DNA N⁶-methyldeoxyadenosine (6 mA) modifications were first found more than 60 years ago but were thought to be only widespread in prokaryotes and unicellular eukaryotes. With the development of high-throughput sequencing technology, 6 mA modifications were found in different multicellular eukaryotes by using experimental methods. However, the experimental methods were time-consuming and costly, which makes it is very necessary to develop computational methods instead. In this study, a machine learning-based prediction tool, named csDMA, was developed for predicting 6 mA modifications. Firstly, three feature encoding schemes, Motif, Kmer, and Binary, were used to generate the feature matrix. Secondly, different algorithms were selected into the prediction model and the ExtraTrees model received the best AUC of 0.878 by using 5-fold cross-validation on the training dataset. Besides, the ExtraTrees model also received the best AUC of 0.893 on the independent testing dataset. Finally, we compared our method with state-of-the-art predictors and the results shown that our model achieved better performance than existing tools.

To investigate the influence of altering operational parameters on the transient flow characteristics within the flow channel of a solid–liquid two-phase centrifugal pump, this study employs a particle model based on the Euler–Euler method. Utilizing the standard k–e model, flow field simulations are conducted using the ANSYS-CFX software. Specifically, the study focuses on throttle regulation scenarios, monitoring and comparing the external and internal flow parameters of the solid–liquid two-phase pump with those of a clear water medium centrifugal pump. The results indicate notable modifications in head, efficiency, and shaft power due to the presence of solid particles in the two-phase flow. Decreasing flow rates during throttle regulation lead to fluctuations in pressure and turbulence energy distribution. Furthermore, under identical operational conditions, the variable working conditions of the solid–liquid pump result in increased flow rates of solid-phase particles near the impeller's outer edge, with particles shifting toward the middle and tail of the vane suction surface. This phenomenon exacerbates wear on the vane tail of the suction surface. Moreover, the study identifies that changing operational conditions in the solid–liquid dual-flow centrifugal pump contribute to increased axial forces, consequently leading to pump vibrations. Overall, this research elucidates the transient flow characteristics within the flow channel of solid–liquid two-phase centrifugal pumps under varied operational conditions, serving as a foundational reference for assessing the stability of such pumps.

To investigate the spatiotemporal evolution mechanism of the axial force on a centrifugal pump acting as a turbine, this study focuses on a single-stage single-suction centrifugal pump and applies dynamic mode decomposition (DMD) to decompose the flow field of the turbine impeller's axial force. The axial force of the impeller under three flow conditions, namely, 1.0Qd, 1.3Qd, and 1.6Qd, is extracted and analyzed. Results show that the DMD method can accurately extract the spatiotemporal coherent structural characteristics of the main modes of axial force, with the first five modes accounting for more than 99.97% of the total mode energy. Under the 1.3Qd condition, the flow field is stable, and the axial force remains constant over time with a very small degree of pressure pulsation. However, under the 1.6Qd condition, the flow field inside the pump becomes complex and unstable, leading to larger changes in axial force compared to the 1.0Qd condition, with an increase in 2.13 times. The amplitude of the pressure pulsation gradually decreases from the impeller inlet to the outlet under both 1.0Qd and 1.6Qd conditions, with vibration caused by the axial force mainly occurring at the impeller inlet. These findings provide a reference basis for improving the stability of centrifugal pumps acting as turbines.

In order to improve the hydraulic performance and operational stability of centrifugal pumps, this paper selects M192-400 centrifugal pumps as the research object to ensure that the centrifugal pump head, efficiency does not decrease and reduce the axial force as much as possible as the optimization goal, using the Plackett–Burman screening test will be centrifugal pump impeller balance hole diameter, radial position, circumferential position, and axial placement angle of four geometric parameters as optimization variables. The optimal Latin hypercubic sampling method was used to create a sample space of 90 groups, and the optimization variables were fitted to the optimization targets using the extreme learning machine. Multi-objective optimization is carried out by the Adaptive Non-dominated Sorting Genetic Algorithms III, and the optimal solutions of impeller balance hole diameter, radial position, circumferential position, and axial placement angle of 14 mm, 68.08 mm, −22°, and 6.62° are obtained by using the hierarchical analysis method and the validity of which is verified. Compared with the initial model, the optimized centrifugal pump increased head by 1.28%, increased efficiency by 0.17%, and reduced axial force by 96.47% to meet the optimization target. Before and after the optimization of the centrifugal pump wall pressure, cross-sectional pressure, velocity, and flow lines, turbulent kinetic energy distribution differences were analyzed. The optimization method of this paper makes the centrifugal pump hydraulic performance improved, the basic balance of the axial force, for the centrifugal pump optimization design to provide a reference basis.