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Influences of the Aerosol Invigoration Effect on Radar Signatures of Deep Convection near Houston TX Using a Bulk Statistical Framework
Get PDFAerosol-deep convective cloud (DCC) interactions remain a frontier in the study of water cycles, energy budgets, climate models, and air quality, partly because it can be difficult to disentangle aerosol impacts from the impacts of variations in the temperature, moisture, and wind fields that strongly affect the structure and evolution of DCCs. Theoretical and observational studies have shown that increased aerosol concentration ingestion by DCC updrafts can promote their invigoration by delaying the warm rain process and increasing latent heat release at the freezing level through a narrowing of the drop size distribution. This mechanism, known as the aerosol invigoration effect (AIE), increases a convective precipitating updraft’s strength and alters microphysical structures throughout its lifetime. However, other recent studies refute claims that the AIE increases updraft strength and instead claim that increased aerosol loading can weaken updrafts and/or reduce precipitation intensity. This study seeks to examine the impact of the AIE on DCCs by using a bulk statistical framework with a sample size of 2500 DCCs observed by ground-based radar observed in the vicinity of Houston, TX in the months of June, July, and August between 2013 – 2021. The vicinity of Houston was chosen for this investigation as regular summertime sea-breeze triggered DCCs occur in low-shear environments and weak synoptic-forcing conditions, with large aerosol concentration differences on varying days. Data are obtained from the Houston-Galveston WSR-88D (KHGX), ECMWF Reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and Texas Commission for Environmental Quality (TCEQ). DCC tracking was completed using the Multi-Cell Identification and Tracking (MCIT) algorithm using radar data from KHGX. Results from a spatial analysis of DCC locations using 2D kernel density estimates show that their locations, initiation times, and aerosol regime are largely governed by the direction and strength of the sea-breeze. Composite difference contoured frequency by altitude diagrams (CFADs) are used to uncover differences in the vertical structure of dual-polarization radar signatures and show that under certain meteorological conditions, differences in radar data consistent with the AIE are present across many DCCs within specific meteorological regimes. These regimes have been shown to promote the AIE by previous work, such as environments with moderate to high instability, low shear, and high free tropospheric relative humidity. Additionally, some meteorological regimes promote inhibition of updraft and precipitation intensity for DCCs under high aerosol mass loading, mainly within an anthropogenic aerosol regime
A Veteran Social Studies Teacher's Journey To Meet The Needs Of Her Students
Get PDFThis autoethnography discusses the ways in which I, a veteran and ambitious social
studies teacher, can evolve as a teacher and learner to meet all the needs of her students. The
literature shows that being a social studies teacher today, is not an easy task. An ambitious
social studies teacher must know their subject matter, create a classroom that balances rigor and
higher order thinking with kindness, know their students well, both inside and outside the
classroom, and give their students a sense of belonging and value. I will address the need for
teachers both veteran and novice to grow and evolve as teachers and learners. To illustrate this, I
use three poignant instances from my classroom to discuss the challenges and rewards of
embedding the competencies necessary to be an ambitious social studies teacher into everyday
classroom practice
Modeling Stellar Surface Features with High-Precision Photometry
Get PDFStarspots are a major source of stellar contamination in transmission spectroscopy of exoplanet atmospheres, and the correction for exoplanet analyses depends on the temperature of the starspots and the covering fraction. Using high-precision data from both space-based and ground-based observatories, we use the starspot modeling program STSP to measure the position and size of stellar surface features on KOI-340, an eclipsing binary consisting of G-type subgiant with an M-dwarf companion and TOI-3884, an M dwarf with a planetary companion. STSP uses a novel technique to measure the spot positions and radii by using the transiting secondary as a magnifying glass to probe down to less than 1\% changes in the surface brightness of the star for high-precision photometry. Our published results on the starspot properties of KOI-340, and our preliminary starspot modeling results for TOI-3884 are presented here. One necessary component to all of our analyses is the contrast of the spot which is related to the spot temperature, photosphere temperature, and the filter of the observed transit. With known spot position and radius, simultaneous multi-filter transits will show different spot signatures that can only be attributed to differences in the contrast (or temperature) of the spot. We have developed a technique to compare the contrast found using simultaneous multi-filter transits to theoretically determined contrast curves, which are determined by interpolating synthetic spectra over a given filter for both the stellar photosphere and a range of spot temperatures. We introduce this technique for HAT-P-11, a K-dwarf with known spot properties and a high-precision (diffuser-aided) simultaneous multi-filter transit obtained using the MuSCAT3 instrument on LCOGT’s 2-m telescope at Haleakala Observatory
A case-based comparison of industrial, ocupational, and suicidal deaths via hydrogen sulfide inhalation in the United States
No full textDeaths due to accidental and intentional inhalation of chemically generated gases are not an uncommon occurrence in the field of forensic pathology and death investigations. Gases such as carbon monoxide, helium, nitrogen, nitrous oxide, and propane are the most encountered in these types of incidences (Azrael et al., 2016). And yet, an unsuspecting gas, hydrogen sulfide (H2S), has recently emerged as a significant cause of inhalation mortality in the United States, but not in an anticipated manner. Previously, hydrogen sulfide-related deaths have been associated with fatalities and accidents in industrial and occupational settings such as oil and gas operations, agricultural settings, confined spaces, power plants, and utility industries (Morii et al., 2010). Post 2007-2008, in the United States, H2S has been utilized as a means of suicide by individuals that intentionally generate the gas, usually, in small, confined spaces. The first documented H2S-mediated suicides emerged in Japan in 2007-2008, following a social media dissemination of an online "how-to" guide (Sams et al., 2013). Subsequently, H2S-related suicidal deaths have also increased in the United States, more specifically, suicides by the intentional generation of the toxic gas. Despite the increase in H2S-mediated suicides within the United States, limited research has been conducted regarding the potential hazards encountered during H2S death scene response, incident investigation, victim recovery operations, and autopsy examinations. This colorless, odorless gas can pose serious health risks to unsuspecting first responders and other medicolegal personnel attempting to save, remove, or process victims on scene and in other investigatory venues. To better elucidate the risk factors and pathology novel to H2S deaths, a survey was deployed using Survey Monkey to members of the ABMDI (American Board of Medicolegal Death Investigators). Autopsy and investigative reports were obtained via email from various Coroner and Medical Examiners' Offices across the United States. Demographic data, incident characteristics, and pathology findings were compared with a previous study wherein preliminary data (n=30 cases) were collected from the National Vital Statistics System (NVSS), NAME (National Association of Medical Examiner's), and public source document searches (Reedy et al., 2011). Our study demonstrated a definitive increase in reported H2S fatalities, post-2008, within the United States. Additionally, our data revealed an increase in suicidal deaths via intentional H2S manufacture and inhalation, as well as the presence of a preponderance of novel, greenish-discolored brain, and central nervous system tissues, unrelated to postmortem decomposition. To ensure the safe investigation and diagnosis of deaths due to H2S inhalation, it is paramount to incorporate a comprehensive, timely combination of scene information, decedent history, as well as the safe collection of appropriate anatomical and toxicological postmortem specimens. Postmortem analyses should include the quantification of thiosulfate levels in blood and/or urine, a detailed history of circumstances surrounding H2S exposure, and a central nervous system (CNS)-focused internal examination or complete autopsy. Additionally, brain and CNS tissue should be thoroughly examined to elucidate a unique and possibly pathognomonic appearance of a greenish discoloration informative to H2S toxicity. This research has implications for the enhanced understanding of personnel and environmental hazards, incident dynamics, asphyxia mechanisms, and postmortem findings associated with H2S-related fatalities
El resurgimiento y la perpetuación de la dinastía Mexica mediante el virrey Español en el Teatro de virtudes políticas que constituyen a un príncipe (1680) de Carlos de Sigüenza y Góngora
No full textThe present thesis traces the process by which the creole savant Carlos de Sigüenza y Góngora (1645-1700) reconfigures the persona of the Spanish viceroy, Tomás de la Cerda (1638-1692), and integrates him into the ancient Mexica dynasty in Teatro de virtudes politicas que consituyen a un príncipe. Unlike previous 16th and 17th-century manuals known as literary “mirrors of princes”, Sigüenza’s Teatro de virtudes audaciously employs the eleven emperors of the ancient Mexica dynasty and a humanized version of the Mexica god Huitzilopochtli as models of political virtues. The novelty of the present study rests in an analysis of Sigüenza’s portrayal of the viceroy as possessing two bodies, one immortal and one mortal, and as being the prophesied prince in the succession of Mexica kings who never die. Sigüenza achieves this portrayal through emblematic art (the devise, impresse, and hieroglyph) in which he Christianizes and Romanizes the Mexica emperors as heroic and authentic historical entities rather than mere symbolic representations. Additionally, a thorough consideration of the 17th-century socio-political framework surrounding Teatro de virtudes signals the notion of an autochthonous viceroy who is challenged to embrace the motherland of the majority of his subjects as his own. Teatro de virtudes indicates its author’s interest in bridging to some extent the ethnic divide between the Spanish and indigenous populations to ensure peaceful coexistence. Although some explanations of Sigüenza’s later work, specifically Alboroto y motín, do not tally with the empathetic portrayal of the indigenous population expressed in Teatro de virtudes, this study also offers a historical contextualization of Alboroto y motín that clarifies much of the posited incongruence
Application of Traffic Speed Deflectometer and AI-Based Models for Structural Calibration and Pavement Management Enhancement
No full textCurrently, different methods are used for the evaluation of pavement conditions including the surface, base, subbase, and subgrade. Among these, Falling Weight Deflectometer (FWD) and/or Fast Falling Weight Deflectometer (FFWD), a non-destructive method, is widely used for pavement condition monitoring and forensic studies. Its ability to back-calculate the elastic moduli or stiffness of different layers can be a useful tool for estimating the remaining life of the pavement based on the rutting and cracking criteria. Currently, several state Departments of Transportation (DOTs) use FWD in their preventive maintenance program at both project level and network level. Collection of FWD and FFWD data requires traffic control and disruptions to traffic flow. The Traffic Speed Deflectometer (TSD) is an instrumented vehicle that is capable of measuring deflection, International Roughness Index (IRI), rut depths and other distresses at traffic speed without requiring any traffic control.
Reflection cracking, edge cracking, rut and longitudinal edge depressions are some of the major problems in asphalt pavements near the intersection of Interstate 35 (I-35) and State Highway 7 (SH-7) in Oklahoma. In this study, over forty (40) miles of FFWD and TSD test data were collected from SH-7 and I-35. More than 380 FFWD tests were conducted at different locations throughout the studied sections. Deflections at different sensors and temperatures during testing were recorded. Also, TSD data were collected from the same pavement sections, as part of a nationwide pool fund study involving the Oklahoma Department of Transportation (ODOT). Specifically, deflections, rut depths, roughness, temperatures, loading and distress data were collected. Thicknesses of different layers were determined from the Ground Penetration Radar (GPR) and from coring. From these data, two different types of pavements were identified under the Asphalt Concrete (AC) layer in the studied section of I-35, namely Jointed Concrete Pavement (JCP) and Continuously Reinforced Concrete Pavement (CRCP). The pavement structure in SH-7, on the other hand, consisted of only AC layers over native subgrade soil. The following laboratory performance tests were conducted on the extracted asphalt cores: Semi-Circular Bend (SCB), Texas Overlay (TO), Tensile Strength Ratio (TSR), Indirect Tensile Asphalt Cracking (IDEAL-CT), and Hamburg Wheel Tracking (HWT). Also, roughness and rut data were collected by Pave3D 8k, in collaboration with the Oklahoma State University team, and compared with the corresponding TSD data. In addition, elastic moduli obtained from the FWD and TSD data were compared in this study. For this purpose, FWD data were used in Modulus 7.0 Software to back-calculate elastic modulus. The deflection values from the TSD data were reformatted and used in Modulus 7.0 to determine the corresponding elastic moduli. A linear elastic model option in KENLAYER was used to determine strains at the bottom of the AC layer. These strains were then used to calibrate the coefficients of the model that are currently used to calculate strains at the bottom of the AC layer from the TSD deflection data. Further, the coefficients used in models by Rhode (1994) and by AASHTO 93 to determine Effective Structural Number (SNeff) were calibrated for both studied sections. Temperature corrections and matching of deflection data obtained from the FWD and TSD testing were needed for these calibrations and their comparison (i.e., FWD vs TSD). Recent developments in Artificial Intelligence (AI)-based models were employed in this study in developing the aforementioned correlations. For comparison purposes, both regression and AI-based models were used to predict the back-calculated elastic moduli values from the TSD data using a Python code. Finally, the predicted elastic moduli (based on the FWD deflections) by linear regressions and Random Forest (RF) models were compared and relative strengths of the AI models discussed
The Last Leaf: A Composition for SATB Choir, Vocal Soloists, and Keyboard Percussion Quartet
Get PDFThe primary aim of this dissertation is to create a new piece of art music, specifically, an ensemble work consisting of SATB choir, soprano soloist, alto soloist, tenor soloist, and a keyboard percussion quartet, including four marimbas, crotales, and chimes. The piece, entitled The Last Leaf, is modeled on a work by the composer David Lang, entitled The Little Match Girl Passion. Lang’s piece utilizes a small chorus and four solo singers, who also play simple percussion parts, which act as an ornamental supplement to the vocal parts. My work, The Last Leaf, incorporates a quartet of keyboard percussionists that are equal contributors to the overall fabric of the piece.
In addition to providing the full score for The Last Leaf, this document elucidates the means by which my piece was constructed. I also detail the specific ways in which Lang’s work, The Little Match Girl Passion, was used a model, and as an aesthetic influence on The Last Leaf. This document also explains the architectural and orchestrational components of my work, specifically its symmetrical designs. Furthermore, I will articulate how my libretto, which uses the text of a short story by O. Henry, also titled The Last Leaf, was paraphrased, broken up into constituent movements, and set to music
These Were the Nights: A Creative Collection by a Misunderstood Giant of a Broken Thing
Get PDFThese Were the Nights: A Creative Collection by a Misunderstood Giant of a Broken Thing is a collection of creative non-fiction and short fiction meditating on Black, multiracial identity, family trauma, and the effects of depression through loss and conflict. These works were written, revised and compiled between the fall of 2021 and spring of 2023
Freshwater Stream Monitoring Process Improvements for Fecal Indicator Impairment Designation
Get PDFRecreational water quality standards for freshwater streams and rivers are important to understand the potential human health risks associated with primary body contact recreation. Indicator bacteria, Enterococcus and Escherichia coli, are used to routinely monitor and assess waterbodies for impairment. The 2020 Clean Water Act 303(d) Integrated Report indicates that approximately 7500 miles of streams and rivers are impaired for both E. coli and Enterococcus in Oklahoma. Fecal indicator bacteria (FIB) sources are often difficult to assess as they are from numerous anthropogenic, wildlife and environmental non-point sources and require consistent monitoring and assessment due to potential dynamic spatial and temporal factors within streams. The Oklahoma water quality standards provide threshold criteria and a general sampling frequency for FIB to make an impairment assessment, but do not provide guidelines for how, when, or where water samples should be collected in a waterbody. Furthermore, there is evidence from recent studies to suggest that Enterococcus may not be the strongest predictor for freshwater impairment criteria and may be of non-enteric origin and that fluorogenic substrate methods (i.e., Enterolert™ [ELT]) used to analyze Enterococcus samples may result in false positives. Resources are often limited for many agencies that routinely monitor these streams and new approaches and tools are needed to develop effective monitoring plans. Given the immense resource effort required to monitor and assess these streams, more research is needed to understand and improve the FIB monitoring process for primary body contact recreation assessment. Therefore, the objectives of this dissertation were to 1) evaluate spatial and temporal factors in Oklahoma streams that may influence FIB, 2) investigate stream sediment as a contributing factor to Enterococcus in streams and rivers, 3) evaluate the ELT enumeration method for applicability to analyze freshwater stream samples for Enterococcus, and 4) explore existing geospatial and water quality data to develop correlation factors and regression equations to improve prediction of FIB for monitoring and assessment.
Studies that were conducted in this dissertation included a 1) field water quality spatiotemporal study at two cross sections in Spring Creek (Ch. 2), 2) spatiotemporal assessment of six streams and two laboratory microcosms for Enterococcus survivability in sediment and water and related environmental factors (Ch. 3), 3) investigation of the ELT method for Enterococcus false positives from stream water and sediment samples (Ch. 4), and 4) development of multiple linear regressions for FIB using water quality monitoring data collected from the Oklahoma Conservation Commission (Ch. 5). In brief, the results of these studies revealed that spatial and temporal factors (i.e., sampling location and time) and water quality and geographical characteristics (i.e., land use) can influence FIB in Oklahoma streams (Ch. 2 and Ch. 3). Furthermore, these spatiotemporal factors can be used to predict FIB concentrations in stream water and sediment (Ch. 3). Enterococcus showed extended survival and stability in stream sediments greater than 31-d under stable laboratory microcosms (Ch. 3). False positive bacteria were identified in 25% of all ELT samples analyzed with greater than 90% of those identified as Paenibacillus spp. from the microcosm and field studies (Ch. 4). Regression equations can be developed from water quality and geospatial variables to provide an initial reconnaissance of the expected FIB concentrations within a stream and/or region (Ch. 5). The outcomes of this work indicate that more emphasis should be placed on evaluation of the sampling process design and methodology for assessing Oklahoma streams for FIB impairment determination
Data balancing approaches in quality, defect, and pattern analysis
Get PDFThe imbalanced ratio of data is one of the most significant challenges in various industrial domains. Consequently, numerous data-balancing approaches have been proposed over the years. However, most of these data-balancing methods come with their own limitations that can potentially impact data-driven decision-making models in critical sectors such as product quality assurance, manufacturing defect identification, and pattern recognition in healthcare diagnostics. This dissertation addresses three research questions related to data-balancing approaches: 1) What are the scopes of data-balancing approaches toward the major and minor samples? 2) What is the effect of traditional Machine Learning (ML) and Synthetic Minority Over-sampling Technique (SMOTE)-based data-balancing on imbalanced data analysis? and 3) How does imbalanced data affect the performance of Deep Learning (DL)-based models?
To achieve these objectives, this dissertation thoroughly analyzes existing reference works and identifies their limitations. It has been observed that most existing data-balancing approaches have several limitations, such as creating noise during oversampling, removing important information during undersampling, and being unable to perform well with multidimensional data. Furthermore, it has also been observed that SMOTE-based approaches have been the most widely used data-balancing approaches as they can create synthetic samples that are easy to implement compared to other existing techniques. However, SMOTE also has its limitations, and therefore, it is required to identify whether there is any significant effect of SMOTE-based oversampled approaches on ML-based data-driven models' performance. To do that, the study conducts several hypothesis tests considering several popular ML algorithms with and without hyperparameter settings. Based on the overall hypothesis, it is found that, in many cases based on the reference dataset, there is no significant performance improvement on data-driven ML models once the imbalanced data is balanced using SMOTE approaches.
Additionally, the study finds that SMOTE-based synthetic samples often do not follow the Gaussian distribution or do not follow the same distribution of the data as the original dataset. Therefore, the study suggests that Generative Adversarial Network (GAN)-based approaches could be a better alternative to develop more realistic samples and might overcome the limitations of SMOTE-based data-balancing approaches. However, GAN is often difficult to train, and very limited studies demonstrate the promising outcome of GAN-based tabular data balancing as GAN is mainly developed for image data generation. Additionally, GAN is hard to train as it is computationally not efficient. To overcome such limitations, the present study proposes several data-balancing approaches such as GAN-based oversampling (GBO), Support Vector Machine (SVM)-SMOTE-GAN (SSG), and Borderline-SMOTE-GAN (BSGAN). The proposed approaches outperform existing SMOTE-based data-balancing approaches in various highly imbalanced tabular datasets and can produce realistic samples. Additionally, the oversampled data follows the distribution of the original dataset.
The dissertation later examines two case scenarios where data-balancing approaches can play crucial roles, specifically in healthcare diagnostics and additive manufacturing. The study considers several Chest radiography (X-ray) and Computed Tomography (CT)-scan image datasets for the healthcare diagnostics scenario to detect patients with COVID-19 symptoms. The study employs six different Transfer Learning (TL) approaches, namely Visual Geometry Group (VGG)16, Residual Network (ResNet)50, ResNet101, Inception-ResNet Version 2 (InceptionResNetV2), Mobile Network version 2 (MobileNetV2), and VGG19. Based on the overall analysis, it has been observed that, except for the ResNet-based model, most of the TL models have been able to detect patients with COVID-19 symptoms with an accuracy of almost 99\%. However, one potential drawback of TL approaches is that the models have been learning from the wrong regions. For example, instead of focusing on the infected lung regions, the TL-based models have been focusing on the non-infected regions. To address this issue, the study has updated the TL-based models to reduce the models' wrong localization.
Similarly, the study conducts an additional investigation on an imbalanced dataset containing defect and non-defect images of 3D-printed cylinders. The results show that TL-based models are unable to locate the defect regions, highlighting the challenge of detecting defects using imbalanced data. To address this limitation, the study proposes preprocessing-based approaches, including algorithms such as Region of Interest Net (ROIN), Region of Interest and Histogram Equalizer Net (ROIHEN), and Region of Interest with Histogram Equalization and Details Enhancer Net (ROIHEDEN) to improve the model's performance and accurately identify the defect region.
Furthermore, this dissertation employs various model interpretation techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM), to gain insights into the features in numerical, categorical, and image data that characterize the models' predictions. These techniques are used across multiple experiments and significantly contribute to a better understanding the models' decision-making processes.
Lastly, the study considers a small mixed dataset containing numerical, categorical, and image data. Such diverse data types are often challenging for developing data-driven ML models. The study proposes a computationally efficient and simple ML model to address these data types by leveraging the Multilayer Perceptron and Convolutional Neural Network (MLP-CNN). The proposed MLP-CNN models demonstrate superior accuracy in identifying COVID-19 patients' patterns compared to existing methods.
In conclusion, this research proposes various approaches to tackle significant challenges associated with class imbalance problems, including the sensitivity of ML models to multidimensional imbalanced data, distribution issues arising from data expansion techniques, and the need for model explainability and interpretability. By addressing these issues, this study can potentially mitigate data balancing challenges across various industries, particularly those that involve quality, defect, and pattern analysis, such as healthcare diagnostics, additive manufacturing, and product quality. By providing valuable insights into the models' decision-making process, this research could pave the way for developing more accurate and robust ML models, thereby improving their performance in real-world applications