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  1. Free, publicly-accessible full text available January 1, 2023
  2. In the mid-ninth century, an earthquake triggered a landslide that blocked the narrow gorge of the Jhelum River where it exits the Kashmir Valley. The landslide impounded a lake that extended ≈100 km along the floor of the valley, implying an impounded volume of ≤21 km 3 , flooding the capital, Srinagar, and much agricultural land. An engineered breach of the landslide was contrived by a Medieval engineer resulting in the catastrophic release of flood waters. Using reasonable assumptions we calculate the probable minimum drainage time of this Medieval flood (<4 days) and maximum downstream surge velocities (≈12 m/s). These would have been sufficientmore »to transport boulders in the bed of the Jhelum with dimensions of ≈6 m, consistent with those currently present in some reaches of the river. Given the morphology of the Jhelum gorge we consider that landslide outburst floods may have been common in Kashmir’s history. Ancient shorelines indicate that paleo-lake volumes in the Kashmir Valley may have exceeded 400 km 3 which, were they released in catastrophic floods, would have been associated with potential downstream outburst velocities >32 m/s, able to transport boulders with dimensions ≈40 m, far in excess of any found in the course of the Jhelum or in the Punjab plains. Their absence suggests that Kashmir’s ancient lakes were not lowered by outburst mechanisms much exceeding those associated with Suyya’s flood. Present-day floods have been many tens of meters shallower than those impounded by landslides in the Jhelum in the past several thousands of years. A challenge for future study will be to date Kashmir’s ancient shorelines to learn how often landslides and major impoundment events may have occurred in the valley.« less
    Free, publicly-accessible full text available October 4, 2022
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  7. Self-driving vehicles are very susceptible to cyber attacks. This paper aims to utilize a machine learning approach in combating cyber attacks on self-driving vehicles. We focus on detecting incorrect data that are injected into the data bus of vehicles. We will utilize the extreme gradient boosting approach, as a promising example of machine learning, to classify such incorrect information. We will discuss in details the research methodology, which includes acquiring the driving data, preprocessing it, artificially inserting incorrect information, and finally classifying it. Our results show that the considered algorithm achieve accuracy of up to 92% in detecting the abnormalmore »behavior on the car data bus.« less