Probabilistic Wildfire Susceptibility from Remote Sensing Using Random Forests and SHAP

A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.

Smoking remains a leading preventable cause of death globally, impacting millions through various health issues. Current medical screenings often fail to detect early signs of smoking-related health problems, resulting in late diagnoses. This study evaluates machine learning methods for assessing smoking-related health risks, analyzing data from 55,691 individuals. It compares three algorithms—Random Forest, XGBoost, and LightGBM—to identify those at high risk, prioritizing clinical interpretability and practical application.

The study introduces the Mobility-Aware Transformer-Message Passing Neural Network (MAT-MPNN) model, designed to enhance the prediction of HIV diagnosis rates across California, Florida, and New England. This model addresses the limitations of traditional Message Passing Neural Networks, which rely on fixed binary adjacency matrices that fail to capture interactions between non-contiguous regions. By integrating a Transformer encoder for temporal features and a Mobility Graph Generator for spatial relationships, MAT-MPNN aims to improve forecasting accuracy in HIV diagnoses.

Burks Tractor, an Idaho-based dealership, has filed a lawsuit against Monarch, a California startup, for breach of contract and warranty violations. The lawsuit claims that the tractors purchased from Monarch are 'unable to operate autonomously' and continue to face significant operational issues, which Burks Tractor describes as 'defective.' This legal action highlights concerns regarding the reliability and functionality of Monarch's autonomous tractor technology.

Traffic safety analysis at signalized intersections is crucial for minimizing vehicle and pedestrian collisions. Traditional crash-based studies face challenges due to data sparsity and latency. This paper introduces a multi-camera computer vision framework for real-time safety assessment through Post-Encroachment Time (PET) computation, tested at the intersection of H Street and Broadway in Chula Vista, California. Utilizing four synchronized cameras, the system processes frames on NVIDIA Jetson AGX Xavier devices with YOLOv11 segmentation for vehicle detection, creating a unified bird's-eye …