A new meta-heuristic algorithm, Cuckoo Catfish Optimizer (CCO), is proposed for numerical optimization problems. It simulates the search, predation, and parasitic behavior observed in cichlids. Early iterations of the algorithm focus on executing a multidimensional enveloping search strategy and a compressed space strategy, combined with an auxiliary search strategy to efectively limit the escape space of cichlids. This phase ensures extensive exploration of the solution space. In the intermediate stage of iteration, the algorithm uses a transition strategy to promote a smooth transition from exploration to exploitation, endowing the algorithm with both a certain degree of exploration capability and exploitation capability. In later stages, the algorithm uses chaotic predation mechanisms to create disturbances around cichlids to improve the exploitation of optimal solutions. Throughout the entire optimization process, the guidance, parasitism, and death mechanisms of individuals are integrated, allowing individuals to adjust their positions in real-time and improve the overall convergence accuracy. This paper rigorously evaluates the performance of CCO through 23 classic test functions and three CEC test suites. The experimental results show that compared with 11 famous algorithms and 10 novel improved algorithms, CCO can obtain the optimal solution in 91.52% of the test functions, demonstrating its excellent ability in solving various numerical optimization problems. Additionally, through the successful application to 6 mechanical optimization problems, 3 photovoltaic cell parameter optimization problems, and 1 path opti- mization problem, the competitiveness of CCO in solving real-world problems is verified and highlighted. The CCO source code can be downloaded here: https://ww2.mathworks.cn/matlabcentral/fileexchange/176828-cuckoo-catfish-optimizer-a-new-meta-heuristic-optimization

The study of the architectural heritage of the Chinese diaspora has an important role and significance in China’s historical and cultural background in the preservation of cultural data, the restoration of images, and in the analysis of human social and ideological conditions. The images from the architectural heritage of the Chinese diaspora usually include frescos, decorative patterns, chandelier base patterns, various architectural styles and other major types of architecture. Images of the architectural heritage of the Chinese diaspora in Jiangmen City, Guangdong Province, China are the research object of this study. A total of 5073 images of diaspora Chinese buildings in 64 villages and 16 towns were collected. In view of the fact that different types of image vary greatly in features while there are only small differences among the features of the same type of image, this study uses the depth learning method to design the Convolutional Neural Network Attention Retrieval Framework (CNNAR Framework). This approach can be divided into two stages. In the first stage, the transfer learning method is used to classify the image in question by transferring the trained parameters of the Paris500K datasets image source network to the target network for training, and thus the classified image is obtained. The advantage of this method is that it narrows the retrieval range of the target image. In the second stage, the fusion attention mechanism is used to extract the features of the images that have been classified, and the distance between similar images of the same type is reduced by loss of contrast. When we retrieve images, we can use the features extracted in the second stage to measure the similarities among them and return the retrieval results. The results show that the classification accuracy of the proposed method reaches 98.3% in the heritage image datasets of the JMI Chinese diaspora architectures. The mean Average Precision (mAP) of the proposed algorithm can reach 76.6%, which is better than several mainstream model algorithms. At the same time, the image results retrieved by the algorithm in this paper are very similar to those of the query image. In addition, the CNNAR retrieval framework proposed in this paper achieves accuracies of 71.8% and 72.5% on the public data sets Paris500K and Corel5K, respectively, which can be greatly generalized and can, therefore, also be effectively applied to other topics datasets. The JMI architectural heritage image database constructed in this study, which is rich in cultural connotations of diaspora Chinese homeland life, can provide strong and reliable data support for the follow-up study of the zeitgeist of the culture reflected in architecture and the integration of Chinese and Western aesthetics. At the same time, through the rapid identification, classification, and retrieval of precious architectural images stored in the database, similar target images can be retrieved reasonably and accurately; then, accurate techniques can be provided to restore old and damaged products of an architectural heritage.

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) an improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.