Engineering最新文献

Practical real-world scenarios such as the Internet, social networks, and biological networks present the challenges of data scarcity and complex correlations, which limit the applications of artificial intelligence. The graph structure is a typical tool used to formulate such correlations, it is incapable of modeling high-order correlations among different objects in systems; thus, the graph structure cannot fully convey the intricate correlations among objects. Confronted with the aforementioned two challenges, hypergraph computation models high-order correlations among data, knowledge, and rules through hyperedges and leverages these high-order correlations to enhance the data. Additionally, hypergraph computation achieves collaborative computation using data and high-order correlations, thereby offering greater modeling flexibility. In particular, we introduce three types of hypergraph computation methods: ① hypergraph structure modeling, ② hypergraph semantic computing, and ③ efficient hypergraph computing. We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional (3D) object recognition, revealing that hypergraph computation can reduce the data requirement by 80% while achieving comparable performance or improve the performance by 52% given the same data, compared with a traditional data-based method. A comprehensive overview of the applications of hypergraph computation in diverse domains, such as intelligent medicine and computer vision, is also provided. Finally, we introduce an open-source deep learning library, DeepHypergraph (DHG), which can serve as a tool for the practical usage of hypergraph computation.

Microparticles have demonstrated value for regenerative medicine. Attempts in this field tend to focus on the development of intelligent multifunctional microparticles for tissue regeneration. Here, inspired by erythrocytes-associated self-repairing process in damaged tissue, we present novel biomimetic erythrocyte-like microparticles (ELMPs). These ELMPs, which are composed of extracellular matrix-like hybrid hydrogels and the functional additives of black phosphorus, hemoglobin, and growth factors (GFs), are generated by using a microfluidic electrospray. As the resultant ELMPs have the capacity for oxygen delivery and near-infrared-responsive release of both GFs and oxygen, they would have excellent biocompatibility and multifunctional performance when serving as microscaffolds for cell adhesion, stimulating angiogenesis, and adjusting the release profile of cargoes. Based on these features, we demonstrate that the ELMPs can stably overlap to fill a wound and realize controllable cargo release to achieve the desired curative effect of tissue regeneration. Thus, we consider our biomimetic ELMPs with discoid morphology and cargo-delivery capacity to be ideal for tissue engineering.

Strain gradient is a normal phenomenon around a heterostructural interface in ultrathin film, and it is important to determine its effect on magnetic interactions to understand interfacial coupling. In this work, ultrathin Pr_0.67Sr_0.33MnO₃ (PSMO) films on different substrates are studied. For PSMO film under different in-plane strain conditions, the saturated magnetization and Curie temperature can be qualitatively explained by double-exchange interaction and the Jahn–Teller distortion. However, the difference in the saturated magnetization with zero field cooling and 5 T field cooling is proportional to the strain gradient. Strain-gradient-induced structural disorder is proposed to enhance phonon–electron antiferromagnetic interactions and the corresponding antiferromagnetic-to-ferromagnetic phase transition via a strong magnetic field during the field cooling process. A non-monotonous structural transition of the MnO₆ octahedral rotation can enlarge the strain gradient in PSMO film on a SrTiO₃ substrate. This work demonstrates the existence of the flexomagnetic effect in ultrathin manganite film, which should be applicable to other complex oxide systems.

The in-band full-duplex (IBFD) wireless system is a promising candidate for 6G and beyond, as it can double data throughput and enormously lower transmission latency by supporting simultaneous in-band transmission and reception of signals. Enabling IBFD systems requires a substantial mitigation of a transmitter (Tx)’s strong self-interference (SI) signal into the receiver (Rx) channel. However, current state-of-the-art approaches to tackle this challenge are inefficient in terms of performance, cost, and complexity, hindering the commercialization of IBFD techniques. In this work, we devise and demonstrate an innovative approach to realize IBFD systems that exhibit superior performance with a low-cost and less-complex architecture in an all-passive module. Our scheme is based on meticulously combining polarization-division multiplexing (PDM) with ferromagnetic nonreciprocity to achieve ultra-high isolation between Tx and Rx channels. Such an unprecedented conception has become feasible thanks to a concurrent dual-mode circulator—a new component introduced for the first time—as a key feature of our module, and a dual-mode waveguide that transforms two orthogonally polarized waves into two orthogonal waveguide modes. In addition, we propose a unique passive tunable secondary SI cancellation (SIC) mechanism, which is embedded within the proposed module and boosts the isolation over a relatively broad bandwidth. We report, solely in the analog domain, experimental isolation levels of 50, 70, and 80 dB over 340, 101, and 33 MHz bandwidth at the center frequency of interest, respectively, with excellent tuning capability. Furthermore, the module is tested in two real IBFD scenarios to assess its performance in connection with Tx-to-Rx leakage and modulation error in the presence of a Tx’s strong interference signal.