Lifeboat Foundation

Dyson spheres and rings have always held a special fascination for me. The concept is simple: build a great big structure either as a sphere or ring to harness the energy from a star. Dyson rings are far more simple and feasible to construct and in a recent paper a team of scientists explore how we might detect them by analyzing the light from distant stars. The team suggests they might be able to detect Dyson rings around pulsars using their new technique.

Like their spherical cousins, Dyson rings remain for now, a popular idea in science fiction yet they are starting to appear more and more in scientific debates. The concept of the ring is similar to the sphere, a megastructure designed to encircle a star, harnessing its energy on a gargantuan scale.

It might consist of a series of satellites or even habitats in a circular orbit with solar collectors and unlike the spheres, require far less resources to build. The concept of the sphere was first proposed by physicist and mathematician Freeman Dyson in 1960. Such structures might be detectable and reveal the existence of intelligent civilizations.

As our devices multiply and data demands grow, traditional wireless systems are hitting their limits. To meet these challenges, we have turned to an innovative solution. At the University of Melbourne and Monash University, we have developed a dual-carrier Modular Optical Phased Array (MOPA) communication system. At the core of our innovation is a groundbreaking concept: a modular phased array.

This design is inspired by the quantum superposition principle, applying its logic to enhance technical performance and efficiency. This cutting-edge technology is designed to make indoor wireless networks faster, more reliable and more secure, while addressing the limitations of traditional systems. Our research is published in the IEEE Open Journal of the Communications Society.

Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments.

Saar Yoskovitz is Co-Founder & CEO at Augury, a pioneer in AI-driven Machine Health and Process Health solutions for industrial sectors.

American manufacturers are at a crossroads, needing to decide between evolution and obsolescence. The tools that historically drove profitability and efficiency are no longer having an impact. Labor is hard to find and harder to keep. The National Association of Manufacturing projects that 2.1 million manufacturing roles will go unfilled by 2030. This hard truth is compounded by findings in Augury’s State of Production Health report, which reveals that 91% of manufacturers say that the mass exodus of industry veterans will worsen the knowledge gap.

An alarming rate of brain drain is looming over the industrial sector. As tenured employees reach retirement age and fewer professionals line up to take their place, more manufacturers are turning to artificial intelligence (AI) to bridge the gap.

A new mRNA therapy tested in mice may target the root cause of the potentially fatal pregnancy disorder preeclampsia. It’s yet to be tested in humans.

‘Self-replicating Robotic Systems’ published in ‘Encyclopedia of Complexity and Systems Science’

A self-replicating machine is a type of autonomous robot that is capable of reproducing itself autonomously using raw materials found in the environment, thus exhibiting self-replication in a way analogous to that found in nature. Homer Jacobson, Edward F. Moore, Freeman Dyson, John von Neumann, Konrad Zuse and in more recent times by K. Eric Drexler in his book on nanotechnology, Engines of Creation (coining the term clanking replicator for such machines) and by Robert Freitas and Ralph Merkle in their review Kinematic Self-Replicating Machines^{moons and asteroid belts for ore and other materials, the creation of lunar factories, and even the construction of solar power satellites in space. The von Neumann probeuniversal constructor, a self-replicating machine that would be able to evolve and which he formalized in a cellular automata environment. Notably, Von Neumann’s Self-Reproducing Automata scheme posited that open-ended evolution requires inherited information to be copied and passed to offspring separately from the self-replicating machine, an insight that preceded the discovery of the structure of the DNA molecule by Watson and Crick and how it is separately translated and replicated in the cell.https://en.m.wikipedia.org/wiki/Self-replicating_machine#:~:...n_probe_is, [ 9 ] A self-replicating machine is an artificial self-replicating system that relies on conventional large-scale technology and automation. The concept, first proposed by Von Neumann no later than the 1940s, has attracted a range of different approaches involving various types of technology. Certain idiosyncratic terms are occasionally found in the literature. For example, the term clanking replicator was once used by Drexler [ 10 ] to distinguish macroscale replicating systems from the microscopic nanorobots or “assemblers” that nanotechnology may make possible, but the term is informal and is rarely used by others in popular or technical discussions. Replicators have also been called “von Neumann machines” after John von Neumann, who first rigorously studied the idea.}

Self replicating alien probes.

Found on Google from phys.org

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Artificial intelligence and nanotechnology.