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Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Since illegal wildlife trade poses challenges to biodiversity and public security, improving people’s wildlife conservation through environmental education has become an important issue. This study analyzes the intervention effect of labeling in wildlife conservation education and explores the underlying mechanism by targeting adolescents. Specifically, it is found that (1) the labeling in wildlife conservation education can improve teenagers’ willingness to protect wildlife; (2) the environmental self-identity is highly correlated with wildlife conservation.

A charge-based model is described for long-channel cylindrical surrounding-gate (SRG) MOSFETs from an intrinsic channel to a heavily doped body. The model is verified by numerical simulations with a wide range of SRG MOSFET geometric parameters and channel doping concentrations, including the undoped channel.

In recent years, more and more metal oxides have been finding critical tribo-catalytic applications. Presently, we have explored the tribo-catalytic conversion of H2O and CO2 using Co3O4 nanoparticles and obtained some surprising results. In an as-received 150 mL glass reactor enclosed with 10 mL of H2O, 0.10 g of Co3O4 nanoparticles, 1 atm of CO2, and a Teflon magnetic rotary disk, we observed the production of as much as 57.41 µmol/L of H2, 0.15 µmol/L of CH4, and 0.21 µmol/L of CO after 5 h of magnetic stirring. Metallic coatings of Cu, Ni, SUS316, Ti, Nb, Mo, and W were further introduced on reactor bottoms separately. For those coatings of Ni, SUS316, Ti, and Nb, the reduction of CO2 was dramatically enhanced, and C2+ products of C2H6 and C2H4 were observed. Especially for the Ti coating, the amounts of H2 and CH4 were increased by 2 and 26 times from those for the glass bottom, respectively, and the amounts of C2H6 and C2H4 were very impressive. The Co3O4 nanoparticles were proven chemically stable under magnetic stirring in water, and hydroxyl radicals and superoxide radicals have been detected for the Co3O4 nanoparticles under magnetic stirring through fluorescence spectroscopy and electron paramagnetic resonance spectroscopy analyses. These findings not only reveal outstanding capability of Co3O4 to generate multicarbon products from H2O and CO2 through tribo-catalysis but also highlight a promising potential of tribo-catalysis as a whole to harness mechanical energy for addressing energy shortages and environmental pollution.