Precise adjustment of the pore size, damage repair, and efficient cleaning is all challenges for the wider application of inorganic membranes. This study reports a simple strategy of combining dry-wet spinning and electrosynthesis to fabricate stainless-steel metal–organic framework composite membranes characterized by customizable pore sizes, targeted reparability, and high catalytic activity for membrane cleaning. The membrane pore size can be precisely customized in the range of 14–212 nm at nanoscale, and damaged membranes can be repaired by targeted treatment in 120 s. In addition, advanced oxidation processes can be used to quickly clean the membrane and achieve 98% flux recovery. The synergistic actions of the membrane matrix and the selective layer increase the adsorption energy of active sites to oxidant, shorten the electron transfer cycle, and enhance the overall catalytic performance. This study can provide a new direction for the development of advanced membranes for water purification and high-efficiency membrane cleaning methods. 相似文献
Neural Computing and Applications - Considering some problems of local linear embedding methods in semi-supervised scenarios, a robust scheme for generating soft labels is designed and a... 相似文献
The Journal of Supercomputing - This study was to evaluate the performance of magnetic resonance imaging (MRI) reconstruction algorithm based on convolutional neural network (CNN) in the diagnosis... 相似文献
The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.
Laser shock forming (LSF) technology employs shock waves to form sheet metal into three-dimensional complex parts, and has application potential in manufacturing sheet metal parts. In this paper, the forming of 2024 aluminum alloy sheet with LSF was investigated through numerical and experimental methods. The numerical model was established with the commercial code ABAQUS/Explicit. The formed conical cup was obtained from the simulation, and validated by the experiment. With the verified numerical model, the deformation behaviors, including deformation velocity, sheet thickness variation and strain distribution, were studied. In addition, the influence of different shock wave pressures on the forming precision was also investigated. The experimental and numerical results show that the metal sheet loaded by shock wave can take the shape of the mold, and the non-uniform thickness is distributed in the formed cup. The investigations also display that there exists reverse deformation at the central region of deforming sheet owing to severe collision during LSF. In order to obtain formed part with better quality, an appropriate pressure of applied shock waves is required. 相似文献
Nano Research - Aggregation-induced emission luminogens (AIEgens) are fluorescent agents that are ideal for bioimaging and have been widely used for organelle targeting, cellular mapping, and... 相似文献