Anomaly detection plays a crucial role in quality control within the manufacturing industry. Traditional computer vision approaches typically adopt reconstruction-based strategies, where the distribution of normal features is modeled to suppress the reconstruction of anomalous regions. Although these methods generally require fewer data, they often struggle to simultaneously improve reconstruction quality and detection performance. Experimental results indicate that high-level semantic context features exhibit strong capabilities in identifying anomalies, while low-level features contribute to the representation of global characteristics. Based on this, the paper uses discrete wavelet transform (DWT) to decompose images into low- and high-frequency components, extracting both global and local features. The input image is encoded in parallel using convolutional encoders and fused with the base features extracted through DWT. To further enhance detection performance, we propose a customized feature fusion module inspired by Feature Pyramid Networks (FPN), which integrates spatial and channel attention mechanisms to strengthen the model's ability to capture inter-feature relationships. Finally, extensive comparative and ablation experiments conducted on public benchmark datasets demonstrate the effectiveness of the proposed method.

Solving Singularly Perturbed Differential Equations (SPDEs) poses computational challenges arising from the rapid transitions in their solutions within thin regions. The effectiveness of deep learning in addressing differential equations motivates us to employ these methods for solving SPDEs. In this paper, we introduce Component Fourier Neural Operator (ComFNO), an innovative operator learning method that builds upon Fourier Neural Operator (FNO), while simultaneously incorporating valuable prior knowledge obtained from asymptotic analysis. Our approach is not limited to FNO and can be applied to other neural network frameworks, such as Deep Operator Network (DeepONet), leading to potential similar SPDEs solvers. Experimental results across diverse classes of SPDEs demonstrate that ComFNO significantly improves accuracy compared to vanilla FNO. Furthermore, ComFNO exhibits natural adaptability to diverse data distributions and performs well in few-shot scenarios, showcasing its excellent generalization ability in practical situations.

Most COVID-19 patients have a positive prognosis, but patients with additional underlying diseases are more likely to have severe illness and increased fatality rates. Numerous studies indicate that cancer patients are more prone to contract SARS-CoV-2 and develop severe COVID-19 or even dying. In the recent transcriptome investigations, it is demonstrated that the fructose metabolism is altered in patients with SARS-CoV-2 infection. However, cancer cells can use fructose as an extra source of energy for growth and metastasis. Furthermore, enhanced living conditions have resulted in a notable rise in fructose consumption in individuals' daily dietary habits. We therefore hypothesize that the poor prognosis of cancer patients caused by SARS-CoV-2 may therefore be mediated through fructose metabolism. Using CRC cases from four distinct cohorts, we built and validated a predictive model based on SARS-CoV-2 producing fructose metabolic anomalies by coupling Cox univariate regression and lasso regression feature selection algorithms to identify hallmark genes in colorectal cancer. We also developed a composite prognostic nomogram to improve clinical practice by integrating the characteristics of aberrant fructose metabolism produced by this novel coronavirus with age and tumor stage. To obtain the genes with the greatest potential prognostic values, LASSO regression analysis was performed, In the TCGA training cohort, patients were randomly separated into training and validation sets in the ratio of 4: 1, and the best risk score value for each sample was acquired by lasso regression analysis for further analysis, and the fifteen genes CLEC4A, FDFT1, CTNNB1, GPI, PMM2, PTPRD, IL7, ALDH3B1, AASS, AOC3, SEPINE1, PFKFB1, FTCD, TIMP1 and GATM were finally selected. In order to validate the model's accuracy, ROC curve analysis was performed on an external dataset, and the results indicated that the model had a high predictive power for the prognosis prediction of patients. Our study provides a theoretical foundation for the future targeted regulation of fructose metabolism in colorectal cancer patients, while simultaneously optimizing dietary guidance and therapeutic care for colorectal cancer patients in the context of the COVID-19 pandemic.

In this paper, a multi-cell joint optimization scheme based on pilot allocation is proposed in order to mitigate pilot contamination for multi- cell massive MIMO systems. This method measures interference of each pilot sequence caused by users multiplexing from adjacent cells exploiting the massive MIMO characteristics of fading channels. The scheme takes jointly optimizing a plurality of cells into account to ensure users in poor channel conditions suffering from less interference after the assignment, and improves the system performance with low computational complexity. Simulation results demonstrate the effectiveness of the proposed scheme.