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At present, the lithium-ion power battery system is the core component of new energy vehicles, and an effective battery management system can improve electrical performance and safety performance for the vehicles. It is worth mentioning that battery cycle life is an important indicator of power battery safety. Moreover, the most direct parameter to characterize battery performance degradation is the remaining capacity of a battery. Therefore, remaining capacity prediction of the battery after multiple charge-discharge cycles has become a research hotspot. In a normal working process, the residual capacity of power batteries cannot be measured by direct testing means, so it is necessary to use battery characteristic parameters that are easy to measure to predict the residual capacity. Many scholars at home and abroad extract characteristic values from dynamic charge-discharge curves, but this method is easily affected by battery operating conditions. In order to avoid the influence of dynamic conditions, voltage increase during the resting time of the battery after the discharge period is used as a health factor to characterize the decline of battery capacity. The mapping model between health factors and battery remaining capacity is established by using backward propagation (BP) algorithm and a support vector machine (SVM). The test data of more than 500 cycles of charge-discharge of an in-service 42 Ah ternary lithium battery are taken as samples. Among the samples, the first 400 cycles of the data are set as training samples. Then, through the subsequent 111 cycles of the data, this paper predicts the remaining capacity of the battery by using the model of the BP algorithm and the SVM respectively. The data results and the change trend of the test data in the two predicted models are the same. The MSE for the model of BP and SVM are 1.4% and 0.6% respectively. The experiment results show that, after multiple charge-discharge cycles, there is an obvious linear relationship between the voltage rise sequence during the shelving period and the maximum remaining capacity of the ternary lithium battery. As health factors, voltage rise parameters can be a theoretical reference for the analysis of the remaining capacity of the same type of ternary lithium batteries in practical application. The BP and SVM models can be used to accurately predict the remaining capacity of batteries. Compared with the BP algorithm, the SVM algorithm has better performance when used for small-scale data, with more accurate and effective prediction. [ABSTRACT FROM AUTHOR]

This research fills the gap in understanding fluid–structure interaction (FSI) from wave diffraction forces on CALM buoys and cylindrical structures, based on the hydrodynamics with connections. Recently, there is an increased application of (un)loading marine hoses for Catenary Anchor Leg Moorings (CALM) buoy systems in the offshore industry due to the need for more flexible marine structures that are cost-saving, easier to install, and service. However, different operational issues challenge these hoses, like during hose disconnection. Also, the fluid behaviour was investigated based on the analytical and numerical models. The numerical modelling involves the boundary element method (BEM) and Orcaflex line theory. Hydrodynamic analysis is conducted on the disconnection-induced load response of marine bonded hoses during normal operation and accidental operation under irregular waves. A comparative study on hose performance during normal operation and accidental operation is also presented. Results of statistical analysis on CALM buoy system shows good motion characteristics. [ABSTRACT FROM AUTHOR]

Abstract Background p27Kip1 plays a major role as a negative regulator of the cell cycle. The regulation of p27Kip1 degradation is mediated by its specific ubiquitin ligase subunits S-phase kinase protein (Skp) 2 and cyclin-dependent kinase subunit (Cks) 1. However, little is known regarding the prognostic utility of p27Kip1, Skp2 and Cks1 expression in renal cell carcinoma. Methods Immunohistochemistry was performed for p27Kip1, Skp2 and Cks1 in tissue microarrays of 482 renal cell carcinomas with follow-up. The data were correlated with clinicopathological features. The univariate and multivariate survival analyses were also performed to determine their prognostic significance. Results Immunoreactivity of p27Kip1, Skp2 and Cks1 was noted in 357, 71 and 82 patients, respectively. Skp2 and Cks1 expression were not noted in chromophobe cancers. A strong correlation was found between Skp2 and Cks1 expression (P < 0.001), both of which were inversely related to p27Kip1 levels (P = 0.006 and P < 0.001), especially in primary and clear-cell cancers. Low p27Kip1 expression and Skp2 expression were correlated with larger tumor size and higher stage, as well as tumor necrosis. Cks1 expression was only correlated with tumor size. In univariate analysis, low p27Kip1 expression, Skp2 and Cks1 expression were all associated with a poor prognosis, while in multivariate analysis, only low p27Kip1 expression were independent prognostic factors for both cancer specific survival and recurrence-free survival in patients with RCC. Conclusion Our results suggest that immunohistochemical expression levels of p27Kip1, Skp2 and Cks1 may serve as markers with prognostic value in renal cell carcinoma.