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Precision Irrigation Management and Water Stress Assessment in Cotton for Sustainable Agriculture
No full textPrecision Irrigation Management and Water Stress Assessment in Cotton for Sustainable Agriculture is a comprehensive study that explores the need for efficient water resource management and precision irrigation in semi-arid cotton production systems. Water scarcity and climate continue to challenge sustainable agriculture, making it urgent to integrate remote sensing, machine learning, and precision irrigation techniques to optimize water use efficiency (WUE) and maintain crop productivity. This dissertation investigates how satellite- and UAS-based remote sensing can improve precision irrigation strategies, assess water stress, and enhance cotton yield prediction. The research is structured around five objectives: (1) assessing precision water management using satellite remote sensing, (2) optimizing cotton water use and yield for precision irrigation, (3) evaluating spatial and temporal variabilities of evapotranspiration (ET) and WUE using satellite-derived ET, (4) predicting cotton yield using satellite remote sensing and machine learning, and (5) flight altitude and sensor angle affect unmanned aerial system cotton plant height assessments.
Water management in cotton production requires a deep understanding of how irrigation treatments influence crop response at different spatial and temporal scales. The objective of this chapter is to assess the effects of irrigation variability on crop growth and yield, and to identify the key remote sensing indicators for precision irrigation management. Variable rate irrigation (VRI) was integrated with high-resolution PlanetScope imagery to monitor cotton phenotypic traits across different irrigation regimes. Results reveal that NDRE exhibited the highest predictive accuracy (R² = 0.90, RMSE = 5.77%) for biomass and leaf area index (LAI), making it a valuable tool for irrigation optimization. While mid-season biomass accumulation was significantly influenced by irrigation (p < 0.001), the final yield variations were primarily driven by site-specific factors such as apparent soil electrical conductivity (ECa), slope, and elevation (p < 0.001), rather than irrigation volume alone. These findings showed the need for site-specific irrigation strategies rather than uniform water applications to enhance irrigation efficiency.
Maximizing cotton productivity under minimal water use is critical for sustainable agricultural systems in semi-arid regions. The objective of this chapter is to identify the optimal irrigation rates that improve WUE while maintaining or increasing cotton yields. A Random Forest machine learning model was used to evaluate the effects of irrigation levels, soil properties, and topography on WUE and yield. Key findings revealed that optimized, site-specific irrigation strategies improved WUE by up to 21.4 % compared to conventional methods, while increasing cotton yields by 31%. Spatial variability significantly influenced irrigation efficiency, with soil ECa, elevation, and slope playing crucial roles in yield response. These findings highlight the importance of tailoring irrigation practices to local field conditions to enhance productivity while reducing unnecessary water use, ensuring both water conservation and optimal yields in water-limited regions.
Understanding the spatial and temporal dynamics of evapotranspiration (ET) and WUE within irrigated cotton fields is crucial for precision irrigation. The objective of this chapter is to quantify the influence of field topography and irrigation treatments on water use efficiency. OpenET-derived satellite data were used to assess spatial and temporal variability in ET and its relationship with WUE and yield. Results show substantial spatial heterogeneity in ET and WUE across fields, with some fields achieving the highest mean WUE (2.20 kg ha-1 mm-1) and the lowest coefficient of variation (CV = 15.79 %), indicating more uniform water use. In contrast, other fields exhibited greater variability (CV = 25.46 % and 31.20 %, respectively), suggesting inconsistent water utilization. A positive correlation between ET and yield was observed, where regions with higher ET had increased cotton lint yield. However, ANOVA results indicate that landscape position significantly influenced ET and WUE (p < 0.05), while irrigation treatments had no significant effect, emphasizing the dominant role of field topography in shaping water use dynamics. These findings highlight the effectiveness of OpenET in monitoring within-field water variability and guiding precision irrigation strategies to optimize WUE and cotton yield.
Accurate and timely yield prediction models are essential for optimizing resource allocation and improving decision-making in precision agriculture. The objective of this chapter is to evaluate the accuracy of different remote sensing datasets in predicting cotton yield and determining the most effective spectral indicators. Sentinel-1 and PlanetScope satellite imagery were compared using machine learning models to assess their effectiveness at different growth stages. Results show that PlanetScope significantly outperformed Sentinel-1, achieving an R² of 0.84 with an RMSE of 7.01 kg ha-1, while Sentinel-1’s best model achieved an R² of 0.76. Among vegetation indices, EVI was the strongest predictor of cotton yield, contributing over 42% feature importance at mid-growth, outperforming NDVI and NDRE indices. Mid-season predictions provided the most actionable insights, allowing early intervention through optimized irrigation and nutrient management before final yield determination. These findings highlight the importance of multi-temporal remote sensing and machine learning in improving data-driven decision-making for sustainable cotton production.
Accurate plant height measurements provide critical insights into crop growth and productivity, playing a key role in precision agriculture applications. The objective of this chapter is to determine the best UAS flight configurations for capturing reliable plant height data. Images were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) to assess their impact on measurement accuracy. Results show that lower-altitude flights (40 m) provided more accurate height measurements than higher-altitude flights (80 m), particularly in early-season growth stages. Additionally, oblique sensor angles (45°) improved plant height estimation accuracy compared to nadir (90°) views, particularly in late-season assessments. These findings establish clear guidelines for optimizing UAS-based crop monitoring, ensuring that remote sensing-derived plant height measurements are both precise and reliable for cotton growth assessments.
The findings of this dissertation collectively demonstrate the potential of integrating remote sensing, machine learning, and site-specific irrigation strategies to enhance water use efficiency, optimize yield, and promote sustainable cotton production in semi-arid regions. By leveraging high-resolution satellite data, OpenET-derived evapotranspiration monitoring, and advanced predictive modeling, this research provides a scalable and data-driven framework for precision agriculture, offering practical solutions to the challenges of water scarcity, climate variability, and resource optimization. The insights gained from this study extend beyond cotton production and have broader implications for sustainable water management in other water-limited cropping systems. Future research should build upon these advancements by incorporating real-time sensor integration, decision support systems, and multi-sensor fusion techniques to refine precision irrigation strategies and enhance agricultural resilience in the face of global climate challenges
From Planning to Practice: Designing a Targeted Professional Development Program to Change Teachers’ Pedagogical Mindsets to Promote Engagement in Instructional Practices.
No full textThis design-based school improvement study examines a suburban high school in the Dallas-Fort Worth metroplex implementing a professional development program to align teachers' pedagogical mindsets and promote engagement in instructional practices.
The researcher implemented professional development sessions designed to promote engagement in the classroom. The sessions focused on goal-setting, quality feedback, and lesson plan alignment. Throughout the process, teacher expectations and experience were examined. As teachers develop interventions and higher expectations for their students, they begin to see more significant opportunities for their students to be successful. This study's framework and blueprint are based on situational theory, which allows adaptability and flexibility to find the most effective changes. The aim is to explore individual teachers' mindsets and classroom experiences. Five teachers in EOC-tested subject areas (English I, English II, Biology, and United States History) participated in the study. The emerging themes centered around student-centered engagement, the educator's role in the classroom, lesson planning and time management, ongoing professional development, and constructive and immediate feedback. Teachers participated in interviews, observations, and professional development sessions to create and practice classroom lesson plans
Motion Prediction in Collision Avoidance Manual Reaching Tasks Considering Cognitive Perceived Risk
No full textHuman motion and coordination involve the complex interplay of cognitive perception and biomechanical capabilities. This connection allows individuals to perform tasks such as reaching, grasping, and navigating through obstacle-filled environments with remarkable efficiency. This dissertation investigates human collision avoidance strategies during manual reaching tasks by focusing on the minimum clearance distance between the upper extremity and three-dimensional obstacles of varying characteristics. By integrating biomechanical and cognitive factors, this research advances the understanding of how cognitive perceived risk influences human motion in obstacle-populated environments. Experimental data were collected from fifteen participants as they performed natural reaching motions around three distinct obstacles using an IMU-based motion capture system. These obstacles varied in shape, size, orientation, and fragility, prompting participants to adjust their motion strategies based on risk perception. This work proposes and tests multiple loss functions to quantify the cost of collision with an obstacle, including a risk factor loss function tailored to each obstacle and parameterized using experimental data. Other loss functions, such as the log-cosh, tilted, and weighted absolute error loss functions, are also evaluated, offering a broader perspective on how different mathematical formulations influence motion prediction. The study incorporates a Bayesian Decision Theory framework to model perceived collision risk, an approach that quantifies the cognitive biases involved in human decision-making under uncertainty. The optimization-based motion prediction algorithm was enhanced by integrating the proposed loss functions as constraints, allowing for the prediction of joint angle profiles and minimum clearance distances. A sensitivity analysis for the tilted and weighted loss functions demonstrated that the parameter plays a crucial role in improving joint angle profiles’ predictions. The results show that the prediction accuracy for shoulder and elbow joint angles was higher than for wrist motions, as evidenced by lower root mean square error values in the joint angle profile comparison with experimental data. Among the loss functions, the risk factor loss function proved most effective for minimum clearance distance prediction, likely due to its obstacle-specific parameterization. However, each loss function exhibited distinct advantages and disadvantages, demonstrating the importance of selecting a loss function based on the specific requirements of the simulation task. The results further demonstrate that obstacle characteristics significantly affect the minimum clearance distance, with wider and more fragile obstacles resulting in greater clearance. No significant differences were found in motion duration or maximum velocity, suggesting that these factors may not directly correlate with perceived risk in static environments. Overall, including perceived risk through the proposed loss functions led to more accurate predictions than traditional non-risk-based simulations, highlighting the importance of including cognitive factors and biomechanical constraints. This research contributes to human motion modeling by combining biomechanical efficiency with cognitive factors, such as perceived risk, into a unified prediction algorithm. The findings highlight the need to incorporate cognitive models of risk perception into optimization-based motion prediction algorithms for applications such as human-robot interaction, ergonomics, and digital human modeling
A Proposed Reconfigurable Lunar Habitat Design for the Support of Human Space Exploration
No full textKevin M. May, University of Colorado, United StatesLynnette N. Wilde, University of Colorado, United StatesCharlie Priebe, University of Colorado, United StatesLynzee Hoegger, University of Colorado, United StatesJonathan Abrams, University of Colorado, United StatesNoah Hassett, University of Colorado, United StatesTucker Peyok, University of Colorado, United StatesICES502: Space ArchitectureThe 54th International Conference on Environmental Systems was held in Prague, Czechia, on 13 July 2025 through 17 July 2025.Future lunar missions will require innovative habitat
systems to support crewed operations on the Moon’s surface
for extended durations. To address these needs, a
reconfigurable lunar habitat was designed, intended to
facilitate extended human exploration beyond low-Earth
orbit. This design incorporates state-of-the-art
technologies, combining highly demonstrated solutions with
emerging advancements to address the Moon’s unique
environmental and operational challenges. The proposed
habitat is designed to support critical functions such as
life support, power generation, communication,
extravehicular activities, and crew health maintenance -
each necessary for enabling longer-duration missions that
contribute to NASA’s vision for future planetary
exploration.
This design approach emphasizes reconfigurability, crew
well-being, and operational efficiency. With a fully
integrated design and a layout that adapts to multiple
operational scenarios, the habitat minimizes reliance on
additional infrastructure and launches. The reconfigurable
interior layout maximizes usable volume, enhancing
habitability within the constraints of a lunar habitat and
supporting evolving mission goals and payload requirements.
Upcoming developments include the design and construction
of a full-scale, medium-fidelity mockup of the habitat
concept, incorporating a fully reconfigurable interior to
simulate various mission activities. Comprehensive
“day-in-the-life” human factors testing will evaluate the
habitat’s design, assessing its ability to support diverse
lunar operations and enhance crew habitability. These
efforts lay a foundation for future human exploration of
the Moon and beyond
Design, Co-Expression, and Evaluation for Assembly of the Structural Proteins from Thermophilic Bacteriophage ΦIN93
No full textBacteriophage ΦIN93 has an icosahedral-like capsid that is believed to be composed of two putative capsid or coat proteins, namely open reading frame (ORF)13 and ORF14. In addition to the two capsid proteins, there are other proteins that may be associated with the structure of the virus. For example, five other proteins (ORF12, ORF16, ORF17, ORF19, and ORF20) in the virus have been identified as putative membrane-associated proteins. It is believed that membrane-associated proteins associate with coat proteins (serve as scaffolding proteins) to promote viral assembly. While the expression/co-expression of ORF13 and ORF14 have been done to assess if they can assemble to form virus-like particles (VLPs), the expression of any of the membrane-associated proteins and their contribution to assembly have never been attempted. In this study, we successfully co-expressed, for the first time, three membrane-associated proteins (ORF12, ORF16, ORF17) in addition to ORF13 and ORF14 in thermophilic bacteria (Thermus thermophilus, HB27:nar strain) and in mesophilic bacteria (BL21 Star). The expression levels of the proteins were higher in BL21 Star than in Thermus thermophilus, HB27:nar. Some of the expressed proteins (especially ORF17) migrated at sizes that were more than their deduced molecular weight (based on amino acid sequence). Co-expression of these proteins did not lead to the formation of structures that we believe are VLPs. Nevertheless, we believe co-expressing these proteins together from different plasmids is a good approach to assess which of them may be required to form VLPs
Test-Retest Reliability and Agreement of an Ultrasound Guide System
No full textBrightness-mode (B-mode) ultrasound is a popular non-invasive imaging modality used to assess skeletal muscle size. While magnetic resonance imaging (MRI) remains the gold standard for measuring muscle size and volume. However, the reliability and agreement of different B-mode ultrasound imaging techniques in conjunction with the various muscle volume estimation equations has not yet been studied. This study aimed to assess the test-retest reliability of a custom-built probe guide (PGT) and traditional (TRAD) ultrasound methods to assess muscle volume using four volume estimation equations. We also aimed to evaluate the slice-by-slice agreement and the agreement within the most accurate (i.e., “best version”) of four different muscle volume estimation equations. This study consisted of forty recreationally active young adults (55% female; 21 3 years, 24.54 4.21 kg/m2) completing one visit to the laboratory. Participants underwent non-invasive ultrasound imaging on their vastus lateralis (VL) across four separate image acquisition sessions, each separated by 10 minutes. Two trials utilized the PGT while the other two used TRAD methods. The cross-sectional area (CSA) from the scans were quantified using an open-source imaging software. Muscle volume was estimated using the truncated cone, Cavalieri, cubic spline interpolation, and DPSO equations. Test-retest reliability statistics were calculated between trials for PGT and TRAD for all four muscle volume estimation equations. Excellent reliability was displayed across trials 1 and 2 for PGT and TRAD for all four muscle volume estimation equations (ICC2,1 = 0.956 - 0.987, SEM% = 2.02 – 5.34%, MD = 3.96 – 10.50 cm3). For the 7-site muscle volume estimations, the slice-by-slice agreement within the four muscle volume estimation equations displayed that as slice number increases error and bias decrease. When using 6 slices, agreement was the best for the truncated cone (SEE = 5.03, TE = 13.59, r = 0.997), Cavalieri (SEE = 4.85, TE = 12.57, r = 0.997), and cubic spline interpolation (SEE = 4.57, TE = 5.01, r = 0.999). The DPSO method displayed excellent agreement (SEE = 4.59, TE = 4.70, r = 0.999) at slice 5 with the lowest amount of bias (-0.96 cm3). The agreement for the “best version” within the 7-site muscle volume estimation equations, the cubic spline interpolation and DPSO displayed good agreement (SEE = 9.30, TE = 27.02, r = 0.997) with a reduced amount of bias (-25.37 cm3, LOA: -43.60 - -7.14). While the Cavalieri and truncated cone also displayed relatively good agreement (SEE = 4.16, TE = 17.97, r = 0.999) with a low amount of bias (17.48 cm3, LOA: 9.30 - 25.63). Overall, the PGT and TRAD methods exhibit excellent reliability for whole muscle volume estimation across all four equations. Equations that incorporate adjustments for missing CSA values (i.e., cubic spline interpolation and DPSO) displayed the best overall agreement for 7-site muscle volume and slice-by-slice analyses
WMU Pictures.
No full textVarious materials from the FBC Lubbock Woman's Missionary Union c. 1960s-1980s
"Enjoy Your Perfect Experience": Decolonizing Study Abroad through a Content and Paradigmatic Narrative Analysis of the Study Abroad Curriculum
No full textStudy abroad is a high-impact educational practice in higher education where students spend time living and learning outside of the US. While study abroad aims to produce critical thinkers who possesses skills necessary to navigate complex global environments, extant scholarship demonstrates that study abroad may not be achieving the stated outcomes. Additionally, calls to decolonize higher education have extended to the practice of study abroad and research shows there is ample opportunity to apply decolonial theory to study abroad.
Study abroad programming relies on curriculum with various components, including academic coursework, housing, and on-site activities and excursions (OSAEs). Study abroad curriculum is designed and delivered by Study Abroad Program Providers (SAPPs) who engage tens of thousands of students annually in study abroad programs around the globe. The top destination for US students to study abroad is Italy.
The purpose of this qualitative research study is to interrogate whether or not the on-site activities and excursions component of the study abroad curriculum indicate the presence of coloniality. To that end, the research questions for this study are: To what extent do the contents of the on-site activities and excursions component of the study abroad curriculum designed and delivered by SAPPs in Italy present coloniality? To what extent do the stories of enacted curriculum of the on-site activities and excursions component of the study abroad curriculum present coloniality? This study engaged SAPP curriculum developers and deliverers of OSAEs in Italy as interview participants and the contents of OSAEs for analysis.
Decolonial curriculum theory was leveraged as the theoretical framework for this study to interrogate the study abroad curriculum. Using extant scholarship, a rubric of Coloniality Indicators (CIs) was created in order to analyze the data for the presence of coloniality. These Coloniality Indicators (CIs) were used in the content analysis and paradigmatic narrative analysis to identify emergent themes in the narratives. The findings were then re-storied into narrative form, specifically a genre called flash fiction. These flash fiction pieces are meant to elicit a compelling engagement of the data from the reader and inspire action.
The study did indicate the presence of coloniality in the curriculum of OSAEs in Italy. Specific recommendations are offered for addressing the presence of coloniality as well as quick wins for easy, implementable actions to address the presence of coloniality in the curriculum. Finally, suggestions for future research and general recommendations to decolonize study abroad are offered
Cultivating Excellence in Education: A Comprehensive Exploration of the Sustained Influence of Project-Based Learning on Academic Achievement - A Meta-Analysis Spanning Over 15 Years
No full textAbstract
This meta-analysis examined the long-term impact of Project-Based Learning (PBL) on academic achievement, engagement, equity, and skill development in K–12 settings. Synthesizing data from studies published over the past 15 years, the research aimed to determine the effectiveness of PBL compared to traditional instructional methods. The analysis incorporated peer-reviewed studies that utilized quantitative, qualitative, and mixed-methods designs, focusing on diverse student populations across elementary, middle, and high school levels.
The study followed a systematic review process, including defined inclusion and exclusion criteria, an extensive database search, and procedures to extract data across multiple studies. Quantitative findings were assessed using effect size measures, while qualitative results were synthesized to capture student engagement, equity outcomes, and the development of 21st-century skills.
Findings indicated that PBL consistently outperformed traditional instruction in promoting academic achievement, especially in STEM subjects and Advanced Placement courses. Students engaged in PBL also demonstrated stronger critical thinking, collaboration, and problem-solving skills. Furthermore, the analysis revealed that PBL reduced achievement gaps among English Language Learners and students from historically underserved communities. Equity-focused implementations yield the most significant outcomes when teachers and administrators are supported by targeted professional development and aligned instructional resources.
This study highlights the potential of PBL as a high-impact, student-centered instructional model that supports deeper learning and academic success across diverse settings. It also identifies best practices and implementation strategies that can guide educators and policymakers in adopting PBL effectively. Recommendations for future research include exploring the longitudinal impact of PBL on college and career readiness, strategies for teacher training, and integrating emerging technologies into project-based frameworks