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Multiple objectives optimization of frequency selective surface (FSS) structures is challenging in electromagnetic wave filter design. For example, one of the sub-objectives, the sidelobe level (SLL), is critical to directional anti-interference, which is complicated and becomes the bottleneck for radar design. Here, we established a dynamic algorithm for fitness function to automatically adjust the weights of multiple objectives in the optimization process of FSS structures. The dynamic algorithm could efficiently evaluate the achieving probability of sub-objectives according to the statistical analysis of the latest individual distribution so that the fitness function could automatically adjusted to focus on the sub-objective difficult to optimize, such as SLL. Computational results from the dynamic algorithm showed that the efficiency of multi-objective optimization was greatly improved by 213%, as compared to the fixed-weighted algorithm of the fitness function. Specifically for SLL, the efficiency rate increased even better, up to 315%. More interestingly, the FSS structures were most improved while picking median value or golden section value as the reference value. Taken together, the current study indicated that the dynamic algorithm with fitness function might be a better choice for FSS structural optimization with SLL suppression and potentially for the better design of lower SLL radar.

In recent years, deep convolutional neural networks (CNNs) have been widely applied to handle low-level vision problems. However, most existing CNN-based approaches can either handle single degeneration each time or treat them jointly through feature entangling, thus likely leading to poor performance when the actual degradation is inconsistent with hypothetical degradation condition. Furthermore, feature coupling will bring a large amount of computation, which may make the methods impractical to real-time mobile scenarios. In order to address these problems, we propose a deep decoupled cooperative learning model which can not only develop the corresponding recover network to deal with each degradation, but also flexibly handle multiple degradations at the same time. Thus, our approach can achieve disentangling and synthesizing single image super-resolution and motion deblurring, which has high practicability. We evaluate the proposed approach on various benchmark datasets, covering both natural images and synthetic images. The results demonstrate its superiority, compared to the state-of-the-art, where image SR and motion deblurring can be accomplished effectively concurrently. The source code of the work is available at https://github.com/hengliusky/Cooperative-Learning-Deblur-SR .

Eutrophication of lakes is one of the most common pressures affecting aquatic biodiversity and ecosystem services. Poyang Lake is of great importance for maintaining regional ecological balance; however, algal blooms have become major environmental problems in this basin, and they are also caused by a variety of problems including biodiversity decline, toxin production and possibly unsafe drinking water. In this study, the community structure of phytoplankton and physicochemical parameters were investigated in different eutrophication area, Poyang Lake Basin. The results showed that the number of phytoplankton species had decreased, but the density and biomass were increased in the current period. The assemblage structure of phytoplankton showed significantly seasonal and spatial variability. The assemblage structure of phytoplankton was related to dissolved oxygen, nitrate nitrogen, chlorophyll-a and ammonium nitrogen, based on redundancy analysis. The study showed that the phytoplankton community structure was influenced by algal blooms and water quality factors. This study provides an important reference for increased biomonitoring and ecohydrological studies in this basin.

The time-interleaved analog-to-digital conversion (TIADC) technique is an effective method for increasing the sampling rate in a waveform digitization system. In this study, a 20-Gsps TIADC system was designed. A wide-bandwidth performance was achieved by optimizing the analog circuits, and a sufficient effective number of bits (ENOB) performance guaranteed using the perfect reconstruction algorithm for mismatch error correction. The proposed system was verified by tests, and the results indicated that a − 3 dB bandwidth of 6 GHz and the ENOB performance of 8.7 bits at 1 GHz and 7.6 bits at 6 GHz were successfully achieved.

The Yangtze River is a biodiversity hotspot, and it has the most diverse fish assemblages on Earth. Dam construction is one of the main causes of fish biodiversity decline. The Three Gorges Dam (TGD) is the world’s largest hydroelectric dam, which is a cause of concern for ecologists worldwide, mainly regarding the negative impacts on biodiversity and the ecological processes in the region. Information on the biodiversity patterns of fish is vital for informing conservation and management strategies. However, few studies have articulated the principal mechanisms underlying the interactions between the biodiversity patterns of fish and anthropogenic habitat alterations after the TGD construction in the Yangtze River Basin. Here, we aimed to analyze the changes in the biodiversity patterns of the fish in the historical and current periods and to explore the effects of geographical and climate characteristics on the biodiversity patterns. The results showed that the fish species diversity in the current period has experienced a significant decrease. An average of 45.3 native species and 19.3 endemic species have been extirpated in the Yangtze River Basin. A total of 113 fish species have been classified as Critically Endangered (22), Endangered (39), Vulnerable (29) and Near Threatened (23). In addition, the alpha and gamma diversity have also decreased significantly. The analysis of the beta diversity showed a significant trend towards taxonomic homogenization. We found significant effects of geographical distance, temperature, precipitation and altitude on the beta diversity. This study will provide important information for the conservation and management of fish biodiversity in the Yangtze River Basin and other areas.

The water Cherenkov detector array (WCDA) is an important part of the large high-altitude air shower observatory (LHAASO), which is in a research and development phase. The central scientific goal of LHAASO is to explore the origin of high-energy cosmic rays of the universe and to push forward the frontier of new physics. To simplify the WCDA’s readout electronics, a prototype of a front-end readout for an application-specific integrated circuit (ASIC) is designed based on the time-over-threshold method to achieve charge-to-time conversion. High-precision time measurement and charge measurement are necessary over a full dynamic range [1–4000 photoelectrons (P.E.)]. To evaluate the performance of this ASIC, a test system is designed that includes the front-end ASIC test module, digitization module, and test software. The first module needs to be customized for different ASIC versions, whereas the digitization module and test software are tested for general-purpose use. In the digitization module, a field programmable gate array-based time-to-digital converter is designed with a bin size of 333 ps, which also integrates an inter-integrated circuit to configure the ASIC test module, and a universal serial bus interface is designed to transfer data to the remote computer. Test results indicate that the time resolution is better than 0.5 ns, and the charge resolution is better than 30% root mean square (RMS) at 1 P.E. and 3% RMS at 4000 P.E., which are beyond the application requirements.