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Exploring the Role of Podcasts: A Study of Agricultural Colleges and Extension Services' Use of Podcasting
No full textThis thesis explores the strategic use of podcasting as an educational and
outreach tool by agricultural colleges, 1890 land grants, non-land grants institutions, and extension services across the United States. This study used the Uses and Gratifications (U&G) Theory to understand the motivations behind creating and promoting agricultural podcasts and how these digital media tools are used to engage audiences. The study was conducted in two phases. Phase One involved a content analysis of 69 agricultural podcasts, evaluating characteristics such as production frequency, topic focus, and distribution platforms. Phase Two consisted of one-on-one interviews with podcast hosts to identify underlying motivations and audience engagement strategies in podcast production. This study demonstrated that agricultural podcasts meet various audience needs, from cognitive gratifications through the delivery of technical information to social and affective gratifications by creating a sense of community and shared experience. Podcasters’ digital tools with traditional outreach methods suggest that podcasts can enhance educational outreach and engagement within the agricultural sector when used strategically. This study provides practical recommendations for educators and communicators looking to adopt podcasting and offers theoretical insights into how new media technologies are reshaping agricultural communication strategies
Utilizing Encryption Keys Derived from Immunoaffinity Interactions as a Basis for Potential Security Enhancements
No full textBioaffinity interactions allow antibodies and antigens to bind and were shown to successfully produce cryptographic keys for encryption in this research. This straightforward immune-system-based construct has shown that data obtained from immunoassay interactions may be utilized to create symmetrical key ciphers. The Advanced Encryption Standard (AES), the current standard method to encrypt and decrypt data, was implemented to show that biomolecules from immune systems can be applied to cryptography for security enhancements. When the sender and receiver use identical protocols and component concentrations, the symmetrical key ciphers can be encrypted and decrypted. Variable immunoassay concentrations, pH, temperature, and data point sorting protocols applied to encryption systems will prevent key repetition and alleviate the ability for unauthorized system access, which solves prominent issues in cryptography. This concept can also strengthen cryptographic processes by providing additional security levels of varying complexity using other indirect methods with this nontraditional immunoaffinity approach to current cipher algorithms
Evaluating Missing Data Recovery Techniques in Two Wave Planned Missing Data Designs for Estimating Latent Growth Parameters
No full textResearchers in longitudinal studies often face the challenge of missing data. This challenge can be addressed through adoption of Planned Missing Data Designs (PMDDs) and modern missing data treatment methods in longitudinal research. These approaches improve data quality, reduce burden, and optimize resources. However, researchers must carefully consider the optimal sample size when implementing PMDDs. The proposed design incorporates intentional missingness across waves for all groups, offering increased flexibility in study design and resource allocation. By eliminating the requirement for a fully observed group, the proposed design effectively reduces resource demands. This strategy also alleviates the logistical and data collection burdens typically placed on researchers and participants in traditional designs that mandate a fully observed group. The proposed wave planned missing design employs modern methods for handling missing data. Researchers can then compare the outcomes of this new proposed design with those of the established wave planned missing design.
This study employs Monte Carlo simulation to demonstrate the feasibility of the proposed wave planned missing data design. This study enables researchers to compare the performance of two types of wave planned missing data designs. It also examines the effectiveness of two modern methods for handling missing data under simulated conditions. This study aims to demonstrate that the proposed wave-planned missing data design can produce results like those from a complete dataset when modern missing data techniques are applied. Furthermore, the study aims to demonstrate that the proposed design performs similarly to the established wave-planned missing data design. It evaluates 24 conditions impacting the design's viability by comparing its performance for estimating growth curve parameters in longitudinal research. The study also introduces a robust visual technique, the ridgeline plot, to enhance the understanding of the performance of the two designs and the two methods for handling missing data.
The study results show that the proposed wave-planned missing data design works well, even with small sample sizes of at least 300 per group. FIML consistently outperforms the PcAux missing data treatment method across most conditions, with a greater extent as sample size increases. The Absolute Relative Bias (ARB) of latent growth estimates is significantly influenced by the magnitude of the population parameter, a trend consistent across both designs. Relative efficiency indicates that PcAux becomes increasingly unreliable in handling missing data as sample size increases, with its performance deteriorating in terms of accuracy, bias, and efficiency in parameter estimation. In contrast, FIML shows increasing efficiency when sample size increases, often achieving relative efficiency greater than 60%. The study demonstrates that the newly proposed wave planned missing design with samples of at least 300 per group performs similarly to the established traditional wave planned missing data design, offering the advantage of reducing the research burden by using FIML missing data treatment method. This makes it a practical alternative, without significantly compromising accuracy, bias, or efficiency in parameter estimation. Overall, the study successfully demonstrates that the proposed design can return unbiased parameter estimates with sufficiently large sample size with FIML missing data treatment method
Filtration Performance Analysis of Different Filter Substrates
Full text linkThe rapid spread of SARS-CoV-2 has created challenges for societies, healthcare settings, business and institutions. To curb virus transmission appropriate personal protective equipment (PPE) including face masks have been recommended given their role in preventing direct transmission of the virus. In the first project government distributed face masks and respirators during the COVID-19 have been tested following ASTM standards. Suggestions were made based on the results obtained. R95 respirator has the highest filtration performance followed by N95 respirator and face masks. N95 has the highest air permeability and filtration performance near R95 and thus may act an effective barrier. Face mask could be an alternative since they have comparable filtration performance and air permeability. The second project discussed about the effects of cold plasma on meltblown polypropylene 3PLY face mask. Three different plasma conditions; low, medium, and high; and nine different runs were made on meltblown polypropylene for three exposure periods; 5, 10 and 15 minutes. Results showed that medium plasma condition for 15 minutes gives the highest increase of filtration performance indicating the potential plasma condition on enhancing filtration performance of polypropylene meltlbown mask. The third project compared the filtration performances of after-market automotive cabin air filters (ACAFs) against ultrafine particles. Both charcoal and standard cabin filter were tested against ultrafine size particles. Charcoal filters were performed better than standard filters. Filters with meltblown layer have superior filtration performances as well. Filter characteristics of cabin filters like GSM, Thickness, and Number of filter layers were positively related to each other and influence the filtration performances of cabin filters
Development of Best Management Practices for Enhancing Soil Health in Semi-Arid Transitional Organic Cropping Systems
Full text linkThe Southern Great Plains (SGP) are marked by challenging climate conditions, such as low annual precipitation (381 to 711 mm) and high wind velocities. The adoption of organic management practices, such as crop rotation, cover cropping, and organic fertilizer usage, can enhance water conservation, build organic matter, and lower the risk of erosion. However, tangible improvements in soil health parameters are crucial to persuade producers to consider organic transition. The objective of this study was to determine the agronomic viability and effects of traditional and non-traditional organic cover crops and crop rotation on soil health parameters, stored soil moisture, and CO2 emissions. Field studies were established in the Texas High Plains (THP) at Lamesa, Texas, and in the Texas Rolling Plains (TRP) at Vernon, Texas, from 2021-2023. The cropping systems at each site were continuous cotton (Gossypium hirsutum L.) (CC), cotton/peanut (CP) (Arachis hypogaea L.) at Lamesa and cotton/mungbean (Vigna radiata L.) (CM) at Vernon, cotton/sesame (Sesamum indicum L.) (CS), and cotton/wheat/forage sorghum sudangrass (Sorghum bicolor x Sorghum sudanense) (CWH). The cover crop treatments were rye at 17 kg ha-1 (Rye17), rye at 34 kg ha-1 (Rye34), fennel/fenugreek (FF) mix at 8.5/8.5 kg ha-1, rye/fennel/fenugreek (RFF) at 25/6/6 kg ha-1. Soils at both sites were characterized for key physical, chemical, and biological soil health parameters, and crop yields and cover crop herbage mass were determined annually. Additionally, at Lamesa, stored soil moisture and CO2 emissions were measured in 2022, and weed cover in 2023.
Crop yields and cover crop herbage mass were generally low because of drought and weed pressure. Still, the cropping system most influenced changes in soil health parameters at both sites. Mean weight diameter (MWD), total microbial biomass, and certain microbial communities (AMF, G+ bacteria) were most improved during the transition period to organic production. Soil organic carbon (SOC) stocks increased from 14 to 43% at Vernon and from 22 to 31% at Lamesa between 2021 and 2023, although differences between cropping systems were minimal. At Lamesa, sorghum sudangrass produced the greatest herbage mass in 2022, which lowered weed pressure, enhanced stored soil moisture, and increased SOC. While high herbage mass resulted in high CO2 flux in 2022, approximately 50% of the C lost was replaced by crop residues. Increases in microbial abundance (fungi, AMF, and G+) allude to positive trends in C sequestration, especially when crop rotation is practiced. Our results highlight that changes in soil health parameters because of organic transition are influenced by ecoregion, land history, and management decisions (plant selection, compost application frequency, planting, and termination timing). We recommend further research to develop regionally appropriate best-management practices for semiarid transitional organic cropping systems to address weed pressure, conserve soil water, and build SOC
Developing a Generative AI Model to Enhance Sentiment Analysis for the Saudi Dialect
Full text linkSentiment Analysis (SA) is a fundamental task in Natural Language Processing (NLP) with broad applications across various real-world domains. While Arabic is a globally significant language with several well-developed NLP models for its standard form, achieving high performance in sentiment analysis for the Saudi Dialect (SD) remains challenging. A key factor contributing to this difficulty is inadequate SD datasets for training of NLP models. This study introduces a novel method for adapting a high-resource language model to a closely related but low-resource dialect by combining moderate effort in SD data collection with generative AI to address this problem of inadequacy in SD datasets. Then, AraBERT was fine-tuned using a combination of collected SD data and additional SD data generated by GPT. The results demonstrate a significant improvement in SD sentiment analysis performance compared to the AraBERT model, which is fine-tuned with only collected SD datasets. This approach highlights an efficient approach to generating high-quality datasets for fine-tuning a model trained on a high-resource language to perform well in a low-resource dialect. Leveraging generative AI enables reduced effort in data collection, making our approach a promising avenue for future research in low-resource NLP tasks
The Impact of Nature-Based Outdoor Play Areas on Play and Learning Behaviors of Children with Autism Spectrum Disorder (ASD)
Full text linkNature plays a vital role in the development of children. Unfortunately, not all children follow the same growth curve. A 2023 WHO report found that 1 in 100 children worldwide have autism spectrum disorder (ASD), totaling about 20 million globally. These children often face challenges in sensory integration, impacting communication, learning, social skills, motor development, and stress-related issues. However, many guidelines and design practices discuss only the indoor environment and design guidelines for children with ASD but rarely show any evidence that nature-based outdoor play areas may help to minimize the difficulties experienced by children with ASD.
This thesis investigates whether a nature-based outdoor play area has any impact on play and learning behaviors of children with ASD. The investigation was conducted by transforming the existing playground of the Burkhart Center for Autism Education and Research at Texas Tech University by incorporating nature-based learning elements and analyzing before-after data. Three types of data were collected - behavior mapping data of children with ASD using ArcGIS Online and Field Map, survey data from parents and caregivers using Qualtrics, and a focus group discussion with the parents. This research shows that nature interventions in a playground for children with ASD impact their play and learning behaviors, and interactions with other children and adults, and they add value to the everyday experiences of children and adults (parents and caregivers). After the intervention, sensory exploration was 3.9 times higher in the nature-play area, STEAM-related activities rose by 1.6 times, and playing with a group of children increased 3.67 times. Major changes Findings suggest that further research (with a larger sample size, experimental design, and permanent interventions) may add valuable insights for designing learning landscapes for children with ASD
SWCPC 438 Negatives #36 B. W. Reynolds, undated.
No full textThe collection features portraits of sixty-one prominent cattle ranchers, both male and female, who were considered to be the “Cattle Kings of Texas.
SWCPC 438 Negatives #9 R. B. Masterson, undated.
No full textThe collection features portraits of sixty-one prominent cattle ranchers, both male and female, who were considered to be the “Cattle Kings of Texas.
SWCPC 438 Negatives #70 Kerville, Texas (duplicate), undated.
No full textThe collection features portraits of sixty-one prominent cattle ranchers, both male and female, who were considered to be the “Cattle Kings of Texas.