraphy, and features that are of interest in the exploration for energy, suitable storage for carbon dioxide, geothermal development, and environmental considerations. The interpretations of these data typically are regarded as partially an art, as it depends on the experience of the interpreter. This is further exacerbated with the big data era as this data is large and takes several days, even longer, to manually interpret. To bridge this gap of speed and subjective bias, machine learning has been successfully applied to automate the geophysical interpretation process. This study improves the application of machine learning systems for practical field deployment through the proposal of novel workflows. This study also documents the first application of diffusion models to solve seismic inverse problems. Traditional seismic inversion lacks a unique solution and suffers from cycle skipping. Here, I propose a new data-driven framework for the direct inversion of velocity from seismic amplitudes, potentially overcoming the limitations and shortcomings associated with traditional methods.
In the second chapter, I propose a novel workflow called SeisSegDiff that utilizes the semantic representations learned by a diffusion model to aid in seismic facies classification with limited training data. I demonstrate that with as little as 5 training cross-sections, the seismic facies can be accurately classified even for facies with limited occurrence. The third chapter investigates the nature of these learned representations and uncovers the optimal model specification for SeisSegDiff. This chapter also discusses the practical usage of this model for field applications. The fourth chapter reformulates the diffusion model used for generative tasks to predict the subsurface velocity from pre-stack seismic data. I demonstrate that this low-resolution velocity prediction from the diffusion model can aid in accelerating classical full waveform inversion. In the fifth chapter, I propose a committee machine that aggregates the gradient boosting, support vector machine, and neural network to improve prediction of the brittleness estimate for mudstone
Global Experiences: How Locality and Space Shape Pedagogical Practices and Experiences between Western Institutions and Ghanaian Musical Centers
Get PDFPedagogical research on African music and dance practices have conventionally focused on ways in which styles and traditions are taught including, but not limited to, modes of transmission, curriculum structure, and cultural responsiveness. These investigations, and subsequent applications, have contributed towards a deeper understanding of pedagogical structures as related to cultural context and effective modes of teaching and learning African music. At the same time, the impact that space and locality play within African music and dance instruction, particularly as related to learning experiences have been largely overlooked. Locality and space involve several factors that range from elements attributed to a given location’s soundscape to components of physical structures and social environments. Broadly, this study explores the role locality and space play within pedagogical practices. More specifically it examines their influence on Ghanaian music practices as taught within music centers in Ghana and outside of the country as well. Using traditional ethnographic modes of investigation, this work analyzes factors of locality and space within pedagogical contexts in Ghana at the Bernard Woma Dagara Music Center and the Dagbe Cultural Institute and Arts Center as well as within the United States of America at West Virginia University, which maintains a connection with both centers. Through this investigation, this study considers how locality and space shape pedagogical structures and further contribute to already existing pedagogical research on African music and dance