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Stripe noise often affects remote sensing images, leading to the degradation of imaging quality and impacting subsequent image processing. Deep learning methods have made remarkable advancements in removing stripes from remote sensing images with their powerful feature extraction capability. However, it is noteworthy that these methods are still insufficient in analysing structural characteristics in the direction of stripes and multi-scale contextual information. To deal with the above issues, we propose a remote sensing image destriping with a two-stage image decomposition network, named TSIDNet, which utilizes the stripe structural characteristics to effectively remove stripe noise while retaining better details. Firstly, stripes are directional, and the structural information of the stripes is mainly concentrated in the image after the differential decomposition of the direction along the stripes. Therefore, a differential decomposition stripe extraction subnetwork (DDSESN) is constructed to generate latent images. Within this subnetwork, we further design a multi-scale cross-fusion residual block (MCRB) and cross-scale fusion attention block (CSFAB) to gradually expand the network's receptive field, which is conducive to extract and remove stripes more completely. Secondly, the features obtained by adding the latent clear image with details extracted from the stripe's reverse direction are input into a DWT decomposition detail enhancement subnetwork (DDDESN), which utilizes the discrete wavelet transform (DWT) and the dense residual network for detail enhancement. Demonstrated through experiments, the proposed TSIDNet enables the removal of image stripes while preserving details, and surpassing the comparative methods in qualitative as well as quantitative evaluations. The code will be provided after acceptance. [ABSTRACT FROM AUTHOR]

AbstractGeopolymers can be classified as an emerging, environmentally-friendly construction material. The dense structure of geopolymers means that they are effective for the immobilization of radioactive ions. However, the mechanical properties are intricately affected by radioactive ions, pore size and initial crack, and the underlying mechanisms require further investigation. This study employs a combined molecular dynamics (MD) and peridynamics (PD) approach to analyze the effects of radioactive ions and how changes in pore size and the presence of an initial crack influence the mechanical performance of geopolymers. The results reveal that Cs and Sr ions exert opposing effects on the mechanical properties of NASH; more specifically, Cs ions negatively affect the mechanical properties of geopolymers. Pore size demonstrates a non-linear influence on performance, with models featuring pore diameters of 40–60 nm exhibiting the poorest mechanical properties. Moreover, the presence of an initial crack in the modeled geopolymer significantly reduces Young’s modulus and the ultimate tensile strength of the material. In the applied cross-scale approach, PD simulations validated and extended the MD results to align more closely with experimental values. This study provides a foundation through which multi-scale modeling can be leveraged to optimize geopolymer performance, particularly in the fields of nuclear waste immobilization and advanced construction materials. [ABSTRACT FROM AUTHOR]

A cross-scale hotspot ignition optimisation model is proposed to characterise the ignition stage in pressed energetic materials (PBXs). In the model, to account for heterogeneous effects, a multi-complex nested ubiquitiform optimisation model is developed to characterise the meso-structure feature of PBX and then used to predict the hotspot distribution of PBX. The intensity of hotspot is obtained based on meso-scale void collapse simulations, and the influence of the interaction between voids on hotspot intensity is considered. Then, a cross-scale hotspot ignition optimisation model is developed based on the hotspot distribution and the Arrhenius model. Based on the physical and chemical properties of the material and without the calibration of experimental parameters, the model can calculate the reaction rate of the hotspot ignition for PBX 9501. Finally, the upper and lower limits of ignition reaction rate are obtained by substituting maximum hotspot energy and minimum hotspot energy into the hotspot ignition optimisation model, which provides a certain basis for the safety evaluation and design of explosives. [ABSTRACT FROM AUTHOR]

This short article highlights unsolved problems of magnetic reconnection in collisionless plasma. Advanced in-situ plasma measurements and simulations have enabled scientists to gain a novel understanding of magnetic reconnection. Nevertheless, outstanding questions remain concerning the complex dynamics and structures in the diffusion region, cross-scale and regional couplings, the onset of magnetic reconnection, and the details of particle energization. We discuss future directions for magnetic reconnection research, including new observations, new simulations, and interdisciplinary approaches. [ABSTRACT FROM AUTHOR]