Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data.

Aggressive driving behavior is mainly motivated by the intention of the driver; therefore, the underlying intention of behavior should be considered in investigating aggressive driving behavior. However, existing aggressive driving behavior prediction methods are not advanced in a compelling of characterizing the driver's intention among a large set of attributes and describing the random process among time-varying transversion. To address this, this paper proposes a prediction method, which is structured with a Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) Network.

The "Golden Hour" and field triage pattern for road trauma patients.

Introduction: Although the term "golden hour" is a well-known concept among trauma system and emergency medical service providers, the relationship between time and trauma patient outcome and the process of prehospital care for road trauma patients in rural settings are poorly understood. As the underlying basis for triage decision-making, the estimated transport interval to trauma center is usually absent in the existing studies.

Method: In this study, the crash data between 2013 and 2017 were obtained from the Fatality Analysis Reporting System, and the estimated intervals were calculated by using a Geographic Information System software.

Mix design optimization and early strength prediction of unary and binary geopolymer from multiple waste streams.

Geopolymer has received increasing amounts of attention recently due to its potential utilization of industrial and urban wastes. However, the variability of source materials and the complexity of mixture design hinder geopolymer applications derived from various waste streams. There is a need for a practical and quick scanning tool for material evaluation and mixture design optimization.