Rented e-bicycles more dangerous than e-scooters in cities
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Nov-2025 13:11 ET (21-Nov-2025 18:11 GMT/UTC)
E-scooters have often been identified as more dangerous than e-bikes, but that picture changes when they are compared on equal terms. A recently published study from Chalmers University of Technology, Sweden, shows in fact that the crash risk is eight times higher for e-bikes than for e-scooters, calculated based on the trip distance with rental vehicles in cities. This surprising result provides a better basis for cities to make decisions on how much to facilitate different types of micromobility.
In 2018 the National Institute of Neurological Disorders and Stroke (NINDS) awarded a five-year, $2.9 million R01 grant to a TTUHSC research team in Amarillo to help uncover and develop much needed therapies for the treatment of ischemic stroke. Due to their high level of productivity and potential to create new medications for stroke injury, NINDS recently awarded a new $3 million competitive renewal that extends the grant for an additional five years to 2030.
The University of Texas at San Antonio launched the College of AI, Cyber and Computing on Sept. 1 bringing together academic programs in artificial intelligence, cybersecurity, computing and data science. The new college positions UT San Antonio at the forefront of technological education and research and brings the number of academic colleges at UT San Antonio to nine.
Earthquake-induced liquefaction of loose, sandy soil can be extensively damaging for built environment. In recent times, chemical grouting of the sand is being used as a method to enhance soil stability and reduce the risk of liquefaction. However, a standardized and effective method to test the resistance is necessary in order to refine the method. In this study, scientists explored the potential of stress-controlled and strain-controlled cyclic triaxial testing.
Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment. Described in a paper published in “Nature Communications,” the tool, called Flexynesis, uses deep neural networks and evaluates multi modal data.