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The ionosphere is the ionized part of the upper atmosphere of the Earth, which plays an important role in atmospheric electricity and forms the inner edge of the magnetosphere. It influences radio propagation significantly, such as the Global Navigation Satellite System (GNSS). Meanwhile, the GNSS is also an essential technique for sensing the variation of ionosphere. During the years of 2019—2023, a large number of Chinese geodesy scientists devoted much efforts to the geodesy related ionosphere. Due to the very limited length, the achievements are carried out from the following six aspects, including: ① The ionospheric correction models for BDS and BDSBAS; ② Real-time global ionospheric monitoring and modeling; ③ The ionospheric 2D and 3D modeling based on GNSS and LEO satellites; ④ The ionospheric prediction based on artificial intelligence; ⑤ The monitoring and mitigation of ionospheric disturbances for GNSS users; ⑥ The ionospheric related data products and classical applications.

Traditional design methods seldom consider accumulation properties, which influence the structure of asphalt mixtures. Therefore, this study is the first to put forward a modified compressible packing model. The filling characteristics of coarse aggregate were studied. Different matrix compaction indexes η and compaction indexes K were selected to study the influence of the modified compressible model on pavement performance. Design methods based on a 4% air void and minimum voids in mineral aggregate were compared through the volumetric parameters and pavement performance based on the modified compressible packing model. The asphalt mixture performances with three gradations were analyzed. The test results showed that as the matrix compaction index η decreased, the optimum asphalt content of both methods increased, while the VMA decreased. Finally, an optimized asphalt mixture design method based on the compressible packing model was proposed, which showed good prospects for application.

Inflammatory responses play a central role in coordinating biomaterial‐mediated tissue regeneration. However, precise modulation of dynamic variations in microenvironmental inflammation post‐implantation remains challenging. In this study, the traditional β‐tricalcium phosphate‐based scaffold is remodeled via ultrathin MXene‐Ti3C2 decoration and Zn2+/Sr2+ ion‐substitution, endowing the scaffold with excellent reactive oxygen species‐scavenging ability, near‐infrared responsivity, and enhanced mechanical properties. The induction of mild hyperthermia around the implant via periodic near‐infrared irradiation facilitates spatiotemporal regulation of inflammatory cytokines secreted by a spectrum of macrophage phenotypes. The process initially amplifies the pro‐inflammatory response, then accelerates M1‐to‐M2 macrophage polarization transition, yielding a satisfactory pattern of osteo‐immunomodulation during the natural bone healing process. Later, sustained release of Zn2+/Sr2+ ions with gradual degradation of the 3D scaffold maintains the favorable reparative M2‐dominated immunological microenvironment that supports new bone mineralization. Precise temporal immunoregulation of the bone healing process by the intelligent 3D scaffold enhances bone regeneration in a rat cranial defect model. This strategy paves the way for the application of β‐tricalcium phosphate‐based materials to guide the dynamic inflammatory and bone tissue responses toward a favorable outcome, making clinical treatment more predictable and durable. The findings also demonstrate that near‐infrared irradiation‐derived mild hyperthermia is a promising method of immunomodulation. [ABSTRACT FROM AUTHOR]

Titanium alloys have been widely used in marine engineering fields. However, because of high biocompatibility, they are vulnerable to biofouling. In this work, based on the micro‐arc oxidation technology and spontaneous galvanic replacement reaction, a temperature‐responsive low‐toxic smart coating consisting of liquid metal particles is designed to control the release of Ga3+, Cu2+, and Cu1+ ions in different temperatures. This technology can ensure the full release of active ingredients within the target temperature range, intelligently maintaining the excellent anti‐biofouling performance, while saving active ingredients. After being immersed in culture media with Sulfate‐Reducing Bacteria (SRB) for 14 days at 10, 20, and 30 °C, at the optimal activity temperature of 30 °C for SRB, the best sample releases the highest amounts of Ga3+, Cu2+, and Cu1+ ions, demonstrating a 99.9% bactericidal rate. When the temperature decreases to 10 °C, the activity level of SRB is very low, and the smart coating can also reduce the released ions correspondingly, still with a 97.3% bactericidal rate. The remarkable anti‐biofouling performance is attributed to the physical damage and lethal ions interaction. Furthermore, the best sample exhibits good corrosion resistance. This work presents a design route for smart anti‐biofouling coatings for temperature‐responsive. [ABSTRACT FROM AUTHOR]

The unpredictability and instability of wind have hindered the development and utilization of wind power. To harness wind energy and ensure a secure and stable power grid after wind power integration, precise predictions of wind power generation are imperative. Here, we apply one-year data from a coastal wind farm in Zhejiang to train a Random Forest (RF) model for predicting wind power generation. The results indicate that the RF model (mean bias (MB) 1.33) outperforms traditional linear models (MB 87.70). An evaluation of prediction-measurement bias shows a strong agreement between the RF model's predictions and actual measurements (MB 1.33), especially when wind speeds exceed 5.7 m/s. While the Weather Research and Forecasting (WRF) model yields less accurate results (R2 0.65) compared to using measured wind velocity data (R2 0.97) for wind power prediction, it remains acceptable as it can capture and forecast peak generating power, meeting daily management requirements. Therefore, our machine learning-based approach offers practical guidance for reducing wind power generation uncertainties. Enhancing wind velocity forecasting through the WRF model could further improve the RF model's accuracy in predicting wind power generation. Our study also verified the future application of RF model in coastal areas of China. [ABSTRACT FROM AUTHOR]