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As electric power grid critical infrastructure grows increasingly heterogeneous, a key question is how to encourage and ensure relatively equitable access to the energy it supplies. With diverse socio-economic regions linked to the grid and various generation types, achieving equitable access to clean energy, as outlined in initiatives like DOE’s Justice40, remains an important aspirational goal. Building on previous work, this paper describes our investigation of a heterogenous grid modeled on the Alaska Railbelt, allowing us to explore the intrinsic inequities and possible mechanisms to enhance the equity across regions. We apply risk and equity metrics to different regions to examine how penalties which change the cost can impact the equity as well as the overall grid’s risk and dynamics. 

As electric power grid critical infrastructure grows increasingly heterogeneous, a key question is how to encourage and ensure relatively equitable access to the energy it supplies. With diverse socio-economic regions linked to the grid and various generation types, achieving equitable access to clean energy, as outlined in initiatives like DOE’s Justice40, remains an important aspirational goal. Building on previous work, this paper describes our investigation of a heterogenous grid modeled on the Alaska Railbelt, allowing us to explore the intrinsic inequities and possible mechanisms to enhance the equity across regions. We apply risk and equity metrics to different regions to examine how penalties which change the cost can impact the equity as well as the overall grid’s risk and dynamics. 

With the ongoing expansion of the aging population, it is increasingly critical to prioritize the safety of older drivers. The objective of this study is to utilize sensor data in order to detect early indications of impairment, thereby facilitating proactive interventions and enhancing road safety for the elderly. This article provides an overview of the research approach, presents significant results, and analyzes the consequences of utilizing in-vehicle sensors i.e. vision and telematics, to mitigate cognitive decline among elderly drivers; in doing so, it promotes progress in the domains of public health and transportation safety by standardizing the use of such devices to automatically assess the drivers’ cognitive functions. 

In response to the expanding role of wind, solar, and storage, increasing demand flexibility, and a changing climate, new analytical methods and metrics to assess resource adequacy are needed. A focus has been on identifying ways to reduce risks of failure. Less attention has been directed to how new analytical approaches can inform the design of planning processes, regulatory standards, and markets. Using mixed methods and a community-engaged approach, data on community preferences and uneven distributions of impacts are used in a demonstration of a coupled socio-technical systems model that has been validated in diverse settings. The research is informed by the physical and institutional infrastructures in the Railbelt power grid of Alaska. The findings illustrate how new analytical tools can inform institutional design and facilitate more affordable, sustainable, and equitable outcomes. 

Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hard-brakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors. 

Given a GPS dataset comprising driving records captured at one-second intervals, this research addresses the challenge of Abnormal Driving Detection (ADD). The study introduces an integrated approach that leverages data preprocessing, dimensionality reduction, and clustering techniques. Speed Over Ground (SOG), Course Over Ground (COG), longitude (lon), and latitude (lat) data are aggregated into minute-level segments. We use Singular Value Decomposition (SVD) to reduce dimensionality, enabling K-means clustering to identify distinctive driving patterns. Results showcase the methodology's effectiveness in distinguishing normal from abnormal driving behaviors, offering promising insights for driver safety, insurance risk assessment, and personalized interventions. 

Abstract Magnetic reconnection is a fundamental plasma process that has been studied with analytical theory, numerical simulations, in situ observations, and laboratory experiments for decades. The models that have been established to describe magnetic reconnection often assume a reconnection plane normal to the current sheet in which an antiparallel magnetic field annihilates. The annihilation points, also known as the X-points, form an x -line, which is believed to be perpendicular to the reconnection plane. Recently, a new study using Magnetospheric Multiscale mission observations has challenged our understanding of magnetic reconnection by providing evidence that the x -line is not necessarily orthogonal to the reconnection plane. In this study we report a second nonorthogonal x -line event with similar features as that in the previous case study, supporting that the sheared x -line phenomenon is not an aberrant event. We employ a detailed directional derivative analysis to identify the x -line direction and show that the in-plane reconnection characteristics are well maintained even with a nonorthogonal x -line. In addition, we find the x -line tends to follow the magnetic field on one side of the current sheet, which suggests an asymmetry across the current sheet. We discuss the possibility that the nonorthogonal x -line arises from an interplay between the two aspects of reconnection: the macroscopic magnetic field topology and microscopic particle kinetics.