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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]

Background: The prevalence of deaths involving synthetic opioids has historically been lower in Texas than most U.S. states but more than quadrupled from January 2020 to January 2022. This paper explores the emergence of fentanyl in a drug supply where black tar heroin predominates, a factor considered protective against fentanyl adulteration, through the perspectives of people who use drugs (PWUD). Objectives: We describe experiences of unintentional exposure to fentanyl, illustrate how some people identify fentanyl in their supply, and present harm reduction strategies that PWUD use to avoid overdose. Methods: Thirty rapid assessment interviews were conducted in July 2021 at 2 mobile outreach sites of a harm reduction organization in Austin, Texas. The brief semistructured interviews were designed to assess participant fentanyl exposure experiences. Results: Participants were clients who reported using heroin or fentanyl in the past week and had lived in Texas for at least 6 months. Seventeen participants identified as male, 10 as female, and 3 as nonbinary. Half identified as white; other participants were Latinx (6), black (2), American Indian (1), and mixed race (6). Two-thirds were unhoused or in transitional housing. The drug supply in Texas has evolved; most participants reported that the heroin and other drugs they obtained contain fentanyl. Participants detected differences by observing changes in the physical characteristics of the drug, experiencing unexpected effects, and using fentanyl test strips. Many had been unintentionally exposed to fentanyl and expressed concerns about fentanyl’s presence. The presence of fentanyl had negative unintended consequences for participants, including adverse effects and developing a dependence on opioids. Conclusion: PWUD in Austin, Texas, report increasing prevalence of unintentional fentanyl exposure, despite the predominance of black tar heroin. Pharmacists can provide crucial supplies and education to safeguard the health of this vulnerable population. [ABSTRACT FROM AUTHOR]

This paper investigates the strategic pricing behavior of a leader in a two-person, noncooperative game when profits depend on the purchasing response of a follower who not only reacts to changing prices instantaneously, but also builds long-lived consumption infrastructure that affects future demand. As an application of such a system, we formulate the relationship between these players in the natural gas and electricity generation industries as a bilevel problem. The leader is a natural gas producer whose objective is to maximize profit; the follower is an electric utility who solves a capacity expansion and dispatch problem with the objective of minimizing the cost of electricity generation and long-run investments. To find solutions, the bilevel problem is reformulated as a mixed-integer linear program by replacing the lower-level player's model with its Karush–Kuhn–Tucker conditions, which are necessary and sufficient for optimality here, and linearizing the upper-level player's objective function using the strong duality conditions of the lower-level problem. After parameterizing the model with publicly available data for Texas, we conduct scenario analyses through 2040, evaluating strategies of the natural gas producer under different policies regarding carbon taxes and incentives for carbon capture and storage (CCS). We then observe how the lower-level player responds to these strategies in terms of the evolution of the generation mix, added capacity, and CO 2 emissions. We also quantify the effects of strategic pricing by running scenarios where natural gas prices are fixed. Key findings include: (1) different levels of carbon tax and CCS incentive can have non-monotonic effects on the optimal natural gas price and producer profit, (2) effects of CCS incentives can spill over to technologies without CCS, and (3) omission of strategic pricing from the model not only decreases the profit for the producer but also can increase the costs for the electricity sector. • Bilevel model for interaction between natural gas producer and electric utility. • Carbon policy effects on electricity capacity mix, generation mix, and CO 2 emissions. • Effectiveness of different reformulations of bilevel programming problems. • Demonstration of how strategic natural gas pricing affects carbon policy outcomes. [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]

Cracks that develop during galvanization of High Mast Illumination Poles (HMIPs), often at the pole-to-base plate connection, are an important concern to US fabricators and highway officials. If they are not detected during fabrication and are allowed to propagate during service, these cracks can pose a significant risk to the public due to the ubiquitous presence of HMIPs in close proximity to roads and highways. Economic losses caused by these cracks include the cost of detailed inspections to ascertain their presence and direct losses associated with discarded poles or repair of cracks that manifest while in service. Modifications to the design of HMIPs and/or the galvanization process to reduce the likelihood of galvanization cracks reduce economic loses, decrease fabrication costs, and improve public safety. This paper presents a parametric study evaluating the effects of geometric configuration and galvanization practices on thermally-induced stress/strain demands during the galvanization of HMIPs. Simulations were performed for poles with standard pole-to-base plate connection details adopted by the Texas DOT. Geometric parameters evaluated in the study included base plate-to-pole thickness ratio, pole shaft geometric shape, and bend radius. Galvanizing practice variables included dwell time, speed and angle of dipping, and cross-section orientation. Simulations were conducted using the commercial software Abaqus, following the methodology validated in a companion paper. In models having the same pole thickness, thermally-induced stresses and strains at critical locations increased with base plate-to-pole thickness ratio. In models with the same base plate thickness, stress and strain demands varied inversely proportional to pole thickness. Stress and strain demands were found to be lower for poles having circular shape than for multi-sided poles, and increases in bend radius led to reductions in localized strain demands. Dwell time was found to have a negligible effect on stress and strain demands, while increased dipping speed and dipping angle resulted in decreased stress and strain demands during galvanizing. • A numerical parametric study was conducted to study galvanization cracks in HMIPs. • The contribution of shape and galvanizing parameters to weld-toe cracks was evaluated. • Base plate-to pole thickness ratio was found to play a key role on crack formation. • Circular poles exhibited the lowest stress/strain demands during galvanizing. • Reducing dipping time and increasing dipping angles reduced plastic deformations. [ABSTRACT FROM AUTHOR]

Highlights • The flux of multivalent cations across non-selective CMX membranes was 1.4 times > sodium. • A CMX-S membrane at the cathode boundary reduced the flux of Ca, Mg, and Ba into the electrode rinse. • Ca, Mg and Ba interfered by blocking transfer sites on the CMX-S membrane at the cathode barrier. • Overall flux was improved by unblocking the CMX-S membrane with periodic polarity reversal. • Soluble Fe precipitated throughout the stack and within the diluate and concentrate tanks. Abstract Electrodialysis is an electrochemically driven membrane technology usually used for treating feed water containing several thousand mg/l total dissolved solids (TDS). This research investigated the use of conventional ED for the treatment of flowback water from shale gas hydraulic fracturing that contains up to tens of thousands of mg/l TDS and contain a mixture of multivalent cations. This paper discusses efforts to treat concentrated brines with electrodialysis and focuses on the mitigation of the detrimental effects of multivalent cations such as calcium, magnesium, barium, iron, and in the feedstock. A previous paper detailed the effects of calcium under defined lab conditions. The apparent calcium fouling (in and around) the electrode cells was mitigated by changing the single cathode chamber boundary membrane to a (CMX-S) that was more selective for monovalent cations. This paper covers efforts undertaken to mitigate problems associated with high concentrations of calcium, barium, iron, and magnesium. Four field samples of produced water from oil and gas operations in from the Marcellus shale formation (generally Pennsylvania, USA) and the Barnett shale formation (generally north central Texas, USA) were also tested. These results from field samples were similar to the results from lab defined tests. Mechanisms of process interference by the different multivalent cations appear different for each species. Calcium, magnesium, barium, and iron were preferentially transported from the diluate to the concentrate compared to sodium by a factor of about 1.4:1 on a charge basis resulting in a suppression of the flux of sodium to the concentrate. A single CMX-S membrane selective for monovalent cations was placed at the cathode barrier resulting in a reduced flux of calcium, magnesium and barium into the electrode rinse. However total ion flux was inhibited caused by apparent blockage of the cation transfer sites on the CMX-S membrane by the stagnant multivalent cations. A series of tests with periodic pulse polarity-reversal (anode and cathode reversed for a brief time) indicted the blockage by calcium and barium was immediately reversable, however, iron was more problematic and may have precipitated on membrane surfaces other than the cathode barrier. [ABSTRACT FROM AUTHOR]

This paper reviews previous work conducted on Laser Ablation – Inductively Coupled Plasma – Mass Spectrometry (LA-ICP-MS) sourcing 33 Clovis period projectile points from the Gault Site (41BL323) in Central Texas. This paper includes new geochemical data from four new primary geologic sources to increase an Edwards Plateau region chert database to 302 primary geologic samples from 204 samples with which to test against the geochemistry of the 33 Clovis artifacts. These artifacts are then assessed with regards to their form, breakage patterns, and distance to source to suggest a blended model of mobility for Clovis period hunter-gatherers in Central Texas. This data suggests that the pattern of movement across the landscape of the Edwards Plateau for these hunter-gatherers may have been a blended pattern alternating between collector and forager based on changing distribution of resources seasonally. This patterning may have been influenced for the Gault Clovis hunter-gatherers reliant on availability of chert resources as well as ecotonal density and diversity of plants and animals; especially, as gauged by Clovis site types and distribution spread throughout much of the Southern Great Plains. • Clovis hunter-gatherers at the Gault Site used chert from over 300 km away. • Breakage and discard patterns of non-local and local chert suggests long distance mobility. • Gault Clovis hunter-gatherer likely alternated their mobility strategies throughout the year. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a research study on a bridge site located along US highway 67 over SH 174 in Cleburne, Texas, where bridge approach slabs have experienced more than 0.4 m (17 in.) of settlement within a span of 16 years after construction. Many treatment methods attempted to mitigate this problem had proven to be ineffective. As part of novel rehabilitation works, the top of existing fill soil on the embankment was replaced with lightweight expanded polystyrene (EPS) geofoam blocks to alleviate the approach slab settlements. This paper describes initial design and construction details of the rehabilitation works performed on the embankment system along with a focus on the early performance details. Field monitoring studies were conducted for almost three years to study the bump/settlements under the EPS geofoam embankment system. Short term measured settlement data was analyzed with hyperbolic model to predict the long term settlements. Numerical finite element studies attempted in this study showed that settlements could be reasonably predicted by modeling these geofoam embankments. Based on the monitoring and modeling studies, the effectiveness of utilizing EPS geofoam as an embankment fill material was addressed to mitigate the differential settlements under a bridge approach slab. [ABSTRACT FROM AUTHOR]

• A two-stage method is proposed for electricity price forecasting. • The method diversifies the data resources improve the forecasting accuracy. • The method is tested using the real-world data of Texas electricity market. • Causal relationship combined with physical models guarantee better results. • Deep neural networks show a great performance in electricity price forecasting. Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved. [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]

This paper attempts to examine the impacts of the spatial structure of landscape on housing premiums in Austin, TX through the years of 2009–2011. Dissimilar to previous studies, this paper highlighted three points. First, landscape patterns was observed—not only the composition but also the configuration in terms of shape, fragmentation, isolation, and connectivity. Second, the definition of neighborhood in this study was closely matched to the nature of a residential neighborhood, which was represented by a residential subdivision that has a name and a high level of homogeneous characteristics (i.e., development age, appraised value, lot size, and housing size). This approach specifically helps understand the impact of landscape configuration at the micro-level neighborhoods. Third, the hierarchical nature of neighborhoods and embedded parcels—nested parcels within the same neighborhood posit a certain level of identity as a whole—was given attention so that multi-level modeling could be employed. Conceptually and theoretically, multi-level modeling is a better approach to examine phenomenon that occur in multi-level units that show a clear hierarchy. The findings indicated that home buyers are willing to pay more to live in neighborhoods with the high ratio of tree cover. This is a finding consistent with previous studies. Meanwhile, the detailed spatial configuration of landscape does not play an important role at the residential neighborhood level. This result urges policy makers to be more scale-sensitive when planning landscape and indicates that micro neighborhoods are not the correct spatial level to discuss the spatial configuration of landscape, greenways, and green network. [ABSTRACT FROM AUTHOR]

Ocean anoxic events (OAE) are characterized by increased organic content of marine sediment on a global scale with accompanying positive excursions in sedimentary organic and inorganic δ 13C values. To sustain the increased C exports and burial required to explain the C isotope excursion, increased supplies of nutrients to the oceans are often invoked during ocean anoxic events. The potential source of nutrients in these events is investigated in this study for Oceanic Anoxic Event 2, which spans the Cenomanian-Turonian boundary. Massive eruptions of one or more Large Igneous Provinces (LIPs) are the proposed trigger for OAE 2. The global warming associated with volcanogenic loading of carbon dioxide to the atmosphere has been associated with increased continental weathering rates during OAE 2, and by extension, enhanced nutrient supplies to the oceans. Seawater interactions with hot basalts at LIP eruption sites can further deliver ferrous iron and other reduced metals to seawater that can stimulate increased productivity in surface waters and increased oxygen demand in deep waters. The relative importance of continental and submarine weathering drivers of expanding ocean anoxia during OAE 2 are difficult to disentangle. In this paper, a box model of the marine Sr cycle is used to constrain the timing and relative magnitudes of changes in the continental weathering and hydrothermal Sr fluxes to the oceans during OAE 2 using a new high-resolution record of seawater 87Sr/86Sr ratios preserved in a marl-limestone succession from the Iona-1 core collected from the Eagle Ford Formation in Texas. The results show that seawater 87Sr/86Sr ratios change synchronously with Os isotope evidence for the onset of massive LIP volcanism 60 kyr before the positive C isotope excursion that traditionally marks the onset of OAE 2. The higher temporal resolution of the seawater Sr isotope record presented in this study warrants a detailed quantitative analysis of the changes in continental weathering and hydrothermal Sr inputs to the oceans during OAE 2. Using an ocean Sr box model, it is found that increasing the continental weathering Sr flux by ∼1.8-times captures the change in seawater 87Sr/86Sr recorded in the Iona-1 core. The increase in the continental weathering flux is smaller than the threefold increase estimated by studies of seawater Ca isotope changes during OAE 2, suggesting that hydrothermal forcing may have played a larger role in the development of ocean anoxic events than previously considered [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]

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]

This paper examines developer practices and self-help housing in serviced subdivisions targeting very low-income Hispanic homesteaders in the Texas border region. Laid out under “Model Subdivision Rules” after federal and state legislative intervention in the early 1990s, this initiative sought to curtail further colonia development and to ensure that any further homesteading followed regulations regarding service and infrastructure provision. Model subdivisions (MSs) proliferated after 1995 and this paper examines developer practices of seller financing, lot sales, and lot repossession for the large number of low-income families who default. Descriptive data are presented about housing costs in a number of different sized model subdivisions, and regression analysis of over 1247 individual lots reveals aggressive rent-seeking developer practices that lead to: (i) high levels of default and repossession; followed by (ii) resale (flips) of lots to other unwary buyers; and (iii) rapid “flipping” (re-sale) of lots by developers soon after repossession. Informal (seller) financing at high interest rates, combined with high private transportation and other consumption costs associated with low-income residence in peri-urban areas leave little cash surplus for self-building and home improvement such that housing conditions are among the worst in the State, and often worse than self-built homes in the colonias that the MSs were designed to replace. The paper concludes with a brief overview of the implications for theory and for a new round of public policy responses. [ABSTRACT FROM AUTHOR]

Cancer is the second leading cause of death in the U.S. Utilizing family health history in cancer prevention holds promise in lessening the burden of cancer. Nevertheless, family health history is underutilized in public health and preventive medicine. Community health workers, also known as lay health educators, are ideal candidates to offer basic cancer family history-based education and services to the general public. The authors developed the first cancer family history-based genomics training program in cancer prevention tailored for community health workers. This paper details the development and pilot testing findings of the training. Specifically, a multidisciplinary research team of geneticists, genetic counselors, health educators, community health workers, and community health worker instructors developed a 7-module, 6-hour, bilingual (English and Spanish) cancer family history-based training focusing on cancer family history-based risk assessment, lifestyle recommendations, and genetic evaluation and testing. The curriculum was based on an integrated theoretical framework, the National Comprehensive Cancer Network guidelines, the community health worker core competencies, and the 4MAT instructional model. The Texas Department of State Health Services approved and certified the curriculum with 2 delivery formats: in-person/face-to-face workshops and online training. A total of 34 community health workers completed the pilot training in person (n=17) and online (n=17) in 2018 and 2019. Participating community health workers' knowledge, attitudes, self-efficacy, and intention in delivering basic cancer family history-based genomics education and services significantly increased on the immediate post-test measures compared with their pretest data. Positive ratings and feedback were also reported by the community health workers. Findings from this pilot study suggest that wider training is warranted for educating more community health workers in the U.S. [ABSTRACT FROM AUTHOR]

Mobile-source emissions are pivotal in quantifying the negative externalities of surface transportation, such as environmental pollution and climate-change, and in evaluating low-carbon traffic strategies. In such assessments, it is important to avoid prospective policy shortcomings. Hence, a wide range of sensitivities of mobile-source emissions must be understood, particularly from a traffic modeling standpoint. This paper takes a step in that direction and explores the effects of certain supply-side network attributes on emissions. Three key elements are investigated: level-of-detail of traffic activity, link speeds in the network, and link lengths. Both aggregated (hourly) and fine-grained (per-second) traffic activities are modeled using a simulation-based dynamic traffic assignment tool. Emissions are modeled using US Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES). System-wide estimates of five criteria pollutants (CO, NO2, PM10, PM2.5, and SO2) and greenhouse-gases (CO2) are developed for a weekday morning peak-hour modeling period. Numerical experiments on a rapidly growing county in Central Texas, US, indicate that emission estimates are sensitive to all the aforementioned supply-side variables. Most notably, median network-wide estimates are found to increase in magnitude with aggregation of traffic activity and speeds. Effects of link lengths appear to be more prominent in high-speed traffic corridors, such as restricted-access highways, than low-speed unrestricted-access arterials. The latter, however, witness more traffic dynamics and subsequently contribute more to deviation in emission estimates across levels-of-detail. The findings highlight the need to be mindful of such physical sensitivities of emissions while enacting policy decisions, which frequently rely on network-based regional emissions inventories. • Microsimulation emissions models are computationally prohibitive for large networks. • We improve geographical coverage and applicability of mobile-source emissions models. • Hourly and per-second traffic activity are simulated using dynamic traffic assignment. • Median emissions estimates increase with aggregation of network-wide traffic activity data. • Criteria pollutants are sensitive to physical supply-side features and network composition. [ABSTRACT FROM AUTHOR]

Food preparation interventions are an increasingly popular target for hands-on nutrition education for adults, children, and families, but assessment tools are lacking. Objective data on home cooking practices, and how they are interpreted through different data collection methods, are needed. The goal of this study was to explore the utility of the Healthy Cooking Index in coding multiple types of home food preparation data and elucidating healthy cooking behavior patterns. Parent–child dyads were recruited between October 2017 and June 2018 in Houston and Austin, Texas for this observational study. Food preparation events were observed and video recorded. Participants also wore a body camera (eButton) and completed a questionnaire during the same event. Parents with a school-aged child were recruited as dyads (n=40). Data collection procedures took place in participant homes during evening meal preparation events. Food preparation data were collected from parents through direct observation during preparation as well as eButton and paper questionnaires completed immediately after the event. All data sets were analyzed using the Healthy Cooking Index coding system and compared for concordance. A paired sample t test was used to examine significant differences between the scores. Cronbach's α and principal components analysis were conducted on the observed Healthy Cooking Index items to examine patterns of cooking practices. Two main components of cooking practices emerged from the principal components analysis: one focused on meat products and another on health and taste enhancing practices. The eButton was more accurate in collecting Healthy Cooking Index practices than the self-report questionnaire. Significant differences were found between participant reported and observed summative Healthy Cooking Index scores (P <0.001), with no significant differences between scores computed from eButton images and observations (P =0.187). This is the first study to examine nutrition optimizing home cooking practices by observational, wearable camera and self-report data collection methods. By strengthening cooking behavior assessment tools, future research will be able to elucidate the transmission of cooking education through interventions and the relationships between cooking practices, disease prevention, and health. [ABSTRACT FROM AUTHOR]

• The credibility of SPST results against field performance data was validated. • The shear resistance of the tested HMA mixes increased with asphalt-binder PG grading, test temperature and decreased with asphalt-binder content (AC). • Shear Strength is a recommended SPST parameter for use in differentiating/screening mixes with respect to HMA shear resistance and rutting performance prediction. • The HMA shear strength measured from the SPST test correlated well with the field HMA rutting. • HMA mixes with higher SPST shear strength, in general, showed low field rutting. The work presented in this paper was conducted to evaluate and validate the Simple Punching Shear Test (SPST) in characterizing the HMA shear properties for rutting performance prediction. The scope of work included SPST sensitivity evaluation in the laboratory with respect to test temperature and HMA mix-design volumetric variables such as the asphalt-binder type and asphalt-binder content, respectively. The corresponding laboratory test results were then validated against field performance data through correlations of the measured HMA shear properties (shear strength) to actual rutting measured in the field. Based on extensive laboratory testing of commonly used Texas coarse-, dense-, and fine-graded mixes (with different mix-design characteristics/combinations), it was found that the SPST is reasonably sensitive to test temperatures and the HMA mix-design volumetric parameters that were investigated in the study. The HMA shear strength generally exhibited a decreasing trend with an increase in the test temperature and asphalt-binder content, respectively. However, as the Performance Grade (PG) of the asphalt-binder increased, so did the HMA shear strength. With respect to SPST field validation, the HMA shear strength measured from the SPST correlated well with the field HMA rutting performance, exhibiting higher rutting resistance (i.e., low rut depths) for mixes with higher HMA shear strength and vice versa. Overall, based on the study findings, the SPST was found to be a viable surrogate laboratory test method for routinely measuring and quantifying the HMA shear properties for predicting field rutting performance. [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]

Accurate forecasting of peak electricity load has long been an active area of research in electricity markets, and power systems planning and operation. Unanticipated climate-induced surges in peak load can lead to supply shortages causing frequent brownouts and blackouts, and large-scale socioeconomic impacts. In this paper, the climate sensitivity of daily peak load is characterized by leveraging advanced statistical machine learning algorithms. More specifically, a rigorously tested and validated predictive model based on the Bayesian additive regression trees algorithm is proposed. Results from this study revealed that maximum daily temperature followed by mean dew point temperature are the most important predictors of the climate-sensitive portion of daily peak load. Among the non-climatic predictors, electricity price was found to have a strong positive association with the daily peak load. Economic growth was observed to have an inverse association with the daily peak load. While the proposed framework is established for the state of Texas, one of the most energy-intensive states with geographic and demographic susceptibility to climatic change, the methodology can be extended to other states/regions. The model can also be used to make short-term predictions of the climate-sensitive portion of daily peak load. • Maximum temperature is the most important predictor of climate-sensitive peak load. • Dew point temperature significantly influences peak load both during summer and winter. • Maximum sustained wind speed lowers daily peak load. • Electricity price has a strong positive association with peak load. • Economic growth of the state is inversely associated with peak load. [ABSTRACT FROM AUTHOR]

Growth of electricity generation from variable renewable resources like wind and solar has raised questions about future grid stability. This paper used several renewable energy penetration scenarios to determine when an electric grid might be more vulnerable to frequency contingencies, such as a generator outage. Unit commitment and dispatch modeling was used to quantify system inertia, an established proxy for grid stability. A case study of the Electric Reliability Council of Texas grid was used to illustrate the method. Results from the modeled scenarios showed that the Texas grid is resilient to major grid changes, even with relatively high penetrations (∼ 30% of annual energy generation compared to 18% in 2017) of renewable energy. However, retiring nuclear power plants and private-use networks in the model led to unstable inertia levels in our results. When the system inertia was constrained to meet a minimum threshold in our model, multiple coal and natural gas combined-cycle plants were dispatched at part-load or at their minimum operating level to maintain stable system inertia levels. This behavior is expected to expand with higher renewable energy penetrations and could occur on other electric grids that are reliant on synchronous generators for inertia support. [ABSTRACT FROM AUTHOR]

• HMA overlays and reflective cracking. • Use of interlayer grid-reinforcement to mitigate reflective cracking in HMA overlays. • The Texas flexible pavements and overlays database – The DSS. • Reflective cracking performance and interlayer-bonding of in-service highway test sections. • Quantifying reflective cracking performance using the grid efficiency factor (GEF) concepts. One of the common methods to mitigate reflective cracking, in existing cracked pavements, is the use of interlayer grid reinforcements in hot-mix asphalt (HMA) overlay construction during maintenance and/or rehabilitation projects. As a means of sharing the practical experience, lessons learned, and demonstrating the performance benefits of using interlayer grid-reinforcements, this paper presents a field case study where different types of grid reinforcements (namely geosynthetic paving mats) were used in HMA overlay construction to mitigate reflective cracking and thereafter, field performance was monitored and evaluated periodically. Two HMA overlay test sections (denoted as Sec13 and Sec14), reinforced with different grid materials (i.e., geosynthetic paving mats) were constructed in 2011 over an existing cracked HMA pavement (with transverse cracks) on an in-service highway US 59 in the Atlanta District of Texas. Field performance was subsequently monitored/evaluated for a period of over seven years against an adjacent Control section (Sec01), without grid reinforcement, on the same US 59 highway. Under the same pavement structure, traffic loading, and climatic conditions, various performance indices were evaluated semi-annually including reflective cracking, rutting, longitudinal surface profiles, and interlayer bonding. While the rutting performance was indifferent on all the three test sections after 7 years of service, 17% of reflective cracking was measured on the Control section versus 4% on the grid-reinforced test sections – demonstrating that the use of grid reinforcement (namely geosynthetic paving mats) has been effective in mitigating reflective cracking from the existing cracked HMA pavement. Similarly, while coring indicated satisfactory interlayer bonding conditions on all the three test sections, the rate of pavement surface roughness deterioration was also hardly different on all the three test sections – albeit that the Control section (Sec01) exhibited superiority in terms of the profile indices (smoothness/serviceability) than the grid-reinforced test sections (Sec13 and Sec14). [ABSTRACT FROM AUTHOR]

Using data on the geographic location of fracking wells in four U.S. states with mandatory disclosure between 2011 and 2013 — Colorado, Oklahoma, Pennsylvania, and Texas — this paper analyzes the socio-demographic characteristics of people living close to fracking activity. Geo-coded well data from the FracFocus registry are merged to blockgroup-level socio-demographic data from the 2006–2010 American Community Survey and population density and land use data from EPA's Smart Location Database 2010. Different buffer zones around fracking wells are applied to capture effects at different spatial scales and to compare only areas with similar geological properties. We explain the distance to the nearest well within a county with fracking activity or within a buffer zone by race/ethnicity, income, educational attainment, various land-use control variables, and county fixed-effects. We find robust evidence that minorities, especially African Americans, disproportionately live near fracking wells, but less consistent evidence for environmental injustice by income or educational attainment. Strong heterogeneity across states can be observed, suggesting that an improvement in disclosure laws in other states, that would make similar analyses possible, is of great importance. [ABSTRACT FROM AUTHOR]

• We describe an equation-oriented rate-based parallel column model. • The model can simulate DWCs of arbitrary configuration. • Vapour flow splits are found by balancing the pressure drop on both sides of the wall. • Good agreement is found when comparing the model to experimental data from The University of Texas. • Heat transfer through the wall is easy to model in a PCM, and is important in the Texas experiments. This paper describes a rate-based parallel column model, which, via the CAPE-OPEN framework, can be used in any CAPE-OPEN compliant process flowsheet simulation package and can be used to model dividing wall columns of arbitrary configuration. Heat transfer across the dividing wall can be modelled in a straightforward way. Vapour split ratios are determined during the solution of the model equations by requiring the pressure on both sides of the dividing wall(s) to be equal. We compare the predictions of the rate-based model to experimental data from the University of Texas at Austin. There is good agreement between model and measured data. A design case study demonstrates the versatility of our method of calculating vapour splits from a pressure balance. [ABSTRACT FROM AUTHOR]

• 1.5 million individual ridesourcing rides used to estimate net effect on energy use. • Commuting and between-ride "deadheading" account for 19% and 26% of ridesourcing VMT. • Ridesource vehicles are 2 MPG more efficient and twice as likely to be hybrid-electric. • Ridesourcing increases energy use by an estimated 41–90% compared to prior mode. This paper identifies major aspects of ridesourcing services provided by Transportation Network Companies (TNCs) which influence vehicles miles traveled (VMT) and energy use. Using detailed data on approximately 1.5 million individual rides provided by RideAustin in Austin Texas, we quantify the additional miles TNC drivers travel: before beginning and after ending their shifts, to reach a passenger once a ride has been requested, and between consecutive rides (all of which is referred to as deadheading); and the relative fuel efficiency of the vehicles that RideAustin drivers use compared to the average vehicle registered in Austin. We conservatively estimate that TNC drivers commute to and from their service areas accounts for 19% of the total ridesourcing VMT. In addition, we estimate that TNC drivers drove 55% more miles between ride requests within 60 min of each other, accounting for 26% of total ridesourcing VMT. Vehicles used for ridesourcing are on average two miles per gallon more fuel efficient than comparable light-duty vehicles registered in Austin, with twice as many are hybrid-electric vehicles. New generation battery electric vehicles with 200 miles of range would be able to fulfill 90% of full-time drivers' shifts on a single charge. We estimate that the net effect of ridesourcing on energy use is a 41–90% increase compared to baseline, pre-TNC, personal travel. [ABSTRACT FROM AUTHOR]

• Coupling of ANN waste forecasts with GIS collection route optimization. • GIS routes with minimum travel distances are sensitive to waste composition. • Results of 36 scenarios yield changes in travel distance of up to 19.9% • Dual compartment trucks reduce travel distance but increase waste collection time. Combining an artificial neural network (ANN) waste prediction model with a geographic information system (GIS) waste collection route optimization, the paper shows how the compositional features of waste materials affect the optimized truck route time, distance, and air emissions. Using data from Austin, Texas, USA, a nonlinear autoregressive ANN model is used to predict the waste generation rate of the recycling and garbage streams for the year 2023 in four sub-areas of the city. This ANN model resulted in mean absolute percentage errors ranging from 10.92% to 16.51%. Modified compositions of the recycling and garbage streams are then used as inputs, along with the year 2023 generation rates, to create 6 modified and 3 non-modified scenarios that reflect possible future changes in waste composition. These waste stream scenarios are then used as input parameters to determine optimal waste collection routes with minimal travel distance in each of the four sub-areas using the GIS vehicle routing problem network analysis tool. Results of these 36 scenarios yield changes in travel distance of up to 19.9%, when compared to the non-modified composition. Further, dual compartment trucks were compared to single compartment trucks and found to save between 10.3 and 16.0% in travel distance and slightly reduce emissions but had a 15.7–19.8% increase in collection time. Results suggest temporal changes in waste composition and characteristics are important in GIS route optimization studies. [ABSTRACT FROM AUTHOR]

Abstract With increasing levels of atmospheric p CO 2 the oceans are becoming progressively more acidic, with the impact of a lowered pH beginning to affect the calcification of numerous invertebrate groups, including foraminifers, pteropods, heteropods and calcareous nannoplankton. Research on the ecology of foraminifera in the Mediterranean Sea, Gulf of California, Caribbean Sea and elsewhere has shown how modern assemblages are responding to acidification. Experimental work in mesocosms and laboratory cultures are also adding to our knowledge of the response to pH changes. Near Ischia (Italy), natural CO 2 vents amongst sea grass meadows are creating low pH environments in which it is possible to observe the response of benthic foraminifera. At a pH of 7.8 the foraminiferal assemblages are already becoming less diverse and below pH 7.6 there are often no calcite-secreting benthic foraminifera. In the Gulf of California, in a deeper-water setting, natural CO 2 (and methane) vents are also lowering sea floor pH. The foraminifera show the impact of this change, although the relatively high carbonate saturation ensures that calcite-secreting foraminifers are able to live and reproduce in these relatively low pH environments, only becoming impacted by dissolution effects once dead. Using data from the Cretaceous–Paleogene boundary in Texas, Alabama and north-west Europe it is clear that the plankton was severely impacted by surface water acidification while the relatively shallow water benthic foraminifera show little change and no visible signs of post-mortem dissolution due to ocean acidification. Highlights • This paper discusses the K/Pg in terms of surface water acidification caused by the K/Pg impact event. • describes depositional environments at K/Pg boundary • provides depositional model for the K/Pg boundary • provides correlaton of the boundary events [ABSTRACT FROM AUTHOR]

Abstract This paper documents a previously undescribed marine palynomorph, Doricodinium obscurum gen. et sp. nov., from Upper Cretaceous sections in the central and southern Western Interior of the USA. Although an archaeopyle has not been confirmed, a case is made here for a probable dinoflagellate cyst, possible peridinioid affinity. The type locality of D. obscurum is the Global boundary Stratotype Section and Point (GSSP) for the base of Turonian Stage, at Pueblo, Colorado, where it consistently occurs in intra-Lower to middle Turonian deposits. The taxon also occurs consistently in intra-Lower Turonian strata from the Iona-1 core, SW Texas, where astronomical age modelling gives an age range of 93.92–93.25 ± 0.12 Ma in that section. Outside the USA, it has until now only been recorded from the Cenomanian–Turonian boundary interval in Pakistan. Highlights • Description of Doricodinium obscurum gen. et sp. nov., a marine palynomorph, a probable dinoflagellate cyst. • Distribution calibrated at the GSSP for the Turonian Stage, Pueblo, Colorado. • Astronomical age modelling of its distribution in the Iona-1 core, SW Texas. [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]

Operating costs and market rules are likely to have an impact on the rewards of participating in a Vehicle to Grid system. This paper investigates these impacts by developing a model of a centralized Vehicle to Grid system and applying it to the 2015 wholesale electricity market in Texas (Houston Hub) for selling energy and capacity. Three scenarios are examined. In the first scenario, electric vehicles are paid based on a fixed retail market price; in the second, they are paid a time-varying retail market price; in the third, the virtual power plant shares 50% of its total reward with the participating vehicles. The results demonstrate that, while this system is always financially profitable to the virtual power plant and the system operator gets grid services, the electric vehicles could lose money. Further, results show that these vehicles with lower per unit output-battery cost could lose more money because of extensive battery over-use and insufficient reward at current market prices. Lower battery costs, subsidies for participation in this system, and more rewarding market products could all make their participation more economically viable. [ABSTRACT FROM AUTHOR]

• A general Natech risk assessment methodology is presented. • The methodology takes into account the possible domino effects. • The methodology relies upon Bayesian networks capabilities. • The methodology is implemented in a real case study. On February 2007, a massive fire in a propane de-asphalting unit in an oil refinery in Texas, USA happened due to liquid propane release from a cracked pipe in a control station injuring four people, damaging extensive equipment, causing significant business interruption, and resulting in more than $50 million losses. The accident was triggered by a natural hazard: freezing of piping at a control station caused an inlet pipe elbow to crack, which in turn, led to the release of high-pressure liquid propane which was rapidly ignited. In addition, there were two near-miss events due to potential domino effects. In fact, the accident could reasonably have resulted in much more severe consequences due to the exposure of large butane storage spheres and chlorine containers, increasing the possibility of a catastrophic domino effect. This paper develops a Natech (natural hazard triggering technological disasters) risk assessment methodology that relies upon Bayesian network capabilities and takes into account the potential Natech domino effects. The methodology is implemented in the intended refinery and mathematically graphically represents the dynamic cause–effect relations between units involved in the scenario, and handles uncertainties among the interactions. In addition, the methodology can provide a risk value for the entire scenario that can be used further for risk-based decision making. [ABSTRACT FROM AUTHOR]

This paper focuses on simulation and analysis of the Houston Ship Channel vessel traffic and operation, through a discrete event model in Arena. The model is applied to mitigate the consequences of the channel closure for constructing a new bridge over the waterway. To evaluate different closure scenarios, real world data is analyzed, and a single factor ANOVA is used to find significant vessel waiting time differences. In addition, Fisher pairwise comparison method is applied to specify the best closure alternative. The results reveal that the best closure scenario will decrease waiting time up to 70%. The model can be used for assessing the performance of the system under different decision making frameworks. [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]

This paper investigates the impact of mass shootings on hotel performance. Collecting the information on 85 mass shootings across Texas in the U.S. from 2013 to 2019, we adopt a staggered Difference-in-Difference design and find that mass shootings lead to an approximately 11.3 % decline in RevPAR for hotels in the current month of shootings. The effects gradually fade away and become insignificant in the following months. In addition, we find that being in the city of a mass shooting can amplify the negative impact of mass shootings on hotel performance with distance control. However, the distance from a hotel to a mass shooting cannot moderate the impact of mass shootings on hotel performance. We conclude that the "city-name effect" rather than the "distance effect" matters. Finally, we conduct a heterogeneity analysis and provide several managerial implications in light of our findings. • Hotel performance is significantly and negatively affected in the first two months of a mass shooting. • Being in the same city with a mass shooting will amplify the negative effect of mass shootings on hotel performance. • The distance cannot moderate the effect of mass shootings on hotel performance. • Mass shootings affect small and independent hotels rather than large and chain ones. [ABSTRACT FROM AUTHOR]

This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may limit their capabilities in extracting complex nonlinear behaviors in the data. Therefore, the present paper comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. This study quantifies event-to-event and site-to-site variability of the ground motions by implementing them as random effect terms to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4–500 km in Oklahoma, Kansas, and Texas since 2005. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring predefined equations or coefficients. Moreover, it is found that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. • ML-based framework is proposed to develop data-driven GMMs. • ML algorithms are adjusted to consider random effect parameters in GMMs. • ML-based GMMs provide higher accuracy than conventional methods of GMMs. • Physically sound features are observed in ML techniques. [ABSTRACT FROM AUTHOR]

This paper reviews the procured capacity of operating reserves and its relationship with increasing penetration of wind energy in the top three United States power markets that have most wind generation, including Electric Reliability Council of Texas (ERCOT), Midcontinent Independent System Operator (MISO), and Southwest Power Pool (SPP). Using hourly data for the period of January 1, 2014, to October 31, 2019, we find there was a stable or even declining trend of procured operating reserve quantities among these three markets, while wind energy production collectively grew by more than 80% during the study period. Our study finds that in all three markets the growing wind energy production has not yet significantly increase the magnitude and temporal pattern of net-load ramp, hence potentially rendering a stable or even declining need of operating reserves. The empirical results in this paper support arguments in extant literature that the impact of wind on the operating reserves is more likely related to deviations from normal operation conditions but not for a contingency event. The geographical distribution of wind resources in all three markets could have also helped to reduce the need of additional operating reserves. • Review operating reserves in ERCOT, MISO, and SPP markets in the U.S. • Investigate the relationship between operating reserves and wind generation. • All three markets did not procure more operating reserves to date. • Operating reserves quantity is potentially related to net-load ramp. [ABSTRACT FROM AUTHOR]

Hurricane Ike, the third costliest hurricane in US history, made landfall in the Galveston Bay Area in September, 2008. Existing literature postulates that maladaptive behavior such as increased alcohol use is often exhibited by disaster survivors in coping with both disaster-related traumatic events and post-disaster stressful events. In addition, it has also been postulated that survivors’ perceptions of social cohesion and social control can potentially serve to moderate such behavior. The purpose of this paper is to study such hypotheses for Hurricane Ike. In particular, we investigate the following four hypotheses: (H1) There is an increase of alcohol use by survivors of Hurricane Ike in the Galveston Bay Area; (H2) There are positive associations between both Ike-related trauma and post-Ike stress events and the increase in alcohol use; (H3) There are negative associations between both perceived social cohesion and social control and the increase in alcohol use following Ike; and finally that (H4) perceived social cohesion and social control serve to moderate the associations between both Ike-related trauma and post-Ike stress events and increased alcohol use after Ike. Using public use survey-weighted data from the Galveston Bay Recovery Study (GBRS) of Ike survivors (N = 658), we tested these hypotheses using logistic regressions controlling for other key socioeconomic variables. Our results confirm H1 and H2. Hypotheses H3 and H4 are partially confirmed with respect to social control, but show that (i) there is a positive association between perceived social cohesion and the increase in alcohol use following Ike, and that (ii) while perceived social cohesion and social control do moderate the association between post-Ike stress events and increased alcohol use, they have no effect on the association between Ike-related trauma and increased alcohol use. [ABSTRACT FROM AUTHOR]

Introduction The primary objective of this paper is to evaluate the safety impacts of red-light running camera (RLC) system installation and then deactivation at 48 intersections in Houston, Texas. The second objective is to evaluate the spillover effect at nearby non-treated intersections in Houston after the deactivation. Methods To accomplish study objectives, an Empirical Bayes (EB) before-after analysis was used. Results The results indicate statistically significant collision reductions on all red-light running (RLR) crash types (37 percent) as well as right-angle RLR crashes (47 percent) at the treated intersections after RLC activation. By way of comparison, the RLC deactivation analysis indicated that crashes increased by 20 percent for all RLR crash types and by 23 percent in right-angle RLR crashes at the formerly treated intersections. After deactivation, all severity RLR crashes increased more than expected at nearby non-treated intersections, which indicates the possibility of an adverse spillover effect. However, fatal/injury crashes associated with rear-end decreased after deactivation at both formerly treated and non-treated intersections, although those rear-end crashes account for smaller proportions when compared to all crash types/right-angle crashes. Practical applications Overall, removing RLC treatments results in a negative reaction to the safety benefits that the treatment provides when it is in place and actively working and to the nearby intersections where the treatment has not been implemented. This study helps define the effects that RLCs have on safety at signalized intersections after installation and deactivation. [ABSTRACT FROM AUTHOR]

Background: Youth experiencing homelessness have elevated rates of mental illness and substance use compared to the general population. However, the extent to which underlying mental health issues may contribute to substance use as a way to manage symptoms and whether mental health treatment may reduce risk for substance use is unclear. This paper investigated these relations in a community sample of homeless youth.Methods: Youth ages 13-24 (N=416) were interviewed as part of a community count and survey of homeless youth in Houston, Texas. A path analysis examined relations among lifetime diagnoses of ADHD, bipolar disorder, and depression; past-month marijuana, alcohol, and synthetic marijuana use, and hypothesized mediators of past-year mental health treatment and perceived unmet need for treatment.Results: Rates of prior mental disorder diagnoses were high, with extensive comorbidity across the three diagnoses (n=114, 27.3% had all three diagnoses). Relations varied by diagnoses and substances. ADHD was positively related to current marijuana use (β=0.55 (0.16), p<0.001), a relation that mental health treatment did not mediate. Depression was positively related to synthetic marijuana use through unmet need (β=0.25 (0.09), p=0.004) and to alcohol use through unmet need (β=0.20 (0.10), p=0.04) CONCLUSIONS: This study provides new information about relations between prior mental health diagnoses and substance use in homeless youth. Findings support the need to consider prior mental disorder diagnoses in relation to current substance use and to assess for whether youth perceive they have unmet needs for mental health treatment. [ABSTRACT FROM AUTHOR]