Optimisation Strategy of Carbon Dioxide Methanation Technology Based on Microbial Electrolysis Cells

Microbial Electrolytic Cell(MEC)is an electrochemical reaction device that uses electrical energy as an energy input and microorganisms as catalysts to produce fuels and chemicals.The regenerative electrochemical system is a MEC improvement system for methane gas produced by biological carbon sequestration technology using renewable energy sources to provide a voltage environment.In response to the influence of fluctuating disturbances of renewable electricity and the long system start-up time,this paper analyzes the characteristics of two strategies,regulating voltage parameter changes and activated sludge pretreatment,on the methane production efficiency of the renewable gas electrochemical system.In this system,the methane production rate of regenerative electrochemical system is increased by 1.4 times through intermittent boosting start-up strategy;based on intermittent boosting,the methane production rate of regenerative electrochemical system is increased by 2 times through sludge pyrolysis pretreatment start-up strategy,and the start-up time is reduced to 10 days.Meanwhile,according to the simulation test results of power input fluctuation and intermittency,the stability standard deviation of its system operation is 75%of the original one,and the recovery rate is about 1 times higher.This study can provide a theoretical basis and technical reference for the early industrial application of microbial CO_(2)methanation technology based on renewable energy.

Adaptive Region Boosting method with biased entropy for path planning in changing environment

Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting （ARB） is proposed to increase the sampling density for difficult areas with different strategies. What＇s more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.

DNA vaccine prime and replicating vaccinia vaccine boost induce robust humoral and cellular immune responses against MERS-CoV in mice

As of December 2022,2603 laboratory-identified Middle East respiratory syndrome coronavirus(MERS-CoV)infections and 935 associated deaths,with a mortality rate of 36%,had been reported to the World Health Organization(WHO).However,there are still no vaccines for MERS-CoV,which makes the prevention and control of MERS-CoV difficult.In this study,we generated two DNA vaccine candidates by integrating MERS-CoV Spike(S)gene into a replicating Vaccinia Tian Tan(VTT)vector.Compared to homologous immunization with either vaccine,mice immunized with DNA vaccine prime and VTT vaccine boost exhibited much stronger and durable humoral and cellular immune responses.The immunized mice produced robust binding antibodies and broad neutralizing antibodies against the EMC2012,England1 and KNIH strains of MERS-CoV.Prime-Boost immunization also induced strong MERS-S specific T cells responses,with high memory and poly-functional(CD107a-IFN-γ-TNF-α)effector CD8t T cells.In conclusion,the research demonstrated that DNA-Prime/VTT-Boost strategy could elicit robust and balanced humoral and cellular immune responses against MERS-CoV-S.This study not only provides a promising set of MERS-CoV vaccine candidates,but also proposes a heterologous sequential immunization strategy worthy of further development.

Towards multi-party targeted model poisoning attacks against federated learning systems

The federated learning framework builds a deep learning model collaboratively by a group of connected devices via only sharing local parameter updates to the central parameter server.Nonetheless,the lack of transparency in the local data resource makes it prone to adversarial federated attacks,which have shown increasing ability to reduce learning performance.Existing research efforts either focus on the single-party attack with impractical perfect knowledge setting and limited stealthy ability or the random attack that has no control on attack effects.In this paper,we investigate a new multi-party adversarial attack with the imperfect knowledge of the target system.Controlled by an adversary,a number of compromised devices collaboratively launch targeted model poisoning attacks,intending to misclassify the targeted samples while maintaining stealthy under different de-tection strategies.Specifically,the compromised devices jointly minimize the loss function of model training in different scenarios.To overcome the update scaling problem,we develop a new boosting strategy by introducing two stealthy metrics.Via experimental results,we show that under both perfect knowledge and limited knowl-edge settings,the multi-party attack is capable of successfully evading detection strategies while guaranteeing the convergence.We also demonstrate that the learned model achieves the high accuracy on the targeted samples,which confirms the significant impact of the multi-party attack on federated learning systems.