A future timeline of the 21st century (updated).
An increasingly globalised humanity is faced with climate change, overpopulation, dwindling resources and technological upheaval.
21st Century
Posted in climatology, sustainability
Replacing a gene in a common maize line leads to higher seed protein content and improved nitrogen use.
Moreover, the house can be dismantled, remodelled, or restructured at a different location whenever the residents want.
About 30 per cent of tears in the anterior cruciate ligament that supports the knee heal within two years with strengthening exercises, challenging the common assumption that surgery is always needed.
KNP Arises, a Delhi-based startup run by Kirti and Sushil Vaishnav, collects used cooking oils from restaurants and converts it into biodiesel, which generates 80% lower carbon emissions as compared to fossil fuels.
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New studies of GRP78, a protein implicated in both COVID-19 and multiple types of cancer, uncover a drug that interferes with its effects.
During Artemis I, NASA’s Space Launch System (SLS) rocket will send the agency’s Orion spacecraft on a trek 40,000 miles beyond the Moon before returning to Earth. To capture the journey, the rocket and spacecraft are equipped with cameras that will collect valuable engineering data and share a unique perspective of humanity’s return to the Moon.
A femtosecond pulse generator is realized using an electro-optic time-lens system integrated on a lithium niobate photonic chip, capable of tunable repetition rates and wavelengths.
For the first time, researchers implemented a type of AI called a deep neural network into a photonic (light-based) device. In doing so, they’ve come closer to making a machine that processes what it “sees” like humans do, very quickly and efficiently.
For instance, the photonic deep neural network can classify a single image in less than 570 picoseconds, or nearly 2 billion images per second. To put things into perspective, the frame rate for fluid footage sits between 23 and 120 frames per second.
“Direct, clock-less processing of optical data eliminates analog-to-digital conversion and the requirement for a large memory module, allowing faster and more energy-efficient neural networks for the next generations of deep learning systems,” wrote the authors from the University of Pennsylvania.