Scientists have released the first X-ray images of a mysterious, sphere-shaped object recovered in Colombia, which locals claim is of alien origin. The so-called'UFO' was spotted in March over the town of Buga, zig-zagging through the sky in a way that defies the movement of conventional aircraft. The object was recovered shortly after it landed and has since been analyzed by scientists, who discovered it features three layers of metal-like material and 18 microspheres surrounding a central nucleus they are calling'a chip.' Dr Jose Luis Velazquez, a radiologist who examined the sphere, reported finding'no welds or joints,' which would typically indicate human fabrication. He and his team concluded: 'It is of artificial origin, in that it shows no evidence of welding, and its internal structure is composed of high-density elements. More testing is needed to establish its origin.'

Microswimmers are sub-millimeter swimming microrobots that show potential as a platform for controllable locomotion in applications including targeted cargo delivery and minimally invasive surgery. To be viable for these target applications, microswimmers will eventually need to be able to navigate in environments with dynamic fluid flows and forces. Experimental studies with microswimmers towards this goal are currently rare because of the difficulty isolating intentional microswimmer motion from environment-induced motion. In this work, we present a method for measuring microswimmer locomotion within a complex flow environment using fiducial microspheres. By tracking the particle motion of ferromagnetic and non-magnetic polystyrene fiducial microspheres, we capture the effect of fluid flow and field gradients on microswimmer trajectories. We then determine the field-driven translation of these microswimmers relative to fluid flow and demonstrate the effectiveness of this method by illustrating the motion of multiple microswimmers through different flows.

Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields.

Integrity and trust are at the heart of humanity and allow for peaceful nations, bonds and trust between multiple parties. However, when that integrity is played with, many become overprotective, people cannot enjoy the common object and once trustful systems are tampered with, connections between communities are disrupted. On a global scale, counterfeit goods are where this integrity is played with the most. Counterfeit pharmaceuticals cause the deaths of millions in developing nations2, counterfeit batteries pose risks to everyday items bursting at any moment and overall, these goods cost the global market over one trillion dollars. Current solutions can easily be reverse-engineered and as the counterfeit epidemic surges, the world needs a solution to make counterfeit goods impossible. The integration of nanotags and harnessing the randomness and uniqueness of quantum dots allow for unclonable tags on each product. These tags are verified by the end-user through a deep learning algorithm. The tags are unclonable by any quantum computer let alone any attacker, ensuring the security of millions of lives and billions of dollars. The global market of counterfeit goods is currently $1.8 trillion and this number is only increasing. During the COVID-19 pandemic, a greater urgency for counterfeiting occurred as the global demand for medical supplies continued to increase (2). Not only does counterfeiting cost the global economy trillions of dollars, but it also results in fake pharmaceutical pills, costing millions of lives (2). To add on, 500 identity frauds happen on a daily basis, showcasing how counterfeit goods are reaching an epidemic level (10). In 2015, 10% of all luxury goods in Europe were counterfeit, and the number continues to increase (10).

Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.