Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Thermomechanical response of aluminum alloys under the combined action of tensile loading and laser irradiations

This study reports the investigation of the thermomechanical behavior of aluminum alloys （AI-1060, A1-6061, and A1-7075） under the combined action of tensile loading and laser irradiations. The continuous wave ytterbium fiber laser （wavelength 1080 nm） was employed as the irradiation source, while tensile loading was provided by the tensile testing machine. The effects of various pre-loading and laser power densities on the failure time, temperature distribution, and the deformation behavior of aluminum alloys are analyzed. The experimental results represent the significant reduction in failure time for higher laser power densities and for high preloading values, which implies that preloading may contribute a significant role in the failure of the material at elevated temperature. Fracture on a microscopic scale was predominantly ductile comprising micro-void nucleation, growth, and coalescence. The AI-1060 specimens behaved plastically to some extent, while A1-6061 and A1-7075 specimens experienced catastrophic failure. The reason and characterization of ma- terial failure by tensile and laser loading are explored in detail. A comparative behavior of under-tested materials is also investigated. This work suggests that studies considering only combined loading are not enough to fully understand the mechanical behavior of under-tested materials. For complete characterization, one should consider the effect of heating as well as loading rate and the corresponding involved processes with the help of thermomechanical coupling and the thermal elastic-plastic theory.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Molecular mechanism and promising treatments of chronic prostatitis/chronic pelvic pain syndrome:An exploratory bibliometric analysis and literature review of preclinical studies

Background:As one of the most common diseases in urology,a large number of preclinical studies have been accumulated to explore the etiological mechanism and potential intervention of chronic prostatitis/chronic pelvic pain syndrome(CP/CPPS).Methods:In this study,we systematically evaluated the current status of preclinical research on CP/CPPS through bibliometrics analysis using VOSviewer and Citespace.Characteristics of publication such as year,country/region,institution,author,journal,citation,and keywords were analyzed.Based on the bibliometrics analysis results of keywords,we summarized the possible mechanisms and promising treatments for CP/CPPS narratively.Results:According to the results of this study,the most common mechanisms involved in CP/CPPS were as follows:Disturbed immune and inflammation mediators,immune cell dysfunction,oxidative stress,dysregulated signaling pathways,apoptosis,gut microbiota,and testosterone metabolism.Traditional Chinese Medicine and extracorporeal shock wave therapy have important potential in the treatment of CP/CPPS.Conclusion:Further translational studies targeting the above mechanisms and validating the objective efficacy of potential treatments indicated by preclinical studies in clinical patients are needed in the future.

Denitrification and microbial community in MBBR using A. donax as carbon source and biofilm carriers for reverse osmosis concentrate treatment

In this study,raw Arundo donax(A.donax)pieces were applied as carbon source and biofilm carriers for denitrification in a lab-scale moving bed biofilm reactor(MBBR)for the treatment of reverse osmosis concentrate gathered from local wastewater reuse plant.At stable phase(about 60 days),efficient denitrification performance was obtained with73.2%±19.5%NO3--N average removal and 8.10±3.45 g N/(m3·day)NO3--N average volumetric removal rate.Mass balance analysis showed that 4.84 g A.donax was required to remove 1 g TN.Quantitative real-time PCR analysis results showed that the copy numbers of 16S r-RNA,narG,nirS,nosZ and anammox gene of carrier biofilm and suspended activated sludge in the declination phase(BF2 and AS2)were lower than those of samples in the stable phase(BF1 and AS1),and relatively higher copy numbers of nirS and nirK genes with lower abundance of narG and nosZ genes were observed.High-throughput sequencing analysis was conducted for BF2 and AS2,and similar dominant phyla and classes with different abundance were obtained.The class Gammaproteobacteria affiliated with the phylum Proteobacteria was the most dominant microbial community in both BF2(52.6%)and AS2(41.7%).The PICRUSt prediction results indicated that 33 predictive specific genes were related to denitrification process,and the relative abundance of 18 predictive specific genes in BF2 were higher than those in AS2.

N2O emission in partial nitritation-anammox process

Nitrous oxide(N2O)is one of the significant greenhouse gases,and partial nitritation-anammox(PNA)process emits higher N2O than traditional nitrogen removal processes.N2O production in PNA mainly occurs in three different pathways,i.e.,the ammonia oxidizing bacteria(AOB)denitrification,the hydroxylamine(NH2 OH)oxidation and heterotrophic denitrifiers denitrification.N2O emission data vary significantly because of the different operational conditions,bioreactor configurations,monitoring systems and quantitative methods.Under the common operational parameter scopes of PNA,N2O emission via NH2 OH oxidation dominates at relatively low dissolved oxygen(DO),low inorganic carbon(IC),high pH or low N02-concentration,while N2O emission via AOB denitrification dominates at relative higher DO,higher IC.lower pH or higher N2O-concentration.AOB are highly enriched while nitriteoxidizing bacteria(NOB)are rarely found in partial nitritation process,and the order Nitrosomonadales of AOB is the dominant group and N2O producer.Anammox bacteria,AOB and certain amount of heterotrophic denitrifying bacteria are observed in the anammox process,the genus Denitratisoma and the heterotrophic denitrifying bacteria in the deep layer of anammox granules are the dominant N2O generation bacteria.In one-stage PNA reactors,anammox bacteria account for a large fraction of the biomass,AOB account for small portion,and NOB account for even less.The microbial community,diversity and N2O producers in one-stage PNA reactors are similar with those in two-stage PNA reactors.The dominant anammox bacteria,AOB and NOB in PNA are the species Candidatus Brocadia,the genera of Nitrotoga,Nitrospira and Nitrobacter,and the genus Nitrosomonas,respectively.The relations between N2O emission pathways and microbial communities need further study in the future.