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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]

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]

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]

• 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]

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]

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

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

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

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

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

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