Luigi Mangione trial date dispute emerges as defense calls Manhattan DA's proposed July 1 start date "unrealistic" due to conflicting federal trial in Brian Thompson case.

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}.

Insider threats (InTs) within organizations are small in number but have a disproportionate ability to damage systems, information, and infrastructure. Existing InT research studies the problem from psychological, technical, and educational perspectives. Proposed theories include research on psychological indicators, machine learning, user behavioral log analysis, and educational methods to teach employees recognition and mitigation techniques. Because InTs are a human problem, training methods that address InT detection from a behavioral perspective are critical. While numerous technological and psychological theories exist on detection, prevention, and mitigation, few training methods prioritize psychological indicators. This literature review studied peer-reviewed, InT research organized by subtopic and extracted critical theories from psychological, technical, and educational disciplines. In doing so, this is the first study to comprehensively organize research across all three approaches in a manner which properly informs the development of an InT education platform.

On Monday, police arrested 26-year-old Luigi Mangione and charged him in the murder of UnitedHealthcare CEO Brian Thompson. Mangione's five-day run from authorities ended after he was spotted eating at a McDonald's in Altoona, Pennsylvania, about 300 miles from Manhattan, where Thompson was gunned down on the morning of December 4. Authorities say they found Mangione carrying fake IDs and a 3D-printed "ghost gun," the model of which is known as the FMDA, or "Free Men Don't Ask." Meanwhile, a flood of mysterious drone sightings across New Jersey and neighboring states caused so much havoc, it quickly gained federal attention. While many people wondered why the US military couldn't just shoot down the drones, the FBI, Department of Homeland Security, and independent experts say the drone mystery may not be much of a mystery, and the drones are probably mostly just airplanes. As for more terrestrial threats, we dove into the far-right realm of "Active Clubs," small groups of young, fitness-focused men who are steeped in extremist ideology and linked to several violent attacks. While the man who helped invent the Active Club network, Robert Rundo, was sentenced in federal court this week, Active Clubs around the world are proliferating.

Luigi Mangione, the man suspected of killing the chief executive of UnitedHealthcare, underwent surgery and was reported missing in San Francisco before the shooting. Brian Thompson, 50, CEO of the healthcare insurance giant, was gunned down last week in Midtown Manhattan, spawning a five-day manhunt that eventually led to Mangione's arrest at a McDonald's restaurant in Altoona, Pa. Questions about Mangione's alleged motives and background have swirled in the media since his arrest Monday. As prosecutors worked to bring him to New York to face charges, new details emerged about his life and his capture. The 26-year-old Ivy League graduate from a prominent Maryland real estate family was charged with murder hours after his arrest.

During an address Thursday, President Joe Biden claimed he taught "political theory" at the University of Pennsylvania. An Altoona, Pennsylvania man has been arrested after allegedly having a sexual relationship with a teenage girl he met on the popular dating app, Tinder, according to reports. An NBC station out of Johnstown-Altoona, Pennsylvania reported that state police spoke with the 14-year-old girl on Sept. 19. During the conversation, the girl reportedly told police she met Steven Ellis, 32, on Tinder after creating a profile that made her appear older. In court documents, police said they learned the teenager and Ellis sent each other explicit messages and photos.

Distributed ledger systems have become more prominent and successful in recent years, with a focus on blockchains and cryptocurrency. This has led to various misunderstandings about both the technology itself and its capabilities, as in many cases blockchain and cryptocurrency is used synonymously and other applications are often overlooked. Therefore, as a whole, the view of distributed ledger technology beyond blockchains and cryptocurrencies is very limited. Existing vocabularies and ontologies often focus on single aspects of the technology, or in some cases even just on one product. This potentially leads to other types of distributed ledgers and their possible use cases being neglected. In this paper, we present a knowledge graph and an ontology for distributed ledger technologies, which includes security considerations to model aspects such as threats and vulnerabilities, application domains, as well as relevant standards and regulations. Such a knowledge graph improves the overall understanding of distributed ledgers, reveals their strengths, and supports the work of security personnel, i.e. analysts and system architects. We discuss potential uses and follow semantic web best practices to evaluate and publish the ontology and knowledge graph.

The performance of rigid pavement is greatly affected by the properties of base/subbase as well as subgrade layer. However, the performance predicted by the AASHTOWare Pavement ME design shows low sensitivity to the properties of base and subgrade layers. To improve the sensitivity and better reflect the influence of unbound layers a new set of improved models i.e., resilient modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this study. An Artificial Neural Network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation datasets in the ANN model consist of 27000 simulation cases with different combinations of pavement layer thickness, layer modulus and slab-base interface bond ratio. To examine the sensitivity of modified MR and k-values on pavement response, eight pavement sections data are collected from the Long-Term Pavement performance (LTPP) database and modeled by using the FE software ISLAB2000. The computational results indicate that the modified MR values have higher sensitivity to water content in base layer on critical stress and deflection response of rigid pavements compared to the results using the Pavement ME design model. It is also observed that the k-values using ANN model has the capability of predicting critical pavement response at any partially bonded conditions whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonding and no bonding).

"Artificial intelligence" (AI) may evoke fears of robots writing their own software code and not taking orders from humans. The real AI, at least in present form, is delivering results in the business world. Technology companies are using powerful computers and advanced statistical models to accelerate their product development. Most are not calling these efforts AI but rather machine learning. As a form of AI, machine learning is making it possible to quickly find relevant patterns in data captured by Internet of Things (IoT) devices and sensors, explains Adam Kahn, vice president of fleets for Netradyne, which has a vision-based fleet safety system called Driveri ("driver eye").