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Swine Origin Probiotics that Promote Health and Growth Performance in Pigs
No full textThe present invention provides probiotic compositions comprising isolated bacterial strains, referred to herein as LactX, StrepX1 and StrepX3. Methods of using the compositions to increase growth rate in pigs and enhance pig production are also provided
The Fathers Have Eaten Sour Grapes: History is the Wrong Test for Establishment Clause Questions in Public Schools
Get PDFThe old quip goes, “As long as there are math tests, students will pray in the schools.” There is, however, a distinction between private and public prayers; the former allows religious students to find peace and comfort in a moment of anxiety, and the latter openly divides the religious majority from the religious and nonreligious minority, ostracizing the minority and exacerbating their nerves. This Note examines the constitutionality of religious exercises initiated by public school faculty and staff at public education’s founding, arguing that the special considerations present when considering prayer in these contexts are too important to be limited by history. This Note will demonstrate that religion in schools cannot be held constitutional based on historical practice alone because the practices promulgated in early American educational history are incompatible with modern values. Part II discusses Establishment Clause jurisprudence. Part III discusses the ambiguities in Kennedy’s historical standard and the history of American public education, noting specifically the policies and practices implemented to effectuate early education reformers’ goals of a “nonsectarian” education. Part IV discusses how those practices are incompatible with modern values, beliefs, and practices, demonstrating the prospective harm posed by Kennedy’s vague historical standard. Part V briefly concludes and implores the Court to consider the impending repercussions of using history, alone and unqualified, as the sole consideration to evaluate religious expression in public schools
How Gender Inequality Drives the Human Trafficking Industry
Get PDFThere are currently more than 40 million people subjected to human trafficking worldwide. People living under forced servitude and sexual exploitation, people in forced marriages, and child soldiers are victims of human trafficking. Research has shown that 71% of these victims are women and girls, making human trafficking a disproportionately gendered issue. However, does gender impact trafficking as a whole? The crux of this study uses a literature review, a comprehensive summary, and a critical analysis of existing works on a specific topic to understand how gender inequality relates to human trafficking. This study will also include a correlation test with the main variables being a country\u27s gender development index score and human trafficking rating from the Global Organized Crime Index to run a correlational study. A country’s gender development index score comes from the difference between men\u27s and women\u27s achievements in three categories: health, education, and command over economic resources. This aims to gain evidence from multiple studies to better understand the knowledge already identified and the gaps in current research. From the evidence taken from the literature review, we can expect that countries with higher gender inequality index scores will have high rates of human trafficking. The study showed a moderate positive correlation between gender inequality and human trafficking rates. The research on gender and trafficking is limited. Still, it is crucial to research this issue further to widen people\u27s understanding of human trafficking and the implications gender inequality has on trafficking to protect individuals better and combat trafficking every day
Vision-Based Multimodal Frameworks for Human Behavioral Analysis: Applications in Group Activity Understanding and Public Health
Get PDFGroup Activity Recognition (GAR) has emerged as a crucial problem in computer vision, with wide-ranging applications in sports analysis, video surveillance, and social scene understanding. Unlike traditional action recognition focused on individuals, GAR requires understanding complex spatiotemporal relationships between multiple actors, their interactions, and the broader context in which these activities occur. This complexity introduces unique challenges, including the need for accurate actor localization, modeling of inter-actor dependencies, and understanding of temporal evolution in group behaviors. While recent advances have shown promise, existing approaches often rely heavily on extensive annotations such as ground-truth bounding boxes and action labels, creating significant barriers to practical deployment and scalability. Additionally, current methods struggle to capture the full spectrum of contextual factors that give meaning to group activities, particularly in real-world applications where multiple modalities of information are available. We first introduce Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition (SPARTAN) and Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition (SoGAR), novel self-supervised frameworks that significantly reduce annotation requirements while maintaining high recognition accuracy. SPARTAN leverages multi-resolution temporal views to capture varied motion characteristics, while SoGAR implements temporal collaborative learning and spatiotemporal cooperative learning strategies. These approaches achieve state-of-the-art performance on multiple benchmark datasets, including JRDB-PAR, NBA, and Volleyball, without requiring person-level annotations. In the multimodal domain, we present three frameworks: Recognize Every Action Everywhere All At Once (REACT), which employs a Vision-Language Encoder for sparse spatial interactions; Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos (HAtt-Flow), which introduces flow conservation principles in attention mechanisms; and LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition (LiGAR), which utilizes LiDAR data as a structural backbone for processing visual and textual information. These frameworks demonstrate significant improvements in capturing cross-modal dependencies and spatial-temporal relationships. Finally, we extend our research to healthcare applications, particularly in analyzing tobacco-related content on social media platforms. We develop Public Health Advocacy Dataset: A Dataset of Tobacco Usage Videos from Social Media (PHAD), Flow-Attention Adaptive Semantic Hierarchical Fusion for Multimodal Tobacco Content Analysis (FLAASH), and A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention (DEFEND) frameworks to address the limitations of current Large Language Models in processing video content. Our experimental results show substantial improvements over existing methods, achieving up to 10.6% gains in F1-score on JRDB-PAR and 5.9% improvement in Mean Per Class Accuracy on the NBA dataset. This thesis advances the field of group activity recognition by reducing reliance on extensive annotations, improving multimodal integration, and demonstrating practical applications in public health monitoring. The proposed frameworks provide a foundation for future research in automated understanding of complex group behaviors while addressing real-world challenges in data annotation and multimodal analysis
Developing Custom Fertilizer Strategies Recirculating Hydroponic Systems
No full textHydroponic systems recirculate nutrient solution to reduce the amount of water used in leafy green production. However, the rootzone of the nutrient solution is complex and influenced by interacting factors such as plant uptake demand, irrigation water quality, supplied fertilizer salts and nutrient concentrations, environmental conditions, and injection of mineral acids/bases to control solution pH. A common strategy for managing nutrients in commercial production is to select a hydroponic solution formulation (i.e. recipe) to supply and maintain a target pH and electrical conductivity (EC) in the recirculating solution over time by automatic injection of mineral acids/bases and concentrated fertilizer stock solutions. During nutrient replenishment of the recirculated solution, nutrients are resupplied at the same ratios at which they are supplied in the initial solution. Often with this approach, the resupply of nutrients is not balanced with plant uptake demand, resulting in root zone nutrient imbalances which can cause yield reductions and motivate growers to dump and replace solution. The objective was to develop a novel strategy for managing nutrients in recirculating solutions designed to supply and maintain optimal macronutrient concentrations and solution pH with basil (Ocimum basilicum) and lettuce (Lactuca sativa) as model crops. The strategy consisted of custom formulating species-specific initial and replenishment solutions formulated using a combination of data on plant tissue nutrient concentrations, previously published nutrient management guidelines and peer-reviewed research, and common grower tools and experience. The custom initial solution for each species was intended to supply macronutrient concentrations at near optimal ratios whereas the custom replenishment solution was intended to replace the nutrients taken up and maintain the concentrations/ratios in the initial solution. The custom strategy was evaluated with basil and lettuce grown for 56 d in deep water culture (DWC) systems and compared to a control strategy consisting of a common 2-part fertilizer formulation for both the initial and replenishment solution. Overall, the custom strategies for basil and lettuce resulted in macronutrients remaining near the initial concentrations supplied whereas the control strategy resulted in macronutrients, particularly calcium and sulfur, which deviated substantially from the initial concentrations. A separate experiment evaluated the custom nutrient management strategy with basil grown in hydroponic nutrient film technique (NFT) systems, where nutrient solutions were formulated using two sources of irrigation water differing in soluble salts and alkalinity. Overall, solution macronutrient concentrations remained more stable over time with the custom strategy compared to the control strategy for both irrigation water qualities. Yield was greatest for basil grown in hydroponic solutions formulated using the high alkalinity irrigation water as a result of the additional nitrogen (N) supplied by nitric acid (HNO3) injection used to neutralize the water alkalinity and adjust solution pH
Comparison of Airborne Lidar-derived Elevation Data in Fayetteville, Arkansas, USA
Get PDFLight detection and ranging (lidar) laser scanners are prominent remote sensing tools to produce high resolution three-dimensional (3D) imagery of the Earth’s surface. These laser scanners combined with global navigation satellite systems (GNSS) and real-time kinematic (RTK) reference stations can generate some of the most accurate ground surface imagery and elevation data for terrain mapping and related applications. Lidar aerial survey is an important tool in industries such as architecture, civil engineering, forestry, geology, geography, and agriculture where digital terrain models (DTMs) can be used to examine the geographical landscape and urban industry. Currently, there are three different common laser scanning systems: traditional airborne lidar, terrestrial laser scanners (TLS), and small unmanned aircraft systems (sUAS)-based lidar. As the integration of sUAS and lidar is in a phase of rapid development, there are ongoing questions regarding the comparative precision and reliability of sUAS versus traditional airborne platforms. In this study, aerial lidar datasets collected on two distinct platforms were compared for benefits to quality and sensitivity to how the data is collected and post-processed. The lidar datasets collected were analyzed and processed using Esri’s ArcGIS Pro software and LAStools produced by Rapidlasso GmbH to generate two DTMs representing ground surface terrain. The DTMs were evaluated based on geomorphological identification, quality assessment, and lidar intensity returns. The results of the DTMs’ Pearson correlation coefficient and relative accuracy indicate strong similarities. However, the dataset collected using traditional airborne lidar was found to be a better representation of the terrain, with vegetation more identifiable in the DTM derived from sUAS lidar. The traditional airborne lidar also benefited from being captured during the “leaf-off” season and was previously post-processed with ground control points (GCPs), resulting in a more accurate point cloud of the bare earth. Future research of sUAS lidar may indicate that a more accurate DTM is possible with the application of GCPs, more analysis of intensity measurements to classify vegetation, manual classification, and evaluation of industry use. As it stands, sUAS derived lidar is more cost-effective than traditional airborne lidar and is linked to more dynamic and versatile remote sensing technologies
Investigating Landslides in Colorado Using GIS-based Methods, Remote Sensing & Electrical Resistivity
No full textLandslides are among the highly damaging natural hazards worldwide. In the USA, active landslides cause damage to homes and infrastructures as well as around 25-50 deaths per year. Colorado is one of the states most highly affected by landslides. According to the most recent Colorado Hazard Mitigation Plan (CHMP), landslides are one of the main hazards that affect Colorado State. For this research, El Paso and Garfield Counties have been selected to be studied because they have an extreme landslide growth risk. Due to the high activity of landslides in El Paso County according to the 2018 CHMP, Colorado Springs City has been selected for this research. In this dissertation, Geographic Information System (GIS), remote sensing, and Electrical Resistivity (ER) have been used to conduct three independent studies focusing on assessing landslides in Colorado. Each independent study has been presented in a separate chapter in a paper format as the paper is either published or being considered for publication in peer-reviewed journals.
The first study addresses the recent zones of landslides in Colorado Springs. Colorado Springs has been experiencing increased landslide activity for 4 decades due to developing new communities on unstable ground. Colorado Springs has developed hillslope areas on its western side; these areas are dominated by unstable shale and landslide deposits. This development decreased the stability of hillslope areas and created vulnerability to landslides. In this study, ER has been used to study Skyway landslides and the Broadmoor Bluffs landslides in Colorado Springs. ER has provided valuable information about the location of the failure surface, the geometry of the landslide surface, the moisture content, and the interface between rock types. This information has not been obtained on this large scale for Colorado Springs before using ER. The produced ER models will guide to future land use and mitigate landslides hazards in the developed areas in the landslide’s areas.
The second study has been dedicated to creating landslide susceptibility maps for Colorado Springs. In this study, the Regression Analysis (RA), Random Forest (RF), and the Analytical Hierarchy Process (AHP) have been integrated into a GIS to establish different GIS-based models. This research has extended the work that has been done before on Colorado Springs. An updated susceptibility map has been created to overcome the limitations of the susceptibility map created by the Colorado Geological Survey (CGS). In addition, the whole city has been considered in this study and several landslide-influencing factors have been investigated. The highest precipitation rates, the presence of shale units, and the high slope values are found to be the main trigger of landslides in Colorado Springs. Most landslides occur in the western, southwestern, and northwestern parts of the city. The landslide susceptibility map produced can help with landslide management, hazard mitigation, and future land use planning in Colorado Springs.
The third study has been focused on evaluating landslides in Garfield County. According to the 2018 CHMP, Garfield County is one of the counties that has been assigned the highest growth risk ratings based on the high risk noted in its local hazard mitigation plan as well as large projected percentages of population growth (ranging from 35% to 42%). The previous landslide hazard mapping is limited to the studied areas, while landslides investigation for the entire Garfield County has not been conducted before. RF has been used to investigate the relationship between landslides events and the significant influencing factors in the entire Garfield County. Five main factors, including precipitation, topographic factors, lithological types, landuse/cover (LuLc) types, and soil types have been investigated. Precipitation showed higher importance than other variables (21%). If the current climate trends continue, the frequency of landslide events will increase. Also, due to the high population growth, development and construction will increase the probability of landslides occurrences in the future. The landslide susceptibility map will help in hazard mitigation and future landuse planning in Garfield County