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1. Resiliency Assessment in Distribution Networks Using GIS Based Predictive Risk Analytics

   https://doi.org/10.1109/TPWRS.2019.2913090  

   Leite, Jonatas Boas; Mantovani, Jose Roberto; Dokic, Tatjana; Yan, Qin; Chen, Po-Chen; Kezunovic, Mladen (January 2019, IEEE Transactions on Power Systems)

   A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the status of the grid. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a GIS platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely tracking of the operating condition. 

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2. “Optimized Asset Management in Distribution Systems Based on Predictive Risk Analysis

   T. Dokic, M. Kezunovic (January 2018, Energies)

   The paper introduces an optimal maintenance scheduler based on predictive assessment of risk of outage and equipment failure in distribution networks. The variety of severe weather conditions are observed and their impact on the network components is quantified. The equipment deterioration and failure rates are observed continuously across the space and time using heterogeneous data. The risk of weather-related outages for each component is generated in real-time, and can be extracted at multiple temporal and spatial scales depending on the application of interest. The optimal maintenance scheduling that minimizes the system risk while maintaining the economic investment limits is developed. The benefits of the framework are presented using a distribution network asset management example. 

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3. Fast Parameter Estimation of Generalized Extreme Value Distribution using Neural Networks

   https://doi.org/10.1002/env.2845  

   Rai, Sweta; Hoffman, Alexis; Lahiri, Soumendra; Nychka, Douglas W; Sain, Stephan R; Bandyopadhyay, Soutir (May 2024, Environmetrics)

   The heavy‐tailed behavior of the generalized extreme‐value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires and so forth. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate‐sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood‐free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network‐based method provides generalized extreme value distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000‐year annual maximum temperature data from the Community Climate System Model version 3 across North America for three atmospheric concentrations: 289 ppm (pre‐industrial), 700 ppm (future conditions), and 1400 ppm , and compare the results with those obtained using the maximum likelihood approach. 

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4. Embracing and Controlling Risk Dependency in Cyber Insurance Policy Underwriting

   Khalili, M.; Liu, M. and (June 2018, The Annual Workshop on the Economics of Information Security (WEIS))

   This paper highlights how cyber risk dependencies can be taken into consideration when underwrit- ing cyber-insurance policies. This is done within the context of a base rate insurance policy framework, which is widely used in practice. Specifically, we show that there is an opportunity for an underwriter to better control the risk dependency and the risk spill-over, ultimately resulting in lower overall cyber risks across its portfolio. To do so, we consider a Service Provider (SP) and its customers as the interdependent insurer’s customers: a data breach suffered by the SP can cause business interruption to its customers. In underwriting both the SP and its customers, we show that the insurer can increase its profit by incentivizing the SP (through a discount on its premium) to invest more in security, thereby decreasing the chance of business interruption to the customers and increasing social welfare. For comparison, we also consider a scenario where the insurer underwrites only the SP’s customers (but not the SP), and receives compensation from the SP’s insurance carrier when losses are attributed to the SP. We show that the insurer cannot outperform the case where it underwrites both the SP and its customers. We use an actual cyber-insurance policy and claims data to calibrate and substantiate our analytical findings. 

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5. Kinematics and Metallicity of the Dwarf Spheroidal Galaxy Andromeda XVIII

   https://doi.org/10.3847/1538-4357/ad55f0  

   Kvasova, Kateryna A; Kirby, Evan N; Beaton, Rachael L (September 2024, The Astrophysical Journal)

   Abstract Andromeda XVIII is an isolated dwarf galaxy 579 kpc away from the nearest large galaxy, M31. It is a candidate “backsplash galaxy” that might have been affected by a close passage to M31. We present new Keck/DEIMOS spectroscopy of Andromeda XVIII to assess the likelihood that it is a backsplash galaxy. We estimated the velocities, metallicities ([Fe/H]), andα-enhancements ([α/Fe]) for 56 probable members. Based on the abundances of 38 stars with low errors (δ[Fe/H] < 0.3), parameters for the simplest chemical evolution models were estimated using the maximum likelihood coupled with a Markov Chain Monte Carlo (MCMC) method. The metallicity distribution is inconsistent with these models, due to a sharp metal-rich cutoff. We estimated Andromeda XVIII’s mean heliocentric velocity, rotation velocity, position angle of the rotation axis, and velocity dispersion using the maximum likelihood coupled with an MCMC. There is no evidence for bulk rotation, though subpopulations might be rotating. The mean heliocentric velocity is −337.2 km s⁻¹, such that the line-of-sight velocity relative to M31 is lower than the escape velocity from M31. Together, the metallicity distribution and the mean velocity are consistent with a sudden interruption of star formation. For possible causes of this quenching, we considered gas loss due to ram pressure stripping during a close passage by M31 or due to a past major merger. However, we cannot rule out internal feedback (i.e., a terminal wind). 

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