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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]

• Integrated techno-economic assessment of the hydrogen value chain in the Permian Basin, spanning production, storage, and transportation. • Identified cost benefits in feedstock and tax credits for Permian-based hydrogen, balanced by transport expenses to Houston. • Scaling up to 412,000 metric tons/year can counterbalance transportation costs, positioning the Permian Basin competitively in the hydrogen market. This paper investigates the untapped potential of the Permian Basin, a multifaceted energy axis in Texas and adjoining states, in the emerging era of decarbonization. Aligned with current policy directives on regional hydrogen hubs, this study explores the viability of developing a hydrogen energy hub in the Permian Basin, thereby producing low-carbon intensity hydrogen from natural gas in the Basin and transporting it to the Greater Houston area. Diverging from existing literature, this study provides an integrated techno-economic evaluation of the entire hydrogen value chain in the Permian Basin, encompassing production, storage, and transportation. Furthermore, it comparatively analyzes the scenario of interest against an optimized base scenario, thereby underlining comparative advantages and disadvantages. The paper concludes that the delivered cost of Permian-based low-carbon intensity hydrogen to the Greater Houston area is $1.85/kg, benchmarked to the scenario, with hydrogen produced close to the Greater Houston area and delivered at $1.42/kg. Our findings reveal that Permian-based low-carbon intensity hydrogen production can achieve cost savings in feedstock ($0.25/kg) and potentially accrue a higher production tax credit due to a shorter gas supply chain to production ($0.33/kg). Nevertheless, a significant cost barrier is the expense of long-haul pipeline transport ($0.90/kg) from the Permian Basin to Houston as opposed to local production. Despite the obstacles, the study identifies a potential breakeven solution where increasing the production scale to at least 412,000 metric ton per year (about 3 steam-reforming plants) in the Permian Basin can effectively lower costs in the transport sector. Hence a scaled-up production can mitigate the cost difference and establish the Permian Basin as a competitive player in the hydrogen market. In conclusion, a SWOT analysis presents Strengths, Weaknesses, Opportunities, and Threats associated with Permian-based hydrogen production. [ABSTRACT FROM AUTHOR]

This paper illustrates the scope and contribution that the discipline of geomorphology can offer in environmental litigation. The case in question was filed by two plaintiffs who contended that the construction of a storage facility on an adjacent property had caused significant disturbance of the topsoil, and that runoff from the impermeable surfaces (e.g., roofs, driveways) had exacerbated the problem by eroding and transporting soil and fill material from the facility directly into three ponds owned by them. They sued for $1.6 million to cover dredging, restoration of the ponds, and disposal of the sediment. Utilizing a range of geomorphological techniques, I was able to determine the most likely source area(s) of eroded sediment within the basin and quantify the extent to which the defendant's property contributed runoff and sediment to the ponds. The lawsuit was dismissed before ever going to trial. In the paper, I discuss the steps involved in the process of being an expert witness and also share some of the advice I have received from attorneys with whom I have worked. I emphasize the importance of setting any case study within a larger spatial context and encourage my fellow geomorphologists to become involved as experts in litigation because, in most instances, we can contribute to an appropriate outcome. [ABSTRACT FROM AUTHOR]

As our environment becomes increasingly urban, the governance of urban regions faces multifaceted challenges. There is growing recognition that studying the governance of urban regions requires looking beyond individual plans and examining networks of plans. Existing work on plan networks focuses on documents, neglecting the agency of the people and governments who create and implement the plans. To help fill this gap, we develop and test an analytical framework for assessing urban governance within a network of plans, focusing on the alignment of plan content, interactions, and the relative efficacy of the plans. We apply this framework in metropolitan Austin, Texas (USA), studying five strategic plans that address the region's built environments, transportation systems, and natural areas over a 20 year-period. The analysis reveals how voluntary regional scenario-planning conducted decades earlier continues to shape city and regional development goals; how alignment between plans on paper can belie poor cross-jurisdictional coordination in practice; and how plans addressing transportation and natural areas are more efficacious for implementation than plans for the built environment. In addition to making plan dynamics visible, the framework thus allows for rigorous, empirical assessment of regional governance through a network of plans. • A framework is proposed to analyze urban-region governance with a network of plans. • The framework is tested with five strategic plans in Austin, Texas (USA). • Evidence is found of a network consisting of documents and agency. • Network-of-plan research benefits from the framework. [ABSTRACT FROM AUTHOR]

Using computer vision techniques such as stereo vision systems for sea state measurement or for offshore structures monitoring can improve the measurement fidelity and accuracy with no significant additional cost. In this paper, two experiments (in-lab/open-sea) are conducted to study the performance of stereo vision system to measure the water wave surface elevation and rigid body heaving motion. For the in-lab experiment, regular water waves are generated in a wave tank for different frequencies and wave heights, where the water surface is scanned by the stereo vision camera installed on the top of the tank. Surface elevation inferred by the stereo vision is verified by an installed stationary side camera that records the water surface through the tank transparent side window, water surface elevation measured by the side camera recordings is extracted using edge detection algorithm. During the in-lab experiment a heaving buoy is installed to test the performance of Visual Simultaneous Localization and Mapping (VSLAM) algorithm to monitor the buoy heave motion. The VSLAM algorithm fuses a buoy onboard stereo vision recordings with an embedded Inertial Measurement Unit (IMU) to estimate the 6-DOF of a rigid body. The Buoy motion VSLAM measurements are verified by a KLT tracking algorithm implemented on the video recordings of the stationary side camera. The open-sea experiment is implemented in Lake Somerville, Texas. The stereo vision system is installed to measure the water surface elevation and directional spectrum of the wind generated irregular waves. The open-sea wave measurements by the stereo vision are verified by a Sofar commercial wave buoys deployed in the testing location. • Edge detection is adequate for wave height measurement in wave tanks. • Stereo vision is effective for in-tank/open-sea wave height measurement. • Stereo vision is effective directional wave spectrum measurement. • KLT algorithm is accurate in planar rigid structure motion measurement. • VSLAM shows better accuracy when measuring small amplitudes than accelerometers. [ABSTRACT FROM AUTHOR]

VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey's (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA. Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data. The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities. • Visible to infrared reflectance spectra were analyzed of uranium-hosting rocks. • These include the Jackson Group, Catahoula, Oakville, and Goliad Formations. • Compositional variability in ferric‑iron, clay, and carbonate minerals was determined. • Some rocks contain detectable zeolite and uranium industrial/critical minerals. • The data and methods in this work will be useful for future remote sensing studies. [ABSTRACT FROM AUTHOR]

Global warming has been a critical issue in recent years. Many leaders and politicians have talked about replacing fossil fuels with R enewable E nergy R esources (RES). Some of them even went further and are talking about running a country with 100% RES soon. Is that economically viable? What would be the estimated cost of such a system? In this paper, a city in west Texas with a 100 MW peak load has been assumed to operate with the wind, solar, and Battery Energy Storage System (BESS) totally disconnected from the grid for a cost evaluation. Real generation and load time series data are used to achieve an optimum combination of the installed capacity of those resources by minimizing the total overnight cost, respecting a maximum of 24 h of faults in which the system cannot provide the load during the period. A Monte Carlo simulation is applied to the previous results to evaluate the influence of faulty devices and increase the robustness of the system. The results for both cases are analyzed and compared. Finally, the total cost of install capacity will be compared with other non-renewable resources. • Challenges of 100% isolated renewable system with wind, solar, and battery. • Algorithm to optimize the size and cost of system elements. • Effects of equipment failures for the designed system reliability. • Viability of the 100% system compared with other available technologies. [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]

As climate change impacts become increasingly severe, it is critical to have a comprehensive understanding of interactions between climate, energy, and water. Previous energy-water nexus studies have focused primarily on specific dependencies, such as water for thermoelectric cooling, rather than the potential for cascading impacts of supply disruptions at broader scales. This paper explores relative energy-water interdependencies across multiple sectors at the scale of the three primary electricity interconnections within the United States. Our results indicate the need for and potential benefits of integrated policy, planning, and management of energy and water resources based on an interdependent systems perspective. • Interconnection-level Sankeys provide insights into potential vulnerabilities • Electricity generation in the Eastern US is susceptible to water disruptions • Energy intensities of public water supply and agriculture are highest in the West • Electricity and industry sectors in Texas grid have the highest fuel dependence [ABSTRACT FROM AUTHOR]

• Geospatial models in weed risk assessments need to be accessible and reproducible. • The Proto4 model provides accurate yet simple to use suitability distributions. • This model will help invasive species prevention and management. "Proto3" is a geospatial model used by the United States Department of Agriculture (USDA) Plant Protection and Quarantine to predict the potential distribution of non-native weed species in the continental U.S. as part of routine weed risk assessments (WRA). While performing as well as other methods, this tool has the benefit of being simple to produce, expanding accessibility and reproducibility. However, it has the tendency to overestimate potential distributions. To address this shortcoming, this paper introduces the "Proto4" model and compares it with the established and mechanistically similar "Proto3" model currently used. Both models overlay Plant Hardiness Zones, precipitation, and Köppen-Geiger climate classes with global distribution of a plant species and rely on semi-qualitative assessments of a plant's affinity for each of the climate categories. However, Proto4 uses more detailed layers of the Plant Hardiness Zones and Köppen-Geiger climate classes, adds elevation as a fourth predictive variable to increase the precision of predictive maps. Additionally, we incorporate uncertainty to spatially distinguish regions of different potential suitability. We compared the performance of both models by estimating the predicted distributions of 30 broadly distributed, invasive plants in the U.S. with Proto3 and Proto4. We found that on average, the Proto4 model produces predicted distributions that are nearly 780,000 square kilometers (an area larger than the state of Texas) smaller than the Proto3, while only failing to capture a median of fewer than 0.5% more georeferenced points. Furthermore, the inclusion of uncertainty classes adds to the utility of Proto4 by distinguishing areas with greater and lesser degrees of evidence that a particular area is suitable for an invasive species, providing more information to help select invasive species prevention and management prioritization strategies. [ABSTRACT FROM AUTHOR]

While a growing number of studies have explored evacuation expenditure for more effective evacuation planning and emergency management, few have investigated the public preferences regarding hurricane evacuation behavior and the cost of evacuation. This paper presents an empirical analysis of hurricane evacuation decisions, using both revealed preference (RP) and stated preference (SP) evacuation expenditure data from Texas residents affected by Hurricane Ike. Furthermore, we have developed a methodology framework based on utility maximization theory and the lifecycle consumption model. This approach allows us to estimate the lower bound of individuals' willingness to pay (WTP) to mitigate hurricane risks and calculate the associated cost-of-evacuation under both voluntary and mandatory evacuation orders. The study was carried out from a transportation cost perspective and included the expenditure of transportation, food, and lodging during hurricane evacuation. The findings provide policy implications for emergency management agencies in coastal communities in terms of hurricane evacuation planning and strengthening disaster management practices in the future. [ABSTRACT FROM AUTHOR]

The ongoing energy transition, including the rapid electrification of energy services, reinforces the importance of secure and reliable electricity infrastructure. Electricity as a critical energy carrier cuts across sectoral boundaries, including but not limited to public health, national security, education, and transportation. Electricity markets treat electricity as a commodity bought and sold in the different restructured markets of the United States and are a critical feature of power systems shaping the energy transition. However, current electricity market developments primarily rely on techno-economic indicators and fall short of incorporating ethical considerations to spur energy transition towards 100% renewable electricity as end-use energy. Even though electricity markets are designed based on the assumption that they are socially valuable, they essentially go unmeasured based on social and ethical parameters and are largely measured using technical and economic tools and criteria. These inadequacies in the development of electricity markets fail to provide energy and other allied services, particularly during and in the aftermath of a disaster, to the people who need them the most. This paper aims to provide an analytical framework using the foundational approach of energy justice for looking at the electricity market development. The paper utilizes the case of the Texas electricity market and its role in dealing with the electricity crisis in Texas during the winter storm of 2021 from the critical lens of energy justice. The paper shows that using the foundational approach of energy justice, the electricity market can be designed more ethically by considering electricity as means rather than only a market commodity. [ABSTRACT FROM AUTHOR]

Replacing legacy high carbon technologies (LHCTs) with new low carbon technologies (NLCTs), particularly NLCTs featuring variable renewable resources, can disproportionally influence the costs associated with maintaining a reliable and competent grid system. Traditional cost metrics and publicly available tools that make use of these metrics fail to capture the total systems cost (TSC) implications of such changes, highlighting the need for a tool that will capture these implications. This paper presents the results of a tool developed to estimate the System Cost of Replacement Energy (SCoRE) when NLCTs replace LHCTs in an operating region. The SCoRE estimates potential changes in the TSC resulting from the replacement. The SCoRE tool is applied to the Electric Reliability Council of Texas (ERCOT) operating region. LHCTs, namely coal- and natural gas-fired power plants are replaced by NLCTs including solar photovoltaics and wind, with and without energy storage, and coal and natural gas power plants equipped with carbon capture and storage (CCS). The results showcase the TSC implications of different replacement scenarios, which for ERCOT include larger increases in the TSC if LHCTs are replaced with wind and solar, in lieu of natural gas power plants equipped with CCS, for all decarbonization targets considered. • Technology transformations to achieve decarbonization will impact system costs. • SCoRE tool estimates system costs implications of LHCT being replaced with NLCT. • Wind and NGCC + CCS is the least cost pathway for 90% decarbonization in ERCOT. • Renewable energy curtailment appears cheaper than co-locating it with energy storage. • Solar PV + Battery storage become impractical at >70% decarbonization. [ABSTRACT FROM AUTHOR]

Guar (Cyamopsis tetragonoloba L.) is a relatively unexplored seed legume crop with a high interest for production of galactomannan (guar gum), which is highly valued for industrial purposes. Until now no crop simulation models exist for guar. This paper describes the adaptation of the CROPGRO module of the Cropping System Model (CSM) to simulate guar growth and yield based on literature knowledge as well as optimization against growth analysis and yield data collected in multiple field experiments conducted in Texas and New Mexico. CSM-CROPGRO is a mechanistic model that simulates daily crop growth and development dependent on daily weather, soil properties, crop management, and genetic parameters. Model adaptation started with CROPGRO-Soybean as the template and involved setting tissue compositions, and especially the cardinal temperature dependencies of processes including the rate of leaf appearance, rate of progress to reproductive events, photosynthesis, pod addition, and seed growth rate, all parameterized in the species file. Adapted cultivar parameters include life cycle phase durations, duration to end of leaf area expansion, pod adding duration, leaf photosynthesis, seed size, seed protein, seed lipid, and individual seed filling duration. Compared to soybean, guar appears more sensitive to cold temperature because the cardinal base temperatures for photosynthesis, rate of leaf appearance, leaf area expansion, and seed growth had to be increased by 2–4 ˚C. The model adaptations highlight the drought tolerance of guar, with slower leaf and root senescence and greater tolerance of N-fixation under drought. The model simulated well the time-series growth of two cultivars under irrigated and rainfed conditions at Chillicothe, TX and gave acceptable simulations of end-of-season biomass, pod, and seed yield of guar in response to different weather, irrigation, soils, and seasons across three sites. This is the first creation of a crop model for guar and will be released in DSSAT. • The CROPGRO model was successfully adapted to simulate growth and yield of Cyamopsis tetragonoloba L. • The model was parameterized for plant composition, cardinal temperatures, growth, partitioning, and drought-tolerance. • Simulated growth dynamics illustrate that guar is drought-tolerant but cold-sensitive. [ABSTRACT FROM AUTHOR]

• Monthly allocation of work days across three locations is examined. • A multiple discrete–continuous extreme value model is employed. • The choice of the third work place location is modeled for the first time. • Policy implications are identified. In this paper, we examine work place location (WPL) preferences of workers in an unpredictable and evolving future by investigating how workers would prefer to allocate their monthly working days among the three WPL alternatives of working from home, from the work office, and from a variable third WPL. In contrast to earlier studies that typically focus on telework as a binary of whether an individual is a teleworker or not, we focus our attention on workers' preferences for specific combinations of all three WPLs over a period of a month (including, but not limited to, only selecting one WPL for all days of the month). In our analysis, we employ the multiple discrete–continuous extreme value (MDCEV) model, using a 2022 stated preference survey of future work preferences of employees residing in the state of Texas. The results indicate that single young women with very young children, those with long commutes and "intolerable" traffic congestion to the work office, individuals with a private study in their homes, self-employed workers, and those in non-essential service occupations have the highest preference for working from home. On the other hand, older men, individuals from low income households, those residing in rural areas, and workers in essential service occupations have the highest preference for the work office. And, for the third WPL, young non-single women with very young children, individuals from low income households, part-time employees, and those in professional, managerial or finance occupations have the highest predisposition. These results should provide valuable insights to urban planners, homebuilders, employers, travel demand modelers, and a whole host of other businesses to achieve specific desired end states. From a data collection standpoint, our study underscores the need to collect detailed information about work patterns in future activity-travel surveys. [ABSTRACT FROM AUTHOR]

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. 1 1 These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively. • Modeling and simulation platform to estimate power demand during extreme weather conditions. • Demonstration of the platform on 2021 winter storm Uri conditions. • 34 GW of peak capacity deficit estimated for the current Electricity Reliability Council of Texas (ERCOT) grid. • 78 GW peak capacity deficit for load electrification, reduced to 47 GW with technology efficiency • Integrating distributed solar PV and storage reduces the potential unmet energy by 31% and 40%, respectively. [ABSTRACT FROM AUTHOR]

A major blackout occurred in the U.S. Texas during a week of unseasonably cold weather starting from February 13, 2021. Despite advanced knowledge of the extreme weather, there was limited mitigation available to minimize the extent of disruptions. The events resulted in a forced reduction of consumption (e.g., rolling blackouts and equipment failures) during a time when electrical energy was already in high demand due to the cold weather. When primary generation systems started to fail from freezing conditions (48.6% loss of normal generation at its peak), the utility operators were forced to progressively issue rolling blackouts to maintain system frequency and avoid a complete blackout. While Texas avoided a complete grid collapse, 4.5 million customer premises were blacked out with economic losses estimated to be at least $130 billion and many lives lost. The cold weather affected the neighboring state of Oklahoma, however, the disruptions to electricity were mild when compared with Texas. This paper documents some of the key contributing factors in this event for the U.S. Southwest region based on factual data available to date. Wider electric energy reliability issues beyond the affected area are also highlighted. • Impact of Winter storm 2021 on the electrical power systems in Texas and Oklahoma. • Generation failure exacerbated by interdependence of electricity on natural gas. • Unique organizational factors and grid architectures yielded different outcomes. [ABSTRACT FROM AUTHOR]

Over the past couple of decades, the number of wildfires and area of land burned around the world has been steadily increasing, partly due to climatic changes and global warming. Therefore, there is a high probability that more people will be exposed to and endangered by forest fires. Hence there is an urgent need to design pervasive systems that effectively assist people and guide them to safety during wildfires. This paper examines the potential of mobile applications to be employed to assist people to escape wildfires in real-time, by means of using and evaluating EscapeWildFire, a system for suggesting evacuation paths to mobile phone users in real-time. Two different evaluation strategies are followed: (a) Considering different areas of the island of Cyprus as case studies with simulated wildfires and actual users and (b) Considering a historic wildfire that occurred in Amarillo, Texas in 2011. Results in both cases show that mobile apps can effectively be employed to assist people to escape wildfires, but their use is heavily dependent on a backend system that supports the precise modelling of the wildfire. The time between the wildfire outbreak and the notification reaching the user is crucial for their safety. Various recommendations for improvements have been recorded based on either the feedback of participants in the case studies or the observations from the Texas wildfire. The code of the mobile app, backend and administration interface used are all open-source; fire authorities around the world are encouraged to further investigate this opportunity and adopt this approach. • A method for assisting citizens to escape wildfires in real-time via mobile apps. • System implementation is realistic solution to the problem under study based on various requirements and design aspects. • Evaluation of method and system in various diverse scenarios. • Implemented system operates correctly, it is accepted by the users and easy to use. • Numerous recommendations recorded, an indication of the problem's complexity. [ABSTRACT FROM AUTHOR]

• Dining choices are analyzed, including eat-in delivery and take-out options. • The impact of dining patters on travel patterns is examined. • A multivariate ordered-response model is employed. • Policy implications are identified. In this paper, we examine the non-home-cooked meal (NHCM) preferences of individuals for their dinner meal by studying the monthly count of NHCM meals by channel type: eat-out, eat-in takeout, and eat-in delivery. Data from a 2022 online survey collected in Texas is employed to estimate a multivariate joint model. Model estimation results indicate that the most frequent customers of the eat-out channel are white individuals, individuals from 3+ motorized vehicle-owning households, those in non-joint families, those in households with no children, full-time employees who never work from home or do so only for a small fraction of their workdays, and those residing in areas with a high density of restaurants. The distinct consumer segments for the eat-in takeout channel include young individuals, those with high household incomes, those working from home all their workdays or a substantial fraction of their workdays, and urban residents; the most enthusiastic consumers of the eat-in delivery channel are white individuals, those with less than three vehicles in the household, individuals with children, urban residents, and those worried about pandemic-related personal health risks. Older individuals, non-white individuals, individuals with a graduate degree, individuals in fewer motorized vehicle-owning households and in joint families, those with children in the household, and rural residents constitute the most committed population segments of the home-cooked meal (HCM) consumption channel. The results suggest the important impact of workplace location on dining channel choice. The results also show clear evidence of complementary and substitution effects at play; the delivery channel complements eating out but substitutes takeout. Similarly, eat-out has a substitution effect on eat-in takeout. These effects have important implications for activity-travel behavior due to emerging technology-based ordering options for dining choices, especially in the aftermath of the COVID pandemic. [ABSTRACT FROM AUTHOR]

This paper investigated the effects of phase change materials (PCMs) on temperature field and rutting performance of asphalt concrete (AC) pavement with a focus on PCM types, contents, and locations. To quantify their effects on rut depth accumulation, a pavement finite element (FE) model was constructed with measured material properties and designed structural configuration. The implemented traffic and environmental conditions were obtained from a FE model updating scheme and a heat transfer model with the consideration of traffic speed and the phase change process. Results showed that using polyethylene glycol of 4000 molar mass encapsulated by SiO 2 as the PCM to replace 10% of the aggregate in the asphalt top sublayer yielded the best rutting performance; a 14% reduction in the rut depth that was accumulated within one month after pavement construction was predicted for a typical summer case in Texas. This study also revealed that incorporating PCMs into AC does not always lead to pavement rutting performance improvement. This is because (1) the lower thermal conductivity of the PCMs can offset the benefits achieved from the increased heat capacity during phase change, and (2) PCMs will not function if they are added in layers where pavement temperature is not high enough for PCMs to undergo the phase change. Therefore, developing PCMs with higher thermal conductivity together with a judicious selection of PCM types and locations will maximize the effectiveness of the pavement temperature and rutting control. [ABSTRACT FROM AUTHOR]

• We evaluate the impact of operational modeling details on resource adequacy results. • Multi-stage probabilistic assessments may better capture operational details. • Thermal generator outages may impact results more than solar forecast errors. • Flexibility provided by hybrids can reduce the number of load-shedding events. • Results are more sensitive to hybrid inverter sizing than other hybrid features. Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1–2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1–2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions. [ABSTRACT FROM AUTHOR]

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. Performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems. • A multi-area parameter error identification method is presented. • Introduces significant speed-up in parameter error identification for large power grids. • Enables scalability of parameter error detection method for large power grids. [ABSTRACT FROM AUTHOR]

The apportionment process that precedes redistricting is generally a staid American political ritual. Recent debates about who should be included in the apportionment basis, however, have raised new questions about representation in the apportionment process. To estimate the effects of excluding non-citizens and children from apportionment, we describe an algorithm to simulate drawing of state legislative districts, based on a previously published algorithm, Seed-Fill-Shift-Repair (SFSR), designed to draw congressional districts. To account for the larger number of districts to draw we implement an adapted search heuristic that is able to efficiently create contiguous and population-balanced maps for state legislative districts, which we call SFSR-G. We use SFSR-G to simulate 1000 maps of upper and lower legislative chambers in 12 states to demonstrate that a shift from total population to citizen voting age population as the apportionment basis will reduce minority–majority and minority-opportunity districts. The paper presents findings for all 12 states investigated, and discusses the important case of Texas at greater length. [ABSTRACT FROM AUTHOR]

As the share of solar photovoltaics (PV) in the power system increases, there is a growing need for flexibility from multiple, possibly interdependent sources to adjust to PV's variability, uncertainty, and diurnal dependence. This paper investigates how stochastic unit commitment leveraging probabilistic solar forecasts can support other flexibility measures under high solar shares. We consider two flexibility measures relevant to day-ahead scheduling: battery energy time-shifting and solar ancillary service provision. Unit commitment and economic dispatch simulations are conducted on a realistic test system based on Texas using day-ahead solar trajectories. The benefits of the two flexibility measures are pronounced when the instantaneous solar share is high, offering cost savings of 10%–20% in the spring. For a Texas-sized system, this translates to hundreds of millions of dollars in cost savings once the installed PV capacity enables instantaneous solar shares regularly exceeding 40%. Using probabilistic forecasts also greatly increases the reliability of upward reserve provision from solar PV, reducing unserved reserves by 50%–100%. Both day-ahead forecast resolution and errors can impact system reliability at high solar shares, but the stochastic formulation has significant value, mitigating reliability impacts on over-forecast days. • Forecast resolution as well as error has reliability impacts at high solar shares. • Flexibility measures considered are battery storage and solar ancillary services. • Stochastic unit commitment greatly improves reliability of solar upward reserves. • Cost savings from flexibility are pronounced for instantaneous solar shares >40%. • Demonstrated on Texas-sized system with realistic day-ahead solar trajectories. [ABSTRACT FROM AUTHOR]

System-wide functionality failure emerges when a collection of components collapse simultaneously. Aiming to detect the early warning of the rapid critical facilities accessibility loss after flooding, this paper presents a network-theory-based detection model to identify the critical point after which the rate of road access loss to critical facilities starts to accelerate. The road network of Harris County, Texas during Hurricane Harvey 2017 flooding and simulated 500-year flooding (1,000 flooding sequences) is used for the case study. We also considered the travel radius for reaching critical facilities (e.g., 5 miles travel radius for hospitals, and 4 miles for groceries and pharmacies) in the accessibility assessment to show the flood impact on daily life. Additionally, we link the critical threshold to the spatial network to identify the corresponding critical roads whose disruption will likely trigger the rapid critical facility accessibility loss. This result will facilitate the decision-making on targeted infrastructure protection. Moreover, we map the number of critical facilities that each network component has access to within reach and reveal the resource redundancy inequity and risk disparity across the network. Integrating with the social vulnerability mapping, our result can help inform the resilience planning toward an equitable community. • Critical facilities accessibility rapidly decreases after the early-warning point. • Accessibility failure early-warning point can help identify critical roads. • Communities' access to critical facilities are affected disproportionately in urban flooding. • Access redundancy mapping can inform equitable resilience planning in infrastructure development. [ABSTRACT FROM AUTHOR]

• Simple TOC measurements can be inadequate for paleoenvironmental reconstruction. • Present-day TOC can be ½ the original content if hydrocarbons were generated. • Decrease is greatest for marine organic matter at high maturity. • A technique used in petroleum-systems modeling makes the necessary corrections. • We advise capturing uncertainty due to the assumptions inherent in this method. Methods for reconstructing present-day TOC values to their original values are widely used in petroleum systems analysis. However, the importance of making these corrections appears to be underappreciated when TOC is used for paleoenvironmental interpretation. In this paper we demonstrate how differences between present-day TOC (TOC PD) values (e.g., as measured via LECO and similar methods) and those at the time of deposition (TOC O) can materially affect paleoenvironmental interpretations that use TOC as a variable. The difference between those two values depends on the thermal maturity of the deposits and the original kerogen type. We illustrate the effects of restoring and using TOC O values on samples from the Upper Cretaceous Eagle Ford Formation of South Texas and the Upper Devonian Lower Bakken Shale of North Dakota. Making this correction requires programmed pyrolysis ("Rock-Eval") data rather than simple TOC measurements. We use plots of molybdenum and sulfur versus TOC PD and TOC O to illustrate the effects of restoring TOC, but any analysis that involves TOC as a variable will be affected. There are uncertainties associated with defining the original organic matter type, and other unknowns that can affect TOC reconstruction. As such we advocate presentation of a range of possible TOC O values, i.e., deliberately capturing uncertainty, rather than a single value. The method used to restore TOC has minimal impact on the interpretations, at least for the two methods employed herein. [ABSTRACT FROM AUTHOR]

• An analytical relationship between the generator terminal bus voltage and current phasors and the internal rotor speed is derived via the Thevenin equivalent. • A reformulation of inertia estimation is proposed based on a first-principles equation derived from Newton's law applied to rotating masses. Specifically, the measured voltage phasor is the model input, whereas the estimated rotor speed and the measured real and reactive power from PMUs are treated as model outputs.. • The rotor speed estimation in the proposed method is online and model-free, a big difference from existing dynamic state estimation methods using detailed generator models. This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise. [ABSTRACT FROM AUTHOR]

• A stochastic hydrological modeling system (SHMS) is proposed for improving streamflow prediction. • Interactions of error parameters have significant contributions to model performance. • The SHMS improves upon the EnKF through a more accurate convergence of model parameters. • The SHMS improves upon the conditional vine copula model for probabilistic streamflow prediction. • Temporal dynamics of parameter sensitivity are examined using the time-varying sensitivity analysis. Streamflow prediction plays a crucial role in water resources systems planning and the mitigation of hydrological extremes such as floods and droughts. Since a variety of uncertainties exist in streamflow prediction, it is necessary to enhance our efforts to robustly address uncertainties and their interactions for improving the reliability of streamflow prediction. This paper presents a stochastic hydrological modeling system (SHMS) for improving daily streamflow prediction by explicitly addressing uncertainties in error and model parameters as well as in forcing data and model outputs. Specifically, the SHMS merges the strengths of the ensemble Kalman filter and the particle filter algorithms for improving the effectiveness and robustness of daily streamflow assimilation. Factorial analysis of variance and variance-based global sensitivity analysis are performed to reveal parameter interactions affecting predictive performance and temporal dynamics of parameter sensitivities, maximizing the accuracy of streamflow prediction. The SHMS has been applied to the Guadalupe River basin located in Texas of the United States to demonstrate feasibility and applicability. Our findings indicate that the SHMS improves upon the well-known ensemble Kalman filter for sequential estimation of hydrological model parameters through a more rapid and accurate convergence of model parameters in streamflow simulation. The SHMS also demonstrates a higher level of skill in streamflow prediction compared to the conditional vine copula model. The proposed SHMS can be applied straightforwardly to other river basins for probabilistic hydrological prediction. [ABSTRACT FROM AUTHOR]

• The 'Earlygold' genome has large homozygous regions that cover 52% of its length. • Thirty-three QTLs, including 12 consistent across years, were found for 24 traits,. • A QTL hotspot on chromosome 4 affects five phenology, fruit quality and leaf traits. • Four times more QTLs were found in the F2 than in the BC1 progeny of 'Earlygold'. • Leaf anthocyanic color at senescence is determined by two independent QTLs. Based on detailed maps, DNA sequences and phenotypic data, there is a great deal of information on the genetics and genomics of 'Earlygold', a historical peach cultivar from the US. The F 2 between 'Texas' almond and 'Earlygold' peach (T × E) was used to construct the first saturated peach linkage map that later became the reference map for the Prunus genus. This population and the first backcross ('Texas' × 'Earlygold') × 'Earlygold' (T1E) yielded information on QTLs for a large number of agronomic traits, and T1E is being used as the basis for constructing a set of introgression lines of 'Texas' fragments into the 'Earlygold' background, currently in progress. This paper describes the construction of a high-density SNP map for 'Earlygold' using an F 2 population, and the QTL analysis of 24 traits. Results of maps and QTLs are compared with those from the 'Earlygold' parent of the T1E map, using the same set of markers and characters. Results show major differences between the two progenies in terms of numbers of markers mapped and the capability of detecting QTLs, with a large increase in the resolution of maps and QTLs when using the F 2 progeny compared to the T1E pseudo-testcross. In addition, we provide data on leaf senescence color, studied for the first time in peach, with two consistent QTLs located in the same position as other color-related genes and QTLs. [ABSTRACT FROM AUTHOR]

The US Gulf of Mexico coastal region has repeatedly been subjected to major flood events. Local geotechnical site characteristics and geomorphology can change due to sediment transport processes during such events. However, field measurements during extreme conditions are challenging. This paper discusses initial attempts at a combined geotechnical and geophysical site investigation of the uppermost layers of riverbeds following severe flooding events at three different rivers in Texas: the Guadalupe, Brazos, and Colorado Rivers in terms of sediment strength derived from a portable free fall penetrometer, backscatter intensity recorded by a chirp sonar, and soil sample characterization. Results show low strength sediments (<40 kPa) along the investigated sections of the Guadalupe and Brazos riverbanks. Although sediments in the center of the Brazos River were characterized with higher strength (>50 kPa) and larger grain sizes (d 50 ∼ 0.3 mm), sediment strength of the Guadalupe and Colorado Rivers displayed more variations around bridge piers. The spatial variations likely resulted from sediment remobilization processes and local scour under severe hydrodynamic conditions. Both, geotechnical and geophysical results, reflected the observed variations in the riverbed sediments; nonetheless, a quantitative correlation among the rivers was impeded by challenges primarily related to limitations of spatial accuracy and the significant riverbed heterogeneity, as well as shallow water limitations of the chirp sonar. • Combined geotechnical and geophysical site characterization at three Texas river sections • Measurements from small vessels and in areas of unknown riverbed conditions • Sediment dynamics were reflected in geotechnical and geophysical data • Qualitative relationship between portable free fall penetrometer and chirp sonar • Three river sections differed significantly in local sediment properties [ABSTRACT FROM AUTHOR]

The frequency of Very Large-Scale Weather Events (VLSWEs) such as Superstorm Sandy in 2012, Hurricane Maria in 2017 and Winter Storm Uri in 2021, among others, in the last decade have led increased conversations around the topic of grid resilience, by grid operators, planners, government agencies and other industry stakeholders. These events, which were to only occur "once in 100 years" exert a heavy toll on life and property. For example, winter storm Uri and the statewide power outages it caused in Texas directly and indirectly led to the death of 210 people and by some estimates caused over $120 billion in economic losses. The 2021 Texas electricity crises, which led to over 4 million people losing electricity and water for almost a week has brought about a renewed interest in the resilience of the electric power system in general and the Electric Reliability Council of Texas (ERCOT) in particular. Historically these conversations have been about grid reliability, but with the increase in frequency and the sheer size of devastation caused by VLSWEs more holistic and robust approaches to addressing their effects on the grid are being considered by industry stakeholders and policy makers. In this paper the events of this crisis are analyzed using a syncretistic approach developed from analyzing and then synthesizing important resilience constructs, frameworks and concepts. [ABSTRACT FROM AUTHOR]

Counterfactual regret minimization (CFR) is a popular and effective method for solving a game with imperfect information. The effect of CFR is limited by the size of the game state space. With the increase in the number of game participants, the game state space will increase rapidly. Although the vanilla CFR is suitable for two-player imperfect-information games, it does not work well in imperfect-information games with three or more players. In this paper, we design a framework for imperfect-information games, which can not only deal with two-player imperfect-information games but also can efficiently solve three-player imperfect-information games. Compared with traditional solving methods, in this framework we propose real-time hand abstraction (RTHA), which can reduce the error caused by the abstraction. We also propose a warm-start online solution of sub-game (WSOS-SG) method, which can improve the accuracy of the action estimation and solve the sub-game in real time. Experimental results show that the agent based on our method achieve better performances than traditional methods. The agent based on our method took part in the 2018 AAAI-ACPC poker competition and won third place in heads-up no-limit Texas hold'em. [ABSTRACT FROM AUTHOR]

• Long short-term memory neural network is proposed for groundwater prediction. • The proposed method relies only on in-situ piezometric observations. • The model is used for daily and monthly prediction for the Edward aquifer. • The results highlight the importance of long-term, groundwater level data. The application of neural networks (NN) in groundwater (GW) level prediction has been shown promising by previous works. Yet, previous works have relied on a variety of inputs, such as air temperature, pumping rates, precipitation, service population, and others. This work presents a long short-term memory neural network (LSTM-NN) for GW level forecasting using only previously observed GW level data as the input without resorting to any other type of data and information about a groundwater basin. This work applies the LSTM-NN for short-term and long-term GW level forecasting in the Edwards aquifer in Texas. The Adam optimizer is employed for training the LSTM-NN. The performance of the LSTM-NN was compared with that of a simple NN under 36 different scenarios with prediction horizons ranging from one day to three months, and covering several conditions of data availability. This paper's results demonstrate the superiority of the LSTM-NN over the simple-NN in all scenarios and the success of the LSTM-NN in accurate GW level prediction. The LSTM-NN predicts one lag, up to four lags, and up to 26 lags ahead GW level with an accuracy (R2) of at least 99.89%, 99.00%, and 90.00%, respectively, over a testing period longer than 17 years of the most recent records. The quality of this work's results demonstrates the capacity of machine learning (ML) in groundwater prediction, and affirms the importance of gathering high-quality, long-term, GW level data for predicting key groundwater characteristics useful in sustainable groundwater management. [ABSTRACT FROM AUTHOR]

• A novel Light Gradient Boosting Machine approach is introduced to model crash counts. • SHAP method is used to quantify the safety effects of key factors. • The effects on different types of crashes are modeled separately and compared. • Modeling crashes by type provides important insights for developing mitigation strategies. • ML and SHAP are useful for developing crash modification factors. Understanding and quantifying the effects of risk factors on crash frequency is of great importance for developing cost-effective safety countermeasures. In this paper, the effects of key crash contributing factors on total crashes and crashes of different collision types are analyzed separately and compared. A novel Machine Learning (ML) method, Light Gradient Boosting Machine (LightGBM), is introduced to model a Texas dataset consisting of vehicle crashes occurred from 2015 to 2017. Compared with other commonly used ML methods such as eXtreme Gradient Boosting (XGBoost), LightGBM performs significantly better in terms of mean absolute error (MAE) and root mean squared error (RMSE). In addition, the SHapley Additive explanation (SHAP) approach is employed to interpret the LightGBM outputs. Significant risk factors are identified, including speed limits, area type, number of lanes, roadway functional class, shoulder width and shoulder type. With the SHAP method, the importance, total effects, and main and interaction effects of risk factors are quantified. The results suggest that the importance of risk factors vary across collision types. Speed limit is a more important risk factor than right/left shoulder width, lane width, and median width for Rear-End (RE) crashes, while the opposite relationship is found for Run-Off-Road (ROR) crashes. Also, it is found that narrow lanes (8ft to 11ft) increase the risk for all types of crashes (i.e., Total , ROR , and RE) in this study. For road segments with 5 or 6 lanes in both directions combined, a lane width greater than or equal to 12ft may help reduce the risk of all types of crashes. These results have important implications for developing accurate crash modification factors and cost-effective safety countermeasures. [ABSTRACT FROM AUTHOR]

APEX (A gricultural P olicy/ E nvironmental e X tender) is an oft-used agroecosystem model but has limited use in groundwater-driven watersheds due to a simplistic representation of groundwater processes. This paper presents the linkage of APEX and the groundwater flow model MODFLOW into a single modeling code. The mapping of recharge, groundwater head, and groundwater-surface water interactions are handled internally via subroutines. The APEX-MODFLOW model is applied to three watersheds in the United States for testing code accuracy and hydrologic state variables and fluxes: the Animas River Watershed, Colorado and New Mexico (3543 km2); the Price River Watershed, Utah (4886 km2); and the Middle Bosque River Watershed, Texas (470 km2). Whereas the hydrology of the Animas River and Price River watersheds is driven by snowmelt and spring runoff, the hydrology of the Middle Bosque River Watershed is driven by summer thunderstorms. The model can be used for scenario analysis in groundwater-driven watersheds. • New integrated hydrologic model links APEX model to MODFLOW model. • Model passes recharge, water table elevation, and gw-sw flux rates internally. • APEX-MODFLOW applied to three watersheds in Utah, Colorado, and Texas, USA. • Model results show importance of including physically based groundwater flow. [ABSTRACT FROM AUTHOR]

Due to the vulnerability of the Edwards Aquifer to urban development, policy measures have been implemented to protect the important water resource in South–Central Texas. This paper analyzed land use change trends across different portions of the Edwards Aquifer to better understand the degree to which protective measures influenced aquifer urbanization rates. Specifically, the National Land Cover Database was used to quantify urban development within the contributing and recharge zones of the Edwards Aquifer across Bexar, Travis, Williamson, Comal, and Hays counties for three time periods: 2001–2006, 2006–2011, and 2011–2016. Bexar County exhibited the greatest reduction in the rate of urbanization within the recharge zone although the percentage of the zone developed remained the highest. Conversely, the pace of recharge zone development in Travis and Williamson County decreased less rapidly, but the percentage of the zone urbanized was lower. Limited urban development was observed across the aquifer in Comal and Hays County during the study period. Overall, the consistent declining rate of urbanization throughout the sensitive Edwards Aquifer recharge zone suggests that the policies protecting the aquifer were largely effective, particularly given the rapid pace of urban growth throughout the region. • Urbanization trends across the Edwards Aquifer were analyzed for 2001–2016. • Important differences were observed between the Austin and San Antonio regions. • The rate of urban development throughout the sensitive recharge zone decreased. • This indicated that aquifer protection policies were largely effective. [ABSTRACT FROM AUTHOR]

Nonmedical exemptions from school-entry vaccine mandates are receiving increased policy and public health scrutiny. This paper examines how expanding the availability of exemptions influences vaccination rates in early childhood and academic achievement in middle school. We leverage 2003 legislation that granted personal belief exemptions (PBE) in Texas and Arkansas, two states that previously allowed exemptions only for medical or religious regions. We find that PBE decreased vaccination coverage among Black and low-income preschoolers by 16.1% and 8.3%, respectively. Furthermore, we find that those cohorts affected by the policy change in early childhood performed less well on standardized tests of academic achievement in middle school. Estimated effects on test scores were largest for Black students and economically disadvantaged students. [ABSTRACT FROM AUTHOR]