The Dutch railway network is one of the busiest in the world, with delays being a prominent concern for the principal passenger railway operator NS. This research addresses a gap in delay prediction studies within the Dutch railway network by employing an XGBoost Classifier with a focus on topological features. Current research predominantly emphasizes short-term predictions and neglects the broader network-wide patterns essential for mitigating ripple effects. This research implements and improves an existing methodology, originally designed to forecast the evolution of the fast-changing US air network, to predict delays in the Dutch Railways. By integrating Node Centrality Measures and comparing multiple classifiers like RandomForest, DecisionTree, GradientBoosting, AdaBoost, and LogisticRegression, the goal is to predict delayed trajectories. However, the results reveal limited performance, especially in non-simultaneous testing scenarios, suggesting the necessity for more context-specific adaptations. Regardless, this research contributes to the understanding of transportation network evaluation and proposes future directions for developing more robust predictive models for delays.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

One of California's riskiest volcanoes has for decades been undergoing geological changes and seismic activity, which are sometimes a precursor to an eruption, but -- thankfully -- no supervolcanic eruptions are expected. That's according to Caltech researchers who have been studying the Long Valley Caldera, which includes the Mammoth Lakes area in Mono County. The caldera was classified in 2018 by the U.S. Geological Survey as one of three volcanoes in the state -- along with 15 elsewhere in the U.S. -- considered a "very high threat," the highest-risk category defined by the agency. The two other volcanoes in California with that classification are Mt. Shasta in Siskiyou County and the Lassen Volcanic Center, which includes Lassen Peak in Shasta County.

The Elko County Sheriff's Office says it has acquired a new drone which it plans to use during special events, search and rescue missions, missing persons, crime scenes and traffic collisions.