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Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.

Bacterial wilt caused by Ralstonia solanacearum is one of the most important diseases in solanaceous plants, including peppers. It generally tends to be more serious under warm−temperature and moist (WM) conditions than at moist room−temperature (RM) conditions. Although immunity mechanisms at room temperature have been intensively studied, the mechanisms underlying WM conditions remain poorly understood. Herein, the pepper cysteine protease CaZingipain2 was expressed and functionally characterized in pepper immunity against R. solanacearum at WM conditions and at room temperature. The results showed that CaZingipain2 localized to the nucleus and was upregulated at the transcript level in pepper plants upon R. solanacearum infection under WM conditions (RSWM). Virus−induced gene silencing of CaZingipain2 significantly increased the susceptibility of pepper plants to RSWM, and was coupled with the downregulation of CaPRP1 and CaMgst3, which are specifically related to pepper immunity against RSWM, according to our previous studies, while its overexpression significantly reduced the susceptibility of N. benethamiana plants to RSWM compared to that of wild−type plants. In addition, our data showed that CaZingipain2 also acts positively in pepper immunity against R. solanacearum infection at room temperature by upregulating the SA− and JA−responsive PR genes, including CaNPR1 and CaDEF1. All these results indicate that CaZingipain2 improves pepper immunity against R. solanacearum under WM conditions and at room temperature by regulating different PR genes.

Solar desalination devices utilizing sustainable solar energy and the abundant resource of seawater has great potential as a response to global freshwater scarcity. Herein, a bilayered solar evaporator was designed and fabricated utilizing a facile paper sheet forming technology, which was composed of cellulose fibers decorated with Fe3O4 nanoparticles as the top absorbent layer and the original cellulose fibers as the bottom supporting substrate. The characterization of the cellulose fibers decorated with Fe3O4 nanoparticles revealed that the in situ formed Fe3O4 nanoparticles were successfully loaded on the fiber surface and presented a unique rough surface, endowing the absorber layer with highly efficient light absorption and photothermal conversion. Moreover, due to its superhydrophilic property, the cellulose fiber-based bottom substrate conferred ultra-speed water transport capability, which could enable an adequate water supply to combat the water loss caused by continuous evaporation on the top layer. With the advantages mentioned above, our designed bilayered paper-based evaporator achieved an evaporation rate ~1.22 kg m−2 h−1 within 10 min under 1 sun irradiation, which was much higher than that of original cellulose cardboard. Based on the simple and scalable manufacture process, the bilayered paper-based evaporator may have great potential as a highly efficient photothermal conversion material for real-world desalination applications.