Fundamental probing limit on the high-order orbital angular momentum of light.

The orbital angular momentum (OAM) of light, possessing an infinite-dimensional degree of freedom, holds significant potential to enhance the capacity of optical communication and information processing in both classical and quantum regimes. Despite various methods developed to accurately measure OAM modes, the probing limit of the highest-order OAM remains an open question. Here, we report an accurate recognition of superhigh-order OAM using a convolutional neural network approach with an improved ResNeXt architecture, based on conjugated interference patterns. A type of hybrid beam carrying double OAM modes is utilized to provide more controllable degrees of freedom for greater recognition of the OAM modes. Our contribution advances the OAM recognition limit from manual counting to machine learning. Results demonstrate that, within our optical system, the maximum recognizable OAM modes exceed l = ±690 with an accuracy surpassing 99.93%, the highest achieved by spatial light modulator to date. Enlarging the active area of the CCD sensor extends the number of recognizable OAM modes to 1300, constrained only by the CCD resolution limit. Additionally, we explore the identification of fractional high-order OAM modes with a resolution of 0.1 from l = ±600.0 to l = ±600.9, achieving a high accuracy of 97.86%.

Learning-enabled data transmission with up to 32 multiplexed orbital angular momentum channels through a commercial multi-mode fiber.

Multiplexing orbital angular momentum (OAM) modes enable high-capacity optical communication. However, the highly similar speckle patterns of adjacent OAM modes produced by strong mode coupling in common fibers prevent the utility of OAM channel demultiplexing. In this paper, we propose a machine learning-supported fractional OAM-multiplexed data transmission system to sort highly scattered data from up to 32 multiplexed OAM channels propagating through a commercial multi-mode fiber parallelly with an accuracy of >99.92%, which is the largest bit number of OAM superstates reported to date (to the best of our knowledge). Here, by learning limited samples, unseen OAM superstates during the training process can be predicted precisely, which reduces the explosive quantity of the dataset. To verify its application, both gray and colored images, encoded by the given system, have been successfully transmitted with error rates of <0.26%. Our work might provide a promising avenue for high-capacity OAM optical communication in scattering environments.

Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Research on the factors influencing the process of prefabricated fragments penetrating finite thickness concrete.

The damage to the back of the target plate is a phenomenon that occurs when concrete is subjected to high-speed impact. In order to study the motion parameters of prefabricated spherical fragments penetrating finite thickness concrete targets at high speeds and the occurrence rules of concrete damage, as well as the impact of target back damage on the motion of fragments, experiments were conducted on 100 mm finite thickness concrete targets with prefabricated spherical fragments. The concrete model parameters in LS-DYNA were modified based on the residual velocity of fragments, and numerical simulations were conducted on the penetration of prefabricated fragments with different impact velocities and concrete target plates with different thicknesses. By analyzing the location of concrete target plate damage, the relationship between concrete thickness and concrete damage was obtained; Combining the motion parameters of fragment penetration process, the phenomenon of concrete collapse was linked to fragment motion, and the influence of concrete thickness on fragment motion parameters was analyzed. The results indicate that the thickness of the finite thickness concrete target plate and the penetration speed of fragments have a significant impact on the damage state of the target back, and further affect the motion change response stage during the penetration process of prefabricated fragments.

Research on the destroy characteristics of PTFE/Cu composite liner to explosive reactive armor.

The jet generated through PTFE based inert material liner has the characteristics of low energy, low density, and large aspect ratio, which can effectively achieve the "penetration without explosion" of explosive reactive armor. PTFE/Cu composite material liner with various densities is prepared, to research the roles of preparation procedure and density in the destroy effect of jet on reactive armor. Through numerical simulation research, it was found that there was no reaction at all in the explosive layer penetrated by the jet generated by the sinter liner molded, while the explosive layer penetrated by the jet generated through the hot-pressing sintering and extrusion molding liner experienced local reactions on the jet impact channel, and the overall explosive layer did not undergo any reaction. Through experimental verification, it has been proven that all three types of jets have achieved "penetration without explosion" on explosive reactive armor.

High resolution spectroscopy of B0u+←X0g+ transitions in 130Te2: Reference lines spanning the 443.2-451.4 nm region.

Molecular tellurium (130Te2) has long been regarded as a promising candidate for a variety of spectroscopic applications, especially as a frequency reference. Absorption lines of 130Te2 were published extensively in the past few decades, however, the spectral resolution was not sufficiently high to be considered a precision spectroscopic reference. Here, a high resolution spectral atlas of 130Te2 Doppler-free saturated absorption transitions is reported, and used to assign multiple ro-vibrational bands in the B(Σu-3)0u+←X(Σu-3)0g+ electronic transition via the Dunham model, giving updated Dunham parameters and diatomic constants for the B(Σu-3)0u+ state at an unprecedented level of precision. Our findings reveal spectroscopic properties of 130Te2 that might eventually contribute to frequency reference in precision measurement such as the quest for non-zero electron's Electric Dipole Moment (eEDM).

Isoform-selective TGF-ß3 inhibition for systemic sclerosis.

BACKGROUND: Transforming growth factor ß (TGF-ß) is implicated as a key mediator of pathological fibrosis, but its pleiotropic activity in a range of homeostatic functions presents challenges to its safe and effective therapeutic targeting. There are three isoforms of TGF-ß, TGF-ß1, TGF-ß2, and TGF-ß3, which bind to a common receptor complex composed of TGF-ßR1 and TGF-ßR2 to induce similar intracellular signals in vitro. We have recently shown that the cellular expression patterns and activation thresholds of TGF-ß2 and TGF-ß3 are distinct from those of TGF-ß1 and that selective short-term TGF-ß2 and TGF-ß3 inhibition can attenuate fibrosis in vivo without promoting excessive inflammation. Isoform-selective inhibition of TGF-ß may therefore provide a therapeutic opportunity for patients with chronic fibrotic disorders. METHODS: Transcriptomic profiling of skin biopsies from patients with systemic sclerosis (SSc) from multiple clinical trials was performed to evaluate the role of TGF-ß3 in this disease. Antibody humanization, biochemical characterization, crystallization, and pre-clinical experiments were performed to further characterize an anti-TGF-ß3 antibody. FINDINGS: In the skin of patients with SSc, TGF-ß3 expression is uniquely correlated with biomarkers of TGF-ß signaling and disease severity. Crystallographic studies establish a structural basis for selective TGF-ß3 inhibition with a potent and selective monoclonal antibody that attenuates fibrosis effectively in vivo at clinically translatable exposures. Toxicology studies suggest that, as opposed to pan-TGF-ß inhibitors, this anti-TGF-ß3 antibody has a favorable safety profile for chronic administration. CONCLUSION: We establish a rationale for targeting TGF-ß3 in SSc with a favorable therapeutic index. FUNDING: This study was funded by Genentech, Inc.