Search

As a commonly used physical intervention, electrical stimulation (ES) has been demonstrated to be effective in the treatment of central nervous system disorders. Currently, researchers are studying the effects of electrical stimulation on individual neurons and neural networks, which are dependent on factors such as stimulation intensity, duration, location, and neuronal properties. However, the exact mechanism of action of electrical stimulation remains unclear. In some cases, repeated or prolonged electrical stimulation can lead to changes in the morphology or function of the neuron. In this study, immunofluorescence staining and Sholl analysis are used to assess changes in the neurite number and axon length to determine the optimal pattern and stimulation parameters of ES for neurons. Neuronal death and plasticity are detected by TUNEL staining and microelectrode array assays, respectively. mRNA sequencing and bioinformatics analysis are applied to predict the key targets of the action of ES on neurons, and the identified targets are validated by western blot analysis and qRT-PCR. The effects of alternating current stimulation (ACS) on neurons are more significant than those of direct current stimulation (DCS), and the optimal parameters are 3 μA and 20 min. ACS stimulation significantly increases the number of neurites, the length of axons and the spontaneous electrical activity of neurons, significantly elevates the expression of growth-associated protein-43 (GAP-43) without significant changes in the expression of neurotrophic factors. Furthermore, application of PI3K/AKT-specific inhibitors significantly abolishes the beneficial effects of ACS on neurons, confirming that the PI3K/AKT pathway is an important potential signaling pathway in the action of ACS.

The flow loss caused by the fan blades in a turbofan engine with a large bypass ratio is significant, and the wake considerably affects the inlet flow of downstream components. Surfaces with bird feather-like convergent–divergent (C–D) riblets have been proven to modulate the boundary layer flow by inducing counter-rotating rolling modes; however, the effects of these surfaces on the total pressure loss and wake turbulence of transonic compressor cascades remain unexplored. In this study, the effects of C–D-riblet surfaces on the total pressure loss and wake turbulence of a transonic compressor cascade were experimentally investigated using a five-hole probe and hot-wire measurements. The flow-loss-control effects of C–D-riblet surfaces with different characteristic lengths were also analyzed. The most significant reduction in the area-averaged total pressure loss (11.23%) was achieved using a C–D-riblet surface with a characteristic length of 30 μm at a Mach number of 0.94; this total pressure loss reduction corresponded to an increase in the mean velocity and a decrease in the turbulence intensity of the wake profile. Furthermore, the effectiveness of the loss control varied significantly with the spreading position of the C–D riblets. The optimal control effect was observed in the divergence-line region, and the control was slightly less effective as the measurement position neared the convergence line. This paper demonstrates the promising potential of using C–D riblets to achieve flow loss control in transonic compressor cascades. [ABSTRACT FROM AUTHOR]

The gradient of flow parameters in a transonic compressor cascade flow field varies significantly, especially in the region of shock waves, which causes a significant challenge to its high-precision flow field prediction. In this study, the position query-guided cross-modal flow field prediction model (PGCM) is proposed to effectively predict the flow field parameter distribution of a transonic compressor cascade. The PGCM utilizes the self-attention mechanism for the global and deep geometric feature extraction of configurations, which contributes to an in-depth understanding of the spatial relationships between coordinate points within the flow field, accurately capturing and analyzing the structural complexity of a compressor cascade flow. In addition, the PGCM integrates the cross-attention mechanism that establishes correlations between different input sequences, which enhances the performance of the model in querying and interpreting flow parameters at specific coordinates. The flow field prediction models are developed to predict the flow parameter distributions of different cascade geometries at Mach numbers of 0.78 and 0.93, respectively. The validation results indicate that the PGCM performs significantly better than the existing convolutional neural network and vision transformer, especially in the prediction of the pressure coefficient Cp distribution. The PGCM is adaptable to the variation of flow conditions and geometrical configurations efficiently and accurate in predicting the flow field of a compressor cascade. This paper demonstrates the promising potential of conducting the multi-modal information fusion to enhance the capability of flow field prediction. [ABSTRACT FROM AUTHOR]

In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the test datasets, we conducted an in-depth analysis of the geometric attributes of the cascades reconstructed using our framework, considering adjustments made to the geometric information of the point cloud. We fine-tuned the input using 1603 data points and quantified the contribution of each point. The outcomes reveal that variations in the suction side of the cascade have a significantly more substantial influence on the field results compared to the pressure side and explain the way graph neural networks work for cascade flow-field prediction, enhancing the comprehension of graph-based flow-field prediction among developers and proves the potential of graph neural networks in flow-field prediction on large-scale point clouds and design. [ABSTRACT FROM AUTHOR]

Electrorheological (ER) fluid, as an intelligent material, is a kind of suspension composed of polarized micro‐ and/or nanoparticles dispersed into insulating oil. In recent years, molybdenum disulfide (MoS2), as the hottest 2D material, has provided a new strategy to develop the high‐performance ER materials. 2D‐based ER composites can provide large surface to enhance the polarization. But the MoS2 ER material has many disadvantages such as low polarization strength and high electrical conductivity. To make up these defects, polyaniline (PANI) is used to combine with lamellar MoS2. Herein, PANI and MoS2 hybrid composite nanoparticles with special morphology are prepared by two‐step method. The coating material has special morphology, high interfacial polarization, specific surface area, and low density. The optimum reaction ratio is obtained by adjusting the proportion of MoS2 and PANI. When the proportion of PANI and MoS2 is 4:1, the material performance is best, in which the shear stresses can reach 150 Pa. In a word, it is proved that the material is an excellent electrorheological material. [ABSTRACT FROM AUTHOR]

As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles. [ABSTRACT FROM AUTHOR]

In this paper, the effects of citric acid (CA) and protein retarder (SC) on the properties of phosphogypsumsulfoaluminate cement composite cementitious system were investigated. The fluidity, setting time and compressive strength were tested to evaluate the workability and mechanical of composite system, and the change of conductivity, phase composition and microstructure were analyzed to clarify the influence mechanism of different retarders. The results show that the strength loss of composite cementitious system under the action of SC is smaller than that of CA when the same setting time is achieved. The addition of two retarders has a certain inhibitory effect on the dissolution of composite system during the hydration induction and the acceleration period, and CA has a greater inhibitory effect than SC at the same content. CA can cause gypsum dihydrate crystals to show a flat and coarse structure, which has a greater impact on the mechanical properties, while SC increases the overall scale of gypsum dihydrate crystals, with little change in crystal morphology, which has a smaller effect on the deterioration of the mechanical properties of composite system. [ABSTRACT FROM AUTHOR]