Shamees Aden has developed a concept for self-repairing running shoes made from 3D-printed synthetic biological material.
Category: biological – Page 12
Satyendra Nath Bose
Satyendra Nath Bose FRS, MP [ 1 ] (/ ˈ b oʊ s / ; [ 4 ] [ a ] 1 January 1894 – 4 February 1974) was an Indian theoretical physicist and mathematician. He is best known for his work on quantum mechanics in the early 1920s, in developing the foundation for Bose–Einstein statistics, and the theory of the Bose–Einstein condensate. A Fellow of the Royal Society, he was awarded India’s second highest civilian award, the Padma Vibhushan, in 1954 by the Government of India. [ 5 ] [ 6 ] [ 7 ]
The eponymous particles class described by Bose’s statistics, bosons, were named by Paul Dirac. [ 8 ] [ 9 ]
A polymath, he had a wide range of interests in varied fields, including physics, mathematics, chemistry, biology, mineralogy, philosophy, arts, literature, and music. He served on many research and development committees in India, after independence. [ 10 ] .
Enzyme-based plastics recycling at an industrial scale could be cost-effective, analysis finds
A successful collaboration involving a trio of research institutions has yielded a roadmap toward an economically viable process for using enzymes to recycle plastics.
The researchers, from the National Renewable Energy Laboratory (NREL), the University of Massachusetts Lowell, and the University of Portsmouth in England, previously partnered on the biological engineering of improved PETase enzymes that can break down polyethylene terephthalate (PET). With its low manufacturing cost and excellent material properties, PET is used extensively in single-use packaging, soda bottles, and textiles.
The new study, published in Nature Chemical Engineering, combines previous fundamental research with advanced chemical engineering, process development, and techno-economic analysis to lay the blueprints for enzyme-based PET recycling at an industrial scale.
The Minds That Left Reality | Diaspora
Greg Egan’s Diaspora is one of the most ambitious and mind-bending science fiction novels ever published. It came out in 1997 and originally started as a short story called “Wang’s Carpets.” That story ended up as a chapter in the novel. Diaspora is: dense, smart, and way ahead of its time.
This is hard science fiction to the core. Egan invents entire new branches of physics. He reimagines life, consciousness, time, space — even what it means to be human. The book doesn’t ease you in. There’s a glossary, invented physics theories like Kozuch Theory, and characters that don’t even have genders. But if you stick with it, what you get isn’t just a story, it’s a look at what the future might actually become.
By the year 2,975, humanity isn’t one species anymore. It’s split into three groups: Fleshers: The biological humans, including the “statics” (unchanged baseline humans) and all sorts of heavily modified versions — underwater people, gene-hacked thinkers, even “dream apes” who gave up speech to live closer to nature. Gleisners: AIs in robotic bodies that live in space. They care about the physical world and experience time like regular humans. They’re kind of old-school — still sending ships to the stars, trying to build things in real space. Citizens: These are digital minds that live entirely in simulated worlds called polises.
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Multimodal LLMs and the human brain create object representations in similar ways, study finds
A better understanding of how the human brain represents objects that exist in nature, such as rocks, plants, animals, and so on, could have interesting implications for research in various fields, including psychology, neuroscience and computer science. Specifically, it could help shed new light on how humans interpret sensory information and complete different real-world tasks, which could also inform the development of artificial intelligence (AI) techniques that closely emulate biological and mental processes.
Multimodal large language models (LLMs), such as the latest models underpinning the functioning of the popular conversational platform ChatGPT, have been found to be highly effective computational techniques for the analysis and generation of texts in various human languages, images and even short videos.
As the texts and images generated by these models are often very convincing, to the point that they could appear to be human-created content, multimodal LLMs could be interesting experimental tools for studying the underpinnings of object representations.
All-topographic neural networks more closely mimic the human visual system
Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a result, in addition to tackling various real-world computational problems, they could help neuroscientists and psychologists to better understand the underpinnings of specific sensory or cognitive processes.
Researchers at Osnabrück University, Freie Universität Berlin and other institutes recently developed a new class of artificial neural networks (ANNs) that could mimic the human visual system better than CNNs and other existing deep learning algorithms. Their newly proposed, visual system-inspired computational techniques, dubbed all-topographic neural networks (All-TNNs), are introduced in a paper published in Nature Human Behaviour.
“Previously, the most powerful models for understanding how the brain processes visual information were derived off of AI vision models,” Dr. Tim Kietzmann, senior author of the paper, told Tech Xplore.