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China has installed an 18-megawatt (MW) offshore wind turbine – the world’s largest by power rating – in Guangdong province.

State-owned power generator manufacturer Dongfang Electric Corporation completed the installation of the massive wind turbine at a coastal test base in Shantou on June 5.

China’s 18-MW offshore wind turbine has a 260-meter (853-foot) rotor diameter and a swept area of 53,000 square meters (570,487 square feet) – equivalent to 7.4 standard football fields.

Apple also announced a ton of other home-grown AI innovations during Monday’s show. This includes AI-generated emojis called Genmoji, a feature that prioritizes important messages, another that summarizes group chats, and Safari will also create AI summaries of webpages.

The financials of Apple’s deal with OpenAI were not disclosed on Monday, and that element remains unclear. While Google has paid Apple billions of dollars for default privileges on Apple products, OpenAI may require an investment to provide computing of this magnitude. Processing AI requests for billions of people is anything but cheap. If Apple pays OpenAI for this, Microsoft may ultimately benefit from this deal.

While researchers have long studied brain dynamics using imaging (fMRI) and electroencephalograms (EEG), advances in neuroscience have only recently provided massive datasets for the brain’s cellular structure. These data opened possibilities for Kovács and his team to apply statistical physics techniques to measure the physical structure of neurons.

For the new study, Kovács and Ansell analyzed publicly available data of 3D brain reconstructions from humans, fruit flies and mice. By examining the brain at nanoscale resolution, the researchers found the samples showcased hallmarks of physical properties associated with criticality.

One such property is the well-known, fractal-like structure of neurons. This nontrivial fractal-dimension is an example of a set of observables, called “critical exponents,” that emerge when a system is close to a phase transition.

A recent study has explored the influence on low-energy fusion processes of isospin composition. This is a key nuclear property that differentiates protons from neutrons. The researchers used and theoretical modeling to investigate the fusion of different nuclei with varying isospin configurations. The results show that the isospin composition of the nuclei in a fusion reaction plays a crucial role in understanding the reaction. The paper is published in the journal Physical Review C.

In this study, researchers at Fisk University and Vanderbilt University used high-performance computational and theoretical modeling techniques to conduct a detailed many-body method study of how the dynamics of isospin influence nuclear fusion at low energies across a series of isotopes. The study also examined how the shape of the nuclei involved affect these dynamics. In systems where the nuclei are not symmetrical, the dynamics of isospin become particularly important, often leading to a lowered fusion barrier, especially in systems rich in neutrons. This phenomenon can be explored using facilities that specialize in the generation of beams composed of exotic, unstable nuclei.

The findings provide critical knowledge regarding the fundamental nuclear processes governing these reactions, which have broad implications for fields such as , astrophysics, and, perhaps someday, fusion-based energy.