Researchers at the University of Windsor developed SH17, a large open-source dataset with 8,099 images and 75,994 labeled instances to improve detection of personal protective equipment (PPE) in manufacturing. Using advanced AI models like YOLOv9, the study achieved over 70% accuracy, offering industries a scalable tool to enhance worker safety and compliance.

New review in iOptics demonstrates how AI is overcoming key challenges in metasurface design, enabling advanced applications in compact optics and computational imaging.

A research team from Fudan University has developed a hydrogel technology based on microenvironment-responsive mechanisms. The material can sense pH changes in the wound environment and dynamically release functional agents, enabling a switch from antibacterial action to tissue repair. Constructed from an interpenetrating network of sodium alginate and carboxymethyl chitosan, and loaded with tannic acid and zinc-doped bioactive glass, the hydrogel rapidly releases antibacterial molecules during infection and gradually delivers regenerative ions during healing—achieving, for the first time, precise, stage-specific control of infected wound treatment.

Reactive planning and control capacity for collaborative robots is essential when the tasks change online in an unstructured environment. This is more difficult for collaborative mobile manipulators (CMM) due to high redundancies. To this end, this paper proposed a reactive whole-body locomotion-integrated manipulation approach based on combined learning and optimization. First, human demonstrations are collected, where the wrist and pelvis movements are treated as whole-body trajectories, mapping to the end-effector (EE) and the mobile base (MB) of CMM, respectively. A time-input kernelized movement primitive (T-KMP) learns the whole-body trajectory, and a multi-dimensional kernelized movement primitive (M-KMP) learns the spatial relationship between the MB and EE pose. According to task changes, the T-KMP adapts the learned trajectories online by inserting the new desired point predicted by M-KMP. Then, the updated reference trajectories are sent to a hierarchical quadratic programming (HQP) controller, where the EE and the MB trajectories tracking are set as the first and second priority tasks, generating the feasible and optimal joint level commands. An ablation simulation experiment with CMM of the HQP is conducted to show the necessity of MB trajectory tracking in mimicking human whole-body motion behavior. Finally, the tasks of the reactive pick-and-place and reactive reaching were undertaken, where the target object was randomly moved, even out of the region of demonstrations. The results showed that the proposed approach can successfully transfer and adapt the human whole-body loco-manipulation skills to CMM online with task changes.

Researchers from Zhejiang Lab have proposed a novel spatiotemporal mode multiplexing technology, coupling pulsed orbital angular momentum (OAM) beams with diffractive deep neural networks (D²NN) and optical fiber delay-line arrays to realize a “space-time” projection mechanism. This enables high-speed, all-optical multidimensional multiplexing with potential scalability to the GHz level, offering a breakthrough solution for large-capacity optical communications in challenging environments such as free space and underwater.

Tellurene, a chiral chain semiconductor with a narrow bandgap and exceptional strain sensitivity, emerges as a pivotal material for tailoring electronic and optoelectronic properties via strain engineering. This study elucidates the fundamental mechanisms of ultrafast laser shock imprinting (LSI) in two-dimensional tellurium (Te), establishing a direct relationship between strain field orientation, mold topology, and anisotropic structural evolution. This is the first demonstration of ultrafast LSI on chiral chain Te unveiling orientation-sensitive dislocation networks. By applying controlled strain fields parallel or transverse to Te’s helical chains, we uncover two distinct deformation regimes. Strain aligned parallel to the chain’s direction induces gliding and rotation governed by weak interchain interactions, preserving covalent intrachain bonds and vibrational modes. In contrast, transverse strain drives shear-mediated multimodal deformations—tensile stretching, compression, and bending—resulting in significant lattice distortions and electronic property modulation. We discovered the critical role of mold topology on deformation: sharp-edged gratings generate localized shear forces surpassing those from homogeneous strain fields via smooth CD molds, triggering dislocation tangle formation, lattice reorientation, and inhomogeneous plastic deformation. Asymmetrical strain configurations enable localized structural transformations while retaining single-crystal integrity in adjacent regions—a balance essential for functional device integration. These insights position LSI as a precision tool for nanoscale strain engineering, capable of sculpting 2D material morphologies without compromising crystallinity. By bridging ultrafast mechanics with chiral chain material science, this work advances the design of strain-tunable devices for next-generation electronics and optoelectronics, while establishing a universal framework for manipulating anisotropic 2D systems under extreme strain rates. This work discovered crystallographic orientation-dependent deformation mechanisms in 2D Te, linking parallel strain to chain gliding and transverse strain to shear-driven multimodal distortion. It demonstrates mold geometry as a critical lever for strain localization and dislocation dynamics, with sharp-edged gratings enabling unprecedented control over lattice reorientation. Crucially, the identification of strain field conditions that reconcile severe plastic deformation with single-crystal retention offers a pathway to functional nanostructure fabrication, redefining LSI’s potential in ultrafast strain engineering of chiral chain materials.