RESUMEN
Optical multiplexing technology plays a crucial role in various fields such as data storage, anti-counterfeiting, and time-resolved biological imaging. Nevertheless, employing single-wavelength phosphorescence for multiplexing often results in spectral overlap among the emission peaks of various channels, which can precipitate crosstalk and misinterpretation in the information-decoding process, thereby compromising the integrity and precision of the encrypted data. This paper proposes a time-divided colorful multiplexing technology based on phosphorescent carbon nanodots with different colors and lifetimes. Using different luminescence colors to symbolize varying information levels helps achieve multitiered information encryption and storage. By modulation of the lifetime and the emission wavelength, intricate information can be encoded, thereby enhancing the intricacy and security of the encryption mechanism. By assigning different data bits to each color, more information can be encoded in the same physical space. This method enables higher-density information storage and fortifies encryption, ensuring the compactness and security of information.
RESUMEN
Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution. Based on CVSTA, we construct a multidimensional refinement GCN (MDR-GCN) that can improve the discrimination among channel-, joint-, and frame-level features for fine-grained actions. Furthermore, we propose a robust decouple loss (RDL) that significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse NTU-RGB + D 120 dataset and NTU-RGB + D X-view version. Our code is publicly available at https://github.com/dingyn-Reno/MDR-GCN.
RESUMEN
The biogeochemical cycle of nitrogen in the aquatic environment is the research hotspot in the world all the time. Nitrification and denitrification are the special processes of the microorganisms, and also the key steps in the nitrogen biogeochemical cycle possessing great significance in the freshwater ecosystem. In the processes of outbreaks of cyanobacterial blooms, total nitrogen, chlorophyll a, dissolved oxygen and pH decreased sharply, whilst dissolved organic carbon and ammonium nitrogen increased. The results of simulation of outbreaks of cyanobacterial blooms using micro-universe system in lab showed that the amoA gene abundance was reduced in the early stage and AOA was replaced by AOB gradually. Our results also showed that the amount of denitrifiers with nirS/nirK was elevated by also 100 times during the bloom outbreak, which can explain the promoted denitrification in the water during cyanobacterial bloom.
