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Oil contamination is problematic in subaqueous environments. Here, a cationic poly(N,N-dimethyl-N-(ethoxycarbonylmethyl)-N-[2'-(methacryloyloxy) ethyl]- ammonium chloride) (PCBMAE) was synthesized, and a one-step approach of dopamine-assisted codeposition was used to generate PCBMAE/ polydopamine (PDA) superhydrophilic and underwater superoleophobic coatings coated on a stainless steel mesh for oil/water separation. The structures, surface morphologies, and properties of the coatings were analyzed by IR, SEM, TGA, and contact angle measurements. During deposition at a PCBMAE and DA mass ratio of 1:1, PCBMAE/PDA mixture particles uniformly adhered to the skeletons of the mesh, increased the surface roughness, created enriched surface micro/nanostructures, and reduced the surface energy of the coating. This endowed the metal mesh with superhydrophilicity and underwater superoleophobicity (oil contact angle 163[degrees]) without blocking the mesh, and the deposition density was 0.26 mg/[cm.sup.2]. The separation efficiency of the coated mesh was 99.6% for the first castor oil/water separation and dropped to 98.0% after 5 cycles. After 25 cycles, the separation efficiency was still higher than 95%. The PCBMAE/PDA codeposited mesh exhibited high oil/water separation efficiency, stable mechanical properties, and weather stability under different harsh environments, making it a promising separation material for future industrial applications. KEYWORDS cationic polymer, dopamine-assisted codeposition, metal mesh, oil/water separation, superhydrophilicity, underwater superoleophobicity, 1 | INTRODUCTION Oil/water separation, whether pollution of oil spill accidents or industrial wastewater, has become an urgent challenge in the world. [1-3] At present, common approaches used for oil/water [...]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The design of lignin nanostructures where interfacial interactions enable enhanced entanglement of colloidal networks can broaden their applications in hydrogel‐based materials and light‐based 3D printing. Herein, an approach for fabricating surface‐active dendritic colloidal microparticles (DCMs) characterized by fibrous structures using nanostructured allylated lignin is proposed for the development of lignin‐based photocurable resins. With allyl‐terminated surface functionality of 0.61 mmol g−1, the entanglement between lignin‐DCM fibrils with a size of 1.4 µm successfully produces only lignin‐based hydrogels with structural integrity through photo‐crosslinking. The colloidal network of lignin dendricolloids reinforces the poly(ethylene glycol) (PEG) hydrogels during a digital light processing (DLP) 3D printing process by generating bicontinuous morphologies, resulting in six‐fold increases in toughness values with respect to the neat PEG hydrogel. The dual effectiveness of photoabsorption and free‐radical reactivity of lignin‐DCMs allow the light‐patterning of rather dilute PEG hydrogels (5–10%) with high geometric fidelity and structural complexity via DLP 3D printing. This study demonstrates a green and effective strategy for the design of 1D lignin‐DCMs that increases the versatility of the nanostructured biopolymer, opening up numerous opportunities for formulating functional hydrogels with robust structure‐property correlations. [ABSTRACT FROM AUTHOR]

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.