No abstract

Social media has been increasingly utilized to spread breaking news and risk communications during disasters of all magnitudes. Unfortunately, due to the unmoderated nature of social media platforms such as Twitter, rumors and misinformation are able to propagate widely. Given this, a surfeit of research has studied false rumor diffusion on Twitter, especially during natural disasters. Within this domain, studies have also focused on the misinformation control efforts from government organizations and other major agencies. A prodigious gap in research exists in studying the monitoring of misinformation on social media platforms in times of disasters and other crisis events. Such studies would offer organizations and agencies new tools and ideologies

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—‐fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R2 value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R2 value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.

The structures of the recently reported ordered perovskites SrLa(FeTi)O 6 and BaLa(FeTi)O 6 have been revisited using Rietveld analysis of the powder x-ray diffraction data.Our studies clearly show that earlier authors have incorrectly reported the tetragonal structure in the I4/m space group for SrLa(FeTi)O 6 and the cubic structure in the Fm3 m space group for BaLa(FeTi)O 6 . The correct structure is found to be orthorhombic in the Pnma space group for SrLa(FeTi)O 6 while cubic in the Pm3 m space group for BaLa(FeTi)O 6 . Thus the BaLa(FeTi)O 6 is not an ordered double perovskite as the occupancies of Ba/La ions at A-site and Fe/Ti ions at B-site are disordered resulting in the primitive cubic unit cell in the Pm 3 m space group.Similarly, the occupancies of Sr/La ions at A-site and Fe/Ti ions at B-site are disordered in SrLa(FeTi)O 6 also, ruling out the ordered perovskite structure.

Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly rightskewed sewer system failure data to predict the failure risk, leveraging a bidimensional spacetime approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodnessof-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.