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Microwave thermal therapy (MWTT) is a novel clinical treatment of tumors via inducing thermal effect and anti‐tumor immune response, exhibiting advantages of deep penetration, minimal invasion, and high heat conversion efficiency. However, effective ablation is difficult to cover the entire tumor area, and heat‐shock protein 90 (HSP90) is often upregulated after ablation to generate heat tolerance of sublethal tumors and weaken the induced antitumor immune effect, resulting in a high rate of tumor recurrence and metastasis. Herein, a multifunctional nanoregulator of metal phenolic network‐coated ZnS (ZnS@Ga‐tannic acid, ZGT) is proposed to release H2S for synergically enhancing MWTT. Under the costimulation of MW radiation and acidic tumor microenvironment, ZGT can improve the MW thermal effects of tumors to promote thermal damage and release H2S to induce tumor apoptosis. The released H2S can also inhibit the production of ATP and then downregulate the expression of HSP90, strengthening the activated systemic antitumor immune response after ablation. This nanoregulator provides a new way for achieving compensation effect for MWTT. The strategy of H2S gas to downregulate HSP90 and positively regulate antitumor immune response introduces a novel direction for reducing the risk of tumor recurrence and metastasis after thermal therapy. [ABSTRACT FROM AUTHOR]

The log messages generated in the system reflect the state of the system at all times. The realization of autonomous detection of abnormalities in log messages can help operators find abnormalities in time and provide a basis for analyzing the causes of abnormalities. First, this paper proposes a log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. This method uses BERT (Bidirectional Encoder Representations from Transformers) and VAE (Variational Auto-Encoder) to extract the semantic features and statistical features of the log sequence, respectively, and the dual features are combined to perform anomaly detection on the log sequence, with a novel contrastive adversarial training method also used to train the model. In addition, this paper introduces the method of obtaining statistical features of log sequence and the method of combining semantic features with statistical features. Furthermore, the specific process of contrastive adversarial training is described. Finally, an experimental comparison is carried out, and the experimental results show that the method in this paper is better than the contrasted log sequence anomaly detection method. [ABSTRACT FROM AUTHOR]

The log messages generated in the system reflect the state of the system at all times. The realization of autonomous detection of abnormalities in log messages can help operators find abnormalities in time and provide a basis for analyzing the causes of abnormalities. First, this paper proposes a log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. This method uses BERT (Bidirectional Encoder Representations from Transformers) and VAE (Variational Auto-Encoder) to extract the semantic features and statistical features of the log sequence, respectively, and the dual features are combined to perform anomaly detection on the log sequence, with a novel contrastive adversarial training method also used to train the model. In addition, this paper introduces the method of obtaining statistical features of log sequence and the method of combining semantic features with statistical features. Furthermore, the specific process of contrastive adversarial training is described. Finally, an experimental comparison is carried out, and the experimental results show that the method in this paper is better than the contrasted log sequence anomaly detection method.