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Autonomous underwater helicopters (AUHs) are complex electromechanical systems consisting of multiple interconnected sub-disciplines, posing a significant challenge for traditional sequential design strategies in managing their interactions. To address this challenge, a multidisciplinary collaborative optimization (MCO) strategy is proposed, integrating structural, hydrodynamic, and energy disciplines to enhance AUH cruising range. Additionally, a novel approximation model is introduced, combining the Logistic-Tent chaos sparrow search algorithm (LTC-SSA) with the backpropagation neural network (BPNN) algorithm. This method aims to replace costly computational fluid dynamics (CFD) and structural mechanics calculations, thus reducing AUH optimization costs. The laminate design of the composite pressure hull follows classic lamination theory (CLT) and the Tsai-Wu failure criterion. Results demonstrate that the multidisciplinary co-design strategy significantly reduces optimization time costs of AUH, from several months to several days. Furthermore, the proposed LTC-SSA-BPNN algorithm accurately evaluates sailing resistance and Tsai-Wu failure index, with maximum errors of 1.64% and 9.32% respectively. Additionally, the composite pressure shell achieves a 30.4% weight reduction and a 4.1% reduction in sailing resistance while maintaining structural stability. Moreover, the maximum cruising range increases by 34.4%. This strategy offers theoretical support for AUH conceptual design and promises performance enhancement in practical engineering applications.

Two hybrid back-propagation neural network (BPNN) models optimized by two heuristic search algorithms, namely genetic algorithm (GA-BP) and particle swarm optimization (PSO-BP), are proposed in this paper to predict radial maximum wall shear stress instead of traditional computational fluid dynamics (CFD) methods. The two proposed models are trained and validated using a database of 150 radial maximum wall-shear-stress values obtained via CFD simulations. The best fit model is identified from various BPNN models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), balancing the trade-off between goodness-of-fit and model complexity. The model performance is evaluated by MAE, RMSE, regression coefficient (R), and Nash-Sutcliffe efficiency (NSE). The best fit model is a three-layered BPNN model consisting of a 4:4:1 topology. In almost all evaluation indicators, the two hybrid BPNN methods outperform three existing algorithms, namely classical BPNN, random forest (RF), and gradient boosting decision tree (GBDT). Both PSO-BP and GA-BP can provide a more precise assessment of radial maximum wall shear stress, with maximum errors being 5.81% and 8.24% respectively. The proposed PSO-BP prediction model is promising and has great feasibility in predicting the radial maximum wall shear stress of UHP water-jet nozzle in engineering applications.

Abstract Background The major dose-limiting toxicity of paclitaxel, one of the most commonly used drugs to treat solid tumor, is painful neuropathy. However, the molecular mechanisms underlying paclitaxel-induced painful neuropathy are largely unclarified. Methods Paw withdrawal threshold was measured in the rats following intraperitoneal injection of paclitaxel. The qPCR, western blotting, protein or chromatin immunoprecipitation, ChIP-seq identification of NFATc2 binding sites, and microarray analysis were performed to explore the molecular mechanism. Results We found that paclitaxel treatment increased the nuclear expression of NFATc2 in the spinal dorsal horn, and knockdown of NFATc2 with NFATc2 siRNA significantly attenuated the mechanical allodynia induced by paclitaxel. Further binding site analysis utilizing ChIP-seq assay combining with gene expression profile revealed a shift of NFATc2 binding site closer to TTS of target genes in dorsal horn after paclitaxel treatment. We further found that NFATc2 occupancy may directly upregulate the chemokine CXCL14 expression in dorsal horn, which was mediated by enhanced interaction between NFATc2 and p300 and consequently increased acetylation of histone H4 in CXCL14 promoter region. Also, knockdown of CXCL14 in dorsal horn significantly attenuated mechanical allodynia induced by paclitaxel. Conclusion These results suggested that enhanced interaction between p300 and NFATc2 mediated the epigenetic upregulation of CXCL14 in the spinal dorsal horn, which contributed to the chemotherapeutic paclitaxel-induced chronic pain.