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Analysis of the Current State of the Beekeeping Industry in Alabama
Get PDFIn this thesis, I investigated the following research questions, “What are characteristics of Alabama beekeepers?” “What are revenue sources for beekeepers in Alabama?” and “What are barriers to expansion of beekeeping operations?” To investigate these questions, we used a survey that asked various operations and revenue questions to Alabama beekeepers. We collected 150 responses. Out of the 150, 62%, or 93 beekeepers classified as hobbyists, meaning they manage 25 hives or less. 32% or 48 out of 150 were sideliner beekeepers, managing anywhere between 26 and 299 hives. The remaining 6% were commercial beekeepers, running 300 plus operations. The results reveal that 80% of the survey participated in honey sales, making it the most popular revenue source by far. The second largest source of income was nuc sales, which 24.3% of the survey participated in. 18 beekeepers out of the 150 (12.3%) use their hives for pollination services, and 20 (13.7) partake in queen sales. The smallest source of revenue was packaged bee sales, which only 7 (4.6%) beekeepers participated in. Although it would require more financial and cost data to know whether beekeeping is feasible in Alabama or not, the results from this research point towards improving feasibility
Training an AI-Based Chatbot to Infer Workplace Competencies
Get PDFCompetency modeling is used within a wide range of contexts within organizations such as selection, development, and promotion decisions to name a few. Assessing relevant competencies comes with its challenges; designing and implementing these models is time consuming and expensive. The purpose of the present study is to design and train AI models to infer competencies through a text-based chat interaction with an AI chatbot. The models were trained using text responses to behaviorally based competency interview questions as predictors and raters used behaviorally anchored rating scales (BARS) to score the responses and these scores were used as the criteria (ground truth). The training sample consisted of 297 full-time employees and the test sample included 210 college students. The psychometric properties of machine-inferred competency scores were examined including reliability (split-half), convergent and discriminant validity, criterion-related validity, and generalizability. The results showed promising evidence for split-half reliability, convergent validity, and generalization of the model, but less promising results for discriminant validity and criterion validity
Evaluation of Dietary Additives on the Antimicrobial Activity against Campylobacter jejuni Colonization in Broilers
Get PDFIn the United States, campylobacteriosis is one of the most reported bacterial foodborne illnesses. Chickens are a natural and common reservoir for Campylobacter jejuni, and poultry chicken meat is the most frequently cited source of human infection. Reducing levels of Campylobacter jejuni in chickens could reduce risk of human exposure and have a significant impact on food safety and public health. The objectives of the present dissertation were to evaluate feed and water additives and their ability to reduce Campylobacter jejuni intestinal colonization in broilers, as well as their impact on gut health and broiler performance. The first 2 trials evaluated the use of yeast cell walls (YCW) as dietary additives in broilers that were and were not inoculated with Campylobacter jejuni. The dietary treatments evaluated were negative control, positive control (bacitracin, 50 g/ton), YCW constant dose (400 g/ton), and YCW step-down dose (800, 400, and 200 g/ton in the starter, grower, and finisher periods, respectively). Trial 1 was inoculated with a 107 –colony-forming-units (CFU)/mL dose at day 21 and trial 2 was inoculated with a 103 CFU/ml dose at day 16. In trial 1, an instance of cross-contamination occurred between broilers inoculated with Campylobacter jejuni and those inoculated with phosphate buffered saline (PBS), leading to Campylobacter colonization in PBS-inoculated birds. In both trials, Campylobacter jejuni cecal colonization levels were comparable among broilers consuming different diets. The average Campylobacter colonization level in trial 1 at day 42 was 8.4 log10 CFU/g of cecal content for both Campylobacter- and PBS-inoculated birds. In trial 2, the average Campylobacter colonization levels were 6, 8.3, and 8.1 log10 CFU/g of cecal contents at days 24, 34, and 42, respectively (P>0.05), and all PBS-inoculated birds were negative for Campylobacter jejuni prevalence. Under the conditions of both trials, the dietary addition of YCW did not have an impact on the innate immune response, growth performance, carcass yield, or carcass contamination after processing in broilers (P>0.05). Additionally, in trial 1, no differences were observed in gut histomorphometry among birds consuming different diets or types of inoculation (P>0.05). Regardless of the inoculation dose, it was observed that Campylobacter jejuni colonization levels in the ceca of broilers reaches high numbers (>8 log10 CFU/g), which suggests that Campylobacter colonization does not follow a linear dose response. For trial 3, acidification of water using organic acids was evaluated to determine the antimicrobial activity against Campylobacter jejuni. Broilers were randomly assigned to 1 of 4 water treatments groups: negative control (water), intermittent organic acid A (2 mL/L; days 1 to 5 then 1 day per week), continuous organic acid A (2 mL/L), and continuous organic acid B (2.6 mL/L) and to 1 of 2 inoculations: PBS or Campylobacter jejuni at 104 CFU/mL administered via oral gavage on day 21. The inclusion of organic acids in water did not affect the levels of Campylobacter cecal colonization at any time point analyzed (P>0.05) and the average Campylobacter cecal colonization levels at day 42 were 8.8 log10 CFU/g. An effect of the inclusion of organic acid A was observed in broiler body weight at day 41, in which broilers consuming water acidified with organic acid A had heavier body weights (P=0.0029) than the age-matched negative controls and broilers consuming organic acid B. No effects of the organic acids were observed in gut histomorphometry or cecal microbiome (P>0.05). However, the type of inoculation impacted the intestinal structure at the jejunum and ileum, where birds inoculated with Campylobacter jejuni had increased crypt depth in the jejunum (P=0.0018) and reduced villus height and villus height to crypt depth ratio in the ileum (P=0.0134 and P=0.0003, respectively) than birds inoculated with PBS. Similarly, inoculation with Campylobacter jejuni significantly impacted the alpha- and beta-diversity of the cecal microbial populations (P<0.01). Based on the overall Campylobacter jejuni recovery from cecal contents of broilers provided with either dietary YCW or organic acids in water, further studies evaluating the efficacy of different additives, alone or in combination, are warranted to successfully reduce Campylobacter colonization in broilers during grow-out
Pavement Marking Retroreflectivity: Exploring Degradation Factors and Relationship with Road Safety
Get PDFThe evaluation of pavement marking materials is primarily based on their retroreflectivity (RL), which refers to how well they reflect vehicle headlights back toward the driver. This study focuses on rural roadways and thermoplastic markings to i) identify key factors contributing to RL degradation, ii) understand the relationship between subjective ratings and measured RL by marking line types, iii) develop RL prediction models without initial RL and marking age information as inputs, and iv) explore the statistical relationship between RL and road crashes. Two years of RL data (2020 and 2021) were collected from ALDOT for the Montgomery area. The associated traffic flow, road, location, and crash information were extracted from six different data sources. Beta regression, multiple linear regression, and binary logit models were employed to uncover statistically significant relationships. The results showed that the effects of factors contributing to RL degradation vary by marking line type. For example, yellow centerlines in residential, business, and mixed-use areas exhibited statistically significant RL degradation. RL degradation for white right edgeline (WREL) was significantly higher on curve segments compared to adjacent straight segments. In addition, the analyses found evidence supporting the hypothesis that officers may struggle to assign accurate subjective ratings, particularly for yellow markings. Moreover, this research demonstrated an effective approach for examining the statistical relationship between RL and road crashes by increasing the sample size through developed RL prediction models. The safety analyses identified that lower RL levels of WREL (below 250 mcd/m²/lux) statistically increase the likelihood of single-vehicle run-off-road (ROR) crashes on curve segments and in dark conditions. The findings can help ALDOT's pavement management system in identifying locations with high RL degradation, enabling more targeted restriping at these specific segments. Moreover, the developed RL prediction models offer local transportation agencies a useful tool for forecasting RL with only one year RL measurement and associated traffic flow information. The results from crash data analyses highlight the importance of maintaining adequate RL levels for highway safety. Overall, the findings of this research align with one of the core principles of the Safe System Approach: ‘Making Our Roads Safer’
Irrigation Effects on Corn (Zea mays) and Soybeans (Glycine max L.) Yield in Clay Soils in the Alabama Blackland Prairie Region
Get PDFPrecise estimates of irrigation yield effects are essential for farmers when making irrigation investment decisions. The project goal is to improve our knowledge of irrigation yield effects in the soils of the Alabama Blackland Prairie region. The study objectives encompass estimating the irrigation yield effects of corn (Zea mays) and soybeans (Glycine max L.) based on terrain, soil, and climate parameters and determining the variables with the highest effect on crop yield. We evaluated two statistical analyses: Integrated Nested Laplace Approximation (INLA) and machine learning. The first analysis, assess the individual effect of irrigation, soil, and terrain properties on crop yield, and the second analysis evaluated four machine learning algorithms, including Sup port Vector Machine, Elastic Net Regression, Stepwise Regression, and Random Forest, under the following aspects: 1) identification of variables influencing yield predictions, 2) yield predictions based on adjacent fields, 3) yield predictions from irrigated fields, 4) yield prediction for a specific year utilizing data from other years within the same field, and 5) yield prediction of one field for a specific year using other fields with crop production in the same years. On an area of 3,400 ha with 22 pivot-irrigated and rainfed fields in the Alabama Blackland Prairie region, the research utilized 183 yield datasets collected from shrink and swell soils from 2012 to 2021. Spa tial derivatives derived from elevation information from the National Elevation Database, soil data from the POLARIS database, and drought indices calculated using precipitation, evapotranspira tion, and temperature data from the Parameter elevation Regressions on Independent Slopes Model (PRISM) Climate Group were integrated into the analysis. Results from INLA showed that terrain variables had a greater effect in corn than in soybeans yield. Although, these variables had the greatest effect, the yield increase or decrease was minimum in both crops. Results from machine learning showed that the accuracy of corn and soybean yield predictions is lower when relying only on one year of training data, where terrain attributes exhibited more significant influence compared to soil and climate properties. Conversely, incorporating data from multiple fields spanning several years and diverse crop yields into the training dataset led to greater accuracy of predictions, where the impact of climate properties is more notorious
Videoconference Use Predictors and Dynamics in Alabama’s Blackbelt Region
Get PDFUtilizing videoconference platforms has become the norm for work and personal use.
However, many adults need help accessing and using videoconference system platforms. The
demographics with low videoconference system access and use numbers are adults living in low
socioeconomic rural (LSR) communities such as Alabama's Blackbelt Region. The Alabama
Blackbelt region is one of the poorest areas in the country, characterized by a predominantly
African American population. LSR communities' use in technology is low due to several factors
in the digital divide concepts such differences in digital skills and affordability of the internet.
This study explores the dynamics behind LSR communities in rural Alabama's use with
videoconference system platforms. This study seeks to gain insight and understanding into the
predictors and dynamics behind the relationships between computer self-efficacy (CSE) and the
Technology Acceptance Model 2 (TAM2) constructs in videoconferencing use among
individuals living in the Blackbelt Region of Alabama. Two hundred and ninety-five adult
participants residing in Macon County, Alabama, were surveyed during this study using a
convenience sample. Results showed significant interactions with CSE among age, education
attainment, and income levels. In addition, only actual use (AU), attitude towards use (ATT), and
perceived ease of use (PEOU) were significant predictors of perceived levels of CSE among
participants residing in Macon County, AL. Although these results show significant interactions,
more research is needed on videoconference systems and the dynamics that affect their use in the
Blackbelt region. The implications of videoconference systems research within the Blackbelt
region have the potential to help significantly narrow the digital divide adding to research on
technology use in low socio-economic rural populations
Embrace the dark side: Identification of the cellular and kinetic characteristics of melanization in the American Cockroach
No full textInsect immune systems are comprised of both cellular and humoral mechanisms that protect these organisms from injury and infection. Of these immune mechanisms, the hemolymphatic melanization reaction is a unique immunological process that is ubiquitous across insect species. Using prophenoloxidases (PPOs) expressed in specific hemocytes, insect species can synthesize cytotoxic melanin. This reaction involves the production of reactive oxygen species to kill infectious microbes. While the mechanism of this cascade is well described, the hemocyte populations participating in have not been standardized across Insecta. Another knowledge gap is that these hemocyte have been incompletely identified via microscopy. Additionally, the kinetic characteristics of this phenomenon are poorly understood with most studies evaluating melanin production as a reaction endpoint. Furthermore, a study correlating the in vivo and in vitro kinetics of melanization with an organism has not been documented. Therefore, thesis has attempted to identify PPO-containing hemocyte via a robust assay and elucidate the kinetics of melanization under various conditions within Periplaneta americana. Using cell sorting and an assay to induce melanization, we found that PPO containing hemocytes in P. americana primarily are characterized by a high affinity for the LEA lectin and high intracellular complexity. Furthermore, we found that this population exhibited a large amount of hemocyte clusters which may be indicative of nodulation. Evaluating the kinetics of melanization using spectrophotometry, we found that the addition of L-DOPA and ethanol to whole hemolymph resulted in the fastest rate and endpoint of melanization in comparison to samples containing only whole hemolymph or whole hemolymph and L-DOPA after about 17 hours. Observing the effects of PAMPs in vivo and in vitro, we found that unmethylated cytosine–guanine dinucleotide (CpG) motifs caused a high degree of melanization. Additionally, we found that supernatant derived from a stationary Serratia marcescens culture resulted in a high number of melanized nodules formed in vivo. Overall, my thesis supports that the previously stated methods can be used for a more robust characterization of PPO-containing cells in contrast to microscopy alone. Additionally, my thesis supports that different immunostimulatory PAMPs may cause a wide range of reactivity in the melanization reaction within P. americana. This thesis may be used to inform future research on germ-free insects, which are known to possess truncated immune systems
Testing several powders for activity against litter beetles (Alphitobius diaperinus) under various conditions
No full textLitter beetles (Alphitobius diaperinus) are significant pests in poultry facilities worldwide, acting as both vectors and reservoirs for a variety of pathogens. They cause structural damage to poultry houses and can contaminate feed and litter with pathogens. Thus, increasing poultry's exposure to disease. These challenges highlight an urgent need for effective and sustainable pest control strategies. However, despite the widespread use of chemical insecticides, their effectiveness is frequently compromised by insecticide resistance, health risks, and environmental concerns. This study explored alternative approaches to managing A. diaperinus, with a particular focus on natural powders, including Bentonite Clay, Biochar, Boric Acid, Diatomaceous Earth, Ground Gypsum, Kaolin Clay, Moroccan Rhassoul Clay, Silicon Dioxide, Talc, Walnut Powder, and Zeolite Clay. Known for their physical and chemical properties, these powders can lead to desiccation, metabolic disruption, and eventual death in insects while posing minimal risks to poultry and the environment. Experiments were conducted at room temperature, as well as at varying temperatures (20°C, 30°C, and 35°C) and different humidities (ranging from 10.3% to 99.7% RH). The results showed that Silicon Dioxide consistently caused high mortality rates in beetles across all tested conditions, establishing it as the most effective treatment overall. Moroccan Rhassoul clay and diatomaceous earth also showed strong efficacy, particularly in drier environments. Zeolite Clay and Talc demonstrated greater effectiveness at elevated temperatures, whereas Kaolin Clay maintained consistent performance in both dry and humid conditions. Boric Acid's effectiveness increased with higher temperatures but declined under high humidity. In contrast, Bentonite Clay, Ground Gypsum, and Walnut Powder had minimal impact on beetle mortality. These results underscore the potential of specific powders as viable alternatives to conventional insecticides, with Silicon Dioxide emerging as the most dependable option across varying conditions. By presenting these alternatives, the study aims to advance the understanding and implementation of sustainable pest control techniques tailored to the poultry industry
Rising Tropospheric Ozone Impacts on Pathogen Resistant Pepper (Capsicum annuum)
Get PDFThe increase of anthropogenic emissions has led to rising of global surface temperatures, as well
as increases in secondary greenhouse gas pollutants. A particularly dangerous secondary
pollutant is tropospheric ozone, which has been identified as the third-most potent
greenhouse gas following carbon dioxide and methane. Ozone is formed by the photochemical
oxidation of primary pollutants such as nitrogen oxides, methane, carbon monoxide, and volatile
organic compounds by ultraviolet (UV) sunlight in the troposphere. The accumulation of ozone has
risen with the industrial revolution, rising the average concentration from 10 parts per billion
(ppb) in the 1800s to 40-50 ppb by the 2010s. This daily maximum concentration fluctuates
globally, depending on factors such as population density, urban development, and intensity of
UV radiation. Increasing concerns over the effects of global climate change has led to research
dedicated to direct impacts on overall plant health, as well as the secondary impacts on plant
mechanisms controlling other environmental response pathways. The aim of this thesis research
is to understand the physiological and transcriptomic impacts elevated ozone will have on plant-host
interactions. Specifically, how disease-resistant cultivars may be affected by combined stress
treatments
Improving Interpretability and Accuracy of Artificial Intelligence in Natural Language and Image Understanding
Get PDFTransformer-based models, such as BERT, have revolutionized natural language processing (NLP) by setting new standards for accuracy and capability. BERT has achieved state-of-the-art (SOTA) results on many NLP tasks and benchmarks, such as GLUE, even surpassing human performance. Despite these successes, there remain questions about whether these models truly understand natural languages like humans do. Our findings reveal that BERT-based classifiers often disregard the sequential order of words when evaluated on GLUE. Using LIME, an attribution method to visualize how much a token contributes towards models’ prediction, we find that instead of understanding sentence meaning, these models rely on superficial cues. Additionally, incorporating BERT into attribution methods to yield more plausible counterfactuals when interpreting text classifiers has proven problematic. Our research shows that BERT is not particularly useful in this context unless the attribution method, such as LIME, produces out-of-distribution samples. The limitations of current NLP benchmarks like GLUE are evident as they do not require models to understand the surrounding context before making predictions. To address this, we introduce the Phrase-in-Context (PiC) benchmark. PiC forces models to comprehend the context first before interpreting the meaning of a phrase, as the meaning is context-dependent. This benchmark poses a significant challenge to models, with even GPT-4 (as of March 2023) only achieving a 64–75% accuracy. Moreover, recognizing the value of text-based explanations, we propose using part-based object descriptors generated by GPT-4 to explain image classification systems. By grounding these texts into specific regions of an object image, we aim to enhance both interpretability and performance. This approach is exemplified by PEEB, a part-based image classifier also based on transformer. PEEB translates class names into descriptive texts, matching visual parts to these descriptions for improved classification. It significantly outperforms existing explainable models, particularly in zero-shot settings, and allows for classifier customization without retraining, thereby advancing both interpretability and accuracy in fine-grained image classification