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UNLABELLED: Wastewater treatment plants (WWTPs) are indispensable biotechnology facilities for modern cities and play an essential role in modern urban infrastructure by employing microorganisms to remove pollutants in wastewater, thus protecting public health and the environment. This study conducted a 13-month bacterial community survey of six full-scale WWTPs in Hong Kong with samples of influent, activated sludge (AS), and effluent to explore their synchronism and asynchronism of bacterial community. Besides, we compared AS results of six Hong Kong WWTPs with data from 1,186 AS amplicon data in 269 global WWTPs and a 9-year metagenomic sequencing survey of a Hong Kong WWTP. Our results showed the compositions of bacterial communities varied and the bacterial community structure of AS had obvious differences across Hong Kong WWTPs. The co-occurrence analysis identified 40 pairs of relationships that existed among Hong Kong WWTPs to show solid associations between two species and stochastic processes took large proportions for the bacterial community assembly of six WWTPs. The abundance and distribution of the functional bacteria in worldwide and Hong Kong WWTPs were examined and compared, and we found that ammonia-oxidizing bacteria had more diversity than nitrite-oxidizing bacteria. Besides, Hong Kong WWTPs could make great contributions to the genome mining of microbial dark matter in the global wanted list. Operational parameters had important effects on OTUs abundance, such as the temperature to the genera of Tetrasphaera, Gordonia and Nitrospira. All these results obtained from this study can deepen our understanding of the microbial ecology in WWTPs and provide foundations for further studies. IMPORTANCE: Wastewater treatment plants (WWTPs) are an indispensable component of modern cities, as they can remove pollutants in wastewater to prevent anthropogenic activities. Activated sludge (AS) is a fundamental wastewater treatment process and it harbors a highly complex microbial community that forms the main components and contains functional groups. Unveiling who is there is a long-term goal of the research on AS microbiology. High-throughput sequencing provides insights into the inventory diversity of microbial communities to an unprecedented level of detail. At present, the analysis of communities in WWTPs usually comes from a specific WWTP and lacks comparisons and verification among different WWTPs. The wide-scale and long-term sampling project and research in this study could help us evaluate the AS community more accurately to find the similarities and different results for different WWTPs in Hong Kong and other regions of the world.

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

Integrated energy systems enable the complementary utilization of various forms of energy. With the rapid increase in installed capacity of distributed generations, their intermittency and randomness have posed significant challenges to the operational efficiency and safety of the system. To address the impact of uncertainties in both energy supply and demand on the economic scheduling of building integrated energy systems, this study first models the operational characteristics of each subsystem, including the power grid, natural gas network, and various coupled equipments. Next, a thermal cell model of the building users is constructed based on the quantitative relationship among temperature, thermal radiation, and thermal load. Then, a day-ahead optimization scheduling model for the building integrated energy systems is established with the objective of minimizing the operating cost. A chance-constrained programming approach is employed to convert the non-linear scheduling model into a mixed-integer second-order cone programming problem easy to solve. Finally, simulation analysis is conducted in the Python environment using the CPLEX solver. The results demonstrate that the proposed model and solution method are capable of effectively characterizing and addressing uncertainty risks in the system, facilitating the consumption of renewable energy, and improving the economic efficiency of system operation.

Objective To achieve reliable control of traction and braking key systems in trains while suppressing the impact of communication delay on train control, a discrete RBF (radial basis function) adaptive robust sliding mode control (abbreviated as ARSMC) method is proposed for critical network systems in high-speed trains. Method The traction and braking network systems in high-speed trains are introduced. An ARSMC method considering fractional delay terms is designed, and its stability is analyzed. The proposed discrete RBF ARSMC method is jointly simulated and analyzed using configuration software on a testing platform. Result & Conclusion The delay in discrete systems is divided into integer and fractional terms. By fully considering the fractional delay terms, the sliding mode traction and braking force with delay compensation under discrete approximate law is derived. The unknown nonlinear function in which is accurately approximated using an RBF neural network with adaptive adjustment characteristics. To suppress strong disturbances encountered during train operation, the sliding mode traction and braking force based on disturbance observer is integrated into the model to enhance its disturbance rejection performance. The proposed discrete RBF ARSMC method demonstrates superior stability and responsiveness compared to other control methods, exhibiting more ideal delay compensation effect and robust performance.