Abstract: A method for chronologically aggregating media content for a specific topic from media content providers includes receiving multi-type media content relating to the specific topic, where the multi-type media content includes at least a first type of media content and a second type of media content. Identifying a first statement in a first portion of the first type of media content that responds to a second statement in a second portion of the second type of media content. Determining a chronological sequence for the first portion of the first type of media content and the second portion of the second type of media content, where the first portion of the first type of media content occurs after the second portion of the first type of media content. Generating a final media content presentation file for the determined chronological sequence.

Abstract: A method for chronologically aggregating media content for a specific topic from media content providers includes receiving multi-type media content relating to the specific topic, where the multi-type media content includes at least a first type of media content and a second type of media content. Identifying a first statement in a first portion of the first type of media content that responds to a second statement in a second portion of the second type of media content. Determining a chronological sequence for the first portion of the first type of media content and the second portion of the second type of media content, where the first portion of the first type of media content occurs after the second portion of the first type of media content. Generating a final media content presentation file for the determined chronological sequence.

Abstract: Aspects of the disclosure provide for a computer-implemented method. In at least some examples, the method includes receiving one or more piece of historical data associated with one or more occurrences of wildfires, training a machine learning model according to the received one or more historical data, receiving one or more piece of substantially real-time data associated with a region of interest, and processing the received one or more piece of substantially real-time data using the machine learning model to determine the probability of the wildfire occurring.

Abstract: An apparatus includes a frame configured to suspend an article of clothing and a plurality of terahertz sensors coupled to the frame. The terahertz sensors are configured to image an article of clothing suspended from the frame from different vantage points from within an interior of the article of clothing. Another apparatus includes a frame configured to suspend an article of clothing and a terahertz sensor coupled to the frame and configured to image an article of clothing suspended from the frame from within an interior of the article of clothing. The terahertz sensor is selectively positionable along the frame thereby enabling imaging from different vantage points.

Abstract: An apparatus includes a frame configured to suspend an article of clothing and a plurality of terahertz sensors coupled to the frame. The terahertz sensors are configured to image an article of clothing suspended from the frame from different vantage points from within an interior of the article of clothing. Another apparatus includes a frame configured to suspend an article of clothing and a terahertz sensor coupled to the frame and configured to image an article of clothing suspended from the frame from within an interior of the article of clothing. The terahertz sensor is selectively positionable along the frame thereby enabling imaging from different vantage points.