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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]

This paper aims to share post-pandemic lessons for destination resilience and the sustainability of events. It offers a new perspective that reimagines the space and place of events as learning destinations enmeshed in complex systems. Complexity arises due to the interactions and interrelationships between numerous stakeholders, activities, and events in the social–ecological destination system, where boundaries are porous, and issues and actions from afar can impact the local community. The case presented here describes the micro-level activities and actions undertaken to engage with destination resilience and sustainable event management and certification at a learning destination in Texas, USA. These situated efforts are shown (i) at the campus-wide level for the university and (ii) with the collaborative, learning-oriented activities undertaken by students in event management classes to pilot test the Sustainable Event Certification Checklist that was developed. They corroborate the general characteristics and criteria of the complex learning destination summarized in the paper, along with identifying and discussing the skills, literacies, and lessons learned to advance destination resilience and the sustainability of events. Participants in the learning destination draw on practical knowledge and develop soft skills to engage in adaptive planning proactively and collaboratively with other stakeholders to address emergent challenges and practical problems in the complex destination and sustainable event domain. [ABSTRACT FROM AUTHOR]

The main challenges of the input current control in synchronous DC-DC buck converters are the nonlinear model of the system, changes of the operating point in a wide range, and the need to use an input LC filter for current smoothing, which may result in the instability of the closed-loop system. In this paper, a step-by-step approach is developed for the design and improvement of a PI-feedforward closed-loop controller. It is shown that a linear PI controller cannot stabilize the closed-loop system properly during wide changes in model parameters, e.g., an equivalent series resistance of the input filter. To cope with the stability issues, a fixed-frequency sliding mode controller (SMC) has been developed in this paper for the implementation of an electro-mechanical actuator (EMA) emulator. Moreover, a systematic approach is proposed for controller tuning and the selection of the SMC's gains. To achieve high power efficiency, high-frequency GaN switches are used for the practical implementation of the DC-DC converter. Despite large changes in the load current, the designed nonlinear controller can track the input current reference satisfactorily. Steady-state and dynamic responses of the proposed SMC are compared with conventional linear controllers. Considering the Lyapunov stability theorem, it is proved that the designed SMC can stabilize the closed-loop system in the entire utilizable domain. The proposed nonlinear SMC controller enjoys a very simple control law. Hence, despite having very high switching and sampling frequencies, it can be easily implemented. The experimental response of the designed synchronous DC-DC buck converter is evaluated experimentally by implementing the control strategy in a TMS320F28335PGFA DSP from Texas Instrument. Moreover, the comprehensive comparison of the proposed SMC controller and a PI-feedforward controller proved the superior performance of the developed closed-loop system, in terms of the transient time response, robustness, and stability of the EMA emulator. [ABSTRACT FROM AUTHOR]

(1) Background: This paper focuses on the development of a community assessment for telehealth using an interprofessional lens, which sits at the intersection of public health and urban planning using multistakeholder input. The paper analyzes the process of designing and implementing a telemedicine plan for the City of Brownsville and its surrounding metros. (2) Methods: We employed an interprofessional approach to CBPR which assumed all stakeholders as equal partners alongside the researchers to uncover the most relevant and useful knowledge to inform the development of telehealth community assessment. (3) Results: Key findings include that: physicians do not have the technology, financial means, or staff to provide a comprehensive system for telemedicine; and due to language and literacy barriers, many patients are not able to use a web-based system of telemedicine. We also found that all stakeholders believe that telehealth is a convenient tool that has the capacity to increase patient access and care. (4) Conclusions: Ultimately, the use of an interprofessional community-based participatory research (CBPR) design allowed our team to bring together local knowledge with that of trained experts to advance the research efforts. [ABSTRACT FROM AUTHOR]

Formation fracturing is the method of choice for developing shale oil and gas reservoirs that constitute a gigantic resource in the U.S.A. and many other countries but are characterized by a low permeability in the nano-Darcy range. The oil production of Texas has increased by about 5 million B/D in 15 years as a result of shale exploitation by massive multistage hydraulic fracturing. The mathematical modeling of this fracturing process is complex and can be approached in several ways. This paper first gives a concise description of the fracturing process as carried out in Texas. Included are the ranges of the key reservoir properties, as well as the injection fluid volumes and pressure, the composition of the injected fluid, proppant type, and volume, and other relevant data. Also included are the number of fracture stages, methods of zonal isolation, and diagnostic techniques used. An important variable considered is flowback, in particular, fluid retention and oil and gas production. High-salinity water production is discussed. Given the above variables, the currently used fracture simulators are briefly considered and compared, both the geomechanics-based and fracture-propagation-based. No single simulator can model the complete process. The directions currently being followed are briefly described. Also discussed is the simulation of the re-fracturing process and its range of success in increasing oil recovery from about the original ~5% to ~8%. Future processes such as plasma fracturing are mentioned, and their future applicability is discussed for further increasing oil recovery. [ABSTRACT FROM AUTHOR]

The utilization of wind energy has become increasingly popular in the United States and many European countries due to its abundant nature and optimized design. While existing wind turbines are predominantly large-scale and not suitable for standalone or distributed power production, Lubbock County in West Texas offers a diverse range of renewable energy options to meet its energy needs. The region relies heavily on utility-scale wind energy sources to supply power to the Texas Grid, replacing conventional fossil fuel-based systems. Currently, standalone solar PV systems are the preferred choice for renewable energy generation. However, West Texas possesses an ample supply of wind energy that can be harnessed to establish a microgrid and provide standalone power to rural communities. Distributed wind energy offers localized power generation, reducing transmission losses and grid strain, while conventional wind farms require long-distance transmission, leading to efficiency gains. By employing the latest technology and optimizing efficiency, even in low-scale generation, a 6-kilowatt permanent magnet alternator-based distributed wind turbine has been designed. This paper focuses on analyzing the techno-economic aspects of implementing this wind turbine in a real-world scenario, taking into account wind attributes, such as velocity and available power, at the specific location. [ABSTRACT FROM AUTHOR]

After the occurrence of a hurricane, assessing damage is extremely important for the emergency managers so that relief aid could be provided to afflicted people. One method of assessing the damage is to determine the damaged and the undamaged buildings post-hurricane. Normally, damage assessment is performed by conducting ground surveys, which are time-consuming and involve immense effort. In this paper, transfer learning techniques have been used for determining damaged and undamaged buildings in post-hurricane satellite images. Four different transfer learning techniques, which include VGG16, MobileNetV2, InceptionV3 and DenseNet121, have been applied to 23,000 Hurricane Harvey satellite images, which occurred in the Texas region. A comparative analysis of these models has been performed on the basis of the number of epochs and the optimizers used. The performance of the VGG16 pre-trained model was better than the other models and achieved an accuracy of 0.75, precision of 0.74, recall of 0.95 and F1-score of 0.83 when the Adam optimizer was used. When the comparison of the best performing models was performed in terms of various optimizers, VGG16 produced the best accuracy of 0.78 for the RMSprop optimizer. [ABSTRACT FROM AUTHOR]

The increase in the concentration of geological gas emissions in the atmosphere and particularly the increase of methane is considered by the majority of the scientific community as the main cause of global climate change. The main reasons that place methane at the center of interest, lie in its high global warming potential (GWP) and its lifetime in the atmosphere. Anthropogenic processes, like engineering geology ones, highly affect the daily profile of gasses in the atmosphere. Should direct measures be taken to reduce emissions of methane, immediate global warming mitigation could be achieved. Due to its significance, methane has been monitored by many space missions over the years and as of 2017 by the Sentinel-5P mission. Considering the above, we conclude that monitoring and predicting future methane concentration based on past data is of vital importance for the course of climate change over the next decades. To that end, we introduce a method exploiting state-of-the-art recurrent neural networks (RNNs), which have been proven particularly effective in regression problems, such as time-series forecasting. Aligned with the green artificial intelligence (AI) initiative, the paper at hand investigates the ability of different RNN architectures to predict future methane concentration in the most active regions of Texas, Pennsylvania and West Virginia, by using Sentinel-5P methane data and focusing on computational and complexity efficiency. We conduct several empirical studies and utilize the obtained results to conclude the most effective architecture for the specific use case, establishing a competitive prediction performance that reaches up to a 0.7578 mean squared error on the evaluation set. Yet, taking into consideration the overall efficiency of the investigated models, we conclude that the exploitation of RNN architectures with less number of layers and a restricted number of units, i.e., one recurrent layer with 8 neurons, is able to better compensate for competitive prediction performance, meanwhile sustaining lower computational complexity and execution time. Finally, we compare RNN models against deep neural networks along with the well-established support vector regression, clearly highlighting the supremacy of the recurrent ones, as well as discuss future extensions of the introduced work. [ABSTRACT FROM AUTHOR]

The University of Texas MD Anderson Cancer Center, a comprehensive cancer center designated by the National Cancer Institute (NCI), defines its service population area as the State of Texas (29.1 M), the second most populous state in the country and the state with the greatest number of uninsured residents in the United States. Consistent with a novel and formal commitment to prevention as part of its core mission, alongside clear opportunities in Texas to drive vaccine uptake, MD Anderson assembled a transdisciplinary team to develop an institutional Framework to increase adolescent HPV vaccination and reduce HPV-related cancer burden. The Framework was developed and activated through a four-phase approach aligned with the NCI Cancer Center Support Grant Community Outreach and Engagement component. MD Anderson identified collaborators through data-driven outreach and constructed a portfolio of collaborative multi-sector initiatives through review processes designed to assess readiness, impact and sustainability. The result is an implementation community of 78 institutions collaboratively implementing 12 initiatives within a shared measurement framework impacting 18 counties. This paper describes a structured and rigorous process to set up the implementation of a multi-year investment in evidence-based strategies to increase HPV vaccination that solves challenges preventing implementation of recommended strategies and to encourage similar initiative replication. [ABSTRACT FROM AUTHOR]

In our title, "chaos" means there is a positive Lyapunov exponent that causes the tornado to move. We are asserting that a positive Lyapunov exponent is not always needed to have a butterfly effect. Lorenz's butterfly effect initially appeared in meteorology and has captured the imaginations of people for applications to all kinds of fields. We feel it is important to understand simpler non-meteorological models to understand the additional aspects of the butterfly effect. This paper presents simple linear map models that lack "chaos" but exhibit a butterfly effect: our simplest model does not have any positive Lyapunov exponents but still exhibits a butterfly effect, that is, temporary exponential growth from a tiny perturbation such as one infected mosquito setting off an epidemic outbreak. We focus on a 24-dimensional version of the map where a significant butterfly effect is observed even though the only Lyapunov exponent is 0. We introduce a linear "infected mosquito" model that shows how off-diagonal matrix entries can cause a finite-time growth rate. We argue that the degree of instability in our systems can be better measured by its finite-time growth rate. Our findings suggest that even in linear systems, off-diagonal matrix entries can significantly impact the system's behavior and be more important than the Lyapunov exponents in higher-dimensional systems. A focus on finite-time growth rates can yield valuable insights into the system's dynamics. [ABSTRACT FROM AUTHOR]

Tree crown width relates directly to wood quality and tree growth. The traditional method used to measure crown width is labor-intensive and time-consuming. Pairing imagery taken by an unmanned aerial vehicle (UAV) with a deep learning algorithm such as a faster region-based convolutional neural network (Faster-RCNN) has the potential to be an alternative to the traditional method. In this study, Faster-RCNN outperformed single-shot multibox detector (SSD) for crown detection in a young loblolly pine stand but performed poorly in a dense, mature loblolly pine stand. This paper proposes a novel Faster-RCNN algorithm for tree crown identification and crown width extraction in a forest stand environment with high-density loblolly pine forests. The new algorithm uses Residual Network 101 (ResNet101) and a feature pyramid network (FPN) to build an FPN_ResNet101 structure, improving the capability to model shallow location feature extraction. The algorithm was applied to images from a mature loblolly pine plot in eastern Texas, USA. The results show that the accuracy of crown recognition and crown width measurement using the FPN_ResNet101 structure as the backbone network in Faster-RCNN (FPN_Faster-RCNN_ResNet101) was high, being 95.26% and 0.95, respectively, which was 4.90% and 0.27 higher than when using Faster-RCNN with ResNet101 as the backbone network (Faster-RCNN_ResNet101). The results fully confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Simple Summary: People with substance use disorders have extremely elevated rates of smoking and, therefore, are a priority population for lung cancer screening. This paper examines the lung cancer screening practices—determining patients' eligibility for lung cancer screening and making referrals to screening—of Texas healthcare organizations that provide services to people with substance use disorders. This work demonstrated that few organizations are determining patients' eligibility and even fewer are making referrals. While not all organizations have the capability to make referrals (i.e., no on-site prescriber), they each have a vital role to play in eligibility determination and patient education. There is a need for researchers to focus intervention and implementation efforts within these organizations to increase capacity and ensure that patients are being navigated to lung cancer screening at multiple touch points across the healthcare continuum. For people at elevated risk for lung cancer, lung cancer screening (LCS) reduces lung cancer mortality. People with non-nicotine substance use disorders (SUDs) have elevated rates of smoking compared with the general population, highlighting them as a priority population for LCS consideration. Although research has shown LCS is underutilized, there is little literature to inform whether organizations that serve individuals with SUDs have existing clinical protocols surrounding LCS. In the current study, we examine the LCS eligibility and referral practices among these organizations. We conducted a statewide needs assessment survey in 2021 to discern how tobacco use was being addressed at Texas organizations that provide treatment or services to individuals with SUDs. Respondents were asked to report on their center's LCS eligibility and referral practices. The analytic sample consists of 125 respondents who represented 23 federally qualified health centers, 29 global local mental health authorities (LMHAs), 12 substance use treatment programs in LMHAs, and 61 standalone substance use treatment centers. Very few respondents indicated that healthcare providers at their center made referrals to LCS for patients (8.8%); a few respondents indicated that their healthcare providers assessed patients' eligibility for LCS but did not make referrals (3.2%). Intervention and implementation efforts are needed in these and other SUD healthcare settings to bolster organizational capacity and ensure that patients are being navigated to lung cancer screening at multiple touch points across the care continuum. [ABSTRACT FROM AUTHOR]

This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery in order to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to USD 61 million dollars of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge releases and topographic data provided by the U.S. Army Corps of Engineers. A measure of fit assessment is performed on the MOP flood maps with the HEC-RAS simulated flood inundation output to quantify spatial differences. Estimating possible flood inundation using individual datasets that vary spatially and temporally allow to gain an understanding of how much each observational dataset contributes to the overall water estimation. Results show that water surfaces estimated by MOP are comparable with the simulated output for the duration of the flood event. Additionally, contributed data, such as Civil Air Patrol, although they may be geographically sparse, become an important data source when fused with other observation data. [ABSTRACT FROM AUTHOR]

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method. [ABSTRACT FROM AUTHOR]

Motivated by the relatively infrequent but very large price spikes in the day-ahead and real-time energy markets operated by the Electric Reliability Council of Texas, this paper proposes an insurance that a small and risk-averse retailer in Texas (i.e., a retail electric provider (REP)) may buy to prevent financial insolvency caused by inadequate risk management. It also demonstrates the insurance's practical design, pricing, and implementation. As participation in the REP's procurement auction is voluntary, the insurance is mutually beneficial for the REP and the insurance seller. Hence, the proposed insurance is a newly developed wholesale market product that deserves consideration by REPs in Texas and competitive retailers elsewhere. [ABSTRACT FROM AUTHOR]

Automatically detecting the wet/dry shoreline from remote sensing imagery has many benefits for beach management in coastal areas by enabling managers to take measures to protect wildlife during high water events. This paper proposes the use of a modified HED (Holistically-Nested Edge Detection) architecture to create a model for automatic feature identification of the wet/dry shoreline and to compute its elevation from the associated DSM (Digital Surface Model). The model is generalizable to several beaches in Texas and Florida. The data from the multiple beaches was collected using UAS (Uncrewed Aircraft Systems). UAS allow for the collection of high-resolution imagery and the creation of the DSMs that are essential for computing the elevations of the wet/dry shorelines. Another advantage of using UAS is the flexibility to choose locations and metocean conditions, allowing to collect a varied dataset necessary to calibrate a general model. To evaluate the performance and the generalization of the AI model, we trained the model on data from eight flights over four locations, tested it on the data from a ninth flight, and repeated it for all possible combinations. The AP and F1-Scores obtained show the success of the model's prediction for the majority of cases, but the limitations of a pure computer vision assessment are discussed in the context of this coastal application. The method was also assessed more directly, where the average elevations of the labeled and AI predicted wet/dry shorelines were compared. The absolute differences between the two elevations were, on average, 2.1 cm, while the absolute difference of the elevations' standard deviations for each wet/dry shoreline was 2.2 cm. The proposed method results in a generalizable model able to delineate the wet/dry shoreline in beach imagery for multiple flights at several locations in Texas and Florida and for a range of metocean conditions. [ABSTRACT FROM AUTHOR]

Background: This paper performs a detailed ordinal logistic regression study in an evaluation of a survey at a university in South Texas, USA. We show that, for categorical data in our case, ordinal logistic regression works well. Methods: The survey was designed according to the guidelines in diet and lifestyle from the American Heart Association and the United States Department of Agriculture and was sent out to all registered students at Texas A&M International University in Laredo, Texas. Data analysis included 601 students' results from the survey. Data analysis was conducted in Rstudio. Results: The results showed that, compared with students who do not have enough whole grain food and exercise, those who have enough in both tend to have normal BMIs. As age increases, BMI tends to be out of the normal range. Conclusions: Because BMI in this research has three categories, applying an ordinal logistic regression model to describe the relationship between an ordered categorical response variable and more explanatory variables has several advantages compared with other models, such as the linear regression model. [ABSTRACT FROM AUTHOR]

Scientific studies have provided evidence that there is a relationship between violent and aggressive behaviors and addictions. Genes involved with the reward system, specifically the brain reward cascade (BRC), appear to be associated with various addictions and impulsive, aggressive, and violent behaviors. In our previous research, we examined the Taq A1 allele (variant D2 dopamine receptor gene) and the DAT-40 base repeat (a variant of the dopamine transporter gene) in 11 Caucasian boys at the Brown School in San Marcus, Texas, diagnosed with intermittent explosive disorder. Thirty supernormal controls were screened to exclude several reward–deficit behaviors, including pathological violence, and genotyped for the DRD2 gene. Additionally, 91 controls were screened to exclude ADHD, pathological violence, alcoholism, drug dependence, and tobacco abuse, and their results were compared with DAT1 genotype results. In the schoolboys vs. supercontrols, there was a significant association with the D2 variant and a trend with the dopamine transporter variant. Results support our hypothesis and the involvement of at least two gene risk alleles with adolescent violent/aggressive behaviors. This study and the research presented in this paper suggest that violent/aggressive behaviors are associated with a greater risk of addiction, mediated via various genes linked to the BRC. This review provides a contributory analysis of how gene polymorphisms, especially those related to the brain reward circuitry, are associated with violent behaviors. [ABSTRACT FROM AUTHOR]

To prevent climate change, Europe and the world must shift to low-carbon and renewable energies. Hydrogen, as an energy vector, provides viable solutions for replacing polluting and carbon-emitting fossil fuels. Gaseous hydrogen can be stored underground and coupled with existing natural gas pipe networks. Salt cavern storage is the best suited technology to meet the challenges of new energy systems. Hydrogen storage caverns are currently operated in the UK and Texas. A preliminary risk analysis dedicated to underground hydrogen salt caverns highlighted the importance of containment losses (leaks) and the formation of gas clouds following blowouts, whose ignition may generate dangerous phenomena such as jet fires, unconfined vapor cloud explosions (UVCEs), or flashfires. A blowout is not a frequent accident in gas storage caverns. A safety valve is often set at a 30 m depth below ground level; it is automatically triggered following a pressure drop at the wellhead. Nevertheless, a blowout remains to be one of the significant accidental scenarios likely to occur during hydrogen underground storage in salt caverns. In this paper, we present modelling the subterraneous and aerial parts of a blowout on an EZ53 salt cavern fully filled with hydrogen. [ABSTRACT FROM AUTHOR]

This study attempts to integrate a Surface Water (SW) model Soil and Water Assessment Tool (SWAT) with an existing steady-state, single layer, unconfined heterogeneous aquifer Analytic Element Method (AEM) based Ground Water (GW) model, named Bluebird AEM engine, for a comprehensive assessment of SW and GW resources and its management. The main reason for integrating SWAT with the GW model is that the SWAT model does not simulate the distribution and dynamics of GW levels and recharge rates. To overcome this issue, often the SWAT model is coupled with the numerical GW model (either using MODFLOW or FEFLOW), wherein the spatial and temporal patterns of the interactions are better captured and assessed. However, the major drawback in integrating the two models (SWAT with—MODFLOW/FEM) is its conversion from Hydrological Response Unit's (HRU)/sub-basins to grid/elements. To couple them, a spatial translation system is necessary to move the inputs and outputs back and forth between the two models due to the difference in discretization. Hence, for effective coupling of SW and GW models, it may be desirable to have both models with a similar spatial discretization and reduce the need for rigorous numerical techniques for solving the PDEs. The objective of this paper is to test the proof of concept of integrating a distributed hydrologic model with an AEM model at the same spatial units, primarily focused on surface water and groundwater interaction with a shallow unconfined aquifer. Analytic Element Method (AEM) based GW models seem to be ideal for coupling with SWAT due to their innate character to consider the HRU, sub-basin, River, and lake boundaries as individual analytic elements directly without the need for any further discretization or modeling units. This study explores the spatio-temporal patterns of groundwater (GW) discharge rates to a river system in a moist-sub humid region with SWAT-AEM applied to the San Jacinto River basin (SJRB) in Texas. The SW-GW interactions are explored throughout the watershed from 2000–2017 using the integrated SWAT-AEM model, which is tested against stream flow and GW levels. The integrated SWAT-AEM model results show good improvement in predicting the stream flow (R2 = 0.65–0.80) and GW levels as compared to the standalone SWAT model. Further, the integrated model predicted the low flows better compared to the standalone SWAT model, thus accounting for the SW-GW interactions. Almost 80% of the stream network experiences an increase in groundwater discharge rate between 2000 and 2017 with an annual average GW discharge rate of 1853 Mm3/year. The result from the study seems promising for potential applications of SWAT-AEM coupling in regions with considerable SW-GW interactions. [ABSTRACT FROM AUTHOR]

Featured Application: This paper aims to develop a landslide model to simulate slump-type landslide tsunamis. The modified Bi-viscous model can be used to simulate the landslide movements in both the in-land area and ocean area. The model is able to describe the developments of the landslide as well as the slip surface. In addition, the model can be applied to hazard asscessments. This paper incorperates Bingham and bi-viscosity rheology models with the Navier–Stokes solver to simulate the dynamics and kinematics processes of slumps for tsunami generation. The rheology models are integrated into a computational fluid dynamics code, Splash3D, to solve the incompressible Navier–Stokes equations with volume of fluid surface tracking algorithm. The change between un-yield and yield phases of the slide material is controlled by the yield stress and yield strain rate in Bingham and bi-viscosity models, respectively. The integrated model is carefully validated by the theoretical results and laboratory data with good agreements. This validated model is then used to simulate the benchmark problem of the failure of the gypsum tailings dam in East Texas in 1966. The accuracy of predicted flood distances simulated by both models is about 73% of the observation data. To improve the prediction, a fixed large viscosity is introduced to describe the un-yield behavior of tailings material. The yield strain rate is obtained by comparing the simulated inundation boundary to the field data. This modified bi-viscosity model improves not only the accuracy of the spreading distance to about 97% but also the accuracy of the spreading width. The un-yield region in the modified bi-viscosity model is sturdier than that described in the Bingham model. However, once the tailing material yields, the material returns to the Bingham property. This model can be used to simulate landslide tsunamis. [ABSTRACT FROM AUTHOR]

Geoenvironmental models were formulated by the U.S. Geological Survey in the 1990s to describe potential environmental effects of extracting different types of ore deposits in different geologic and climatic regions. This paper presents a geoenvironmental model for roll-front (roll-type) uranium deposits in the Texas Coastal Plain. The model reviews descriptive and quantitative information derived from environmental studies and existing databases to depict existing conditions and potential environmental concerns associated with mining this deposit type. This geoenvironmental model describes how features of the deposits including host rock; ore and gangue mineralogy; geologic, hydrologic, and climatic settings; and mining methods (legacy open-pit and in situ recovery [ISR]) influence potential environmental effects from mining. Element concentrations in soil and water are compared to regulatory thresholds to depict ambient surface water and groundwater conditions. Although most open-pit operations in this region have been reclaimed, concerns remain about groundwater quality at three of the four former mills that supported former open-pit mines and are undergoing closure activities. The primary environmental concerns with ISR mining are (1) radon gas at active ISR operations, (2) radiation or contaminant leakage during production and transport of ISR resin or yellowcake, (3) uranium excursions into groundwater surrounding active ISR operations, and (4) contamination of groundwater after ISR mining. Although existing regulations attempt to address these concerns, some problems remain. Researchers suggest that reactive transport modeling and a better understanding of geology, stratigraphy, and geochemistry of ISR production areas could minimize excursions into surrounding aquifers and improve results of groundwater restoration. [ABSTRACT FROM AUTHOR]

A previously completed mineral resources assessment of the Texas Coastal Plain indicated the potential for the future discovery of uranium resources. Geoenvironmental assessments that include the hydrogeologic framework can be used as a tool to understand the potential effects of mining operations. The hydrogeologic framework for this study focused on the composite hydrogeologic unit of the tract permissive for the occurrence of uranium consisting of the upper part of the Miocene-age Fleming Formation/Lagarto Clay, Pliocene-age Goliad and Pleistocene-age Willis Sands, Pleistocene-age Lissie and Beaumont Formations, and Holocene-age alluvial sediments (fluvial alluvium and eolian sand deposits). This composite hydrogeologic unit, which contains the Chicot and Evangeline aquifers of the Gulf Coast aquifer system, is intended for inclusion in a regional-scale geoenvironmental assessment of as yet undiscovered uranium resources. This article provides (1) a brief literature review describing the geologic and hydrogeologic settings, (2) the methodology used to develop a composite hydrogeologic framework, and (3) descriptions and maps of the land-surface altitude, composite hydrogeologic unit base and midpoint depth, water-level altitude, depth of water, unsaturated and saturated zone thickness, and transmissivity and hydraulic conductivity. A composite hydrogeologic unit, created by combining geologic and hydrogeologic data and maps for individual geologic and hydrogeologic units, is intended for use as a tool in a geoenvironmental assessment to evaluate potential contaminant migration through various avenues. Potential applications include using the hydrogeologic framework as an input into a geoenvironmental assessment to help estimate the potential for (1) runoff of contaminants into surface water, (2) infiltration of contaminants into the groundwater (aquifers), or (3) movement of contaminants from the mining area through wind, groundwater-flow, or streamflow in a given permissive tract. The procedures outlined in this paper also provide a method for developing hydrogeologic frameworks that can be applied in other areas where mining may occur. [ABSTRACT FROM AUTHOR]

Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land, dry sand or debris, wet sand, and water. Unmanned aircraft system (UAS) remote sensing that can acquire imagery with sub-decimeter pixel size provides opportunities to map these four beach zones. This paper attempts to delineate four beach zones based on UAS hyperspatial RGB (Red, Green, and Blue) imagery, namely imagery of sub-decimeter pixel size, and feature textures. Besides the RGB images, this paper also uses USGS (the United States Geological Survey) Munsell HSV (Hue, Saturation, and Value) and CIELUV (the CIE 1976 (L*, u*, v*) color space) images transformed from an RGB image. The four beach zones are identified based on the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) textures. Experiments were conducted with South Padre Island photos acquired by a Nikon D80 camera mounted on the US-16 UAS during March 2014. The results show that USGS Munsell hue can separate land and water reliably. GLCM and LBP textures can slightly improve classification accuracies by both unsupervised and supervised classification techniques. The experiments also indicate that we could reach acceptable results on different photos while using training data from another photo for site-specific UAS remote sensing. The findings imply that parallel processing of classification is feasible. [ABSTRACT FROM AUTHOR]

This paper investigates the spatial dimension of socioeconomic and demographic factors behind COVID-19 vaccine hesitancy. With a focus on a county with considerable sociodemographic diversity in the state of Texas, USA, we apply regression models to census-tract-level data of the unvaccinated population. In addition to disparities in accessing the vaccination service, particularly for residents in rural areas, empirical results confirm under-vaccination among lower socioeconomic neighborhoods and communities with signs of distrust in government. The spatial model regressions further underscore the impact that vaccine hesitancy among residents in one community spread to its nearby communities. This observed spatial spillover effect is attributable to the geographic interactions of similar socioeconomic groups. [ABSTRACT FROM AUTHOR]

Power systems optimization is generally subject to the compromise between performance and cost. The 2021 Texas grid outage illustrates the worldwide dangers for the regional-centralized power grid, with comparable advantages to safety and flexibility for the distributed energy system. The storage of household batteries helps balance grid load and increase system stability and flexibility. However, household storage battery is still not widely used today because of its high costs. Currently, research on increasing household battery storage applicability is focused largely on optimizing economic strategies, such as configuration, dispatching and subsidy policies, which rely substantially more on technologies and financial perspectives. Consumers are not 'rational' individuals, and non-economic incentives can affect their decisions without raising prices. This paper consequently proposes to encourage users to acquire household battery storage to increase efficiency of power dispatching and economic advantages based on behavioral economics. In this paper, an empirical research builds upon the utility model of behavioral economics incentives and purchase willingness. Moreover, the multi-objective genetic algorithm is utilized to optimize the dispatching of household battery storage by using grid variance and user revenues as optimizing goals. The results of this paper show that the behavioral economics incentive improves intention to buy the household battery energy storage by 10.7% without raising subsidies. By improving the energy dispatching strategy, peak-load shifting performance and user revenues are improved by 4.2% and 10.6%, respectively. [ABSTRACT FROM AUTHOR]

Accuracy assessment of classification results has important significance for the application of remote sensing images, which can be achieved by sampling methods. However, the existing sampling methods either ignore spatial correlation or do not consider spatial heterogeneity. Here, we proposed a multi-level non-uniform spatial sampling method (MNSS) for the accuracy assessment of classification results. Taking the remote sensing image of Kobo Askov, Texas, USA, as an example, the classification result of this image was obtained by Support Vector Machine (SVM) classifier. In the proposed MNSS, the studied spatial region was zoned from high to low resolution based on the features of spatial correlation. Then, the sampling rate of each zone was deduced from the low to high resolution based on the spatial heterogeneity. Finally, the positions of sample points were allocated in each zone, and the classification results of the sample points were obtained. We also used other sampling methods, including a random sampling method (SRS), stratified sampling method (SS), and spatial sampling of the gray level co-occurrence matrix method (GLCM), to obtain the classification results of the sample points (2-m resolution). Five categories of ground objects in the same region were used as the ground truth data. We than calculated the overall accuracy, Kappa coefficient, producer accuracy, and user accuracy to estimate the accuracy of the classification results. The results showed that MNSS was the strictest inspection method as shown by the minimum value of accuracy. Moreover, MNSS overcame the shortcoming of SRS, which did not consider the spatial correlation of sample points, and overcame the shortcomings of SS and GLCM, which had redundant information between sample points. This paper proposes a novel sampling method for the accuracy assessment of classification results of remote sensing images. [ABSTRACT FROM AUTHOR]

Inductive power transfer (IPT) systems have become more and more popular recently. To improve transient responses and load disturbance responses, this paper proposes a predictive controller design for a three-winding inductive power transfer (IPT) system. First, a three-winding IPT is presented. Next, a predictive controller is designed based on augmented variables and a performance index. Finally, a digital signal processor, TMS 320F2808, made by Texas Instrument, is used to execute the predictive control algorithms and to control the switching states of the power devices. An IPT system, with DC 220 V input, DC 130 V output, and a rated power of 2 kW, is implemented. A buck converter is used to provide an adjustable output voltage and output current to charge a battery set. Experimental results show that the proposed predictive controllers of the IPT system have better performance than proportional-integral (PI) controllers, including faster transient responses and better load disturbance responses. [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]

This study examines how implied speaker nationality, which serves as a proxy for bilingual/monolingual status, influences social perception and linguistic evaluation. A modified matched-guise experiment was created with the speech of eight bilingual U.S. Spanish speakers from Texas talking about family traditions; the speech stimuli remained the same, but the social information provided about the speakers–whether they were said to be from Mexico (implied monolingual) or from Texas (implied bilingual)–varied. Based on 140 listeners' responses (77 L2 Spanish listeners, 63 heritage Spanish listeners), quantitative analyses found that overall listeners evaluated 'Mexico' voices as more able to teach Spanish than 'Texas' voices. However, only heritage listeners perceived 'Mexico' voices as being of higher socioeconomic status and of more positive social affect than 'Texas' voices. Qualitative comments similarly found that heritage listeners evaluated 'Mexico' voices more favorably in speech quality and confidence than 'Texas' voices. The implications are twofold: (i) the social information of implied monolingualism/bilingualism influences listeners' social perceptions of a speaker, reflecting monoglossic language ideologies; and (ii) there exists indeterminacy between language and social meaning that varies based on differences in lived experiences between L2 and heritage Spanish listeners. Extending on previous findings of indeterminacy between linguistic variants and meaning, the current study shows this also applies to (implied) language varieties, demonstrating the role of language ideologies in mediating social perception. [ABSTRACT FROM AUTHOR]

Murine typhus is an acute febrile illness caused by Rickettsia typhi, an obligately intracellular Gram-negative coccobacillus. Rats (Rattus species) and their fleas (Xenopsylla cheopis) serve as the reservoir and vector of R. typhi, respectively. Humans become infected when R. typhi-infected flea feces are rubbed into flea bite wounds or onto mucous membranes. The disease is endemic throughout much of the world, especially in tropical and subtropical seaboard regions where rats are common. Murine typhus is reemerging as an important cause of febrile illness in Texas and Southern California, where an alternate transmission cycle likely involves opossums (Didelphis virginiana) and cat fleas (Ctenocephalides felis). Although primarily an undifferentiated febrile illness, a range of neurologic manifestations may occur, especially when treatment is delayed. Serology is the mainstay of diagnostic testing, but confirmation usually requires demonstrating seroconversion or a fourfold increase in antibody titer from acute- and convalescent-phase sera (antibodies are seldom detectable in the first week of illness). Thus, early empiric treatment with doxycycline, the drug of choice, is imperative. The purpose of this review is to highlight murine typhus as an important emerging and reemerging infectious disease, review its neurologic manifestations, and discuss areas in need of further study. [ABSTRACT FROM AUTHOR]