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}.

Gold Treasure Worth a Fortune Was Hidden in a Forest. For years, Jason Rohrer put out bizarre, beloved video games. Now, with Project Skydrop, he launches the real-world treasure hunt of his dreams. The muddy trail levels out and we stop to catch our breath. Which is good, because hiking with my eyes covered has been a pain in the ass. A voice says: "You can take your blindfold off now." I squint as I get my bearings. Then, after a bit more hiking and some bushwhacking, I finally see it. The thing no one is supposed to know the location of, at least for another few weeks. I have to fight a lizard-brain instinct to reach for it.

This pilot study aims to develop a deep learning model for predicting seismocardiogram (SCG) signals in the dorsoventral direction from the SCG signals in the right-to-left and head-to-foot directions ($\textrm{SCG}_x$ and $\textrm{SCG}_y$). The dataset used for the training and validation of the model was obtained from 15 healthy adult subjects. The SCG signals were recorded using tri-axial accelerometers placed on the chest of each subject. The signals were then segmented using electrocardiogram R waves, and the segments were downsampled, normalized, and centered around zero. The resulting dataset was used to train and validate a long short-term memory (LSTM) network with two layers and a dropout layer to prevent overfitting. The network took as input 100-time steps of $\textrm{SCG}_x$ and $\textrm{SCG}_y$, representing one cardiac cycle, and outputted a vector that mapped to the target variable being predicted. The results showed that the LSTM model had a mean square error of 0.09 between the predicted and actual SCG segments in the dorsoventral direction. The study demonstrates the potential of deep learning models for reconstructing 3-axis SCG signals using the data obtained from dual-axis accelerometers.

Vlocity, Inc., a cloud software company, announced the launch of automated claims features in their apps. The launch includes end-to-end management of property and casualty (P&C) insurance claims for policyholders, agents and claims handlers, and enables dynamic, digital claims interactions from any device. New features include peril-driven adjudication and an adjuster workbench that enhance a carrier's ability to run their entire business on Salesforce. Carriers can download pre-configured claims processes from Vlocity's Insurance Process Library and leverage a modern, optimized user experience. Carriers, if they prefer, can create a completely new experience from scratch in a code-free environment using Vlocity's intuitive design interface.

When Nathan Kecy graduated from Plymouth State University in New Hampshire a decade ago with a bachelor's degree in communications, he found himself with about $10,000 in debt and few clear career options. He first found work as a door-to-door salesman ("a pyramid scheme," he recalls) and then in telemarketing. Finally he landed a job as an infrastructure specialist for Datamatic, a Texas-based water-meter-technology company. He was traveling across the country installing meters, making a decent salary. But he lost his job after the company restructured in 2012, he said, and soon he found that his skills weren't easily transferable to a new field; Datamatic's technology was proprietary, and his expertise in the company's installation program wasn't appealing to employers outside that particular industry.