Causal Disentanglement Domain Generalization for time-series signal fault diagnosis.

Domain generalization-based fault diagnosis (DGFD) presents significant prospects for recognizing faults without the accessibility of the target domain. Previous DGFD methods have achieved significant progress; however, there are some limitations. First, most DGFG methods statistically model the dependence between time-series data and labels, and they are superficial descriptions to the actual data-generating process. Second, most of the existing DGFD methods are only verified on vibrational time-series datasets, which is insufficient to show the potential of domain generalization in the fault diagnosis area. In response to the above issues, this paper first proposes a DGFD method named Causal Disentanglement Domain Generalization (CDDG), which can reestablish the data-generating process by disentangling time-series data into the causal factors (fault-related representation) and no-casual factors (domain-related representation) with a structural causal model. Specifically, in CDDG, causal aggregation loss is designed to separate the unobservable causal and non-causal factors. Meanwhile, the reconstruction loss is proposed to ensure the information completeness of the disentangled factors. We also introduce a redundancy reduction loss to learn efficient features. The proposed CDDG is verified on five cross-machine vibrational fault diagnosis cases and three cross-environment acoustical anomaly detection cases by comparing it with eight state-of-the-art (SOTA) DGFD methods. We publicize the open-source time-series DGFD Benchmark containing CDDG and the eight SOTA methods. The code repository will be available at https://github.com/ShaneSpace/DGFDBenchmark.

Self-reported and self-facilitated theranostic oxygen nano-economizer for precise and hypoxia alleviation-potentiated photodynamic therapy.

Photodynamic therapy (PDT) has been extensively investigated for cancer treatment by virtue of singlet oxygen-induced oxidative damage to tumors. Nevertheless, the therapeutic efficiency of PDT is still limited by the low singlet oxygen yield attributed to the improper irradiation duration and the tumor hypoxic microenvironment. To tackle these challenges, we elaborately design a theranostic oxygen nano-economizer to self-report the optimal irradiation duration and alleviate tumor hypoxia simultaneously, which is engineered by fluorescent 9,10-anthracenyl bis (benzoic acid) (DPA)-MOF, tetrakis (4-carboxyphenyl) porphyrin (TCPP), triphenyl phosphine (TPP) and redox-responsive lipid-PEG (DSPE-SS-PEG2k). Upon laser irradiation, the fluorescence of DPA-MOF could be quenched, thereby self-reporting the optimal irradiation duration for sufficient PDT. The decoration of DSPE-SS-PEG2k and TPP endows the theranostic oxygen nano-economizer with a tumor-specific response and mitochondrial targeting capability, respectively. Notably, singlet oxygen generated from TCPP reduces oxygen consumption by disrupting the entire oxidative phosphorylation (OXPHOS) pathway in the mitochondria of tumor cells, further improving the level of singlet oxygen in a self-facilitated manner for hypoxia alleviation-potentiated PDT. As expected, such a self-reported and self-facilitated theranostic oxygen nano-economizer exhibits potent antitumor activity in the 4T1 tumor-bearing mouse model. This study offers a theranostic paradigm for precise and hypoxia alleviation-potentiated cancer therapy.

Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance.

OBJECTIVE: As one of the commonly used control signals of brain-computer interface (BCI), steady-state visual evoked potential (SSVEP) exhibits advantages of stability, periodicity and minimal training requirements. However, SSVEP retains the non-linear, non-stationary and low signal-to-noise ratio (SNR) characteristics of EEG. The traditional SSVEP extraction methods regard noise as harmful information and highlight the useful signal by suppressing the noise. In the collected EEG, noise and SSVEP are usually coupled together, the useful signal is inevitably attenuated while the noise is suppressed. Also, an additional band-pass filter is needed to eliminate the multi-scale noise, which causes the edge effect. APPROACH: To address this issue, a novel method based on underdamped second-order stochastic resonance (USSR) is proposed in this paper for SSVEP extraction. MAIN RESULTS: A synergistic effect produced by noise, useful signal and the nonlinear system can force the energy of noise to be transferred into SSVEP, and hence amplifying the useful signal while suppressing multi-scale noise. The recognition performances of detection are compared with the widely-used canonical coefficient analysis (CCA) and multivariate synchronization index (MSI). SIGNIFICANCE: The comparison results indicate that USSR exhibits increased accuracy and faster processing speed, which effectively improves the information transmission rate (ITR) of SSVEP-based BCI.