Aperture-Adjustable and Repairable Metal-Based MOFs Catalysis Membrane for Water Purification

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.     

Applying deep learning in football ankle injury for value of high-power magnetic resonance bioimaging evaluation

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...     

A Survey on Long-Tailed Visual Recognition

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.

     

高效能空气净化器评价技术要求国家标准研究

本文论述了编制高效能空气净化器产品评价技术要求国家标准的意义和作用,介绍了标准研制的总体思路和原则,高效能空气净化器的定性和定量评价指标要求,测试、计算和评价方法等,通过国标的研制与实施,将引领并促进高效能空气净化器产品的生产与应用.     

Aggregation-induced emission luminogen-assisted stimulated emission depletion nanoscopy for super-resolution mitochondrial visualization in live cells

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...     

构建信息对抗专业的知识体系和实践教学体系

针对我校信息对抗技术专业的发展方向和培养定位,结合我校办学特色,提出该专业的知识体系和实践教学体系。通过对当前现状和存在问题的分析,研究探讨符合实际的专业教学和人才培养模式。     

Anybus串行网关在HOLLIAS MACS系统Profibus现场总线中的应用

介绍Anybus串行网关在石西油田油气处理站HOLLIASMACS系统中的应用。     

WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.