In order to eliminate marine litter, a Dutch start-up c […]
In order to eliminate marine litter, a Dutch start-up company has created a “ocean vacuum cleaner”, hoping to build a floating U-shaped fence up to 3 meters deep on the sea by a huge pipe with a total length of more than 600 meters. The garbage is wiped out and the ship is regularly cleaned and transported to the shore.
On September 8 this year, the "Ocean Vacuum Cleaner" that sailed from the Sanmen City Golden Gate Bridge successfully completed the test in the Pacific Ocean for about two weeks. After various assessments, it is expected that it will continue to sail to the Pacific garbage belt. Contribute to reducing plastic waste floating on the ocean.
The marine vacuum cleaner was built by Ocean Cleanup, a household vacuum cleaner company founded by Dutch youth Boyan Slat. The concept is to build a 3 meter deep floating U-shaped fence on the sea with huge pipes over 600 meters in length, along the sea breeze, ocean currents and waves. Move, the drifting garbage collected on the Pacific garbage belt will be exhausted, and then handed over to the ship for regular cleaning and transportation.
To observe the moving time is too fast and too slow to be invalid
Although the test has been completed, before the official start of the task of cleaning up marine debris, the development team of the marine vacuum cleaner needs to verify whether it has sufficient capacity, so the 450 km offshore area is designated as the test area and tested. It will continue to move to the Pacific Garbage Belt, 2,200 kilometers from the US West Coast.
According to the official Ocean Cleanup presshandheld vacuum cleaner release, U-shaped is the most efficient form of marine vacuum cleaners. Whether it can maintain shape trapping in the changing environment of the ocean is a very important part.
The company mentioned that the research team spent two or two days setting the marine vacuum cleaner into a U-shape. In most experiments, the U-shape showed excellent efficiency, and the entire process only had a 2-hour opening span to reduce the length of the efficiency. The best shape can be maintained for the rest of the time.
The speed of sailing on the surface of the water is also the key to the effectiveness of the marine vacuum cleaner. If it moves slower than the garbage, it will not be able to maintain the garbage in the area, or catch up with the marine debris drifting in front. The experiment also does move faster than garbage, but in the performance of the Pacific garbage belt, they will continue to observe.
The ability of marine vacuum cleaners to adapt to wind direction and wave changes is another test goal. Marine vacuum cleaners need to be able to change their direction with the wind direction in order to maintain plastic waste in the fence. By means of integrated sea breeze, ship towing, etc., they observed that under the sea breeze blowing in different directions such as 45 degrees, 90 degrees, 180 degrees, the marine vacuum cleaner can smoothly change its direction.
Japan Research AI Analyze Magnetic Field Predict Earthquake Tsunami Damage Reduction
A team at Tokyo Metropolitan University uses machine learning techniques in artificial intelligence (AI) to analyze small changes in the Earth's magnetic field, and the systems they create are expected to predict natural disasters earlier than existing methods.
The research team led by the school's associate professor Kan Okubo said, "The earthquake and tsunami are accompanied by local changes in the geomagnetic field. For earthquakes, this is mainly a piezomagnetic effect. A large amount of stress accumulated along the fault will cause the geomagnetic field. Local changes; for tsunami, sudden and huge ocean movements cause changes in atmospheric pressure, which in turn affects the ionosphere and subsequently changes the geomagnetic field. Both can be performed through observation points located at multiple locations. Detection, by detecting changes in the earth's magnetic field, instantaneously detects the occurrence of a disaster.
Provide effective warnings to allow the public to escape
The machine learning algorithm developed by the research team, using 500,000 data points collected in 2015, the system can be used with a highly sensitive detector network to provide an effective early warning system that minimizes losses and saves lives. An accurate warning system allows residents to have enough time to escape.