Postsynaptic density (PSD) is an electron-dense structure that contains various scaffolding and signaling proteins. Shank1 is a master regulator of the synaptic scaffold located at glutamatergic synapses, and has been proposed to be involved in multiple neurological disorders. In this study, we investigated the role of shank1 in an in vitro Parkinson's disease (PD) model mimicked by 6-OHDA treatment in neuronal SN4741 cells. The expression of related molecules was detected by western blot and immunostaining. We found that 6-OHDA significantly increased the mRNA and protein levels of shank1 in SN4741 cells, but the subcellular distribution was not altered. Knockdown of shank1 via small interfering RNA (siRNA) protected against 6-OHDA treatment, as evidenced by reduced lactate dehydrogenase (LDH) release and decreased apoptosis. The results of RT-PCR and western blot showed that knockdown of shank1 markedly inhibited the activation of endoplasmic reticulum (ER) stress associated factors after 6-OHDA exposure. In addition, the downregulation of shank1 obviously increased the expression of PRDX3, which was accompanied by the preservation of mitochondrial function. Mechanically, downregulation of PRDX3 via siRNA partially prevented the shank1 knockdowninduced protection against 6-OHDA in SN4741 cells. In summary, the present study has provided the first evidence that the knockdown of shank1 protects against 6-OHDA-induced ER stress and mitochondrial dysfunction through activating the PRDX3 pathway.

When a submersible is sitting on a seabed, it could lose buoyancy because of the bottom-sitting adsorption effect. In this article, a numerical calculation model and experimental scheme for eliminating the bottom-sitting adsorption effect of under-sea equipment were established. An analysis of the hydrostatic pressure variation on a submersible’s bottom was carried out, and a submerged water jet which was based on the method of soil liquefaction was proposed to solve the problem of reducing hydrostatic pressure. It was shown that a water jet could liquefy soil to restore hydrostatic pressure on the submersible’s bottom, and there was an optimal jet velocity to form the largest liquefied soil thickness. A rectangular pulsed jet was the best way to liquefy soil in terms of efficiency and the liquefaction degree, which can be seen from the calculation of the two-dimensional two-phase flow. Through the calculation of the three-dimensional two-phase flow, it was found that the soil liquefaction developed from the periphery to the center, and a variation in jet liquefaction with the top wall constraint was obtained. Finally, an experiment was carried out to prove that a submerged water jet could eliminate the bottom-sitting adsorption effect of a submersible. The results showed that the submerged jet was an efficient way to liquefy soil, and a submersible could quickly recover hydrostatic pressure on the bottom and refloat up independently.

Autonomous collision avoidance (CA) for maritime autonomous surface ships (MASSs) in complex navigational environments requires accurate environment modelling and efficient CA algorithms. This study proposes a fast construction method for complex environmental potential field models applicable to vector charts. It accurately represents all complex static obstacles, including concave polygons, and dynamically adjusts the range of influence of a potential field according to its danger level. For dynamic ships, a dynamic obstacle modelling method, based on the quaternion ship domain, is designed to highlight the uncertain motion characteristics of ships. Based on the coupled modelling of dynamic and static obstacles, the virtual potential field of target ships, constrained by the International Regulations for Preventing Collisions at Sea (COLREGs), was constructed. By combining the first-order Nomoto model and optimal field prediction strategy, an optimal CA path, satisfying ship dynamics and COLREGs constraints, was realised, and the local optimal problem of the traditional artificial potential field method was solved. Simulation results show that the proposed method accomplishes fast and accurate modelling of complex environments and realises autonomous path planning and real-time multivessel CA tasks for MASSs in complex water considering the COLREGs constraints. Thus, the proposed method can be implemented in actual ships.

In this paper, the relationship between room temperature and outside temperature, wall temperature, system operation, and energy consumption is investigated through mathematical modeling and simulation experiments. The thermodynamic parameters of a typical room are used in the study, and the characteristics of the heating and cooling system are considered. 1. This paper analyses the variation of room temperature, wall temperature, switching state, and heating power with time. The results show that the room and wall temperatures are influenced by the external temperature and system operation, while the switching state and heating power are regulated by the room temperature. The correlation between temperature and heating power is quantified by calculating the correlation coefficient matrix. The results show that there is a positive correlation between room temperature and heating power, while there is a negative correlation between wall temperature and room temperature and heating power. 2. This paper investigates the effect of external temperature on room temperature and wall temperature. The results show that an increase in external temperature leads to a decrease in room temperature and wall temperature. In addition, it is found that the indoor temperature is more sensitive to changes in external temperature within the range of changes in external temperature. 3. It is based on the steady-state solution curves between temperature and external temperature, as well as thermodynamic plots of system operation and energy consumption. The visual presentation of the effect of external temperature on the system provides a reference for optimizing building energy use and designing efficient heating and cooling systems. This study provides insight into the relationship between temperature change and energy transfer in building thermodynamic processes, guiding for achieving sustainable energy utilization and reducing environmental impacts. It is of great significance for optimizing the design of building energy systems and improving the efficiency of energy use.

Efficient and effective prediction of propeller performance, especially for hydrodynamic performance and fluctuating pressure, have always been areas of interest for ship engineers and researchers. With the rapid blooming of machine learning based surrogate models, it is important to identify proper features that can represent crucial information from numerous features of propellers' geometry design. This paper used five different dimension reduction methods to conduct the feature selection process from propellers geometry. The reduced features are then used as inputs for a random forest based surrogate model to predict both the hydrodynamic performance and fluctuation pressure under cavitation. The experiment results on three test propellers showed that dimension reduction methods such as factor analysis, principal component analysis and random forest feature selection could yield a higher precision for the surrogate model compare to using all features or the main features selected by experience, while manifold learning methods such as isometric feature mapping and locally linear embedding were not good at extracting propellers' geometry. This paper innovatively proposes dimension reduction methods that can automatically extract main features from propellers' complex geometry. With these main features selected, Artificial Intelligence models can yield a higher precision on propellers’ performance predication. Furthermore, these dimension reduction methods have high generalization ability on different types of propellers, which enables a smarter and more cost-efficient way for the preliminary design process of ship propellers.

This paper presents a novel multimodal tensor fusion network based on the self-attention mechanism (MTFN-SAM) for classroom fatigue recognition. The LSTM network is first employed to learn the fatigue information of different modalities over time. Then, a self-attention mechanism is utilized to capture the connections within the modal features. Finally, a multimodal tensor fusion layer is employed to fuse features from multiple modalities, including electroencephalogram (EEG), heart rate (HR), galvanic skin response (GSR), and acceleration (ACC). The classification accuracy of the experimental results was compared with TFN, LWF, LSTM, and other fusion algorithms, confirming that the proposed MTFN-SAM network had better performance in multimodal feature fusion. Additionally, we found that as the number of modalities increased, the information contained in the multimodal fusion became more abundant, as confirmed by the experimental results (94.11% accuracy after EEG+HR+GSR+ACC fusion). Thus, the method could efficiently identify students’ classroom fatigue.

The ultimate strength of medium-thick composite pressure shells under hydrostatic pressure was studied by experimental test and finite element method in this paper. The ultimate strength, damage form and strain response of cylindrical shells were obtained by hydrostatic destruction test, which carried out for two T700 carbon fiber/epoxy composite cylindrical pressure shells with different length. Meanwhile, finite element models were built to predict the strength failure pressure based on the progressive damage mechanism of composite materials and its initial defects. The finite element results were in close agreement with the experimental results. A parametric study was carried out to discuss the effect of radius-length ratio and radius-thickness ratio on ultimate strength. Based on the numerical results, an empirical formula was proposed as a function of radius-length ratio and radius-thickness ratio to predict the ultimate strength of medium-thick composite pressure shells.

Fatigue failure is a typical failure mode of welded structures. It is of great engineering significance to predict the remaining fatigue life of structures after a certain period of service. In this paper, a two-stage hybrid deep learning approach is proposed only using the response of structures under fatigue loading to predict the remaining fatigue life. In the first stage, a combination of convolutional neural network (CNN), squeeze-and-excitation (SE) block, and long short-term memory (LSTM) network is employed to calculate health indicator values based on the current measured data sequence. In the second stage, a particle filtering-based algorithm is utilized to predict the remaining fatigue life using the previously calculated health indicators. Experimental results on different welded specimens under the same loading conditions demonstrate that the hybrid deep learning approach achieves superior prediction accuracy and generalization ability compared to CNN, LSTM, or CNN-LSTM models in the first stage. Moreover, the average relative deviation between the predicted and actual fatigue life is less than 6% during the final quarter of the crack propagation and fracture stage.