Flying microrobots have garnered growing research interest owing to their technological intricacies and suitability for various applications leveraging miniaturized size. Electrohydrodynamic (EHD) thrust offers advantages by generating propulsion without moving parts, but real-world use is limited by insufficient thrust generation, manufacturing challenges, fragility, and cost. This work presents the design and development of an optimized ion-propelled flying microrobot that excels in low weight, high thrust-to-weight ratio, and cost efficiency. Regarding design, multiphysics simulations guided structural optimization to increase thrust while decreasing weight. For materials, metal-coated polyethylene terephthalate (PET) film was selected to leverage the combined merits of metal conductivity and polymer flexibility, light weight, and low cost, enabling further weight reduction, easy assembly, robustness, and cost-effectiveness. Various experiments, including voltage-current measurements, ionic wind speed, thrust quantification, and airflow visualization, directed design refinements and validated performance. Through structural optimization, the maximum wind speed attained 2.25 m/s. Flight demonstrations with payloads evidenced the microrobot can stably fly at an inherent 16 mg weight while carrying an additional 72 mg load, achieving a record 5.5 thrust-to-weight ratio. These results open possibilities to incorporate microelectronics, enabling autonomous flight functionality.

Precise cancer classification is essential to the successful diagnosis and treatment of cancers. Although semisupervised dimensionality reduction approaches perform very well on clean datasets, the topology of the neighborhood constructed with most existing approaches is unstable in the presence of high-dimensional data with noise. In order to solve this problem, a novel local and global preserving semisupervised dimensionality reduction based on random subspace algorithm marked as RSLGSSDR, which utilizes random subspace for semisupervised dimensionality reduction, is proposed. The algorithm first designs multiple diverse graphs on different random subspace of datasets and then fuses these graphs into a mixture graph on which dimensionality reduction is performed. As themixture graph is constructed in lower dimensionality, it can ease the issues on graph construction on highdimensional samples such that it can hold complicated geometric distribution of datasets as the diversity of random subspaces. Experimental results on public gene expression datasets demonstrate that the proposed RSLGSSDR not only has superior recognition performance to competitive methods, but also is robust against a wide range of values of input parameters.