DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity.Here,we apply the Bloom Filter,a space-efficient probabilistic data structure,to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin-yang codec is developed.

Due to differences in the physical and chemical properties of CO2 and N2, along with a reduction in the momentum ratio between oxidant and fuel streams, the local extinction of oxy-fuel flames is significantly more pronounced compared to conventional air–fuel flames, which poses challenges in the design and operation of oxy-fuel burners. This study further validates the species-weighted flamelet/progress variable (FPV) model proposed in previous work [Jiang et al., 2023], particularly in oxy-fuel flames characterized by highly local extinction. Large eddy simulations were conducted on the Sandia oxy-fuel jet diffusion flame at various Reynolds numbers. The predictions are systematically compared with experimental data, and the influence of Reynolds number on local extinction is thoroughly analyzed. The results demonstrate that the numerical simulation effectively predicts mean temperature, species mass fractions, differential diffusion parameters (ZHC), and local extinction in oxy-fuel jet flames across a wide range of Reynolds numbers. The errors in predicted mean temperature and mass fractions exhibit a slight increase with rising Reynolds numbers, yet remain below 15 %. As the Reynolds number increases from 12,000 to 18,000, the predicted peak ZHC decreases by 30 %, and the beneficial effect of preferential diffusion of H2 weakens, while the adverse effect of CO2 on combustion becomes stronger.

Corals establish symbiotic relationships with microorganisms, especially endosymbiotic photosynthetic algae. Although other microbes have been commonly detected in coral tissues, their identity and beneficial functions for their host are unclear. Here, we confirm the beneficial outcomes of the inoculation of bacteria selected as probiotics and use fluorescence in situ hybridization (FISH) to define their localization in the coral Pocillopora damicornis. Our results show the first evidence of the inherent presence of Halomonas sp. and Cobetia sp. in native coral tissues, even before their inoculation. Furthermore, the relative enrichment of these coral tissue-associated bacteria through their inoculation in corals correlates with health improvements, such as increases in photosynthetic potential, and productivity. Our study suggests the symbiotic status of Halomonas sp. and Cobetia sp. in corals by indicating their localization within coral gastrodermis and epidermis and correlating their increased relative abundance through active inoculation with beneficial outcomes for the holobiont. This knowledge is crucial to facilitate the screening and application of probiotics that may not be transient members of the coral microbiome.

Memristive devices are gaining importance in the semiconductor industry for applications in information storage, artificial intelligence cryptography and telecommunication. Memristive devices fabricated by solution-processing methods can be integrated into a wide variety of large-area substrates, which has motivated their use in applications requiring flexible, stretchable, transparent and biocompatible devices. Several studies on solution-processed memristors have claimed excellent electrical performance; however, in many cases such claims are based on scarce measurements conducted on only one device, using unreliable testing protocols or using device structures that are too large for the target applications. Understanding the reliability of a memristive structure is important to avoid hyped expectations, attract potential investments in such technology, and realistically understand its potential impact on society and on the market. In this Perspective, we analyse which solution-processed memristors have so far exhibited the highest and most reliable electronic performance, irrespective of the type of material used and the application targeted. For that group of memristors, we also discuss the switching mechanism and potential applications, as well as possible improvements in terms of device technology. We describe the outlook of this field with aims of increasing the impact and technology readiness of solution-processed memristors

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.