Sludge ratio affects the start-up performance and functional bacteria distribution of a hybrid CANON system

Venture financing risk assessment and risk control algorithm for small and medium-sized enterprises in the era of big data

The existing risk assessment and control methods of enterprise risk financing have a large error in mobile data, which leads to inaccurate risk assessment results and low-risk optimization control efficiency. In order to improve the accuracy of risk financing risk assessment for small and medium-sized enterprises (SMEs) and risk control optimization efficiency, this article proposes risk assessment and risk control algorithms for SMEs in the era of big data. Through verifying the information of the loan application and supplementing the data during the loan period, invoke the existing enterprise financing risk database, establish the SME venture financing risk assessment model; build the risk evaluation index system according to the characteristics of the enterprise production organization, process characteristics, and the development of the socioeconomic and technical environment; apply the GA–PSO algorithm to the design of the SME risk financing risk control scheme, and complete the SME risk financing risk assessment and risk control. The experimental results show that the risk optimization control efficiency of the control algorithm reaches more than 70%, and the risk assessment accuracy of SMEs reaches over 95%, and the runtime less than 80 ms, with good convergence performance of risk assessment and control, strong risk optimization control ability, and accurate evaluation effect.

Varied interspecies interactions between anammox and denitrifying bacteria enhanced nitrogen removal in a single-stage simultaneous anammox and denitrification system

The simultaneous anammox and denitrification (SAD) system has received growing interest for the enhanced nitrogen removal, while the ecological traits of microbial community including spatial distribution characteristics, assembly processes and interspecies interactions have not been fully unraveled. The present study applied metagenomics and ecological analysis methods to gain the ecological traits of microbial communities in the SAD system across different organic substrate loadings. Results showed that organic matter significantly affected the bioreactor performance, and the optimal total nitrogen removal efficiency reached 93.4 ± 0.7% under the COD concentrations of 180 ± 18.2 mg/L. Functional organisms including Candidatus Brocadia (3.9%), Denitratisoma (1.6%), Dokdonella (4.4%) and Thauera (4.6%) obviously enriched under the optimal organic loading conditions. Moreover, microbial communities were significantly governed by deterministic process under high organic concentrations, and the denitrifying organisms displayed important ecological roles in the communities. Although anammox bacteria obviously enriched at the middle of bioreactor, it possessed the highest expression activities at both bottom and middle sites. Denitrifying bacteria that enriched at the bottom sites strongly achieved nitrate reduction and provided nitrite for anammox bacteria, while these organisms trended to compete nitrite with anammox bacteria at the middle site. These findings highlight the importance of microbial ecology in the SAD systems, which may expand our understanding of the synergistic patterns between anammox and denitrifying bacteria.