(Purpose/Significance). This paper aims at the problems of inaccurate recommendation effect caused by data sparseness and cold start in the traditional collaborative filtering-based book personalized recommendation algorithm. So this paper proposes a collaborative filtering recommendation algorithm which improves the similarity solution method and the filling method of missing data. (Method/Process). By considering the influence of the users common rating book collection on the similarity calculation, the average rating value of all books is used as the threshold, and the users common rating weight is introduced into the users similarity calculation. As for data filling, according to the users average rating, the basic attributes such as the age and gender of users are coded, and then Euclidean distance is initially calculated, making hierarchical clustering on users. Whats more, Shope-one algorithm is used to calculate the filling value of the former m similar usersand add the weight value of the degree simultaneously to get the final filling value, so as to improve the data filling method. (Result/Conclusion). Experiments were carried out with the data set of Book-Crossing Data set through Python. The experimental results show that the improved collaborative filtering algorithm has a significantly improvement in the accuracy and quality of book recommendation.

Large-scale use of electric vehicles will greatly increase the traffic pressure on urban road network. Therefore, planning of charging stations for electric vehicles considering charging demand and transportation network is particularly important for the coordinated development of electric vehicles and intelligent transportation. Under the condition of bounded rationality, this paper considers such factors as the travel utility perception difference between the users of fuel vehicles and electric vehicles, the time-varying of traffic flow, the location and service level of charging stations. On this basis, combining the cumulative prospect theory, dynamic traffic flow allocation and charging demands, a two-level programming model is established to solve the problem of charging station site selection. The upper layer is a system optimal model, the goal is to minimize the travel time of the network. The lower model describes the time-variability of departure time and the randomness of charging and travel behaviors, establishes the dynamic user equilibrium model and designs the heuristic algorithm. The validity of the model and algorithm is verified by a numerical example. Through the simulation experiment, the optimal location scheme of charging station under different electric vehicle proportion is obtained, and the driving characteristics of two types of vehicles are analyzed. Compared with the traditional model, it is found that the charging station planning considering bounded rationality can achieve higher road network traffic efficiency with fewer charging piles.

To address the challenges in identifying effective fault features and achieving sufficient diagnostic accuracy and robustness in variable-speed printing press bearings, where complex mixed-condition vibration signals exhibit non-stationarity, strong nonlinearity, ambiguous time-frequency characteristics, and overlapping fault features across multiple operating conditions, this paper proposes an adaptive optimization signal decomposition method combined with dual-modal time-series and image deep feature fusion for variable-speed multi-condition bearing fault diagnosis. First, to overcome the strong parameter dependency and significant noise interference of traditional adaptive decomposition algorithms, the Crested Porcupine Optimization Algorithm is introduced to adaptively search for the optimal noise amplitude and integration count of ICEEMDAN for effective signal decomposition. IMF components are then screened and reorganized based on correlation coefficients and variance contribution rates to enhance fault-sensitive information. Second, multidimensional time-domain features are extracted in parallel to construct time-frequency images, forming time-sequence-image bimodal inputs that enhance fault representation across different dimensions. Finally, a dual-branch deep learning model is developed: the time-sequence branch employs gated recurrent units to capture feature evolution trends, while the image branch utilizes SE-ResNet18 with embedded channel attention mechanisms to extract deep spatial features. Multimodal feature fusion enables classification recognition. Validation using a bearing self-diagnosis dataset from variable-speed hybrid operation and the publicly available Ottawa variable-speed bearing dataset demonstrates that this method achieves high-accuracy fault identification and strong generalization capabilities across diverse variable-speed hybrid operating conditions.

In this paper, a conformal acoustic black hole (CABH) based on the Zhukovski mapping is proposed. The design of the CABH incorporates the ABH technique and the conformal mapping method and is realized by varying the plate thickness. The uniqueness of the CABH is that it has the function of ABH flexural wave cloaking, meanwhile, its vibration reduction performance is consistent with the ordinary ABH. This work validated the ABH flexural wave broadband cloaking performance of the CABH by the scattering measure, then experimentally verified the significant vibration reduction performance of the CABH above the cut-on frequency by the linear average acceleration level. The conformal mapping method extends the function of the ordinary ABH, and the CABH can be used to replace the ordinary ABH for multi-functional applications of the ABH. This study has potential applications in vibration control of perforated wall panels in mechanical equipment.