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This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people's attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1's findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2's results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling. [ABSTRACT FROM AUTHOR]

As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs). [ABSTRACT FROM AUTHOR]

Nurses are at high risk of burnout and subsequent mental health concerns due to problems with overstaffing, immense workload volume, and personal health risks associated with the job. Effective mental health treatments are available but potential barriers to receiving care may prevent nurses from benefiting. The Emotional Health and Well-Being Clinic (EHWC) at Houston Methodist is an outpatient mental health clinic offering therapy and medication management services for employees and employee dependents of our institution. The EHWC is uniquely positioned to observe how nurses utilize mental health services and to address barriers to effective care for this vital group of healthcare professionals. This paper provides descriptive data on the utilization of mental health services by nurses in the EHWC and a discussion of possible challenges faced by this group when seeking care. Based on these data, we propose potential solutions to ensure that nurses can achieve maximum benefit from outpatient mental health services. [ABSTRACT FROM AUTHOR]

Deep soil moisture data have wide applications in fields such as engineering construction and agricultural production. Therefore, achieving the real-time monitoring of deep soil moisture is of significant importance. Current soil monitoring methods face challenges in conducting the large-scale, real-time monitoring of deep soil moisture. This paper innovatively proposes a real-time prediction approach to deep soil moisture combining GNSS-R data and a water movement model in unsaturated soil. This approach, built upon surface soil moisture data retrieved from GNSS-R signal inversion, integrates soil–water characteristics and soil moisture values at a depth of 1 m. By employing a deep soil moisture content prediction model, it provides predictions of soil moisture at depths from 0 to 1 m, thus realizing the large-scale, real-time dynamic monitoring of deep soil moisture. The proposed approach was validated in a study area in Goodwell, Texas County, Oklahoma, USA. Predicted values of soil moisture at a randomly selected location in the study area at depths of 0.1 m, 0.2 m, 0.5 m, and 1 m were compared with ground truth values for the period from 25 October to 19 November 2023. The results indicated that the relative error (δ) was controlled within the range of ±14%. The mean square error (MSE) ranged from 2.90 × 10 − 5 to 1.88 × 10 − 4 , and the coefficient of determination ( R 2 ) ranged from 82.45% to 89.88%, indicating an overall high level of fitting between the predicted values and ground truth data. This validates the feasibility of the proposed approach, which has the potential to play a crucial role in agricultural production, geological disaster management, engineering construction, and heritage site preservation. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of meeting increasing energy needs by assessing the potential of bioenergy as a sustainable resource option in South Central Texas. Available agricultural crop residues suitable for bioenergy production are evaluated from the 21 counties in South Central Texas Regional Water Planning Area (Region L). The residues produced and available for bioenergy are quantified according to the production areas for each field crop and tree area. Residue-to-product ratios of field crops are determined according to crop type and production quantity. Biomass potential of trees is calculated based on tree density and biomass production per tree. The results demonstrate that the potential productions of utilizable agricultural wastes are in the range of 898.7 t kt–1421.39 kt for Region L. The average annual energy potential is estimated at 19.27 PJ, and ranges between 14.36 and 24.18 PJ. The average potential biomass-based electricity production could compensate significant amount of coal-based electricity generated in the Texas and when agricultural wastes are available. [ABSTRACT FROM AUTHOR]

Simple Summary: People experiencing homelessness are at increased risk of dying from tobacco-related cancers due to their elevated tobacco use rates but are not offered evidence-based tobacco dependence interventions by homeless-serving agencies within the United States. Through pre- and post-implementation provider interviews, this qualitative study explored the factors enabling and inhibiting organizational readiness to implement a comprehensive tobacco-cessation intervention within three homeless-serving agencies. Although the organizational readiness was initially high, at the post-implementation, changing contextual factors, primarily resource privations, undermined the provider change efficacy and limited the program implementation. These findings support the value and acceptability of implementing tobacco-cessation interventions within homeless-serving agencies, and they identify the factors needed to build organizational capacity for successful implementation. Despite the high tobacco use rates (~80%) and tobacco-related cancers being the second leading cause of death among people experiencing homelessness within the United States, these individuals rarely receive tobacco use treatment from homeless-serving agencies (HSAs). This qualitative study explored the enablers and inhibitors of implementing an evidence-based tobacco-free workplace (TFW) program offering TFW policy adoption, specialized provider training to treat tobacco use, and nicotine replacement therapy (NRT) within HSAs. Pre- and post-implementation interviews with providers and managers (n = 13) pursued adapting interventions to specific HSAs and assessed the program success, respectively. The organizational readiness for change theory framed the data content analysis, yielding three categories: change commitment, change efficacy and contextual factors. Pre- to post-implementation, increasing challenges impacted the organizational capacity and providers' attitudes, wherein previously enabling factors were reframed as inhibiting, resulting in limited implementation despite resource provision. These findings indicate that low-resourced HSAs require additional support and guidance to overcome infrastructure challenges and build the capacity needed to implement a TFW program. This study's findings can guide future TFW program interventions, enable identification of agencies that are well-positioned to adopt such programs, and facilitate capacity-building efforts to ensure their successful participation. [ABSTRACT FROM AUTHOR]

The effectiveness of COVID-19 vaccines depends on widespread vaccine uptake. Employing a telephone-administered weighted survey with 19,502 participants, we examined the determinants of COVID-19 vaccine acceptance among adults in Texas. We used multiple regression analysis with LASSO-selected variables to identify factors associated with COVID-19 vaccine uptake and intentions to receive the vaccine among the unvaccinated. The prevalence of unvaccinated individuals (22%) was higher among those aged 18–39, males, White respondents, English speakers, uninsured individuals, those facing financial challenges, and individuals expressing no concern about contracting the illness. In a fully adjusted regression model, higher odds of being unvaccinated were observed among males (aOR 1.11), the uninsured (aOR 1.38), smokers (aOR 1.56), and those facing financial struggles (aOR 1.62). Conversely, Asians, Blacks, and Hispanics were less likely to be unvaccinated compared to Whites. Among the unvaccinated, factors associated with stronger intent to receive the vaccine included age (over 65 years), Black and Hispanic ethnicity, and perceived risk of infection. Hispanic individuals, the uninsured, those covered by public insurance, and those facing financial challenges were more likely to encounter barriers to vaccine receipt. These findings underscore the importance of devising tailored strategies, emphasizing nuanced approaches that account for demographic, socioeconomic, and attitudinal factors in vaccine distribution and public health interventions. [ABSTRACT FROM AUTHOR]

This article focuses on developing models that estimate suspended sediment concentrations (SSCs) for the Lower Brazos River, Texas, U.S. Historical samples of SSCs from gauge stations and satellite imagery from Landsat Missions and Sentinel Mission 2 were utilized to develop models to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural networks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate SSCs for the Brazos River as a baseline comparison of the models that used satellite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram analysis on the test data set developed for the Brazos River data. Fifteen of the models developed using satellite imagery as inputs performed with a coefficient of determination R2 above 0.69, with the three best performing models having an R2 of 0.83 to 0.85. One of the best performing models was then utilized to estimate the SSCs before, during, and after Hurricane Harvey to evaluate the impact of this storm on the sediment dynamics along the Lower Brazos River and the model's ability to estimate SSCs. [ABSTRACT FROM AUTHOR]

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P–P reflection and P–S volumes derived from the well's vertical section. The Wolfcamp formation can be seen in two 3D volumes (P–P and P–S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north–south direction and 1500 ft (460 m) in the west–east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) –8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes. [ABSTRACT FROM AUTHOR]

West Texas has been a seismically active region in the past decade due to the injection of industrial wastewater and hydrocarbon exploitation. The newly founded Texas seismological network has provided a catalog that characterizes the intense seismicity down to a magnitude of 1.5 Ml. However, there are numerous small-magnitude events (Ml < 1.0) occurring every day that are not analyzed and reported, due to the prohibitively high workload to manually verify the picks from automatic picking methods. We propose to apply an advanced deep learning method, the earthquake compact convolutional transformer (EQCCT), to unleash our power in analyzing hundreds of small earthquakes per day in West Texas. The EQCCT method is embedded in an integrated-detection-and-location framework to output a highly complete earthquake catalog, given a list of available seismic stations, in a seamless way. The EQCCT has enabled us to detect and locate 50-times more earthquakes (mostly smaller than magnitude 1) than we previously could. We applied the EQCCT-embedded detection and location workflow to the Culberson and Mentone earthquake zone (CMEZ) in West Texas and detected thousands of earthquakes per month for consecutively three months. Further relocation of the new catalog revealed an unprecedentedly high-resolution and precise depiction of shallow and deep basement-rooted faults. The highly complete catalog also offers significant insights into the seismo-tectonic status of the CMEZ. Association with nearby injection activities also revealed a strong correlation between the rate of injected fluid volume and the number of small earthquakes. [ABSTRACT FROM AUTHOR]

The COVID-19 vaccine is safe and effective for children, yet parental hesitancy towards vaccinating children against the virus persists. We conducted a telephone-administered weighted survey in Texas to examine parents' sociodemographic factors and medical conditions associated with COVID-19 vaccination intention for parents with unvaccinated children ages 5–17 years. We collected responses from 19,502 participants, of which 4879 were parents of children ages 5–17 years. We conducted multiple logistic regression with Lasso-selected variables to identify factors associated with children's vaccination status and parents' intention to vaccinate their children. From the unweighted sample, less than half of the parents (46.8%) had at least one unvaccinated child. These parents were more likely to be White, English-speaking, not concerned about illness, privately insured, and unvaccinated for COVID-19 themselves (p < 0.001). In the adjusted regression model, parents who were unvaccinated (vs. having COVID-19 booster, aOR = 28.6) and financially insecure (aOR = 1.46) had higher odds of having unvaccinated children. Parents who were Asian (aOR = 0.50), Black (aOR = 0.69), Spanish-speaking (aOR = 0.57), concerned about illness (aOR = 0.63), had heart disease (aOR = 0.41), and diabetes (aOR = 0.61) had lower odds of having unvaccinated children. Parents who were Asian, Black, Hispanic, Spanish-speaking, concerned about illness for others, and vaccine-boosted were more likely to have vaccination intention for their children (p < 0.001). Children's vaccination is essential to reduce COVID-19 transmission. It is important to raise awareness about the value of pediatric COVID-19 vaccination while considering parents' sociodemographic and medical circumstances. [ABSTRACT FROM AUTHOR]

In this study, we investigate the impact of COVID-19 on academic achievement in Texas public schools. Demographic and Grade 5 STAAR test data were collected from 1155 public school districts for 2018–2019 and 2020–2021. Multiple regression was adopted to analyze the differences between rural and non-rural districts, as well as the impact of demographic characteristics on students' achievement. The results reveal significant differences in demographic characteristics between the two academic years, with non-rural districts exhibiting a greater decline in academic achievement than rural districts. Additionally, the findings suggest that higher teacher salaries correlate with better academic performance across various subjects and that English learners require additional support to acquire content knowledge and skills. We further confirm that the COVID-19 pandemic has disrupted the academic learning experience of Texas students, with rural districts displaying more resilience than non-rural districts. [ABSTRACT FROM AUTHOR]

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca 2 + , Cl − , and Na + . We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. [ABSTRACT FROM AUTHOR]

This study investigates the understudied area of motivational factors influencing the rental intention of electric vehicles (EVs) within the context of their integration into urban transportation to combat air pollution and reduce carbon footprints and explores the critical factors influencing consumer behavior towards EV rental, focusing on hedonic motivation, service level, consumer habits, and willingness to pay. Utilizing multiple linear regression analysis on 302 valid samples from Texas, USA, the research identifies the significant impact of these factors on rental intention. Notably, the service level emerges as the most influential predictor while emphasizing the unique and less studied role of hedonic and personal characteristics as essential antecedents of rental intention. The findings, supplemented by a Monte Carlo simulation, reveal that these personal and motivational characteristics are pivotal in shaping rental intentions, accounting for approximately 47.2% of the variance in rental intention. The study contributes valuable insights into the EV rental market, offering theoretical implications for the EV literature and practical strategies for car rental enterprises to tap into consumer patterns effectively. [ABSTRACT FROM AUTHOR]

Approximately 30% of the private land in Texas, USA is under absentee ownership. Understanding who absentee landowners are and their land management behaviors is vital for the protection of privately owned landscapes and the ecosystem services that they support, including surface water quality. By focusing on absentee landowners with properties in five watersheds in Texas, we utilized the theory of place attachment to gain insights into absentee landowners' land management decisions and their involvement in water quality conservation programs, such as watershed protection plans (WPPs). By conducting a mail-out survey, we obtained 100 responses, which were analyzed using an exploratory factor analysis and a series of nonparametric assessments. The results revealed that, contrary to the term "absentee", the landowners in our study demonstrated strong feelings of place attachment and heightened land stewardship. Based on these findings, we suggest that instead of considering absentee landowners as obstacles to collaborative conservation initiatives, such as WPPs, natural resource practitioners should recognize and capitalize on the emotional attachment that these landowners have to their properties, thereby fostering their involvement. By demonstrating the owner–land relationship and its behavioral outcomes among absentee landowners, this study provides a novel contribution to the existing literature on place attachment in the context of private land management and conservation. [ABSTRACT FROM AUTHOR]

This comparison of five power plants in West Texas is intended to provide various decision-makers and stakeholders with a holistic picture of the life-cycle environmental impacts associated with these power plants. A key contribution of this analysis is that we assumed all power plants generate the same amount of electricity over a 30-year life, taking a 500 MW combined-cycle gas turbine (CCGT) plant as a benchmark. Also, in two cases, we added battery storage to wind and solar PV facilities to render them nearly as dispatchable as the CCGT. We included the entire supply chain supporting electricity generation, which encompassed raw material sourcing, processing, manufacturing, operations, and product end of life, also called "cradle to grave". We report on 18 environmental impacts using ReCiPe midpoint (H) impact assessment. The supply chains are global, and impacts are felt differently by host communities across the world. The results can help stakeholders identify hotspots across numerous supply chains with the highest environmental impacts. We discuss some remedial measures and challenges to inform future analysis by the research community. [ABSTRACT FROM AUTHOR]

Wheat offers winter forage for cattle grazing and is one of the most valuable cash crops in Texas. In this study, we evaluate the impacts of climate change projections on winter wheat grain yields in five major wheat producing counties in Texas (Deaf Smith, Ochiltree, Hansford, Moore, and Parmer). For this purpose, extant soil and climate data were utilized in conjunction with Agricultural Policy Environmental eXtender (APEX) and Coupled Model Intercomparison Project—Phase 5 (CMIP 5) climate projections to determine the most reasonable future trajectory of Texas winter wheat yields. The results indicate that Deaf Smith and Parmer counties are projected to experience the greatest yield decrease, 33.33%, about 696 kg/ha under the CMIP5 RCP4.5 (Texas projected temperature increase between 2.2 and 3.3 °C) 2046–2070 scenario compared to a 1981–2017 baseline. The maximum percentage yield increase was noticed in Ochiltree County under the CMIP5 RCP8.5 2071–2095 scenario, with an 84.2% (about 1857 kg/ha) yield increase compared to the 1981–2017 baseline. Parmer County is projected to experience the greatest yield decrease of 20%, about 348 kg/ha, under the RCP4.5 2046–2070 scenario when compared to the 1981–2005 baseline. The maximum percentage yield increase is projected for Ochiltree County—a 105.9% increase, about 2089 kg/ha—under the RCP8.5 2071–2095 scenario when compared to the 1981–2005 baseline. In general, with few exceptions, winter wheat yields are projected to rise under the projected climate scenarios. [ABSTRACT FROM AUTHOR]

Enduring community–academic partnerships are essential for expediting the successful dissemination and implementation of promising interventions and programs, particularly for complex public health issues such as cancer prevention and control. The purpose of this case study was to understand the combined voices of a diverse group of stakeholders to outline the essential factors needed to translate research into sustainable cancer care within communities. System dynamics group model building was used to develop system maps of the factors impacting equitable access to cancer care services among three stakeholder groups (Group A: survivors and family members, n = 20; Group B: providers and administrators in community agencies/organizations, n = 40; Group C: administrators from a cancer institute, academic universities, foundations, and healthcare facilities that coordinate care, n = 25) in central Texas, USA. The lead researcher identified factors involved in transitions of care and their linkages with each other. The analysis of this work displays these connections visually. These models represent the ripple effect of factors influencing the transition of care for stakeholders who are invested in cancer care outcomes. All three groups identified medical mistrust, a culturally sensitive and diverse provider workforce, and care coordination as three essential factors (i.e., themes). Group A also identified caregiver navigation. The groups varied in their emphasis on upstream vs. downstream social drivers of health, with Group B emphasizing the former and Group C emphasizing the latter. To achieve cancer care equity, all stakeholder groups agreed on the importance of addressing the impact of social drivers as critical gaps. Eliminating or reducing these impacts allows each stakeholder group to work more efficiently and effectively to improve cancer care for patients. [ABSTRACT FROM AUTHOR]

Sporisorium reilianum, the causal agent of sorghum (Sorghum bicolor (L.) Moench) head smut, is present in most sorghum-producing regions. This seed replacement fungal disease can reduce yield by up to 80% in severely infected fields. Management of this disease can be challenging due to the appearance of different pathotypes within the pathogenic population. In this research, the genetic variability and pathogenicity of isolates collected from five Texas Counties was conducted. Due to the lack of available space, 21 out of 32 sequenced isolates were selected and evaluated for virulence patterns on the six sorghum differentials, Tx7078, BTx635, SC170-6-17 (TAM2571), SA281 (Early Hegari), Tx414, and BTx643. The results reveal the occurrence of a new pathotype, 1A, and four previously documented US pathotypes when the 21 isolates were evaluated for virulence patterns on the differentials. The most prevalent was pathotype 5, which was recovered from Brazos, Hidalgo, Nueces, and Willacy Counties, Texas. This pathotype was followed by 1A and 6 in frequency of recovery. Pathotype 4 was identified only from isolates collected from Hidalgo County, while pathotype 1 was from Burleson County, Texas. It appeared that the previous US head smut pathotypes (2 and 3) are no longer common, and the new pathotypes, 1A, 5, and 6, are now predominant. The phylogenetic tree constructed from the single-nucleotide polymorphism (SNP) data through the neighbor-joining method showed high genetic diversity among the tested isolates. Some of the diverse clades among the tested isolates were independent of their sampled locations. Notably, HS37, HS49, and HS65 formed a clade and were classified as 1A in the virulence study, while HS 61 and HS 66, which were collected from Nueces County, were grouped and identified as pathotype 5. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ETr across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning. [ABSTRACT FROM AUTHOR]

In Texas, the cotton fleahopper (Pseudatomoscelis seriatus (Reuter)) is considered a highly economically damaging pest of cotton (Gossypium hirsutum L.). Current control methods rely heavily on foliar chemical insecticides throughout the growing season. Considering the cost of insecticides and the critical timeliness of their application, chemical control methods are often not optimized to reduce potential yield losses. The Mpp51Aa2.834_16 gene in cotton (ThryvOn) has shown effectiveness against thrips and several piercing and sucking mirid insect pests, suggesting it has the potential to mitigate yield losses caused by the cotton fleahopper. Choice and no-choice caged feeding assays were conducted to assess the impact of cotton fleahoppers on ThryvOn cotton square retention under controlled laboratory conditions. In the choice assay, feeding by cotton fleahoppers significantly reduced square retention in the gene-lacking cotton to 46%, while the ThryvOn cotton retained 60% of the squares. In the no-choice assay, cotton fleahopper nymph feeding significantly reduced square retention in the cotton not expressing Mpp51Aa2 to 61%, whereas the ThryvOn cotton was unaffected. Based on the differences in square retention observed in both the choice and no-choice feeding assays, our findings indicate that the Mpp51Aa2 protein influences cotton fleahopper feeding preferences and the susceptibility of cotton plants to damage caused by cotton fleahoppers. Our study offers confirmation of the activity of ThryvOn on cotton fleahoppers observed in the field. The ThryvOn trait's activity towards cotton fleahoppers is consistent with that found for other mirid pests in cotton. [ABSTRACT FROM AUTHOR]