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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]

Background: Human papillomavirus (HPV) infections can cause cancers of the cervix, vagina, vulva, penis, anus, and oropharynx. The most recently approved HPV vaccine, Gardasil-9, protects against HPV infection and can prevent HPV-associated invasive cancers. However, Gardasil-9 is one of the most underused vaccines in the US today. Young adults are at risk for HPV infection, but many are not vaccinated. This study uses a randomized controlled trial (RCT) to test an innovative multilevel intervention to increase HPV vaccination rates among young adults. In this paper, we describe the research protocol. Methods: The study uses a two by three factorial design. A total of 1200 young adults in Texas, age 18–26 years, who have not been previously fully vaccinated against HPV will be randomly assigned to one of six conditions to receive: (1) standard CDC information about HPV vaccination (control); (2) video narratives about HPV vaccination; (3) written narratives about HPV vaccination; or (4–6) enhanced access to HPV vaccine combined with (4) standard CDC information, (5) video narratives, or (6) written narratives. The two primary outcomes are the rate of HPV vaccination initiation by 3-month follow-up and rate of HPV vaccination completion by 9-month follow-ups. We will determine the impact of the individual level intervention (i.e., persuasive narratives through video or written format), the systemic level intervention (i.e., enhanced access to HPV vaccines), and the combination of both levels, on HPV vaccination initiation and completion. We will also use purposive sampling to select participants to take part in semi-structured interviews/focus groups to better understand the mechanisms of the intervention. Discussion: Recruitment and data collection began in March 2022. We expect to complete data collection by March 2026. We expect that narratives, enhanced access, and the combination of both will improve HPV vaccination initiation and completion rates among young adults. If proven successful, these individual- and system-level interventions can be easily disseminated in regions with low HPV vaccination rates to improve HPV vaccination, and ultimately decrease HPV-related cancer burden. Trial Registration: NCT05057312. [ABSTRACT FROM AUTHOR]

Climate change and the associated sea level rise (SLR) are presenting newfound challenges to the port systems and coastal transportation infrastructure of southeast Texas. This paper introduces a Geographic Information Systems (GIS) based model designed to simulate inundation scenarios under various sea-level projections, aiming to assess the vulnerabilities of both port facilities and road networks. The study area encompasses a specific region within Jefferson County, southeast Texas, encompassing three major ports: Port Arthur, Beaumont, and Orange. Utilizing a high-resolution (1-m) Digital Elevation Model (DEM) derived from the 2017 LiDAR dataset, this model is integrated with NASA's sea-level rise projections to compute the extent and volume of inundation across low, medium, and high SLR scenarios. Drawing from monthly mean sea level data spanning from 1958 to 2020, the lowest SLR projections, derived from the relative sea-level trend measured at the Sabine Pass, TX gauge station, indicate a yearly increase of 6.16 mm, with a 95% confidence interval of +/- 0.74 mm. Projections for 2050 and 2,100 show the lowest SLR at 0.17 m and 0.48 m, respectively. In contrast, the medium to high RSLR projections under the IPCC SSP3-7.0 scenario for 2050 and 2,100 stand at 0.54 m and 1.34 m, respectively. The findings reveal that, under medium to high SLR scenarios, the extent of inundated areas in the study region is expected to expand by 12.4% in 2050 and 19.9% in 2,100, compared to the lowest SLR projection. Additionally, the length of submerged roadways is predicted to increase by 6.9% in 2050 and 13.3% in 2,100, in comparison to the lowest SLR projection. It is worth noting that some margin of error may be introduced due to factors such as the width of the port area and access roads, the high-resolution DEM, and the alignment of computed inundated areas with the existing topography. Overall, the manuscript highlights the urgency of proactive planning and underscores the importance of safeguarding critical infrastructure in the context of climate change and SLR. [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]

Two-dimensional (2D), depth-averaged shallow water equation (SWE) models are routinely used to simulate flooding in coastal areas – areas that often include vast networks of channels and flood-control topographic features and/or structures, such as barrier islands and levees. Adequately resolving these features within the confines of a 2D model can be computationally expensive, which has led to coupling 2D simulation tools to less expensive one-dimensional (1D) models. Under certain 1D–2D coupling approaches, this introduces internal constraints that must be considered in the generation of the 2D computational mesh used. In this paper, we further develop an existing automatic unstructured mesh generation tool for SWE models, ADMESH+, to sequentially (i) identify 1D constraints from the raw input data used in the mesh generation process, namely the digital elevation model (DEM) and land–water delineation data; (ii) distribute grid points along these internal constraints, according to feature curvature and user-prescribed minimum grid spacing; and (iii) integrate these internal constraints into the 2D mesh size function and mesh generation processes. The developed techniques, which include a novel approach for determining the so-called medial axis of a polygon, are described in detail and demonstrated on three test cases, including two inland watersheds with vast networks of channels and a complex estuarine system on the Texas, USA, coast. [ABSTRACT FROM AUTHOR]

In this paper, we present price and transaction information of fed cattle marketings to explore if differences exist in the quality grade of cattle marketed under different transaction types. In particular, we explore regional differences in marketings for cash, formula, forward, grid, and negotiated grid transactions from 2012 to 2022. Analysis shows that despite an industry trend toward higher-quality grade animals, most lowquality grade cattle are marketed in Texas, Oklahoma, or New Mexico using nonnegotiated pricing methods. [ABSTRACT FROM AUTHOR]

Carbon capture and sequestration from a stationary source comprises four distinct engineering processes: separation of CO2 from the other flue gases, compression, transportation, and injection into the chosen storage site. An analysis of the thermodynamic and transport properties of CO2 shows that dissolving this gas in seawater at depths more than 600 m is, most likely, an optimal long-term storage method; and that for transportation, the CO2 must be in the denser supercritical state at pressures higher than 7.377 MPa. The separation, compression, transportation, and injection processes require significant energy expenditures, which are determined in this paper using realistic equipment efficiencies, for the cases of two currently in operation coal power plants in Texas. The computations show that the total energy requirements for carbon removal and sequestration are substantial, close to one-third of the energy currently generated by the two power plants. The cost analysis shows that two parameters - the unit cost of the pipeline and the discount factor of the corporation - have a very significant effect on the annualized cost of the CCS process. Doubling the unit cost of the pipeline increases the total annualized cost of the entire CCS project by 36% and increasing the discount rate from 5% to 15% increases this annualized cost by 32%. [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI), such as increased mortality rates, property damage, and reduced productivity, due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. These findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies. • Framework for the value of customer resilience by extreme temperature-related outages • Resilience valuation using customer characteristics and grid variables in simulations • Loss scenarios using customer factors and temperature-based damage models • Applying resilience valuation to 2021 Winter Storm Uri use-case [ABSTRACT FROM AUTHOR]

• An unprecedented very large-scale T&D dataset covering Texas is made publicly available. • The sheer size of this dataset is intended to provide a challenge for the scientific community. • This lays the foundation for demonstrating the coordination of T&D. • It can help foster the improvement of algorithms and tools for years to come. Distributed energy resources, such as rooftop solar, have rapidly expanded in recent years, given declining costs and the desire to reduce carbon emissions. With more energy resources located in the lower-voltage distribution system, it is increasingly helpful to utilize combined transmission and distribution (T&D) system models to analyze interactions between these normally-distinct subsystems. This paper proposes a methodology for creating very large-scale, highly detailed, combined T&D systems that are synthetic—that is, free from non-public data—yet still realistic. The methodology creates very large-scale combined T&D systems by merging the most up-to-date techniques for creating synthetic distribution feeder networks with the latest methods for building synthetic, meshed bulk-power transmission networks. This methodology is demonstrated on a T&D system geolocated in Texas, and benchmarked with co-simulation results. Validation demonstrates that the resulting syn -texas-TDgrid synthetic test system realistically represents characteristics found in actual networks, addressing the lack of available T&D test systems. With over 15,000 feeders and 46 million electrical nodes, this T&D dataset has applications for research in optimal power flow algorithms, voltage control, reconfiguration, and T&D coordination schemes under high adoption of distributed energy resources. [ABSTRACT FROM AUTHOR]

Freshwater mussels preserve valuable information about hydrology, climate, and population dynamics, but developing seasonal chronologies can be problematic. Using clumped isotope thermometry, we produced high-resolution reconstructions of modern and historic (~ 1900) temperatures and δ18Owater from mussel shells collected from an impounded river, the Brazos in Texas, before and after damming. We also performed high-resolution growth band analyses to investigate relationships between mussel growth rate, rainfall, and seasonal temperature. Reconstructed δ18Owater and temperature vary little between the modern (3R5) and historic shell (H3R). However, a positive relationship between reconstructed δ18Owater and growth rate in H3R indicates that aside from diminished growth in winter, precipitation and flow rate are the strongest controls on mussel growth in both modern and pre-dam times. Overall, our results demonstrate (1) the impact, both positive and negative, of environmental factors such as flow alteration and temperature on mussel growth and (2) the potential for clumped isotopes in freshwater mussels as a paleohydrology and paleoclimate proxies in terrestrial environments. [ABSTRACT FROM AUTHOR]

Responding to the underutilization of mental health services in Asian American communities, we examined factors associated with their willingness to use mental health counseling. Applying Andersen's Behavioral Health Service Model, we focused on the role of mental health needs and prior use of mental health counseling in shaping the attitudes toward mental health counseling of diverse groups of Asian Americans. We conducted a secondary analysis of data from 2,609 Asian Americans aged 18 or older who participated in the Asian American Quality of Life (AAQoL) survey conducted in central Texas. Logistic regression models of willingness to use mental health counseling were tested with predisposing (age, gender, marital status, education, nativity, and English-speaking ability), mental health needs (mental distress and self-rated mental health) and enabling (prior use of mental health counseling) variables. About 67% of the overall sample indicated their willingness to use mental health counseling. Individuals who met the criteria for mental distress showed 17% lower odds of willingness to use mental health counseling. The enabling role of prior use of mental health counseling was supported; those who had used counseling were over three times more likely to be willing to use counseling in the future than their counterparts without such an experience. Given the inverse association between mental health needs and the willingness to use mental health counseling, further attention should be paid to improving Asian Americans' recognition of mental health symptoms and awareness of the benefit of mental health services. The enabling role of prior use of counseling also highlights the importance of increasing the exposure to mental health services for Asian Americans. In efforts to promote mental health literacy, reduce cultural stigma, and advocate for mental health service use, consideration of cultural and linguistic diversity within the Asian American population is imperative. [ABSTRACT FROM AUTHOR]

Anthropogenic activity in coastal areas can damage marine habitats and alter marine mammal behavior and habitat use. Understanding behavioral associations with diverse habitat features in industrialized coastal areas is crucial for marine mammal conservation management. A shore-based digital theodolite was used to assess the behavioral states and habitat use of common bottlenose dolphins (Tursiops truncatus) off Port Aransas, Texas across seasons. The relationship between behavioral states and environmental and geographical variables were analyzed. Behavioral hotspots were identified. Dolphins frequently foraged (46%), indicating the area is an important feeding ground. Dolphins also traveled (25%) and milled (22%), with less frequent occurrences of socializing (6%) and resting (1% of observations). Season, time of day, and distance to shoreline were significant predictors of foraging and traveling behavioral states. Dolphins engaged in all behavioral states closer to seawall shorelines than to mangroves and natural seagrass beds, suggesting that hard shoreline features may influence daily activity patterns. Despite daily anthropogenic operations, bottlenose dolphins use features of the industrialized area (i.e., deeply dredged channels, human-engineered seawalls) to engage in a variety of behaviors. Monitoring of dolphin behavioral states and habitat use in active ship channels are needed to assess changes from baseline data from increasing coastal development and vessel activities. [ABSTRACT FROM AUTHOR]

Feral populations of aoudad (Ammotragus lervia) occur in Texas bighorn sheep (Ovis canadensis) habitat and pose several conceptual ecological threats to bighorn sheep re-establishment efforts. The potential threat of disease transmission from aoudad to bighorn sheep may exacerbate these issues, but the host competency of aoudad and subsequent pathophysiology and transmissibility of pneumonic pathogens involved in the bighorn sheep respiratory disease complex is largely unknown. Because the largest population-limiting diseases of bighorn sheep involve pathogens causing bronchopneumonia, we evaluated the host competency of aoudad for Mycoplasma ovipneumoniae and leukotoxigenic Pasteurellaceae. Specifically, we described the shedding dynamics, pathogen carriage, seroconversion, clinical patterns, and pathological effects of experimental infection among wild aoudad held in captivity. We found that aoudad are competent hosts capable of maintaining and intraspecifically transmitting Mycoplasma ovipneumoniae and Pasteurellaceae and can shed the bacteria for 53 days after exposure. Aoudad developed limited clinical signs and pathological findings ranged from mild chronic lymphohistiocytic bronchointerstitial pneumonia to severe and acute suppurative pneumonia, similarly, observed in bighorn sheep infected with Mycoplasma spp. and Pasteurellaceae bacteria, respectively. Furthermore, as expected, clinical signs and lesions were often more severe in aoudad inoculated with a combination of Mycoplasma ovipneumoniae and Pasteurellaceae as compared to aoudad inoculated with only Mycoplasma ovipneumoniae. There may be evidence of interindividual susceptibility, pathogenicity, and/or transmissibility, indicated by individual aoudad maintaining varying severities of chronic infection who may be carriers continuously shedding pathogens. This is the first study to date to demonstrate that aoudad are a conceptual disease transmission threat to sympatric bighorn sheep populations due to their host competency and intraspecific transmission capabilities. [ABSTRACT FROM AUTHOR]

The article focuses on the emergence of clade 2.3.4.4b avian influenza A(H5N1) in North America and its potential impact on mammalian hosts, including humans, contrasting with the susceptibility of cattle primarily to influenza D virus. Topics include the investigation into influenza virus infections among dairy cattle in Texas, where veterinary diagnostic laboratories initially explored other pathogens before considering influenza A virus as a possible cause.

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Habitat restoration efforts are often limited by cost, making it important that available funds are used efficiently and effectively to achieve desired restoration goals. In this study, we evaluate habitat provision for oysters and motile epifauna on restored oyster reefs in a northwestern Gulf of Mexico estuary constructed with discrete high vertical relief (~0.6m "reef mounds") or continuous low vertical relief (<0.08 m "reef flats"). Habitat provision on reef mounds exceeded that on reef flats within one month of construction and supported 0.75x higher oyster density, 2x higher motile epifauna biomass, and 3.6x higher motile epifauna density one year after construction. Oyster density on reef mounds remained relatively high throughout the study period, with ~2x higher oyster densities than reef flats 18 months after construction and ~1.5x higher oyster densities by the end of the study. Both reef mounds and reef flats increased oyster and epifaunal densities compared to unrestored areas. Although on-reef oyster densities were higher on reef mounds than reef flats, the total restored oyster areal density and volumetric density was higher in restored reef flat areas, primarily because the restored flats area had no gaps within its restoration boundaries. Our findings have practical value for better predicting restoration outcomes and achieving desired restoration goals based on restored oyster reef height, with reef mounds maximizing on-reef oyster and epifaunal densities and reef flats maximizing the total number of oysters per area restored or volume of substrate purchased. Understanding the benefits and tradeoffs between restoration designs will allow resource managers to improve cost-efficiencies in future restoration projects. [ABSTRACT FROM AUTHOR]

Objective: The Atlantic Tripletail Lobotes surinamensis is a globally distributed subtropical and tropical fish species that inhabits estuaries throughout the northern Gulf of Mexico (GOM), particularly during warm months. Little is known about distribution and residency patterns within estuaries, as the species is rarely caught in the recreational fishery, and virtually no commercial fishery exists for the species in the GOM. Methods: We used data from a long‐term fishery‐independent gill‐net survey to model estuarine distribution throughout Texas and to relate environmental variables to the Atlantic Tripletail catch. Result: Although there were no observable temporal trends in catch over the time series (1990–2022), the most recent 6 years included record catch in six of the 10 major Texas estuaries, possibly indicating a recent pulse in abundance. Catch throughout the time series was spatially aggregated in a small number of "hot spots" observed coastwide. Latitude was the best predictor of catch, although wind fetch and wind aspect (wind direction in relation to shoreline direction) were important predictors, and catch was highest near GOM inlets. The Texas Parks and Wildlife Department gill‐net sampling program caught a range of Atlantic Tripletail between 171 and 880 mm total length, indicating a potential gear bias against juveniles. Conclusion: Despite this gear bias, these data shed light on the factors that drive Atlantic Tripletail estuarine distribution and abundance in the northwestern GOM. Wind‐driven passive movements in the estuary, combined with active selection of polyhaline habitats near GOM inlets, might be primary drivers of Atlantic Tripletail catch, thus supporting findings from previous studies. Impact statementAtlantic Tripletail are a popular recreational fishing species throughout the Gulf of Mexico. In Texas estuaries, Atlantic Tripletail are clustered in hot spots, which are typified by windward shorelines, and present mainly during the high‐use fishing season. They are rarely observed at water temperatures below 20°C. [ABSTRACT FROM AUTHOR]

Around 60% of households in Texas currently rely on electricity for space heating. As decarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantly increasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change is expected to increase temperatures, the potential for summer heat waves, and associated electricity demand for cooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions about how they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability. This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model, and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrification of residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps could significantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winter peaking events and increased reliability risks. As heating electrification intensifies, system planners will need to balance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongside the benefits (improved efficiency and reductions in emissions). Plain Language Summary: Electric heat pump adoption could help abate the impacts of climate change on overall power system reliability by reducing summer cooling demand in a warmer world because heat pumps are a more efficient alternative to standard air conditioning. It would also carry greenhouse gas emissions benefits in a decarbonized power grid. Yet widespread adoption of heat pumps (especially low‐efficiency heat pumps) could cause more extreme winter peaking events. During these extreme cold events, higher peak demand could result in an increased probability of outages. Higher efficiency heat pumps would help avoid these winter reliability impacts, but these more efficient heat pumps are also currently more expensive. From a grid planning perspective, planners need to account for a range of future peak demand dynamics across different heat pump adoption scenarios and climate futures. Key Points: Widespread electric heat pump adoption and climate change will increase uncertainty in seasonal peaking patterns and grid reliabilityFuture peaking behavior depends on the frequency and severity of extreme heat and cold events and the type of heat pumps widely adoptedShifts toward winter peaking could carry large resource adequacy risks, depending on pump efficiency and climate scenario [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]

In recent years, there has been much focus on the use of wastewater‐based epidemiology (WBE) in urban centers, particularly for SARS‐CoV‐2 monitoring. However, less is known about the application of WBE in rural settings or in areas of limited resources. Most WBE programs in low‐resource communities have occurred outside the United States. To reap the benefits, WBE would need to be tailored to better reflect the socioeconomic challenges, technical barriers, communication limitations, and variable wastewater infrastructures associated with rural communities. The objective of this review is to evaluate the potential opportunities and challenges of deploying the current SARS‐CoV‐2 monitoring methodologies in small, rural communities, with a particular focus on rural Texas. For this, we conducted an inventory of rural communities in the state of Texas and their wastewater infrastructure. Based on specific rural examples, we evaluated the potential of current WBE methodologies used in urban settings to monitor for emerging biological agents of concern such as SARS‐CoV‐2. Our findings include an overview of rural wastewater capacity across rural Texas, a look at current WBE efforts to detect SARS‐CoV‐2, and recommendations for future implementation in two cities in rural counties, Kerrville and Valentine. WBE is a rapidly evolving public health tool with several notable advantages associated with cost, access, and adaptability. It is of particular use in resource‐limited communities that often exhibit healthcare disparities. This study presents the first overview of the feasibility of implementing WBE in the rural settings of Texas. We provide several recommendations and suggest alternatives that may be of use when planning an expansion of WBE into these areas. [ABSTRACT FROM AUTHOR]

This study presents the first report of per‐ and polyfluoroalkyl substances (PFAS) in water samples collected in the Arroyo Colorado (n = 15), irrigation canals (n = 6), stormwater and wastewater retention ponds (n = 7), as well as drinking waters (n = 2) across the Arroyo Colorado watershed. Of the 30 PFAS monitored in this study, 14 were detected in the samples in various combinations. Short‐chain PFAS (less than 8 carbon atoms) were observed in most samples. Water collected from the Arroyo Colorado showed significant spatial variabilities, with high total PFAS concentrations observed near possible point sources ‐ a municipal airport and wastewater treatment facilities. PFAS concentrations were generally higher in water samples collected in stormwater and wastewater retention ponds than in the Arroyo Colorado and irrigation canals. PFAS in stormwater retention ponds likely came from roadway runoff. Short‐chain PFAS were observed in the two municipal water samples, but they were below the current U.S. EPA regulation limits or are not currently regulated. This study provides useful information for water quality in this region and provides insights into PFAS occurrence in a rapidly urbanizing area. [ABSTRACT FROM AUTHOR]

Sleep is a pillar of human health and wellbeing. In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment in the bedroom. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive—problems that may increase due to climate change. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. We used a thermal manikin to experimentally show that many passive and low-energy strategies are highly effective in supplementing or replacing HVAC systems during sleep. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69–91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC. [ABSTRACT FROM AUTHOR]

In recent years, heat stress has affected potato production more frequently, resulting in lower marketable yields and reduced tuber quality. In order to develop heat-tolerant potatoes, it is necessary to select under heat-stress conditions and consider traits affected by heat stress. The Texas A&M Potato Breeding Program has selected potatoes under high-temperature stress for several decades. Ten potato cultivars, representing heat tolerant and sensitive clones based on past performance in Texas, were included in field trials for three years at the two main locations used by the Texas Breeding Program (Dalhart and Springlake, TX) to assess if the Texas field locations are suitable for heat tolerance screening. Both locations were confirmed as appropriate for heat stress screening. However, Springlake was a more stressful location since it had significantly lower yields of marketable tubers and increased percentages of tuber defects. Planting time did not have a significant effect at the most stressful location. The same ten potato clones were included in greenhouse experiments with contrasting temperatures (normal versus heat stress). There was confirmation that heat stress conditions resulted in significantly lower marketable yields, specific gravity, dormancy, and significantly higher percentages of tuber defects; however, significant differences existed between potato clones. Under heat stress conditions, Russet Burbank had a high percent of tubers with external defects, whereas Atlantic showed the highest percentage of internal defects (mainly internal heat necrosis). Vanguard Russet produced the highest marketable yield while maintaining a low percentage of external and internal defects. Russet Burbank and Atlantic were heat-sensitive controls for external and internal tuber defects, respectively. In contrast, Vanguard Russet can be used as a reliable heat-tolerant control. Including appropriate controls in heat stress studies will help identify clones with heat tolerance. [ABSTRACT FROM AUTHOR]

Introduction: The design-build (DB) delivery method is used to deliver increasingly complex transportation infrastructure projects associated with higher uncertainty. As such, allocating risks in the contract between the owner and design-builder becomes challenging and often leads to higher initial bids, increased contingency, or claims. Learnings from implementation worldwide have underlined the need for improving risk allocation in DB contracts. Most existing studies address risk allocation mechanisms to manage contingency at the contract level. Other studies have recognized the need for owners to adapt their processes to better allocate risks in DB contracts. This study explored the influential factors for risk assessment and allocation for complex DB infrastructure projects, addressing the opportunity to improve transportation owners' risk allocation processes before the design-builder is selected and the DB contract is awarded. Method: The objectives of this work were achieved by utilizing empirical data collected through 20 interviews with Texas Department of Transportation and private sector experts. The interview data were analyzed using inductive and axial coding. Inductive coding allowed themes to emerge without a pre-existing framework, identifying six influential factors and six pertinent risks on complex DB projects. Results: These factors include the (i) Quality of DB teams, (ii) Level of up-front investigation, (iii) Limitations on the timing of letting, (iv) Design optimization opportunities, (v) Project-specific requirements, and (vi) Relationships with third parties. Through axial coding, the interaction and frequency between the factors and risks were also examined. The coded interactions demonstrated how the identified factors influence allocation for six pertinent risks including right-of-way acquisition, stakeholder approval, site conditions, permits and third-party agreements, railroad interaction, and utility adjustments and coordination. Findings indicate that the evaluation of these interactions can shift the risk allocation from baseline norms established by an agency to correspond to project-specific needs. Contribution: In contributing to the infrastructure project management, this is the first study to examine the factors that influence risk allocation in complex DB projects and examine interactions with pertinent risks, setting the foundation for optimizing allocation based on project-specific needs. In practice, the findings presented in this study can guide owners in adapting their allocation practices, managing, and developing their strategic plan for delivering complex DB projects. The findings can also assist contractors in pricing risks more efficiently and increase competitive bidding. [ABSTRACT FROM AUTHOR]

Introduction: Type 2 diabetes impacts millions and poor maintenance of diabetes can lead to preventable complications, which is why achieving and maintaining target A1C levels is critical. Thus, we aimed to examine inequities in A1C over time, place, and individual characteristics, given known inequities across these indicators and the need to provide continued surveillance. Methods: Secondary de-identified data from medical claims from a single payer in Texas was merged with population health data. Generalized Estimating Equations were utilized to assess multiple years of data examining the likelihood of having non-target (>7% and ≥7%, two slightly different cut points based on different sources) and separately uncontrolled (>9%) A1C. Adults in Texas, with a Type 2 Diabetes (T2D) flag and with A1C reported in first quarter of the year using data from 2016 and 2019 were included in analyses. Results: Approximately 50% had A1Cs within target ranges (<7% and ≤7%), with 50% considered having non-target (>7% and ≥7%) A1Cs; with 83% within the controlled ranges (≤9%) as compared to approximately 17% having uncontrolled (>9%) A1Cs. The likelihood of non-target A1C was higher among those individuals residing in rural (vs urban) areas (P <.0001); similar for the likelihood of reporting uncontrolled A1C, where those in rural areas were more likely to report uncontrolled A1C (P <.0001). In adjusted analysis, ACA enrollees in 2016 were approx. 5% more likely (OR = 1.049, 95% CI = 1.002-1.099) to have non-target A1C (≥7%) compared to 2019; in contrast non-ACA enrollees were approx. 4% more likely to have non-target A1C (≥7%) in 2019 compared to 2016 (OR = 1.039, 95% CI = 1.001-1.079). In adjusted analysis, ACA enrollees in 2016 were 9% more likely (OR = 1.093, 95% CI = 1.025-1.164) to have uncontrolled A1C compared to 2019; whereas there was no significant change among non-ACA enrollees. Conclusions: This study can inform health care interactions in diabetes care settings and help health policy makers explore strategies to reduce health inequities among patients with diabetes. Key partners should consider interventions to aid those enrolled in ACA plans, those in rural and border areas, and who may have coexisting health inequities. [ABSTRACT FROM AUTHOR]

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) airocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey's 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series. [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 three-dimensional coupled hydrodynamic–biogeochemical model with multiple nutrient and plankton functional groups was developed and adapted to the Gulf of Mexico to investigate the role of nutrients and the complexity of plankton community in dissolved oxygen (DO) dynamics. A 15-year hindcast was achieved covering the period of 2006–2020. Extensive model validation against in situ data demonstrates that the model was capable of reproducing vertical distributions of DO and spatial distributions of bottom DO concentration, as well as their interannual variations. The study demonstrates that bottom DO dynamics and hypoxia evolution are significantly influenced by both physical processes and local biochemistry, with sedimentary oxygen consumption and vertical diffusion identified as key contributors. Summer hydrodynamics play a critical role in nutrient distribution and limitation: a notable expansion of Si limitation was simulated when coastal currents shifted eastward or northward. This effect, especially pronounced on the western part of the Louisiana–Texas shelf, underscores the importance of nutrient limitation in shaping DO dynamics. The model identifies a bi-peak primary production pattern in spring and early summer, aligned with satellite chlorophyll a variations, attributed to the complexity of the plankton community and interactions among different plankton groups. Our findings emphasize the necessity of integrating sophisticated plankton community dynamics into biogeochemical models to understand primary production variability and its impact on bottom hypoxia. [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]

Drought has long posed an existential threat to society. Engineering and technological advancements have enabled the development of complex, interconnected water supply systems that buffer societies from the impacts of drought, enabling growth and prosperity. However, increasing water demand from population growth and economic development, combined with more extreme and prolonged droughts due to climate change, poses significant challenges for governments in the 21st century. Improved understanding of the cascading multisectoral impacts and adaptive responses resulting from extreme drought can aid in adaptive planning and highlight key processes in modeling drought impacts. The record drought spanning 2008 to 2015 in the Colorado Basin in the state of Texas, United States, serves as an outstanding illustration to assess multisectoral impacts and responses to severe, multi-year drought. The basin faces similar water security challenges to those across the western US, such as groundwater depletion and sustainability, resource competition between agriculture and growing urban populations, limited options for additional reservoir expansion, and the heightened risk of more severe and frequent droughts due to climate change. By analyzing rich, high-quality data sourced from nine different local, state, and federal sources, we demonstrate that characterizing regional multisector dynamics is crucial to predicting and understanding future vulnerability and possible approaches to reduce impacts to human and natural systems in the face of extreme drought conditions. This review reveals that, despite the severe hydrometeorological conditions of the drought, the region's advanced economy and existing water infrastructure effectively mitigated economic and societal impacts. [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]

The Permian Basin is one of the most prolific, and currently one of the most active, oil and gas basins in the USA. The Lower Permian strata in the Permian Basin have produced more than 14 billion barrels of oil (BBO), making it the largest volume of hydrocarbon in the basin. Sedimentation in the Midland Basin during late Leonardian through early Guadalupian (ca. 272e269 Ma) resulted in progradation of shelf edge and ultimately closure of the basin by Middle Permian time. We analyzed a merged seismic survey covering parts of the Permian Basin (i.e., Central Basin Platform and Midland Basin) in Andrews, Ector, and Midland Counties, Texas. The seismic survey and well logs show the presence of gently dipping (ca. 1°) clinoforms in the Upper San Andres and Grayburg Formations on the eastern edge of the Central Basin Platform and western Midland Basin. The seismic attributes, curvature, and spectral decomposition identify low sinuosity slope channels oriented north-south, but such channels do not appear beyond the slope. The shelf edge shifts from north to south during deposition of the Upper San Andres and Grayburg Formations. We identify five basinward shifts noted by the migration of the shelf edge toward the basin center and the presence of channel features along the depositional slope. The petrophysical analysis indicates that channels cut into carbonate rocks and are filled by siliciclastic sediments; this interpretation is supported by the most negative curvature anomalies along the channel axes caused by the differential compaction between the carbonate and siliciclastic rocks. There are a few channels with a northwest-southeast strike, which matches the direction of the Concho Lineament observed by satellite data. Such observations are consistent with previous interpretations of the northern Midland Basin closure during Middle-Late Permian time. [ABSTRACT FROM AUTHOR]

Effective habitat management for rare and endangered species requires a thorough understanding of their specific habitat requirements. Although machine learning models have been increasingly used in the analyses of habitat use by wildlife, the primary focus of these models has been on generating spatial predictions. In this study, we used machine learning models in combination with simulated management actions to guide planning and inform managers. We used data from 61 whooping cranes (Grus americana) tagged with GPS telemetry collars between 2009 and 2018 near Aransas National Wildlife Refuge in coastal Texas. We included variables based on topography, land use classification, vegetation height, plant phenology, drought, storm surge events, and both wild and prescribed fires. We then built models at multiple scales: population level, home range level, and roosting and daytime within home range level. We simulated responses to the two primary management actions used to enhance whooping crane habitat on Aransas National Wildlife Refuge: prescribed fire and removal of woody vegetation. At the population and home range scales, land use classification variables had the highest importance values, whereas the combined elevation and bathymetry layer was the most important predictor at both roosting and daytime within home range scales. Our findings revealed that the effects of fire, although generally modest, varied spatially. Areas dominated by estuarine wetlands exhibited higher predicted use within the first months after a fire, whereas those dominated by palustrine wetlands were more likely to be avoided in the immediate postfire years. Our simulation of vegetation removal identified the areas on Aransas National Wildlife Refuge where whooping cranes were predicted to benefit the most if vegetation were removed. These techniques can be used by other researchers wanting to examine and predict the effects of potential management actions on target species habitat. [ABSTRACT FROM AUTHOR]

Wildlife reintroduction site selection requires the consideration of not only a species' ecology but also socio‐political factors that may impact conservation efforts. These socio‐political dimensions may be especially important for endangered carnivore reintroductions on private lands in the United States, where landowner support for the reintroduction is a necessity given landowner concerns about ecological and legal impacts of carnivore restoration. We designed an assessment to identify potential sites for reintroduction of the federally endangered ocelot (Leopardus pardalis pardalis) in Texas, a state where over 97% of lands are privately owned. We incorporated International Union for Conservation of Nature recommendations into a geospatial analysis evaluating potential reintroduction sites based on site size, ecological and life history requirements of ocelots, potential natural and anthropogenic threats, and the socio‐political context of each site. We identified the five highest‐ranking sites that had species‐specific suitable landscape structure of woody cover, fine‐scale vegetative cover, minimal natural and anthropogenic threats, and that present land ownership patterns that are logistically feasible for conservation planners to navigate. Our assessment provided information for ocelot conservation planning and established a framework for incorporating private land data into large‐scale assessments of wildlife reintroduction sites on private lands. [ABSTRACT FROM AUTHOR]

Activation functions play an important role in deep learning models by introducing non-linearity to the output of a neuron, enabling the network to learn complex patterns and non-linear relationships in data and make predictions on more complex tasks. Deep learning models' most commonly used activation functions are Purelin, Sigmoid, Tansig, Rectified Linear Unit (ReLU), and Exponential Linear Unit (ELU), which exhibit limitations such as non-differentiability, vanishing gradients, and neuron inactivity with negative values. These functions are typically defined over a finite range, and their outputs are integers or real numbers. Using fractional calculus in designing activation functions for neural networks has shown promise in improving the performance of deep learning models in specific applications. These activation functions can capture more complex non-linearities than traditional integer-order activation functions, improving performance on tasks such as image classification and time series prediction. This paper focuses on deriving and testing linear and non-linear fractional-order forms of activation functions and their variants. The linear activation function includes Purelin. In contrast, the non-linear activation functions are Binary Step, Sigmoid, Tansig, ReLU, ELU, Gaussian Error Linear Unit (GELU), Hexpo, and their variants. Besides, the standard formula has been implemented and used in developing the fractional-order linear activation function. Furthermore, various expansion series, such as Euler and Maclaurin, have been used to design non-linear fractional-order activation functions and their variants. The single- and multi-layer fractional-order neural network models have been developed using the designed fractional-order activation functions. The simulation study uses developed fractional-order neural network models for predicting the Texas wind turbine systems' generated power. The performance of single and multi-layer fractional-order neural network models has been evaluated by changing the activation functions in the hidden layer while keeping the Purelin function constant at the output layer. Experiments on neural network models demonstrate that the designed fractional-order activation functions outperform traditional functions like Sigmoid, Tansig, ReLU, ELU, and their variants, effectively addressing limitations. • Activation functions are derived into fractional functions using fractional calculus. • The fractional neural network models have been made by adopting derived functions. • A Case study has been done on the Texas wind turbine with the models to predict the power. • The models' performance has been evaluated using the fractional functions. [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]

Given the availability of high-quality and high-spatial-resolution digital elevation maps (DEMs) from the United States Geological Survey's 3D Elevation Program (3DEP), derived mostly from light detection and ranging (lidar) sensors, we examined the effects of these DEMs at various spatial resolutions on the quality of flood inundation map (FIM) extents derived from a terrain index known as Height Above Nearest Drainage (HAND). We found that using these DEMs improved the quality of resulting FIM extents at around 80 % of the catchments analyzed when compared to using DEMs from the National Hydrography Dataset Plus High Resolution (NHDPlusHR) program. Additionally, we varied the spatial resolution of the 3DEP DEMs at 3, 5, 10, 15, and 20 m (meters), and the results showed no significant overall effect on FIM extent quality across resolutions. However, further analysis at coarser resolutions of 60 and 90 m revealed a significant degradation in FIM skill, highlighting the limitations of using extremely coarse-resolution DEMs. Our experiments demonstrated a significant burden in terms of the computational time required to produce HAND and related data at finer resolutions. We fit a multiple linear regression model to help explain catchment-scale variations in the four metrics employed and found that the lack of reservoir flooding or inundation upstream of river retention systems was a significant factor in our analysis. For validation, we used Interagency Flood Risk Management (InFRM) Base Level Engineering (BLE)-produced FIM extents and streamflows at the 100- and 500-year event magnitudes in a sub-region in eastern Texas. [ABSTRACT FROM AUTHOR]

Background: Latinos/Hispanics are at higher risk for developing gastric cancer (GC) compared with non‐Hispanic whites, and social determinants of health (SDoH) are thought to contribute. Aims/Materials and Methods: This study addressed SDoH and their interactions contributing to disparities in the testing and treatment of Helicobacter pylori (HP) infection and diagnosis of GC and its known precursors, among Latinos/Hispanics relative to non‐Latinos at two affiliated but independent health systems in San Antonio, Texas, using a mixed methods approach. Results: Secondary data abstraction and analysis showed that GCs represented 2.6% (n = 600) of our population. Men and older individuals were at higher GC risk. Individuals with military insurance were 2.7 times as likely to be diagnosed as private insurance. Latinos/Hispanics had significantly (24%) higher GC risk than Whites. Poverty and lack of insurance contributed to GC risk among the minorities classified as other (Asians, Native Americans, Multiracial; all p < 0.01). All SDoH were associated with H. pylori infection (p < 0.001). Qualitative analysis of patient and provider interviews showed providers reporting insurance as a major care barrier; patients reported appointment delays, and lack of clinic staff. Providers universally agreed treatment of H. pylori was necessary, but disagreed on its prevalence. Patients did not report discussing H. pylori or its cancer risk with providers. Discussion/Conclusion: These data indicate the importance of considering SDoH in diagnosis and treatment of GC and its precursors, and educating providers and patients on H. pylori risks for GC. [ABSTRACT FROM AUTHOR]