NEW DELHI一Authorities in northern Indian state of Uttarakhand on February 23 said the death number in the dam disaster that happened earlier this month in the state has risen to 70 and the search for missing people was underway. “As of now, a total of 70 bodies and 29 human body parts have been got back from different places,” reads a statement issued by police. t4Out of these, 40 bodies and one body part have been identified.”

Police said DNA collection of the unidentified bodies was being carried out. According to police, missing reports of 205 persons have been filed so far. “So far DNA samples of 110 relatives, 58 bodies and 28 body parts have been sent for matching to forensic (法医的）science laboratory (FSL) Dehradun,” the statement said Meanwhile, the search operation in the underway.

On Feb. 7, a glacier burst and then into a dam in Uttarakhand, causing a huge flood. The floodwaters tore open a dam and a flood of water hurtled down flooding the area in its course, damaging two hydropower (水力发电）projects and leading to numerous deaths，besides endangering hundreds of others. The majority of the missing in the disaster are believed to be workers from two hydropower plants.

Immediately after the incident rescuers rushed to carry out efforts on several sites, including a tunnel more than 200 meters long to save the trapped ones. Apart from locals, authorities moved in disaster response force personnel，troops，paramilitaries (准军事部队）and military helicopters to the state to help with rescue efforts.

Artificial intelligence (AI) is showing promise in earthquake prediction, challenging the long-held belief that it is impossible. Researchers at the University of Texas, Austin, have developed an Al algorithm (算法) that correctly predicted 70% of earthquakes a week in advance during a test in China and provided accurate strength calculations for the predicted earthquakes.

The AI was provided with a set of statistical features based on the team’s knowledge of earthquake physics, and then instructed to train itself using a five-year database of earthquake recordings. Once trained, the AI provided its prediction by listening for signs of incoming earthquakes within the background rumblings (轰隆隆) in the Earth.

This work is clearly a milestone in research for AI-driven earthquake prediction. “You don’t see earthquakes coming,” explains Alexandros Savvaidis, a senior research scientist who leads the Texas Seismological Network Program (TexNet). “It’s a matter of milliseconds, and the only thing you can control is how prepared you are. Even with the 70% accuracy (精确度), that’s a huge result and could help decrease economic and human losses and has the potential to remarkably improve earthquake preparation worldwide.”

While it is unknown whether the same approach will work at other locations, the researchers are confident that their AI algorithm could produce more accurate predictions if used in areas with reliable earthquake tracking networks. The next step is to test artificial intelligence in Texas, since UT’s Bureau TexNet has 300 earthquake stations and over six years worth of continuous records, making it an ideal location for these purposes.

Finally, the authors hope to combine the system with physics-based models. This strategy could prove especially important where data is poor or lacking. “That may be a long way to go, but many advances such as this one, taken together, are what moves science forward.” concludes Scott Tinker, the bureau’s director.

On the day the tornado hit, my husband, Jimmy, 67, and I, 65, came around at 9 p. m.. The first warning is from some text on the TV Jimmy was watching. He ran upstairs to find me in our third-floor bedroom, and we changed the channel from the national television to our local Pensacola, Florida, station.

Soon the tornado was on top of us. It was the loudest thing I had ever heard. The house shook, and the power went out. And the wind began to roar (咆哮) through the house, most likely through windows and the door to our garage. Everything was moving. And the back wall of the house came off and flew into the darkness outside. We had three flights of steps to get to the storeroom down there, the relative safety of the first floor.

I didn’t know how or if we would make it down the steps. It felt as if there was no floor as the wind lifted me off my feet. As we finally reached the last flight of steps, our front door blew open. Suddenly, a three -foot-long tree branch (树枝) flew over our heads, missing us by inches.

By the time I reached the storeroom, the tornado had been over us for about a minute, Jimmy pushed me down to the storeroom floor, but he couldn’t get inside himself because of the wind. I held Jimmy’s arm as the tornado blew the door open. My knees were full of glass, but I felt no pain. If I had let go, Jimmy would have flown right out of the house.

All of a sudden, Jimmy lifted off his feet. I thought he was gone. And then everything stopped. He landed on his feet. In those first quiet moments, I couldn’t believe it was over. Our neighbor said the storm lasted four minutes. In that time, four of the twelve town houses in our unit were completely destroyed. Luckily, none of us were seriously injured.